3e:null 3f:["$","title",null,{"children":"MODFLOW Paper | Karthik Ramaswamy | Observable"}] 45:T1838,// Figure 1. Model domain and farm layout (manuscript v3, Section 2.2; Table 1) // Stylized, homogeneous domain: an n x n-section CID in the centre of a four-section APM buffer, // so the domain is (n + 8) x (n + 8) sections. Change cidN to 1, 2, 3, 5 or 7 (Table 2). const cidN = 3; const bufferN = 4; const fig1 = (() => { const domainN = cidN + 2 * bufferN; const NS = "http://www.w3.org/2000/svg"; const mk = (tag, attrs = {}, parent) => { const e = document.createElementNS(NS, tag); for (const [k, v] of Object.entries(attrs)) e.setAttribute(k, v); if (parent) parent.appendChild(e); return e; }; const svg = mk("svg", {viewBox: "0 0 1000 520", width: "100%", style: "max-width: 1000px; font-family: Helvetica, Arial, sans-serif;"}); const defs = mk("defs", {}, svg); const m = mk("marker", {id: "fig1-arrow", viewBox: "0 0 10 10", refX: 9, refY: 5, markerWidth: 6, markerHeight: 6, orient: "auto-start-reverse"}, defs); mk("path", {d: "M0,0 L10,5 L0,10 z", fill: "#222"}, m); const txt = (x, y, s, a = {}) => { const t = mk("text", {x, y, "font-size": 14, fill: "#222", ...a}, svg); t.textContent = s; return t; }; // ---- (a) model domain ---- const ax = 40, ay = 60, aw = 400, cs = aw / domainN; txt(ax, 32, "(a) Model domain, " + cidN + " × " + cidN + "-section CID", {"font-size": 16, "font-weight": "bold"}); const inCID = (i) => i >= bufferN && i < bufferN + cidN; const memberClass = (i, j) => { if (cidN === 1) return "S"; const ei = i === bufferN || i === bufferN + cidN - 1; const ej = j === bufferN || j === bufferN + cidN - 1; return ei && ej ? "C" : ei || ej ? "E" : "I"; }; for (let i = 0; i < domainN; i++) { for (let j = 0; j < domainN; j++) { const member = inCID(i) && inCID(j); mk("rect", {x: ax + i * cs, y: ay + j * cs, width: cs, height: cs, fill: member ? "#9ecae1" : "#f3e1c6", stroke: "#8c8c8c", "stroke-width": 0.8}, svg); if (member) txt(ax + (i + 0.5) * cs, ay + (j + 0.5) * cs + 5, memberClass(i, j), {"text-anchor": "middle", fill: "#08306b", "font-size": Math.min(15, cs * 0.45)}); } } mk("rect", {x: ax + bufferN * cs, y: ay + bufferN * cs, width: cidN * cs, height: cidN * cs, fill: "none", stroke: "#08519c", "stroke-width": 3}, svg); mk("rect", {x: ax, y: ay, width: aw, height: aw, fill: "none", stroke: "#333", "stroke-width": 2}, svg); txt(ax + 1.5 * cs, ay + 1.5 * cs + 5, "APM", {"text-anchor": "middle", fill: "#8c510a", "font-size": 15}); txt(ax + (bufferN + cidN / 2) * cs, ay + bufferN * cs - 8, "CID (" + cidN + " × " + cidN + " sections)", {"text-anchor": "middle", fill: "#08519c", "font-size": 14, stroke: "#f3e1c6", "stroke-width": 4, "paint-order": "stroke"}); mk("line", {x1: ax + 2, y1: ay + aw + 20, x2: ax + bufferN * cs - 2, y2: ay + aw + 20, stroke: "#222", "stroke-width": 1.3, "marker-start": "url(#fig1-arrow)", "marker-end": "url(#fig1-arrow)"}, svg); txt(ax + (bufferN * cs) / 2, ay + aw + 40, "APM buffer: " + bufferN + " sections", {"text-anchor": "middle", "font-size": 13}); txt(ax + aw, ay + aw + 24, "C corner · E edge · I interior member", {"text-anchor": "end", "font-size": 12, fill: "#08306b"}); txt(ax + aw, ay + aw + 42, "no-flow outer boundary", {"text-anchor": "end", "font-size": 12, "font-style": "italic"}); // ---- (b) one producer: a section with four quarter-section pivots ---- const bx = 560, by = 70, bw = 380, qs = bw / 2; const cellsPerQuarter = 7; // 804.7 m / 7 = 115 m cells const g = qs / cellsPerQuarter; const rPivot = (Math.sqrt(486000 / Math.PI) / 804.67) * qs; // 48.6 ha pivot in a 64.7 ha quarter txt(bx - 20, 32, "(b) One producer (section): 4 pivots, 4 wells", {"font-size": 16, "font-weight": "bold"}); txt(bx + bw / 2, by - 12, "4 quarter sections of 64.7 ha (160 ac)", {"text-anchor": "middle", "font-size": 13}); for (let k = 0; k <= 2 * cellsPerQuarter; k++) { mk("line", {x1: bx + k * g, y1: by, x2: bx + k * g, y2: by + bw, stroke: "#d9d9d9", "stroke-width": 0.6}, svg); mk("line", {x1: bx, y1: by + k * g, x2: bx + bw, y2: by + k * g, stroke: "#d9d9d9", "stroke-width": 0.6}, svg); } const centres = [[0, 0], [1, 0], [0, 1], [1, 1]].map(([i, j]) => [bx + (i + 0.5) * qs, by + (j + 0.5) * qs]); for (const [cx, cy] of centres) { mk("circle", {cx, cy, r: rPivot, fill: "#a1d99b", "fill-opacity": 0.8, stroke: "#41ab5d", "stroke-width": 1.5}, svg); } for (const [i, j] of [[0, 0], [1, 0], [0, 1], [1, 1]]) { mk("rect", {x: bx + i * qs, y: by + j * qs, width: qs, height: qs, fill: "none", stroke: "#636363", "stroke-width": 1.2}, svg); } mk("rect", {x: bx, y: by, width: bw, height: bw, fill: "none", stroke: "#252525", "stroke-width": 2}, svg); for (const [cx, cy] of centres) { mk("path", {d: "M" + cx + "," + (cy - 7) + " L" + (cx + 7) + "," + (cy + 5) + " L" + (cx - 7) + "," + (cy + 5) + " Z", fill: "#111"}, svg); } const [pcx, pcy] = centres[1]; txt(pcx, pcy + 28, "pivot", {"text-anchor": "middle", fill: "#006d2c", "font-size": 13}); txt(pcx, pcy + 44, "48.6 ha (120 ac)", {"text-anchor": "middle", fill: "#006d2c", "font-size": 13}); const [wcx, wcy] = centres[2]; txt(wcx, wcy + 26, "well", {"text-anchor": "middle", "font-size": 13}); txt(bx + bw / 2, by + bw + 30, "1 section = 1.609 km (259 ha); grid 115 m (7 cells per quarter side)", {"text-anchor": "middle", "font-size": 13}); const fig = document.createElement("figure"); fig.style.cssText = "margin: 0; font: 12px sans-serif; max-width: 1000px;"; const cap = document.createElement("figcaption"); cap.style.cssText = "margin-top: 8px; line-height: 1.4;"; cap.textContent = "Figure 1. (a) Model domain for a " + cidN + " × " + cidN + "-section cooperative irrigation district " + "(CID, blue) surrounded by a " + bufferN + "-section buffer of annual-profit-maximizing (APM) producers. " + "C, E and I mark corner, edge and interior members. (b) One producer: a 259-ha section with four " + "quarter-section pivots, each supplied by its own well, overlaid on the 115-m model grid."; fig.append(svg, cap); return fig; })(); display(fig1);46:T119c,// Figure 2. Annual coupling of MODFLOW 6 and the producer decision models (manuscript v3, Section 2.7) const fig2 = (() => { const NS = "http://www.w3.org/2000/svg"; const mk = (tag, attrs = {}, parent) => { const e = document.createElementNS(NS, tag); for (const [k, v] of Object.entries(attrs)) e.setAttribute(k, v); if (parent) parent.appendChild(e); return e; }; const svg = mk("svg", {viewBox: "0 0 960 450", width: "100%", style: "max-width: 960px; font-family: Helvetica, Arial, sans-serif; font-size: 13px;"}); const defs = mk("defs", {}, svg); const m = mk("marker", {id: "fig2-arrow", viewBox: "0 0 10 10", refX: 9, refY: 5, markerWidth: 7, markerHeight: 7, orient: "auto-start-reverse"}, defs); mk("path", {d: "M0,0 L10,5 L0,10 z", fill: "#333"}, m); // text with simple subscripts: "w_{max}" renders "max" as a subscript const rich = (t, s) => { const parts = s.split(/(_\{[^}]*\})/); for (const p of parts) { if (!p) continue; if (p.startsWith("_{")) { const sub = mk("tspan", {"baseline-shift": "sub", "font-size": "75%"}, t); sub.textContent = p.slice(2, -1); } else { const sp = mk("tspan", {}, t); sp.textContent = p; } } }; const box = (x, y, w, h, title, lines, fill, stroke) => { mk("rect", {x, y, width: w, height: h, rx: 6, fill, stroke, "stroke-width": 1.5}, svg); const t0 = mk("text", {x: x + w / 2, y: y + 26, "text-anchor": "middle", "font-weight": "bold", "font-size": 15, fill: "#111"}, svg); rich(t0, title); lines.forEach((s, i) => { const t = mk("text", {x: x + w / 2, y: y + 50 + i * 17, "text-anchor": "middle", fill: "#222"}, svg); rich(t, s); }); }; const arrow = (pts) => mk("polyline", {points: pts.map((p) => p.join(",")).join(" "), fill: "none", stroke: "#333", "stroke-width": 1.5, "marker-end": "url(#fig2-arrow)"}, svg); const label = (x, y, s, anchor = "middle") => { const t = mk("text", {x, y, "text-anchor": anchor, "font-style": "italic", fill: "#333", stroke: "white", "stroke-width": 4, "paint-order": "stroke"}, svg); rich(t, s); }; box(15, 150, 225, 150, "1. Aquifer state, year t", ["MODFLOW heads →", "saturated thickness b of each", "quarter section; feasible", "capacity class w_{max}(b);", "leakage history"], "#deebf7", "#6b8fb3"); box(310, 40, 260, 140, "2a. APM producers", ["one-year problem (T = 1): crop,", "irrigation trigger, capacity", "class or dryland maximizing", "this year's net return"], "#fdd0a2", "#d9822b"); box(310, 270, 260, 150, "2b. CID members (LPM)", ["receding-horizon MIP over", "years t … 30; belief about", "leakage: naive (φ = 0) or", "informed (φ̂ estimated", "from history)"], "#c7e9c0", "#41ab5d"); box(655, 40, 290, 140, "3. MODFLOW 6, year t", ["120-d season: each well pumps", "V / 120 d (MAW, rate scaling", "if short); 245-d recovery;", "net flow across CID boundary"], "#deebf7", "#6b8fb3"); box(655, 270, 290, 150, "4. Realized economics", ["delivered volume Ṽ and simulated", "pumping depth d̃ → energy and", "repair cost (Eq. 1); net return", "per pivot (Eq. 8); outputs per", "well-year"], "#f0f0f0", "#7a7a7a"); arrow([[240, 195], [308, 130]]); label(262, 150, "b, w_{max}"); arrow([[240, 255], [308, 320]]); label(266, 318, "b, w_{max}, φ̂", "end"); arrow([[570, 110], [653, 110]]); label(611, 102, "plans"); arrow([[570, 345], [612, 345], [612, 150], [653, 150]]); label(618, 250, "plans", "start"); arrow([[800, 180], [800, 268]]); label(808, 229, "delivered Ṽ, depth d̃", "start"); arrow([[800, 40], [800, 14], [127, 14], [127, 148]]); label(463, 18, "heads → start of year t + 1"); const fig = document.createElement("figure"); fig.style.cssText = "margin: 0; font: 12px sans-serif; max-width: 960px;"; const cap = document.createElement("figcaption"); cap.style.cssText = "margin-top: 8px; line-height: 1.4;"; cap.textContent = "Figure 2. Annual coupling of the groundwater flow model and the producer decision models. " + "The aquifer state at the start of each year determines the capacity class available to each well; APM producers " + "solve a one-year problem and CID members a receding-horizon program; MODFLOW 6 simulates the season with the " + "planned pumping, and realized returns use the delivered water and the simulated pumping depth."; fig.append(svg, cap); return fig; })(); display(fig2);47:T1b55,// Aquifer data: USGS High Plains aquifer boundary (Qi, 2010; DS-543) and 2009 saturated thickness // (McGuire et al., 2012; SIR 2012-5177), ArcInfo binary grid of 500-m cells, values in feet. // Both use USGS Albers (NAD83; parallels 29.5/45.5; lon0 -96; lat0 23) and are reprojected here to lon/lat. import * as shapefile from "npm:shapefile"; import proj4 from "npm:proj4"; import * as d3 from "npm:d3"; const albers = "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0 +units=m +no_defs"; const toLL = proj4(albers, "EPSG:4326"); // forward: Albers -> lon/lat; inverse: lon/lat -> Albers const FT = 0.3048; // d3.geo expects clockwise exterior rings; flip any feature that came out inside-out const rewind = (f) => { if (d3.geoArea(f) <= 2 * Math.PI) return f; const rev = (poly) => poly.map((ring) => ring.slice().reverse()); const c = f.geometry.type === "Polygon" ? rev(f.geometry.coordinates) : f.geometry.coordinates.map(rev); return {...f, geometry: {...f.geometry, coordinates: c}}; }; const reproj = (c) => (typeof c[0] === "number" ? toLL.forward(c) : c.map(reproj)); // ---- Aquifer boundary ---- const hpRaw = await shapefile.read(await FileAttachment("hp_bound2010.shp").arrayBuffer()); const hpAquifer = {type: "FeatureCollection", features: hpRaw.features.map((f) => rewind({type: "Feature", properties: {}, geometry: {type: f.geometry.type, coordinates: reproj(f.geometry.coordinates)}}))}; // ---- Saturated thickness grid reader (ArcInfo binary grid, float, uncompressed tiles) ---- const hdr = new DataView(await FileAttachment("hdr.adf").arrayBuffer()); const bnd = new DataView(await FileAttachment("dblbnd.adf").arrayBuffer()); const tileIndex = new DataView(await FileAttachment("w001001x.adf").arrayBuffer()); const tileData = new DataView(await FileAttachment("w001001.adf").arrayBuffer()); const cellSize = hdr.getFloat64(256, false); // 500 m const tilesPerRow = hdr.getInt32(288, false); const tileW = hdr.getInt32(296, false), tileH = hdr.getInt32(304, false); const [gx0, gy0, gx1, gy1] = [0, 8, 16, 24].map((o) => bnd.getFloat64(o, false)); const nCols = Math.round((gx1 - gx0) / cellSize), nRows = Math.round((gy1 - gy0) / cellSize); // thickness in feet at grid column c, row r (row 0 = north edge) const thickAt = (c, r) => { if (c < 0 || r < 0 || c >= nCols || r >= nRows) return NaN; const t = Math.floor(r / tileH) * tilesPerRow + Math.floor(c / tileW); const off = tileIndex.getInt32(100 + t * 8, false) * 2; const size = tileIndex.getInt32(104 + t * 8, false) * 2; if (!size) return NaN; const v = tileData.getFloat32(off + 2 + ((r % tileH) * tileW + (c % tileW)) * 4, false); return v < -1e30 ? NaN : v; }; // thickness in metres at a lon/lat point const thickLLm = ([lon, lat]) => { const [x, y] = toLL.inverse([lon, lat]); return thickAt(Math.floor((x - gx0) / cellSize), Math.floor((gy1 - y) / cellSize)) * FT; }; // ---- Thickness bands (m) over the study-area map extent ---- const thickBreaksM = [0.3, 10, 20, 30, 40, 60]; const cornersA = []; for (const lon of [-103.8, -101.5, -99.2]) for (const lat of [35.8, 37.7]) cornersA.push(toLL.inverse([lon, lat])); const ac0 = Math.floor((d3.min(cornersA, (p) => p[0]) - gx0) / cellSize), ac1 = Math.ceil((d3.max(cornersA, (p) => p[0]) - gx0) / cellSize); const ar0 = Math.floor((gy1 - d3.max(cornersA, (p) => p[1])) / cellSize), ar1 = Math.ceil((gy1 - d3.min(cornersA, (p) => p[1])) / cellSize); const subW = ac1 - ac0, subH = ar1 - ar0; const subVals = new Float64Array(subW * subH); for (let r = 0; r < subH; r++) { for (let c = 0; c < subW; c++) { const v = thickAt(ac0 + c, ar0 + r); subVals[r * subW + c] = Number.isFinite(v) ? v * FT : -1; } } const sx0 = gx0 + ac0 * cellSize, sy1 = gy1 - ar0 * cellSize; const thicknessBands = d3.contours().size([subW, subH]).thresholds(thickBreaksM)(subVals).map((g) => rewind({ type: "Feature", properties: {value: g.value}, geometry: {type: "MultiPolygon", coordinates: g.coordinates.map((poly) => poly.map((ring) => ring.map(([x, y]) => toLL.forward([sx0 + x * cellSize, sy1 - y * cellSize]))))} })); // ---- Texas County: distribution of 2009 saturated thickness (checks Table 1 / Section 3.1) ---- const texasCo = geo.panhandle.find((d) => d.id === "40139"); const tcCorners = d3.geoBounds(texasCo); const tcA = [[tcCorners[0][0], tcCorners[0][1]], [tcCorners[1][0], tcCorners[1][1]], [tcCorners[0][0], tcCorners[1][1]], [tcCorners[1][0], tcCorners[0][1]]].map((p) => toLL.inverse(p)); const tc0 = Math.floor((d3.min(tcA, (p) => p[0]) - gx0) / cellSize), tc1 = Math.ceil((d3.max(tcA, (p) => p[0]) - gx0) / cellSize); const tr0 = Math.floor((gy1 - d3.max(tcA, (p) => p[1])) / cellSize), tr1 = Math.ceil((gy1 - d3.min(tcA, (p) => p[1])) / cellSize); let tcCells = 0; const tcVals = []; for (let r = tr0; r < tr1; r++) { for (let c = tc0; c < tc1; c++) { const ll = toLL.forward([gx0 + (c + 0.5) * cellSize, gy1 - (r + 0.5) * cellSize]); if (!d3.geoContains(texasCo, ll)) continue; tcCells++; const v = thickAt(c, r); if (Number.isFinite(v) && v > 0) tcVals.push(v * FT); } } tcVals.sort(d3.ascending); const shareAtLeast = (b) => tcVals.filter((v) => v >= b).length / tcVals.length; const texasCoThickness = { cellsInCounty: tcCells, aquiferShareOfCounty: tcVals.length / tcCells, p10: d3.quantileSorted(tcVals, 0.1), p25: d3.quantileSorted(tcVals, 0.25), median: d3.quantileSorted(tcVals, 0.5), p75: d3.quantileSorted(tcVals, 0.75), p90: d3.quantileSorted(tcVals, 0.9), mean: d3.mean(tcVals), max: tcVals[tcVals.length - 1], shareAtLeast18m: shareAtLeast(18), shareAtLeast25m: shareAtLeast(25), shareAtLeast35m: shareAtLeast(35) }; const tcTable = document.createElement("table"); tcTable.style.cssText = "font: 13px sans-serif; border-collapse: collapse; margin: 4px 0;"; const tcRows = [ ["Texas County, 2009 saturated thickness (McGuire et al., 2012)", ""], ["Share of county area over the aquifer", (100 * texasCoThickness.aquiferShareOfCounty).toFixed(0) + "%"], ["10th / 25th / 50th / 75th / 90th percentile (m)", [texasCoThickness.p10, texasCoThickness.p25, texasCoThickness.median, texasCoThickness.p75, texasCoThickness.p90].map((v) => v.toFixed(1)).join(" / ")], ["Mean / maximum (m)", texasCoThickness.mean.toFixed(1) + " / " + texasCoThickness.max.toFixed(1)], ["Share of aquifer area with b ≥ 18 / 25 / 35 m", [texasCoThickness.shareAtLeast18m, texasCoThickness.shareAtLeast25m, texasCoThickness.shareAtLeast35m].map((v) => (100 * v).toFixed(0) + "%").join(" / ")] ]; tcRows.forEach(([a, b], i) => { const tr = document.createElement("tr"); const td1 = document.createElement("td"), td2 = document.createElement("td"); td1.textContent = a; td2.textContent = b; td1.style.cssText = "padding: 3px 12px 3px 0;" + (i === 0 ? " font-weight: bold;" : ""); td2.style.cssText = "padding: 3px 0; text-align: right;"; tr.append(td1, td2); tcTable.append(tr); }); display(tcTable);48:T111a,// Figure S1 (suggested supplementary figure). Study area: Texas County, Oklahoma, on the 2009 saturated // thickness of the High Plains aquifer. Supports Section 2.1; the model domain itself (Figure 1) is stylized. import * as Plot from "npm:@observablehq/plot"; const figS1 = (() => { const extent = {type: "MultiPoint", coordinates: [[-103.6, 35.9], [-99.4, 37.6], [-103.6, 37.6], [-99.4, 35.9]]}; const kmPerDeg = 111.32 * Math.cos((36.1 * Math.PI) / 180); const scaleBar = [[-103.35, 36.1], [-103.35 + 50 / kmPerDeg, 36.1]]; // 50 km const texasCo = geo.panhandle.find((d) => d.id === "40139"); const towns = [ {name: "Guymon", lon: -101.4816, lat: 36.6828}, {name: "Goodwell", lon: -101.6363, lat: 36.5953}, {name: "Boise City", lon: -102.513, lat: 36.7292}, {name: "Beaver", lon: -100.5193, lat: 36.8161} ]; const stateLabels = [ {name: "COLORADO", lon: -102.6, lat: 37.3}, {name: "KANSAS", lon: -100.6, lat: 37.3}, {name: "TEXAS", lon: -101.3, lat: 36.2}, {name: "NEW MEXICO", lon: -103.25, lat: 36.55}, {name: "OKLAHOMA", lon: -99.7, lat: 36.3} ]; const halo = {stroke: "white", strokeWidth: 3}; const mapMain = Plot.plot({ width: 760, color: {type: "threshold", domain: thickBreaksM.slice(1), scheme: "blues", legend: true, label: "Saturated thickness, 2009 (m)"}, projection: {type: "conic-equal-area", parallels: [35, 38], rotate: [101.5, 0], domain: extent, inset: 10}, marks: [ Plot.geo(geo.neighbors, {fill: "#f4f4f2", stroke: "#999", strokeWidth: 0.8}), Plot.geo(thicknessBands, {fill: (d) => d.properties.value}), Plot.geo(hpAquifer, {stroke: "#555", strokeWidth: 0.6}), Plot.geo(geo.okCounties, {stroke: "#bbb", strokeWidth: 0.5}), Plot.geo(geo.panhandle, {stroke: "#333", strokeWidth: 1}), Plot.geo(texasCo, {stroke: "#d95f02", strokeWidth: 2.5}), Plot.text(geo.panhandle, Plot.centroid({text: (d) => d.properties.name.toUpperCase() + " CO.", fontSize: 10, fontWeight: "bold", fill: "#444", dy: 26, ...halo})), Plot.text(stateLabels, {x: "lon", y: "lat", text: "name", fontSize: 11, fill: "#888", fontWeight: "bold", ...halo}), Plot.dot(towns, {x: "lon", y: "lat", r: 2.5, fill: "black"}), Plot.text(towns.filter((d) => d.name !== "Goodwell"), {x: "lon", y: "lat", text: "name", dy: -9, fontSize: 10, fill: "black", ...halo}), Plot.text(towns.filter((d) => d.name === "Goodwell"), {x: "lon", y: "lat", text: "name", dx: -6, textAnchor: "end", fontSize: 10, fill: "black", ...halo}), Plot.line(scaleBar, {stroke: "black", strokeWidth: 2}), Plot.text([scaleBar[0]], {text: ["0"], dy: -9, fontSize: 9, fill: "black"}), Plot.text([scaleBar[1]], {text: ["50 km"], dy: -9, fontSize: 9, fill: "black"}), Plot.text([[-99.65, 37.45]], {text: ["N ↑"], fontSize: 13, fontWeight: "bold", fill: "black"}), Plot.frame({stroke: "#666"}) ] }); const mapLocator = Plot.plot({ width: 300, projection: "albers-usa", marks: [ Plot.geo(geo.states, {fill: "#eee", stroke: "white", strokeWidth: 0.5}), Plot.geo(hpAquifer, {fill: "#9ecae1", fillOpacity: 0.8}), Plot.geo(geo.oklahoma, {stroke: "#666", strokeWidth: 0.8}), Plot.geo(texasCo, {fill: "#d95f02"}) ] }); const panel = (tag, node) => { const d = document.createElement("div"); const b = document.createElement("div"); b.textContent = tag; b.style.cssText = "font: bold 13px sans-serif; margin-bottom: 2px;"; d.append(b, node); return d; }; const fig = document.createElement("figure"); fig.style.cssText = "margin: 0; font: 12px sans-serif; max-width: 760px;"; const cap = document.createElement("figcaption"); cap.style.cssText = "margin-top: 8px; line-height: 1.4;"; cap.textContent = "Figure S1. Study area. (a) Texas County (orange outline) in the Oklahoma Panhandle, shaded by the " + "2009 saturated thickness of the High Plains (Ogallala) aquifer (McGuire et al., 2012); the thin grey line is the " + "aquifer boundary (Qi, 2010). Goodwell is the site of the EPIC calibration. (b) Location within the United States, " + "with the High Plains aquifer (blue) and Texas County (orange). The model domain (Figure 1) is stylized and homogeneous."; fig.append(panel("(a)", mapMain), panel("(b)", mapLocator), cap); return fig; })(); display(figS1);49:T83f6,// Full-design results (432 MODFLOW-optimization runs; results/design/ in the project; rerun 16 Oct 2026 with // identical results). Regenerate this cell with gen_data_cells.py after any rerun of the design. // designRuns: one row per aquifer setting x CID size x belief (27 x 5 x 2 = 270), from design_summary.csv. // K (m/d), Sy, b0 (m), n = CID size (sections), mode = naive | informed, // G = CID member NPV gain vs the all-APM baseline (%), kappa = capture ratio G / G*, // R10, R5 = retention of deferred water at years 10 and 5 (%; blank where nothing had been deferred yet), // L30 = water leaked to neighbours by year 30 (Mm3), D = peak cumulative deferral (Mm3), // Gn = APM neighbour NPV gain (%), Gstar = no-neighbour gain G* (%), // dC, dN = total NPV change of the CID members and of the APM neighbours ($), land = dC + dN ($), // shareN = neighbours' share of the positive gains (0-1; blank when neither gains), npvA = CID pivot NPV under all-APM ($). // compBySize: members worse off than all-APM, by CID size and compensation rule (compensation_summary.csv). // makeWholeFeasible: districts (aquifer settings) in which the make-whole rule is feasible, by size and belief. const designRuns = d3.csvParse(`K,Sy,b0,n,mode,G,kappa,R10,L30,D,Gn,Gstar,R5,dC,dN,land,shareN,npvA 7.62,0.125,18,1,naive,-3.905,-0.4937,22.9,0.148,0.152,0.0,7.91,21.4,-10182,49,-10133,1.0,65184 7.62,0.125,18,1,informed,-3.858,-0.4877,34.6,0.086,0.089,0.0,7.91,11.7,-10060,21,-10039,1.0,65184 7.62,0.125,18,4,naive,-2.559,-0.3235,39.3,0.478,0.528,0.001,7.91,45.6,-26689,144,-26545,1.0,65184 7.62,0.125,18,4,informed,-1.539,-0.1946,49.0,0.302,0.453,0.0,7.91,33.2,-16053,83,-15970,1.0,65184 7.62,0.125,18,9,naive,-0.291,-0.0368,51.2,0.687,1.077,0.001,7.91,60.4,-6826,240,-6586,1.0,65184 7.62,0.125,18,9,informed,0.07,0.0088,55.4,0.54,1.014,0.001,7.91,58.7,1648,183,1831,0.0999,65184 7.62,0.125,18,25,naive,-0.452,-0.0571,67.7,1.575,2.92,0.001,7.91,74.8,-29479,430,-29049,1.0,65184 7.62,0.125,18,25,informed,2.534,0.3204,67.3,0.576,2.412,0.001,7.91,73.3,165170,332,165502,0.002,65184 7.62,0.125,18,49,naive,2.066,0.2612,76.2,1.778,5.72,0.001,7.91,81.6,263915,618,264533,0.0023,65184 7.62,0.125,18,49,informed,2.5,0.3161,76.9,1.398,5.223,0.001,7.91,81.1,319385,534,319919,0.0017,65184 7.62,0.125,25,1,naive,-3.729,-0.3231,-2.4,0.729,0.719,0.072,11.54,19.7,-17431,27075,9644,1.0,116862 7.62,0.125,25,1,informed,-1.224,-0.1061,-58.1,0.152,0.183,0.047,11.54,11.7,-5723,17675,11952,1.0,116862 7.62,0.125,25,4,naive,-2.659,-0.2304,16.6,2.867,2.88,0.498,11.54,43.6,-49722,223359,173637,1.0,116862 7.62,0.125,25,4,informed,-0.699,-0.0606,-44.8,0.629,0.727,0.086,11.54,33.1,-13069,38754,25685,1.0,116862 7.62,0.125,25,9,naive,-0.699,-0.0606,33.9,5.501,6.479,1.02,11.54,58.6,-29425,534167,504742,1.0,116862 7.62,0.125,25,9,informed,0.04,0.0035,-21.1,1.249,1.633,0.112,11.54,49.8,1665,58586,60251,0.9724,116862 7.62,0.125,25,25,naive,1.839,0.1594,54.6,11.868,17.991,1.682,11.54,73.6,214905,1132298,1347203,0.8405,116862 7.62,0.125,25,25,informed,1.425,0.1235,43.0,6.12,8.587,0.705,11.54,69.6,166498,474256,640754,0.7402,116862 7.62,0.125,25,49,naive,3.632,0.3147,66.1,18.262,35.225,1.978,11.54,80.7,831907,1626970,2458877,0.6617,116862 7.62,0.125,25,49,informed,3.341,0.2895,66.7,12.26,24.409,1.334,11.54,77.7,765168,1097181,1862349,0.5891,116862 7.62,0.125,35,1,naive,-5.278,-0.4586,5.5,1.473,1.519,0.008,11.51,8.2,-37356,4807,-32549,1.0,176930 7.62,0.125,35,1,informed,-2.383,-0.207,3.9,0.918,0.948,0.007,11.51,7.4,-16865,3855,-13010,1.0,176930 7.62,0.125,35,4,naive,-3.908,-0.3395,17.7,5.161,5.612,0.033,11.51,24.1,-110635,22257,-88378,1.0,176930 7.62,0.125,35,4,informed,-1.74,-0.1512,14.1,3.396,3.778,0.019,11.51,22.0,-49259,13102,-36157,1.0,176930 7.62,0.125,35,9,naive,0.048,0.0042,26.7,9.001,10.418,0.166,11.51,38.5,3087,131552,134639,0.9771,176930 7.62,0.125,35,9,informed,-0.298,-0.0259,22.2,6.704,8.899,0.084,11.51,36.6,-18971,66801,47830,1.0,176930 7.62,0.125,35,25,naive,2.533,0.2201,44.2,16.702,23.317,0.434,11.51,58.3,448109,442324,890433,0.4968,176930 7.62,0.125,35,25,informed,4.049,0.3518,41.9,15.512,22.923,0.365,11.51,57.1,716315,371772,1088087,0.3417,176930 7.62,0.125,35,49,naive,3.86,0.3354,53.7,24.978,45.262,0.691,11.51,67.5,1338623,860303,2198926,0.3912,176930 7.62,0.125,35,49,informed,5.419,0.4708,54.5,28.484,45.386,0.831,11.51,67.2,1879299,1035439,2914738,0.3552,176930 7.62,0.175,18,1,naive,-1.571,-0.1964,18.8,0.152,0.158,0.0,8.0,26.4,-5134,66,-5068,1.0,81705 7.62,0.175,18,1,informed,-1.162,-0.1452,7.9,0.06,0.063,0.0,8.0,15.4,-3798,21,-3777,1.0,81705 7.62,0.175,18,4,naive,-0.466,-0.0582,41.2,0.51,0.649,0.096,8.0,51.9,-6094,29977,23883,1.0,81705 7.62,0.175,18,4,informed,0.443,0.0554,25.0,0.123,0.257,0.0,8.0,39.9,5791,66,5857,0.0113,81705 7.62,0.175,18,9,naive,0.523,0.0654,56.0,0.948,1.445,0.245,8.0,65.6,15379,89687,105066,0.8536,81705 7.62,0.175,18,9,informed,2.045,0.2556,59.4,0.432,1.05,0.163,8.0,56.3,60144,59766,119910,0.4984,81705 7.62,0.175,18,25,naive,1.598,0.1998,71.7,1.864,3.987,0.444,8.0,78.6,130585,209105,339690,0.6156,81705 7.62,0.175,18,25,informed,3.255,0.4069,72.9,1.151,3.694,0.318,8.0,78.3,265924,149429,415353,0.3598,81705 7.62,0.175,18,49,naive,3.482,0.4352,79.4,2.338,7.856,0.571,8.0,84.4,557676,328524,886200,0.3707,81705 7.62,0.175,18,49,informed,4.328,0.541,79.9,1.967,7.43,0.519,8.0,84.4,693118,298633,991751,0.3011,81705 7.62,0.175,25,1,naive,-0.945,-0.2855,12.7,0.067,0.069,0.0,3.31,10.6,-5199,122,-5077,1.0,137499 7.62,0.175,25,1,informed,-0.909,-0.2746,5.7,0.045,0.046,0.0,3.31,10.6,-5001,97,-4904,1.0,137499 7.62,0.175,25,4,naive,-0.993,-0.3,40.0,0.343,0.384,0.001,3.31,31.0,-21850,476,-21374,1.0,137499 7.62,0.175,25,4,informed,-1.034,-0.3124,41.5,0.436,0.489,0.001,3.31,31.0,-22756,572,-22184,1.0,137499 7.62,0.175,25,9,naive,-1.024,-0.3094,51.4,0.728,0.997,0.001,3.31,47.9,-50671,901,-49770,1.0,137499 7.62,0.175,25,9,informed,-0.941,-0.2843,53.5,0.608,0.989,0.001,3.31,47.9,-46584,836,-45748,1.0,137499 7.62,0.175,25,25,naive,0.424,0.1281,-25.1,0.233,1.153,0.001,3.31,,58367,563,58930,0.0096,137499 7.62,0.175,25,25,informed,0.349,0.1054,64.4,0.965,1.607,0.001,3.31,,47936,1047,48983,0.0214,137499 7.62,0.175,25,49,naive,1.167,0.3526,,-0.464,2.26,0.0,3.31,,314558,167,314725,0.0005,137499 7.62,0.175,25,49,informed,0.873,0.2637,72.3,3.44,7.123,0.004,3.31,,235140,3624,238764,0.0152,137499 7.62,0.175,35,1,naive,-3.549,-0.5143,5.6,1.161,1.189,0.006,6.9,12.4,-29386,4068,-25318,1.0,207003 7.62,0.175,35,1,informed,-1.167,-0.1691,5.0,0.826,0.844,0.005,6.9,9.5,-9660,3417,-6243,1.0,207003 7.62,0.175,35,4,naive,-2.623,-0.3801,20.5,3.71,4.241,0.016,6.9,29.9,-86860,12321,-74539,1.0,207003 7.62,0.175,35,4,informed,-2.295,-0.3326,18.2,3.584,4.25,0.015,6.9,31.9,-76028,12080,-63948,1.0,207003 7.62,0.175,35,9,naive,0.14,0.0203,28.2,6.067,8.161,0.029,6.9,46.4,10402,26438,36840,0.7176,207003 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30.48,0.125,25,4,informed,-16.36,-0.7754,-1.0,6.208,6.387,0.902,21.1,8.7,-421021,557047,136026,1.0,160845 30.48,0.125,25,9,naive,-16.737,-0.7932,9.9,19.251,20.129,2.396,21.1,24.4,-969120,1726646,757526,1.0,160845 30.48,0.125,25,9,informed,-10.967,-0.5198,3.0,9.886,11.757,1.157,21.1,20.2,-635033,833532,198499,1.0,160845 30.48,0.125,25,25,naive,-9.174,-0.4348,22.8,43.348,50.405,4.427,21.1,38.2,-1475587,4101059,2625472,1.0,160845 30.48,0.125,25,25,informed,-4.343,-0.2058,12.4,21.819,32.659,2.224,21.1,33.3,-698523,2060451,1361928,1.0,160845 30.48,0.125,25,49,naive,-3.478,-0.1648,34.0,69.154,92.899,5.676,21.1,51.6,-1096319,6427004,5330685,1.0,160845 30.48,0.125,25,49,informed,0.486,0.023,28.8,40.396,65.965,3.678,21.1,46.2,153150,4164443,4317593,0.9645,160845 30.48,0.125,35,1,naive,-23.454,-1.4595,0.5,3.753,3.75,0.678,16.07,6.6,-219021,506288,287267,1.0,233460 30.48,0.125,35,1,informed,-5.943,-0.3698,-1.5,1.131,1.159,0.183,16.07,3.4,-55497,136481,80984,1.0,233460 30.48,0.125,35,4,naive,-21.378,-1.3303,6.7,14.107,14.384,1.817,16.07,19.6,-798556,1629338,830782,1.0,233460 30.48,0.125,35,4,informed,-5.345,-0.3326,-0.9,4.015,4.082,0.648,16.07,9.7,-199648,580775,381127,1.0,233460 30.48,0.125,35,9,naive,-17.693,-1.101,11.9,28.679,30.013,2.684,16.07,32.3,-1487007,2806857,1319850,1.0,233460 30.48,0.125,35,9,informed,-3.429,-0.2134,-8.4,5.33,5.613,1.133,16.07,17.2,-288170,1185376,897206,1.0,233460 30.48,0.125,35,25,naive,-13.126,-0.8168,22.1,71.014,80.222,4.72,16.07,49.8,-3064327,6346748,3282421,1.0,233460 30.48,0.125,35,25,informed,-2.846,-0.1771,14.9,22.621,26.755,1.981,16.07,36.1,-664425,2663773,1999348,1.0,233460 30.48,0.125,35,49,naive,-7.943,-0.4943,31.1,115.437,153.961,6.114,16.07,61.5,-3634489,10048438,6413949,1.0,233460 30.48,0.125,35,49,informed,0.119,0.0074,27.2,38.597,55.178,2.554,16.07,50.5,54225,4197049,4251274,0.9872,233460 30.48,0.175,18,1,naive,-10.219,-0.5527,3.8,1.404,1.42,0.102,18.49,2.3,-54979,43938,-11041,1.0,134501 30.48,0.175,18,1,informed,-4.251,-0.2299,2.7,0.603,0.613,0.003,18.49,5.3,-22869,1216,-21653,1.0,134501 30.48,0.175,18,4,naive,-5.677,-0.307,12.9,4.777,5.004,0.438,18.49,15.5,-122172,226443,104271,1.0,134501 30.48,0.175,18,4,informed,-0.861,-0.0466,4.9,1.929,2.008,0.222,18.49,8.5,-18536,114692,96156,1.0,134501 30.48,0.175,18,9,naive,-4.025,-0.2177,21.2,10.285,11.376,1.2,18.49,29.3,-194879,723087,528208,1.0,134501 30.48,0.175,18,9,informed,3.101,0.1677,4.7,2.938,4.368,0.352,18.49,-14.6,150171,212175,362346,0.5856,134501 30.48,0.175,18,25,naive,0.805,0.0435,39.7,21.57,28.557,2.197,18.49,46.0,108327,1702249,1810576,0.9402,134501 30.48,0.175,18,25,informed,5.834,0.3155,31.7,9.388,12.234,0.87,18.49,-18.8,784705,674088,1458793,0.4621,134501 30.48,0.175,18,49,naive,3.515,0.1901,52.5,35.18,55.471,3.044,18.49,58.6,926679,2882010,3808689,0.7567,134501 30.48,0.175,18,49,informed,7.767,0.4201,52.2,19.3,32.206,1.589,18.49,25.4,2047663,1505017,3552680,0.4236,134501 30.48,0.175,25,1,naive,-19.418,-1.1586,4.5,2.985,3.025,0.065,16.76,6.6,-159868,42809,-117059,1.0,205825 30.48,0.175,25,1,informed,-11.996,-0.7158,2.8,1.659,1.683,0.009,16.76,6.0,-98761,5963,-92798,1.0,205825 30.48,0.175,25,4,naive,-13.925,-0.8308,11.7,10.776,11.058,1.438,16.76,18.2,-458562,1136540,677978,1.0,205825 30.48,0.175,25,4,informed,-6.354,-0.3791,9.9,4.71,5.51,0.244,16.76,16.2,-209255,192894,-16361,1.0,205825 30.48,0.175,25,9,naive,-11.187,-0.6675,17.3,21.345,22.877,2.148,16.76,29.7,-828959,1981083,1152124,1.0,205825 30.48,0.175,25,9,informed,-3.064,-0.1828,12.9,9.76,11.989,1.244,16.76,29.5,-227061,1147487,920426,1.0,205825 30.48,0.175,25,25,naive,-4.801,-0.2865,29.7,45.112,54.24,3.229,16.76,49.5,-988103,3828484,2840381,1.0,205825 30.48,0.175,25,25,informed,-0.306,-0.0183,20.5,17.406,25.774,1.691,16.76,43.1,-63069,2004573,1941504,1.0,205825 30.48,0.175,25,49,naive,-0.736,-0.0439,38.1,71.96,103.136,3.925,16.76,61.6,-296939,5687044,5390105,1.0,205825 30.48,0.175,25,49,informed,2.076,0.1239,34.8,41.467,66.383,2.68,16.76,62.4,837662,3883095,4720757,0.8226,205825 30.48,0.175,35,1,naive,-6.216,-0.7489,3.5,2.155,2.171,-0.583,8.3,,-74662,-559978,-634640,,300273 30.48,0.175,35,1,informed,-5.81,-0.7,3.0,1.529,1.587,-0.686,8.3,,-69781,-659156,-728937,,300273 30.48,0.175,35,4,naive,-3.446,-0.4152,11.3,7.331,7.377,0.226,8.3,,-165581,261070,95489,1.0,300273 30.48,0.175,35,4,informed,-3.843,-0.463,7.5,5.303,5.321,-0.024,8.3,,-184653,-28070,-212723,,300273 30.48,0.175,35,9,naive,-2.141,-0.258,23.1,14.1,14.638,0.543,8.3,,-231410,729893,498483,1.0,300273 30.48,0.175,35,9,informed,-1.202,-0.1448,17.7,7.798,10.468,0.274,8.3,,-129884,368778,238894,1.0,300273 30.48,0.175,35,25,naive,-0.01,-0.0012,41.5,30.76,35.603,0.979,8.3,,-3140,1692708,1689568,1.0,300273 30.48,0.175,35,25,informed,0.442,0.0533,35.0,15.894,25.548,0.532,8.3,,132725,919680,1052405,0.8739,300273 30.48,0.175,35,49,naive,1.387,0.1671,55.2,46.849,69.238,1.219,8.3,,816430,2577468,3393898,0.7594,300273 30.48,0.175,35,49,informed,1.581,0.1905,54.9,32.75,51.97,0.92,8.3,,930269,1945322,2875591,0.6765,300273 30.48,0.225,18,1,naive,-6.325,-0.4583,-9.2,1.342,1.276,1.166,13.8,7.4,-41200,607637,566437,1.0,162847 30.48,0.225,18,1,informed,-1.66,-0.1203,-41.1,0.587,0.607,1.126,13.8,5.7,-10812,586802,575990,1.0,162847 30.48,0.225,18,4,naive,-3.863,-0.2799,0.9,4.621,4.427,1.589,13.8,15.2,-100663,993803,893140,1.0,162847 30.48,0.225,18,4,informed,-1.061,-0.0769,-49.4,2.036,2.317,1.336,13.8,10.8,-27637,835593,807956,1.0,162847 30.48,0.225,18,9,naive,-1.371,-0.0993,12.6,8.704,8.892,1.734,13.8,24.0,-80391,1264711,1184320,1.0,162847 30.48,0.225,18,9,informed,-0.801,-0.058,-33.4,4.976,5.537,1.724,13.8,22.1,-46970,1257417,1210447,1.0,162847 30.48,0.225,18,25,naive,1.535,0.1112,34.5,20.069,24.254,2.445,13.8,46.5,250039,2293049,2543088,0.9017,162847 30.48,0.225,18,25,informed,1.478,0.1071,11.5,13.074,15.257,2.123,13.8,42.1,240651,1991076,2231727,0.8922,162847 30.48,0.225,18,49,naive,3.695,0.2678,45.0,30.693,44.046,2.813,13.8,60.5,1179385,3225244,4404629,0.7322,162847 30.48,0.225,18,49,informed,3.962,0.2871,39.5,27.328,37.889,2.642,13.8,57.4,1264487,3028430,4292917,0.7054,162847 30.48,0.225,25,1,naive,-4.576,-0.6045,5.4,1.555,1.59,0.003,7.57,17.4,-46265,2592,-43673,1.0,252756 30.48,0.225,25,1,informed,-1.323,-0.1748,4.7,0.612,0.629,0.001,7.57,23.2,-13378,1016,-12362,1.0,252756 30.48,0.225,25,4,naive,-3.699,-0.4886,18.1,5.541,5.858,0.06,7.57,40.4,-149607,58207,-91400,1.0,252756 30.48,0.225,25,4,informed,-0.708,-0.0935,16.3,1.989,2.256,0.003,7.57,48.4,-28646,3257,-25389,1.0,252756 30.48,0.225,25,9,naive,-3.926,-0.5186,13.3,13.284,14.494,0.394,7.57,49.2,-357231,446370,89139,1.0,252756 30.48,0.225,25,9,informed,-3.783,-0.4997,19.7,6.862,8.75,0.038,7.57,45.3,-344223,43142,-301081,1.0,252756 30.48,0.225,25,25,naive,-1.666,-0.2201,33.5,28.244,36.784,0.879,7.57,67.6,-421151,1279593,858442,1.0,252756 30.48,0.225,25,25,informed,-0.986,-0.1303,29.5,16.581,22.804,0.397,7.57,64.4,-249112,577648,328536,1.0,252756 30.48,0.225,25,49,naive,0.72,0.0951,48.1,38.426,65.986,1.039,7.57,78.3,356444,1848873,2205317,0.8384,252756 30.48,0.225,25,49,informed,1.567,0.207,52.8,31.637,50.63,0.897,7.57,80.6,776473,1596536,2373009,0.6728,252756 30.48,0.225,35,1,naive,-2.197,-0.7709,7.9,1.102,1.178,0.097,2.85,,-31563,110944,79381,1.0,359173 30.48,0.225,35,1,informed,-1.889,-0.6628,2.2,0.875,0.869,0.096,2.85,,-27144,110472,83328,1.0,359173 30.48,0.225,35,4,naive,-2.311,-0.8109,31.3,5.022,5.099,0.217,2.85,,-132807,299062,166255,1.0,359173 30.48,0.225,35,4,informed,-2.489,-0.8733,22.3,2.575,3.472,0.124,2.85,,-143017,171359,28342,1.0,359173 30.48,0.225,35,9,naive,-1.745,-0.6123,48.6,10.604,11.157,0.266,2.85,,-225696,427302,201606,1.0,359173 30.48,0.225,35,9,informed,-1.612,-0.5656,39.0,6.722,7.902,0.245,2.85,,-208463,394136,185673,1.0,359173 30.48,0.225,35,25,naive,-0.914,-0.3207,66.4,21.957,30.176,0.337,2.85,,-328270,698208,369938,1.0,359173 30.48,0.225,35,25,informed,-1.089,-0.3821,58.9,15.558,21.7,0.294,2.85,,-391010,607443,216433,1.0,359173 30.48,0.225,35,49,naive,-0.323,-0.1133,74.7,33.634,57.646,0.391,2.85,,-227389,987458,760069,1.0,359173 30.48,0.225,35,49,informed,-0.148,-0.0519,75.0,30.622,57.459,0.37,2.85,,-103986,935872,831886,1.0,359173`, d3.autoType); const compBySize = [{"n":1,"rule":"equal_share","members":54,"worse":51},{"n":1,"rule":"gain_share","members":54,"worse":51},{"n":1,"rule":"make_whole","members":54,"worse":51},{"n":1,"rule":"none","members":54,"worse":51},{"n":4,"rule":"equal_share","members":216,"worse":196},{"n":4,"rule":"gain_share","members":216,"worse":196},{"n":4,"rule":"make_whole","members":216,"worse":196},{"n":4,"rule":"none","members":216,"worse":196},{"n":9,"rule":"equal_share","members":486,"worse":369},{"n":9,"rule":"gain_share","members":486,"worse":369},{"n":9,"rule":"make_whole","members":486,"worse":347},{"n":9,"rule":"none","members":486,"worse":372},{"n":25,"rule":"equal_share","members":1350,"worse":500},{"n":25,"rule":"gain_share","members":1350,"worse":500},{"n":25,"rule":"make_whole","members":1350,"worse":394},{"n":25,"rule":"none","members":1350,"worse":542},{"n":49,"rule":"equal_share","members":2646,"worse":392},{"n":49,"rule":"gain_share","members":2646,"worse":392},{"n":49,"rule":"make_whole","members":2646,"worse":274},{"n":49,"rule":"none","members":2646,"worse":634}]; const makeWholeFeasible = [{"n":1,"mode":"informed","feasible":2,"districts":27},{"n":1,"mode":"naive","feasible":1,"districts":27},{"n":4,"mode":"informed","feasible":4,"districts":27},{"n":4,"mode":"naive","feasible":1,"districts":27},{"n":9,"mode":"informed","feasible":8,"districts":27},{"n":9,"mode":"naive","feasible":5,"districts":27},{"n":25,"mode":"informed","feasible":19,"districts":27},{"n":25,"mode":"naive","feasible":15,"districts":27},{"n":49,"mode":"informed","feasible":24,"districts":27},{"n":49,"mode":"naive","feasible":22,"districts":27}]; // Shared figure settings (same colours as the manuscript PNGs) const beliefColor = {domain: ["informed", "naive"], range: ["#2a78d6", "#eb6834"]}; const sizeTicks = [1, 4, 9, 25, 49]; const figure = (plotEl, caption) => { const fig = document.createElement("figure"); fig.style.cssText = "margin: 0; font: 12px sans-serif; max-width: 1000px;"; const cap = document.createElement("figcaption"); cap.style.cssText = "margin-top: 8px; line-height: 1.4;"; cap.innerHTML = caption; fig.append(plotEl, cap); return fig; }; // Table helper: columns = [{label, value: (row) => string, align}], header rows above as [[{label, span}]] const table = (caption, columns, rows, {groups = null, note = null} = {}) => { const wrap = document.createElement("figure"); wrap.style.cssText = "margin: 0; font: 12.5px sans-serif; max-width: 1000px; overflow-x: auto;"; const cap = document.createElement("figcaption"); cap.style.cssText = "margin-bottom: 6px; line-height: 1.4;"; cap.innerHTML = caption; const t = document.createElement("table"); t.style.cssText = "border-collapse: collapse; font-variant-numeric: tabular-nums;"; const th = (html, align = "right", span = 1, top = false) => { const c = document.createElement("th"); c.innerHTML = html; c.colSpan = span; c.style.cssText = `padding: 3px 8px; text-align: ${align}; font-weight: 600; border-bottom: 1px solid #888;` + (top ? " border-top: 1.5px solid #222;" : ""); return c; }; const thead = document.createElement("thead"); if (groups) for (const g of groups) { const tr = document.createElement("tr"); for (const x of g) tr.append(th(x.label, "center", x.span ?? 1, true)); thead.append(tr); } const hr = document.createElement("tr"); columns.forEach((c) => hr.append(th(c.label, c.align ?? "right", 1, !groups))); thead.append(hr); const tbody = document.createElement("tbody"); rows.forEach((r, i) => { const tr = document.createElement("tr"); for (const c of columns) { const td = document.createElement("td"); td.innerHTML = c.value(r, i); td.style.cssText = `padding: 2px 8px; text-align: ${c.align ?? "right"};` + (i === rows.length - 1 ? " border-bottom: 1.5px solid #222;" : ""); tr.append(td); } tbody.append(tr); }); t.append(thead, tbody); wrap.append(cap, t); if (note) { const p = document.createElement("div"); p.style.cssText = "margin-top: 6px; line-height: 1.4; color: #444;"; p.innerHTML = note; wrap.append(p); } return wrap; }; const pm = (x, k = 2) => { if (x == null || Number.isNaN(x)) return "–"; const s = Math.abs(x).toFixed(k); return (Number(s) === 0 ? "" : x > 0 ? "+" : "−") + s; }; display(`${designRuns.length} design rows, ${compBySize.length} compensation rows`); 4a:Tf63,// Figure 3. CID member gain by district size (manuscript alpha, Section 4.2; Table 3) const sizeStats = (rows, value) => d3.flatRollup(rows, (v) => ({ mean: d3.mean(v, value), median: d3.median(v, value), q1: d3.quantile(v, 0.25, value), q3: d3.quantile(v, 0.75, value), min: d3.min(v, value), max: d3.max(v, value), pos: 100 * d3.mean(v, (d) => (value(d) > 0 ? 1 : 0)), k: v.length }), (d) => d.mode, (d) => d.n).map(([mode, n, s]) => ({mode, n, ...s})); const offset = (d) => d.n * (d.mode === "naive" ? 0.93 : 1.075); // separate the two beliefs at each size const pct = (x) => (x > 0 ? "+" : "") + x.toFixed(2) + "%"; // Plot's log axis drops some tick labels (e.g. 9), so label the five CID sizes explicitly const sizeLabels = (extra = {}) => Plot.text(sizeTicks.map((n) => ({n, ...extra})), {x: "n", text: (d) => String(d.n), frameAnchor: "bottom", dy: 20, fontSize: 10, fill: "currentColor", ...Object.fromEntries(Object.keys(extra).map((k) => [k, k]))}); const fig3 = (() => { const stats = sizeStats(designRuns, (d) => d.G); const plot = Plot.plot({ width: 760, height: 450, marginLeft: 58, marginRight: 72, marginBottom: 52, x: {type: "log", domain: [0.85, 58], ticks: sizeTicks, tickFormat: () => "", label: "CID size (sections, log scale) →", labelOffset: 44}, y: {grid: true, label: "↑ Member NPV gain vs all-APM baseline (%)", tickFormat: (d) => (d > 0 ? "+" : "") + d}, color: {...beliefColor, legend: true, tickFormat: (d) => (d === "informed" ? "Informed members" : "Naive members")}, marks: [ Plot.ruleY([0], {stroke: "#52514e"}), sizeLabels(), Plot.text([{x: 0.9, y: 0, t: "no gain vs all-APM"}], {x: "x", y: "y", text: "t", dy: -8, textAnchor: "start", fill: "#52514e", fontSize: 10}), Plot.dot(designRuns, {x: offset, y: "G", fill: "mode", r: 1.8, fillOpacity: 0.3}), Plot.areaY(stats, {x: "n", y1: "q1", y2: "q3", fill: "mode", fillOpacity: 0.14}), Plot.lineY(stats, {x: "n", y: "mean", stroke: "mode", strokeWidth: 2.2}), Plot.dot(stats, {x: "n", y: "mean", fill: "mode", r: 4.5, stroke: "white", strokeWidth: 1.2, tip: true, title: (d) => `${d.mode === "informed" ? "Informed" : "Naive"} members, ${d.n}-section CID\n` + `mean ${pct(d.mean)} (IQR ${pct(d.q1)} to ${pct(d.q3)})\nrange ${pct(d.min)} to ${pct(d.max)}\n` + `district gains in ${d.pos.toFixed(0)}% of ${d.k} settings`}), Plot.text(stats.filter((d) => d.n === 49), {x: "n", y: "mean", text: (d) => (d.mode === "informed" ? "Informed" : "Naive"), dx: 9, textAnchor: "start", fill: "#222", fontSize: 11}) ] }); return figure(plot, "Figure 3. Small districts lose to their neighbors; member gains turn positive only at 9 to 49 sections. " + "Gain in net present value (NPV) of cooperative irrigation district (CID) members relative to the all-APM baseline " + "(G, Equation 13), by district size and member belief about leakage. Each small dot is one of the 27 aquifer settings " + "(3 hydraulic conductivities K × 3 specific yields S_y × 3 initial saturated thicknesses b₀), offset " + "horizontally by belief; lines join the means over settings and shaded bands span the interquartile range. Naive members " + "assume the water they save stays under their own land; informed members learn a leakage rate from observed aquifer behavior " + "(Section 2.6). One-section districts lose on average (−7.00% naive, −3.89% informed) and gain in only one of 54 cases. " + "Mean gains turn positive between 9 and 25 sections for informed districts and between 25 and 49 sections for naive " + "districts, reaching +1.63% and +2.86% at 49 sections, where 81% and 89% of settings show a gain. 30-year horizon, 4% " + "discount rate. Hover over a mean marker for its values. Gains for each setting: Figure S4 and Table S4."); })(); display(fig3); 4b:Tc7b,// Figure 4. Capture ratio by district size (manuscript alpha, Sections 3.3, 3.5 and 4.2; Table 4) const fig4 = (() => { const rows = designRuns.filter((d) => d.Gstar >= 2).map((d) => ({...d, kp: 100 * d.kappa})); const nSettings = new Set(rows.map((d) => `${d.K}|${d.Sy}|${d.b0}`)).size; const stats = sizeStats(rows, (d) => d.kp); const fmt = (x) => (x > 0 ? "+" : "") + x.toFixed(0) + "%"; const plot = Plot.plot({ width: 760, height: 450, marginLeft: 58, marginRight: 72, marginBottom: 52, x: {type: "log", domain: [0.85, 58], ticks: sizeTicks, tickFormat: () => "", label: "CID size (sections, log scale) →", labelOffset: 44}, y: {grid: true, label: "↑ Share of the potential gain G* that members keep, κ (%)", tickFormat: (d) => (d > 0 ? "+" : "") + d}, color: {...beliefColor, legend: true, tickFormat: (d) => (d === "informed" ? "Informed members" : "Naive members")}, marks: [ Plot.ruleY([0], {stroke: "#52514e"}), sizeLabels(), Plot.text([{x: 0.9, y: 0, t: "keeps none of the potential gain"}], {x: "x", y: "y", text: "t", dy: -8, textAnchor: "start", fill: "#52514e", fontSize: 10}), Plot.dot(rows, {x: offset, y: "kp", fill: "mode", r: 1.8, fillOpacity: 0.3}), Plot.areaY(stats, {x: "n", y1: "q1", y2: "q3", fill: "mode", fillOpacity: 0.14}), Plot.lineY(stats, {x: "n", y: "median", stroke: "mode", strokeWidth: 2.2}), Plot.dot(stats, {x: "n", y: "median", fill: "mode", r: 4.5, stroke: "white", strokeWidth: 1.2, tip: true, title: (d) => `${d.mode === "informed" ? "Informed" : "Naive"} members, ${d.n}-section CID\n` + `median κ ${fmt(d.median)} (IQR ${fmt(d.q1)} to ${fmt(d.q3)})\nmean κ ${fmt(d.mean)}; ${d.k} settings`}), Plot.text(stats.filter((d) => d.n === 49), {x: "n", y: "median", text: (d) => (d.mode === "informed" ? "Informed" : "Naive"), dx: 9, textAnchor: "start", fill: "#222", fontSize: 11}) ] }); return figure(plot, "Figure 4. Even 49-section districts keep only a fifth to a quarter of what conservation is worth. " + "Capture ratio κ = G / G*, the share of the potential gain from long-term management that district members keep when " + "their neighbors maximize annual profit, by district size and belief. G* is the gain of the all-LPM control run in the same " + `aquifer setting, in which no water can leak away (Equation 13). Lines show the median over the ${nSettings} aquifer settings ` + "with G* ≥ 2% and shaded bands the interquartile range; small dots are individual settings. The setting with G* = 0.04% " + "(K = 7.62 m/d, S_y = 0.225, b₀ = 25 m) is excluded because κ is unstable there (Section 3.5). " + "κ = 100% would mean that leakage costs the district nothing; κ below zero means that members are worse off than under APM. " + "Median κ is −55% (naive) and −29% (informed) for one-section districts and rises to +20% and +28% at 49 sections. In pooled " + "regressions with aquifer-setting fixed effects, the slope of κ in ln(size) roughly doubles beyond 4 to 9 sections (Table 4). " + "κ for each setting: Table S5."); })(); display(fig4); 4c:Tb97,// Figure 5. Member gain by size for each hydraulic conductivity and specific yield (manuscript alpha, Section 4.4) const fig5 = (() => { const stats = d3.flatRollup(designRuns, (v) => ({ mean: d3.mean(v, (d) => d.G), lo: d3.min(v, (d) => d.G), hi: d3.max(v, (d) => d.G) }), (d) => d.K, (d) => d.Sy, (d) => d.mode, (d) => d.n).map(([K, Sy, mode, n, s]) => ({K, Sy, mode, n, ...s})); const plot = Plot.plot({ width: 900, height: 660, marginLeft: 58, marginRight: 72, marginTop: 30, marginBottom: 52, x: {type: "log", domain: [0.85, 58], ticks: sizeTicks, tickFormat: () => "", label: "CID size (sections, log scale) →", labelOffset: 44}, y: {grid: true, labelAnchor: "center", label: "↑ Member NPV gain vs all-APM baseline (%)", tickFormat: (d) => (d > 0 ? "+" : "") + d}, fx: {label: null, tickFormat: (d) => `K = ${d} m/d`}, fy: {label: null, tickFormat: (d) => `Sy = ${d}`}, color: {...beliefColor, legend: true, tickFormat: (d) => (d === "informed" ? "Informed members" : "Naive members")}, marks: [ Plot.frame({stroke: "#e1e0d9"}), Plot.ruleY([0], {stroke: "#52514e"}), sizeLabels({fy: 0.225}), Plot.areaY(stats, {fx: "K", fy: "Sy", x: "n", y1: "lo", y2: "hi", fill: "mode", fillOpacity: 0.14}), Plot.lineY(stats, {fx: "K", fy: "Sy", x: "n", y: "mean", stroke: "mode", strokeWidth: 1.8}), Plot.dot(stats, {fx: "K", fy: "Sy", x: "n", y: "mean", fill: "mode", r: 3, stroke: "white", strokeWidth: 0.8, tip: true, title: (d) => `K = ${d.K} m/d, Sy = ${d.Sy}, ${d.n}-section CID, ${d.mode}\n` + `mean over b0 ${pct(d.mean)} (range ${pct(d.lo)} to ${pct(d.hi)})`}) ] }); return figure(plot, "Figure 5. Leakier aquifers erase more of the district's gain. CID member gain relative to the all-APM baseline by " + "district size, for each combination of hydraulic conductivity K (columns) and specific yield S_y (rows). Lines " + "show the mean over the three initial saturated thicknesses (b₀ = 18, 25 and 35 m) and shaded bands their range; " + "all panels share one vertical scale. Losses in small districts grow with K: one-section districts lose 12.0% (naive) and " + "5.6% (informed) on average at K = 30.48 m/d, against 2.8% and 1.8% at K = 7.62 m/d, and the largest losses (about 20% for " + "naive one-section districts) occur at K = 30.48 m/d and S_y = 0.125. These conductive, low-storage aquifers also " + "offer the largest potential gains (Figure S3): conservation is worth most where it leaks most. Knowing about leakage helps " + "most where leakage is fastest; informed members outperform naive ones by 4.4 percentage points on average at K = 30.48 m/d " + "against 0.6 points at K = 7.62 m/d. At 49 sections the differences narrow, and mean informed gains are 2.8–3.0% at all three " + "levels of K. Each of the 27 settings separately: Figure S4."); })(); display(fig5); 4d:Tbb2,// Figure 6. Members worse off than all-APM, by size and compensation rule (manuscript alpha, Sections 3.4 and 4.5) const fig6 = (() => { const rules = [ ["none", "No compensation", "#898781"], ["gain_share", "Equal or gain share", "#2a78d6"], // equal_share gives identical counts in this design ["make_whole", "Make-whole", "#1baf7a"] ]; const labelOf = new Map(rules.map(([r, l]) => [r, l])); const rows = compBySize.filter((d) => labelOf.has(d.rule)) .map((d) => ({...d, label: labelOf.get(d.rule), share: (100 * d.worse) / d.members})); const feasible = d3.rollup(makeWholeFeasible, (v) => (100 * d3.sum(v, (d) => d.feasible)) / d3.sum(v, (d) => d.districts), (d) => d.n); const plot = Plot.plot({ width: 760, height: 430, marginLeft: 58, marginBottom: 40, fx: {label: "CID size (sections) →", axis: "bottom", padding: 0.18}, x: {axis: null, domain: rules.map((r) => r[1]), paddingInner: 0.08}, y: {domain: [0, 100], grid: true, label: "↑ CID members worse off than all-APM (%)"}, color: {domain: rules.map((r) => r[1]), range: rules.map((r) => r[2]), legend: true}, marks: [ Plot.barY(rows, {fx: "n", x: "label", y: "share", fill: "label", tip: true, title: (d) => `${d.n}-section CID, ${d.label}\n${d.worse} of ${d.members} members worse off (${d.share.toFixed(0)}%)`}), Plot.text(rows.filter((d) => d.rule === "make_whole"), {fx: "n", x: "label", y: "share", dy: -8, fontSize: 10, fill: "#52514e", text: (d) => `${feasible.get(d.n).toFixed(0)}%`}), Plot.ruleY([0]) ] }); return figure(plot, "Figure 6. Most members of small districts are worse off, and compensation cannot fix it. Share of CID members " + "(producers of one section) who are worse off than in the all-APM baseline, by district size and budget-balanced compensation " + "rule (Section 3.4), pooled over the 27 aquifer settings and both beliefs (54 districts per size). Equal sharing and gain sharing " + "give identical results in the homogeneous domain and are shown as one bar. Numbers above the make-whole bars give the share of " + "districts in which the make-whole rule is feasible, that is, in which the gains of members who gain cover the losses of members " + "who lose. Without compensation, 94% of the members of one-section districts and 91% of the members of four-section districts " + "are worse off, and transfers change nothing there because the district as a whole loses in most settings. At 49 sections the " + "share worse off falls from 24% without compensation to 15% with equal or gain sharing and 10% with make-whole, which is feasible " + "in 85% of districts but at no size in every setting. Losses are concentrated on the boundary: at 49 sections, 63% of corner, 37% " + "of edge and 7% of interior members are worse off without compensation. Hover over a bar for member counts. By position: Figure S7 " + "and Table S7."); })(); display(fig6); 4e:T9a8,// Figure S3. Potential gain G* in each aquifer setting (all-LPM control runs; manuscript alpha, Section 4.1; SI Text S5) const figS3gstar = (() => { const settings = Array.from(d3.group(designRuns, (d) => `${d.K}|${d.Sy}|${d.b0}`).values(), (v) => ({K: v[0].K, Sy: v[0].Sy, b0: v[0].b0, Gstar: v[0].Gstar})); const plot = Plot.plot({ width: 760, height: 300, marginLeft: 70, marginBottom: 44, x: {type: "band", domain: ["7.62", "15.24", "30.48"], label: "Hydraulic conductivity K (m/d)"}, y: {type: "band", domain: ["0.125", "0.175", "0.225"], label: "Specific yield Sy"}, fx: {label: null, tickFormat: (d) => `Initial thickness b0 = ${d} m`}, color: {type: "linear", scheme: "blues", domain: [0, 27], legend: true, label: "Potential gain G* (%)"}, marks: [ Plot.cell(settings, {fx: "b0", x: (d) => String(d.K), y: (d) => String(d.Sy), fill: "Gstar", inset: 1, tip: true, title: (d) => `K = ${d.K} m/d, Sy = ${d.Sy}, b0 = ${d.b0} m\nG* = ${d.Gstar.toFixed(2)}%`}), Plot.text(settings, {fx: "b0", x: (d) => String(d.K), y: (d) => String(d.Sy), text: (d) => d.Gstar.toFixed(1), fill: (d) => (d.Gstar > 15 ? "white" : "#0b0b0b"), fontSize: 11}) ] }); return figure(plot, "Figure S3. The potential gain from long-term management, G*, is largest in conductive, low-storage, thin aquifers. NPV gain per pivot (%) when every producer in the domain follows long-term profit maximization (all-LPM control run), relative to the all-APM baseline in the same aquifer setting (Equation 13). With no non-conserving neighbors, no conserved water can leak away, so G* is the benchmark for the capture ratio (Figure 4). Panels show initial saturated thickness b₀; columns hydraulic conductivity K; rows specific yield S_y. G* ranges from 0.04% to 26.6% across the 27 settings (median 9.3%); averaged over the other two properties it is 7.0%, 11.2% and 14.6% at K = 7.62, 15.24 and 30.48 m/d, 16.2%, 10.5% and 6.1% at S_y = 0.125, 0.175 and 0.225, and 12.6%, 11.0% and 9.3% at b₀ = 18, 25 and 35 m. The baseline setting (K = 15.24 m/d, S_y = 0.175, b₀ = 25 m) gives 14.45%, reproducing the stand-alone gain used to verify the coupling (Section 2.8). The setting with G* = 0.04% (K = 7.62 m/d, S_y = 0.225, b₀ = 25 m) is excluded from the tests that use the capture ratio. Values with NPVs: Table S3."); })(); display(figS3gstar); 4f:Tca4,// Figure S5. Retention and leakage of deferred water by district size (manuscript alpha, Sections 3.2 and 4.3; Table 3; SI Text S6) const figS5retention = (() => { const panel = (tag, rows, value, yLabel, yFmt) => { const stats = sizeStats(rows, value); const plot = Plot.plot({ width: 450, height: 380, marginLeft: 56, marginRight: 20, marginBottom: 52, x: {type: "log", domain: [0.85, 58], ticks: sizeTicks, tickFormat: () => "", label: "CID size (sections, log scale) →", labelOffset: 44}, y: {grid: true, nice: true, label: yLabel}, color: {...beliefColor, legend: true, tickFormat: (d) => (d === "informed" ? "Informed members" : "Naive members")}, marks: [ Plot.ruleY([0]), sizeLabels(), Plot.areaY(stats, {x: "n", y1: "q1", y2: "q3", fill: "mode", fillOpacity: 0.14}), Plot.lineY(stats, {x: "n", y: "median", stroke: "mode", strokeWidth: 2.2}), Plot.dot(stats, {x: "n", y: "median", fill: "mode", r: 4.5, stroke: "white", strokeWidth: 1.2, tip: true, title: (d) => `${d.mode === "informed" ? "Informed" : "Naive"}, ${d.n}-section CID\nmedian ${yFmt(d.median)} ` + `(IQR ${yFmt(d.q1)} to ${yFmt(d.q3)}); ${d.k} settings`}) ] }); const d = document.createElement("div"); const b = document.createElement("div"); b.textContent = tag; b.style.cssText = "font: bold 13px sans-serif; margin-bottom: 2px;"; d.append(b, plot); return d; }; const f0 = (x) => x.toFixed(0) + "%"; const a = panel("(a) Retention of deferred water at year 10", designRuns.filter((d) => d.R10 !== null), (d) => d.R10, "↑ R(10), deferred water still under the district (%)", f0); const b = panel("(b) Water leaked to neighbors by year 30", designRuns.filter((d) => d.D > 0), (d) => (100 * d.L30) / d.D, "↑ L(30) as a share of peak deferral (%)", f0); const row = document.createElement("div"); row.style.cssText = "display: grid; grid-template-columns: repeat(2, 470px); gap: 16px;"; row.append(a, b); return figure(row, "Figure S5. Small districts lose almost all the water they defer; larger districts keep about half. Fate of the water a district defers relative to the all-APM baseline, by district size and belief (blue: informed; orange: naive members). (a) Retention at year 10, R(10) = 1 − L(10)/D(10), the share of the cumulative deferred water still under the district (Equation 11). (b) Cumulative net outflow to the neighbors by year 30, L(30), as a share of the peak cumulative deferral D. Lines show medians over the 27 aquifer settings and shaded bands the interquartile range; settings without deferral are omitted. One-section districts keep only 4–5% of the deferred water at year 10 and lose 97–98% of their peak deferral by year 30. At 49 sections median retention reaches 55% for both beliefs and the leaked share falls to about half. Informed members defer about half as much water as naive members in districts of up to nine sections and so lose less in absolute terms, but their retention is slightly lower up to 25 sections, which is why retention serves as a diagnostic rather than as the primary outcome (Section 3.2)."); })(); display(figS5retention); 50:T6c8,// Table 3. District outcomes by size (manuscript alpha, Sections 4.2 and 4.3), computed from designRuns const table3 = (() => { const kp = designRuns.filter((d) => d.Gstar >= 2); const pair = (rows, f, n) => ["naive", "informed"].map((m) => f(rows.filter((d) => d.n === n && d.mode === m))); const rows = sizeTicks.map((n) => { const G = pair(designRuns, (v) => d3.mean(v, (d) => d.G), n); const P = pair(designRuns, (v) => (100 * d3.mean(v, (d) => (d.G > 0 ? 1 : 0))), n); const k = pair(kp, (v) => d3.mean(v, (d) => d.kappa), n); const R = pair(designRuns, (v) => d3.median(v, (d) => d.R10), n); const L = pair(designRuns, (v) => d3.median(v, (d) => d.L30), n); return {n, G, P, k, R, L}; }); const sg = (x, k) => (x > 0 ? "+" : x < 0 ? "−" : "") + Math.abs(x).toFixed(k); const both = (v, f) => `${f(v[0])} / ${f(v[1])}`; return table("Table 3. District outcomes by size. Each cell gives naive / informed values. G: mean over the 27 aquifer settings. Settings with G > 0: share of the 27 settings in which the district gains. κ: mean over the 26 settings with G* ≥ 2%. R(10) and L(30): medians over the 27 settings.", [ {label: "CID size (sections)", value: (r) => r.n, align: "left"}, {label: "G (%)", value: (r) => both(r.G, (x) => (r.n >= 25 ? sg(x, 2) : "−" + Math.abs(x).toFixed(2)))}, {label: "Settings with G > 0 (%)", value: (r) => both(r.P, (x) => x.toFixed(0))}, {label: "κ", value: (r) => both(r.k, (x) => (r.n >= 25 ? sg(x, 2) : "−" + Math.abs(x).toFixed(2)))}, {label: "R(10) (%)", value: (r) => both(r.R, (x) => x.toFixed(1))}, {label: "L(30) (Mm³)", value: (r) => both(r.L, (x) => x.toFixed(1))} ], rows); })(); display(table3); 51:Taca,// Table 4. Segmented regressions of the capture ratio on ln(size) with aquifer-setting fixed effects // (manuscript alpha, Sections 3.5 and 4.2). Same models as scripts/12_hypothesis_tests.py, fitted here by least squares. const olsAIC = (X, y) => { // least squares via the normal equations (Gaussian elimination with partial pivoting) const k = X[0].length, n = y.length; const A = Array.from({length: k}, (_, i) => Array.from({length: k + 1}, (_, j) => j < k ? d3.sum(X, (r) => r[i] * r[j]) : d3.sum(X, (r, t) => r[i] * y[t]))); for (let c = 0; c < k; c++) { let p = c; for (let r = c + 1; r < k; r++) if (Math.abs(A[r][c]) > Math.abs(A[p][c])) p = r; [A[c], A[p]] = [A[p], A[c]]; for (let r = 0; r < k; r++) { if (r === c) continue; const f = A[r][c] / A[c][c]; for (let j = c; j <= k; j++) A[r][j] -= f * A[c][j]; } } const beta = A.map((row, i) => row[k] / row[i]); const rss = d3.sum(X, (r, t) => (y[t] - d3.sum(r, (x, j) => x * beta[j])) ** 2); return {aic: n * Math.log(rss / n) + 2 * k, beta}; }; const kappaRegressions = (() => { const out = []; for (const mode of ["naive", "informed"]) { const d = designRuns.filter((r) => r.Gstar >= 2 && r.mode === mode); const keys = [...new Set(d.map((r) => `${r.K}|${r.Sy}|${r.b0}`))]; const fe = (r) => keys.map((s) => (s === `${r.K}|${r.Sy}|${r.b0}` ? 1 : 0)); const y = d.map((r) => r.kappa), x = d.map((r) => Math.log(r.n)); const lin = olsAIC(d.map((r, t) => [...fe(r), x[t]]), y); out.push({mode, model: "Linear", bp: null, aic: lin.aic, dAIC: 0, before: lin.beta.at(-1), after: null}); for (const c of [4, 9, 25]) { const s = olsAIC(d.map((r, t) => [...fe(r), x[t], Math.max(0, x[t] - Math.log(c))]), y); out.push({mode, model: "Segmented", bp: c, aic: s.aic, dAIC: s.aic - lin.aic, before: s.beta.at(-2), after: s.beta.at(-2) + s.beta.at(-1)}); } } return out; })(); const table4 = table("Table 4. Pooled regressions of the capture ratio κ on ln(district size) with aquifer-setting fixed effects, 130 districts per belief (26 settings × 5 sizes). ΔAIC is relative to the linear model; slopes are per unit of ln(size).", [ {label: "Belief", value: (r, i) => (i % 4 === 0 ? (r.mode === "naive" ? "Naive" : "Informed") : ""), align: "left"}, {label: "Model", value: (r) => r.model, align: "left"}, {label: "Breakpoint (sections)", value: (r) => r.bp ?? "—"}, {label: "AIC", value: (r) => pm(r.aic, 1).replace("+", "")}, {label: "ΔAIC", value: (r) => (r.dAIC === 0 ? "0" : pm(r.dAIC, 1))}, {label: "Slope before → after", value: (r) => (r.after == null ? r.before.toFixed(2) : `${r.before.toFixed(2)} → ${r.after.toFixed(2)}`)} ], kappaRegressions); display(table4); 52:T5c4,// Table 5. Variance explained by the main effect of each design factor (eta-squared; manuscript alpha, Section 4.4) const etaSquared = (rows, value, factor) => { const v = rows.filter((d) => value(d) != null); const grand = d3.mean(v, value), sst = d3.sum(v, (d) => (value(d) - grand) ** 2); const ss = d3.sum(d3.rollups(v, (g) => g.length * (d3.mean(g, value) - grand) ** 2, factor), ([, s]) => s); return ss / sst; }; const table5 = (() => { const kp = designRuns.filter((d) => d.Gstar >= 2); const factors = [ ["CID size", (d) => d.n], ["Hydraulic conductivity K", (d) => d.K], ["Specific yield Sy", (d) => d.Sy], ["Initial saturated thickness b₀", (d) => d.b0]]; const rows = factors.map(([label, f]) => ({label, k: etaSquared(kp, (d) => d.kappa, f), r: etaSquared(designRuns, (d) => d.R10, f)})); rows.push({label: "Aquifer properties combined", k: d3.sum(rows.slice(1), (d) => d.k), r: d3.sum(rows.slice(1), (d) => d.r)}); rows.push({label: "Belief (naive / informed)", k: etaSquared(kp, (d) => d.kappa, (d) => d.mode), r: etaSquared(designRuns, (d) => d.R10, (d) => d.mode)}); return table("Table 5. Share of variance explained by the main effect of each design factor (η²). κ: 26 aquifer settings; R(10): 27 settings.", [ {label: "Factor", value: (r) => r.label, align: "left"}, {label: "κ", value: (r) => r.k.toFixed(2)}, {label: "R(10)", value: (r) => r.r.toFixed(2)} ], rows); })(); display(table5); 53:T1337a,// Data behind the Supporting Information figures and tables (generated by gen_data_cells.py from results/design). // controls: all-LPM control vs all-APM baseline per aquifer setting (controls.csv): NPV per pivot ($) and G* (%). // members: one row per setting x size x belief x member position, from compensation_summary.csv: // members = number of members in that position; gain, gainGS, gainMW = mean member gain (% of all-APM NPV) with no // compensation, gain sharing and make-whole; worse* = members worse off than all-APM; trMW = mean annual make-whole // transfer per member ($/yr, positive = received); mwFeasible = 1 if make-whole keeps every member whole. // timeSeries: annual cumulative deferral D, leakage L (Mm3) and retention R (%) for Sy = 0.175, b0 = 25 m and three K // (from the run JSON zone budgets). phiPath: informed members' estimated leakage rate by year, K = 15.24 m/d. // bmin: smallest saturated thickness (m) that sustains each well-capacity class (wellcap.py, Equation 6). // runtime: run times of the 16 Oct 2026 batch (status.csv), minutes per run. const controls = [{"K":7.62,"Sy":0.125,"b0":18.0,"npvA":65184,"npvL":70343,"Gstar":7.91},{"K":7.62,"Sy":0.125,"b0":25.0,"npvA":116862,"npvL":130348,"Gstar":11.54},{"K":7.62,"Sy":0.125,"b0":35.0,"npvA":176930,"npvL":197302,"Gstar":11.51},{"K":7.62,"Sy":0.175,"b0":18.0,"npvA":81705,"npvL":88241,"Gstar":8.0},{"K":7.62,"Sy":0.175,"b0":25.0,"npvA":137499,"npvL":142051,"Gstar":3.31},{"K":7.62,"Sy":0.175,"b0":35.0,"npvA":207003,"npvL":221288,"Gstar":6.9},{"K":7.62,"Sy":0.225,"b0":18.0,"npvA":99016,"npvL":103534,"Gstar":4.56},{"K":7.62,"Sy":0.225,"b0":25.0,"npvA":144595,"npvL":144647,"Gstar":0.04},{"K":7.62,"Sy":0.225,"b0":35.0,"npvA":222582,"npvL":243334,"Gstar":9.32},{"K":15.24,"Sy":0.125,"b0":18.0,"npvA":90334,"npvL":106041,"Gstar":17.39},{"K":15.24,"Sy":0.125,"b0":25.0,"npvA":140004,"npvL":164383,"Gstar":17.41},{"K":15.24,"Sy":0.125,"b0":35.0,"npvA":209142,"npvL":243533,"Gstar":16.44},{"K":15.24,"Sy":0.175,"b0":18.0,"npvA":114539,"npvL":127565,"Gstar":11.37},{"K":15.24,"Sy":0.175,"b0":25.0,"npvA":171767,"npvL":196583,"Gstar":14.45},{"K":15.24,"Sy":0.175,"b0":35.0,"npvA":265663,"npvL":284371,"Gstar":7.04},{"K":15.24,"Sy":0.225,"b0":18.0,"npvA":131230,"npvL":138251,"Gstar":5.35},{"K":15.24,"Sy":0.225,"b0":25.0,"npvA":200449,"npvL":213555,"Gstar":6.54},{"K":15.24,"Sy":0.225,"b0":35.0,"npvA":303780,"npvL":319540,"Gstar":5.19},{"K":30.48,"Sy":0.125,"b0":18.0,"npvA":101507,"npvL":128492,"Gstar":26.59},{"K":30.48,"Sy":0.125,"b0":25.0,"npvA":160845,"npvL":194776,"Gstar":21.1},{"K":30.48,"Sy":0.125,"b0":35.0,"npvA":233460,"npvL":270972,"Gstar":16.07},{"K":30.48,"Sy":0.175,"b0":18.0,"npvA":134501,"npvL":159368,"Gstar":18.49},{"K":30.48,"Sy":0.175,"b0":25.0,"npvA":205825,"npvL":240312,"Gstar":16.76},{"K":30.48,"Sy":0.175,"b0":35.0,"npvA":300273,"npvL":325203,"Gstar":8.3},{"K":30.48,"Sy":0.225,"b0":18.0,"npvA":162847,"npvL":185315,"Gstar":13.8},{"K":30.48,"Sy":0.225,"b0":25.0,"npvA":252756,"npvL":271893,"Gstar":7.57},{"K":30.48,"Sy":0.225,"b0":35.0,"npvA":359173,"npvL":369418,"Gstar":2.85}]; const members = d3.csvParse(`K,Sy,b0,n,mode,position,members,gain,gainGS,gainMW,worse,worseGS,worseMW,trMW,mwFeasible 7.62,0.125,18,1,informed,single,1,-3.86,-3.86,-3.86,1,1,1,0,0 7.62,0.125,18,1,naive,single,1,-3.9,-3.9,-3.9,1,1,1,0,0 7.62,0.125,18,4,informed,corner,4,-1.54,-1.54,-1.54,4,4,4,0,0 7.62,0.125,18,4,naive,corner,4,-2.56,-2.56,-2.56,4,4,4,0,0 7.62,0.125,18,9,informed,corner,4,-0.83,0.07,0.0,4,0,0,126,1 7.62,0.125,18,9,informed,edge,4,1.28,0.07,0.16,0,0,0,-169,1 7.62,0.125,18,9,informed,interior,1,-1.14,0.07,0.0,1,0,0,172,1 7.62,0.125,18,9,naive,corner,4,-2.07,-0.29,-0.59,4,4,4,224,0 7.62,0.125,18,9,naive,edge,4,1.66,-0.29,0.0,0,4,0,-250,0 7.62,0.125,18,9,naive,interior,1,-0.97,-0.29,-0.27,1,1,1,105,0 7.62,0.125,18,25,informed,corner,4,-2.29,2.53,0.0,4,0,0,346,1 7.62,0.125,18,25,informed,edge,12,1.74,2.53,1.52,0,0,0,-33,1 7.62,0.125,18,25,informed,interior,9,5.73,2.53,5.01,0,0,0,-109,1 7.62,0.125,18,25,naive,corner,4,-0.3,-0.45,-0.24,4,4,4,9,0 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30.48,0.125,35,49,informed,corner,4,-3.06,0.12,0.0,4,0,0,1650,1 30.48,0.125,35,49,informed,edge,20,-0.34,0.12,0.01,16,0,0,189,1 30.48,0.125,35,49,informed,interior,25,1.0,0.12,0.23,0,0,0,-415,1 30.48,0.125,35,49,naive,corner,4,-12.16,-7.94,-12.16,4,4,4,0,0 30.48,0.125,35,49,naive,edge,20,-10.13,-7.94,-10.13,20,20,20,0,0 30.48,0.125,35,49,naive,interior,25,-5.52,-7.94,-5.52,25,25,25,0,0 30.48,0.175,18,1,informed,single,1,-4.25,-4.25,-4.25,1,1,1,0,0 30.48,0.175,18,1,naive,single,1,-10.22,-10.22,-10.22,1,1,1,0,0 30.48,0.175,18,4,informed,corner,4,-0.86,-0.86,-0.86,4,4,4,0,0 30.48,0.175,18,4,naive,corner,4,-5.68,-5.68,-5.68,4,4,4,0,0 30.48,0.175,18,9,informed,corner,4,1.03,3.1,1.03,0,0,0,0,1 30.48,0.175,18,9,informed,edge,4,4.24,3.1,4.24,0,0,0,0,1 30.48,0.175,18,9,informed,interior,1,6.81,3.1,6.81,0,0,0,0,1 30.48,0.175,18,9,naive,corner,4,-4.31,-4.02,-4.31,4,4,4,0,0 30.48,0.175,18,9,naive,edge,4,-3.9,-4.02,-3.9,4,4,4,0,0 30.48,0.175,18,9,naive,interior,1,-3.39,-4.02,-3.39,1,1,1,0,0 30.48,0.175,18,25,informed,corner,4,4.57,5.83,4.57,0,0,0,0,1 30.48,0.175,18,25,informed,edge,12,5.37,5.83,5.37,0,0,0,0,1 30.48,0.175,18,25,informed,interior,9,7.01,5.83,7.01,0,0,0,0,1 30.48,0.175,18,25,naive,corner,4,-2.55,0.81,0.0,4,0,0,792,1 30.48,0.175,18,25,naive,edge,12,1.37,0.81,0.91,0,0,0,-143,1 30.48,0.175,18,25,naive,interior,9,1.54,0.81,1.02,0,0,0,-161,1 30.48,0.175,18,49,informed,corner,4,5.7,7.77,5.7,0,0,0,0,1 30.48,0.175,18,49,informed,edge,20,7.42,7.77,7.42,0,0,0,0,1 30.48,0.175,18,49,informed,interior,25,8.38,7.77,8.38,0,0,0,0,1 30.48,0.175,18,49,naive,corner,4,-1.66,3.52,0.0,4,0,0,517,1 30.48,0.175,18,49,naive,edge,20,2.61,3.52,2.51,0,0,0,-30,1 30.48,0.175,18,49,naive,interior,25,5.07,3.52,4.88,0,0,0,-59,1 30.48,0.175,25,1,informed,single,1,-12.0,-12.0,-12.0,1,1,1,0,0 30.48,0.175,25,1,naive,single,1,-19.42,-19.42,-19.42,1,1,1,0,0 30.48,0.175,25,4,informed,corner,4,-6.35,-6.35,-6.35,4,4,4,0,0 30.48,0.175,25,4,naive,corner,4,-13.92,-13.92,-13.92,4,4,4,0,0 30.48,0.175,25,9,informed,corner,4,-4.12,-3.06,-4.12,4,4,4,0,0 30.48,0.175,25,9,informed,edge,4,-2.28,-3.06,-2.28,4,4,4,0,0 30.48,0.175,25,9,informed,interior,1,-1.95,-3.06,-1.95,1,1,1,0,0 30.48,0.175,25,9,naive,corner,4,-11.45,-11.19,-11.45,4,4,4,0,0 30.48,0.175,25,9,naive,edge,4,-10.65,-11.19,-10.65,4,4,4,0,0 30.48,0.175,25,9,naive,interior,1,-12.32,-11.19,-12.32,1,1,1,0,0 30.48,0.175,25,25,informed,corner,4,-3.29,-0.31,-1.66,4,4,4,777,0 30.48,0.175,25,25,informed,edge,12,0.58,-0.31,0.0,0,12,0,-278,0 30.48,0.175,25,25,informed,interior,9,-0.17,-0.31,-0.11,8,9,8,25,0 30.48,0.175,25,25,naive,corner,4,-8.83,-4.8,-8.83,4,4,4,0,0 30.48,0.175,25,25,naive,edge,12,-4.59,-4.8,-4.59,12,12,12,0,0 30.48,0.175,25,25,naive,interior,9,-3.29,-4.8,-3.29,9,9,9,0,0 30.48,0.175,25,49,informed,corner,4,-0.1,2.08,0.0,4,0,0,48,1 30.48,0.175,25,49,informed,edge,20,0.77,2.08,0.77,0,0,0,-1,1 30.48,0.175,25,49,informed,interior,25,3.47,2.08,3.46,0,0,0,-7,1 30.48,0.175,25,49,naive,corner,4,-7.34,-0.74,-2.6,4,4,4,2257,0 30.48,0.175,25,49,naive,edge,20,-3.18,-0.74,-1.13,20,20,20,977,0 30.48,0.175,25,49,naive,interior,25,2.27,-0.74,-0.13,4,25,4,-1143,0 30.48,0.175,35,1,informed,single,1,-5.81,-5.81,-5.81,1,1,1,0,0 30.48,0.175,35,1,naive,single,1,-6.22,-6.22,-6.22,1,1,1,0,0 30.48,0.175,35,4,informed,corner,4,-3.84,-3.84,-3.84,4,4,4,0,0 30.48,0.175,35,4,naive,corner,4,-3.45,-3.45,-3.45,4,4,4,0,0 30.48,0.175,35,9,informed,corner,4,-1.93,-1.2,-1.84,4,4,4,66,0 30.48,0.175,35,9,informed,edge,4,-0.91,-1.2,-0.86,4,4,4,31,0 30.48,0.175,35,9,informed,interior,1,0.56,-1.2,0.0,0,1,0,-387,0 30.48,0.175,35,9,naive,corner,4,-2.58,-2.14,-2.58,4,4,4,0,0 30.48,0.175,35,9,naive,edge,4,-1.79,-2.14,-1.79,4,4,4,0,0 30.48,0.175,35,9,naive,interior,1,-1.76,-2.14,-1.76,1,1,1,0,0 30.48,0.175,35,25,informed,corner,4,-1.17,0.44,0.0,4,0,0,810,1 30.48,0.175,35,25,informed,edge,12,0.26,0.44,0.18,0,0,0,-54,1 30.48,0.175,35,25,informed,interior,9,1.4,0.44,0.98,0,0,0,-288,1 30.48,0.175,35,25,naive,corner,4,-1.52,-0.01,-0.03,4,4,4,1034,0 30.48,0.175,35,25,naive,edge,12,-0.72,-0.01,-0.01,12,12,12,490,0 30.48,0.175,35,25,naive,interior,9,1.6,-0.01,0.0,0,9,0,-1113,0 30.48,0.175,35,49,informed,corner,4,-0.64,1.58,0.0,4,0,0,442,1 30.48,0.175,35,49,informed,edge,20,1.25,1.58,1.21,0,0,0,-28,1 30.48,0.175,35,49,informed,interior,25,2.2,1.58,2.13,0,0,0,-48,1 30.48,0.175,35,49,naive,corner,4,-1.48,1.39,0.0,4,0,0,1026,1 30.48,0.175,35,49,naive,edge,20,0.4,1.39,0.51,8,0,0,71,1 30.48,0.175,35,49,naive,interior,25,2.63,1.39,2.31,0,0,0,-221,1 30.48,0.225,18,1,informed,single,1,-1.66,-1.66,-1.66,1,1,1,0,0 30.48,0.225,18,1,naive,single,1,-6.32,-6.32,-6.32,1,1,1,0,0 30.48,0.225,18,4,informed,corner,4,-1.06,-1.06,-1.06,4,4,4,0,0 30.48,0.225,18,4,naive,corner,4,-3.86,-3.86,-3.86,4,4,4,0,0 30.48,0.225,18,9,informed,corner,4,-1.51,-0.8,-1.18,4,4,4,128,0 30.48,0.225,18,9,informed,edge,4,-0.81,-0.8,-0.63,4,4,4,68,0 30.48,0.225,18,9,informed,interior,1,2.08,-0.8,0.0,0,1,0,-783,0 30.48,0.225,18,9,naive,corner,4,-1.85,-1.37,-1.78,4,4,4,28,0 30.48,0.225,18,9,naive,edge,4,-1.36,-1.37,-1.31,4,4,4,21,0 30.48,0.225,18,9,naive,interior,1,0.52,-1.37,0.0,0,1,0,-195,0 30.48,0.225,18,25,informed,corner,4,-0.39,1.48,0.0,4,0,0,149,1 30.48,0.225,18,25,informed,edge,12,2.35,1.48,2.16,0,0,0,-71,1 30.48,0.225,18,25,informed,interior,9,1.15,1.48,1.22,4,0,0,29,1 30.48,0.225,18,25,naive,corner,4,-0.09,1.54,0.0,4,0,0,33,1 30.48,0.225,18,25,naive,edge,12,2.23,1.54,2.21,0,0,0,-8,1 30.48,0.225,18,25,naive,interior,9,1.33,1.54,1.32,0,0,0,-5,1 30.48,0.225,18,49,informed,corner,4,0.34,3.96,0.34,0,0,0,0,1 30.48,0.225,18,49,informed,edge,20,2.6,3.96,2.6,0,0,0,0,1 30.48,0.225,18,49,informed,interior,25,5.63,3.96,5.63,0,0,0,0,1 30.48,0.225,18,49,naive,corner,4,0.27,3.7,0.27,0,0,0,0,1 30.48,0.225,18,49,naive,edge,20,3.42,3.7,3.42,0,0,0,0,1 30.48,0.225,18,49,naive,interior,25,4.46,3.7,4.46,0,0,0,0,1 30.48,0.225,25,1,informed,single,1,-1.32,-1.32,-1.32,1,1,1,0,0 30.48,0.225,25,1,naive,single,1,-4.58,-4.58,-4.58,1,1,1,0,0 30.48,0.225,25,4,informed,corner,4,-0.71,-0.71,-0.71,4,4,4,0,0 30.48,0.225,25,4,naive,corner,4,-3.7,-3.7,-3.7,4,4,4,0,0 30.48,0.225,25,9,informed,corner,4,-4.06,-3.78,-4.06,4,4,4,0,0 30.48,0.225,25,9,informed,edge,4,-3.97,-3.78,-3.97,4,4,4,0,0 30.48,0.225,25,9,informed,interior,1,-1.92,-3.78,-1.92,1,1,1,0,0 30.48,0.225,25,9,naive,corner,4,-3.48,-3.93,-3.48,4,4,4,0,0 30.48,0.225,25,9,naive,edge,4,-4.32,-3.93,-4.32,4,4,4,0,0 30.48,0.225,25,9,naive,interior,1,-4.1,-3.93,-4.1,1,1,1,0,0 30.48,0.225,25,25,informed,corner,4,-0.0,-0.99,-0.0,4,4,4,0,0 30.48,0.225,25,25,informed,edge,12,-2.27,-0.99,-2.05,12,12,12,127,0 30.48,0.225,25,25,informed,interior,9,0.29,-0.99,0.0,0,9,0,-169,0 30.48,0.225,25,25,naive,corner,4,-0.93,-1.67,-0.82,4,4,4,60,0 30.48,0.225,25,25,naive,edge,12,-3.6,-1.67,-3.2,12,12,12,235,0 30.48,0.225,25,25,naive,interior,9,0.58,-1.67,0.0,0,9,0,-340,0 30.48,0.225,25,49,informed,corner,4,0.1,1.57,0.1,0,0,0,0,1 30.48,0.225,25,49,informed,edge,20,1.85,1.57,1.85,0,0,0,0,1 30.48,0.225,25,49,informed,interior,25,1.58,1.57,1.58,0,0,0,0,1 30.48,0.225,25,49,naive,corner,4,-0.0,0.72,0.0,4,0,0,2,1 30.48,0.225,25,49,naive,edge,20,-0.13,0.72,0.13,8,0,0,156,1 30.48,0.225,25,49,naive,interior,25,1.52,0.72,1.3,4,0,0,-125,1 30.48,0.225,35,1,informed,single,1,-1.89,-1.89,-1.89,1,1,1,0,0 30.48,0.225,35,1,naive,single,1,-2.2,-2.2,-2.2,1,1,1,0,0 30.48,0.225,35,4,informed,corner,4,-2.49,-2.49,-2.49,4,4,4,0,0 30.48,0.225,35,4,naive,corner,4,-2.31,-2.31,-2.31,4,4,4,0,0 30.48,0.225,35,9,informed,corner,4,-1.61,-1.61,-1.61,4,4,4,0,0 30.48,0.225,35,9,informed,edge,4,-1.74,-1.61,-1.74,4,4,4,0,0 30.48,0.225,35,9,informed,interior,1,-1.11,-1.61,-1.11,1,1,1,0,0 30.48,0.225,35,9,naive,corner,4,-1.68,-1.75,-1.68,4,4,4,0,0 30.48,0.225,35,9,naive,edge,4,-1.6,-1.75,-1.6,4,4,4,0,0 30.48,0.225,35,9,naive,interior,1,-2.59,-1.75,-2.59,1,1,1,0,0 30.48,0.225,35,25,informed,corner,4,-1.79,-1.09,-1.79,4,4,4,0,0 30.48,0.225,35,25,informed,edge,12,-1.17,-1.09,-1.17,12,12,12,0,0 30.48,0.225,35,25,informed,interior,9,-0.67,-1.09,-0.67,9,9,9,0,0 30.48,0.225,35,25,naive,corner,4,-2.36,-0.91,-2.03,4,4,4,274,0 30.48,0.225,35,25,naive,edge,12,-1.43,-0.91,-1.23,12,12,12,166,0 30.48,0.225,35,25,naive,interior,9,0.41,-0.91,0.0,0,9,0,-344,0 30.48,0.225,35,49,informed,corner,4,-1.54,-0.15,-0.4,4,4,4,954,0 30.48,0.225,35,49,informed,edge,20,-1.11,-0.15,-0.28,20,20,20,683,0 30.48,0.225,35,49,informed,interior,25,0.84,-0.15,0.0,0,25,0,-699,0 30.48,0.225,35,49,naive,corner,4,-2.31,-0.32,-1.11,4,4,4,997,0 30.48,0.225,35,49,naive,edge,20,-1.19,-0.32,-0.57,20,20,20,514,0 30.48,0.225,35,49,naive,interior,25,0.69,-0.32,0.0,0,25,0,-571,0`, d3.autoType); const timeSeries = d3.csvParse(`K,n,mode,year,D,L,R 7.62,1,naive,1,0.046,0.027,40.7 7.62,1,naive,2,0.046,0.035,23.7 7.62,1,naive,3,0.046,0.038,16.7 7.62,1,naive,4,0.046,0.04,12.9 7.62,1,naive,5,0.046,0.041,10.6 7.62,1,naive,6,0.046,0.042,9.0 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30.48,4,naive,23,10.722,10.672,0.5 30.48,4,naive,24,10.722,10.698,0.2 30.48,4,naive,25,10.722,10.719,0.0 30.48,4,naive,26,10.722,10.736,-0.1 30.48,4,naive,27,10.722,10.75,-0.3 30.48,4,naive,28,10.722,10.76,-0.4 30.48,4,naive,29,10.722,10.769,-0.4 30.48,4,naive,30,10.722,10.776,-0.5 30.48,4,informed,1,2.024,1.215,40.0 30.48,4,informed,2,4.028,2.694,33.1 30.48,4,informed,3,4.152,3.192,23.1 30.48,4,informed,4,5.01,3.972,20.7 30.48,4,informed,5,5.01,4.198,16.2 30.48,4,informed,6,5.01,4.33,13.6 30.48,4,informed,7,5.499,4.653,15.4 30.48,4,informed,8,5.499,4.799,12.7 30.48,4,informed,9,5.499,4.889,11.1 30.48,4,informed,10,5.499,4.953,9.9 30.48,4,informed,11,5.51,5.006,9.1 30.48,4,informed,12,5.191,4.95,4.6 30.48,4,informed,13,5.191,4.924,5.1 30.48,4,informed,14,5.191,4.916,5.3 30.48,4,informed,15,5.191,4.915,5.3 30.48,4,informed,16,5.191,4.917,5.3 30.48,4,informed,17,5.191,4.922,5.2 30.48,4,informed,18,5.191,4.926,5.1 30.48,4,informed,19,5.191,4.931,5.0 30.48,4,informed,20,5.191,4.936,4.9 30.48,4,informed,21,4.709,4.932,-4.7 30.48,4,informed,22,4.709,4.904,-4.1 30.48,4,informed,23,4.709,4.868,-3.4 30.48,4,informed,24,4.709,4.835,-2.7 30.48,4,informed,25,4.709,4.805,-2.0 30.48,4,informed,26,4.709,4.78,-1.5 30.48,4,informed,27,4.709,4.758,-1.0 30.48,4,informed,28,4.709,4.739,-0.6 30.48,4,informed,29,4.709,4.723,-0.3 30.48,4,informed,30,4.709,4.71,-0.0 30.48,9,naive,1,4.554,2.008,55.9 30.48,9,naive,2,8.665,4.451,48.6 30.48,9,naive,3,14.791,7.887,46.7 30.48,9,naive,4,18.787,11.19,40.4 30.48,9,naive,5,18.888,13.276,29.7 30.48,9,naive,6,18.515,14.339,22.6 30.48,9,naive,7,18.66,14.956,19.9 30.48,9,naive,8,19.483,16.07,17.5 30.48,9,naive,9,20.305,16.796,17.3 30.48,9,naive,10,21.127,17.479,17.3 30.48,9,naive,11,21.949,18.15,17.3 30.48,9,naive,12,22.051,18.815,14.7 30.48,9,naive,13,22.156,19.35,12.7 30.48,9,naive,14,22.26,19.685,11.6 30.48,9,naive,15,22.36,19.949,10.8 30.48,9,naive,16,22.461,20.17,10.2 30.48,9,naive,17,22.566,20.363,9.8 30.48,9,naive,18,22.671,20.536,9.4 30.48,9,naive,19,22.772,20.694,9.1 30.48,9,naive,20,22.877,20.839,8.9 30.48,9,naive,21,21.897,20.975,4.2 30.48,9,naive,22,21.492,21.134,1.7 30.48,9,naive,23,21.492,21.173,1.5 30.48,9,naive,24,21.492,21.203,1.3 30.48,9,naive,25,21.492,21.231,1.2 30.48,9,naive,26,21.492,21.257,1.1 30.48,9,naive,27,21.492,21.281,1.0 30.48,9,naive,28,21.492,21.304,0.9 30.48,9,naive,29,21.492,21.325,0.8 30.48,9,naive,30,21.492,21.345,0.7 30.48,9,informed,1,4.554,2.008,55.9 30.48,9,informed,2,8.665,4.451,48.6 30.48,9,informed,3,8.945,5.5,38.5 30.48,9,informed,4,11.989,7.504,37.4 30.48,9,informed,5,11.757,8.29,29.5 30.48,9,informed,6,10.812,8.575,20.7 30.48,9,informed,7,11.195,8.92,20.3 30.48,9,informed,8,10.363,8.98,13.3 30.48,9,informed,9,10.363,8.991,13.2 30.48,9,informed,10,10.363,9.026,12.9 30.48,9,informed,11,10.363,9.07,12.5 30.48,9,informed,12,9.646,9.093,5.7 30.48,9,informed,13,9.646,9.084,5.8 30.48,9,informed,14,9.646,9.073,5.9 30.48,9,informed,15,9.646,9.064,6.0 30.48,9,informed,16,9.646,9.058,6.1 30.48,9,informed,17,9.646,9.054,6.1 30.48,9,informed,18,9.646,9.052,6.2 30.48,9,informed,19,9.716,9.071,6.6 30.48,9,informed,20,9.821,9.11,7.2 30.48,9,informed,21,9.681,9.393,3.0 30.48,9,informed,22,9.561,9.52,0.4 30.48,9,informed,23,9.561,9.592,-0.3 30.48,9,informed,24,9.561,9.641,-0.8 30.48,9,informed,25,9.561,9.677,-1.2 30.48,9,informed,26,9.561,9.705,-1.5 30.48,9,informed,27,9.561,9.726,-1.7 30.48,9,informed,28,9.561,9.741,-1.9 30.48,9,informed,29,9.561,9.752,-2.0 30.48,9,informed,30,9.561,9.76,-2.1 30.48,25,naive,1,12.65,3.56,71.9 30.48,25,naive,2,24.997,8.06,67.8 30.48,25,naive,3,40.894,14.641,64.2 30.48,25,naive,4,51.13,21.021,58.9 30.48,25,naive,5,50.768,25.64,49.5 30.48,25,naive,6,48.602,28.14,42.1 30.48,25,naive,7,47.368,29.62,37.5 30.48,25,naive,8,44.856,31.764,29.2 30.48,25,naive,9,47.109,33.285,29.3 30.48,25,naive,10,49.385,34.74,29.7 30.48,25,naive,11,51.658,36.185,30.0 30.48,25,naive,12,51.949,37.635,27.6 30.48,25,naive,13,52.235,39.007,25.3 30.48,25,naive,14,52.515,39.954,23.9 30.48,25,naive,15,52.796,40.75,22.8 30.48,25,naive,16,53.08,41.447,21.9 30.48,25,naive,17,53.371,42.073,21.2 30.48,25,naive,18,53.657,42.64,20.5 30.48,25,naive,19,53.949,43.161,20.0 30.48,25,naive,20,54.24,43.644,19.5 30.48,25,naive,21,51.516,44.094,14.4 30.48,25,naive,22,48.79,44.512,8.8 30.48,25,naive,23,46.565,44.796,3.8 30.48,25,naive,24,45.853,44.882,2.1 30.48,25,naive,25,45.853,44.926,2.0 30.48,25,naive,26,45.853,44.968,1.9 30.48,25,naive,27,45.853,45.007,1.8 30.48,25,naive,28,45.853,45.044,1.8 30.48,25,naive,29,45.853,45.079,1.7 30.48,25,naive,30,45.853,45.112,1.6 30.48,25,informed,1,12.65,3.56,71.9 30.48,25,informed,2,24.997,8.06,67.8 30.48,25,informed,3,25.774,10.441,59.5 30.48,25,informed,4,25.026,12.734,49.1 30.48,25,informed,5,24.326,13.834,43.1 30.48,25,informed,6,21.944,14.416,34.3 30.48,25,informed,7,22.069,14.876,32.6 30.48,25,informed,8,17.568,14.497,17.5 30.48,25,informed,9,17.619,14.4,18.3 30.48,25,informed,10,18.251,14.502,20.5 30.48,25,informed,11,18.883,14.691,22.2 30.48,25,informed,12,18.883,15.185,19.6 30.48,25,informed,13,18.883,15.421,18.3 30.48,25,informed,14,18.883,15.576,17.5 30.48,25,informed,15,19.077,15.731,17.5 30.48,25,informed,16,19.271,15.88,17.6 30.48,25,informed,17,19.563,16.042,18.0 30.48,25,informed,18,19.855,16.21,18.4 30.48,25,informed,19,20.146,16.381,18.7 30.48,25,informed,20,20.438,16.555,19.0 30.48,25,informed,21,18.274,16.878,7.6 30.48,25,informed,22,17.474,16.983,2.8 30.48,25,informed,23,17.474,17.037,2.5 30.48,25,informed,24,17.474,17.098,2.2 30.48,25,informed,25,17.474,17.159,1.8 30.48,25,informed,26,17.474,17.217,1.5 30.48,25,informed,27,17.474,17.27,1.2 30.48,25,informed,28,17.474,17.32,0.9 30.48,25,informed,29,17.474,17.365,0.6 30.48,25,informed,30,17.474,17.406,0.4 30.48,49,naive,1,24.795,5.108,79.4 30.48,49,naive,2,52.052,11.893,77.2 30.48,49,naive,3,82.937,21.499,74.1 30.48,49,naive,4,103.136,31.098,69.8 30.48,49,naive,5,101.876,39.13,61.6 30.48,49,naive,6,97.409,43.694,55.1 30.48,49,naive,7,94.036,46.498,50.6 30.48,49,naive,8,84.912,49.476,41.7 30.48,49,naive,9,83.15,51.893,37.6 30.48,49,naive,10,87.311,54.074,38.1 30.48,49,naive,11,91.775,56.234,38.7 30.48,49,naive,12,92.337,58.414,36.7 30.48,49,naive,13,92.909,60.558,34.8 30.48,49,naive,14,93.466,62.344,33.3 30.48,49,naive,15,94.03,63.708,32.2 30.48,49,naive,16,94.586,64.908,31.4 30.48,49,naive,17,95.149,65.998,30.6 30.48,49,naive,18,95.703,67.004,30.0 30.48,49,naive,19,96.254,67.941,29.4 30.48,49,naive,20,96.825,68.819,28.9 30.48,49,naive,21,91.483,69.647,23.9 30.48,49,naive,22,86.149,70.43,18.2 30.48,49,naive,23,80.908,71.124,12.1 30.48,49,naive,24,76.693,71.535,6.7 30.48,49,naive,25,74.222,71.691,3.4 30.48,49,naive,26,73.459,71.774,2.3 30.48,49,naive,27,73.459,71.831,2.2 30.48,49,naive,28,73.459,71.878,2.2 30.48,49,naive,29,73.459,71.921,2.1 30.48,49,naive,30,73.459,71.96,2.0 30.48,49,informed,1,24.795,5.108,79.4 30.48,49,informed,2,52.052,11.893,77.2 30.48,49,informed,3,55.001,15.786,71.3 30.48,49,informed,4,66.383,20.48,69.1 30.48,49,informed,5,64.132,24.106,62.4 30.48,49,informed,6,59.464,26.471,55.5 30.48,49,informed,7,57.739,28.198,51.2 30.48,49,informed,8,48.889,29.246,40.2 30.48,49,informed,9,46.83,30.539,34.8 30.48,49,informed,10,48.45,31.599,34.8 30.48,49,informed,11,52.736,32.872,37.7 30.48,49,informed,12,53.117,34.292,35.4 30.48,49,informed,13,53.659,35.459,33.9 30.48,49,informed,14,54.048,36.318,32.8 30.48,49,informed,15,54.521,37.034,32.1 30.48,49,informed,16,55.065,37.702,31.5 30.48,49,informed,17,55.612,38.321,31.1 30.48,49,informed,18,56.157,38.903,30.7 30.48,49,informed,19,56.714,39.456,30.4 30.48,49,informed,20,57.272,39.987,30.2 30.48,49,informed,21,51.925,40.499,22.0 30.48,49,informed,22,46.829,41.032,12.4 30.48,49,informed,23,44.236,41.278,6.7 30.48,49,informed,24,42.606,41.344,3.0 30.48,49,informed,25,42.124,41.348,1.8 30.48,49,informed,26,42.124,41.366,1.8 30.48,49,informed,27,42.124,41.391,1.7 30.48,49,informed,28,42.124,41.417,1.7 30.48,49,informed,29,42.124,41.442,1.6 30.48,49,informed,30,42.124,41.467,1.6`, d3.autoType); const phiPath = [{"n":1,"year":1,"phi":0.0},{"n":1,"year":2,"phi":0.0},{"n":1,"year":3,"phi":0.9},{"n":1,"year":4,"phi":0.9},{"n":1,"year":5,"phi":0.9},{"n":1,"year":6,"phi":0.9},{"n":1,"year":7,"phi":0.9},{"n":1,"year":8,"phi":0.9},{"n":1,"year":9,"phi":0.9},{"n":1,"year":10,"phi":0.9},{"n":1,"year":11,"phi":0.9},{"n":1,"year":12,"phi":0.9},{"n":1,"year":13,"phi":0.9},{"n":1,"year":14,"phi":0.9},{"n":1,"year":15,"phi":0.9},{"n":1,"year":16,"phi":0.9},{"n":1,"year":17,"phi":0.9},{"n":1,"year":18,"phi":0.9},{"n":1,"year":19,"phi":0.9},{"n":1,"year":20,"phi":0.9},{"n":1,"year":21,"phi":0.9},{"n":1,"year":22,"phi":0.9},{"n":1,"year":23,"phi":0.9},{"n":1,"year":24,"phi":0.9},{"n":1,"year":25,"phi":0.9},{"n":1,"year":26,"phi":0.9},{"n":1,"year":27,"phi":0.9},{"n":1,"year":28,"phi":0.9},{"n":1,"year":29,"phi":0.9},{"n":1,"year":30,"phi":0.9},{"n":9,"year":1,"phi":0.0},{"n":9,"year":2,"phi":0.0},{"n":9,"year":3,"phi":0.7437},{"n":9,"year":4,"phi":0.4035},{"n":9,"year":5,"phi":0.3226},{"n":9,"year":6,"phi":0.3114},{"n":9,"year":7,"phi":0.3158},{"n":9,"year":8,"phi":0.2981},{"n":9,"year":9,"phi":0.2965},{"n":9,"year":10,"phi":0.2952},{"n":9,"year":11,"phi":0.2944},{"n":9,"year":12,"phi":0.2942},{"n":9,"year":13,"phi":0.2941},{"n":9,"year":14,"phi":0.2926},{"n":9,"year":15,"phi":0.2926},{"n":9,"year":16,"phi":0.2925},{"n":9,"year":17,"phi":0.2925},{"n":9,"year":18,"phi":0.2925},{"n":9,"year":19,"phi":0.2924},{"n":9,"year":20,"phi":0.2924},{"n":9,"year":21,"phi":0.2924},{"n":9,"year":22,"phi":0.2924},{"n":9,"year":23,"phi":0.2924},{"n":9,"year":24,"phi":0.2924},{"n":9,"year":25,"phi":0.2924},{"n":9,"year":26,"phi":0.2923},{"n":9,"year":27,"phi":0.2922},{"n":9,"year":28,"phi":0.2914},{"n":9,"year":29,"phi":0.2915},{"n":9,"year":30,"phi":0.2915},{"n":49,"year":1,"phi":0.0},{"n":49,"year":2,"phi":0.0},{"n":49,"year":3,"phi":0.2472},{"n":49,"year":4,"phi":0.118},{"n":49,"year":5,"phi":0.0969},{"n":49,"year":6,"phi":0.0896},{"n":49,"year":7,"phi":0.086},{"n":49,"year":8,"phi":0.0802},{"n":49,"year":9,"phi":0.0796},{"n":49,"year":10,"phi":0.0797},{"n":49,"year":11,"phi":0.0796},{"n":49,"year":12,"phi":0.0795},{"n":49,"year":13,"phi":0.0793},{"n":49,"year":14,"phi":0.0788},{"n":49,"year":15,"phi":0.0771},{"n":49,"year":16,"phi":0.0757},{"n":49,"year":17,"phi":0.0747},{"n":49,"year":18,"phi":0.0738},{"n":49,"year":19,"phi":0.0731},{"n":49,"year":20,"phi":0.0723},{"n":49,"year":21,"phi":0.0716},{"n":49,"year":22,"phi":0.0709},{"n":49,"year":23,"phi":0.0703},{"n":49,"year":24,"phi":0.0697},{"n":49,"year":25,"phi":0.0691},{"n":49,"year":26,"phi":0.0686},{"n":49,"year":27,"phi":0.0681},{"n":49,"year":28,"phi":0.0676},{"n":49,"year":29,"phi":0.0671},{"n":49,"year":30,"phi":0.0667}]; const bmin = [{"wc":380,"K":7.62,"Sy":0.125,"bmin":14.5},{"wc":380,"K":7.62,"Sy":0.175,"bmin":14.4},{"wc":380,"K":7.62,"Sy":0.225,"bmin":14.2},{"wc":380,"K":15.24,"Sy":0.125,"bmin":10.9},{"wc":380,"K":15.24,"Sy":0.175,"bmin":10.9},{"wc":380,"K":15.24,"Sy":0.225,"bmin":10.8},{"wc":380,"K":30.48,"Sy":0.125,"bmin":8.6},{"wc":380,"K":30.48,"Sy":0.175,"bmin":8.5},{"wc":380,"K":30.48,"Sy":0.225,"bmin":8.5},{"wc":760,"K":7.62,"Sy":0.125,"bmin":20.1},{"wc":760,"K":7.62,"Sy":0.175,"bmin":19.9},{"wc":760,"K":7.62,"Sy":0.225,"bmin":19.7},{"wc":760,"K":15.24,"Sy":0.125,"bmin":14.8},{"wc":760,"K":15.24,"Sy":0.175,"bmin":14.6},{"wc":760,"K":15.24,"Sy":0.225,"bmin":14.5},{"wc":760,"K":30.48,"Sy":0.125,"bmin":11.1},{"wc":760,"K":30.48,"Sy":0.175,"bmin":11.0},{"wc":760,"K":30.48,"Sy":0.225,"bmin":11.0},{"wc":1140,"K":7.62,"Sy":0.125,"bmin":24.4},{"wc":1140,"K":7.62,"Sy":0.175,"bmin":24.2},{"wc":1140,"K":7.62,"Sy":0.225,"bmin":24.0},{"wc":1140,"K":15.24,"Sy":0.125,"bmin":17.8},{"wc":1140,"K":15.24,"Sy":0.175,"bmin":17.7},{"wc":1140,"K":15.24,"Sy":0.225,"bmin":17.5},{"wc":1140,"K":30.48,"Sy":0.125,"bmin":13.2},{"wc":1140,"K":30.48,"Sy":0.175,"bmin":13.1},{"wc":1140,"K":30.48,"Sy":0.225,"bmin":13.0},{"wc":1520,"K":7.62,"Sy":0.125,"bmin":28.2},{"wc":1520,"K":7.62,"Sy":0.175,"bmin":27.9},{"wc":1520,"K":7.62,"Sy":0.225,"bmin":27.7},{"wc":1520,"K":15.24,"Sy":0.125,"bmin":20.5},{"wc":1520,"K":15.24,"Sy":0.175,"bmin":20.3},{"wc":1520,"K":15.24,"Sy":0.225,"bmin":20.1},{"wc":1520,"K":30.48,"Sy":0.125,"bmin":15.0},{"wc":1520,"K":30.48,"Sy":0.175,"bmin":14.9},{"wc":1520,"K":30.48,"Sy":0.225,"bmin":14.8},{"wc":1900,"K":7.62,"Sy":0.125,"bmin":31.5},{"wc":1900,"K":7.62,"Sy":0.175,"bmin":31.2},{"wc":1900,"K":7.62,"Sy":0.225,"bmin":31.0},{"wc":1900,"K":15.24,"Sy":0.125,"bmin":22.8},{"wc":1900,"K":15.24,"Sy":0.175,"bmin":22.6},{"wc":1900,"K":15.24,"Sy":0.225,"bmin":22.4},{"wc":1900,"K":30.48,"Sy":0.125,"bmin":16.7},{"wc":1900,"K":30.48,"Sy":0.175,"bmin":16.5},{"wc":1900,"K":30.48,"Sy":0.225,"bmin":16.4},{"wc":2280,"K":7.62,"Sy":0.125,"bmin":34.6},{"wc":2280,"K":7.62,"Sy":0.175,"bmin":34.2},{"wc":2280,"K":7.62,"Sy":0.225,"bmin":33.9},{"wc":2280,"K":15.24,"Sy":0.125,"bmin":24.9},{"wc":2280,"K":15.24,"Sy":0.175,"bmin":24.7},{"wc":2280,"K":15.24,"Sy":0.225,"bmin":24.5},{"wc":2280,"K":30.48,"Sy":0.125,"bmin":18.2},{"wc":2280,"K":30.48,"Sy":0.175,"bmin":18.0},{"wc":2280,"K":30.48,"Sy":0.225,"bmin":17.9},{"wc":2660,"K":7.62,"Sy":0.125,"bmin":37.3},{"wc":2660,"K":7.62,"Sy":0.175,"bmin":37.0},{"wc":2660,"K":7.62,"Sy":0.225,"bmin":36.7},{"wc":2660,"K":15.24,"Sy":0.125,"bmin":26.9},{"wc":2660,"K":15.24,"Sy":0.175,"bmin":26.7},{"wc":2660,"K":15.24,"Sy":0.225,"bmin":26.5},{"wc":2660,"K":30.48,"Sy":0.125,"bmin":19.6},{"wc":2660,"K":30.48,"Sy":0.175,"bmin":19.4},{"wc":2660,"K":30.48,"Sy":0.225,"bmin":19.2},{"wc":3040,"K":7.62,"Sy":0.125,"bmin":40.0},{"wc":3040,"K":7.62,"Sy":0.175,"bmin":39.6},{"wc":3040,"K":7.62,"Sy":0.225,"bmin":39.3},{"wc":3040,"K":15.24,"Sy":0.125,"bmin":28.8},{"wc":3040,"K":15.24,"Sy":0.175,"bmin":28.5},{"wc":3040,"K":15.24,"Sy":0.225,"bmin":28.3},{"wc":3040,"K":30.48,"Sy":0.125,"bmin":20.9},{"wc":3040,"K":30.48,"Sy":0.175,"bmin":20.7},{"wc":3040,"K":30.48,"Sy":0.225,"bmin":20.5}]; const runtime = [{"n":1,"kind":"alllpm","runs":27,"median":1.8,"max":11.7,"sum":73.3},{"n":1,"kind":"apm","runs":27,"median":0.7,"max":0.8,"sum":18.4},{"n":1,"kind":"informed","runs":27,"median":1.8,"max":10.8,"sum":69.7},{"n":1,"kind":"naive","runs":27,"median":2.2,"max":11.1,"sum":79.9},{"n":4,"kind":"apm","runs":27,"median":0.8,"max":0.8,"sum":20.6},{"n":4,"kind":"informed","runs":27,"median":2.8,"max":13.5,"sum":99.7},{"n":4,"kind":"naive","runs":27,"median":3.8,"max":14.2,"sum":137.8},{"n":9,"kind":"apm","runs":27,"median":0.9,"max":1.0,"sum":23.5},{"n":9,"kind":"informed","runs":27,"median":4.7,"max":13.9,"sum":150.0},{"n":9,"kind":"naive","runs":27,"median":6.9,"max":25.3,"sum":240.9},{"n":25,"kind":"apm","runs":27,"median":1.1,"max":1.2,"sum":29.5},{"n":25,"kind":"informed","runs":27,"median":9.5,"max":26.3,"sum":286.3},{"n":25,"kind":"naive","runs":27,"median":14.9,"max":57.9,"sum":534.0},{"n":49,"kind":"apm","runs":27,"median":1.4,"max":1.5,"sum":36.4},{"n":49,"kind":"informed","runs":27,"median":14.0,"max":39.6,"sum":392.7},{"n":49,"kind":"naive","runs":27,"median":21.4,"max":105.9,"sum":825.1}]; const positionOrder = ["single", "corner", "edge", "interior"]; display(`${controls.length} controls, ${members.length} member rows, ${timeSeries.length} time-series rows`); 54:T13e0,// Figure S2. Deferral, leakage and retention by year in the baseline aquifer (SI Text S4; manuscript Section 3.2) const figS2timepaths = (() => { const base = timeSeries.filter((d) => d.K === 15.24); const sizes = [1, 9, 49]; const label = (tag, node) => { const d = document.createElement("div"); const b = document.createElement("div"); b.textContent = tag; b.style.cssText = "font: bold 12px sans-serif; margin-bottom: 2px;"; d.append(b, node); return d; }; const tags = "abcdefg"; const xYear = {domain: [0.5, 30.5], ticks: [1, 5, 10, 15, 20, 25, 30], label: "Year →"}; const dl = sizes.map((n, i) => label(`(${tags[i]}) ${n}-section CID: D(t) and L(t)`, Plot.plot({ width: 320, height: 230, marginLeft: 46, marginRight: 12, marginBottom: 36, x: xYear, y: {grid: true, nice: true, label: "↑ Mm³"}, color: beliefColor, marks: [ Plot.ruleY([0]), Plot.lineY(base.filter((d) => d.n === n), {x: "year", y: "D", stroke: "mode", strokeWidth: 2}), Plot.lineY(base.filter((d) => d.n === n), {x: "year", y: "L", stroke: "mode", strokeWidth: 2, strokeDasharray: "5,3"}), Plot.tip(base.filter((d) => d.n === n), Plot.pointerX({x: "year", y: "D", title: (d) => `${d.mode}, year ${d.year}\nD = ${d.D.toFixed(2)} Mm³, L = ${d.L.toFixed(2)} Mm³`})) ] }))); const rr = sizes.map((n, i) => label(`(${tags[i + 3]}) ${n}-section CID: R(t)`, Plot.plot({ width: 320, height: 230, marginLeft: 46, marginRight: 12, marginBottom: 36, x: xYear, y: {grid: true, domain: [-60, 100], label: "↑ Retention R(t) (%)"}, color: beliefColor, marks: [ Plot.ruleY([0]), Plot.ruleX([5, 10], {stroke: "#b5b3ac", strokeDasharray: "2,3"}), Plot.lineY(base.filter((d) => d.n === n && d.R != null), {x: "year", y: (d) => Math.max(-60, d.R), stroke: "mode", strokeWidth: 2}), Plot.tip(base.filter((d) => d.n === n && d.R != null), Plot.pointerX({x: "year", y: (d) => Math.max(-60, d.R), title: (d) => `${d.mode}, year ${d.year}\nR = ${d.R.toFixed(1)}%`})) ] }))); const ph = label("(g) Leakage rate estimated by informed members, φ̂", Plot.plot({ width: 660, height: 210, marginLeft: 46, marginRight: 90, marginBottom: 36, x: xYear, y: {grid: true, domain: [0, 1], label: "↑ φ̂ (per year)"}, marks: [ Plot.ruleY([0.9], {stroke: "#b5b3ac", strokeDasharray: "2,3"}), Plot.text([{year: 1, phi: 0.9}], {x: "year", y: "phi", text: () => "upper bound 0.9", dy: -7, textAnchor: "start", fill: "#52514e", fontSize: 10}), ...[[1, null], [9, "6,3"], [49, "2,2"]].map(([n, dash]) => Plot.lineY(phiPath.filter((d) => d.n === n), {x: "year", y: "phi", stroke: "#2a78d6", strokeWidth: 2, strokeDasharray: dash})), Plot.text(phiPath.filter((d) => d.year === 30), {x: "year", y: "phi", text: (d) => `${d.n} section${d.n > 1 ? "s" : ""}`, dx: 6, textAnchor: "start", fill: "#222", fontSize: 10}), Plot.tip(phiPath, Plot.pointerX({x: "year", y: "phi", title: (d) => `${d.n}-section CID, year ${d.year}\nφ̂ = ${d.phi.toFixed(3)}`})) ] })); const legend = Plot.legend({color: {...beliefColor, tickFormat: (d) => (d === "informed" ? "Informed members" : "Naive members")}}); const key = document.createElement("div"); key.style.cssText = "font: 12px sans-serif; margin: 2px 0 6px;"; key.textContent = "Solid: cumulative deferral D(t); dashed: cumulative leakage L(t)."; const grid = document.createElement("div"); grid.style.cssText = "display: grid; grid-template-columns: repeat(3, 320px); gap: 10px 14px;"; grid.append(...dl, ...rr); const wrap = document.createElement("div"); wrap.append(legend, key, grid, ph); return figure(wrap, "Figure S2. Deferral, leakage and retention over the 30-year horizon in the baseline aquifer (K = 15.24 m/d, S_y = 0.175, b₀ = 25 m). (a–c) Cumulative water deferred by the district relative to the all-APM baseline, D(t) (solid lines, Equation 9), and cumulative net outflow to the neighbors, L(t) (dashed lines, Equation 10), for districts of 1, 9 and 49 sections; each panel has its own vertical scale. (d–f) Retention R(t) = 1 − L(t)/D(t) (Equation 11) for the same districts; values below zero mean that more water has left the district than it has deferred so far. Vertical lines mark years 5 and 10, at which Table 3 and Data Set S1 report R. (g) Leakage rate φ̂ estimated by informed members (Equation 7, bounded at 0.9). Naive and informed districts make identical decisions in years 1 and 2, before any leakage has been observed (the one-section districts until year 6). D(t) is not monotonic: informed districts and both 49-section districts defer most in years 2–3 and then pump part of the deferred water, while the naive 1- and 9-section districts, after an early dip, build their deferral up again to a peak in year 26. Because D(t) can shrink while L(t) changes slowly, R(t) becomes unstable late in the horizon, which is why R is reported at years 5 and 10 (Section 3.2)."); })(); display(figS2timepaths); 55:Ta22,// Figure S4. Member gain by size in each of the 27 aquifer settings (SI Text S5; complements Figure 5) const figS4settings = (() => { const rows = designRuns.map((d) => ({...d, row: `K ${d.K} · Sy ${d.Sy}`})); const rowOrder = [...new Set(rows.map((d) => d.row))]; const plot = Plot.plot({ width: 760, height: 1000, marginLeft: 62, marginRight: 118, marginTop: 28, marginBottom: 52, x: {type: "log", domain: [0.85, 58], ticks: sizeTicks, tickFormat: () => "", label: "CID size (sections, log scale) →", labelOffset: 44}, y: {grid: true, labelAnchor: "center", label: "↑ Member gain G (%)", tickFormat: (d) => (d > 0 ? "+" : "") + d, ticks: [-20, -10, 0]}, fx: {label: null, tickFormat: (d) => `b0 = ${d} m`}, fy: {label: null, domain: rowOrder}, color: {...beliefColor, legend: true, tickFormat: (d) => (d === "informed" ? "Informed members" : "Naive members")}, marks: [ Plot.frame({stroke: "#e1e0d9"}), Plot.ruleY([0], {stroke: "#52514e"}), sizeLabels({fy: rowOrder.at(-1)}), Plot.lineY(rows, {fx: "b0", fy: "row", x: "n", y: "G", stroke: "mode", strokeWidth: 1.6}), Plot.dot(rows, {fx: "b0", fy: "row", x: "n", y: "G", fill: "mode", r: 2.4, tip: true, title: (d) => `K = ${d.K} m/d, Sy = ${d.Sy}, b0 = ${d.b0} m\n${d.n}-section CID, ${d.mode}\nG = ${pct(d.G)}, G* = ${d.Gstar.toFixed(2)}%`}) ] }); return figure(plot, "Figure S4. Member gain by district size in each of the 27 aquifer settings. Gain in NPV of CID members relative to the all-APM baseline (G, Equation 13), for naive (orange) and informed (blue) members. Rows are combinations of hydraulic conductivity K (m/d) and specific yield S_y; columns are initial saturated thicknesses b₀; all panels share one vertical scale. Figure 5 shows the mean over b₀; values are listed in Table S4. In 52 of the 54 setting–belief combinations, 49-section districts gain more than one-section districts, although gains do not always rise at every step (they do in 35 of the 54); the two exceptions are the near-zero setting described below. The largest gain is +7.8% (informed, 49 sections, K = 30.48 m/d, S_y = 0.175, b₀ = 18 m) and the largest loss −23.5% (naive, one section, K = 30.48 m/d, S_y = 0.125, b₀ = 35 m). In the setting with almost no potential gain (K = 7.62 m/d, S_y = 0.225, b₀ = 25 m), naive and informed districts make identical choices at 1 to 25 sections and gains stay within about 0.1% of zero."); })(); display(figS4settings); 56:Tfc9,// Figure S6. Gains of the APM neighbours and of the landscape (SI Text S6; manuscript Section 4.3) const figS6neighbours = (() => { const xSize = {type: "log", domain: [0.85, 58], ticks: sizeTicks, tickFormat: () => "", label: "CID size (sections) →", labelOffset: 36}; const panel = (tag, plot) => { const d = document.createElement("div"); const b = document.createElement("div"); b.textContent = tag; b.style.cssText = "font: bold 12px sans-serif; margin-bottom: 2px;"; d.append(b, plot); return d; }; const common = {width: 320, height: 270, marginLeft: 50, marginRight: 14, marginBottom: 44, color: beliefColor}; const a = sizeStats(designRuns, (d) => d.Gn); const pa = Plot.plot({...common, x: xSize, y: {grid: true, label: "↑ Neighbor gain (%)"}, marks: [Plot.ruleY([0]), sizeLabels(), Plot.areaY(a, {x: "n", y1: "q1", y2: "q3", fill: "mode", fillOpacity: 0.14}), Plot.lineY(a, {x: "n", y: "mean", stroke: "mode", strokeWidth: 2}), Plot.dot(a, {x: "n", y: "mean", fill: "mode", r: 3.5, stroke: "white", tip: true, title: (d) => `${d.mode}, ${d.n}-section CID\nmean neighbor gain ${pct(d.mean)} (IQR ${pct(d.q1)} to ${pct(d.q3)})`})]}); const money = d3.flatRollup(designRuns, (v) => [["District", d3.median(v, (d) => d.dC) / 1e6], ["Neighbors", d3.median(v, (d) => d.dN) / 1e6]], (d) => d.mode, (d) => d.n).flatMap(([mode, n, pairs]) => pairs.map(([who, v]) => ({mode, n, who, v}))); const pb = Plot.plot({...common, x: xSize, y: {grid: true, label: "↑ Median NPV change ($ million)"}, marks: [Plot.ruleY([0]), sizeLabels(), Plot.lineY(money.filter((d) => d.who === "District"), {x: "n", y: "v", z: "mode", stroke: "mode", strokeWidth: 2}), Plot.lineY(money.filter((d) => d.who === "Neighbors"), {x: "n", y: "v", z: "mode", stroke: "mode", strokeWidth: 2, strokeDasharray: "5,3"}), Plot.dot(money, {x: "n", y: "v", fill: "mode", r: 3, tip: true, title: (d) => `${d.mode}, ${d.n}-section CID\n${d.who}: median ${d.v.toFixed(2)} $ million`}), ]}); const c = sizeStats(designRuns.filter((d) => d.shareN != null), (d) => d.shareN); const pc = Plot.plot({...common, x: xSize, y: {grid: true, domain: [0, 1], label: "↑ Neighbors' share of gains ψ+"}, marks: [sizeLabels(), Plot.areaY(c, {x: "n", y1: "q1", y2: "q3", fill: "mode", fillOpacity: 0.14}), Plot.lineY(c, {x: "n", y: "median", stroke: "mode", strokeWidth: 2}), Plot.dot(c, {x: "n", y: "median", fill: "mode", r: 3.5, stroke: "white", tip: true, title: (d) => `${d.mode}, ${d.n}-section CID\nmedian ψ+ ${d.median.toFixed(2)} (IQR ${d.q1.toFixed(2)} to ${d.q3.toFixed(2)})`})]}); const row = document.createElement("div"); row.style.cssText = "display: grid; grid-template-columns: repeat(3, 320px); gap: 14px;"; row.append(panel("(a) Neighbors' gain", pa), panel("(b) District (solid) vs neighbors (dashed)", pb), panel("(c) Neighbors' share of the gains", pc)); const wrap = document.createElement("div"); wrap.append(Plot.legend({color: {...beliefColor, tickFormat: (d) => (d === "informed" ? "Informed districts" : "Naive districts")}}), row); return figure(wrap, "Figure S6. The neighbors gain at every district size and take most of the gains created by small districts. (a) Mean NPV gain of the APM producers in the four-section buffer relative to the all-APM baseline (G_N), with the interquartile range over the 27 aquifer settings. (b) Median total NPV change of the district members (ΔV_C, solid) and of their neighbors (ΔV_N, dashed), in millions of dollars over 30 years. (c) Neighbors' share of the positive gains, ψ⁺ = max(ΔV_N, 0) / [max(ΔV_N, 0) + max(ΔV_C, 0)], median and interquartile range; ψ⁺ = 1 when the district loses and its neighbors gain. Unlike ψ (Equation 14), ψ⁺ is bounded between 0 and 1 (Text S6). Blue: informed; orange: naive districts."); })(); display(figS6neighbours); 57:Tf61,// Figure S7. Member gain by position, and make-whole transfers (SI Text S7; manuscript Section 4.5) const figS7members = (() => { const posLabel = {single: "Corner", corner: "Corner", edge: "Edge", interior: "Interior"}; const rows = members.map((d) => ({...d, pos: posLabel[d.position]})); const xSize = {type: "log", domain: [0.85, 58], ticks: sizeTicks, tickFormat: () => "", label: "CID size (sections) →", labelOffset: 36}; // member-weighted statistics over settings for each size x position x belief const stat = (v, f) => ({mean: d3.sum(v, (d) => d.members * f(d)) / d3.sum(v, (d) => d.members), q1: d3.quantile(v, 0.25, f), q3: d3.quantile(v, 0.75, f), k: v.length}); const g = d3.flatRollup(rows, (v) => stat(v, (d) => d.gain), (d) => d.pos, (d) => d.mode, (d) => d.n) .map(([pos, mode, n, s]) => ({pos, mode, n, ...s})).sort((a, b) => a.n - b.n); const feas = rows.filter((d) => d.mwFeasible === 1 && d.n > 1); const t = d3.flatRollup(feas, (v) => stat(v, (d) => d.trMW), (d) => d.pos, (d) => d.mode, (d) => d.n) .map(([pos, mode, n, s]) => ({pos, mode, n, ...s})).sort((a, b) => a.n - b.n); const common = {width: 900, height: 270, marginLeft: 64, marginRight: 20, marginBottom: 44, marginTop: 24, color: beliefColor, fx: {domain: ["Corner", "Edge", "Interior"], label: null}}; const pa = Plot.plot({...common, x: xSize, y: {grid: true, label: null, tickFormat: (d) => (d > 0 ? "+" : "") + d}, marks: [Plot.frame({stroke: "#e1e0d9"}), Plot.ruleY([0]), sizeLabels(), Plot.areaY(g, {fx: "pos", x: "n", y1: "q1", y2: "q3", fill: "mode", fillOpacity: 0.14}), Plot.lineY(g, {fx: "pos", x: "n", y: "mean", stroke: "mode", strokeWidth: 2}), Plot.dot(g, {fx: "pos", x: "n", y: "mean", fill: "mode", r: 3.5, stroke: "white", tip: true, title: (d) => `${d.pos} members, ${d.n}-section CID, ${d.mode}\nmean ${pct(d.mean)} (IQR ${pct(d.q1)} to ${pct(d.q3)}); ${d.k} districts`})]}); const pb = Plot.plot({...common, x: xSize, y: {grid: true, label: null, tickFormat: (d) => (d > 0 ? "+" : "") + d}, marks: [Plot.frame({stroke: "#e1e0d9"}), Plot.ruleY([0]), sizeLabels(), Plot.lineY(t, {fx: "pos", x: "n", y: "mean", stroke: "mode", strokeWidth: 2}), Plot.dot(t, {fx: "pos", x: "n", y: "mean", fill: "mode", r: 3.5, stroke: "white", tip: true, title: (d) => `${d.pos} members, ${d.n}-section CID, ${d.mode}\nmean transfer ${d.mean.toFixed(0)} $/yr; ${d.k} feasible districts`})]}); const label = (tag, node) => { const d = document.createElement("div"); const b = document.createElement("div"); b.textContent = tag; b.style.cssText = "font: bold 12px sans-serif; margin-bottom: 2px;"; d.append(b, node); return d; }; const wrap = document.createElement("div"); wrap.append(Plot.legend({color: {...beliefColor, tickFormat: (d) => (d === "informed" ? "Informed districts" : "Naive districts")}}), label("(a) Member gain without compensation (%), by position (single member of a 1-section CID shown as corner)", pa), label("(b) Annual make-whole transfer ($ per member per year; + = received), districts where the rule is feasible", pb)); return figure(wrap, "Figure S7. Losses fall on the district boundary, and make-whole transfers flow from interior to corner members. (a) Mean gain of members relative to the all-APM baseline without compensation, by position and district size, with the interquartile range over the 27 aquifer settings. A one-section district has a single member, shown with the corner members because it borders neighbors on all sides; four-section districts consist of corner members only. (b) Mean annual make-whole transfer per member ($ per member per year over 30 years, Equation 17; positive values are received), over the districts in which the rule is feasible. Blue: informed; orange: naive districts. Values are listed in Table S7."); })(); display(figS7members); 58:T2087,// Tables S2-S8 of the Supporting Information, computed from the data cells (Table S1 is descriptive and in the SI only) const settingLabel = (d) => [d.K.toFixed(2), d.Sy.toFixed(3), d.b0.toFixed(0)]; const settingCols = [ {label: "K (m/d)", value: (r) => r.K.toFixed(2), align: "left"}, {label: "S_y", value: (r) => r.Sy.toFixed(3)}, {label: "b₀ (m)", value: (r) => r.b0.toFixed(0)} ]; const flatSetting = (r) => r.K === 7.62 && r.Sy === 0.225 && r.b0 === 25; const usd = (x) => (x < 0 ? "−" : "") + Math.abs(Math.round(x)).toLocaleString("en-US"); // ---- Table S2: capacity thresholds const tableS2 = (() => { const combos = [7.62, 15.24, 30.48].flatMap((K) => [0.125, 0.175, 0.225].map((Sy) => ({K, Sy}))); const rows = d3.groups(bmin, (d) => d.wc).map(([wc, v]) => ({wc, v})); return table("Table S2. Smallest saturated thickness b_min (m) at which one well can sustain each well-capacity class for the 120-day season with its pumping level at least 5 m above the aquifer base (Equation 6; r_w = 0.15 m), for each combination of hydraulic conductivity K and specific yield S_y.", [ {label: "Class (L/min)", value: (r) => r.wc.toLocaleString("en-US"), align: "left"}, ...combos.map(({K, Sy}) => ({label: Sy.toFixed(3), value: (r) => r.v.find((d) => d.K === K && d.Sy === Sy).bmin.toFixed(1)})) ], rows, {groups: [[{label: "", span: 1}, {label: "K = 7.62 m/d", span: 3}, {label: "K = 15.24 m/d", span: 3}, {label: "K = 30.48 m/d", span: 3}]], note: "Column headings under each K give S_y."}); })(); // ---- Table S3: controls const tableS3 = table("Table S3. Net present value per pivot in the all-APM baseline and in the all-LPM control run, and the potential gain G*, for the 27 aquifer settings (30 years, 4%). † Excluded from the tests that use the capture ratio (G* < 2%).", [ ...settingCols, {label: "NPV, all-APM ($/pivot)", value: (r) => usd(r.npvA)}, {label: "NPV, all-LPM ($/pivot)", value: (r) => usd(r.npvL)}, {label: "G* (%)", value: (r) => r.Gstar.toFixed(2) + (flatSetting(r) ? "†" : "")} ], controls); // ---- Tables S4, S5: G and kappa by setting const bySetting = (value, digits, mark) => { const rows = Array.from(d3.group(designRuns, (d) => `${d.K}|${d.Sy}|${d.b0}`).values(), (v) => ({K: v[0].K, Sy: v[0].Sy, b0: v[0].b0, v})); const cell = (m, n) => ({label: String(n), value: (r) => { const x = value(r.v.find((d) => d.mode === m && d.n === n)); return pm(x, digits) + (mark && flatSetting(r) ? "†" : ""); }}); return {rows, cols: [...settingCols, ...sizeTicks.map((n) => cell("naive", n)), ...sizeTicks.map((n) => cell("informed", n))], groups: [[{label: "Aquifer setting", span: 3}, {label: "Naive members, CID size (sections)", span: 5}, {label: "Informed members, CID size (sections)", span: 5}]]}; }; const tableS4 = (() => { const t = bySetting((d) => d.G, 2, false); return table("Table S4. Member gain G (%) relative to the all-APM baseline for every aquifer setting, district size (sections) and belief.", t.cols, t.rows, {groups: t.groups}); })(); const tableS5 = (() => { const t = bySetting((d) => d.kappa, 2, true); return table("Table S5. Capture ratio κ = G / G* for every aquifer setting, district size (sections) and belief. † G* = 0.04%; κ is unstable and the setting is excluded from the tests that use κ.", t.cols, t.rows, {groups: t.groups}); })(); // ---- Table S6: neighbours and landscape const tableS6 = (() => { const rows = d3.flatRollup(designRuns, (v) => ({ GN: d3.mean(v, (d) => d.Gn), dC: d3.median(v, (d) => d.dC) / 1e3, dN: d3.median(v, (d) => d.dN) / 1e3, land: d3.median(v, (d) => d.land) / 1e3, nbMore: 100 * d3.mean(v, (d) => (d.dN > d.dC ? 1 : 0)), landPos: 100 * d3.mean(v, (d) => (d.land > 0 ? 1 : 0)), psi: d3.median(v.filter((d) => d.shareN != null), (d) => d.shareN) }), (d) => d.n, (d) => d.mode).map(([n, mode, s]) => ({n, mode, ...s})) .sort((a, b) => a.n - b.n || (a.mode === "naive" ? -1 : 1)); return table("Table S6. Gains of the district and of its APM neighbors by district size, over the 27 aquifer settings. G_N: mean neighbor gain (%). ΔV_C, ΔV_N, ΔV_C + ΔV_N: median total NPV change of the district, the neighbors and both ($1,000). N > C: settings in which the neighbors gain more dollars than the district (%). Total > 0: settings in which the combined value rises (%). ψ⁺: median neighbors' share of the positive gains (Text S6).", [ {label: "Size", value: (r) => (r.mode === "naive" ? r.n : ""), align: "left"}, {label: "Belief", value: (r) => r.mode, align: "left"}, {label: "G_N (%)", value: (r) => pm(r.GN, 2)}, {label: "ΔV_C", value: (r) => usd(r.dC)}, {label: "ΔV_N", value: (r) => usd(r.dN)}, {label: "ΔV_C + ΔV_N", value: (r) => usd(r.land)}, {label: "N > C (%)", value: (r) => r.nbMore.toFixed(0)}, {label: "Total > 0 (%)", value: (r) => r.landPos.toFixed(0)}, {label: "ψ⁺", value: (r) => r.psi.toFixed(2)} ], rows); })(); // ---- Table S7: members by position const tableS7 = (() => { const rows = d3.flatRollup(members, (v) => { const m = d3.sum(v, (d) => d.members); const by = (mode) => v.filter((d) => d.mode === mode); const feas = v.filter((d) => d.mwFeasible === 1); return {members: m, gN: d3.mean(by("naive"), (d) => d.gain), gI: d3.mean(by("informed"), (d) => d.gain), none: (100 * d3.sum(v, (d) => d.worse)) / m, gs: (100 * d3.sum(v, (d) => d.worseGS)) / m, mw: (100 * d3.sum(v, (d) => d.worseMW)) / m, tr: feas.length ? d3.sum(feas, (d) => d.members * d.trMW) / d3.sum(feas, (d) => d.members) : null}; }, (d) => d.n, (d) => d.position).map(([n, position, s]) => ({n, position, ...s})) .sort((a, b) => a.n - b.n || positionOrder.indexOf(a.position) - positionOrder.indexOf(b.position)); return table("Table S7. Members by position and district size, pooled over the 27 aquifer settings and both beliefs. Gain: mean member gain without compensation (%), naive / informed. Worse off: share of members worse off than in the all-APM baseline with no compensation, equal or gain sharing (identical here), and make-whole. Transfer: mean annual make-whole transfer per member ($/yr; positive = received) in the districts where the rule is feasible.", [ {label: "Size", value: (r, i) => (i === 0 || rows[i - 1].n !== r.n ? r.n : ""), align: "left"}, {label: "Position", value: (r) => r.position, align: "left"}, {label: "Members", value: (r) => r.members}, {label: "Gain (%)", value: (r) => `${pm(r.gN, 1)} / ${pm(r.gI, 1)}`}, {label: "None", value: (r) => r.none.toFixed(0)}, {label: "Equal / gain share", value: (r) => r.gs.toFixed(0)}, {label: "Make-whole", value: (r) => r.mw.toFixed(0)}, {label: "Transfer ($/yr)", value: (r) => (r.n === 1 || r.tr == null ? "—" : pm(r.tr, 0))} ], rows, {groups: [[{label: "", span: 4}, {label: "Worse off than all-APM (%)", span: 3}, {label: "", span: 1}]]}); })(); // ---- Table S8: run times const tableS8 = (() => { const kinds = {apm: "All-APM baseline", naive: "Naive CID", informed: "Informed CID", alllpm: "All-LPM control"}; const rows = [...runtime].sort((a, b) => a.n - b.n || Object.keys(kinds).indexOf(a.kind) - Object.keys(kinds).indexOf(b.kind)); return table("Table S8. Run times of the 432 design runs (16 October 2026 batch, 23 parallel workers), by district size and run type: minutes per run and total CPU-hours.", [ {label: "Size", value: (r, i) => (i === 0 || rows[i - 1].n !== r.n ? r.n : ""), align: "left"}, {label: "Run type", value: (r) => kinds[r.kind], align: "left"}, {label: "Runs", value: (r) => r.runs}, {label: "Median (min)", value: (r) => r.median.toFixed(1)}, {label: "Maximum (min)", value: (r) => r.max.toFixed(1)}, {label: "Total (CPU-h)", value: (r) => (r.sum / 60).toFixed(1)} ], rows, {note: `Total: ${(d3.sum(runtime, (d) => d.sum) / 60).toFixed(1)} CPU-hours for ${d3.sum(runtime, (d) => d.runs)} runs.`}); })(); display(tableS2); display(tableS3); display(tableS4); display(tableS5); display(tableS6); display(tableS7); display(tableS8); 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states.features.find((d) => d.id === \"40\"),\n // CO, KS, NM, TX, OK\n neighbors: states.features.filter((d) => [\"08\", \"20\", \"35\", \"48\", \"40\"].includes(d.id)),\n okCounties,\n // Cimarron (40025), Texas (40139), Beaver (40007)\n panhandle: okCounties.filter((d) => [\"40025\", \"40139\", \"40007\"].includes(d.id))\n};\n\ngeo.panhandle.map((d) => d.properties.name)","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"$45","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"$46","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"$47","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"$48","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"$49","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"$4a","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"$4b","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"$4c","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"$4d","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"$4e","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"$4f","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"## Manuscript tables\n\nTables 3–5 of the manuscript, computed from the design data above. Tables 1–2 (parameters and design) are descriptive and live in the manuscript only.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"$50","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"$51","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"$52","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"## Supporting Information\n\nFigures S2–S7 and Tables S2–S8 of the Supporting Information. Figure S1 (study area) is above. Text S1–S8 and Table S1 (model checks) are in the SI document (`manuscript/…/CID_SI`). SI numbering follows first citation in the SI: S2 time paths, S3 potential gain, S4 gains by setting, S5 retention and leakage, S6 neighbors, S7 members.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"$53","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"$54","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"$55","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"$56","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"$57","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"$58","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W59","stdlib":"2"}} 44:"$43:body"