35:null 36:["$","title",null,{"children":"The Lochsa Basin Explored, Elevation and SWE | Stellios Bonadurer | Observable"}] 3c:T915,The Lochsa river contains a world-class stretch of whitewater which brings in boaters from around the world. The stretch is run both by private boaters and commercial outfitters. Beginning around mid-may Lochsa season exists in a very special moment where spring temperatures rise, melting snowpack. The season is ephemeral, peaking in popularity around Memorial Day. The extent of the season varies from year to year, with some years memorialized in the community - often due to high flow or other factors. The remoteness of the Lochsa brings significant expense, both for private boaters who have to plan their trips around river conditions and particularly for commercial outfits who depend on favorable conditions to plan their seasons. Because of these reasons it is helpful to look ahead in winter to make predictions on if it's the season to finally make it out, or for outfitters how much commercial activity to expect and how many guides to staff. Most of the year the Lochsa runs at levels below what is considered 'runnable', for commercial outfits this level is ~4000 cfs while private boaters will run a wider range of flow rates. This creates a lower bound to days we consider runnable. At typical levels the Lochsa is a high volume pool/drop river, featuring large class IV rapids separated by flat sections which allow for recovery from flips and swims. At high water the Lochsa becomes a continuous stretch of class V/V+ rapids leaving no room for error. Due to the risk involved with high water Locsha, commercial outfits (typically) will cancel trips above ~14,000 cfs. This adds an upper bound to the days we look at for commercial runnability. The flow of the Lochsa is determined by the snowpack in a section of the Bitteroot mountains. Common knowledge dictates that the more snowpack that exists in the watershed on a given date in spring can inform water levels in spring. Thus my primary hypothesis is that **years with more SWE (Stored Water Equivalent) on 4/1 will have more days above our lower threshold**. Because this hypothesis is relatively straightforward and can be well explored as a simple bivariate relation, a secondary hypothesis is that **higher amounts/proportions of SWE at high elevations, defined as the upper third of the basin, will be correlated with more runnable days.**3d:T1353,viewof selectedDate = { const minimumDate = selectedAnnualSweMetadata.coverage_start const maximumDate = selectedAnnualSweMetadata.coverage_end const initialDate = `${selectedYear}-04-01` const form = html`` const input = form.querySelector('input[type="date"]') const playButton = form.querySelector('[data-action="play"]') const buttons = form.querySelectorAll("button") for (const button of buttons) { button.style.font = "inherit" button.style.padding = "0.32rem 0.65rem" button.style.cursor = "pointer" } const parseDate = value => new Date(`${value}T00:00:00Z`) const formatDate = date => date.toISOString().slice(0, 10) const minDate = parseDate(minimumDate) const maxDate = parseDate(maximumDate) let timer = null function isLeapDay(date) { return date.getUTCMonth() === 1 && date.getUTCDate() === 29 } function calendarIndex(date) { const monthDay = d3.utcFormat("%m-%d")(date) return dailyCalendar.findIndex( day => d3.utcFormat("%m-%d")(day) === monthDay ) } function isAvailable(date) { const index = calendarIndex(date) return ( index >= 0 && !unavailableDayIndexes.has(index) ) } function clamp(date) { if (date < minDate) return new Date(minDate) if (date > maxDate) return new Date(maxDate) return date } function emit() { form.value = input.value form.dispatchEvent( new CustomEvent("input", {bubbles: true}) ) } function setDate(date) { date = clamp(date) // The arrays intentionally omit February 29. if (isLeapDay(date)) { date.setUTCDate(date.getUTCDate() + 1) } input.value = formatDate(clamp(date)) emit() } function stop() { if (timer !== null) { clearInterval(timer) timer = null } playButton.textContent = "Play" } function shiftDay(amount) { const date = parseDate(input.value) date.setUTCDate(date.getUTCDate() + amount) if (isLeapDay(date)) { date.setUTCDate(date.getUTCDate() + Math.sign(amount)) } setDate(date) } function shiftMonth(amount) { const current = parseDate(input.value) const originalDay = current.getUTCDate() const target = new Date(Date.UTC( current.getUTCFullYear(), current.getUTCMonth() + amount, 1 )) const lastDayOfTargetMonth = new Date(Date.UTC( target.getUTCFullYear(), target.getUTCMonth() + 1, 0 )).getUTCDate() target.setUTCDate( Math.min(originalDay, lastDayOfTargetMonth) ) setDate(target) } function advance() { let candidate = parseDate(input.value) // Search for the next available daily frame. for (let attempts = 0; attempts < 365; attempts++) { candidate.setUTCDate(candidate.getUTCDate() + 1) if (candidate > maxDate) { candidate = new Date(minDate) } if (!isLeapDay(candidate) && isAvailable(candidate)) { setDate(candidate) return } } stop() } form.querySelector('[data-action="previous-month"]').onclick = () => { stop() shiftMonth(-1) } form.querySelector('[data-action="previous-day"]').onclick = () => { stop() shiftDay(-1) } form.querySelector('[data-action="next-day"]').onclick = () => { stop() shiftDay(1) } form.querySelector('[data-action="next-month"]').onclick = () => { stop() shiftMonth(1) } playButton.onclick = () => { if (timer === null) { playButton.textContent = "Pause" timer = setInterval(advance, 160) } else { stop() } } input.oninput = () => { stop() if (input.value.endsWith("-02-29")) { const date = parseDate(input.value) date.setUTCDate(1) date.setUTCMonth(2) input.value = formatDate(date) } emit() } invalidation.then(stop) emit() return form }3e:T62a,lochsaSweMapFinal = Plot.plot({ width, height: 620, margin: 18, title: selectedDateHasSwe ? `Lochsa snow water equivalent — ${selectedDate}` : `Lochsa snow water equivalent — ${selectedDate} (data unavailable)`, projection: { type: "mercator", domain: basin, inset: 10 }, color: { type: "linear", scheme: "blues", domain: [0, sweColorMaximum], clamp: true, unknown: "#d9d9d9", label: "SWE (mm)", legend: true }, marks: [ Plot.geo(gridGeoJSON.features, { fill: (d, i) => selectedDaySwe[i] === dailySweManifest.nodata ? NaN : selectedDaySwe[i], stroke: null, tip: true, title: (d, i) => { const swe = selectedDaySwe[i] return swe === dailySweManifest.nodata ? `SWE unavailable\nElevation: ${d.properties.elevation_m.toFixed(0)} m` : `SWE: ${swe.toLocaleString()} mm\nElevation: ${d.properties.elevation_m.toFixed(0)} m` } }), Plot.geo(tributaries, { stroke: "#6b7280", strokeOpacity: 0.8, strokeWidth: 0.7 }), Plot.geo(lochsaMainstem, { stroke: "#d55e00", strokeWidth: 2.2 }), Plot.geo(basin, { fill: "none", stroke: "#202020", strokeWidth: 1.2 }), Plot.dot(lochsaGauge, { x: "longitude", y: "latitude", r: 4, fill: "#f7f7f7", stroke: "#111111", strokeWidth: 1.5, tip: true, title: d => `${d.name}\n${d.siteId}` }), Plot.frame({ stroke: "#777" }) ] })3f:T933,flowTimeSeriesFinal = Plot.plot({ width, height: 400, marginLeft: 70, marginBottom: 45, title: `Lochsa River daily mean discharge — ${selectedYear}`, subtitle: "Other SWE-covered years shown in gray", x: { type: "utc", domain: [ selectedSeasonBounds.yearStart, selectedSeasonBounds.yearEnd ], ticks: d3.utcMonth.every(1), tickFormat: d3.utcFormat("%b"), label: "Date" }, y: { domain: [0, flowYMaximum], grid: true, label: "Daily mean discharge (CFS)" }, marks: [ // Before the boating season. Plot.rect( [{ x1: selectedSeasonBounds.yearStart, x2: selectedSeasonBounds.seasonBegin, y1: 0, y2: flowYMaximum }], { x1: "x1", x2: "x2", y1: "y1", y2: "y2", fill: "#d1d5db", fillOpacity: 0.65 } ), // After the boating season. Plot.rect( [{ x1: selectedSeasonBounds.seasonEndExclusive, x2: selectedSeasonBounds.yearEnd, y1: 0, y2: flowYMaximum }], { x1: "x1", x2: "x2", y1: "y1", y2: "y2", fill: "#d1d5db", fillOpacity: 0.65 } ), // Other years. Plot.line(otherYearsFlow, { x: "plotDate", y: "discharge_cfs", z: "year", stroke: "#8b8b8b", strokeOpacity: 0.09, strokeWidth: 1 }), // Commercial boundaries. Plot.ruleY( [ flowThresholds.commercial.lowerCfs, //flowThresholds.commercial.upperCfs ], { stroke: "#0072b2", strokeWidth: 1.7, strokeDasharray: "6,4" } ), // Selected year. Plot.line(selectedYearFlow, { x: "date", y: "discharge_cfs", stroke: "#222222", strokeWidth: 2.2 }), // Selected SWE date. Plot.ruleX([selectedFlowDate], { stroke: "#d55e00", strokeWidth: 2 }), Plot.dot( selectedYearFlow.filter( d => +d.date === +selectedFlowDate ), { x: "date", y: "discharge_cfs", r: 4, fill: "#d55e00", stroke: "white", tip: true, title: d => `${selectedYear}\n${d3.format(",")(d.discharge_cfs)} CFS` } ), Plot.frame({ stroke: "#777" }) ] })40:T66b,swePrivateDaysPlotFinal = Plot.plot({ width, height: 480, marginLeft: 65, marginBottom: 55, title: `${selectedSweDateLabel} snowpack and runnable days`, subtitle: "Each point represents one year", x: { domain: [700,3000], label: `${selectedSweDateLabel} basin SWE (million m³)`, grid: true, nice: true }, y: { domain: [0,100], label: "Runnable days", grid: true, nice: true, zero: true }, marks: [ Plot.linearRegressionY(annualAnalysisMetrics, { x: d => d[selectedSweDate], y: "privateRunnableDays", stroke: "#555555", strokeWidth: 2 }), Plot.dot(annualAnalysisMetrics, { x: d => d[selectedSweDate], y: "privateRunnableDays", r: 5, fill: "#0072b2", stroke: "white", strokeWidth: 1, tip: true, title: d => `${d.year}\n` + `${selectedSweDateLabel} SWE: ` + `${d3.format(",.1f")(d[selectedSweDate])} million m³\n` + `Private runnable days: ${d.privateRunnableDays}` }), Plot.dot( annualAnalysisMetrics.filter( d => d.year === selectedYear ), { x: d => d[selectedSweDate], y: "privateRunnableDays", r: 7, fill: "#d55e00", stroke: "white", strokeWidth: 1.5 } ), Plot.text( annualAnalysisMetrics.filter( d => d.year === selectedYear ), { x: d => d[selectedSweDate], y: "privateRunnableDays", text: d => d.year, dy: -12, fill: "#8c3700", fontWeight: "bold" } ), Plot.frame() ] })41:T4d4,upperThirdElevationMapFinal = Plot.plot({ width, height: 620, margin: 18, title: "Upper third of the Lochsa basin by elevation", subtitle: `Cells at or above ${d3.format(",.0f")(upperThirdElevation)} meters`, projection: { type: "mercator", domain: basin, inset: 10 }, color: { type: "categorical", domain: [ "Lower two-thirds", "Upper third" ], range: [ "#d9d9d9", "#0072b2" ], legend: true }, marks: [ Plot.geo(gridGeoJSON.features, { fill: (feature, i) => upperThirdMask[i] ? "Upper third" : "Lower two-thirds", stroke: null, tip: true, title: (feature, i) => `${upperThirdMask[i] ? "Upper third" : "Lower two-thirds"}\n` + `Elevation: ${d3.format(",.0f")( feature.properties.elevation_m )} m` }), Plot.geo(tributaries, { stroke: "#737373", strokeOpacity: 0.75, strokeWidth: 0.7 }), Plot.geo(lochsaMainstem, { stroke: "#d55e00", strokeWidth: 2.2 }), Plot.geo(basin, { fill: "none", stroke: "#202020", strokeWidth: 1.2 }), Plot.frame({ stroke: "#777" }) ] })42:T42e,comparisonMetricsTableFinal = Inputs.table( comparisonMetricsWithFlow, { columns: [ "selection", "totalSwe", "upperThirdSwe", "upperThirdShare", "commercialRunnableDays", "privateRunnableDays", "highWaterDays" ], header: { selection: "Year/comparison", date: "SWE date", totalSwe: "Total SWE", upperThirdSwe: "Upper-third SWE", upperThirdShare: "Upper Third %", commercialRunnableDays: "Commercial days", privateRunnableDays: "Private runnable days", highWaterDays: "High-water days" }, format: { totalSwe: value => d3.format(",.1f")(value), upperThirdSwe: value => d3.format(",.1f")(value), upperThirdShare: value => value == null ? "—" : `${d3.format(".1f")(value)}%`, commercialRunnableDays: d3.format("d"), privateRunnableDays: d3.format("d"), highWaterDays: d3.format("d") }, rows: 3 } )43:Tf59,comparisonSweMapsFinal = { const { monthDay, sweA, sweB, difference } = comparisonSweFrames const nodata = dailySweManifest.nodata // Use a symmetric difference scale centered on zero. const differenceMaximum = Math.max( 100, Math.ceil( d3.max( difference, value => Number.isFinite(value) ? Math.abs(value) : 0 ) / 100 ) * 100 ) const mapWidth = Math.max( 300, Math.floor((width - 24) / 3) ) function mapMarks(values, isDifference = false) { return [ Plot.geo(gridGeoJSON.features, { fill: (feature, cellIndex) => { const value = values[cellIndex] if (isDifference) { return Number.isFinite(value) ? value : NaN } return value === nodata ? NaN : value }, stroke: null, tip: true, title: (feature, cellIndex) => { const value = values[cellIndex] const elevation = feature.properties.elevation_m const valueLabel = isDifference ? Number.isFinite(value) ? `${d3.format("+,.0f")(value)} mm` : "Unavailable" : value === nodata ? "Unavailable" : `${d3.format(",.0f")(value)} mm` const elevationRegion = upperThirdMask[cellIndex] ? "Upper elevation third" : "Lower two-thirds" return ( `SWE: ${valueLabel}\n` + `Elevation: ${d3.format(",.0f")(elevation)} m\n` + elevationRegion ) } }), Plot.geo(tributaries, { stroke: "#737373", strokeOpacity: 0.65, strokeWidth: 0.6 }), Plot.geo(lochsaMainstem, { stroke: "#202020", strokeWidth: 1.8 }), Plot.geo(basin, { fill: "none", stroke: "#202020", strokeWidth: 1.1 }), Plot.frame({ stroke: "#777" }) ] } const commonMapOptions = { width: mapWidth, height: 440, margin: 15, projection: { type: "mercator", domain: basin, inset: 8 } } const absoluteSweColor = { type: "linear", scheme: "blues", domain: [0, sweColorMaximum], clamp: true, unknown: "#d9d9d9", label: "SWE (mm)", legend: true } const firstMap = Plot.plot({ ...commonMapOptions, title: `${comparisonYearA}-${monthDay}`, color: { ...absoluteSweColor }, marks: mapMarks(sweA) }) const secondMap = Plot.plot({ ...commonMapOptions, title: `${comparisonYearB}-${monthDay}`, color: { ...absoluteSweColor }, marks: mapMarks(sweB) }) const differenceMap = Plot.plot({ ...commonMapOptions, title: `${comparisonYearB} minus ${comparisonYearA}`, color: { type: "diverging", scheme: "rdbu", pivot: 0, domain: [ -differenceMaximum, differenceMaximum ], clamp: true, unknown: "#d9d9d9", label: "SWE difference mm", legend: true }, marks: mapMarks(difference, true) }) const mapRow = html`

` mapRow.append( firstMap, secondMap, differenceMap ) const explanation = html`

Difference is ${comparisonYearB} minus ${comparisonYearA}. Blue cells had more SWE in ${comparisonYearB}; red cells had more SWE in ${comparisonYearA}.

` const container = html`

` container.append(mapRow, explanation) return container }44:T53a,annualSweTotals = { const results = [] const cellCount = dailySweManifest.cell_count const nodata = dailySweManifest.nodata const formatMonthDay = d3.utcFormat("%m-%d") const dateIndexes = new Map( sweSnapshotDates.map(snapshot => [ snapshot.key, dailyCalendar.findIndex( date => formatMonthDay(date) === snapshot.monthDay ) ]) ) for (const yearMetadata of dailySweManifest.years) { const values = await loadAnnualSwe(yearMetadata.year) const result = { year: yearMetadata.year } for (const snapshot of sweSnapshotDates) { const dayIndex = dateIndexes.get(snapshot.key) const offset = dayIndex * cellCount let totalCubicMeters = 0 let validCells = 0 for (let cellIndex = 0; cellIndex < cellCount; cellIndex++) { const sweMillimeters = values[offset + cellIndex] if (sweMillimeters !== nodata) { const cellAreaKm2 = Number(gridCells[cellIndex].cell_area_km2) // mm × km² × 1,000 = cubic meters totalCubicMeters += sweMillimeters * cellAreaKm2 * 1000 validCells++ } } result[snapshot.key] = validCells > 0 ? totalCubicMeters / 1_000_000 : null } results.push(result) } return results }45:T43b,//Get all SWE totals for our selected year selectedYearDailySwe = { const results = [] const cellCount = dailySweManifest.cell_count const nodata = dailySweManifest.nodata const formatMonthDay = d3.utcFormat("%m-%d") for ( let dayIndex = 0; dayIndex < dailyCalendar.length; dayIndex++ ) { const offset = dayIndex * cellCount let totalCubicMeters = 0 let validCells = 0 for ( let cellIndex = 0; cellIndex < cellCount; cellIndex++ ) { const sweMillimeters = selectedAnnualSwe[offset + cellIndex] if (sweMillimeters !== nodata) { totalCubicMeters += sweMillimeters * Number(gridCells[cellIndex].cell_area_km2) * 1000 validCells++ } } const monthDay = formatMonthDay( dailyCalendar[dayIndex] ) results.push({ date: new Date( `${selectedYear}-${monthDay}T00:00:00Z` ), totalSweMillionM3: validCells > 0 ? totalCubicMeters / 1_000_000 : null }) } return results }46:T6a6,runnableSeasonMetric = ( yearFlow, thresholds, beginDate, endDate ) => { if (!yearFlow.length) { return { year: null, commercialRunnableDays: 0, privateRunnableDays: 0, highConsequenceDays: 0, daysObserved: 0 } } const year = Number(yearFlow[0].year) const monthDay = value => value instanceof Date ? d3.utcFormat("%m-%d")(value) : String(value).slice(-5) const seasonBegin = new Date( `${year}-${monthDay(beginDate)}T00:00:00Z` ) const seasonEnd = new Date( `${year}-${monthDay(endDate)}T23:59:59Z` ) const seasonFlow = yearFlow .map(d => ({ date: d.date instanceof Date ? d.date : new Date( `${String(d.date).slice(0, 10)}T00:00:00Z` ), discharge_cfs: Number(d.discharge_cfs) })) .filter(d => !Number.isNaN(+d.date) && Number.isFinite(d.discharge_cfs) && d.date >= seasonBegin && d.date <= seasonEnd ) const commercialRunnableDays = seasonFlow.filter( d => d.discharge_cfs >= thresholds.commercial.lowerCfs && d.discharge_cfs <= thresholds.commercial.upperCfs ).length const privateRunnableDays = seasonFlow.filter( d => d.discharge_cfs >= thresholds.private.lowerCfs ).length const highConsequenceDays = seasonFlow.filter( d => d.discharge_cfs > thresholds.highConsequence.lowerCfs ).length return { year, commercialRunnableDays, privateRunnableDays, highConsequenceDays, // Keeps old code using daysRunnable functional. daysRunnable: commercialRunnableDays, daysObserved: seasonFlow.length, seasonBegin, seasonEnd } }47:T5ee,flowTimeSeries = Plot.plot({ width, height: 400, marginLeft: 70, marginBottom: 45, title: `Lochsa River daily mean discharge — ${selectedYear}`, x: { type: "utc", domain: [ new Date(Date.UTC(selectedYear, 0, 1)), new Date(Date.UTC(selectedYear + 1, 0, 1)) ], ticks: d3.utcMonth.every(1), tickFormat: d3.utcFormat("%b"), label: "Date" }, y: { domain: [0, flowYMaximum], grid: true, label: "Daily mean discharge (CFS)" }, marks: [ Plot.rect( [{ x1: new Date(Date.UTC(selectedYear, 0, 1)), x2: new Date(Date.UTC(selectedYear + 1, 0, 1)), y1: flowThresholds.lowerCfs, y2: flowThresholds.upperCfs }], { x1: "x1", x2: "x2", y1: "y1", y2: "y2", fill: "#56b4e9", fillOpacity: 0.15 } ), Plot.ruleY( [flowThresholds.lowerCfs, flowThresholds.upperCfs], { stroke: "#0072b2", strokeWidth: 1.5, strokeDasharray: "6,4" } ), Plot.line(selectedYearFlow, { x: "date", y: "discharge_cfs", stroke: "#222", strokeWidth: 1.5 }), Plot.ruleX([selectedFlowDate], { stroke: "#d55e00", strokeWidth: 2 }), Plot.dot( selectedYearFlow.filter( d => +d.date === +selectedFlowDate ), { x: "date", y: "discharge_cfs", r: 4, fill: "#d55e00", tip: true } ), Plot.frame() ] })48:T824,flowTimeSeries2 = Plot.plot({ width, height: 400, marginLeft: 70, marginBottom: 45, title: `Lochsa River daily mean discharge — ${selectedYear}`, x: { type: "utc", domain: [ selectedSeasonBounds.yearStart, selectedSeasonBounds.yearEnd ], ticks: d3.utcMonth.every(1), tickFormat: d3.utcFormat("%b"), label: "Date" }, y: { domain: [0, flowYMaximum], grid: true, label: "Daily mean discharge (CFS)" }, marks: [ // Shade the part of the year before the boating season. Plot.rect( [{ x1: selectedSeasonBounds.yearStart, x2: selectedSeasonBounds.seasonBegin, y1: 0, y2: flowYMaximum }], { x1: "x1", x2: "x2", y1: "y1", y2: "y2", fill: "#d1d5db", fillOpacity: 0.65 } ), // Shade the part of the year after the boating season. Plot.rect( [{ x1: selectedSeasonBounds.seasonEndExclusive, x2: selectedSeasonBounds.yearEnd, y1: 0, y2: flowYMaximum }], { x1: "x1", x2: "x2", y1: "y1", y2: "y2", fill: "#d1d5db", fillOpacity: 0.65 } ), // Commercial runnable-flow boundaries. Plot.ruleY( [ flowThresholds.commercial.lowerCfs, flowThresholds.commercial.upperCfs ], { stroke: "#0072b2", strokeWidth: 1.7, strokeDasharray: "6,4" } ), // Daily mean discharge. Plot.line(selectedYearFlow, { x: "date", y: "discharge_cfs", stroke: "#222222", strokeWidth: 1.6 }), // Selected SWE date. Plot.ruleX([selectedFlowDate], { stroke: "#d55e00", strokeWidth: 2 }), Plot.dot( selectedYearFlow.filter( d => +d.date === +selectedFlowDate ), { x: "date", y: "discharge_cfs", r: 4, fill: "#d55e00", stroke: "white", tip: true } ), Plot.frame({ stroke: "#777" }) ] })49:T625,lochsaSweMap = Plot.plot({ width, height: 620, margin: 18, title: selectedDateHasSwe ? `Lochsa snow water equivalent — ${selectedDate}` : `Lochsa snow water equivalent — ${selectedDate} (data unavailable)`, projection: { type: "mercator", domain: basin, inset: 10 }, color: { type: "linear", scheme: "blues", domain: [0, sweColorMaximum], clamp: true, unknown: "#d9d9d9", label: "SWE (mm)", legend: true }, marks: [ Plot.geo(gridGeoJSON.features, { fill: (d, i) => selectedDaySwe[i] === dailySweManifest.nodata ? NaN : selectedDaySwe[i], stroke: null, tip: true, title: (d, i) => { const swe = selectedDaySwe[i] return swe === dailySweManifest.nodata ? `SWE unavailable\nElevation: ${d.properties.elevation_m.toFixed(0)} m` : `SWE: ${swe.toLocaleString()} mm\nElevation: ${d.properties.elevation_m.toFixed(0)} m` } }), Plot.geo(tributaries, { stroke: "#6b7280", strokeOpacity: 0.8, strokeWidth: 0.7 }), Plot.geo(lochsaMainstem, { stroke: "#d55e00", strokeWidth: 2.2 }), Plot.geo(basin, { fill: "none", stroke: "#202020", strokeWidth: 1.2 }), Plot.dot(lochsaGauge, { x: "longitude", y: "latitude", r: 4, fill: "#f7f7f7", stroke: "#111111", strokeWidth: 1.5, tip: true, title: d => `${d.name}\n${d.siteId}` }), Plot.frame({ stroke: "#777" }) ] })4a:T547,### Iteration The enhanced timeseries plot better shows what we are after, grey bars focus the viewer to the reasonable season period and horizontal bars show the thresholds we are looking for. The table is helpful just for looking at the data as we iterate, not sure if this will be part of the main body of the project. Also considered a plot for that but deemed that unnecessary for now. For the geo-raster of SWE I am pleased with how it looks, especially when using the play feature to observe a season. It will be helpful to compute some metrics, such as total SWE on a given day. It's hard with the controls so far above to get a feel for things, but when assembled in the final product it will look good I think. The main concern here is that the graphic doesn't necessary help support the hypothesis, or benefit from the raster aspect of the presentation. Colors will be refined once we have a more concrete way of presenting the map. To further this we enhance the table to include SWE on 4 spring days, and a scatter plot between a chosen day and days for private boating. We choose private boating for this because having an upper cut off may affect the relationship significantly. To improve the timeseries plot I added the data for other years, greyed out behind. It now looks a bit busy but maybe the extra context is worthwhile. 4b:T92d,flowTimeSeries3 = Plot.plot({ width, height: 400, marginLeft: 70, marginBottom: 45, title: `Lochsa River daily mean discharge — ${selectedYear}`, subtitle: "Other SWE-covered years shown in gray", x: { type: "utc", domain: [ selectedSeasonBounds.yearStart, selectedSeasonBounds.yearEnd ], ticks: d3.utcMonth.every(1), tickFormat: d3.utcFormat("%b"), label: "Date" }, y: { domain: [0, flowYMaximum], grid: true, label: "Daily mean discharge (CFS)" }, marks: [ // Before the boating season. Plot.rect( [{ x1: selectedSeasonBounds.yearStart, x2: selectedSeasonBounds.seasonBegin, y1: 0, y2: flowYMaximum }], { x1: "x1", x2: "x2", y1: "y1", y2: "y2", fill: "#d1d5db", fillOpacity: 0.65 } ), // After the boating season. Plot.rect( [{ x1: selectedSeasonBounds.seasonEndExclusive, x2: selectedSeasonBounds.yearEnd, y1: 0, y2: flowYMaximum }], { x1: "x1", x2: "x2", y1: "y1", y2: "y2", fill: "#d1d5db", fillOpacity: 0.65 } ), // Other years. Plot.line(otherYearsFlow, { x: "plotDate", y: "discharge_cfs", z: "year", stroke: "#8b8b8b", strokeOpacity: 0.09, strokeWidth: 1 }), // Commercial boundaries. Plot.ruleY( [ flowThresholds.commercial.lowerCfs, flowThresholds.commercial.upperCfs ], { stroke: "#0072b2", strokeWidth: 1.7, strokeDasharray: "6,4" } ), // Selected year. Plot.line(selectedYearFlow, { x: "date", y: "discharge_cfs", stroke: "#222222", strokeWidth: 2.2 }), // Selected SWE date. Plot.ruleX([selectedFlowDate], { stroke: "#d55e00", strokeWidth: 2 }), Plot.dot( selectedYearFlow.filter( d => +d.date === +selectedFlowDate ), { x: "date", y: "discharge_cfs", r: 4, fill: "#d55e00", stroke: "white", tip: true, title: d => `${selectedYear}\n${d3.format(",")(d.discharge_cfs)} CFS` } ), Plot.frame({ stroke: "#777" }) ] })4c:T515,annualAnalysisTable = Inputs.table( annualAnalysisMetrics, { columns: [ "year", "jan1Swe", "feb1Swe", "mar1Swe", "apr1Swe", "commercialRunnableDays", "privateRunnableDays", "highConsequenceDays", "daysObserved" ], header: { year: "Year", jan1Swe: "Jan. 1 SWE (million m³)", feb1Swe: "Feb. 1 SWE (million m³)", mar1Swe: "Mar. 1 SWE (million m³)", apr1Swe: "Apr. 1 SWE (million m³)", commercialRunnableDays: "Commercial days", privateRunnableDays: "Private boating days", highConsequenceDays: "High-consequence days", daysObserved: "Observed days" }, format: { year: d3.format("d"), jan1Swe: value => value == null ? "Unavailable" : d3.format(",.1f")(value), feb1Swe: value => value == null ? "Unavailable" : d3.format(",.1f")(value), mar1Swe: value => value == null ? "Unavailable" : d3.format(",.1f")(value), apr1Swe: value => value == null ? "Unavailable" : d3.format(",.1f")(value), commercialRunnableDays: d3.format("d"), privateRunnableDays: d3.format("d"), highConsequenceDays: d3.format("d"), daysObserved: d3.format("d") }, sort: "year", rows: 23 } )4d:T66e,swePrivateDaysPlot = Plot.plot({ width, height: 480, marginLeft: 65, marginBottom: 55, title: `${selectedSweDateLabel} snowpack and runnable days`, subtitle: "Each point represents one year", x: { domain: [700,3000], label: `${selectedSweDateLabel} basin SWE (million m³)`, grid: true, nice: true }, y: { domain: [0,100], label: "Private runnable days", grid: true, nice: true, zero: true }, marks: [ Plot.linearRegressionY(annualAnalysisMetrics, { x: d => d[selectedSweDate], y: "privateRunnableDays", stroke: "#555555", strokeWidth: 2 }), Plot.dot(annualAnalysisMetrics, { x: d => d[selectedSweDate], y: "privateRunnableDays", r: 5, fill: "#0072b2", stroke: "white", strokeWidth: 1, tip: true, title: d => `${d.year}\n` + `${selectedSweDateLabel} SWE: ` + `${d3.format(",.1f")(d[selectedSweDate])} million m³\n` + `Private runnable days: ${d.privateRunnableDays}` }), Plot.dot( annualAnalysisMetrics.filter( d => d.year === selectedYear ), { x: d => d[selectedSweDate], y: "privateRunnableDays", r: 7, fill: "#d55e00", stroke: "white", strokeWidth: 1.5 } ), Plot.text( annualAnalysisMetrics.filter( d => d.year === selectedYear ), { x: d => d[selectedSweDate], y: "privateRunnableDays", text: d => d.year, dy: -12, fill: "#8c3700", fontWeight: "bold" } ), Plot.frame() ] })4e:T4c3,selectedSweRelationship = { const x = annualAnalysisMetrics.map( d => d[selectedSweDate] ) const y = annualAnalysisMetrics.map( d => d.privateRunnableDays ) const observations = x.length const meanX = d3.mean(x) const meanY = d3.mean(y) const sumXX = d3.sum( x, value => (value - meanX) ** 2 ) const sumYY = d3.sum( y, value => (value - meanY) ** 2 ) const sumXY = d3.sum( d3.range(observations), i => (x[i] - meanX) * (y[i] - meanY) ) const correlation = sumXY / Math.sqrt(sumXX * sumYY) const slope = sumXY / sumXX const intercept = meanY - slope * meanX const predictions = x.map( value => intercept + slope * value ) const residualSumOfSquares = d3.sum( d3.range(observations), i => (y[i] - predictions[i]) ** 2 ) const rSquared = 1 - residualSumOfSquares / sumYY return { date: selectedSweDateLabel, observations, correlation, rSquared, explainedVariancePercent: rSquared * 100, slope, intercept, sweVariance: sumXX / (observations - 1), runnableDaysVariance: sumYY / (observations - 1), rootMeanSquaredError: Math.sqrt(residualSumOfSquares / observations) } }4f:T6dc,selectedYearSweAndFlow = { const commonX = { type: "utc", domain: selectedYearBounds } const swePlot = Plot.plot({ width, height: 260, marginLeft: 75, marginBottom: 20, title: `Basin snowpack and Lochsa discharge — ${selectedYear}`, x: { ...commonX, axis: null }, y: { label: "Basin SWE (million m³)", grid: true, zero: true }, marks: [ Plot.line(selectedYearDailySwe, { x: "date", y: "totalSweMillionM3", stroke: "#0072b2", strokeWidth: 2 }), Plot.ruleX([selectedFlowDate], { stroke: "#d55e00", strokeWidth: 1.5 }), Plot.frame() ] }) const flowPlot = Plot.plot({ width, height: 280, marginLeft: 75, marginTop: 10, marginBottom: 45, x: { ...commonX, ticks: d3.utcMonth.every(1), tickFormat: d3.utcFormat("%b"), label: "Date" }, y: { domain: [0, flowYMaximum], label: "Daily mean discharge (CFS)", grid: true }, marks: [ Plot.ruleY( [ flowThresholds.commercial.lowerCfs, flowThresholds.commercial.upperCfs ], { stroke: "#0072b2", strokeDasharray: "6,4", strokeWidth: 1.5 } ), Plot.line(selectedYearFlow, { x: "date", y: "discharge_cfs", stroke: "#222222", strokeWidth: 1.6 }), Plot.ruleX([selectedFlowDate], { stroke: "#d55e00", strokeWidth: 1.5 }), Plot.frame() ] }) const container = html`

` container.append(swePlot, flowPlot) return container }50:Tb93,threeYearSweMaps = { const comparisonYears = [2017, 2018, 2023] const monthDay = selectedDate.slice(5) const nodata = dailySweManifest.nodata const cellCount = dailySweManifest.cell_count const dayIndex = dailyCalendar.findIndex( date => d3.utcFormat("%m-%d")(date) === monthDay ) if (dayIndex < 0) { throw new Error(`Could not find ${monthDay} in dailyCalendar`) } const annualArrays = await Promise.all( comparisonYears.map(year => loadAnnualSwe(year)) ) const mapWidth = Math.max( 300, Math.floor((width - 24) / 3) ) const maps = comparisonYears.map((year, yearIndex) => { const annualSwe = annualArrays[yearIndex] const dailySwe = annualSwe.subarray( dayIndex * cellCount, (dayIndex + 1) * cellCount ) const hasData = dailySwe.some( value => value !== nodata ) return Plot.plot({ width: mapWidth, height: 440, margin: 15, title: hasData ? `${year}-${monthDay}` : `${year}-${monthDay} — unavailable`, projection: { type: "mercator", domain: basin, inset: 8 }, color: { type: "linear", scheme: "blues", domain: [0, sweColorMaximum], clamp: true, unknown: "#d9d9d9", label: "SWE (mm)", // Show one legend because every map uses the same scale. legend: yearIndex === 0 }, marks: [ Plot.geo(gridGeoJSON.features, { fill: (feature, cellIndex) => { const swe = dailySwe[cellIndex] return swe === nodata ? NaN : swe }, stroke: null, tip: true, title: (feature, cellIndex) => { const swe = dailySwe[cellIndex] const elevation = feature.properties.elevation_m return swe === nodata ? `${year}-${monthDay}\nSWE unavailable\nElevation: ${elevation.toFixed(0)} m` : `${year}-${monthDay}\nSWE: ${swe.toLocaleString()} mm\nElevation: ${elevation.toFixed(0)} m` } }), Plot.geo(tributaries, { stroke: "#6b7280", strokeOpacity: 0.7, strokeWidth: 0.6 }), Plot.geo(lochsaMainstem, { stroke: "#d55e00", strokeWidth: 1.8 }), Plot.geo(basin, { fill: "none", stroke: "#202020", strokeWidth: 1.1 }), Plot.dot(lochsaGauge, { x: "longitude", y: "latitude", r: 3, fill: "white", stroke: "#111111" }), Plot.frame({ stroke: "#777" }) ] }) }) const container = html`

` container.append(...maps) return container }51:T4cf,upperThirdElevationMap = Plot.plot({ width, height: 620, margin: 18, title: "Upper third of the Lochsa basin by elevation", subtitle: `Cells at or above ${d3.format(",.0f")(upperThirdElevation)} meters`, projection: { type: "mercator", domain: basin, inset: 10 }, color: { type: "categorical", domain: [ "Lower two-thirds", "Upper third" ], range: [ "#d9d9d9", "#0072b2" ], legend: true }, marks: [ Plot.geo(gridGeoJSON.features, { fill: (feature, i) => upperThirdMask[i] ? "Upper third" : "Lower two-thirds", stroke: null, tip: true, title: (feature, i) => `${upperThirdMask[i] ? "Upper third" : "Lower two-thirds"}\n` + `Elevation: ${d3.format(",.0f")( feature.properties.elevation_m )} m` }), Plot.geo(tributaries, { stroke: "#737373", strokeOpacity: 0.75, strokeWidth: 0.7 }), Plot.geo(lochsaMainstem, { stroke: "#d55e00", strokeWidth: 2.2 }), Plot.geo(basin, { fill: "none", stroke: "#202020", strokeWidth: 1.2 }), Plot.frame({ stroke: "#777" }) ] })52:T7b5,comparisonSweMetrics = { const nodata = dailySweManifest.nodata function summarize(values) { let totalCubicMeters = 0 let upperCubicMeters = 0 let validCells = 0 for (let i = 0; i < gridCells.length; i++) { const sweMillimeters = values[i] if (sweMillimeters !== nodata) { const cellAreaKm2 = Number(gridCells[i].cell_area_km2) const volumeCubicMeters = sweMillimeters * cellAreaKm2 * 1000 totalCubicMeters += volumeCubicMeters if (upperThirdMask[i]) { upperCubicMeters += volumeCubicMeters } validCells++ } } const totalSwe = totalCubicMeters / 1_000_000 const upperThirdSwe = upperCubicMeters / 1_000_000 return { totalSwe, upperThirdSwe, upperThirdShare: totalSwe > 0 ? (upperThirdSwe / totalSwe) * 100 : null, validCells } } const first = summarize(comparisonSweFrames.sweA) const second = summarize(comparisonSweFrames.sweB) return [ { selection: String(comparisonYearA), date: `${comparisonYearA}-${comparisonSweFrames.monthDay}`, totalSwe: first.totalSwe, upperThirdSwe: first.upperThirdSwe, upperThirdShare: first.upperThirdShare, validCells: first.validCells }, { selection: String(comparisonYearB), date: `${comparisonYearB}-${comparisonSweFrames.monthDay}`, totalSwe: second.totalSwe, upperThirdSwe: second.upperThirdSwe, upperThirdShare: second.upperThirdShare, validCells: second.validCells }, { selection: `${comparisonYearB} minus ${comparisonYearA}`, date: comparisonSweFrames.monthDay, totalSwe: second.totalSwe - first.totalSwe, upperThirdSwe: second.upperThirdSwe - first.upperThirdSwe, upperThirdShare: null, validCells: Math.min( first.validCells, second.validCells ) } ] }53:T52b,comparisonMetricsWithFlow = { const flowByYear = new Map( annualRunnableMetrics.map(d => [ Number(d.year), d ]) ) const flowA = flowByYear.get( Number(comparisonYearA) ) const flowB = flowByYear.get( Number(comparisonYearB) ) return comparisonSweMetrics.map( (row, index) => { if (index === 0) { return { ...row, commercialRunnableDays: flowA.commercialRunnableDays, privateRunnableDays: flowA.privateRunnableDays, highWaterDays: flowA.highConsequenceDays } } if (index === 1) { return { ...row, commercialRunnableDays: flowB.commercialRunnableDays, privateRunnableDays: flowB.privateRunnableDays, highWaterDays: flowB.highConsequenceDays } } // Difference row: second year minus first year. return { ...row, commercialRunnableDays: flowB.commercialRunnableDays - flowA.commercialRunnableDays, privateRunnableDays: flowB.privateRunnableDays - flowA.privateRunnableDays, highWaterDays: flowB.highConsequenceDays - flowA.highConsequenceDays } } ) }54:T458,comparisonMetricsTable = Inputs.table( comparisonMetricsWithFlow, { columns: [ "selection", "date", "totalSwe", "upperThirdSwe", "upperThirdShare", "commercialRunnableDays", "privateRunnableDays", "highWaterDays" ], header: { selection: "Year/comparison", date: "SWE date", totalSwe: "Total SWE (million m³)", upperThirdSwe: "Upper-third SWE (million m³)", upperThirdShare: "SWE in upper third", commercialRunnableDays: "Commercial days", privateRunnableDays: "Private runnable days", highWaterDays: "High-water days" }, format: { totalSwe: value => d3.format(",.1f")(value), upperThirdSwe: value => d3.format(",.1f")(value), upperThirdShare: value => value == null ? "—" : `${d3.format(".1f")(value)}%`, commercialRunnableDays: d3.format("d"), privateRunnableDays: d3.format("d"), highWaterDays: d3.format("d") }, rows: 3 } )55:Tf56,comparisonSweMaps = { const { monthDay, sweA, sweB, difference } = comparisonSweFrames const nodata = dailySweManifest.nodata // Use a symmetric difference scale centered on zero. const differenceMaximum = Math.max( 100, Math.ceil( d3.max( difference, value => Number.isFinite(value) ? Math.abs(value) : 0 ) / 100 ) * 100 ) const mapWidth = Math.max( 300, Math.floor((width - 24) / 3) ) function mapMarks(values, isDifference = false) { return [ Plot.geo(gridGeoJSON.features, { fill: (feature, cellIndex) => { const value = values[cellIndex] if (isDifference) { return Number.isFinite(value) ? value : NaN } return value === nodata ? NaN : value }, stroke: null, tip: true, title: (feature, cellIndex) => { const value = values[cellIndex] const elevation = feature.properties.elevation_m const valueLabel = isDifference ? Number.isFinite(value) ? `${d3.format("+,.0f")(value)} mm` : "Unavailable" : value === nodata ? "Unavailable" : `${d3.format(",.0f")(value)} mm` const elevationRegion = upperThirdMask[cellIndex] ? "Upper elevation third" : "Lower two-thirds" return ( `SWE: ${valueLabel}\n` + `Elevation: ${d3.format(",.0f")(elevation)} m\n` + elevationRegion ) } }), Plot.geo(tributaries, { stroke: "#737373", strokeOpacity: 0.65, strokeWidth: 0.6 }), Plot.geo(lochsaMainstem, { stroke: "#202020", strokeWidth: 1.8 }), Plot.geo(basin, { fill: "none", stroke: "#202020", strokeWidth: 1.1 }), Plot.frame({ stroke: "#777" }) ] } const commonMapOptions = { width: mapWidth, height: 440, margin: 15, projection: { type: "mercator", domain: basin, inset: 8 } } const absoluteSweColor = { type: "linear", scheme: "blues", domain: [0, sweColorMaximum], clamp: true, unknown: "#d9d9d9", label: "SWE (mm)", legend: true } const firstMap = Plot.plot({ ...commonMapOptions, title: `${comparisonYearA}-${monthDay}`, color: { ...absoluteSweColor }, marks: mapMarks(sweA) }) const secondMap = Plot.plot({ ...commonMapOptions, title: `${comparisonYearB}-${monthDay}`, color: { ...absoluteSweColor }, marks: mapMarks(sweB) }) const differenceMap = Plot.plot({ ...commonMapOptions, title: `${comparisonYearB} minus ${comparisonYearA}`, color: { type: "diverging", scheme: "rdbu", pivot: 0, domain: [ -differenceMaximum, differenceMaximum ], clamp: true, unknown: "#d9d9d9", label: "SWE difference (mm)", legend: true }, marks: mapMarks(difference, true) }) const mapRow = html`

` mapRow.append( firstMap, secondMap, differenceMap ) const explanation = html`

Difference is ${comparisonYearB} minus ${comparisonYearA}. Blue cells had more SWE in ${comparisonYearB}; red cells had more SWE in ${comparisonYearA}.

` const container = html`

` container.append(mapRow, explanation) return container }56:[{"id":"6bff6719af2d7e47a8fa19a6ffed7ba023e6f4595f11f5a268d64eca3339c3370237500db8a76247d6ab50a1b50d4e022b5b217a3fdbfbe1d59a3670bbe34a95","href":"https://static.observableusercontent.com/files/6bff6719af2d7e47a8fa19a6ffed7ba023e6f4595f11f5a268d64eca3339c3370237500db8a76247d6ab50a1b50d4e022b5b217a3fdbfbe1d59a3670bbe34a95","name":"lochsa_april1_swe.csv","type":"text/csv","lastModified":1791925384608,"size":3129630},{"id":"292b22e0f23de21419dcdb558936a3cc613edab763c1ba582b90986c2b9e37b8b495aab974b635cae6fdd7f2b5b9e3502e22fe0938c04a81dcb51f4c3ecd4f84","href":"https://static.observableusercontent.com/files/292b22e0f23de21419dcdb558936a3cc613edab763c1ba582b90986c2b9e37b8b495aab974b635cae6fdd7f2b5b9e3502e22fe0938c04a81dcb51f4c3ecd4f84","name":"lochsa_daily_discharge.csv","type":"text/csv","lastModified":1791925384659,"size":2260904},{"id":"91fd6ec24ed8ce726d6b9db8c46c0c38175fc187b6aa282e95eb1e3aefcdd1e11e7f28e37d3048b2ec0a706159158ddd508c06b32cef65bc9b0ed3e8031714ea","href":"https://static.observableusercontent.com/files/91fd6ec24ed8ce726d6b9db8c46c0c38175fc187b6aa282e95eb1e3aefcdd1e11e7f28e37d3048b2ec0a706159158ddd508c06b32cef65bc9b0ed3e8031714ea","name":"lochsa_hydrorivers.geojson","type":"application/geo+json","lastModified":1791928095297,"size":150449},{"id":"d1c7fad8e65b13d2934e10aad2e2b72fdd5a7fd50a8bfc0b02f553e0806e71d4df40cc0e17b15ff15d73243f21745487d284a56d84964c98005f8dd816b80f51","href":"https://static.observableusercontent.com/files/d1c7fad8e65b13d2934e10aad2e2b72fdd5a7fd50a8bfc0b02f553e0806e71d4df40cc0e17b15ff15d73243f21745487d284a56d84964c98005f8dd816b80f51","name":"lochsa_daily_swe.zip","type":"application/zip","lastModified":1792002982160,"size":30647469},{"id":"b17ff5cc39871556f59effac88faa552124cde3b476fe33b3453dca90a8b4c4d13f367860958ce7af8e403bee96fcc416f28842a6c1291161dafe1c7715b3ade","href":"https://static.observableusercontent.com/files/b17ff5cc39871556f59effac88faa552124cde3b476fe33b3453dca90a8b4c4d13f367860958ce7af8e403bee96fcc416f28842a6c1291161dafe1c7715b3ade","name":"lochsa_basin.geojson","type":"application/geo+json","lastModified":1791961570302,"size":68717}] 3a:{"id":"fd08ae0d9d85cfcb","slug":"the-lochsa-basin-explored-elevation-and-swe","title":"The Lochsa Basin Explored, Elevation and SWE","thumbnail":"a7c5a27c4a2d29766b298e196957738f9ba6f0242188bf871b3656faa630ac7e","access_level":"public","create_time":"$D2026-10-14T01:12:57.116Z","update_time":"$D2026-10-19T04:56:58.771Z","view_time":null,"first_public_time":"$D2026-10-19T04:57:16.763Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"a671c05dcf792fdc","login":"stellios-bonadurer","name":"Stellios Bonadurer","avatar_url":"https://avatars-next.observableusercontent.com/afa19a89070b70681752496266e084ba7d02fb86d99e8df269c443c46f5d6a87","tier":"starter_2024","bio":"","following":false},"version":580,"publish_version":580,"role":null,"authored":false,"authors":[{"type":"individual","id":"5e4ec071dd9acf5d","login":"stellios-bonadurer","name":"Stellios Bonadurer","avatar_url":"https://avatars-next.observableusercontent.com/afa19a89070b70681752496266e084ba7d02fb86d99e8df269c443c46f5d6a87","tier":null,"bio":"","following":false}],"members":[{"type":"team","id":"a671c05dcf792fdc","login":"stellios-bonadurer","name":"Stellios Bonadurer","avatar_url":"https://avatars-next.observableusercontent.com/afa19a89070b70681752496266e084ba7d02fb86d99e8df269c443c46f5d6a87","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"The Lochsa Basin Explored, Elevation and SWE","cells":[{"id":1,"value":"# The Lochsa Basin Explored, Elevation and SWE","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"## Exploration of the Data\n\nBefore making this analysis I felt it necessary to get a feel for the data. I wanted a visual representation of snowpack in the basin, and to be able to observe the course of a year. Below is a year selector which allows us to choose which year to investigate. SWE data begins in 2004, so that is our first year. Below that we can choose an individual date to view SWE/flow, as well as a play/pause function that iterates through days.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"viewof selectedYear = Inputs.range(\n [availableYears[0], availableYears[availableYears.length - 1]],\n {\n label: \"SWE and flow year\",\n step: 1,\n value: defaultYear\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":122,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":124,"value":"### Next Steps\n\nAn animation of SWE/flow over the course of the year says little regarding our hypothesis, but it serves as a way to get a feel for the data and was useful to direct the rest of this project. The next step was to calculate data regarding runnable days.\n\nWe create a table, limited to the years we have data for SWE and flow rate. From that we calculate the number of runnable days.\n\nI wanted next to understand the relationship between SWE on a given date and find the best date to use for comparison. I compared the 1st of Jan, Feb, Mar, Apr, and May and found that April 1 snowpack showed the highest\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"The black line represents the best fit line, with the shaded area being the 95% confidence interval of that best fit line.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":128,"value":"relationshipSummary2 = md`\n### ${selectedSweRelationship.date} relationship\n\n- **Pearson correlation:** ${d3.format(\".3f\")(\n selectedSweRelationship.correlation\n)}\n- **R²:** ${d3.format(\".3f\")(\n selectedSweRelationship.rSquared\n)}\n- **Variance explained:** ${d3.format(\".1f\")(\n selectedSweRelationship.explainedVariancePercent\n)}%\n- **Regression slope:** ${d3.format(\".3f\")(\n selectedSweRelationship.slope\n)} additional runnable days per million m³ of SWE\n- **RMSE:** ${d3.format(\".1f\")(\n selectedSweRelationship.rootMeanSquaredError\n)} days\n- **Years:** ${selectedSweRelationship.observations}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"## Interpretation\n\nOur initial hypothesis is somewhat supported, years with higher SWE on April 1 tend to have more days above our lower threshold. There is a moderate correlation indicated by a Pearson correlation of .515. We see that this isn't very prescriptive. With more SWE data we might have found a stronger correlation, however there is high variance so it is hard to say. In any case, this is still a bivariate relation, the rest of this analysis will attempt to take advantage of geographic data.\n\nIn order to understand the shape of the Lochsa basin, below is a plot indicating which cells are in the upper third of elevations.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":130,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"## Comparing Two Years\n\nIn order to see how SWE elevation may river flows we examine the scatterplot above to choose years with similar levels of SWE at April 1 with similar total SWE but different flow profiles. We identify that 2023 appears to be a bit of an outlier, so we create a new mini hypothesis: **How does the elevation of SWE differ in 2023, when compared to more typical years (2017/2018).**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":132,"value":"viewof comparisonYearA = Inputs.select(\n availableYears,\n {\n label: \"First comparison year\",\n value: 2018,\n format: d3.format(\"d\")\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"viewof comparisonYearB = Inputs.select(\n availableYears,\n {\n label: \"Second comparison year\",\n value: 2023,\n format: d3.format(\"d\")\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":134,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":135,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"## Conclusion\n\nAfter spending some time comparing years, there is not an obvious pattern of high elevation snow creating better flow years. In fact, anecdotally it appears the opposite may be true (lower proportion of high elevation snow has more runnable days.)\n\nUltimately this analysis did not support the hypothesis, but more so served as a way to visualize the differences. All plots were examined in colorblind modes and had reasonable colors.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"## Helper Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"### Define Thresholds for Analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"flowThresholds = Object.freeze({\n commercial: {\n lowerCfs: 4000,\n upperCfs: 14000\n },\n\n private: {\n lowerCfs: 4000\n },\n\n highConsequence: {\n lowerCfs: 14000\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"isRunnableFlow = dischargeCfs =>\n flowThresholds.inclusive\n ? dischargeCfs >= flowThresholds.lowerCfs &&\n dischargeCfs <= flowThresholds.upperCfs\n : dischargeCfs > flowThresholds.lowerCfs &&\n dischargeCfs < flowThresholds.upperCfs","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"flowYMaximum = 25000\n // Math.max(\n // flowThresholds.upperCfs,\n // d3.max(selectedYearFlow, d => d.discharge_cfs) ?? 0\n // ) * 1.05","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"runnableSeason = Object.freeze({\n begin: \"04-01\",\n end: \"09-30\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"### Load Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"gridCells = dailySweArchive\n .file(\"lochsa_grid_cells.csv\")\n .csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"april1Swe = dailySweArchive\n .file(\"lochsa_april1_swe.csv\")\n .csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"dailyFlow = FileAttachment(\"lochsa_daily_discharge.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"basin = FileAttachment(\"lochsa_basin.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"rivers = FileAttachment(\"lochsa_hydrorivers.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"### Load SWE Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"dailySweArchive = FileAttachment(\"lochsa_daily_swe.zip\").zip()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"dailySweManifest = dailySweArchive\n .file(\"manifest.json\")\n .json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"### Intake Geographic Data Sets","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"//Convert the grid cells to geographic polygons\ngridGeoJSON = ({\n type: \"FeatureCollection\",\n features: gridCells.map(d => ({\n type: \"Feature\",\n properties: d,\n geometry: {\n type: \"Polygon\",\n coordinates: [[\n [d.west, d.south],\n [d.west, d.north],\n [d.east, d.north],\n [d.east, d.south],\n [d.west, d.south]\n ]]\n }\n }))\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"//Grab Main Stem (for Plotting)\nlochsaMainstem = ({\n type: \"FeatureCollection\",\n features: rivers.features.filter(\n d => d.properties.is_lochsa_mainstem\n )\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"//Grab tributaries\ntributaries = ({\n type: \"FeatureCollection\",\n features: rivers.features.filter(\n d => !d.properties.is_lochsa_mainstem\n )\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"//Lochsa Gauge\nlochsaGauge = [{\n name: \"Lochsa River near Lowell\",\n siteId: \"USGS-13337000\",\n longitude: -115.5872222,\n latitude: 46.1508333\n}]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"### SWE Data Availability Details","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"availableYears = Array.from(\n new Set(april1Swe.map(d => d.year))\n).sort((a, b) => a - b)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"defaultYear = availableYears.includes(2025)\n ? 2025\n : availableYears[availableYears.length - 1]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"### Year Selector set for both datasets","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"selectedYearSwe = april1Swe.filter(\n d => d.year === selectedYear\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"selectedPostSweFlow = selectedYearFlow.filter(\n d => d.days_from_swe_date >= 0\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"### Helper Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"#### Date Related Objects/Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"dailyCalendar = d3.utcDay.range(\n new Date(Date.UTC(2001, 0, 1)),\n new Date(Date.UTC(2002, 0, 1))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"unavailableDayIndexes = {\n const formatMonthDay = d3.utcFormat(\"%m-%d\")\n\n const coverageEndIndex = dailyCalendar.findIndex(\n date =>\n formatMonthDay(date) ===\n selectedAnnualSweMetadata.coverage_end.slice(5)\n )\n\n const unavailable = new Set()\n\n // Permanently missing dates within the historical archive.\n for (\n const isoDate of\n selectedAnnualSweMetadata.source_missing_dates ?? []\n ) {\n const index = dailyCalendar.findIndex(\n date => formatMonthDay(date) === isoDate.slice(5)\n )\n\n if (index >= 0) unavailable.add(index)\n }\n\n // Unpublished dates after partial-year coverage.\n for (\n let index = coverageEndIndex + 1;\n index < dailyCalendar.length;\n index++\n ) {\n unavailable.add(index)\n }\n\n return unavailable\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"viewof selectedMonth = Inputs.select(\n d3.range(1, 13),\n {\n label: \"Month\",\n value: 4,\n format: month => d3.utcFormat(\"%B\")(\n new Date(Date.UTC(2001, month - 1, 1))\n )\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"daysInSelectedMonth = new Date(\n Date.UTC(2001, selectedMonth, 0)\n).getUTCDate()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"dayOfYear =\n dailyCalendar.findIndex(\n date =>\n d3.utcFormat(\"%m-%d\")(date) === selectedDate.slice(5)\n ) + 1","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"selectedYearBounds = [\n new Date(Date.UTC(selectedYear, 0, 1)),\n new Date(Date.UTC(selectedYear + 1, 0, 1))\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"//Used to convert the user defined bounds to utc format for plotting\nselectedSeasonBounds = {\n const yearStart = new Date(Date.UTC(selectedYear, 0, 1))\n const yearEnd = new Date(Date.UTC(selectedYear + 1, 0, 1))\n\n const seasonBegin = new Date(\n `${selectedYear}-${runnableSeason.begin}T00:00:00Z`\n )\n\n const seasonEnd = new Date(\n `${selectedYear}-${runnableSeason.end}T00:00:00Z`\n )\n\n return {\n yearStart,\n yearEnd,\n seasonBegin,\n\n // Make the configured ending date inclusive.\n seasonEndExclusive: d3.utcDay.offset(seasonEnd, 1)\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"#### Get Appropriate SWE Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"selectedAnnualSwe = {\n const filename =\n `lochsa_swe_${selectedYear}.uint16.bin.gz`\n\n const compressed = await dailySweArchive\n .file(filename)\n .arrayBuffer()\n\n const signature = new Uint8Array(compressed, 0, 2)\n\n const decompressed =\n signature[0] === 0x1f && signature[1] === 0x8b\n ? await new Response(\n new Blob([compressed])\n .stream()\n .pipeThrough(new DecompressionStream(\"gzip\"))\n ).arrayBuffer()\n : compressed\n\n const values = new Uint16Array(decompressed)\n const expectedLength =\n dailySweManifest.day_count *\n dailySweManifest.cell_count\n\n if (values.length !== expectedLength) {\n throw new Error(\n `${filename}: expected ${expectedLength.toLocaleString()} values, received ${values.length.toLocaleString()}`\n )\n }\n\n return values\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"selectedAnnualSweMetadata = dailySweManifest.years.find(\n year => year.year === selectedYear\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"selectedDaySwe = selectedAnnualSwe.subarray(\n (dayOfYear - 1) * gridCells.length,\n dayOfYear * gridCells.length\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"selectedDateHasSwe = selectedDaySwe.some(\n value => value !== dailySweManifest.nodata\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"#### Create Data Tables for SWE content at certain dates","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"sweSnapshotDates = [\n {key: \"jan1Swe\", monthDay: \"01-01\"},\n {key: \"feb1Swe\", monthDay: \"02-01\"},\n {key: \"mar1Swe\", monthDay: \"03-01\"},\n {key: \"apr1Swe\", monthDay: \"04-01\"},\n {key: \"may1Swe\", monthDay: \"05-01\"}\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"loadAnnualSwe = async year => {\n const filename = `lochsa_swe_${year}.uint16.bin.gz`\n\n const compressed = await dailySweArchive\n .file(filename)\n .arrayBuffer()\n\n const signature = new Uint8Array(compressed, 0, 2)\n\n const decompressed =\n signature[0] === 0x1f && signature[1] === 0x8b\n ? await new Response(\n new Blob([compressed])\n .stream()\n .pipeThrough(new DecompressionStream(\"gzip\"))\n ).arrayBuffer()\n : compressed\n\n return new Uint16Array(decompressed)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":86,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"annualAnalysisMetrics = {\n const runnableByYear = new Map(\n annualRunnableMetrics.map(d => [Number(d.year), d])\n )\n\n return annualSweTotals.map(swe => {\n const flow = runnableByYear.get(Number(swe.year))\n\n return {\n year: swe.year,\n\n jan1Swe: swe.jan1Swe,\n feb1Swe: swe.feb1Swe,\n mar1Swe: swe.mar1Swe,\n apr1Swe: swe.apr1Swe,\n may1Swe: swe.may1Swe,\n\n commercialRunnableDays:\n flow?.commercialRunnableDays ?? null,\n\n privateRunnableDays:\n flow?.privateRunnableDays ?? null,\n\n highConsequenceDays:\n flow?.highConsequenceDays ?? null,\n\n daysObserved:\n flow?.daysObserved ?? null\n }\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":99,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"#### Get Appropriate Flow Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":54,"value":"selectedYearFlow = dailyFlow\n .filter(d => Number(d.year) === Number(selectedYear))\n .map(d => ({\n ...d,\n date:\n d.date instanceof Date\n ? d.date\n : parseFlowDate(String(d.date).slice(0, 10)),\n discharge_cfs: Number(d.discharge_cfs)\n }))\n .filter(d =>\n d.date instanceof Date &&\n !Number.isNaN(+d.date) &&\n Number.isFinite(d.discharge_cfs)\n )\n .sort((a, b) => d3.ascending(a.date, b.date))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"otherYearsFlow = dailyFlow\n .filter(d =>\n availableYears.includes(Number(d.year)) &&\n Number(d.year) !== Number(selectedYear)\n )\n .map(d => {\n const month = Number(d.month)\n const day = Number(d.day)\n\n const plotDate = new Date(\n Date.UTC(selectedYear, month - 1, day)\n )\n\n // Exclude February 29 when selectedYear is not a leap year.\n const validDate =\n plotDate.getUTCMonth() === month - 1 &&\n plotDate.getUTCDate() === day\n\n return validDate\n ? {\n year: Number(d.year),\n plotDate,\n discharge_cfs: Number(d.discharge_cfs)\n }\n : null\n })\n .filter(d => d !== null)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"selectedFlowDate =\n selectedDate instanceof Date\n ? selectedDate\n : parseFlowDate(String(selectedDate).slice(0, 10))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"parseFlowDate = d3.utcParse(\"%Y-%m-%d\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"({\n selectedYear,\n rows: selectedYearFlow.length,\n first: selectedYearFlow[0],\n last: selectedYearFlow.at(-1),\n maximumCfs: d3.max(selectedYearFlow, d => d.discharge_cfs)\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"### Data Processing and Math","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"#### Calculates annual metrics based on thresholds","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"annualRunnableMetrics = Array.from(\n d3.group(dailyFlow, d => Number(d.year)),\n ([year, yearFlow]) =>\n runnableSeasonMetric(\n yearFlow,\n flowThresholds,\n runnableSeason.begin,\n runnableSeason.end\n )\n).sort((a, b) => d3.ascending(a.year, b.year))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"#### Plotting Aids","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"globalSweMaximum = {\n let maximum = 0\n const nodata = dailySweManifest.nodata\n\n for (const yearMetadata of dailySweManifest.years) {\n const filename =\n `lochsa_swe_${yearMetadata.year}.uint16.bin.gz`\n\n const compressed = await dailySweArchive\n .file(filename)\n .arrayBuffer()\n\n const signature = new Uint8Array(compressed, 0, 2)\n\n const decompressed =\n signature[0] === 0x1f && signature[1] === 0x8b\n ? await new Response(\n new Blob([compressed])\n .stream()\n .pipeThrough(new DecompressionStream(\"gzip\"))\n ).arrayBuffer()\n : compressed\n\n const values = new Uint16Array(decompressed)\n\n for (const value of values) {\n if (value !== nodata && value > maximum) {\n maximum = value\n }\n }\n }\n\n return maximum\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"sweColorMaximum =\n Math.ceil(globalSweMaximum / 100) * 100","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"## Iterative Steps","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"### Basic Plot","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"elevationCheck = ({\n minimum: Math.min(...gridCells.map(d => d.elevation_m)),\n maximum: Math.max(...gridCells.map(d => d.elevation_m)),\n uniqueValues: new Set(gridCells.map(d => d.elevation_m)).size\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"lochsaAreaMap = Plot.plot({\n width,\n height: 620,\n margin: 18,\n\n projection: {\n type: \"mercator\",\n domain: basin,\n inset: 10\n },\n\n color: {\n type: \"linear\",\n scheme: \"cividis\", //cividis is perceptually uniform\n domain: [500, 2550],\n clamp: true,\n label: \"Elevation (m)\",\n legend: true\n },\n\n marks: [\n Plot.geo(gridGeoJSON.features, {\n fill: d => d.properties.elevation_m,\n stroke: null\n }),\n\n Plot.geo(tributaries, {\n stroke: \"#77a9c2\",\n strokeOpacity: 0.75,\n strokeWidth: 0.7\n }),\n\n Plot.geo(lochsaMainstem, {\n stroke: \"#075985\",\n strokeWidth: 2.2\n }),\n\n Plot.geo(basin, {\n fill: \"none\",\n stroke: \"#202020\",\n strokeWidth: 1.2\n }),\n\n Plot.dot(lochsaGauge, {\n x: \"longitude\",\n y: \"latitude\",\n r: 4,\n fill: \"#f7f7f7\",\n stroke: \"#111111\",\n strokeWidth: 1.5,\n tip: true,\n title: d => `${d.name}\\n${d.siteId}`\n }),\n\n Plot.frame({stroke: \"#777\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"#### Initial Thoughts\n\nWe have a plot that shows the Lochsa basin, with 1km cells representing elevation. This is a good baseline, using the cividis makes the perceptually uniform color scheme easy. Pre-processing the data makes creating such plots much more straightforward.\n\nTo accompany the raster plots and inform development we will add a timeseries plot of the flowrate for a given year. (Most of the setup work for our datasets was done at this stage.)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"selectedYearRunnableMetric = runnableSeasonMetric(\n selectedYearFlow,\n flowThresholds,\n runnableSeason.begin,\n runnableSeason.end\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"### Iteration\n\nThis gives a plot of what we were looking for and confirms the general pattern I had hypothesized. It makes it clear for 'runnable days' we should filter from between April 1 until late summer. A few days in February are not typically run.\n\nNow that we have this we will make a table of years and days runnable. Also we will add some more features to the plot to enhance the indicators for our parameters.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"runnableMetricsTable = Inputs.table(\n annualRunnableMetrics,\n {\n columns: [\n \"year\",\n \"commercialRunnableDays\",\n \"privateRunnableDays\",\n \"highConsequenceDays\"\n ],\n\n header: {\n year: \"Year\",\n commercialRunnableDays: \"Commercial days\",\n privateRunnableDays: \"Private boating days\",\n highConsequenceDays: \"High-consequence days\"\n },\n\n format: {\n year: d3.format(\"d\"),\n commercialRunnableDays: d3.format(\"d\"),\n privateRunnableDays: d3.format(\"d\"),\n highConsequenceDays: d3.format(\"d\")\n },\n\n sort: \"year\",\n rows: 20\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"$4a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"aprilSwePrivateDays = annualAnalysisMetrics.filter(\n d =>\n Number.isFinite(d.apr1Swe) &&\n Number.isFinite(d.privateRunnableDays)\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"viewof selectedSweDate = Inputs.select(\n new Map([\n [\"January 1\", \"jan1Swe\"],\n [\"February 1\", \"feb1Swe\"],\n [\"March 1\", \"mar1Swe\"],\n [\"April 1\", \"apr1Swe\"],\n [\"May 1\", \"may1Swe\"]\n ]),\n {\n label: \"Snowpack measurement date\",\n value: \"apr1Swe\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"selectedSweDateLabel = new Map([\n [\"jan1Swe\", \"January 1\"],\n [\"feb1Swe\", \"February 1\"],\n [\"mar1Swe\", \"March 1\"],\n [\"apr1Swe\", \"April 1\"],\n [\"may1Swe\", \"May 1\"]\n]).get(selectedSweDate)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":97,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"relationshipSummary = md`\n### ${selectedSweRelationship.date} relationship\n\n- **Pearson correlation:** ${d3.format(\".3f\")(\n selectedSweRelationship.correlation\n)}\n- **R²:** ${d3.format(\".3f\")(\n selectedSweRelationship.rSquared\n)}\n- **Variance explained:** ${d3.format(\".1f\")(\n selectedSweRelationship.explainedVariancePercent\n)}%\n- **Regression slope:** ${d3.format(\".3f\")(\n selectedSweRelationship.slope\n)} additional runnable days per million m³ of SWE\n- **RMSE:** ${d3.format(\".1f\")(\n selectedSweRelationship.rootMeanSquaredError\n)} days\n- **Years:** ${selectedSweRelationship.observations}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"### Iteration\n\nWe see a somewhat strong relationship between April 1 SWE and runnable days, however there aren't enough data to enrich any conclusion. Based on the Pearson correlation, April 1 SWE seems to have the closest relationship to runnable days and will be used from now on. Just as an exploratory addition I will make a timeseries plot with gauge data and SWE to see what it looks like. For the final timeseries plot I will remove the upper boundary because that is not pertinent to our further analysis.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":101,"value":"Obviously some issues with this and the labeling, I just thought it was cool.\n\nFrom our analysis, we see a decent Pearson coefficient of .515. We see an outlier in 2023 with an anomalous short season compared to similar snowpacks. To explore the geographic impact of SWE distribution we will examine 2023 compared to 2017 and 2018 to see if that can help us understand the difference better.\n\nIt has become clear to me that this project is out of scope and a thorough analysis would veer into climate modeling with temperature, and other factors. Trying to keep the scope limited and the raster component relevant.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":114,"value":"### Iteration and Consideration\n\nThe above plot is good, however having just one legend small like that messes up the alignment. In order to fix this I just added a legend per plot, which may be redundant but it looks nice to me.\n\nWe want to examine the impact of high elevation snowpack. To do this we are going to arbitrarily decide that the top 1/3 of elevations are 'high elevation'. Below is a map.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":107,"value":"upperThirdElevation = {\n const cells = gridCells\n .map(d => ({\n elevation: Number(d.elevation_m),\n area: Number(d.cell_area_km2)\n }))\n .sort((a, b) =>\n d3.ascending(a.elevation, b.elevation)\n )\n\n const totalArea = d3.sum(cells, d => d.area)\n const lowerTwoThirdsArea = totalArea * (2 / 3)\n\n let accumulatedArea = 0\n\n for (const cell of cells) {\n accumulatedArea += cell.area\n\n if (accumulatedArea >= lowerTwoThirdsArea) {\n return cell.elevation\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":108,"value":"upperThirdMask = gridCells.map(\n d => Number(d.elevation_m) >= upperThirdElevation\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":109,"value":"comparisonSweFrames = {\n const monthDay = selectedDate.slice(5)\n\n const dayIndex = dailyCalendar.findIndex(\n date => d3.utcFormat(\"%m-%d\")(date) === monthDay\n )\n\n if (dayIndex < 0) {\n throw new Error(`Could not find ${monthDay}`)\n }\n\n const [annualA, annualB] = await Promise.all([\n loadAnnualSwe(comparisonYearA),\n loadAnnualSwe(comparisonYearB)\n ])\n\n const cellCount = dailySweManifest.cell_count\n const start = dayIndex * cellCount\n const end = start + cellCount\n\n const sweA = annualA.subarray(start, end)\n const sweB = annualB.subarray(start, end)\n const difference = new Float64Array(cellCount)\n const nodata = dailySweManifest.nodata\n\n for (let i = 0; i < cellCount; i++) {\n difference[i] =\n sweA[i] === nodata || sweB[i] === nodata\n ? NaN\n : sweB[i] - sweA[i]\n }\n\n return {\n monthDay,\n sweA,\n sweB,\n difference\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":111,"value":"comparisonSweMetricsTable = Inputs.table(\n comparisonSweMetrics,\n {\n columns: [\n \"selection\",\n \"date\",\n \"totalSwe\",\n \"upperThirdSwe\",\n \"upperThirdShare\",\n \"validCells\"\n ],\n\n header: {\n selection: \"Year/comparison\",\n date: \"Date\",\n totalSwe: \"Total SWE (million m³)\",\n upperThirdSwe: \"Upper-third SWE (million m³)\",\n upperThirdShare: \"SWE in upper third\",\n validCells: \"Valid cells\"\n },\n\n format: {\n totalSwe: d3.format(\",.1f\"),\n upperThirdSwe: d3.format(\",.1f\"),\n upperThirdShare: value =>\n value == null\n ? \"—\"\n : d3.format(\".1f\")(value) + \"%\",\n validCells: d3.format(\",d\")\n },\n\n rows: 3\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":116,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"### Iteration\n\nI think the colors here are appropriate and look good. They are perceptually uniform, and testing them using various colorblind modes creates reasonable shading.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W56","stdlib":"2"}} 3b:"$3a:body"