2c:null 2d:["$","title",null,{"children":"What Happens when El Niño Comes to Vietnam? | Sifan Liu | Observable"}] 32:T41c,By taking the [ADB Data Visualization Challenge](https://challenges.adb.org/en/challenges/dataviz4climate/) on visualizing climate change & resilience, we want to leverage the Observable platform as a powerful tool to **bridge the gap between comprehensive climate-related research reports and intuitive public communication products**. This Observable project is based on a series of reports from international organizations that adopt in-depth and innovative economic models to estimate the climate impact on the agricultural sector in the Southeast Asia region. Through Observable, we want to generate intuitive and interactive visualizations that **support the delivery of critical messages and increase the visibility** of the importance of this research topic in order to raise awareness of building a resilient future for Southeast Asia. ** In this data story, we briefly address**: - *What's the general pattern of climate impact on Vietnam?* - *What can be done to counter these impacts?* - *What lessons can be drawn at the regional level?* 33:T9fe,**A vulnerable agricultural sector** [El Niño–Southern Oscillation (ENSO)](https://www.ncei.noaa.gov/access/monitoring/enso/) events have essential impacts on Vietnam's climate, agriculture, economy, and society. Historical data show that the two phases of ENSO, El Niño and La Niña, tend to depress and increase average rainfall, respectively.

What's ENSO?

${md` **“El Niño and the Southern Oscillation”** El Niño and the Southern Oscillation, also known as ENSO is a periodic fluctuation (i.e., every 2–7 years) in sea surface temperature (El Niño) and the air pressure of the overlying atmosphere (Southern Oscillation) across the equatorial Pacific Ocean. The presence of an El Niño, or its opposite – La Niña – sufficiently modifies the general flow of the atmosphere to affect normal weather conditions in many parts of the world. `}

The country is highly exposed to ENSO-related climate shocks that negatively affect agricultural production. The irregular rainfall and temperature that come with El Niño events typically result in negative impacts on crop yield and livestock production in different sub-regions, with severe economy-wide ramifications.

South-East Asia and India struck by the worst drought in 2016

${md` **“One of their worst heat waves in history”** With the heat wave and drought brought by El Niña in 2016, South-East Asia and India suffered one of their worst heat waves in history. About 330 million people, equivalent to about a quarter of the country's population, were affected by drought. This is the longest El Niño (18 months) in the region. Rainfall and river flows were only 50 to 70% of the average. In some areas, rainfall was reduced to only 20% of the average. `}

**Reduced rainfall during El Niño** In this section, we explore the uncertainty brought by El Niño events to Vietnam's rainfall. Based on rainfall difference data from 1980 to 2015 in Vietnam, figure 1 below shows that El Niño events have brought reduced rainfall across Vietnam. However, the decrease in rainfall varies at the sub-regional level, from 2% in the South to 30% in the North. 34:T569,

El Niño depresses rainfall across Vietnam, from 2% in the South to 30% in the North

${dotPltLegend1}
${dotPltLegend2}

${dotPlt}

Figure 1. Average Rainfall Changes During El Niño Phases in 1980–2015 Between October and June across Crop Areas in Vietnam

35:T6f3,

El Niño events are highly correlated with depressed rice yields during the winter-spring season

${viewof regionNames}

${viewof filterYear}

${linePlt}

El Niño event

Figure 2. Winter–Spring Rice Yields in Regions of Vietnam Most Vulnerable to El Niño, 1995–2016

36:T923,

Poverty rates saw largest increase for households headed by rural female during El Niño, but policy interventions can mitigate.
Compare the poverty indicator between without intervention and with intervention scenarios

${viewof policyIntervention}

${barPlt}

Figure 3. Change in Poverty Headcount Rates During Strong El Niño Events and Intervention Scenarios by Location and Gender of
the Household Head

37:T78b,

${seed}

Drought-tolerant seed varieties

E.g., providing farmers with more drought-resistant maize, rice, and vegetable varieties

${irrigation}

Additional irrigation

E.g., increasing the amount of cultivated land that uses irrigation infrastructure

${exportBan}

Rice export restrictions

E.g., introducing a partial ban on rice exports implemented over the course of a year

${warehouse}

Grain storage and distribution

E.g., supplying 1 million tons of rice from public and private stocks

${socialTransfer}

Social transfers

E.g., providing short-term cash transfers to poorer households equal to about US $20 per person over a given year

38:T41b,

Migration as an Adaptation Strategy in Response to Climate Change

${md` Migration is usually induced by various drivers, amongst which climate change and environmental disruptions play an increasingly important role. According to [International Displacement Monitoring Centre](https://www.internal-displacement.org/sites/default/files/publications/documents/grid2021_idmc.pdf). About 30.7 million disaster displacements were recorded in 2020. Most of these displacements happened in East Asia and the Pacific region. Extreme events like typhoons, floods, earthquakes, and volcanic eruptions triggered 12.1 million new displacements, reaching the highest figure since 2016 and above the region’s 10-year average of 11.2 million. Powerful storms and flooding fuelled by climatic variations such as La Niña triggered 94 percent of them. `}

39:T67f,## Source - Sutton, William R.; Srivastava, Jitendra P.; Rosegrant, Mark; Thurlow, James; Sebastian, Leocardio. 2019. *Striking a Balance : Managing El Niño and La Niña in Vietnam’s Agriculture*. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31524 License: CC BY 3.0 IGO. - Sutton, William R.; Srivastava, Jitendra P.; Rosegrant, Mark; Thurlow, James; Vasileiou, Ioannis. 2019. *Striking a Balance : Managing El Niño and La Niña in Myanmar’s Agriculture*. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31523 License: CC BY 3.0 IGO. - Sutton, William R.; Srivastava, Jitendra P.; Rosegrant, Mark; Valmonte-Santos, Rowena; Ashwill, Maximillian. 2019. *Striking a Balance : Managing El Niño and La Niña in Philippines’ Agriculture*. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31521 License: CC BY 3.0 IGO. - Sutton, William R.; Srivastava, Jitendra P.; Rosegrant, Mark; Koo, Jawoo; Robertson, Ricky. 2019. *Striking a Balance : Managing El Niño and La Niña in Lao PDR’s Agriculture*. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31520 License: CC BY 3.0 IGO. - Sutton, William R.; Srivastava, Jitendra P.; Koo, Jawoo; Vasileiou, Ioannis; Pradesha, Angga. 2019. *Striking a Balance : Managing El Niño and La Niña in Cambodia’s Agriculture*. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31522 License: CC BY 3.0 IGO. - _Global Report on Internal Displacement 2021_, Internal Displacement Monitoring Centre (IDMC) 3a:T45c,//code for figure 1 - the dot map plot dotPlt = addTooltips( Plot.plot({ center:true, width: width*0.29, height:550, marginTop:50, x: { ticks: 0, label: "" }, y: { ticks: 0, label: "" }, color: { legend: false, //set it to false and create a stand-alone legend label: "Average rainfall changes(%)", //scheme: "blues", range: ["#cbc9e2", "#54278f"], reverse: true, // sort: true, // domain: ["North", "Center", "South"] //set the order }, marks: [ Plot.dot(vietnamDotMap, {x: "X", y: "Y", fill: "RainfallDiffFromNetural", r: "CropValue", title: (d) => `${d.Subregion}\n \n Average rainfall changes in Oct–Jun\n during El Niño phases: \n${d.RainfallDiffFromNetural}% \n\n Crop area (thousand hectares): \n${(d.CropValue/1000)}` } )] }), //set tooltip styles { fill: "darkGray", opacity: 0.5 } )3b:Tf5a,//code for figure 2 - the line plot linePlt = Plot.plot({ width: 1100, marginRight: 0, // comment out the color specification for now, as I decided to draw each line individually // color: { // range: ["#4e79a7", "#f28e2b", "#e15759", "#76b7b2"] //using #1-4 of the Tableau10 color scheme to show that the regions here are different than the North/Center/South definition in an earlier chart //https://observablehq.com/@hamzaamjad/color-palettes // }, x: { tickFormat: d3.format("d"), domain: [1995, 2016], ticks: 22, label: "" }, y: { domain: [0,8], grid: true, label: "Rice yields (tons/hectare)", labelOffset: 16 }, marks: [ Plot.areaY(vietnamENSOData, {x: "Year", y: vietnamENSODataEdited, fill: "lightGray"}), //vietnam Plot.line( vietnamLineData.filter(d => d.Category === "Vietnam" ), { filter: d => d.Year <= filterYear, x: "Year", y: "Value", stroke: "#76b7b2", z: "Category", strokeWidth: 3, curve: "linear", title: "Category" } ), Plot.text(["Vietnam"], { frameAnchor: "right", dx: -167, dy: -95, fill: "#76b7b2", fontSize: 12, fontWeight: "bold", lineAnchor: "bottom", text: (d) => d }), //Central Highland Plot.line( vietnamLineData.filter(d => regionNames.includes("Central Highland")? d.Category === "Central Highland" : ""), { filter: d => d.Year <= filterYear, x: "Year", y: "Value", stroke: "#4e79a7", z: "Category", strokeWidth: 3, curve: "linear", title: "Category" } ), Plot.text(regionNames.includes("Central Highland")? ["Central Highland"] : [""], { frameAnchor: "right", dx: -155, dy: -30, fill: "#4e79a7", fontSize: 12, fontWeight: "bold", lineAnchor: "bottom", text: (d) => d }), //Mekong River Delta Plot.line( vietnamLineData.filter(d => regionNames.includes("Mekong River Delta")? d.Category === "Mekong River Delta" : ""), { filter: d => d.Year <= filterYear, x: "Year", y: "Value", stroke: "#f28e2b", z: "Category", strokeWidth: 3, curve: "linear", title: "Category" } ), Plot.text(regionNames.includes("Mekong River Delta")? ["Mekong River Delta"] : [""], { frameAnchor: "right", dx: -70, dy: -150, fill: "#f28e2b", fontSize: 12, fontWeight: "bold", lineAnchor: "bottom", text: (d) => d }), //South Central Coast Plot.line( vietnamLineData.filter(d => regionNames.includes("South Central Coast")? d.Category === "South Central Coast" : ""), { filter: d => d.Year <= filterYear, x: "Year", y: "Value", stroke: "#e15759", z: "Category", strokeWidth: 3, curve: "linear", title: "Category" } ), Plot.text(regionNames.includes("South Central Coast")? ["South Central Coast"] : [""], { frameAnchor: "right", dx: -677, dy: 0, fill: "#e15759", fontSize: 12, fontWeight: "bold", lineAnchor: "bottom", text: (d) => d }), // Plot.text(vietnamLineData, Plot.selectLast({x: "Year", y: "Value", z: "Category", // text: "Category", textAnchor: "start", // dx: 10})), //Plot.area(vietnamLineData, {x1: "Year", y1: 2, y2: 8, stroke: "Category"}) ] }) 3c:T783,//code for figure 3 - the bar plot barPlt = addTooltips( Plot.plot({ width: 1000, marginRight: 120, marginLeft: 70, color: { range: ["#b07aa1", "#ff9da7"], legend: false }, //using #8-9 of the Tableau10 color scheme}, x: { axis: "", //drop axis & tick, use legend instead //tickRotate:-30, label: "" }, y: { domain: [0, 3], label: "Percentage point changes in poverty headcount rates", labelAnchor: "center", }, facet: { data: vietnamPolicyIntervention, x: "DemographicGroup", padding: 100, label: "", axis: "top" }, marks: [ Plot.barY(vietnamPolicyIntervention.filter(d => d.InterventionMethod === policyIntervention), {x: "InterventionYesNo", y: "Value", fill: "InterventionYesNo", insetLeft: 8, insetRight: 8, facet: true, //very important! title: (d) => `${d.Value} \nPercentage point changes \nin poverty headcount rates` }), Plot.ruleY([0]), Plot.frame({stroke: "#ABB0B8"}) ] }), //set tooltip styles { fill: "darkGray", opacity: 0.5 } )3d:T9b5,//code for figure 4 - arrow plot arrowPlt = addTooltips( Plot.plot({ width: 1100, marginRight: 100, marginLeft: 70, x: { domain: ["Without interventions", "All combined"], axis: "", //don't show the axis ("Without interventions" & "All combined" as Plot current doesn't support natively wrapping text in axis) https://github.com/observablehq/plot/issues/460 }, y: { domain: [-2, 0.15], label: "Percentage change in GDP (%)", labelAnchor: "center", }, facet: { data: regionalData, x: "Country", label: "", axis: "top", }, marks: [ Plot.frame(), Plot.ruleY(arrowPltInputs.includes("Show 0% GDP change line")? [0] : [],{ stroke: "darkGray", strokeDasharray: [5,5] }), Plot.arrow(regionalData, { x1: "Policy1", y1: "y1", x2: "Policy2", y2: "y2", bend: false, title: (d) => `From ${d.y1} to ${d.y2} \nGDP percentage changes \nbefore and after policy interventions` }), //direct labeling of values Plot.text(regionalData, { //frameAnchor: "left", x: "Policy1", y: "y1", dx: -20, //dy: 10, fill: "#b07aa1", fontSize: 12, fontWeight: "bold", //lineAnchor: "bottom", text: (d) => arrowPltInputs.includes("Show GDP % change value (without intervention)")? d.y1 : "" }), Plot.text(regionalData, { //frameAnchor: "right", x: "Policy2", y: "y2", dx: 20, //dy: -10, fill: "#ff9da7", fontSize: 12, fontWeight: "bold", //lineAnchor: "bottom", text: (d) => arrowPltInputs.includes("Show GDP % change value (with intervention)")? d.y2 : "" }), //draw x-axis tick/label content as text elements Plot.text(regionalData, { //frameAnchor: "left", x: "Policy1", y: -1.8, //dx: -5, //dy: 10, fill: "#b07aa1", fontSize: 12, fontWeight: "bold", //lineAnchor: "bottom", text: d=> ["Without intervention"], lineWidth: 5 }), Plot.text(regionalData, { //frameAnchor: "right", x: "Policy2", y: -1.8, //dx: 5, //dy: -10, fill: "#ff9da7", fontSize: 12, fontWeight: "bold", //lineAnchor: "bottom", text: d=> ["With intervention"], lineWidth: 5 }), ] }), { opacity: 0.5 } )3e:[{"id":"578a21ff43540be657beb60f48304ea617c77f909bcce6c13491b92c7c122be1fe533b1552d8cb8062dc0ab3bd816b68f75c8a160062a5c98a1f9a6d35a3fbd3","href":"https://static.observableusercontent.com/files/578a21ff43540be657beb60f48304ea617c77f909bcce6c13491b92c7c122be1fe533b1552d8cb8062dc0ab3bd816b68f75c8a160062a5c98a1f9a6d35a3fbd3","name":"unsplash-img@3.jpg","type":"image/jpeg","lastModified":1669389114677.016,"size":359388},{"id":"5cf072160955724266be31bee92b65602c24c0841ee4adb7b31c67573ca9273986c1511a454d1921fce935b41b7bc39a0fec1dcd10ad47421ae34a9f9eea5674","href":"https://static.observableusercontent.com/files/5cf072160955724266be31bee92b65602c24c0841ee4adb7b31c67573ca9273986c1511a454d1921fce935b41b7bc39a0fec1dcd10ad47421ae34a9f9eea5674","name":"figure 2 - 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By marking the occurrence of El Niño events as gray bars in the background, we can observe that El Niño typically, but not always, corresponds to depressed rice yields during the winter-spring season (January to April). \n\nCrop production losses mean higher food prices, lower real consumption for households, and **rising poverty**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":875,"value":"$35","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3060,"value":"---\n## What can be done?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":320,"value":"\n\n**Farmers, Market, and Government**\n\nDespite the negative El Niño shocks illustrated above, policy measures can be taken to reduce El Niño-related losses such as reduced food production and increased poverty, and improve Vietnam’s preparedness and resiliency. We synthesize model estimate results from the literature and show in figure 3 what different policy interventions could do to attenuate the adverse impacts of El Niño events on poverty.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1060,"value":"$36","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3042,"value":"The five policy interventions could be categorized into measures for farmers, the market, and the government, respectively. Examples of each intervention is provided below.\n- Farmers can utilize **drought-tolerant seed varieties** at the farm level and adjust crop management, for example, by adding **additional crop irrigation**.\n- Market-wise, policy interventions can be adopted to address social and economic issues. In Vietnam, **rice export restrictions** or **grain storage and distributions** can alleviate rising food prices.\n- Moreover, the government could use **social transfers** to target poor households to reduce the burden on household consumption.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1939,"value":"$37","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3065,"value":"---\n## And it's Not Just Vietnam...","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":322,"value":"\n\n**Mitigating El Niño's GDP impacts in the region**\n\nEl Niño events are **regional** by nature. Besides Vietnam, other countries in the Southeast Asia region also suffer from El Niño events on social and economic dimensions. Similar simulations have been conducted in the series reports for the other four Southeast Asia countries: Cambodia, Lao PDR, Myanmar, and the Philippines. \n\nThe impacts of El Niño events on agriculture have economy-wide implications: the agriculture sector, along with downstream processing and spillover across sectors, compose the entire agriculture food system and contributes significantly to the region's GDP. \n\nFigure 4 shows that introducing all potential interventions elaborated above concurrently is by far the most effective at minimizing GDP losses during El Niño, even leading to GDP gains during El Niño in Myanmar. \n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2431,"value":"\n \n \n \n \n

\n Implimenting all policy interventions could mitigate El Niño’s GDP impacts across five ASEAN countries\n
\n ${viewof arrowPltInputs}\n
\n

\n\n

\n ${arrowPlt}\n

\n \n

\n Figure 4. GDP Changes During Strong El Niño Events with Intervention Scenarios in Five Countries\n

\n \n

\n \n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3067,"value":"**Migration as an adaptation strategy in response to climate change**\n\nOne related topic to this climate impact story is climate migration, which we plan to explore further in a future story. Migration is usually induced by various drivers, amongst which climate change and environmental disruptions play an increasingly important role.\n\nAccording to the report from the [International Displacement Monitoring Center](https://www.internal-displacement.org/sites/default/files/publications/documents/grid2021_idmc.pdf). About 30.7 million disaster displacements were recorded in 2020. Most of these displacements happened in East Asia and the Pacific region. Extreme events like typhoons, floods, earthquakes, and volcanic eruptions triggered 12.1 million new displacements, reaching the highest figure since 2016 and above the region’s 10-year average of 11.2 million. Powerful storms and flooding fueled by climatic variations triggered 94 percent of them.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2425,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1453,"value":"---\n# Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1455,"value":"## Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":335,"value":"vietnamDotMap = FileAttachment(\"figure 1 - rainfall differance and crop area@1.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"vietnamLineData = FileAttachment(\"figure 2 - rice yields.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":284,"value":"vietnamENSOData = FileAttachment(\"figure 2 - vietnam ENSO data.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":296,"value":"vietnamENSODataEdited = vietnamENSOData.map(d => d.Yvalue !== 8 ? NaN : d.Yvalue)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":998,"value":"vietnamPolicyIntervention = FileAttachment(\"figure 3 - policy scenarios in vietnam.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2314,"value":"regionalData = FileAttachment(\"figure 4v4 - policy scenarios in region@3.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":1536,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1468,"value":"## Viz code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2746,"value":"#### Figure 1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":337,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1596,"value":"//code for figure 1 - rainfall legend\ndotPltLegend1 = dotPlt.legend(\"color\", {label: \"Average rainfall changes(%)\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1606,"value":"//code for figure 1 - area dot legend\ndotPltLegend2 = {\n const svg = d3.select(DOM.svg())\n .attr(\"height\", 30)\n .attr(\"width\", 200 + 15)\n \n // Call it on a g element\n svg.append(\"g\")\n .call(legend);\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1608,"value":"//code for figure 1 - area dot legend setting\nlegend = legendCircle()\n .scale(\n d3.scaleSqrt()\n .domain([0, 500000 ])\n .range([0, 11])\n )\n .tickValues([100000, 500000])\n .tickFormat((d, i, e) => i === e.length - 1 ? d/1000 + \" thousand hectares of crop area\" : d/1000)\n .tickSize(15); // defaults to 5","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2752,"value":"#### Figure 2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":90,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1840,"value":"//code for figure 2 - process data\nregionNames.push(\"Vietnam\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2627,"value":"// code for figure 2 - defining years to be used in the animation\nyears = Array.from(new Set(vietnamLineData.map(d => d.Year)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2629,"value":"// code for figure 2 - year filter for animation\nviewof filterYear = {\n await visibility();\n return Scrubber(years, {\n autoplay: true,\n delay: 200,\n loop: false,\n //loopDelay: 1000\n})\n} ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1814,"value":"// code for figure 2 - multiple selection\nviewof regionNames = Inputs.checkbox([\"South Central Coast\", \"Central Highland\", \"Mekong River Delta\"], {label: \"Vietnam regions most vulnerable to El Niño\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2755,"value":"#### Figure 3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1006,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1002,"value":"//code for figure 3 - select input\nviewof policyIntervention = Inputs.select([\"Drought-tolerant varieties\",\n \"Additional irrigation\",\n \"Rice export restrictions\",\n \"Grain storage and distribution\",\n \"Social transfers\",\n \"All combined\"], \n {label: \"Select one policy intervention scenario\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2760,"value":"#### Figure 4","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2303,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2514,"value":"// code for figure 4 - input checkboxes\nviewof arrowPltInputs = Inputs.checkbox([\"Show GDP % change value (without intervention)\", \"Show GDP % change value (with intervention)\", \"Show 0% GDP change line\"], {label: \"\", value: [\"Show GDP % change value (without intervention)\", \"Show GDP % change value (with intervention)\", \"Show 0% GDP change line\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1458,"value":"## Setting","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":623,"value":"CSS style settings\n ","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":345,"value":"import {Plot, addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1600,"value":"import { legendCircle } from \"@harrystevens/circle-legend\";","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2624,"value":"import {Scrubber} from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2899,"value":"## Credit","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3074,"value":"Our [Data Whale team](https://www.data-whale.com/) would like to thank ADB and Observable for making this data visualization challenge possible! We are grateful for everyone's help along the way, including the organizers, judges, and mentors. A special shoutout to our mentors for the very helpful comments we received and the connections we built!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2930,"value":"Cover image by Peter Hammer from *[Unsplash](https://unsplash.com/photos/ggQy5lGtwb0)*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1960,"value":"//export ban icon by Teewara soontorn from Noun Project https://thenounproject.com/browse/icons/term/ban-shipping/\nexportBan = FileAttachment(\"export-ban.png\").image({ width: 80})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1961,"value":"//irrigation icon by Anwar Hossain from Noun Project https://thenounproject.com/browse/icons/term/irrigation/\nirrigation = FileAttachment(\"irrigation.png\").image({ width: 80})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1962,"value":"//seed icon by ToZIcon from Noun Project https://thenounproject.com/browse/icons/term/wheat/\nseed = FileAttachment(\"seed.png\").image({ width: 80})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1964,"value":"//warehouse icon by Ganjar Adicipta from Noun Project https://thenounproject.com/browse/icons/term/warehouse/\nwarehouse = FileAttachment(\"warehouse.png\").image({ width: 80})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1963,"value":"//social transfer icon by Daouna Jeong from Noun Project https://thenounproject.com/browse/icons/term/networking/\nsocialTransfer = FileAttachment(\"social-transfer.png\").image({ width: 80})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3e","stdlib":"1"}} 31:"$30:body"