2f:T422,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 visualized 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?* 30:T47d,**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. 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 server economy-wide ramifications. **Through the Perspective of Rainfalls and Rice Yield** In this section, we explore the uncertainty brought by El Niño events to Vietnam's rainfall and rice production through interactive visualizations. In Figure 1, based on rainfall difference data from 1980 to 2015 in Vietnam, it is shown that El Niño events have different implications at the sub-region level. On average, El Niño depresses rainfall in all of Vietnam's sub-regions from 2% in the South to 30% in the North. 31:T8d0,--- ## Policy Interventions Can Help **Focus on the Production-Commodity-Consumption Agricultural Value Chain and beyond** The El Niño shocks to agriculture have significant economic and social impacts: the agricultural production changes as illustrated above result in the fluctuation of farmers' income and food prices, downstream processing, input production, and trading, which further influence household consumption, poverty, and GDP. Policy interventions can help reduce El Niño-related losses and improve Vietnam’s preparedness and resiliency for future El Niño events. - Farmers can utilize **drought-tolerant seed varieties** at the farm level and adjust crop management, for example, by **adding additional crop irrigation**. - 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. - Moreover, the government could use **social transfers** to target poor households to reduce the burden on household consumption. **One El Niño, Different Stories** In Figure 3, we synthesize model estimate results from the literature and leverage the interactive capability from Observable to showcase what the government, market, and farmers can do to attenuate the adverse impacts of El Niño events on poverty in Vietnam. The drop-down box allows the audience to select a specific policy intervention and compare the scenarios. By comparing the different policy scenarios in Figure 3, it can be seen that social transfers are most effective in alleviating poverty increase, while additional irrigation and adopting drought-tolerant seed varieties are not as helpful. Another takeaway from the visualization is that rural households are more adversely affected than urban households during El Niño events. Specifically, women, who play a central role in Vietnam's agriculture, are more vulnerable to these events. Without any policy change, rural female-headed households are severely affected: 2.7% more people living in rural female-headed households (represented in the third purple bar) fall into poverty during a strong El Niño even, which is much higher than that of other categories. 32:T4a4,

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

${viewof policyIntervention} ${barPlt} No policy change With policy change

33:T597,--- ## And it's Not Just Vietnam... **Shared Climate, Differentiated Contexts** El 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. However, because of the different country contexts, the impacts of policy interventions vary among countries. Through Figure 4, we visualize the impacts of El Niño events on GDP and the effectiveness of the policy interventions across countries. **Across the five countries**, the Philippines (-1.57%) and Vietnam's (-1.5%) GDPs are more adversely influenced by El Niño. **Among the policy interventions**, - Farm-level management such as using drought-tolerant crop varieties and expanding irrigation could mitigate GDP losses during El Niño in all countries, especially in Myanmar and Cambodia. - However, introducing temporary rice export restrictions generally does not reduce GDP losses during El Niño. - And expanding social transfers such as cash transfer systems, though improving household welfare like poverty rate (Figure 3), could not reduce GDP losses during El Niño. - Lastly, with all the potential interventions concurrently applied, these countries' GDP losses are most effectively attenuated. 34:T61f,## 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. 35:T8b9,//code for the line plot - figure 2 linePlt = Plot.plot({ width: 1100, marginRight: 80, 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"}), Plot.line(vietnamLineData, {x: "Year", y: "Value", stroke: "Category", 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 }), Plot.text(["Central Highland"], { frameAnchor: "right", dx: -155, dy: -30, fill: "#4e79a7", fontSize: 12, fontWeight: "bold", lineAnchor: "bottom", text: (d) => d }), Plot.text(["Mekong River Delta"], { frameAnchor: "right", dx: -70, dy: -150, fill: "#f28e2b", fontSize: 12, fontWeight: "bold", lineAnchor: "bottom", text: (d) => d }), Plot.text(["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"}) ] }) 36:T68d,//code for the bar plot - figure 3 barPlt = addTooltips( Plot.plot({ width: 900, 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", 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} )37:[{"id":"09b018ffeaf4e9a72a9e5f513009fd72251fa859c80e975ace070a836a341cde70d013d2225aa4eb1f9c9e7461ec6eb7562b2f0176089f18e6e05cb1fc818f89","href":"https://static.observableusercontent.com/files/09b018ffeaf4e9a72a9e5f513009fd72251fa859c80e975ace070a836a341cde70d013d2225aa4eb1f9c9e7461ec6eb7562b2f0176089f18e6e05cb1fc818f89","name":"figure 1 - rainfall differance and crop area.csv","type":"text/csv","lastModified":1668843774942.539,"size":3013},{"id":"5cf072160955724266be31bee92b65602c24c0841ee4adb7b31c67573ca9273986c1511a454d1921fce935b41b7bc39a0fec1dcd10ad47421ae34a9f9eea5674","href":"https://static.observableusercontent.com/files/5cf072160955724266be31bee92b65602c24c0841ee4adb7b31c67573ca9273986c1511a454d1921fce935b41b7bc39a0fec1dcd10ad47421ae34a9f9eea5674","name":"figure 2 - rice yields.csv","type":"text/csv","lastModified":1668843774942.539,"size":2407},{"id":"799e7bf304f1d2a11e5171b8ad829b38cb569a9e152d7856de46a55ff6175b101e2f60b19bbac5ccf42fdb92e7858f336262036fd43387f328f1c40572150fe6","href":"https://static.observableusercontent.com/files/799e7bf304f1d2a11e5171b8ad829b38cb569a9e152d7856de46a55ff6175b101e2f60b19bbac5ccf42fdb92e7858f336262036fd43387f328f1c40572150fe6","name":"figure 2 - vietnam ENSO data.csv","type":"text/csv","lastModified":1668843774942.539,"size":308},{"id":"c851083bc1fffd33ee4cef0d2952482a8a64482995d7a9ef9d9fbdcf76a83021e4d5d96c8400b2d90451b505f5d4e0b957f2cdb7c88d8ed903fb8aa1ad054150","href":"https://static.observableusercontent.com/files/c851083bc1fffd33ee4cef0d2952482a8a64482995d7a9ef9d9fbdcf76a83021e4d5d96c8400b2d90451b505f5d4e0b957f2cdb7c88d8ed903fb8aa1ad054150","name":"figure 3 - policy scenarios in vietnam.csv","type":"text/csv","lastModified":1668843774942.539,"size":2880},{"id":"6de2790fcb2578c9c0b9d2e2dbb5785a3d402414f5c8a48389b3503c8d3fdfeaf1f33d9dc3a83ec3d59fcf4f1e1e3f79e4c4520550f8ae3eb6a5fc54a0bdb0a6","href":"https://static.observableusercontent.com/files/6de2790fcb2578c9c0b9d2e2dbb5785a3d402414f5c8a48389b3503c8d3fdfeaf1f33d9dc3a83ec3d59fcf4f1e1e3f79e4c4520550f8ae3eb6a5fc54a0bdb0a6","name":"figure 4 - policy scenarios in region.csv","type":"text/csv","lastModified":1668843774942.539,"size":1361},{"id":"578a21ff43540be657beb60f48304ea617c77f909bcce6c13491b92c7c122be1fe533b1552d8cb8062dc0ab3bd816b68f75c8a160062a5c98a1f9a6d35a3fbd3","href":"https://static.observableusercontent.com/files/578a21ff43540be657beb60f48304ea617c77f909bcce6c13491b92c7c122be1fe533b1552d8cb8062dc0ab3bd816b68f75c8a160062a5c98a1f9a6d35a3fbd3","name":"unsplash-img@3.jpg","type":"image/jpeg","lastModified":1668843774942.539,"size":359388}] 2d:{"title":"","cells":[{"id":1581,"value":"forked on 11/19/22 as a back-up of the notebook before the final submission","pinned":false,"hidden":false,"mode":"md"},{"id":0,"value":"# What Happens when El Niño Comes to Vietnam?","pinned":false,"hidden":false,"mode":"md"},{"id":1399,"value":"unsplashImg = FileAttachment(\"unsplash-img@3.jpg\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1236,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":41,"value":"---\n## What are El Niño's Impacts in Vietnam?","pinned":false,"hidden":false,"mode":"md"},{"id":8,"value":"$30","pinned":false,"hidden":false,"mode":"md"},{"id":508,"value":"\n \n \n \n

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

\n \n

\n ${dotPlt}\n

\n \n","pinned":false,"hidden":false,"mode":"html"},{"id":1562,"value":"Would the significant decreases in rainfall during El Niño years have any implications for rice yield? To answer this question, in Figure 2, we visualize the rice yields across years in three regions of Vietnam that are most vulnerable to El Niño: the South Central Coast, the Central Highlands, and the Mekong River Delta. By marking the occurrence of El Niño events as gray bars in the background, we can observe that the depressed rice yields during the winter are closely associated with El Niño events. ","pinned":false,"hidden":false,"mode":"md"},{"id":875,"value":"\n \n \n \n

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

\n
\n ${linePlt}\n \n \n El Niño event \n \n \n \n

\n \n\n","pinned":false,"hidden":false,"mode":"html"},{"id":320,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":1060,"value":"$32","pinned":false,"hidden":false,"mode":"html"},{"id":322,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":1166,"value":"\n \n \n \n

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

\n ${heatmapPlt}\n \n

\n \n","pinned":false,"hidden":false,"mode":"html"},{"id":1453,"value":"---\n# Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":1455,"value":"## Data","pinned":false,"hidden":false,"mode":"md"},{"id":335,"value":"vietnamDotMap = FileAttachment(\"figure 1 - rainfall differance and crop area.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":80,"value":"vietnamLineData = FileAttachment(\"figure 2 - rice yields.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":284,"value":"vietnamENSOData = FileAttachment(\"figure 2 - vietnam ENSO data.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":296,"value":"vietnamENSODataEdited = vietnamENSOData.map(d => d.Yvalue !== 8 ? NaN : d.Yvalue)","pinned":false,"hidden":false,"mode":"ojs"},{"id":998,"value":"vietnamPolicyIntervention = FileAttachment(\"figure 3 - policy scenarios in vietnam.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":387,"value":"regionalPolicy = FileAttachment(\"figure 4 - policy scenarios in region.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1536,"value":"$34","pinned":false,"hidden":false,"mode":"md"},{"id":1468,"value":"## Viz code","pinned":false,"hidden":false,"mode":"md"},{"id":337,"value":"//code for the dot map plot - figure 1\ndotPlt = addTooltips(\n Plot.plot({\n center:true,\n width: width*0.29,\n height:500,\n x: {\n ticks: 0,\n label: \"\"\n },\n y: {\n ticks: 0,\n label: \"\"\n },\n color: {\n legend: true,\n label: \"Average rainfall changes(%)\",\n //scheme: \"blues\",\n range: [\"#cbc9e2\", \"#54278f\"],\n reverse: true,\n // sort: true,\n // domain: [\"North\", \"Center\", \"South\"] //set the order\n },\n marks: [\n Plot.dot(vietnamDotMap, {x: \"X\", \n y: \"Y\", \n fill: \"RainfallDiffFromNetural\", \n r: \"CropValue\",\n title: (d) =>\n `${d.Subregion}\\n \\n Average rainfall changes in Oct–Jun\\n during El Niño phases: \\n${d.RainfallDiffFromNetural}% \\n\\n Crop area (hectare): \\n${d.CropValue}` \n }\n )]\n }),\n //set tooltip styles\n { fill: \"darkGray\", opacity: 0.5}\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":90,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1002,"value":"//code for select input of the bar plot - figure 3\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"},{"id":1006,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":383,"value":"//code for the heatmap plot - figure 4\nheatmapPlt = Plot.plot({\n width: 1000,\n height: 300,\n marginLeft: 80,\n marginTop: 100,\n marginRight: 100,\n x: {\n axis: \"top\",\n label: \"Policy intervention scenarios\",\n reverse: false,\n tickRotate: -15,\n domain: [\"Without interventions\",\n \"Drought-tolerant varieties\",\n \"Additional irrigation\",\n \"Rice export restrictions\",\n \"Grain storage and distribution\",\n \"Social transfers\",\n \"All combined\"],\n },\n y: {\n label: \"\"\n },\n color: {\n // type: \"diverging\",\n scheme: \"Oranges\",\n legend: true,\n reverse: true,\n label: \"GDP Changes (%)\"\n },\n // facet: {\n // data: regionalPolicy,\n // x: \"PolicyIntervention\"\n // },\n marks: [\n Plot.cell(regionalPolicy, {\n x: \"Policy\",\n y: \"Country\",\n fill: \"Value\"\n }),\n Plot.text(regionalPolicy, {\n x: \"Policy\",\n y: \"Country\",\n text: \"Value\"\n }),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1458,"value":"## Setting","pinned":false,"hidden":false,"mode":"md"},{"id":623,"value":"CSS style settings\n ","pinned":false,"hidden":false,"mode":"html"},{"id":345,"value":"import {Plot, addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W37","stdlib":"1","titling":"auto"}