Dataset of Drought and Flood Impact on Croplands in Southeast Asia between 1980 to 2019

Published: 21 September 2021| Version 4 | DOI: 10.17632/nv7jbmkynm.4
Venkatappa Manjunatha


Droughts and floods have been intensified in recent years and adversely affected agriculture sectors, but only a handful of studies exist to assess their impacts on croplands and production. The purpose of our dataset is to help researchers and scientists to access the temporal climate data geographically and assess the climate change and its impact on croplands in the Monsoon Climate Region (MCR) and Equatorial Climate Region (ECR) of Southeast Asia (SEA) during the crop growing seasons over 40 years period. Here, we used TerraClimate global high-resolution gridded Palmer Drought Severity Index (PDSI) datasets in Google Earth Engine and assessed the droughts and floods and their impacts on rainfed croplands and crop production in SEA. The Global Food Security-support Analysis cropland data in the GEE was used to assess drought damages on rainfed croplands and crop production. We created over 47,192 grid points geographically with a 10 × 10-kilometer resolution, in order to calculate the drought and flood impact on rainfed croplands during the primary crop-growing seasons in the MCR and ECR of SEA. In this data tree datasets were including, two raw data and Geographic Information System (GIS) shapefile data. The first raw dataset folder contains monthly temporal Palmar drought Severity index (PDSI) by country between 1980 to 2019, while the second raw gridded point dataset covers average drought frequencies by 10 km × 10 km geographically in MCR (Myanmar, Thailand, Lao PDR, Cambodia, and Vietnam) and ECR (Malaysia, Singapore, Indonesia, the Philippines, and Brunei Darussalam) regions with five years interval between 1980 to 2019 . The third data in GIS Shapefile format (GIS data folder) comprehends drought and their severities levels on rainfed croplands by country in SEA in 2010. The maps data in Maps folder which represents severity classes of drought and wet conditions (floods) impact on rainfed croplands, and the level of need for policy interventions by country in the MCR and ECR countries in SEA .


Steps to reproduce

All data including, raw data, ArcGIS shapefiles and maps were stored by country. Each country folder includes 3 subfolders: GIS data, Maps, and Raw data. The data users: researchers, NGOs, development agencies, and policymakers can download the data by using below methods. 1.Download the CSV files. 2.Analyze drought conditions temporally using Microsoft excel or any relevant software’s that support reading the CSV files 3.Convert gridded data CSV file to ArcGIS shapefiles by using ArcGIS or Qgis XY Table To Point (Data Management tool) or using Qgis by adding an X and Y coordinate to Point Data. Procedure for ArcMap users: Add the CSV file to ArcMap using the Add Data button, at which point it will be added as an ArcMap Layer. You must be under the Source tab to view these files. Right-click on the name of the new ArcMap layer and go to Display XY Data. Procedure for Qgis users: In Qgis, use the Create a Layer from a Delimited Text File tool, click on Browse, and specify the path to the data file you downloaded. In the File format section, select Custom delimiters and check the Tab. The Geometry definition section will be auto populated if it finds suitable X and Y coordinate fields. In our case, they are LONGITUDE and LATITUDE. 4. Use Interpolation Method in ArcMap or QGIS to analyze the drought intensities 5. Overly cropland data on gridded spatial data and assess the drought impacts on croplands. Google Earth Engine users: To upload CSV datasets, use the Asset Manager in the Code Editor. The Asset Manager is on the Assets tab at the left side of the Code Editor. Use the Importing Table Data to uploading CSV table data into GEE. You can import an asset to your script in Code Editor by hovering over the asset name in the Asset Manager and import the CSV data.


Asian Institute of Technology


Drought, Flood, Crop Production, Climate Data, Permanent Cropland