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Digital Agriculture and Flood Mapping for Kazakhstan's Changing Climate

Project Overview

Water supply, demand, and associated hydrological events are among the most pressing issues for the sustainability of local-regional socioeconomic-environmental systems (SES) in Kazakhstan. When facing the rapidly changing climate, land use, and globalization, it is pertinent to develop an independent and integrated forecasting system for SES functions and dynamics. Here, we propose to geospatially map the increasing, catastrophic flooding events along Kazakhstan’s major rivers.

The objective of this initial project is to first construct accurate spatial maps of historical flooding zones based on high resolution remote sensing images and digital elevation models (DEMs) and to develop an empirical Water Supply Stress Index Model (WaSSI). We then will customize an in situ hydrological model, Soil and Water Assessment Tool (SWAT), to forecast the spatial and temporal changes in flooding and affected zones under different climate and land use scenarios. The Greater Almaty Region (GAR) will be used as our testbed before this knowledge and technology is expanded throughout Kazakhstan.

Example:Flooding as a result of precipitation

Figure 1. Example map of flooding as a result of precipitation. NOTE: Landsat will be used for classification purposes.

Research Tasks

Flooding zones can be delineated from stream flow and elevation. While high resolution DEMs are available (e.g., 3 m), our primary challenge is to predict stream flows (i.e., frequency, magnitude, and duration of peak flows) that depend on three key conditions: precipitation, glacial melting, and land use and cover (LUC). The changes in lake/reservoirs or water level and the amount of evapotranspiration lost from the land surfaces are additional fluxes that directly affect the stream flow. Fortunately, all of these conditional variables and fluxes can now be precisely measured/molded with high-resolution remote sensing images.

With geospatial statistics of population, agriculture, and economic statistics, we can assess the historical impact through an empirical WaSSI and the hydrologically-integrated Global Change Assessment Model (GCAM). By parameterizing a mechanistic model (e.g., SAWT) to map flooding zones and high-risk zones, we can then predict future conditions and flooding. To achieve our proposed objectives, four specific research tasks will be performed in partnership with multiple institutions and agencies in Kazakhstan.

Task 1:
Construct a comprehensive, high-resolution geospatial database for GAR (1980s – present)

Task 2:
Map historical flooding zones along the major rivers/lakes within GAR and estimate the crucial parameters for the models

Task 3:
Conduct flexible and convenient hands-on training courses for land managers, policy makers, educators, and students from Kazakhstan

Task 4:
Perform integrated assessment of flooding through vulnerability analysis and identify hot spots that need proactive planning

Conceptual Framework

DEMO Conceptual Framework

Figure 2. DEMO project: mapping flooding in two watersheds near Almaty and Astana. The blue texts/lines represent the proposed tasks, while the blacks indicate the possible demo products if the data can be made available during the project (March-July, 2018).


Completed Data

Data Type
Time Scale
Spatial Scale
Climate Data Precip, temp, coordinates, etc. shape/text/raster Monthly 1901-2016 Country
Land Classification Land cover raster 500-1000 m 2000's Country
DEM SRTM raster 30 m Country
Basic GIS Data River shape Some watershed
Image Data MODIS raster 250-1000 m 2000's Entire country
Image Data Landsat raster 30 m 1970's Entire country

To Be Completed Data

Data Type
Time Scale
Spatial Scale
Climate Data Precip, temp, coordinates, wind speed, radiation shape/text/excel/csv Hourly-daily Long-term historical records Country
Stream Flows
Land Classification Land cover raster 30m 1970's Country
Land Classification Land use raster 5m 2000's Urban areas/some watershed
Soil map Soil type/depth/texture/bulk density raster Country
DEM AW3D DEM raster 0.5 m Some watershed
Lidar point cloud Building structure Urban area
3D vector 3D map building shape/TAB Urban area
Basic GIS data Rivers, roads, lakes, residential sites, administrative boundaries shape Country
Topographic map Digital/hardcopy 1:50,000



Some watershed

Image data SPOT



raster <2.5 m

2.4 m

<2 m

Some watershed

Team Members

Dr. Jiquan Chen Professor, Principal Investigator
Dr. Jiaguo Qi Professor, Principal Investigator
Dr. Fei Li Postdoctoral Research Associate
Dr. Lifeng Luo Professor
Dr. Alan Arbogast Professor
Marat Beksultanov
Diana Dushniyazova
Gulnaz Iskakova
Maira Kussainova


Center for Global Change and Earth Observations

202 Manly Miles Bldg. 1405 South Harrison Road
Michigan State University, East Lansing, MI 48823