One of the main activities of CAREC within the framework of Climate Adaptation and Mitigation for Aral Sea Basin (CAMP4ASB) is to support hydrometeorological services in improving the quality of forecasting in the face of climate change.
Snow cover monitoring and forecasting the availability of water resources are of particular importance in the Central Asian region. Due to the underdevelopment of the observing network, especially in high mountain areas, methods based on remote sensing applications are currently being used to assess the snow cover in the region. Thus, CAREC, within the framework of the CAMP4ASB project, together with a specialist from the Potsdam Institute for the Study of the Earth (GFZ), Dr. A. Gafurov, adapted the MODSNOW tool for 4 Central Asian countries and held a series of training seminars on the use of this tool. MODSNOW tool is currently used online by specialists of hydrometeorological services.
To assess this tool's effectiveness, there was a study conducted for the period 2018-2019 on the example of 10 river basins in Central Asian countries. Based on the results of this assessment, a report was prepared on the results of forecasting for the growing seasons from April to September and from May to September, as well as for each month from April to September using the MODSNOW model.
Summing up, the accuracy of hydrological forecasts using the MODSNOW model for 12 of 18 river basins was 100%. These results prove that the MODSNOW model provides reasonably accurate forecasts based solely on the snow cover area. Considering that the average accuracy of forecasts for all river basins involved in the study was 75% for the entire growing season and 78.2% for monthly forecasts, we can conclude that such tools allow us to make more accurate forecasts for the region and react to them accordingly. The results of this report also prove that, given the geographic location of the region and the complexity of physical monitoring in high mountain areas, it is possible to provide more accurate hydrological forecasts in Central Asia using the remote sensing method.
You can find more detailed information on the results of the report here.