Agricultural drought in Iran is one of the natural disasters, which causes large economical and social damages. Iran is located in an arid to semi-arid region and is facing widespread droughts regularly. The agricultural sector is with 80-90 % by far the largest user of water in Iran and is often the first sector to be affected by drought. This emphasizes the vulnerability of the agricultural sector to drought and the need for more research to understand and develop tools that would be helpful in planning to mitigate the impacts of drought. Reliable drought assessment would be beneficial for forecasting of crop production, operational decision making on farms, for early warning, for identification of potential vulnerability of areas, for mitigation of drought impact and finally food security.
Although agrohydrological models like SWAP offer the possibilities for assessing of drought impact on crop yield, such models may become inaccurate at regional scale because of uncertainty of input parameters like irrigation scheduling, soil hydraulic parameters and planting dates. Hence to reduce the uncertainty in application of SWAP at regional scales, remotely sensed data of leaf area index LAI and relative evapotranspiration ET/ETp were used in combination with a geographical information system.
To insert the valuable information from remotely sensed land surface data into the SWAP model at regional scale, a simple updating assimilation technique was used. The SWAP model was implemented in a distributed way using the spatial distributed information of soil types, land use and water supply on a raster basis with a grid size of 250 m. In order to link spatial information data with SWAP, a coupling program was developed for transferring of in- and output data from one system to the other, as well as to run the model for each pixel.
Simulation with assimilation of both LAI and ET/ETp -data at both the regional and field scale (bias about %) was very promising in forecasting crop production one month in advance. However, longer term predictions i.e. two months in advance, resulted in a higher bias between the simulated and statistical data. It appeared that in the assimilation process LAI data have a dominant influence. Because of this dominant influence, it is suggested to repeat the assimilation process using the LAI data of the most advanced satellite i.e. IRS-P6 (ResourceSAT1&2) with higher spatial and temporal resolution.