GLUE Analysis and Optimal Operation for Diyala River Basin in Iraq Using Variable Infiltration Capacity Model

Uncertainty in both hydrologic behavior and model characterization is a concern for current and future water resource system planning, operation, and management. To develop optimal dam operation schemes under future uncertainty, the sensitivity of the precipitation-runoff response to changes in hydro-climatic forcing must be quantified. To achieve this purpose, accurate (observational and modeled) data should be implemented. Herein, many data sources were compared to representative hydrologic datasets. Due to limited availability of observed daily data, a random temporal cascade method was used to downscale the monthly precipitation into daily. Then, four interpolation methods were compared to transform the point into gridded data. Furthermore, a regression technique coupled with Kriging method was developed. The method is based on regressing modeled data (from VIC dataset) with the observed gridded temperature by relating the regression to the geometry of each grid. The sensitivity and identifiability of the Variable Infiltration Capacity model (VIC) for the Diyala River basin in Iraq were evaluated using GLUE technique. Diyala River is a Tigris River tributary in eastern Iraq. Its total length and basin area are about 216.5 km and 16,763.7 km2, respectively. Seven candidate parameters of VIC model (b_infilt, Ds, Ws, Dsmax, and depths of soil layer 1, 2, and 3) associated with the infiltration and surface runoff production processes are examined for 14000 random sets. The comparison between the different data showed that neither the observations from Tropical Rainfall Measurement Mission nor the VIC modeled data is accurate for gridded precipitation; therefore, a downscaling technique was applied. Moreover, the comparison between four different interpolation techniques revealed that the Kriging method is the most accurate. The optimal model performance was found to be 0.731 NSCE. Also, the GLUE analysis results implied that the depth of the second soil layer depth is the most sensitive parameter. Future work will be create synthetic climatic scenarios by CLIGEN weather generator to capture future climate variation. The response of the hydrologic and water resource system of the basin will examined by these scenarios. Alternative dam rule curves will be proposed to enhance the dam operation, if needed.