An integrative Hydrological, Ecological and Economical (HEE) modeling system for assessing water resources and ecosystem production: Calibration and validation in the upper and middle parts of the Yellow River Basin, China

Effective management of water resources in arid and semi-arid areas demands studies that cross over the disciplinaries of natural and social sciences. An integrated Hydrological, Ecological and Economical (HEE) modeling system at regional scale has been developed to assess water resources use and ecosystem production in arid and semi-arid areas. As a physically-based distributed modeling system, the HEE modeling system requires various input parameters including those for soil, vegetation, topography, groundwater, and water and agricultural management at different spatial levels. A successful implementation of the modeling system highly depends on how well it is calibrated. This paper presented an automatic calibration procedure for the HEE modeling system and its test in the upper and middle parts of the Yellow River basin. Previous to calibration, comprehensive literature investigation and sensitivity analysis were performed to identify important parameters for calibration. The automatic calibration procedure was base on conventional Monte Carlo sampling method together with a multi-objective criterion for calibration over multi-site and multi-output. The multi-objective function consisted of optimizing statistics of mean absolute relative error (MARE), Nash-Sutcliffe model efficiency coefficient (E_NS), and coefficient of determination (R²). The modeling system was calibrated against streamflow and harvest yield data from multiple sites/provinces within the basin over 2001 by using the proposed automatic procedure, and validated over 1993-1995. Over the calibration period, the mean absolute relative error of simulated daily streamflow was within 7% while the statistics R² and E_NS of daily streamflow were 0.61 and 0.49 respectively. Average simulated harvest yield over the calibration period was about 9.2% less than that of observations. Overall calibration results have indicated that the calibration procedures developed in this study can efficiently calibrate the modeling system in the study area. Annual validation results for average streamflow and harvest yield showed relative large errors which were associated with irrigation water use and reservoir impact. The validation results of streamflow for sites in upper reaches have shown close relationship with observations which indicated the liability of calibrated parameter values in predicting watershed responses. The information and results provided by the study will be helpful to watershed modelers and model users in calibrating complex watershed models and contribute knowledge to interdisciplinary modeling for water resources management in the study area.