Land-Use and Land Cover Is Driving Factor of Runoff Yield: Evidence from A Remote Sensing-Based Runoff Generation Simulation

Abstract

The spatial distribution of water storage capacity has always been the critical content of the study of saturation-excess runoff. Xin′anjiang model uses the water storage capacity curve (WSCC) to characterize the distribution of water storage capacity for runoff yield calculation. However, the mathematical and physical foundations of WSCC are unclear, which is impossible to simulate runoff generation with complex basins accurately. To fill this gap, we considered the dominant role of basin physical characteristics in water storage capacity and developed a new integrated approach to solve the spatial distribution of water storage capacity (L-WSCC) to account for the spatiotemporal dynamics of their impact on runoff generation. The main contribution of L-WSCC was to confer WSCC more physical meaning and the spatial distribution of water storage capacity was explicitly represented more accurately, so as to better express the runoff generation and provide a new approach for runoff yield calculation in non-data basin. L-WSCC was applied to Misai basin in China and promising results had been achieved, which verified the rationality of the method (the mean Nash–Sutcliffe efficiency (NSE):0.86 and 0.82 in daily and hourly scale, respectively). Compared with WSCC, the performance of L-WSCC was improved (mean NSE: 0.82 > 0.78, mean absolute value of flood peak error (PE): 12.74% < 21.66%). Moreover, the results of local sensitivity analyses demonstrated that land-use and land cover was the major driving factor of runoff yield (the change of mean absolute error (ΔMAE): 131.38%). This work was significant for understanding the mechanisms of runoff generation, which can be used for hydrological environmental management and land-use planning.

Study on the Contribution of Land Use and Climate Change to Available Water Resources in Basins Based on Vector Autoregression (VAR) Model

“…(2) Difference between LU and PREP on AWRs change Most studies have shown that surface vegetation can reduce water resources in basins, but the degree of reduction has not been quantified [54,55]. Through the VAR model constructed in this study, it was found that the contribution rates of AD, WD, GD, CD, and PREP to changes in AWRs were 23.76%, 4.40%, 4.34%, 4.56%, and 6.09%, respectively.…”