Linking land use/land cover with climatic and geomorphologic factors in regional mean annual streamflow models with geospatial regression approach

Abstract

Estimates of annual streamflow in connection with key natural and anthropogenic factors are necessary and important for different purposes, such as water resource planning and management, sediment and nutrient loading in streams and rivers, hydropower, and navigation. This study is an attempt to use the spatial statistical regression approach to develop regression models for mean annual streamflow at regional scale while adequately dealing with the common spatial dependency issue in input and output variables used in regression models. The proposed modeling approach is illustrated with a case study of the Upper Mississippi River Basin. The R-squared and the Nash–Sutcliffe model efficiency coefficient of the regional model were 0.993 and 0.985, respectively, while those of the sub-regional model were 0.995 and 0.990, respectively. Methodologically, the proposed model provided an effective way to utilize an extensive spatial dataset of various climatic, geomorphologic, and land cover variables for a large region like the Upper Mississippi River Basin to assess and compare the impact of various factors on mean annual streamflow at regional scale. Furthermore, the model was able to handle spatial dependency in data.