Diagnosis toward predicting mean annual runoff in ungauged basins

Abstract

Abstract. Prediction of mean annual runoff is of great interest but still poses a\nchallenge in ungauged basins. The present work diagnoses the prediction in\nmean annual runoff affected by the uncertainty in estimated distribution of\nsoil water storage capacity. Based on a distribution function, a water\nbalance model for estimating mean annual runoff is developed, in which the\neffects of climate variability and the distribution of soil water storage\ncapacity are explicitly represented. As such, the two parameters in the\nmodel have explicit physical meanings, and relationships between the\nparameters and controlling factors on mean annual runoff are established.\nThe estimated parameters from the existing data of watershed characteristics\nare applied to 35 watersheds. The results showed that the model could\ncapture 88.2\u2009% of the actual mean annual runoff on average across the\nstudy watersheds, indicating that the proposed new water balance model is\npromising for estimating mean annual runoff in ungauged watersheds. The\nunderestimation of mean annual runoff is mainly caused by the\nunderestimation of the area percentage of low soil water storage capacity\ndue to neglecting the effect of land surface and bedrock topography. Higher\nspatial variability of soil water storage capacity estimated through the\nheight above the nearest drainage (HAND) and topographic wetness index (TWI)\nindicated that topography plays a crucial role in determining the actual\nsoil water storage capacity. The performance of mean annual runoff\nprediction in ungauged basins can be improved by employing better estimation\nof soil water storage capacity including the effects of soil, topography,\nand bedrock. It leads to better diagnosis of the data requirement for\npredicting mean annual runoff in ungauged basins based on a newly developed\nprocess-based model finally.