Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

Xiaomang Liu,Koulin Hsu,Tiantian Yang,Changming Liu,Soroosh Sorooshian

doi:10.5194/hess-21-169-2017

Abstract

Abstract. On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basins on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. The evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.

Highlights

Precipitation is one of the essential meteorological inputs of a hydrologic model and the key driving force for a hydrologic cycle
Owing to a harsh environment, the existing meteorological stations managed by the Chinese Meteorological Administration only form an extremely sparse network, which creates great challenges for water resources management and operation
The runoff coefficients are 0.22, 0.27, and 0.26 in the upper Yangtze River (UYZR) based on gauge-based precipitation, Global Land Data Assimilation System (GLDAS) precipitation, and PERSIANN-CDR precipitation, respectively

Summary

Introduction

Precipitation is one of the essential meteorological inputs of a hydrologic model and the key driving force for a hydrologic cycle. Three methods are generally used to measure precipitation: traditional gauge observations, meteorological radar observations, and satellite observations (Ashouri et al, 2015). In many remote regions and mountainous areas, rain gauges and meteorological radar networks are either sparse or non-existent. Satellite-based precipitation is of great importance in such regions. There is a great potential for using satellite-based precipitation estimates on the Tibetan Plateau known as the “roof of the world” with an average elevation of over 4000 m (Yao et al, 2012). Owing to a harsh environment, the existing meteorological stations managed by the Chinese Meteorological Administration only form an extremely sparse network, which creates great challenges for water resources management and operation. Streamflow simulation using the limited gauge-based rainfall information might not be reliable due to the input uncertainties with such a poor spatial resolution.

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Conclusion