Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau

Huiting Lu,Lindsay C. Stringer,Guohua Liu,Martin Dallimer,Tiantian Jin,Jieyuan Zhu,Gang Wu,Yan Yan

doi:10.3390/w12051498

Abstract

Understanding the spatiotemporal characteristics of water yield and its influencing factors is important for water resources management. In this study, we used the seasonal water yield model (SWYM) to assess the spatiotemporal water yield changes of the Lhasa River Basin from 1990 to 2015, and analyzed its influencing factors by focusing on precipitation, land cover, and normalized difference vegetation index (NDVI) change. We first examined the model through Morris screening sensitivity analysis and validated it with the observed flow data. Spatiotemporal variation of three indices of water yield, baseflow, quick flow, and local recharge were then assessed. Results showed that from 1990 to 2015, the baseflow, local recharge, and quick flow decreased by 67.03%, 80.21%, and 37.03%, respectively. The spatial pattern of water yield remained mostly unchanged. According to the contribution analysis, precipitation and NDVI change were the main factors affecting water yield in the Lhasa River Basin, while land cover change began to exert greater influence after 2010. A combination of climate change and human activities therefore drive water yield change, especially through vegetation change. Water resources management strategies should thus take into account the combination of rapidly changing climate and human activities.

Highlights

Among the many ecosystem services that influence human wellbeing, water yield is of great importance as many agricultural, industrial, and domestic activities depend on it [1,2,3]
Important factors, high σ values the presence of non-linearity and/or and local recharge, theirwith sensitivity factorsindicating were nearly the same: P and Curve number (CN) were the interactions two most with other factors; Rain events (RE), Kc, and were less important factors and have less non-linearity important factors, with high σ values indicating the presence of non-linearity and/or interactions and/or fewer interactions; Threshold flow accumulation (TFA)
The results showed that precipitation was the main factor influencing the water yield, followed by Normalized Difference Vegetation Index (NDVI), while land cover change began to exert greater influence after 2010

Summary

Introduction

Among the many ecosystem services that influence human wellbeing, water yield is of great importance as many agricultural, industrial, and domestic activities depend on it [1,2,3]. The spatiotemporal variation in water yield is important, and often leads to the challenge of how to allocate water resources between different seasons, and between upstream and downstream areas [2,5]. For arid and semi-arid regions, especially where the climate is highly seasonal, the baseflow that is slowly released by upstream areas due to the interception of vegetation or soil during the rainy season is highly valuable for downstream residents [6,7]. Understanding the spatiotemporal variation in water yield including the baseflow, local recharge, and quick flow as well as their driving factors is critical for developing appropriate water resources management strategies [3,8,9,10]

Objectives

Results

Discussion

Conclusion