Predictive Contributions of Snowmelt and Rainfall to Streamflow Variations in the Western United States

Xiaohui Zheng,Qiguang Wang,Qing Sun,Qi Li,Lihua Zhou

doi:10.1155/2018/3765098

Abstract

This study used long-term in situ rainfall, snow, and streamflow data to explore the predictive contributions of snowmelt and rainfall to streamflow in six watersheds over the Western United States. Analysis showed that peak snow accumulation, snow-free day, and snowmelt slope all had strong correlation with peak streamflow, particularly in inland basins. Further analysis revealed that the variation of snow accumulation anomaly had strong lead correlation with the variation of streamflow anomaly. Over the entire Western United States, inner mountain areas had lead times of 4–10 pentads. However, in coastal areas, nearly all sites had lead times of less than one pentad. The relative contributions of rainfall and snowmelt to streamflow in different watersheds were calculated based on the snow lead time. The geographic distribution of annual relative contributions revealed that interior areas were dominated by snowmelt contribution, whereas the rainfall contribution dominated coastal areas. In the wet season, the snowmelt contribution increased in the western Pacific Northwest, whereas the rainfall contribution increased in the southeastern Pacific Northwest, southern Upper Colorado, and northern Rio Grande regions. The derived results demonstrated the predictive contributions of snowmelt and rainfall to streamflow. These findings could be considered a reference both for seasonal predictions of streamflow and for prevention of hydrological disasters. Furthermore, they will be helpful in the evaluation and improvement of hydrological and climate models.

Highlights

Introduction e Western UnitedStates (WUS) is a semiarid region that covers more than half the land area of the U.S [1]. is area, which receives little precipitation during summer, relies considerably on the wintertime precipitation phase and snowpack accumulation to sustain a multitude of ecosystem goods and services [2]. us, the regional ecology and economy are both vulnerable to water resource anomalies caused by seasonal hydroclimatic variations [3]
Two principal precipitation mechanisms a ect the Western UnitedStates (WUS). e rst is associated with eastward-moving winter Paci c storms, which bring heavy precipitation to coastal areas and the western highlands, thereby re ecting the considerable orographic e ects generated by upslope motions [42,43,44]. e second precipitation system is associated with subtropical summer monsoon rainfall
Over the entire WUS, the correlations among the metrics were stronger in inland regions compared with coastal areas. us, these strong significant factors could be used as primary predictors for the runoff amounts during the wet season since the peak streamflow represents the state of the high-flow season

Summary

Introduction

Introduction e Western UnitedStates (WUS) is a semiarid region that covers more than half the land area of the U.S [1]. is area, which receives little precipitation during summer, relies considerably on the wintertime precipitation phase and snowpack accumulation to sustain a multitude of ecosystem goods and services [2]. us, the regional ecology and economy are both vulnerable to water resource anomalies caused by seasonal hydroclimatic variations [3]. Recent hydrological disasters in the WUS have been closely related to climate change [4,5,6]. Erefore, it is of great importance to conduct accurate and effective hydrological forecasting for the WUS within the context of climate change. Streamflow is a hydroclimatic variable that directly influences drought- and flood-related disasters. It is affected both by natural factors, such as the precipitation system, soil state, and land surface, and by human factors, including land use changes and water use efficiency. Because of the considerable reliance of the WUS on snow as a water resource, snow accumulation represents a factor of first-order importance regarding regional water supply [10].

Methods

Results

Conclusion