Abstract
Because of the unique climate characteristics, the runoff law in mid-temperate zone is very different from other regions in spring. Accurate runoff simulation and forecasting is of great importance to spring flood control and efficient use of water resources. Baishan reservoir is located in the upper Second Songhua River Basin in Northeast China, where snowmelt is an important source of runoff that contributes to the water supply. This study utilized long-term hydrometeorological data, in the contributing area of Bashan reservoir, to investigate factors and time-lag effects on spring snowmelt and to establish a snowmelt-runoff model. Daily precipitation, temperature, and wind data were collected from three meteorological stations in this region from 1987–2016. Daily runoff into the Baishan reservoir was selected for the same period. The snowmelt period was identified from March 23 to May 4 through baseflow segmentation with the Eckhardt recursive digital filtering method combined with statistical analyses. A global sensitivity analysis, based on the back propagation neural network method, was used to identify daily radiation, wind speed, mean temperature, and precipitation as the main factors affecting snowmelt runoff. Daily radiation, precipitation, and mean temperature factors had a two-day lag effect. Based on these factors, an empirical snowmelt runoff model was established by genetic algorithm (GAS) to estimate the snowmelt runoff in this area. The model showed an acceptable performance with coefficient of determination (R2) of 73.6%, relative error (Re) of 25.10%, and Nash-Sutcliffe efficiency coefficient (NSE) of 66.2% in the calibration period of 1987–2010, while reasonable performance with R2 of 62.3%, Re of 27.2%, and NSE of 46.0% was also achieved during the 2011–2016 validation period.
Highlights
Snow is an important form of precipitation in the surface water cycle because snow accumulation and ablation processes occur over much global land area near the latitude of 40°N, especially in the inland areas that account for roughly one-sixth of the world’s population and about one quarter of the global Gross Domestic Product[1]
The study area is located in the seasonal frozen soil region, so the cumulative effect of accumulated snow has a great influence on snowmelt runoff
Our study area is subject to seasonal frozen soil, where the extreme little snow year was screened through the amount of accumulated snow from November 1 in the previous year to March 31 of the current year based on the meteorological data
Summary
Snow is an important form of precipitation in the surface water cycle because snow accumulation and ablation processes occur over much global land area near the latitude of 40°N, especially in the inland areas that account for roughly one-sixth of the world’s population and about one quarter of the global Gross Domestic Product[1]. There is a large amount of seasonal snow cover in the mid-temperate zone of northeast China, due to its unique climate characteristics. Streamflow simulation and forecasting is of great importance to spring flood control and water resources availability for irrigation, reservoir operation, and hydropower generation in spring, especially in mid-temperate zone of northeast China. The present study takes Baishan basin, the main watershed of the mid-temperate zone in northeast China, as an example aimed to (1) identify the impact factors of spring snowmelt runoff and their time lag effects on runoff, and (2) establish an empirical snowmelt runoff model to estimate the spring snowmelt runoff. The primary goal of this study was to provide an effective tool for snowmelt runoff simulation, which could be used to examine reasonable water resources operations in mid-temperate zone of northeast China. The drainage area contributing runoff to the Baishan reservoir is selected as the study area (Fig. 1)
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