Identification of key influence factors and an empirical formula for spring snowmelt-runoff: A case study in mid-temperate zone of northeast China. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Tian, Lin; Li, Hongyan; Li, Fengping; Li, Xiubin; Du, Xinqiang; Ye, Xueyan
• Is Part Of: Scientific reports. Volume 8:Issue 1(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-018-35282-x
• Languages: English
• ISSNs: 2045-2322

Abstract

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.