Identification of key influence factors and an empirical formula for spring snowmelt-runoff: A case study in mid-temperate zone of northeast China

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.

Elucidating Flavonoid and Antioxidant Activity in Edible and Medicinal Herbs Woodwardia japonica (L.f.) Sm. Based on HPLC-ESI-TOF-MS and Artificial Neural Network Model: Response to Climatic Factors

“…On the basis of an improved BP algorithm, network training combined the optimized initial weight of the GAs with the limited supervised adjustment of learning rates [ 44 ]. In order to improve the BP net learning algorithm avoiding local defects, BP net was optimized by the genetic algorithm.…”

Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting

“…(3) Compared with previous studies (Yang, 2015;Tian et al, 2018), this study significantly has reduced the requirements of multiple input variables for predicting snowmelt runoff, by selecting the critical influencing factors. Based on this, a mid-long term forecasting model with a lead time of one month is implemented.…”

Snow as an Important Natural Reservoir for Runoff and Soil Moisture in Northeast China

“…Feng and Chen (2016) investigated snow intensity changes in Northeast China based on meteorological station data. Tian et al (2018) studied snow changes in the upper Second Songhua River basin in Northeast China, which has an area of only about 1.5% of the total area of Northeast China. Despite the progresses, the previous studies did not analyze snow influence on runoff/soil moisture specifically or investigated regions that only cover a small fraction of Northeast China.…”