A systematic assessment of watershed-scale nonpoint source pollution during rainfall-runoff events in the Miyun Reservoir watershed

Abstract

The assessment of peak flow rate, total runoff volume, and pollutant loads during rainfall process are very important for the watershed management and the ecological restoration of aquatic environment. Real-time measurements of rainfall-runoff and pollutant loads are always the most reliable approach but are difficult to carry out at all desired location in the watersheds considering the large consumption of material and financial resources. An integrated environmental modeling approach for the estimation of flash streamflow that combines the various hydrological and quality processes during rainstorms within the agricultural watersheds is essential to develop targeted management strategies for the endangered drinking water. This study applied the Hydrological Simulation Program-Fortran (HSPF) to simulate the spatial and temporal variation in hydrological processes and pollutant transport processes during rainstorm events in the Miyun Reservoir watershed, a drinking water resource area in Beijing. The model performance indicators ensured the acceptable applicability of the HSPF model to simulate flow and pollutant loads in the studied watershed and to establish a relationship between land use and the parameter values. The proportion of soil and land use was then identified as the influencing factors of the pollution intensities. The results indicated that the flush concentrations were much higher than those observed during normal flow periods and considerably exceeded the limits of Class III Environmental Quality Standards for Surface Water (GB3838-2002) for the secondary protection zones of the drinking water resource in China. Agricultural land and leached cinnamon soils were identified as the key sources of sediment, nutrients, and fecal coliforms. Precipitation volume was identified as a driving factor that determined the amount of runoff and pollutant loads during rainfall processes. These results are useful to improve the streamflow predictions, provide useful information for the identification of highly polluted areas, and aid the development of integrated watershed management system in the drinking water resource area.

A Novel Combined Prediction Model for Monthly Mean Precipitation With Error Correction Strategy

“…Blóschl et al [5] put forward 23 problems in hydrological research, and how to solve the instability of the model has become the focus of many researchers. Therefore, it is of great significance to establish an effective prediction model to accurately predict rainfall [6], [7]. Now, a lot of the traditional forecasting methods of precipitation time series are based on statistics.…”

Long-Term Rainfall Forecast Model Based on The TabNet and LightGbm Algorithm

“…Therefore, it is of great significance to establish an accurate and good generalized rainfall prediction model 5,6 . At present, there are various methods for predicting the probability of rainfall.Suning Liu 7 developed a recursive approach to long-term prediction of monthly precipitation using genetic programming.Hongya Li 8 used Multicellular Gene Expression Programming algorithm for modeling the historical precipitation data series decomposed by Empirical Mode Decomposition.Bo Xiang 9 used the rainfall data from 2011 to 2018 in Chongqing, China, and established a rainfall prediction model based on LightGbm.Guohui Li 10 combined the variational mode decomposition, the improved butterfly optimization algorithm, the least squares support vector machine model predicted the precipitation of two stations in Shaanxi Province With decades of data accumulation, neural networks stand out among many methods by virtue of their excellent processing capabilities for massive data.Jinle Kang 11 deployed Long Short-Term Memory (LSTM) network models for predicting the precipitation based on meteorological data from 2008 to 2018 in Jingdezhen City.…”

Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

“…Hydrologic models have become a vital tool in dealing with many practical and challenging issues that arise in watershed management. Hydrologists usually apply hydrologic models to two scenarios: continuous modeling and event-based modeling [1][2][3][4][5][6][7]. Generally, event-based modeling considers an individual rainfall-runoff event in isolation, as opposed to continuous modeling, which simulates streamflow over a longer period [5].…”

“…Distributed or semi-distributed hydrologic models has been extensively used for streamflow modeling, such as the Soil and Water Assessment Tool (SWAT), Storm Water Management Model (SWMM), Hydrological Simulation Program-Fortran (HSPF), Hydrologic Prediction for the Environment (HYPE), Mike SHE, and Hydrologic Modeling System (HEC-HMS) [24][25][26][27][28][29]. Recently, the HSPF models shows its promising ability in both continuous modeling [30][31][32][33] and event-based modeling [7,[34][35][36]. However, to our knowledge, a clear understanding of how parameter estimation and predictive uncertainty related to the HSPF model vary between different scenarios and different event types has not been fully achieved.…”