基于GWLF模型的于桥水库入库河流水文模拟及氮磷负荷估算

流域模型技术应用是当前开展面源污染防治的重要工具,而水文过程的准确模拟是进行污染负荷估算的首要环节和关键步骤。为了弄清近年来于桥水库入库河流氮、磷输入负荷,选取GWLF模型对水平口子流域的水文过程进行模拟,首先利用2006—2018年气象、水文资料率定模型水文参数,然后将参数推广到整个流域,对2019—2020年3条主要入库河流流量进行模拟,最后乘以相应河流断面的总氮、总磷浓度估算氮磷输入负荷。结果显示：GWLF模型适用于研究区的水文过程模拟,校准期和验证期的纳氏系数分别为0.89和0.91,平均相对误差分别为12.2%和13.1%;2020年总氮入库负荷为3 977.0 t,其中引滦调水贡献占57.0%,3条入库河流共贡献43.0%;总磷入库负荷为48.8 t,其中引滦调水贡献占68.6%,3条入库河流共贡献31.4%。GWLF模型输入数据需求量较少,模型参数较少,模拟效果较好,适用于中小型流域的水资源和水环境管理,具有一定的推广应用前景。     

THE EFFECTS OF CLIMATE CHANGE ON STREAM FLOW AND NUTRIENT LOADING'

ABSTRACT: This study assesses the potential impact of climate change on stream flow and nutrient loading in six watersheds of the Susquehanna River Basin using the Generalized Watershed Loading Function (GWLF). The model was used to simulate changes in stream flow and nutrient loads under a transient climate change scenario for each watershed. Under an assumption of no change in land cover and land management, the model was used to predict monthly changes in stream flow and nutrient loads for future climate conditions. Mean annual stream flow and nutrient loads increased for most watersheds, but decreased in one watershed that was intensively cultivated. Nutrient loading slightly decreased in April and late summer for several watersheds as a result of early snowmelt and increasing evapotranspiration. Spatial and temporal variability of stream flow and nutrient loads under the transient climate scenario indicates that different approaches for future water resource management may be useful.     

MODELING THE HYDROCHEMISTRY OF THE CANNONSVILLE WATERSHED WITH GENERALIZED WATERSHED LOADING FUNCTIONS (GWLF)1

ABSTRACT: A previous modeling study used the Generalized Watershed Loading Functions (GWLF) model to simulate stream‐flow, and nutrient and sediment loads to Cannonsville Reservoir from the West Branch Delaware River (WBDR). We made several model revisions, calibrated key parameters, and tested the original GWLF model and a revised GWLF model using more recent data. Model revisions included: addition of unsaturated leakage between unsaturated and saturated subsurface reservoirs; revised timing of sediment export; inclusion of urban sediments and dissolved nutrients; tracking of particulate nutrients from point sources; and revised timing of septic system loads. The revision of sediment yield timing resulted in significant improvements in monthly sediment and particulate phosphorus predictions as compared to the original model. Addition of unsaturated leakage improved hydrologic predictions during low flow months. The other model changes improve realism without adding significant model complexity or data requirements. Goodness of fit of revised model predictions versus stream measurements, as measured by the Nash‐Sutcliff coefficient of model efficiency, exceeded 0.8 for streamflow‐0.7 for sediment yield and dissolved nitrogen (N) and 0.6 for particulate and dissolved phosphorus (P). The revised GWLF model, with limited calibration, provides reasonable estimates of monthly streamflow, and nutrient and sediment loads in the Cannonsville watershed.     

Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model

This study evaluates the ability of the Catchment SIMulation (CSIM) hydrologic model to describe seasonal and regional variations in river discharge over the entire Baltic Sea drainage basin (BSDB) based on 31 years of monthly simulation from 1970 through 2000. To date, the model has been successfully applied to simulate annual fluxes of water from the catchments draining into the Baltic Sea. Here, we consider spatiotemporal bias in the distribution of monthly modeling errors across the BSDB since it could potentially reduce the fidelity of predictions and negatively affect the design and implementation of land‐management strategies. Within the period considered, the CSIM model accurately reproduced the annual flows across the BSDB; however, it tended to underpredict the proportion of discharge during high‐flow periods (i.e., spring months) and overpredict during the summer low flow periods. While the general overpredictions during summer periods are spread across all the subbasins of the BSDB, the underprediction during spring periods is seen largely in the northern regions. By implementing a genetic algorithm calibration procedure and/or seasonal parameterization of subsurface water flows for a subset of the catchments modeled, we demonstrate that it is possible to improve the model performance albeit at the cost of increased parameterization and potential loss of parsimony.     

GWLF模型的原理、结构及应用

GWLF(Generalized Watershed Loading Function)模型是一个半分布式、半经验式的流域负荷模型。它能够利用GIS及RS提供的空间数据,在中型尺度流域的范围内进行非点源污染负荷估算,模型比较适合于数据量少,参数相对缺乏的地区。重点介绍了GWLF模型的污染物负荷估算的原理,同时将GWLF模型应用于天津市于桥水库流域,利用沙河流域1999年水量、水质数据进行校准,初步估算出于桥水库上游流域的非点源负荷。