Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium)

Although rainfall input uncertainties are widely identified as being a key factor in hydrological models, the rainfall uncertainty is typically not included in the parameter identification and model output uncertainty analysis of complex distributed models such as SWAT and in maritime climate zones. This paper presents a methodology to assess the uncertainty of semi-distributed hydrological models by including, in addition to a list of model parameters, additional unknown factors in the calibration algorithm to account for the rainfall uncertainty (using multiplication factors for each separately identified rainfall event) and for the heteroscedastic nature of the errors of the stream flow. We used the Differential Evolution Adaptive Metropolis algorithm (DREAM_(zs)) to infer the parameter posterior distributions and the output uncertainties of a SWAT model of the River Senne (Belgium). Explicitly considering heteroscedasticity and rainfall uncertainty leads to more realistic parameter values, better representation of water balance components and prediction uncertainty intervals.     

山前平原地下水侧向补给潜力空间变异模拟

SWAT模型利用分辨率较高的DEM数据,可以估算山前地下水侧向补给的空间变异,并可以计算不同降水年型下补给量的差别。模拟需要对研究区进行填洼、流向确定、汇流和河网水系的提取四步运算,并对河道临界支撑面积的取值进行分析。分别模拟了平水年和丰水年两种降雨年型下研究区13个出水口的地下水侧向补给潜力。在研究区,由于地形的变化,导致山与平原交接断面上出水口的集水面积变异非常大,因此山前平原所接受的地下水侧向补给量的空间变异也非常大。不同的降雨年型下,降雨量转化为地下水侧向补给量的比例也不同。平水年的转化比例较小,丰水年的转化比例较大。应用SWAT模型时,与ArcView的拓展模块有机结合,可以更方便提取完整的大流域,划分出合理的子流域。     

拉萨河流域径流对土地利用和气候变化的响应分析

拉萨河流域处青藏高原中南部,因其独特的地理位置是对气候变化较为敏感的区域之一,同时也是青藏高原人口和耕地较为密集区域。在建立SWAT模型对拉萨河流域水循环过程进行模拟的基础上,通过设置不同气候情景与土地利用状况,分析近30 a来拉萨河流域径流变化的成因,并研究径流对气候因子变化的敏感性。结果表明:①气候变化与土地利用对径流影响占比分别约为82.95%和17.05%,主要原因在于近30 a拉萨河流域土地利用情况变化不大,而气温、降水则呈显著增加趋势;②降水每增加10%,流域径流约增加11.8%,且径流对降水变化敏感性的空间差异性较小;③气温每增加1 ℃,流域总径流约增加2.5%,但径流随气温变化的空间差异性较大,其中,中上游地区径流减小0.7%,下游地区径流约增加3.6%。     

变化环境下韩江生态流量演变特征分析

选取位于粤东、闽西南地区的韩江流域为研究对象,基于CA-Markov模型对2050年流域土地利用空间格局进行预测,构建SWAT分布式水文模型,以未来土地利用情景和气候变化情景为变量进行水文模拟,分析不同情景下韩江生态流量的时空演变特征。结果表明,未来城镇化扩张将使梅江支流中上游成为韩江流域内生态流量对土地利用变化最为敏感的区域;土地利用和气候变化将导致韩江流域枯水期流量整体减小,枯水期流量对环境变化更加敏感;韩江流域生态流量变化特征将呈现从西南到东北由升到降的趋势,梅江支流中上游地区的生态流量将得到改善;梅江和汀江两大支流上游区域生态流量对气候变化更为敏感;韩江流域径流总量下降,但丰枯流量分化加剧,长期来看枯季生态流量保障风险可能进一步增大。     

Climate change impact on water quality in the integrated Mahabad Dam watershed-reservoir system

Climate change, besides global warming, is expected to intensify the hydrological cycle, which can impact watershed nutrient yields and affect water quality in the receiving water bodies. The Mahabad Dam Reservoir in northwest Iran is a eutrophic reservoir due to excessive watershed nutrient input, which could be exacerbated due to climate change. In this regard, a holistic approach was employed by linking a climate model (CanESM2), watershed-scale model (SWAT), and reservoir water quality model (CE-QUAL-W2). The triple model investigates the cumulative climate change effects on hydrological parameters, watershed yields, and the reservoir’s water quality. The SDSM model downscaled the output of the climate model under moderate (RCP4.5) and extreme (RCP8.5) scenarios for the periods of 2021–2040 and 2041–2060. The impact of future climate conditions was investigated on the watershed runoff and total phosphorus (TP) load, and consequently, water quality status in the dam’s reservoir. The results of comparing future conditions (2021–2060) with observed present values under moderate to extreme climate scenarios showed a 4–7% temperature increase and a 6–11% precipitation decrease. Moreover, the SWAT model showed a 9–16% decline in streamflow and a 12–18% decline in the watershed TP load for the same comparative period. Finally, CE-QUAL-W2 model results showed a 3–8% increase in the reservoir water temperature and a 10–16% increase in TP concentration. It indicates that climate change would intensify the thermal stratification and eutrophication level in the reservoir, especially during the year’s warm months. This finding specifies an alarming condition that demands serious preventive and corrective measures.     

A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services

Selection of strategies that help reduce riverine inputs requires numerical models that accurately quantify hydrologic processes. While numerous models exist, information on how to evaluate and select the most robust models is limited. Toward this end, we developed a comprehensive approach that helps evaluate watershed models in their ability to simulate flow regimes critical to downstream ecosystem services. We demonstrated the method using the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program–FORTRAN (HSPF) model, and Distributed Large Basin Runoff Model (DLBRM) applied to the Maumee River Basin (USA). The approach helped in identifying that each model simulated flows within acceptable ranges. However, each was limited in its ability to simulate flows triggered by extreme weather events, owing to algorithms not being optimized for such events and mismatched physiographic watershed conditions. Ultimately, we found HSPF to best predict river flow, whereas SWAT offered the most flexibility for evaluating agricultural management practices.     

Coupling watershed modeling,public engagement,and soil analysis improves decision making for targeting P retention wetland locations

Constructed and restored wetlands can be effective sinks for particulate and dissolved phosphorus (P) if properly managed, but identifying suitable P retention wetland locations remains challenging. From a landscape perspective, Soil and Water Assessment Tool (SWAT) models identify locations within target watersheds with high nutrient loads that exhibit appropriate site characteristics and hydrodynamics. However, soil properties vary at the field scale, dictating the capacity of wetland systems to remove P and ultimately determining if a given wetland will operate as a sink or source of P over time. Land ownership and site access further complicate identification of P retention wetland locations. As a result, optimization and identification of P retention wetland locations requires analysis at both 1) watershed and 2) field scales, and 3) public engagement. In response, a survey effort linked SWAT model results that identified locations with target watersheds with field soil P storage capacity data and interested landowners. Results suggest that several locations recommended for their high SWAT-predicted P loading and landowner interest were in fact not well suited for project implementation due to soil P saturation and legacy P constraints. These findings highlight the need to couple watershed models with field scale soils analysis to identify locations for P retention wetlands in order to avoid unintended P release. Additionally, increased collaboration with social scientists and others familiar with public engagement strategies is needed to improve outreach activities targeting regional water quality improvements. Practical applications for nutrient retention wetland site selection are also discussed.     

快速城市化条件下基于SLEUTH-SWAT模型的济南市水文响应分析

为更好地反映快速城市化未来长期的区域水文响应,以济南市为例,利用2000年、2005年、2010年、2015年土地利用、道路演变等数据,驱动SLEUTH模型对快速城市化进行了模拟和合理预测,生成了2020年、2030年土地利用数据。以此作为输入数据,运用SWAT模型定量分析了快速城市化和气候变化对径流、蒸发等水文要素的影响。结果表明:未来济南市仍有大量耕地转化为城市用地,2030年城市面积将达到640 km~2。不考虑气候变化,总径流和地表径流将分别增加4%和12.5%,蒸发和下渗分别减少0.78%和2.51%,核心城市化子流域地表径流将增加40%以上。依据年降雨量选取了平水、极丰和极枯三种降水情景,极枯和极丰情景下2030年济南市地表径流深分别为15.02 mm和101.44 mm,极丰情景下重点城区径流深可达300 mm以上。建议未来济南市核心城区加强防洪管理,重点防范城市内涝及洪水灾害。     

CMADS驱动SWAT模型在水循环模拟中的应用——以洱海流域为例

基于大气数据驱动水文模型的输出结果开展水循环模拟研究是大气和水文学界的研究热点。利用中国大气同化驱动数据集CMADS驱动SWAT模型,模拟2009～2016年期间洱海流域关键水循环要素的时空分布特征。结果表明:①CMADS数据集可很好地驱动SWAT模型,在洱海流域适用性较好,可用于水循环模拟研究。②从时间上看,洱海流域年降水量、实际蒸散和年产水量均呈现出先减少后增加的趋势,分别以4.6,29.3,15.0 mm/a的速率递增,多年平均降水量、多年平均实际蒸散发和多年平均产水量分别为792.8,565.5,286.1 mm。从空间上看,洱海西部降水最丰沛,东部地区次之,北部地区最低;实际蒸散发空间差异性相对较小,高值区主要分布于洱海湖区周围;产水量空间分布差异性较大,洱海西部产水量最大,其次为洱海北部和东部山区。③湿润度呈现出增加的趋势,而产水系数表现出减少的趋势,多年平均湿润度和产水系数分别为0.61和0.43;实际蒸散发与降水变化趋势一致,但与潜在蒸散发变化趋势相反,表明水分条件是限制洱海流域潜热的主要因子。     

An automatic parameter calibration method for the SWAT model in runoff simulation

Runoff simulation is highly significant for hydrological monitoring, flood peak simulation, water resource management, and basin protection. Runoff simulation by distributed hydrological models, such as the soil and water assessment tool (SWAT) model which is the most widely used, is becoming a hotspot for hydrological forecasting research. However, parameter calibration is inefficient and inaccurate for the SWAT model. An automatic parameter calibration (APC) method of the SWAT model was developed by hybrid of the genetic algorithm (GA) and particle swarm optimization (PSO). Multi‐station and multi‐period runoff simulation and accuracy analysis were conducted in the basin of the Zhangjiang River on the basis of this hybrid algorithm. For example, in the Yaoxiaba Station, the calibration results produced an R² of 0.87 and Nash Sutcliffe efficiency (NSE) index of 0.85, while verification results revealed an R² of 0.83 and NSE of 0.83. Results of this study show that the proposed method can effectively improve the efficiency and simulation accuracy of the model parameters. It can be concluded that the feasibility and applicability of GA‐PSO as an APC method for the SWAT model were confirmed via case studies. The proposed method can provide theoretical guidance for many hydrological research fields, such as hydrological simulation, flood prevention, and forecasting.