基于SWAT模型的径流还原方法研究—以大汶河流域为例

在现有下垫面条件下还原天然径流是水资源配置的重要基础性工作。利用SWAT分布式水文模型进行径流还原计算。以大汶河流域为例,选取流域内受人类活动影响较小、能大致反映流域天然径流情况的雪野水库、黄前水库以及东周水库所控制的3个子流域,采用SUFI-2方法进行模型参数率定、验证和不确定性分析;根据就近性与相似性原则,进行全流域参数展布,并通过Arc SWAT2012分析计算大汶河流域内泰安市各分区地表水资源量。结果表明:3个典型子流域的P-factor均大于0.64,Rfactor均小于0.72,率定期和验证期的相关性系数和纳什效率系数均高于0.77,径流模拟值和实测值拟合程度高。通过SWAT模型还原天然径流是可行的。     

不同分布式水文模型在大凌河中游对比运用研究

不同水文模型在不同流域都具有一定的适用性,为对比分布式水文模型SWAT模型和VIC模型在大凌河中游的适用性及模拟精度,以大凌河中游大城子水文站以上流域为研究区域,基于大城子水文站2000-2010年水文数据,对比分析两种分布式水文模型在大凌河中游的适用性及模拟精度。研究成果表明:SWAT模型更适合于大凌河中游的水量模拟,SWAT模型模拟相对误差均值为5.21%,低于VIC模型模拟的相对误差(相对误差均值为8.01%),且SWAT模型模拟的确定性系数均值为0.74,优于VIC模型模拟的确定系数(确定系数均值为0.68)。研究成果对于大凌河中游水文模拟及流域水文预报预警提供参考价值。     

基于格尔木河流域的SWAT模型水文特征情景模拟研究

以2008—2016年格尔木河流域实测径流量数据为基础,构建SWAT分布式水文模型,采用SUFI-2算法进行参数率定、验证及不确定性分析,并设置不同的气候情景(RCP2.6、RCP4.5和RCP6.0),预测流域2022—2050年的径流变化趋势,分析了研究区未来降水、气温的变化趋势并探究了这些气候要素对格尔木河流域径流的影响。结果表明：(1)SWAT模型在格尔木河流域径流过程的模拟中具有较好的适用性,率定期R²和E_NS分别为0.84和0.73,验证期R²和E_NS分别为0.74和0.70;(2)径流预测不确定性较小;(3)未来流域降水呈现增加趋势而气温降低;(4)未来时段流域径流增加显著,且降水是控制流域径流的主要因素。     

东北半干旱地区流域分布式水文模拟——以洮儿河流域为例

以东北半干旱地区典型流域-洮儿河流域为研究对象,应用SWAT模型对流域水文过程进行了模拟研究;选择流域上游子流域和中下游子流域分别进行参数敏感性分析,识别出影响模拟结果的敏感参数,研究发现部分参数敏感性存在空间变异性,分析主要原因在于气候和下垫面的空间异质性导致了流域上下游产流模式存在差异。采用1988-1997年水文气象数据进行模型率定和验证,结果表明：干流水文站月流量过程率定期Nash-Sutcliffe 效率系数平均值为0.78,验证期为0.72,相关系数都达到0.86以上,水量误差大多在20%以内,对日过程的模拟也有较高的精度;枯水年模拟结果较差,主要是因为流域降水站数量不够,难以反映降水的时空分布。对于水文、气象等资料相对缺乏的东北半干旱地区,SWAT模型的模拟结果总体令人满意,可以应用于与流域径流相关的各种模拟分析,研究成果对进一步加强洮儿河流域水资源综合管理提供了依据和手段。     

WEP模型全局参数敏感性分析及其在汉江上游流域的应用

水文模型敏感性分析是用来确定控制模型效率关键参数非常有效的过滤工具之一,并可以帮助理解模型结构,乃至发现模型的结构缺陷,从而有可能改善模型结构.本文通过将全局敏感性分析方法的LH-OAT方法应用到WEP-L模型.并在汉江上游向家坪以上流域进行了成功应用.研究发现.通过LH-OAT方法分析,对于汉江上游向家坪以上流域,河道曼宁糙率和坡面曼宁糙率是影响NaSh效率系数最重要的参数.通过对这两个参数的手动调参,模拟的日Nash效率系数从0.35提高到0.75.     

SWAT模型在海拉尔河流域径流模拟中的应用研究

以海拉尔河上游流域作为研究区域,基于Arc GIS构建SWAT分布式水文模型对流域水文过程进行模拟,通过对流域的基础数据整合,模型采用1999~2003年实测径流数据进行参数率定,将2004~2010年实测径流数据作为模型的验证期,对模型在海拉尔河上游的适用性进行研究。通过对月和年径流模拟值和实测值的比较,率定期和验证期的Nash系数Ens和相关系数R2分别在0.861~0.873和0.877~0.899之间。基于这两个评价标准可知:SWAT模型在海拉尔河上游流域有良好的适用性,可以为该流域的水资源管理提供依据。     

分布式水文概念模型率定方案比较研究

本文在基于新安江模型的分布式水文概念模型的基础上,以伊河东湾流域为研究对象,分别采用集总式、半集总式、半分布式三种率定方案进行模型率定和径流模拟,并与传统的集总式水文模型进行成果比较。研究结果表明:总的来说,分布式模型的模拟结果略好于集总式模型;由于考虑了不同子流域参数变异性,半分布式方案要优于其他方案;但随着模型结构的复杂性增加,参数率定的难度增大,模拟结果的不确定性增多,分布式水文概念模型的模拟效率还有待于进一步的提高。     

基于SWAT模型的三江平原沼泽性河流的径流模拟

三江平原位于中国东北部,是国家重要的商品粮基地,水资源的变化影响区域农业经济的发展。应用SWAT(Soil and Water Assessment Tool)模型模拟挠力河上游流域径流变化特征,为研究沼泽性河流水文变化特征提供有效的方法。根据水文相似性原理和参数移植法把校准的模型应用于七星河流域,进行无资料流域的径流模拟,年径流模拟校准期和验证期的Nash-Sutcliffe效率系数、相关系数R2、相对误差PBIAS值分别为0.84、0.94、-5.70和0.91、0.93、-6.46,表明SWAT模型可以应用到七星河流域。     

基于SWAT模型的绍兴城市径流时空演变规律分析

为克服较大尺度城市径流的定量难题,引入了分布式流域水文模型(SWAT),对绍兴城市径流开展了定量模拟及时空演变规律分析。经DEM建模分析,将绍兴城市置于合适尺度的流域内建立SWAT模型;在敏感性分析的基础上,利用径流监测数据对模型进行了校准与验证,模型评价结果表明,所有站点校准期与验证期的平均相对误差Re(-5.29%~8.81%)、判定系数R2(0.91~0.96)和效率系数Ens(0.90~0.94)均满足年水平和月水平径流定量要求。在此基础上,以1992~2011年的降雨为驱动模拟得到了子流域的月径流数据;最后利用地统计学方法(GSM)对绍兴城市径流的时空演变规律进行了分析,并得到了关键源区,为绍兴城市水资源保护与利用提供科学依据。     

分布式水文模型DHSVM在西北高寒山区流域的适用性研究

分布式水文-土壤-植被模型（Distributed Hydrology Soil Vegetation Model,DHSVM）是基于栅格离散的分布式水文模型,对地表水热循环的各个过程能进行很精细地刻画,被广泛应用于世界各地很多类型的流域的高时空分辨率的水文模拟,然而它在高寒山区的适用性并不清楚。基于300 m数字高程模型,应用DHSVM模型对典型的高寒山区流域八宝河流域2001-2009年的水文过程展开模拟,并采用流域出口祁连站的水文实测数据对模型进行了精度评价。参数敏感性分析表明,土壤横向导水率、田间持水量和植被反照率等是该区域主要的敏感性参数。模型默认参数会高估高寒山区流域的潜在蒸散发量,导致夏季径流量远小于观测值。通过参数率定,模型校准期（2001-2004）的模拟日径流和月径流Nash效率系数分别达到0.72和0.87;而模型验证期（2005-2009）分别为0.60和0.74。结果表明,DHSVM模型基本具备了模拟高寒山区流域降水-径流过程的能力。然而,由于DHSVM模型缺少对高寒山区流域土壤的冻融过程的刻画,春季径流的模拟精度明显受到影响,需要在将来重点改进。     

Spatiotemporal characteristics of earthquake disaster losses in China from 1993 to 2016

The present study analyzes the runoff response during extreme rain events over the basin of Subarnarekha River in India using soil and water assessment tool (SWAT). The SWAT model is configured for the Subarnarekha River basin with 32 sub-basins. Three gauging stations in the basin (viz., Adityapur, Jamshedpur and Ghatshila) were selected to assess the model performance. Daily stream flow data are taken from Central Water Commission, India—Water Resources Information System. Calibration and validation of the model were performed using the soil and water assessment tool-calibration uncertainty programs (SWAT-CUPs) with sequential uncertainty fitting (SUFI-2) algorithm. The model was run for the period from 1982 to 2011 with a calibration period from 1982 to 1997 and a validation period from 1998 to 2011. The sensitivity of basin parameters has been analyzed in order to improve the runoff simulation efficiency of the model. The study concluded that the model performed well in Ghatshila gauging station with a Nash–Sutcliffe efficiency (NSE) of 0.68 during calibration and 0.62 during validation at daily scale. The model, thus calibrated and validated, was then applied to evaluate the extreme monsoon rain events in recent years. Five extreme events were identified in Jamshedpur and Ghatshila sub-basins of Subarnarekha River basin. The simulation results were found to be good for the extreme events with the NSE of 0.89 at Jamshedpur and 0.96 at Ghatshila gauging stations. The findings of this study can be useful in runoff simulation and flood forecasting for extreme rainfall events in Subarnarekha River basin.     

SWAT manual calibration and parameters sensitivity analysis in a semi-arid watershed in North-western Morocco

Being a laborious approach, manual calibration of hydrologic model in a semi-arid context requires in-depth knowledge of the watershed and as much as possible field input data to obtain reliable simulations. In this study, manual calibration and relative sensitivity analysis approaches of the SWAT model (Soil and Water Assessment Tool) were applied for water balance in a 1993 km² watershed (on the R’dom river) located in North-western Morocco. The watershed is located in a semi-arid area dominated by agro-forestry activities. The objectives of this study were (i) to perform a local sensitivity analysis of the SWAT model taking into consideration the watershed characteristics and (ii) to implement a detailed methodology of manual calibration and validation of the model in a semi-arid context. Sensitivity analysis has been carried out on 12 different SWAT input parameters, and has revealed that 4 input parameters only were the most influential ones on flow components of the R’dom watershed. Model manual calibration was conducted along 2006 and 2007 by comparing measured and predicted monthly and daily discharges and taking Nash-Sutcliffe coefficient (NSE), determination coefficient (R ²), and percent bias (PBIAS) as goodness-of-fit indicators. Validation has been performed by the same approach through 2008 and 2009 period. All final NSE values were above 0.5, R ² values exceeded 0.7, and PBIAS lower than 25% demonstrating satisfactory model performances over the study watershed conditions. The SWAT model set-up with measured input data, manually calibrated and validated, reflects well the real hydrologic processes occurring in the R’dom watershed and can be used to assess current and future conditions and to evaluate alternative management practices.     

Parameter estimation of SWAT and quantification of consequent confidence bands of model simulations

Soil and Water Assessment Tool (SWAT) is a river basin scale model widely used to study the impact of land management practices in large, complex watersheds. Even though model output uncertainties are generally recognized to affect watershed management decisions, those uncertainties are largely ignored in model applications. The uncertainties of SWAT simulations are quantified using various methods, but simultaneous attempt to calibrate a model so as to reduce the uncertainty are seldom done. This study aims to use an uncertainty reduction procedure that helps calibrate the SWAT model. The shuffled complex evolutionary metropolis algorithm for uncertainty analysis is employed for this purpose, and is demonstrated using the data from the St. Joseph River basin, USA. The values of the performance indices, the r² and the Nash–Sutcliffe efficiency (NSE) for the simulations during calibration period was found to be 0.81 (same for r² and NSE) and 0.79 for validation period indicating a good simulation by the model. The results also indicate that the algorithm helps reduce the uncertainty (percentage of coverage?=?62% and average width?=?19.2 m³/s), and also identifies the plausible range of parameters that simulate the processes with less uncertainty. The confidence bands of simulations are obtained that can be employed in making uncertainty-based decisions on watershed management practices.     

Use of SWAT to determine the effects of climate and land use changes on streamflow and sediment concentration in the Purna River basin,India

A semi-distributed, physically based, basin-scale Soil and Water Assessment Tool (SWAT) model was developed to determine the key factors that influence streamflow and sediment concentration in Purna river basin in India and to determine the potential impacts of future climate and land use changes on these factors. A SWAT domain with a Geographical Information System (GIS) was utilized for simulating and determining monthly streamflow and sediment concentration for the period 1980–2005 with a calibration period of 1980–1994 and validation period of 1995 to 2005. Additionally, a sequential uncertainty fitting (SUFI-2) method within SWAT-CUP was used for calibration and validation purpose. The overall performance of the SWAT model was assessed using the coefficient of determination (R²) and Nash–Sutcliffe efficiency parameter (E_NS) for both calibration and validation. For the calibration period, the R² and E_NS values were determined to be 0.91 and 0.91, respectively. For the validation period, the R² and E_NS were determined to be 0.83 and 0.82, respectively. The model performed equally well with observed sediment data in the basin, with the R² and E_NS determined to be 0.80 and 0.75 for the calibration period and 0.75 and 0.65 for the validation, respectively. The projected precipitation and temperature show an increasing trend compared to the baseline condition. The study indicates that SWAT is capable of simulating long-term hydrological processes in the Purna river basin.     

岩溶地下河在SWAT中的概化方法——以毕节倒天河流域为例

岩溶地下河的存在严重影响了土壤和水评估工具（SWAT）在岩溶地区的普遍适用性。针对岩溶地下河在SWAT中的概化问题,提出基于数字高程模型（DEM）预处理结合SWAT流域自动识别功能的方法,将岩溶地下河暴露于地表,把岩溶区复杂的地表－地下二元结构简化为地表一元结构,并以贵州毕节地下河发育的倒天河流域为例进行应用。结果表明:① 对比未经概化地下河建立的模型,概化地下河建立的模型识别流域面积增大30.73%,子流域个数增加29.27%,水文响应单元（HRU）个数增加43.82%;② 参数取最大物理意义范围时,未经概化地下河建立的模型p因子=0.64,不满足建模条件,物理模型本身存在问题;③ 概化地下河建立的模型月步长模拟结果:校准期R2=0.96,NS=0.96,验证期R2=0.94,NS=0.93,月尺度模拟效果非常好。岩溶地下河概化方法使SWAT在流域划分方面更加合理,搭建的模型模拟更加合理。该研究拓展了SWAT模型在岩溶区的应用。      

考虑径流成分的雅鲁藏布江月径流模拟

通过构建雅鲁藏布江流域分布式水文模型SWAT,并以1989~1998年为率定期,1999~2005年为验证期对3个水文站(奴各沙、羊村、奴下)的月径流进行了模拟。从模型参数组里选取径流成分模拟不同的2组参数,以Nash-Sutcliffe效率系数(NS)、相关系数(R~2)及相对误差(PBIAS)为评价指标,来分析模型的模拟结果好坏。结果表明:率定期内,在径流成分1下奴各沙、羊村站和奴下站的R~2值高达0.9以上,NS系数分别为0.9、0.92和0.72,PBIAS的值为7.1%、18.5%及34.2%,但径流成分与实际情况出入较大。而径流成分2下,模拟得到的评价指标虽然不是很高,但径流成分比较合理。验证期总体结论一致。因此,建议在模型率定验证中要充分考虑径流成分的作用,从而提高模型模拟的精度。     

耦合SWAT与RIEMS模拟黑河干流山区径流

以黑河干流山区为研究区,采用1：100 000植被类型图、1：1 000 000土壤类型图和气象水文观测数据,耦合SWAT水文模型与RIEMS高分辨率区域气候模式,模拟1995-2010年月径流变化过程,探讨水文模型气象驱动数据的优化方法和气候水文模型耦合的区域适宜性。RIEMS气候模式输出精度较高,降水、温度、湿度、风速的相关系数均在0.80以上,均通过了0.01显著性水平检验,时空分辨率达到6 h和3 km。构建虚拟气象站点,弥补气象观测站点稀少且分布不均匀的不足,对水文模型气象驱动数据进行优化;遵循多时间尺度、多变量和多站点的原则来校准模型。结果表明,径流模拟值与观测值的过程趋势拟合程度较好,NSE均在0.60以上,PBIAS介于±20%之间,R²达到0.70以上。径流模拟在枯水期表现较好,在丰水期存在一定的误差,主要是受降水驱动数据偏高的影响,气候模式模拟能力需要提高,水文模型空间插值方法和气候水文模型耦合方案需进一步完善。总体来看,耦合SWAT模型与RIEMS模式能够较好地模拟黑河干流山区水文过程,可为流域水资源的预测和管理提供科学依据。     

Hierarchical approach to hydrological model calibration

Hydrological models have been widely used for water resources management. Successful application of hydrological models depends on careful calibration and uncertainty analysis. Spatial unit of water balance calculations may differ widely in different models from grids to hydrological response units (HRU). The Soil and Water Assessment Tool (SWAT) software uses HRU as the spatial unit. SWAT simulates hydrological processes at sub-basin level by deriving HRUs by thresholding areas of soil type, land use, and slope combinations. This may ignore some important areas, which may have great impact on hydrological processes in the watershed. In this study, a hierarchical HRU approach was developed in order to increase model performance and reduce computational complexity simultaneously. For hierarchical optimization, HRUs are first divided into two-HRU types and are optimized with respect to some relevant influence parameters. Then, each HRU is further divided into two. Each child HRU inherits the optimum parameter values of the parent HRU as its initial value. This approach decreases the total calibration time while obtaining a better result. The performance of the hierarchical methodology is demonstrated on two basins, namely Sarisu-Eylikler and Namazgah Dam Lake Basins in Turkey. In Sarisu-Eylikler, we obtained good results by a combination of curve number (CN2), soil hydraulic conductivity, and slope for generating HRUs, while in Namazgah use of only CN2 gave better results.