基于SWAT的内蒙古西柳沟孔兑径流模拟研究

使用SWAT模型对内蒙古十大孔兑之一的西柳沟流域进行了径流模拟,并使用SWAT-CUP结合SUFI-2算法进行了参数敏感性分析、参数率定、模型验证.研究结果表明:率定期和验证期线性拟合系数R2,Nash-Sutcliffe效率系数分别均超过了0.6和0.5,说明SWAT模型在西柳沟流域具有一定的适用性.比较模拟径流和实测径流的水文过程线,发现模拟径流在夏汛时期模拟效果较好,春汛及其他降雨较少的月份模拟效果较差,原因可能是西柳沟流域处于干旱半干旱地区,降雨时空分布不均且其流量较小,除汛期外,其它月份流量都小于1m3/s.为更好地模拟该流域的径流,SWAT模型仍有待改进.     

SWAT模型及SUFI-2算法在秃尾河上游流域径流模拟中的应用

通过构建处于干旱和半干旱地区的秃尾河流域(上游)的水文、气象、土地利用土壤等数据库,利用基于分布式的SWAT模型对该流域的水文过程和产流机制进行模拟,并通过改进的SUFI-2算法对模型参数进行不确定分析、率定和验证,探讨SWAT模型在于旱和半干旱流域的适应性.采用1977-1983年的月径流实测数据进行模型率定,用1984-1989年的月径流数据进行模型验证.验证结果表明:该研究流域年径流相关系数(R2)和模型效应系数(Ens)均小于0.6(通常认为当模拟结果期望值大于0.6时,模型能比较准确地描述了研究流域的水文过程);月径流相关系数和模型效应系数超过或者接近0.6的水平;汛期(5-10月)模型径流相关系数和效应系数最高(超过或接近0.8).研究表明通过改进的SWAT-SUFI模型能较好地模拟研究流域的汛期径流,而对全年径流和枯水期的径流模拟不够理想.     

基于支持向量机的石羊河流域径流模拟适用性评价

基于VC维和结构化风险最小理论的支持向量机方法因具有较好的学习和泛化能力而在预测预报领域得到广泛的应用。文中选取当月平均降水量、上月平均降水量以及当月平均相对湿度、平均最高气温和平均最低气温五个预报因子,采用Gridsearch算法优化参数,建立了基于支持向量机的月径流预报模型,并将其应用于石羊河流域八个子流域,定量分析其适用性。结果表明:模型在率定期和验证期模拟的平均Nash-Sutcliffe效率系数分别为0.831和0.806,相对误差分别在6%和5%以内;除个别峰值模拟较小之外,流量序列整体模拟效果较好;模型在丰水时段模拟值小于实测值,枯水时段模拟值大于实测值,在平水时段和枯水时段的模拟效果要优于丰水月份。因此,支持向量机模型在石羊河流域具有较好的适用性,可用于该流域的中长期水文预报。     

基于CMADS气象数据集的延河流域径流量变化模拟及分析

文中对中国大气同化驱动数据集(CMADS)和传统气象站数据的降水空间分布、变化趋势和相关性进行了分析,并基于以上两种数据分别驱动SWAT水文模型,模拟延河流域的多年径流量.同时,利用甘谷驿水文站的实测径流数据进行模型参数的率定和验证,构建适合研究区域特征的分布式水文模型.结果 表明:1) CMADS和传统气象站数据的年...     

基于SUFI-2算法的SWAT模型在陕西黑河流域径流模拟中的应用

参数率定及敏感性分析是用来提高模型精度,确定模型关键参数及改善模型结构的非常有效的方法。本文基于SUFI-2算法,通过SWAT模型对陕西黑河流域进行了模拟,同时基于SUFI-2算法对模型参数进行了敏感性分析,结果表明：（1）影响黑河流域径流模拟结果精度的主影响因子是scs径流曲线参数;（2）用2005年到2011年的实测径流资料对模型进行了率定与验证,模拟确定性系数 R2和模型效率系数ENS均高于0．8;（3）通过SUFI-2算法与SCE-UA算法比较,发现SUFI-2算法所需时间短,精度更高。     

基于SWAT模型的开都河流域径流模拟

在遥感和GIS技术支持下,应用SWAT(Soil and Water Assessment Tool)分布式模型对开都河流域1988-2009年逐月径流进行模拟。结果表明:模拟结果与实测径流较吻合,校准期(1990-2000年)效率系数为0.58,平均相对误差为-5.7%,线性拟合度为0.8,验证期(2000-2009年)的结果与校准期接近,均达到了模型的评价标准,结果具有一定的可信度,SWAT模型适用于开都河流域的径流模拟;季节性融雪是研究区径流形成的重要组成,结合研究区的特性,确定了研究区的各项积雪/融雪参数,提高了冬春季节径流模拟的准确性。     

气候变化下乌伦古河流域农业面源污染负荷响应

以新疆乌伦古河流域为研究区,基于SWAT模型构建适用于该流域的面源污染分布式水文模型,采用情景设置法设定9种气候变化情景。分析不同气候变化情景对流域径流及面源污染负荷的影响,识别重点影响区域,为流域面源污染防控提供科学依据。结果表明：（1）径流、总氮和总磷在率定期和验证期的决定系数R²均在0.75以上,纳什系数E_ns均在0.55以上,建立的SWAT模型能够用于乌伦古河流域农业面源污染负荷模拟;（2）增加20%的降水量,径流量增加48.01%、总氮负荷增加23.19%、总磷负荷增加29.65%,气温变化对径流量的影响小于降水量变化对径流量的影响;（3）各子流域年均单位面积总氮负荷为0.01～164.79 kg·hm^-2·a^-1,单位面积总磷负荷为0.01～45.10 kg·hm^-2·a^-1,气候变化对面源污染影响较大区域主要分布在福海县的阿尔达乡、富蕴县的吐尔洪乡、库尔特乡、恰库尔图镇,及青河县的阿热勒乡、阿热勒托别镇、塔克什肯镇等。说明降水增多是乌伦古河流...     

GIS在分布式水文模型中的应用——以北京雁栖河流域为例

在GIS支持下,由北京雁栖河流域数字高程模型DEM自动生成子流域单元,每个子流域单元的降雨径流过程利用一个水箱模型来模拟,该水箱模型用一个两参数的非线性分布函数来模拟上土壤层的蓄水容量空间分布特征,计算每个子流域单元TOPMODEL模型地形指数ln(a/tanβ)的空间累积分布曲线,并对其作无量纲化处理,通过地形指数ln(a/tanβ)的空间累积分布曲线的无量纲化曲线的最佳曲线拟合确定每个子流域单元土壤蓄水容量空间变异性参数,这使得模型中的土壤蓄水容量空间变异性参数成为一个有确定物理意义的参数,也使得所建立的模型更加具有物理基础。结果表明:无论从模型的率定期还是从模型的验证期来看,模型计算的径流与实测的日径流过程线都呈现出很好的一致性和吻合性,这说明本文建立的模型对雁栖河流域的日径流量的分布式模拟是有效的。     

基于GRID的流域动态水平衡模型研究

水量平衡研究是水资源评价、规划与管理的基础。以往的水平衡模型大多是基于流域、子流域,为了以网格为单元动态模拟水平衡过程,在本文中利用GIS工具建立了基于GRID的泾河流域动态水量平衡模型,该模型空间尺度为1km×1km网格,时间尺度为月。模型的率定是利用泾河流域马莲河把口水文站的1995至1997年逐月实测径流数据,由此获取了模型的主要参数。模型率定结果表明模拟径流曲线与实测径流曲线的变化趋势基本吻合,模拟值和监测值的中值绝对误差为6.72m3/s,相关系数为0.932。最后,将所建模型用于泾河流域逐月逐网格的降水、蒸发、土壤蓄水与地下蓄水,以及形成的径流总量的动态模拟。模拟结果表明泾河流域的径流量主要集中在流域南部与中部,流域北部径流相对较低,这与流域降水分布关系密切。泾河流域的土壤蓄水与地下蓄水量的分布类似,且与径流有一定相关性。该模型模拟精度较高,基本可以满足水资源评价、规划与管理的要求。     

基于小波变换和GRNN神经网络的径流模型在雅砻江流域中的应用

对雅砻江甘孜站控制流域月降水量和气温做 Harr小波变换 ,并作为 GRNN神经网络的输入 ,对雅砻江甘孜站径流进行模拟和预测验证 ,效果较好。但该模型对气温变化不敏感。故应用不引进小波变换的 GRNN神经网络模型 ,并采用全球变化成果 ,在不同的气候情景下 ,对甘孜站径流进行预测。结果表明 ,在未来一段时间内 ,甘孜站径流量会有一定程度的增加。径流量对气温的响应不同于西北内陆河流域 ,原因是该流域月径流量随气温的升高而增加。可利用该模拟对甘孜站径流进行反延或预测 ,并利用甘孜、温波二站径流量关系 ,计算温波站径流量 ,为南水北调工程径流设计服务     

Simulation of hydrological processes of mountainous watersheds in inland river basins: taking the Heihe Mainstream River as an example

The hydrological processes of mountainous watersheds in inland river basins are complicated.It is absolutely significant to quantify mountainous runoff for social,economic and ecological purposes.This paper takes the mountainous watershed of the Heihe Mainstream River as a study area to simulate the hydrological processes of mountainous watersheds in inland river basins by using the soil and water assessment tool(SWAT)model.SWAT simulation results show that both the Nash–Sutcliffe efficiency and the determination coefficient values of the calibration period(January 1995 to December 2002)and validation period(January 2002 to December 2009)are higher than 0.90,and the percent bias is controlled within±5%,indicating that the simulation results are satisfactory.According to the SWAT performance,we discussed the yearly and monthly variation trends of the mountainous runoff and the runoff components.The results show that from 1996 to 2009,an indistinctive rising trend was observed for the yearly mountainous runoff,which is mainly recharged by lateral flow,and followed by shallow groundwater runoff and surface runoff.The monthly variation demonstrates that the mountainous runoff decreases slightly from May to July,contrary to other months.The mountainous runoff is mainly recharged by shallow groundwater runoff in January,February,and from October to December,by surface runoff in March and April,and by lateral flow from May to September.     

河南省不同生态区CERES-Maize模型参数确定及精度验证

本研究将河南夏玉米主产区划分为4个生态区,利用全省18个站近十余年农气观测资料对CERESMaize模型进行参数调试和验证,其中2003—2005年为模型调参年份,2006—2010年为模型验证年份。根据各生态区的多站点调参验证的平均状态,获得4套模型区域品种参数。由各生态区夏玉米品种遗传参数可知,I区全生育期所需积温最多,其次是Ⅲ区和Ⅳ区;单穗潜在最大籽粒数Ⅰ区最高,Ⅲ区次之,Ⅳ区最低;灌浆速率参数Ⅲ区略小,其他地区较相近。各生态区生物量和产量的模拟和验证结果表明,归一化均方根误差NRMSE均小于20%,模型对各生态区生物量和产量模拟能力较强。但各生态区模拟效果有一定差异,其中生物量调试结果中观测值与模拟值均值较接近,验证结果中实测值较模拟值普遍偏大,尤其是Ⅰ区和Ⅱ区。在产量验证中,Ⅰ区和Ⅱ区模拟值略低于观测值,而Ⅲ区和Ⅳ区模拟值略高于观测值。Ⅰ-Ⅲ区生物量和产量的观测值和模拟值相关系数r均通过显著检验,模型对于这些地区生物量及产量增减的变化趋势模拟较好。对生物量和叶面积指数的动态模拟及验证结果表明,地上部总生物量动态增长的拟合效果较好。叶面积指数观测值略滞后于模拟值,但总体趋势吻合度较好。     

Adaptability of machine learning methods and hydrological models to discharge simulations in datasparse glaciated watersheds

The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources. In this study, long shortterm memory(LSTM), a state-of-the-art artificial neural network algorithm, is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia. Two other classic machine learning methods, namely extreme gradient boosting(XGBoost) and support vector regression(SVR), along with a distributed hydrological model(Soil and Water Assessment Tool(SWAT) and an extended SWAT model(SWAT_Glacier) are also employed for comparison. This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data. The two typical basins in this study are the main tributaries(the Kumaric and Toxkan rivers) of the Aksu River in the south Tianshan Mountains, which are dominated by snow and glacier meltwater and precipitation. Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations. The performance metrics Nash-Sutcliffe efficiency coefficient(NS) and correlation coefficient(R~2) of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin, and NS and R~2 are also higher than 0.70 in the Toxkan River Basin. Compared to classic machine learning algorithms, LSTM shows significant advantages over most evaluating indices. XGBoost also has high NS value in the training period, but is prone to overfitting the discharge. Compared with the widely used hydrological models, LSTM has advantages in predicting accuracy, despite having fewer data inputs. Moreover, LSTM only requires meteorological data rather than physical characteristics of underlying data. As an extension of SWAT, the SWAT_Glacier model shows good adaptability in discharge simulation, outperforming the original SWAT model, but at the cost of increasing the complexity of the model. Compared with the oftentimes complex semi-distributed physical hydrological models, the LSTM method not only eliminates the tedious calibration process of hydrological parameters, but also significantly reduces the calculation time and costs. Overall, LSTM shows immense promise in dealing with scarce meteorological data in glaciated catchments.     

Impact of climate change on the streamflow in the glacierized Chu River Basin,Central Asia

Catchments dominated by meltwater runoff are sensitive to climate change as changes in precipitation and temperature inevitably affect the characteristics of glaciermelt/snowmelt, hydrologic circle and water resources. This study simulated the impact of climate change on the runoff generation and streamflow of Chu River Basin(CRB), a glacierized basin in Central Asia using the enhanced Soil and Water Assessment Tool(SWAT). The model was calibrated and validated using the measured monthly streamflow data from three discharge gauge stations in CRB for the period 1961–1985 and was subsequently driven by downscaled future climate projections of five Global Circulation Models(GCMs) in Coupled Model Inter-comparison Project Phase 5(CMIP5) under three radiative forcing scenarios(RCP2.6, RCP4.5 and RCP8.5). In this study, the period 1966–1995 was used as the baseline period, while 2016–2045 and 2066–2095 as the near-future and far-future period, respectively. As projected, the climate would become warmer and drier under all scenarios in the future, and the future climate would be characterized by larger seasonal and annual variations under higher RCP. A general decreasing trend was identified in the average annual runoff in glacier(–26.6% to –1.0%), snow(–21.4% to +1.1%) and streamflow(–27.7% to –6.6%) for most of the future scenario periods. The projected maximum streamflow in each of the two future scenarios occurred one month earlier than that in the baseline period because of the reduced streamflow in summer months. Results of this study are expected to arouse the serious concern about water resource availability in the headwater region of CRB under the continuously warming climate. Changes in simulated hydrologic outputs underscored the significance of lowering the uncertainties in temperature and precipitation projection.     

基于辐射的潜在蒸散发量估算方法在黑河流域的适用性分析

准确估算流域潜在蒸散发量(PET)具有重要意义。文中基于黑河流域16个国家基本气象站1990～2000年的历史气象数据,利用FAO56-PM法和其它6种基于辐射的PET估算方法,对各站点PET进行估算。以PM法的计算结果作为标准,对6种方法进行参数校正,并对其估算精度进行评价。结果表明:采用初始参数时,Hargreaves法在黑河流域的PET估算精度最高。经过参数校正,Makkink法估算PET的精度最高。因此,在计算黑河流域PET时,推荐使用经过参数校正后的Makkink法。     

A new parallel framework of distributed SWAT calibration

With the development of large-scale hydrologic modeling, computational efficiency is becoming more and more important. Rapid modeling and analysis are needed to deal with emergency environmental disasters. The Soil and Water Assessment Tool(SWAT) is a popular hydrologic model, which is less applied in large-scale watershed simulation because of its sequential characteristics. For improving the computational efficiency of the SWAT model, we present a new parallel processing solution for hydrologic cycle and calibration based on MPI(Message Passing Interface). We partitioned sub-basins during the processes based on a load balancing method. Then the calibration was parallelized using a master-slave scheme, in which different input parameters were allocated to different processes to run the hydrologic cycle and compute the function value. Because of the slow convergence and local optimization of the SCE-UA(Shuffled Complex Evolution-developed by University of Arizona) algorithm in SWAT calibration, a genetic algorithm(GA) is developed to optimize the calibration step. Then by dividing the default communicator into several sub-communicators, all the hydrologic cycles were parallelized in their own sub-communicators to achieve further acceleration. In this paper the results show speedups for the hydrologic cycle calculations, as well as in the optimized calibration step. In the case study, we tested the parallel hydrologic cycle by four processes, and got a speedup of 3.06. In the calibration section, after applying the GA optimization, with 10 cores, we got a speed increase of 8.0 in our GA parallel framework compared with the GA sequential calibration, which is much better than the original SWAT calibration. After the sub-communicators added, this process was speeded up even further. The study demonstrated that the GA parallel framework with multi-sub-communicators is an effective and efficient solution for the hydrologists in large scale hydrology simulations.     

黑河流域出山径流模拟

国际水文科学协会已提出针对干旱半干旱区的"无资料地区的水文预测"项目。水已成为制约西北干旱半干旱区社会经济发展和生活改善的主要因素。预测全球变化和人类活动下的水循环过程和水资源变化格局是重要的。文中以黑河上游为研究区,应用IHACRES模型模拟黑河上游降水-径流过程。结果表明:IHACRES模型可以较好地模拟研究区的日出山径流量;率定期确定性系数达0.766,验证期确定性系数为0.71;确定黑河上游地区的基流指数为0.539;黑河上游经莺落峡水文站向中游输送水量为14.9亿m3,其中地表径流与基流分别占46.1%和53.9%;模型模拟受流域面积大及降雪等因素影响,使模拟精度降低;最后指出IHACRES模型适合缺乏资料地区水文模拟的研究。