Entropy‐copula method for single‐site monthly streamflow simulation

Investigating the dynamic characteristic of hydrological processes is of vital significance for environmental protection. In this study, the stepwise cluster analysis (SCA) method was used for monthly streamflow simulation in a hypothetical case. According to SCA, a cluster tree was formulated through training the data of monthly temperature, precipitation and streamflow from 2004 to 2010. Then, the generated tree was used to reproduce monthly streamflow in calibration period (i.e., 2004-2010) and validation period (i.e., 2011-2013). A comparison of SCA and multiple linear regression (MLR) was conducted to reflect the complex relationship of meteorological parameters (e.g., precipitation) and hydrological parameters. Model performance was assessed using Nash-Sutcliffe efficiencies (NSE), the determination coefficient (R 2), the root-mean-square error (RMSE) and the mean absolute error (MAE). NSE and R2 obtained from SCA are higher than that obtained from MLR. RMSE and MAE obtained from SCA are smaller than that obtained from MLR, indicating a better coincidence between simulated streamflow and the observed values in SCA. Results indicated that SCA has advantage in revealing the nonlinear relationship among precipitation, temperature and streamflow.

Application of an inappropriate Digital Elevation Model (DEM) might lead to uncertainty in modelling of the hydrological cycle. The novelty of this work is the development of a comprehensive framework to evaluate the effect of DEM resolution (12 to 500 m), source (TanDEM-X, SRTM, AW3D30 and ASTER GDEM2), resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority) and area threshold (1000 to 50,000 ha) on Soil and Water Assessment Tool (SWAT) outputs based on five criteria: (1) river network extraction, (2) streamflow simulation, (3) topography, slope and basin characteristics, (4) hydrological and (5) water quality simulations. Kelantan River Basin, a tropical basin in Peninsular Malaysia was selected as study area. The major findings are summarized as follows: (1) TanDEM-X had better river network extraction capability than ASTER GDEM2, (2) better monthly streamflow simulations were obtained between 20 m and 60 m DEM resolutions, with the smallest area threshold (1000 ha), (3) TanDEM-X and SRTM DEMs outperformed ASTER GDEM2 on monthly streamflow simulation, (4) DEM resolution, source and resampling technique were insensitive to most of the hydrological components, except the lateral flow, (5) area threshold was sensitive to SWAT-simulated surface runoff, soil water content and evapotranspiration, (6) DEM scenarios had a larger impact on sediment yield simulations compared to the total nitrogen and total phosphorus simulations. We recommend a preliminary assessment of DEM uncertainties on SWAT outputs to obtain more reliable modelling outputs.

평창유역의 적설량을 모의하기 위하여 HSPF 모형을 적용하였다. 미래 적설량을 평가하기 위해 CIMIP3에서 제공하는 A1, A1B, B1의 온실가스 배출시나리오에 기반한 GCMs를 이용하였으며, HSPF 모형과 GCMs의 통계학적 오류를 최소화 하기 위해 편의보정(Bias-correction)과 시간적 분해모형(Temporal disaggregation)을 적용하였다. 모형의 검 보정 결과 모의된 유출량과 적설량의 경우 모형 효율이 높게 나타났으며, 특히 모형의 검정 후 상관계수를 분석한 결과 월별 유출량의 상관계수는 0.94로 나타났다. 월별 적설량, 또한, 상관계수가 0.91로 나타나 보정된 HSPF 모형이 평창지역에 대한 유출량과 적설량을 잘 모의하고 있는 것으로 판단된다. GCMs를 이용한 2018년 평창올림픽 경기장의 적설량을 분석한 결과 1월에는 17.62%, 2월에는 9.38%, 3월에는 7.25%의 적설량이 감소되는 것으로 나타났다. Hydrological simulation Program-Fortran (HSPF) model was used to simulate streamflow and snow depth at Pyengchang watershed. The selected Global Climate Models (GCMs) provided by the Coupled Model Intercomparision Project Phase 3 (CMIP3) were utilized to evaluate streamflow and snow depth driven by future climate scenarios, including A1, A1B, and B1. Bias-correlation and temporal downscaling processes have been performed to minimize systematic errors between GCMs and HSPF. Based on simulated monthly streamflow and snow depth after calibration, the results indicate that HSPF performs well. The correlation coefficient between the observed and simulated monthly streamflow is 0.94. Snow depth simulations also show high correlation coefficient, which is 0.91. The results indicate that snow depth in 2018 at Pyongchang winter olympic venues will decrease by 17.62%, 9.38%, and 7.25% in January, February, and March respectively, based on streamflow realizations induced by all GCMs ensembles.

This paper presents a multimodel regression‐sampling algorithm (MRS) for monthly streamflow simulation. MRS is motivated from the acknowledgment that typical nonparametric models tend to simulate sequences exhibiting too close a resemblance to historical records and parametric models have limitations in capturing complex distributional and dependence characteristics, such as multimodality and nonlinear autocorrelation. The aim of MRS is to generate streamflow sequences with rich scenarios while properly capturing complex distributional and dependence characteristics. The basic assumptions of MRS include: (1) streamflow of a given month depends on a feature vector consisting of streamflow of the previous month and the dynamic aggregated flow of the past 12 months and (2) streamflow can be multiplicatively decomposed into a deterministic expectation term and a random residual term. Given a current feature vector, MRS first relates the conditional expectation to the feature vector through an ensemble average of multiple regression models. To infer the conditional distribution of the residual, MRS adopts the k‐nearest neighbor concept. More precisely, the conditional distribution is estimated by a gamma kernel smoothed density of historical residuals inside the k‐neighborhood of the given feature vector. Rather than obtaining the residuals from the averaged model only, MRS retains all residuals from all the original regression models. In other words, MRS perceives that the original residuals put together would better represent the covariance structure between streamflow and the feature vector. By doing so, the benefit is that a kernel smoothed density of the residual with reliable accuracy can be estimated, which is hardly possible in a single‐model framework. It is the smoothed density that ensures the generation of sequences with rich scenarios unseen in historical record. We evaluated MRS at selected stream gauges and compared with several existing models. Results show that (1) compared with typical nonparametric models, MRS is more apt at generating sequences with richer scenarios and (2) in contrast to parametric models, MRS can reproduce complex distributional and dependence characteristics. Since MRS is flexible at incorporating different covariates, it can be tailored for other potential applications, such as hydrologic forecasting, downscaling, as well as postprocessing deterministic forecasts into probabilistic ones.

PDF HTML阅读 XML下载 导出引用 引用提醒 气候变化对淮河流域中上游汛期极端流量影响的SWAT模拟 DOI: 10.5846/stxb201609281957 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金（41475062）；国家重大基础研究计划（2010CB428506）；水利部公益性行业科研项目（200901042，200901024）；福建省教育厅B类项目（JB12016）水利部公益性行业科研项目（200901042，200901024） Effects of climate change on extreme streamflow in flood season in the upper and middle basin of the Huaihe River by SWAT simulation Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:致洪暴雨主要是3天以上连续强降水，是淮河流域洪涝的直接原因。构建淮河流域中上游SWAT模型，用RegCM3在SRES A2排放情景下的模拟结果（2071-2100年）驱动SWAT模型，研究气候变化对淮河流域中上游汛期极端流量的影响。结果表明：（1）在SRES A2排放情景下，淮河流域中上游未来（2071-2100年）气温升高，降水量增加，降水的空间差异增大；颖河流域中游年降水量有较大幅度的减少，呈现暖干化的趋势；汛期极端过程降水增加，汛期最大9 d降水量平均增幅都在10%以上。（2）在SRES A2排放情景下的气候变化将导致淮河流域中上游汛期极端流量大幅度增加，干流5个水文站汛期最大9 d平均流量的增幅都在20%以上。（3）淮河流域中上游极端流量的概率分布更加集中，更大的极端流量出现的频率更高，研究流域下游更容易出现较大的极端流量。（4）研究流域下游极端流量概率对极端流量变化更敏感，下游也面临着更大的洪涝风险。 Abstract:Consecutive rainstorm events are a main flash-flood-producing phenomenon on a basin scale, and an extreme consecutive rainstorm is more likely to cause catastrophic floods in the Huaihe River Basin. We constructed a SWAT model for the upper and middle basin of the Huaihe River and the drove model using the RegCM3 outputs under SRES A2 Greenhouse Gas Emission Scenarios, during the period from January 1, 2071 to December 31, 2100, to investigate the extreme streamflow response to the climate change during the flood seasons. First, we analyzed the relationship between the maximum 3-13 day precipitation amount and the maximum 3-13 day mean streamflow and determined the maximum 9 day precipitation amount as an indicator of the extreme consecutive rainstorm and the maximum 9-day mean streamflow as an indicator of the extreme streamflow in the flood season. Second, we constructed a SWAT model for the upper and middle basin of the Huaihe River to simulate the maximum 9-day mean streamflow in the flood season. Thirdly, we drove the model using the RegCM3 outputs and simulated the maximum 9-day mean streamflow during the period from January 1, 2071 to December 31, 2100, under SRES A2 Greenhouse Gas Emission Scenarios and the period from January 1,1961 to December 31, 1990 (reference period) respectively. Finally, we compared the results of the maximum 9-day mean streamflow in the flood season under two climate scenarios, and investigated the response of the extreme flow to climate change from the perspective of the probability characteristics. The SWAT model constructed for the upper and middle basin of the Huaihe River simulated monthly streamflow accurately based on the observational data during the period from 1958 to 2009. Simultaneously, the simulation results also reproduced the prominent characteristics of the maximum 9-day mean streamflow in the flood season. The results indicated that, under SRES A2 Scenarios, during the period from January 1, 2071 to December 31, 2100:(1) RegCM3 predicted that annual mean temperature will rise, annual precipitation amount will increase, and the spatial contrast of precipitation will increase in the upper and middle basin of the Huaihe River. However, the annual precipitation amount decreased in the middle basin of the Yinghe River and showed a warming and drying trend. Simultaneously, extreme consecutive precipitation in the flood season increased, especially in the southern basin of the lower reaches. (2) The extreme streamflow in the flood season increased more remarkably, and the maximum 9-day mean streamflow in the flood season increased more than 20% on average in five hydrological gauges. (3) The probability distribution of extreme streamflow was more concentrated and the frequency of the extreme streamflow was much higher, with much higher flood risk in the middle and lower basin. (4) The probability of extreme streamflow in the lower basin responded more sensitively to the extreme streamflow changes, which means flood risks increase owing to climate change. These conclusions have practical significance for flood risk management and control in the Huaihe River Basin under the climate change scenario. 参考文献 相似文献 引证文献

The soil and water assessment tool (SWAT) is a widely accepted semi-distributed agrohydrological model. In the present study, the SWAT has been calibrated for the agriculture-dominated Kantamal catchment of Mahanadi river basin, Odisha, India, to simulate streamflow at the Kantamal gauging station. The model was run for 19 years from 2000 to 2018 to simulate monthly streamflow. In addition, the model has been calibrated for the period 2004 to 2012, considering the initial 4 years (2000-2003) as a warm-up period and validated for 6 years from 2013 to 2018. Uncertainty analysis was carried out using the SUFI-2 algorithm in SWAT-CUP. The sensitivity of the parameters was determined using the t-stat and p-value. Fifteen distinguished parameters were selected for sensitivity analysis (SA). The performance of the model was evaluated satisfactorily on monthly time scale streamflow simulation using Nash- Sutcliffe Efficiency (NSE), coefficient of determination (R2), and percentage BIAS (PBIAS). The P- and R- factors were used to assess the degree of uncertainty. The values of NSE, R2, and PBIAS were found to be 0.91, 0.94, and -0.07 during the calibration period and 0.82, 0.87, and -0.16 during the validation period, respectively. The P- and R- factors values were 0.77 and 0.98 during calibration and 0.79 and 0.86 during the validation period, respectively. The simulated streamflow is well fitted within the 95% prediction uncertainty (95PPU) band of the SUFI-2 algorithm during the calibration and validation periods, indicating a satisfactory model performance under parameter uncertainty. Overall, it is depicted that the SWAT model can be successfully used for streamflow modeling and water resources assessment in agriculture-dominated catchments.

In this paper kernel estimates of the joint and conditional probability density functions are used to generate synthetic streamflow sequences. Streamflow is assumed to be a Markov process with time dependence characterized by a multivariate probability density function. Kernel methods are used to estimate this multivariate density function. Simulation proceeds by sequentially resampling from the conditional density function derived from the kernel estimate of the underlying multivariate probability density function. This is a nonparametric method for the synthesis of streamflow that is data‐driven and avoids prior assumptions as to the form of dependence (e.g., linear or nonlinear) and the form of the probability density functions (e.g., Gaussian). We show, using synthetic examples with known underlying models, that the nonparametric method presented is more flexible than the conventional models used in stochastic hydrology and is capable of reproducing both linear and nonlinear dependence. The effectiveness of this model is illustrated through its application to simulation of monthly streamflow from the Beaver River in Utah.

Estimation of water balance and water yield in the Reedy Fork-Buffalo Creek Watershed in North Carolina using SWAT

Lack of availability of land, water and other natural resources along with climate change has caused a major threat for increasing the agricultural production on par with the increasing population. Bharathapuzha river is considered as the cradle of civilization in Kerala state, India. The river flow is highly affected by increased water use and reduced recharge caused due to increasing population, urbanization and unscientific management practices. Even though, watershed development activities in river basins help in conserving water in the upstream areas for agricultural, domestic and other uses, very few research have been done to understand its effect on the flow regime in the lower reaches. A number of dams, check dams and other conservation structures have been constructed in Bharathapuzha catchment area during the past and hence its effect on the downstream flow need to be studied. The calibrated and validated SWAT model was applied to Bharathapuzha river basin for studying the impact of watershed interventions on the water balance of the area. The simulated monthly streamflow was analyzed during 2007 to 2011 after addition of Water Retention Structures (WRS) @ 0.05%, 0.1% and 0.2% of the land area. The results indicated that even though the total annual flow decreased, the flow during the summer months (baseflow) increased due to the addition of the conservation structures. The increase in river flow was highest during the period January to April when the river had lean flow. It was found that SWAT model can be used as an effective tool to study the impact of water retention structures in a watershed. The conservation structures help to store water in the upper reaches for irrigation and domestic purpose, and at the same time it helps in increasing the summer flow. This helps to maintain a better environmental flow regime. Hence while planning the conservation measures in a watershed so as to meet the demand of the upstream users, care should be taken to avoid the negative impact that can happen in the downstream.

Water contamination caused by the presence of excessive amounts of nitrate can be catastrophic for aquatic ecosystems and human health. Due to these high risks, a great deal of emphasis has been placed on finding effective measures to reduce nitrate concentrations in rivers and aquifers. In this study, we used the SWAT model based on grid-cells of 5 minutes of resolution for assessing the processes involved in nitrate loads generation and transport into aquifers and rivers and for providing basin management strategies of nitrate reduction. We applied the model in the Po River Basin (Italy), one of the most densely populated and highly agriculturally exploited area in the Mediterranean basin. The model was successfully calibrated and validated in eight monitoring stations along the Po River for the period 2000–2012. Simulated monthly streamflow and nitrate concentrations were in good agreement with observations, obtaining values of bias around ±25% in both calibration and validation. Among the tested scenarios of nitrogen reduction from agricultural sources, red clover cover crop after corn, coupled with a targeted reduction of mineral fertilizers and the limitation of nitrogen manure leads to a reduction of nitrate leaching and nitrogen emissions of around 37%.

Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia

Sediment yield estimation along with identification of soil erosion mechanisms is essential for developing sophisticated management approaches, assessing, and balancing different management scenarios and prioritizing better soil and water conservation planning and management. At the watershed scale, land management practices are commonly utilized to minimize sediment loads. The goal of this research was to estimate sediment yield and prioritize the spatial dispersion of sediment-producing hotspot areas in the Nashe catchment using the Soil and Water Assessment Tool (SWAT). Moreover, to reduce catchment sediment output, this study also aims to assess the effectiveness of certain management practices. For calibration and validation of the model, monthly stream flow and sediment data were utilized. The model performance indicators show good agreement between measured and simulated stream flow and sediment yields. The study examined four best management practice (BMP) scenarios for the catchment’s designated sub-watersheds: S0 (baseline scenario), S1 (filter strip), S2 (stone/soil bunds), S3 (contouring), and S4 (terracing). According to the SWAT model result, the watershed’s mean yearly sediment output was 25.96 t/ha. yr. under baseline circumstances. The model also revealed areas producing the maximum sediment quantities indicating the model’s effectiveness for implementing and evaluating the sensitivity of sediment yield to various management strategies. At the watershed scale, treating the watershed with various management scenarios S1, S2, S3, and S4 decreased average annual sediment yield by 34.88%, 57.98%, 39.55%, and 54.77%, respectively. The implementations of the soil/stone bund and terracing scenarios resulted in the maximum sediment yield reduction. The findings of this study will help policymakers to make better and well-informed decisions regarding suitable land use activities and best management strategies.

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using 30 years of monthly data (1991–2020), split into 80% for training (1991–2014) and 20% for testing (2015–2020). Initially, only historical streamflow data were used for predictions, followed by including meteorological factors to assess their impact on streamflow. Subsequently, sequence analysis was conducted to explore various input-output sequence window combinations. We then evaluated the influence of each factor on streamflow by testing all possible combinations to identify the optimal feature combination for prediction. Our results indicate that the Random Forest Regression model consistently outperformed others, especially after integrating all meteorological factors with historical streamflow data. The best performance was achieved with a 24-month look-back period to predict 12 months of streamflow, yielding a Root Mean Square Error of 2.25 and R-squared (R2) of 0.80. Finally, to assess model generalizability, we tested the best model at other locations—Greenwood Springs (Colorado River), Maybell (Yampa River), and Archuleta (San Juan) in the basin.

The spatial variability of precipitation is often considered to be a major source of uncertainty for hydrological models. The widely used Soil and Water Assessment Tool (SWAT) is insufficient to calculate a sub-basin's mean areal precipitation (MAP) since it only uses data from the rainfall station nearest to the centroid of each sub-basin. Therefore, Inverse Distance Weighting (IDW), Thiessen Polygons (TP) and Ordinary Kriging (OK) were applied as alternative interpolation methods in this study to calculate sub-basin MAP. The MAP results from the four methods used for the Xixian Basin were quite different in terms of amount and spatial distribution. The SWAT model performance was then assessed at monthly and daily timescales, based on Nash–Sutcliffe efficiency (NSE), the Coefficient of Determination (R2) as well as Percentage Bias (PBIAS) at the basin outlet. The results under different network densities and spatial distributions of gauge stations indicated that the modified MAP models did not have an advantage over the default Nearest Neighbour (NN) method in simulating monthly streamflow. However, the modified areal precipitation obtained through IDW and TP showed relatively high accuracy in simulating daily flows as the applied rainfall stations changed. The difference in terms of estimated rainfall and streamflow in this study confirmed that evaluation of interpolation methods is necessary before building a SWAT model.

Model parameter calibration is a fundamentally important stage that must be completed before applying a model to address practical problems. In this study, we describe an automatic calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling‐based optimization (ASMO) algorithm. We use this framework to calibrate catchment‐specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25° spatial resolution over 10 major river basins of China from 1960 to 1979. We found that three parameters—the infiltration parameter (B) and two of the soil depth parameters (D1, D2)—are highly sensitive in most basins, while other parameter sensitivities are strongly related to the dynamic environment of the basin. Compared with directly calibrating the seven parameters recommended for the default calibration procedure, our framework not only reduced the computing time by two thirds through opting out of insensitive parameters (type I error) but also improved the Nash‐Sutcliffe model efficiency coefficient (NSE) for optimized results when it identified a missing sensitive parameter (type II error) in the case study river basins. Results show that the SA‐based ASMO framework is an effective and efficient model‐optimization technique for matching simulated streamflow with observations across China. The NSE for monthly streamflow ranged from 0.75 to 0.97 and from 0.71 to 0.97 during the validation and calibration periods, respectively. The calibrated parameters can be applied directly in streamflow simulations across China, and the proposed calibration framework holds important implications for relevant simulation studies in other regions.