Model For Streamflow Simulation Research Articles

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow. However, existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months. To address this limitation, this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations. This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas. The up-to-down sequential method, which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach, was used to determine the structures of multivariate D-vine copulas. The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station, the inflow control station of the Longyangxia Reservoir in the Yellow River Basin. The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow. This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Assessment of the Damage Incurred by biodiversity and ecological degradation via HEC-HMS simulations and of Stream Flow Assessment and Analysis (SAAS).A case study.

Findings on the quality of River Ikpoba samplings is an essential matter because water resources management coupled with hydro-chemical interfaces is of the essence, especially for monitoring and discovering of surface-water contaminants origins. Thus, River Ikpoba samplings were scrutinized using Stream Flow Simulation Model (SFSM) and Multivariate Statistical techniques. Runoff occasion due to land use change shows increase in curve number at peak runoff of 11.60m3/s, while the maximum rainfall is greater in the circumstances up to 141.0 mm. All though the year, not only that envisaged medians always greater than the mean monthly scrutinized, but the anticipated minimum temperature is also bigger, i.e. even more unadventurous prognosis of temperature result in warmer climate scenario in the study region. As the computed runoff occasion p-value (0.998) is greater than the significance level alpha=0.05, one cannot reject the null hypothesis H0. Hence, it was concluded that the simulated flow data are closely related to the observed flow data. Likewise, an increase in curve number (from 74.58 to 79.99) resulted in a corresponding increase in outflow (from 8.00cms to 10.350cms) was observed. Indicating, with increasing curve number, the peak discharge and direct runoff volume also increases proportionately.

Hydrological modeling using the Soil and Water Assessment Tool in urban and peri-urban environments: the case of Kifisos experimental subbasin (Athens, Greece)

Abstract. SWAT (Soil and Water Assessment Tool) is a continuous-time, semi-distributed, river basin model widely used to evaluate the effects of alternative management decisions on water resources. This study examines the application of the SWAT model for streamflow simulation in an experimental basin with mixed-land-use characteristics (i.e., urban/peri-urban) using daily and hourly rainfall observations. The main objective of the present study was to investigate the influence of rainfall resolution on model performance to analyze the mechanisms governing surface runoff at the catchment scale. The model was calibrated for 2018 and validated for 2019 using the Sequential Uncertainty Fitting (SUFI-2) algorithm in the SWAT-CUP program. Daily surface runoff was estimated using the Curve Number method, and hourly surface runoff was estimated using the Green–Ampt and Mein–Larson method. A sensitivity analysis conducted in this study showed that the parameters related to groundwater flow were more sensitive for daily time intervals, and channel-routing parameters were more influential for hourly time intervals. Model performance statistics and graphical techniques indicated that the daily model performed better than the subdaily model (daily model, with NSE = 0.86, R2 = 0.87, and PBIAS = 4.2 %; subdaily model with NSE = 0.6, R2 = 0.63, and PBIAS = 11.7 %). The Curve Number method produced higher discharge peaks than the Green–Ampt and Mein–Larson method and better estimated the observed values. Overall, the general agreement between observations and simulations in both models suggests that the SWAT model appears to be a reliable tool to predict discharge in a mixed-land-use basin with high complexity and spatial distribution of input data.

A Comparative Evaluation of Conceptual Rainfall–Runoff Models for a Catchment in Victoria Australia Using eWater Source

Hydrological modelling at a catchment scale was conducted to investigate the impact of climate change and land-use change individually and in combination with the available streamflow in the Painkalac catchment using an eWater Source hydrological model. This study compares the performance of three inbuilt conceptual models within eWater Source, such as the Australian water balance model (AWBM), Sacramento and GR4J for streamflow simulation. The three-model performance was predicted by bivariate statistics (Nash–Sutcliff efficiency) and univariate (mean, standard deviation) to evaluate the efficiency of model runoff predictions. Potential evapotranspiration (PET) data, daily rainfall data and observed streamflow measured from this catchment are the major inputs to these models. These models were calibrated and validated using eight objective functions while further comparisons of these models were made using objective functions of a Nash–Sutcliffe efficiency (NSE) log daily and an NSE log daily bias penalty. The observed streamflow data were split into three sections. Two-thirds of the data were used for calibration while the remaining one-third of the data was used for validation of the model. Based on the results, it was observed that the performance of the GR4J model is more suitable for the Painkalac catchment in respect of prediction and computational efficiency compared to the Sacramento and AWBM models. Further, the impact of climate change, land-use change and combined scenarios (land-use and climate change) were evaluated using the GR4J model. The results of this study suggest that the higher climate change for the year 2065 will result in approximately 45.67% less streamflow in the reservoir. In addition, the land-use change resulted in approximately 42.26% less flow while combined land-use and higher climate change will produce 48.06% less streamflow compared to the observed flow under the existing conditions.

Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin

Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin

Development of an efficient input selection method for NN based streamflow model

In this paper, using a neural network-based streamflow simulation model (NNSSM), we simulate the runoff of the Ajichai River. The selection of suitable inputs is an essential step toward developing NNSSM. For this aim, we investigate a novel application of the Genetic Classification Algorithm (GCA) as an input variable selection (IVS) method in comparison with the Self-Organizing Map (SOM) and Binary Fully Informed Particle Swarm Optimization (BFIPSO). In another innovative application, we establish Social Choice (SC) for the final ranking of selected data using SOM. Next, the model was improved by adding seasonality indexes. The results indicate the superiority of GCA. The average (maximum) Nash-Sutcliffe index for GCA was found to be 0.63(0.84), while it was 0.55(0.71) and 0.58(0.77) for SOM-SC and BFIPSO, respectively. Moreover, GCA took less than 30 min for each run, while for SOM-SC and BFIPSO at least 3 and 48 h were needed under the same circumstances.

Hydrological Modeling in the Chaohu Lake Basin of China—Driven by Open-Access Gridded Meteorological and Remote Sensing Precipitation Products

This study assessed the performance of two well-known gridded meteorological datasets, CFSR (Climate Forecast System Reanalysis) and CMADS (China Meteorological Assimilation Driving Datasets), and three satellite-based precipitation datasets, TRMM (Tropical Rainfall Measuring Mission), CMORPH (Climate Prediction Center morphing technique), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), in driving the SWAT (Soil and Water Assessment Tool) model for streamflow simulation in the Fengle watershed in the middle–lower Yangtze Plain, China. Eighteen model scenarios were generated by forcing the SWAT model with different combinations of three meteorological datasets and six precipitation datasets. Our results showed that (1) the three satellite-based precipitation datasets (i.e., TRMM, CMORPH, and CHIRPS) generally provided more accurate precipitation estimates than CFSR and CMADS. CFSR and CMADS agreed fairly well with the gauged measurements in maximum temperature, minimum temperature, and relative humidity, but large discrepancies existed for the solar radiation and wind speed. (2) The impact of precipitation data on simulated streamflow was much larger than that of other meteorological variables. Satisfactory simulations were achieved using the CMORPH precipitation data for daily streamflow simulation and the TRMM and CHIRPS precipitation data for monthly streamflow simulation. This suggests that different precipitation datasets can be used for optimal simulations at different temporal scales.

Effect of sensitivity analysis on parameter optimization: Case study based on streamflow simulations using the SWAT model in China

Parameter optimization is an essential step in hydrological simulations, especially for solving practical problems. However, parameter optimization is usually intractable for complex models with a large number of parameters. In this study, a parameter optimization system based on Sensitive Parameter Combinations (SPCs) was developed, which comprised four parameter sensitivity analysis (SA) methods and a sensitive parameter optimization method. In particular, parameter SA was used to screen out the relatively sensitive parameters with significant impacts on the model output, and instead of using All Parameter Combinations (APCs), the SPCs were optimized with a global optimization method. This system was applied to the Soil and Water Assessment Tool (SWAT) model for daily streamflow simulation and monthly evaluation in four watersheds of China. The results showed that no more than 10 sensitive parameters were identified from 27 adjustable parameters for each watershed. In particular, four parameters (CN2, SOL_K, ALPHA_BNK, and SLSUBBSN) were relatively sensitive in all watersheds. Compared with optimizing APCs, despite the number of parameters was reduced by almost 2/3 in the optimization of SPCs, the accuracy was still very close (the maximum Nash–Sutcliffe coefficient (NSE) difference was 0.024 and the minimum difference was 0.002) and the optimization speed was doubled. In the comparison of monthly streamflow optimization, the SPCs were in good agreement with the APCs and had an obvious improvement for the default simulation. The NSE values of the SPCs optimization were greater than 0.88 during the calibration period in all watersheds and greater than 0.83 during the validation period in three watersheds. These findings indicate that optimizing the sensitivity parameters can greatly reduce the computational costs of SWAT streamflow simulations while ensuring their accuracy.

Performance evaluation of Tanh and AWBM rainfall-runoff models

The present study compares the performance of two rainfall-runoff models including Tanh model and AWBM model for streamflow simulation. The Tanh model is a 2-parameter empirical model, and the AWBM is a 9-parameter conceptual model. The Tullaroop Creek sub-catchment at central Victoria is selected as the study area. Rainfall and streamflow data measured from the catchment are the major inputs to these models. The Tanh function is used as a simple rainfall-runoff relationship for the Tullaroop Creek catchment and it estimates annual runoff by using curve fitting of the rainfall runoff plot. The AWBM model is calibrated and validated for two periods: 1974-1987 and 1987-2000, respectively. The Nash–Sutcliffe Efficiency is computed to evaluate the efficacy of model predictions. From the results obtained, it is found that the AWBM model can provide adequate estimates of annual runoff at the study catchments than Tanh model.

AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts

AbstractThe common strategy for real-time streamflow forecasting involves a precalibrated rainfall-runoff model for streamflow simulation together with a statistical postprocessing model of simulat...

Incorporating snow model and snowmelt runoff model for streamflow simulation in a snow-dominated mountainous basin in the western Hindukush-Himalaya region

Incorporating snow model and snowmelt runoff model for streamflow simulation in a snow-dominated mountainous basin in the western Hindukush-Himalaya region

Impacts of combined land-use and climate change on streamflow in two nested catchments in the Southeastern United States

Stream restoration aims to improve hydrologic, geomorphic, and ecological processes and provides an opportunity for ecological design in an urban context. Urban stream restoration and stormwater management involve conventional and low impact development strategies that may employ ecological engineering techniques. Urban flood-risks may increase rapidly due to the combination of land-use and climate change. This is a major research frontier because the changes are poorly understood, especially for the combined impacts of climate and land-use change in small catchments. Climate and land-use change may affect hydrologic systems in a complex, non-linear, and non-additive manner. Streamflow simulation models can measure the relative magnitude of land-use and climate change on hydrologic response to precipitation events. Based on a simulation model calibrated with instrumental rainfall and streamflow data, this study compares simulated urban stormflow response to projected land-use and climate change in two highly urbanized catchments in Columbia, South Carolina, USA, using the U.S. Environmental Protection Agency (EPA)’s Storm Water Management Model (SWMM). Runoff responses for moderate-magnitude storms are contrasted using three land-use change scenarios with differing imperviousness and three climate-change scenarios. Land-use effects on runoff were proportionally greater in the smaller of the two catchments. At the headwater and downstream gauges, a warm/wet climate-change scenario caused larger increases than land-use changes in peak discharges and total event runoff volumes from late spring to early fall. The maximum monthly increase in peak discharge and total runoff volume from all simulations of climate change was for the warm/wet climate scenario in both catchments. However, the combined climate and land-use change scenario produced a 117.9% increase in peak discharge, a slightly (up to 8.6% in September) greater increase in peak discharge than the sum of changes from the individual scenarios (82.6% from climate change and 26.7% from land-use change) at the headwater gauge for the far term (2045–2074). These results suggest a complexity and nonlinearity of the impacts of combined land-use and climate change on hydrological processes, and have implications for stream restoration projects, integrated climate-impact assessments, urban planning, and policy decisions related to storm runoff and water quality.

Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation

With the increased availability of remote sensing products, more hydrological variables (e.g., soil moisture and evapotranspiration) other than streamflow data are introduced into the calibration procedure of a hydrological model. However, how the incorporation of these hydrological variables influences the calibration results remains unclear. This study aims to analyze the impact of remote sensing soil moisture data in the joint calibration of a distributed hydrological model. The investigation was carried out in Qujiang and Ganjiang catchments in southern China, where the Dem-based Distributed Rainfall-runoff Model (DDRM) was calibrated under different calibration schemes where the streamflow data and the remote sensing soil moisture are assigned to different weights in the objective function. The remote sensing soil moisture data are from the SMAP L3 soil moisture product. The results show that different weights of soil moisture in the objective function can lead to very slight differences in simulation performance of soil moisture and streamflow. Besides, the joint calibration shows no apparent advantages in terms of streamflow simulation over the traditional calibration using streamflow data only. More studies including various remote sensing soil moisture products are necessary to access their effect on the joint calibration.

Parameter sensitivity analysis of SWAT model for streamflow simulation with multisource precipitation datasets

Abstract Streamflow in the Shiyang River basin is numerically investigated based on the soil and water assessment tool (SWAT). The interpolation precipitation datasets of GSI, multisource satellite and reanalysis precipitation datasets including TRMM, CMDF, CFSR, CHIRPS and PGF are specially applied as the inputs for SWAT model, and the sensitivities of model parameters, as well as streamflow prediction uncertainties, are discussed via the sequential uncertainty fitting procedure (SUFI-2). Results indicate that streamflow simulation can be effectively improved by downscaling the precipitation datasets. The sensitivities of model parameters vary significantly with respect to different precipitation datasets and sub-basins. CN2 (initial SCS runoff curve number for moisture condition II) and SMTMP (base temperature of snow melt) are found to be the most sensitive parameters, which implies that the generations of surface runoff and snowmelt are extremely crucial for streamflow in this basin. Moreover, the uncertainty analysis of streamflow prediction indicates that the performance of simulation can be further improved by parameter optimization. It also demonstrates that the precipitation data from satellite and reanalysis datasets can be applied to streamflow simulation as effective inputs, and the dependences of parameter sensitivities on basin and precipitation dataset are responsible for the variation of simulation performance.

Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation

Among several components of the hydrology cycle, streamflow is one of the essential process necessarily needed to be studied. The establishment of an accurate and reliable forecasting soft computing model for this process is highly vital for water resource planning and management. The influence of the climatological environment on streamflow is central and studying its influence is very significant from the hydrology perspective. It has been noticed that the application of machine learning models considerably become predominant in solving and capturing the complexity of hydrological applications. This research presents the implementation of a novel hybrid model called Multivariate Adaptive Regression Spline integrated with Differential Evolution (MARS-DE) to forecast streamflow pattern in semi-arid region. To achieve this, monthly time series streamflow data at Baghdad station, coordinated at Tigris River, Iraq, is inspected. For the model validation, Least Square Support Vector Regression (LSSVR) and standalone MARS models are conducted. To demonstrate the analysis of the undertaken models, several statistical indicators are computed to verify the modeling accuracies. Based on the achieved results, the MARS-DE model exhibited an excellent hybrid predictive modeling capability for monthly time scale streamflow in semi-arid region. Quantitatively; MARS-DE, LSSVR and MARS models achieved the minimum root mean square error (RMSE) and mean absolute error (MAE) values of 46.64–35.25 m3/s, 57.50–49.20 m3/s and 78.01–62.65 m3/s, respectively. In conclusion, several perspectives are suggested for further studies to enhance the forecasting capability of the model.

Intercomparison of a Lumped Model and a Distributed Model for Streamflow Simulation in the Naoli River Watershed, Northeast China

Water supply availability has significant impacts on the biggest base for commodity grain production: The Sanjiang Plain in northeast China. The SWAT (soil and water assessment tool) model and IHACRES (identification of unit hydrographs and component flows from rainfall, evapotranspiration and streamflow data) model were used for modelling streamflow variability in the upper Naoli River watershed to determine the applicability of hydrological models to the marsh rivers. Both the SWAT and IHACRES models were suitable for streamflow simulation, having R2 (coefficient of determination) and NS (Nash–Sutcliffe) values greater than 0.7, and PBIAS (percent bias) smaller than 25%. The IHACRES model was easy to use, with less data-preparation, and was found to be a better choice for runoff simulation in a watershed less affected by human activity. The simulation result was better in primeval times, i.e., 1956–1966, than the period 1967–2005, when its performance was found to be unfavorable. In contrast, the complex, processes-based SWAT model was found to be more appropriate for simultaneously simulating streamflow variability. In addition, the effects of land use change and human activities in the watershed—where agricultural activities are intensive—were evaluated. The study found that the SWAT model was potentially suitable for water resource planning and management.

Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia

The accuracy and sufficiency of precipitation data play a key role in environmental research and hydrological models. They have a significant effect on the simulation results of hydrological models; therefore, reliable hydrological simulation in data-scarce areas is a challenging task. Advanced techniques can be utilized to improve the accuracy of satellite-derived rainfall data, which can be used to overcome the problem of data scarcity. Our study aims to (1) assess the accuracy of different satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Climate Data Record (PERSIANN-CDR), and China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) by comparing them with gauged rainfall data; and (2) apply them for runoff simulations for the Han River Basin in South Korea using the SWAT model. Based on the statistical measures, that is, the proportion correct (PC), the probability of detection (POD), the frequency bias index (FBI), the index of agreement (IOA), the root-mean-square-error (RMSE), the mean absolute error (MAE), the coefficient of determination (R2), and the bias, the rainfall data of the TRMM and CMADS show a better accuracy than those of PERSIANN and PERSIANN-CDR when compared to rain gauge measurements. The TRMM and CMADS data capture the spatial rainfall patterns in mountainous areas as well. The streamflow simulated by the SWAT model using ground-based rainfall data agrees well with the observed streamflow with an average Nash-Sutcliffe efficiency (NSE) of 0.68. The four satellite rainfall products were used as inputs in the SWAT model for streamflow simulation and the results were compared. The average R2, NSE, and percent bias (PBIAS) show that hydrological models using TRMM (R2 = 0.54, NSE = 0.49, PBIAS = [−52.70–28.30%]) and CMADS (R2 = 0.44, NSE = 0.42, PBIAS = [−29.30–41.80%]) data perform better than those utilizing PERSIANN (R2 = 0.29, NSE = 0.13, PBIAS = [38.10–83.20%]) and PERSIANN-CDR (R2 = 0.25, NSE = 0.16, PBIAS = [12.70–71.20%]) data. Overall, the results of this study are satisfactory, given that rainfall data obtained from TRMM and CMADS can be used to simulate the streamflow of the Han River Basin with acceptable accuracy. Based on these results, TRMM and CMADS rainfall data play important roles in hydrological simulations and water resource management in the Han River Basin and in other regions with similar climate and topographical characteristics.

Applicability of SWAT Model for Streamflow Simulation in a Highly Managed Agricultural Watershed

Applicability of SWAT Model for Streamflow Simulation in a Highly Managed Agricultural Watershed

A periodic spatial vine copula model for multi-site streamflow simulation

Operational strategies of the Brazilian Electric Sector have direct impacts on operating costs, energy prices, planning the expansion of the system, etc. These decisions are taken under a high level of uncertainty, as the future availability of water for energy generation is a stochastic variable. Computational models, routinely based on stochastic optimization, support these decisions. Some of them make use of streamflow scenarios as entries. In this way, the aim of this paper is to develop a sophisticated statistical model for multi-site stochastic streamflow simulation. Our approach is based on the extension of vine copulas for high dimensional spatial applications. The proposed model copes with both temporal and spatial dependencies of streamflows. At the same time, it can simulate numerous sites concurrently. We tested our approach on streamflow data from 39 Brazilian hydroelectric power plants. The results indicate that the model can simulate streamflow scenarios largely preserving the features observed in the recorded streamflow data.

Hydrological modelling of the Vistula and Odra river basins using SWAT

ABSTRACTThis paper presents a large-scale application of the SWAT model for water balance and natural streamflow simulation in the entire basins of the Vistula and the Odra, covering most of the territory of Poland. A tailored calibration approach was designed to achieve satisfactory goodness-of-fit across a range of catchment sizes. Model calibration and evaluation driven by high-resolution climate data showed overall good behaviour for 80 benchmark catchments divided into eight clusters, and spatial evaluation for 30 gauges showed that the designed regionalization scheme performed well (median KGE of 0.76). Basin-averaged estimates of blue and green water flow and green water storage estimated using the calibrated model were 185, 517 and 206 mm, respectively. This study provides a basis for future work, such as assessing climate change impacts on hydrology, assessing flow alterations, and water quality simulation. The model output is publicly available through an online research data archive (doi:10.4121/uuid:b8ab4f5f-f692-4c93-a910-2947aea28f42).EDITOR A. CastellarinASSOCIATE EDITOR G. Thirel