Watershed Simulation Research Articles

Coupling Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation in Watersheds

AbstractWe assess the overall watershed system representation via fully coupling a generic reservoir operation model with a conceptual rainfall‐runoff model. The performance of the coupled model is evaluated comprehensively by examining watershed outflow simulations, model parameter values, and a key internal flux of the watershed model (here reservoir inflow). Five published generic reservoir operation models are coupled with a watershed rainfall‐runoff model, and results are compared across the coupled models and one additional model called ResIgnore that ignores reservoir operation. Traditional loosely coupled watershed hydrologic models (where calibrated inflow is routed through reservoir operation models) are used as baselines to examine the differences in simulation performance and parameterization obtained from the fully coupled models. We find that fully coupling the Generic Data‐Driven Reservoir Operation Model (GDROM) and the Dynamically Zoned Target Release (DZTR) reservoir operation models with the rainfall‐runoff model obtains robust simulations of watershed outflow with realistic parameterization, suggesting that they can be reliably integrated into large‐scale hydrological models for simulating streamflow in heavily dammed watersheds. Our results also show that compared to ResIgnore, the fully coupled watershed models more accurately simulate the entire distribution of watershed outflow, obtain more realistic values of model parameters, and simulate reservoir inflow with higher accuracy. Finally, we note that the prediction intervals of watershed outflow obtained from the GDROM‐ and DZTR‐based fully coupled models consistently envelop observed watershed outflow across the study watersheds, indicating that GDROM and DZTR can be suitable reservoir components of large‐scale hydrology models.

Evaluating the effects of DEM and soil data resolution on streamflow and sediment yield simulations in the Upper Blue Nile basin.

While the availability of "big data" on biophysical parameters through citizen science and/or from public/private sources is expected to help in addressing data scarcity issues, there is little understanding of whether and/or how such data will improve watershed simulations. This research aimed to evaluate whether improvements in resolutions of Digital Elevation Model (DEM) and soil data will enhance streamflow and sediment yield simulations and thereby improve soil and water management decisions. The study was conducted in two different-sized watersheds (Anjeni and Gilgel Abay with ~ 1 km2 and ~ 1655 km2 area, respectively) in the Upper Blue Nile basin in Ethiopia. Effects of DEM and soil data resolutions on streamflow and sediment yield were evaluated using the Soil and Water Assessment Tool (SWAT). The results showed that the effect of DEM and soil data resolution on streamflow and sediment yield simulation was scale dependent finer resolution DEM and soil datasets improved streamflow and sediment yield simulations in the smaller Anjeni watershed, whereasDEM resolution had no effect in the bigger Gilgel Abay watershed. Small watersheds are often used to understand watershed processes, and thus theuse of finer-resolution spatial data for watershed simulations could result in better results. Findings from the smaller Anjeni watershed suggested that the combined use of finer resolution DEM and soil data could potentially improve sediment yield simulations although the lack of observed sediment yield data did not allow verification of this at the larger Gilgel Abay watershed.

Runoff–Sediment Simulation of Typical Small Watershed in Loess Plateau of China

The implementation of measures such as check dams and terraces in the Loess Plateau of China has had a groundbreaking impact on water and sediment conditions. The question of how to accurately simulate the runoff–sediment process under complex underlying surface conditions has become key to clarifying the water cycle law. This study focused on the Chenggou River basin, a small watershed located in the Loess Plateau, to examine the effect of the underlying surface characteristics on the runoff production process, and the spatial distribution of the dominant runoff process in the runoff generation mechanism was determined according to the land application, slope and vegetation coverage of the watershed. A runoff–sediment model was constructed that was combined with the traditional hydrological physical mechanism and a deep learning algorithm. Different parameters were calibrated depending on the spatial distribution of the dominant runoff process and we then ran the runoff–sediment simulation model to very its serviceability in the typical watershed of the Loess Plateau. Different parameters were calibrated for each type of hydrological response unit (HRU), according to the division of each HRU and the actual flood process, to calculate the runoff yield of each HRU. An LSTM algorithm was used for flow routing and a CSLE algorithm was used to simulate soil erosion. The results show that there were 29 flood events in the Chenggou River basin from 2013 to 2017. The average runoff depth had an 8.86% margin of error, while the peak flow had a slightly higher 9.44% deviation. The Nash efficiency coefficient was 0.84, and the relative error of soil erosion was 14.45%. The model simulation effect is good and can be applied to the typical watershed of the Loess Plateau. The model can provide a scientific basis for the highly efficient and sustainable utilization of water resources, ecological environment construction and the sustainable development of agriculture.

Comparison of Flood Frequency at Different Climatic Scenarios in Forested Coastal Watersheds

Climate change-induced extreme precipitation causes coastal flooding. A streamflow simulation in coastal watersheds, Wolf River Watershed (WRW) and Jourdan River Watershed (JRW), was conducted using the Soil and Water Assessment Tool (SWAT) to compare variation in flow at different climates and to analyze the flood frequency. Baseline models were auto-calibrated with SWAT calibration and uncertainty programs (SWAT-CUP). Kling–Gupta efficiency (KGE), defined as the objective function in SWAT-CUP, ranged from 0.8 to 0.7 in WRW and from 0.55 to 0.68 in JRW during the calibration–validation process. Results indicated reliability of the model performances. Monthly averaged baseline flow was 1% greater than historical and 8.9% lower than future climate in WRW. In JRW, monthly averaged baseline flow was 11% greater than historical and 5.7% lower than future climate. Flood frequency analysis showed the highest 1% exceedance probability in annual maximum series (AMS) of baseline model in WRW, whereas AMS of projected model was estimated the highest in JRW. This study aids in preparing for future flood management.

Evaluating Monthly Flow Prediction Based on SWAT and Support Vector Regression Coupled with Discrete Wavelet Transform

Reliable and accurate streamflow prediction plays a critical role in watershed water resources planning and management. We developed a new hybrid SWAT-WSVR model based on 12 hydrological sites in the Illinois River watershed (IRW), U.S., that integrated the Soil and Water Assessment Tool (SWAT) model with a Support Vector Regression (SVR) calibration method coupled with discrete wavelet transforms (DWT) to better support modeling watersheds with limited data availability. Wavelet components of the simulated streamflow from the SWAT-Calibration Uncertainty Procedure (SWAT-CUP) and precipitation time series were used as inputs to SVR to build a hybrid SWAT-WSVR. We examined the performance and potential of the SWAT-WSVR model and compared it with observations, SWAT-CUP, and SWAT-SVR using statistical metrics, Taylor diagrams, and hydrography. The results showed that the average of RMSE-observation’s standard deviation ratio (RSR), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and root mean square error (RMSE) from SWAT-WSVR is 0.02, 1.00, −0.15, and 0.27 m3 s−1 in calibration and 0.14, 0.98, −1.88, and 2.91 m3 s−1 in validation on 12 sites, respectively. Compared with the other two models, the proposed SWAT-WSVR model possessed lower discrepancy and higher accuracy. The rank of the overall performance of the three SWAT-based models during the whole study period was SWAT-WSVR > SWAT-SVR > SWAT-CUP. The developed SWAT-WSVR model supplies an additional calibration approach that can improve the accuracy of the SWAT streamflow simulation of watersheds with limited data.

Experimental analysis of soil moisture response to rainfall in a typical grassland hillslope under different vegetation treatments

The responses of soil moisture to rainfall are of great significance for watershed hydrological modeling. However, few studies have been done to investigate these responds on hillslope in a typical semi-arid grassland region. This study used high temporal resolution soil moisture data to explore the soil moisture dynamics, response conditions and its controls of 0–40 cm soil profile in the upslope (14°), midslope (9°), and downslope (4°) of a typical grassland inland river basin under bare ground (BG), stubble (SG), and natural grassland (CK) treatments. The results showed that soil water content and water storage increased in the downslope direction, and all showed as BG > SG > CK. The dry and wet changes in fast-changing layer (5 cm) and active layer (10 cm) were rapid, while soil moisture below 20 cm was relatively stable and fluctuated only in heavy or continuous rainfalls. The soil moisture response process varied greatly under different rainfall, rainfall intensity and antecedent soil moisture conditions, which explained 41.1% of the total difference. The rainfall replenishment threshold and the required initial soil profile water content of soil moisture response in 5 cm, 10 cm and 20 cm soil layers were 5.8 mm, 8.0 mm, 11.4 mm and 8.7 vol%, 9.4 vol%, 10.8 vol%, respectively. Soil properties, vegetation characteristics and topography could explain 38.8%, 14.5% and 5.6% of the soil moisture variation on the hillslope. In addition, under the comprehensive influence of environmental factors, changes in soil moisture of the upslope were significantly affected by soil sand content, the differences in the midslope were mainly due to soil clay content and belowground biomass, whereas the vegetation characteristics were the main factors in the downslope. This study can contribute to the further understanding of slope-scale ecohydrological processes and hydrological simulation of semi-arid grassland watersheds.

Assimilation of surface soil moisture jointly retrieved by multiple microwave satellites into the WRF-Hydro model in ungauged regions: Towards a robust flood simulation and forecasting

This study investigates how assimilation of surface soil moisture jointly retrieved by multiple microwave satellites affects flood simulation and forecasting based on the experiments of simulation (Sim), Open Loop (OL), and Ensemble Kalman Filter (EnKF) in small and medium-sized watersheds without gauged soil moisture. We developed a framework for data assimilation (DA) of satellite soil moisture into the WRF-Hydro model based on the EnKF algorithm. Three statistical metrics to evaluate the impacts of DA, including net error reduction, normalized error reduction, and effectiveness criterion, are all positive values (>6.0%), indicating that DA gains reduced errors. Meanwhile, the deterministic coefficients of the EnKF experiment are also greater than those of the OL experiment. It is obvious that multi-satellite retrieved soil moisture and DA technology can improve the accuracy of flood simulation and forecasting in ungauged regions and play an important and positive role in hydrological forecasting.

Application of HEC–HMS for runoff simulation of Gojeb Watershed, Southwest Ethiopia

Application of HEC–HMS for runoff simulation of Gojeb Watershed, Southwest Ethiopia

Impact of riverbed topography on hydrology in small watersheds using Soil and Water Assessment Tool

In various watershed-based hydrological models, river topography information is generated by utilizing empirical expressions developed for large catchments. Therefore, identified the impact of channel geometry factors on hydrologic simulation in small watersheds and evaluation of reproducibility of river flow rate are required. We applied the Soil and Water Assessment Tool (SWAT) to small agricultural watersheds and compared measurements obtained from a river with DEM-generated values. The channel geometry factors determined using DEM were underestimated in comparison with measurements; however, using the measured channel geometry as the model input led to a 14.7% increase in the R2 and Nash–Sutcliffe coefficient (NSE) values in the daily average flow. In addition, the R2 and NSE values were more accurate by approximately 26% and 47% than the DEM-based optimized values obtained through SWAT-Calibration and Uncertainty Program (CUP)/Sequential Uncertainty Fitting ver.2 (SUFI-2) optimization, based on measured riverbed topography.

Utilizing hydrograph transform methods and a hydrologic modeling system in rainfall-runoff simulation of a semi-arid watershed in Algeria in north-west Africa

Utilizing hydrograph transform methods and a hydrologic modeling system in rainfall-runoff simulation of a semi-arid watershed in Algeria in north-west Africa

B-flood 1.0: an open-source Saint-Venant model for flash-flood simulation using adaptive refinement

Abstract. The French Riviera is very often threatened by flash floods. These hydro-meteorological events, which are fast and violent, have catastrophic consequences on life and property. The development of forecasting tools may help to limit the impacts of these extreme events. Our purpose here is to demonstrate the possibility of using b-flood (a subset of the Basilisk library http://basilisk.fr/, last access: 8 November 2021), which is a 2D tool based on the shallow-water equations and adaptive mesh refinement. The code is first validated using analytical test cases describing different flow regimes. It is then applied to the Toce river valley physical model produced by ENEL-HYDRO in the framework of the CADAM project and on a flash-flood case over the urbanized Toce area produced during the IMPACT project. Finally, b-flood is applied to the flash flood of October 2015 in Cannes in south-eastern France, which demonstrates the feasibility of using software based on the shallow-water equations and mesh refinement for flash-flood simulation in small watersheds (less than 100 km2) and on a predictive computational timescale.

Continuous Simulation of Highly Urbanized Watershed to Quantify Nutrients’ Loadings

The United States has witnessed various extreme land use changes over the years. These changes led to alterations in watersheds’ characteristics, impacting their water quality and quantity. To quantify this impact in highly urbanized watersheds such as the Chicago Metropolitan Area, it is crucial to examine the characteristics and imperviousness distribution of urban land uses and available point and non-point sources. In this paper, the effect of urban runoff and nutrient loadings to water bodies in the Chicago River Watershed resulting from level (III) detailed urban land uses is investigated. A watershed scale hydrologic and water quality simulation using BASINS/HSPF model was developed for the highly urbanized watershed. Appropriate considerations were given to the effective impervious area (EIA). The results from the five-year calibrated water quality simulation were reasonably reflected with observed data in the study area and nutrient loadings of both point and non-point sources for 44 different land uses were found. The export coefficients (EC) values obtained are site-specific depending on conditions and variables at the watershed level such as physical characteristics, land use management practices, hydro-meteorological and topographical data, while using a continuous simulation approach and watershed perspective analysis. This is the first attempt to measure and model nutrients’ loadings using detailed land use types in the Chicago River Watershed. The proposed continuous calibrated and validated model can be used in the investigation and analysis of different scenarios and possible future conditions and land utilization.

Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation

AbstractThe hydrological model is one of the key methods in hydrological research and management, which can be divided into physical model and mathematical model. In order to explore the applicability of different models in hydrological simulation, the physical model Hydrologic Simulation Program‐FORTRAN (HSPF), the mathematical models Autoregressive Integrated Moving Average (ARIMA) and Back Propagation Neural Network (BPNN) are built to simulate the hydrological process of Sanya River, China. First, the result shows that all three models have good performance, but the HSPF model has the best performance followed by the ARIMA and the BPNN model with the lowest performance. Next, the effect of time step on the performance of the BPNN and ARIMA models is obviously greater than for the HSPF model. Finally, the applicability of various models is discussed by combining the modeling process and data requirements of the model, and the applicability of the mathematical model in the hydrological simulation of watersheds with insufficient data is confirmed.

Streamflow simulation in data-scarce basins using Bayesian and physics-informed machine learning models

AbstractHydrologic predictions at rural watersheds are important but also challenging due to data shortage. Long Short-TermMemory (LSTM) networks are a promising machine learning approach and have demonstrated good performance in streamflow predictions. However, due to its data-hungry nature, most of LSTM applications focused on well-monitored catchments with abundant and high quality observations. In this work, we investigate predictive capabilities of LSTM in poorly monitored watersheds with short observation records. To address three main challenges of LSTM applications in data-scarce locations, i.e., overfitting, uncertainty quantification (UQ), and out-of-distribution prediction, we evaluate different regularization techniques to prevent overfitting, apply a Bayesian LSTM for UQ, and introduce a physics-informed hybrid LSTM to enhance out-of-distribution prediction. Through case studies in two diverse sets of catchments with and without snow influence, we demonstrate that: (1) when hydrologic variability in the prediction period is similar to the calibration period, LSTM models can reasonably predict daily streamflow with Nash-Sutcliffe efficiency above 0.8, even with only two years of calibration data. (2) When the hydrologic variability in the prediction and calibration periods is dramatically different, LSTM alone does not predict well, but the hybrid model can improve the out-of-distribution prediction with acceptable generalization accuracy. (3) L2 norm penalty and dropout can mitigate overfitting, and Bayesian and hybrid LSTM have no overfitting. (4) Bayesian LSTM provides useful uncertainty information to improve prediction understanding and credibility. These insights have vital implications for streamflow simulation in watersheds where data quality and availability are a critical issue.

A time-varying distributed unit hydrograph and its application in flood simulation of small mountainous watersheds

Flood forecasting is an important non-engineering measure to reduce flood damage. Small mountainous watersheds are characterized by rapid flow concentration and steeply rising and falling floods. Their flow concentration process exhibits strong time dependence, which is not well-captured by conventional unit hydrographs. In this paper, four mountainous watersheds were studied, and an algorithm for generating a time-varying distributed unit hydrograph (TVDUH) was proposed. The algorithm was integrated with the China Flash Flood Hydrological Model (CNFF-HM) to simulate small watersheds in the study area. The results showed relatively large Nash-Sutcliffe efficiency (NSE) of about 0.8 for the watersheds during the calibration and validation periods. Then the TVDUH was compared with the time-invariant one. The results showed that the TVDUH could better reflected the flow concentration characteristics of small mountainous watersheds, as the method took into account the effect of rainfall intensity on flow concentration during the computation process. And the TVDUH has great application potential in flood forecasting in mountainous watersheds to reduce the loss of flood disasters.

Improving flood simulation capability of the WRF-Hydro-RAPID model using a multi-source precipitation merging method

Flash floods-caused losses are rapidly increasing due to climate change induced extreme weather events and economic development in the world. The WRF-Hydro-RAPID model coupled with land surface model and a vector-based flow routing module is able to simulate discharge at any reach of a watershed, making it a good tool for flood simulation and forecasting. We investigated the flood simulation capability of the WRF-Hydro-RAPID model and evaluated the utility of a multi-source precipitation merging method based on the mixed geographically weighted regression model and Bi-square function (MGWR-BI algorithm), which produces precipitation as forcing with improved quality and resolution, to enhance the simulation accuracy of the WRF-Hydro-RAPID model for the Daheba Watershed, a first-order sub-basin of the Yangtze River Basin. The merged precipitation data have substantial higher quality than the downscaled original CPC MORPHing technique satellite precipitation data (CMORPHd) (r = 0.64–0.74 and RMSE = 1.59–6.64 mm/h for the merged data vs. r = −0.11–0.06 and RMSE = 3.31–8.25 mm/h for the CMORPHd data) by comparing to the ground observations. Floods are better forecasted and simulated by the WRF-Hydro-RAPID model driven by the merged precipitation data than the CMORPHd precipitation data for the four nested medium and small watersheds. Performance of the WRF-Hydro-RAPID model at the watershed outlet station does not differ those at the three inner stations, proving that the WRF-Hydro-RAPID model has a consistent performance in space. The combination of the WRF-Hydro-RAPID with the precipitation merging method makes it a valuable tool for flood simulation of medium and small watersheds.

Evaluating applicability of multi-source precipitation datasets for runoff simulation of small watersheds: a case study in the United States

ABSTRACT Small watersheds are ideal objects for studying the evolution of hydrological and water resources at small scales. Whether precipitation products can meet the runoff simulation of small watersheds is the main purpose of this study. With NOAA-CPC-US precipitation as a reference, in nine small watersheds of the United States, accuracy of the precipitation products such as PERSIANN, PERSIANN-CDR, TRMM-3B42V7, GPM-IMERG, StageIV, and ERA5 is analyzed. By driving the CREST hydrological model using these datasets, the runoff simulation effects were evaluated. Result shows the precipitation products match the NOAA-CPC-US from high to low in the order of StageIV, PERSIANN-CDR, GPM-IMERG, PERSIANN, ERA5, and finally TRMM-3B42 V7. These datasets have relatively low accuracy in the northern high latitude area and the western mountains, while accuracy is better in the central, southern, and eastern parts of the United States. In the runoff simulation effectiveness evaluation, the daily runoff of watersheds is simulated during the same verification period. The results show that: NOAA-CPC-US and StageIV have good simulation effect in small watersheds. In the northern and western United States, the PERSIANN, PERSIANN-CDR, GPM-IMERG, and ERA5 for runoff simulation should be used with caution. TRMM-3B42V7 is not suitable for runoff simulation in small watershed.

A coupled atmospheric–hydrologic modeling system with variable grid sizes for rainfall–runoff simulation in semi-humid and semi-arid watersheds: how does the coupling scale affects the results?

Abstract. The coupled atmospheric–hydrologic modeling system is an effective way to improve the accuracy of rainfall–runoff modeling and extend the lead time in real-time flood forecasting. The aim of this study is to explore the appropriate coupling scale of the coupled atmospheric–hydrologic modeling system, which is established by the Weather Research and Forecasting (WRF) model and the gridded Hebei model with three different sizes (1 km×1 km, 3 km×3 km and 9 km×9 km). The Hebei model is a conceptual rainfall–runoff model designed to describe a mixed runoff generation mechanism, including both storage excess and infiltration excess, in the semi-humid and semi-dry area of northern China. The soil moisture storage capacity and infiltration capacity of different grids in the gridded Hebei model are obtained and dispersed using the topographic index. The lumped Hebei model is also used to establish the lumped atmospheric–hydrologic coupled system as a reference system. Four 24 h storm events occurring at two small- and medium-scale sub-watersheds in northern China are selected as case studies. Contrastive analyses of the flood process simulations from the gridded and lumped systems are carried out. The results show that the flood simulation results may not always be improved with higher-dimension precision and more complicated system, and the grid size selection has a strong relationship with the rainfall evenness. For the storm events with uniform spatial distribution, the coupling scale has less impact on flood simulation results, and the lumped system also performs well. For the storm events with uneven spatiotemporal distribution, the corrected rainfall can improve the simulation results significantly, and higher resolution leads to better flood process simulation. The results can help to establish the appropriate coupled atmospheric–hydrologic modeling system to improve the flood forecasting accuracy.

Artificial Neural Networks–Based Model Parameter Transfer in Streamflow Simulation of Brazilian Atlantic Rainforest Watersheds

AbstractThis paper presents an assessment of the calibration and transfer of artificial neural networks (ANNs) to simulate streamflow at Brazilian Atlantic Rainforest basins. Primary data consisted...

Flood hydrograph simulation using the SWMM model: A semiarid zone watershed case study, Shiraz Khoshk River, Iran

AbstractIn the past few decades, considerable efforts have been made to model rainfall‐runoff and to predict the hydrological impacts of various factors using computer technology. The Environmental Protection Agency Storm Water Management Model (SWMM) has been proved to be a powerful tool which simulates the flow of watersheds with different characteristics. The SWMM model is a dynamic rainfall‐runoff simulation model used for single event and/or continuous runoff simulation. It is widely used throughout the world for analysis, planning, and design related to storm water of drainage systems in urban areas, with many applications in nonurban areas as well. This study aimed to assess the performance of the SWMM model for flood simulation in the Shiraz Khoshk River originated from an arid zone watershed. The study area with a combination of natural and urban watersheds (900.3 km2) is located in South‐West of Iran in Fars Province. In this study, the model was calibrated using 12 flood events recorded at the Eghbal‐Abad gauge station. Then, for validation, 10 and 4 other hydrographs were simulated for the Nahre‐Azam and Chenarsukhte gauge stations, respectively. The calibration and validation results show a good agreement between the observed and simulated flows (R2 = 0.96 for the calibration, 0.91 and 0.87 for validation). The model performance criteria were plausible (e.g., Nash‐Sutcliff coefficient = 0.91 for calibration; and 0.88 and 0.86 for validation). Therefore, the results indicate the high efficiency of the model to simulate the flow in this watershed, useful for predicting the impact of various management scenarios on flow characteristics.Recommendations for resource managers The SWMM model can be successfully applied to semiarid zone watersheds. The model is suitable for urban and nonurban watershed simulations.