Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach

Abstract. Based on the Representative Elementary Watershed (REW) approach, the modelling tool REWASH (Representative Elementary WAterShed Hydrology) has been developed and applied to the Geer river basin. REWASH is deterministic, semi-distributed, physically based and can be directly applied to the watershed scale. In applying REWASH, the river basin is divided into a number of sub-watersheds, so called REWs, according to the Strahler order of the river network. REWASH describes the dominant hydrological processes, i.e. subsurface flow in the unsaturated and saturated domains, and overland flow by the saturation-excess and infiltration-excess mechanisms. The coupling of surface and subsurface flow processes in the numerical model is realised by simultaneous computation of flux exchanges between surface and subsurface domains for each REW. REWASH is a parsimonious tool for modelling watershed hydrological response. However, it can be modified to include more components to simulate specific processes when applied to a specific river basin where such processes are observed or considered to be dominant. In this study, we have added a new component to simulate interception using a simple parametric approach. Interception plays an important role in the water balance of a watershed although it is often disregarded. In addition, a refinement for the transpiration in the unsaturated zone has been made. Finally, an improved approach for simulating saturation overland flow by relating the variable source area to both the topography and the groundwater level is presented. The model has been calibrated and verified using a 4-year data set, which has been split into two for calibration and validation. The model performance has been assessed by multi-criteria evaluation. This work represents a complete application of the REW approach to watershed rainfall-runoff modelling in a real watershed. The results demonstrate that the REW approach provides an alternative blueprint for physically based hydrological modelling.

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Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-2-639-2005 ◽

2005 ◽

Vol 2 (3) ◽

pp. 639-690 ◽

Cited By ~ 1

Author(s):

G. P. Zhang ◽

H. H. G. Savenije

Keyword(s):

River Basin ◽

Overland Flow ◽

Model Performance ◽

Source Area ◽

Subsurface Water ◽

Watershed Hydrology ◽

Rainfall Runoff ◽

Data Set ◽

Physically Based ◽

Infiltration Excess

Abstract. Based on the Representative Elementary Watershed (REW) approach, the modelling tool REWASH (Representative Elementary WAterShed Hydrology) has been developed and applied to the Geer river basin. REWASH is deterministic, semi-distributed, physically based and can be directly applied to the watershed scale. In applying REWASH, the river basin is divided into a number of sub-watersheds, so called REWs, according to the Strahler order of the river network. REWASH describes the dominant hydrological processes, i.e. subsurface flow in the unsaturated and saturated domains, and overland flow by the saturation-excess and infiltration-excess mechanisms. Through flux exchanges among the different spatial domains of the REW, surface and subsurface water interactions are fully coupled. REWASH is a parsimonious tool for modelling watershed hydrological response. However, it can be modified to include more components to simulate specific processes when applied to a specific river basin where such processes are observed or considered to be dominant. In this study, we have added a new component to simulate interception using a simple parametric approach. Interception plays an important role in the water balance of a watershed although it is often disregarded. In addition, a refinement for the transpiration in the unsaturated zone has been made. Finally, an improved approach for simulating saturation overland flow by relating the variable source area to both the topography and the groundwater level is presented. The model has been calibrated and verified using a 4-year data set, which has been split into two for calibration and validation. The model performance has been assessed by multi-criteria evaluation. This work is the first full application of the REW approach to watershed rainfall-runoff modelling in a real watershed. The results demonstrate that the REW approach provides an alternative blueprint for physically based hydrological modelling.

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Modelling stream flow with a discrete rainfall–runoff model and 37 GHz PDBT microwave observations: the Xiangjiang River basin case study

10.5194/hess-2016-129 ◽

2016 ◽

Author(s):

Haolu Shang ◽

Massimo Menenti ◽

Li Jia

Keyword(s):

Ground Water ◽

River Basin ◽

Overland Flow ◽

Subsurface Flow ◽

Base Flow ◽

Rainfall Runoff ◽

Xiangjiang River ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Xiangjiang River Basin

Abstract. A discrete rainfall–runoff model has been developed, which uses retrievals of Water Saturated Soil (WSS) and inundation area from 37 GHz microwave observations. The model was implemented at three levels of increasing complexity using field-measured ground water table, WSS and inundated area, and precipitation data. The three levels, defined by the key-variables are: (1) precipitation and base flow; (2) overland flow, infiltrated flow and base flow; (3) overland flow, potential subsurface flow and base flow. The base flow is estimated from observed ground water table depth, while overland and infiltrated flows are estimated from precipitation and the WSS and inundated area. A linear scaling method is developed to estimate the potential subsurface flow. The three model implementations are calibrated with the gauge measurements of 10-day average river discharge in 2002 and 2005 respectively at Changsha station, downstream of Xiangjiang River basin, China. The discrete rainfall–runoff model assumes that specific runoff is determined by antecedent precipitations over a variable period of time. This duration is a model parameter varying between 10 and 150 days. The performance of the discrete rainfall–runoff model increased with the duration of antecedent precipitation for all three implementations in both years. With a duration of 150 days, the model reaches its best performance: Nash–Sutcliffe Efficiency, NSE, for the 1st implementation was ≥ 0.90 with relative RMSE ≤ 22 %; NSE ≈ 0.99 with relative RMSE ≤ 5 % for the 2nd implementation, and NSE ≥ 0.99 with relative RMSE ≤ 4 % for the 3rd one. These good performances prove that the retrievals of WSS and inundated area clearly improve model accuracy, thus justifying the choices of parameters and the method to estimate the potential subsurface flow. The set of parameters driving each implementation is an indication of dominant hydrological processes, particularly water storage, in determining the catchment response to rainfall. Significant differences in the annual water yield have been observed across the three implementations. The relative RMSE in each season demonstrates the possible recharge period of the ground water in Xiangjiang River basin.

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Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽

10.3390/w13152032 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2032

Author(s):

Pâmela A. Melo ◽

Lívia A. Alvarenga ◽

Javier Tomasella ◽

Carlos R. Mello ◽

Minella A. Martins ◽

...

Keyword(s):

Complex Terrain ◽

Overland Flow ◽

Hydrological Model ◽

Model Performance ◽

Soil Parameters ◽

Vegetation Model ◽

Landform Classification ◽

Physically Based ◽

And Performance ◽

Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

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Analysis of the development of a hydrological balance for future decades in the Senianska depression in the Eastern Slovak lowland

Slovak Journal of Civil Engineering ◽

10.2478/v10189-010-0020-6 ◽

2010 ◽

Vol 18 (4) ◽

pp. 30-40 ◽

Cited By ~ 1

Author(s):

M. Tegelhoffová

Keyword(s):

River Basin ◽

Overland Flow ◽

Water Levels ◽

Water Control ◽

Mike She ◽

Hydrological Balance ◽

Small Water ◽

Model Domain ◽

Physically Based ◽

Set Up

Analysis of the development of a hydrological balance for future decades in the Senianska depression in the Eastern Slovak lowlandThe goal of the article was to analyze the hydrological balance for future decades in a pilot area in the Eastern Slovak lowland. The aim was to set up the physically-based Mike SHE hydrological model for the modeling hydrological balance in the selected wetland ecosystem in the Eastern Slovak Lowland. The pilot area - the Senianska depression is located near the village of Senne, between the Laborec and Uh Rivers. Specifically, it is a traditional landscape of meadows, marshes, cultivated soil, small water control structures and forests. To get a complete model set up for simulating elements of the hydrologic balance in the pilot area, it was necessary to devise a model for a larger area, which includes the pilot area - the Senianska depression. Therefore, both the Mike SHE model was set up for the Laborec River basin (a model domain of 500 × 500 m) and the Čierna voda River basin (a model domain of 100 × 100 m), for the simulation period of 1981-2007, is order to get the boundary conditions (overland flow depth, water levels, discharges and groundwater table) for the model of the pilot area. The Mike SHE model constructed for the pilot area - the Senianska depression (a model domain of 1 × 1 m) -was used to simulate the elements of the hydrological balance for the existing conditions during the simulation period of 1983-2007 and for climate scenarios for the simulation period of 1983-2100. The results of the simulated elements of the hydrological balance for the existing conditions were used for a comparison of the evolution of the hydrologic conditions in the past, for identifying wet and flooded areas and for identifying the spatial distribution of the actual evapotranspiration in the pilot area. The built-up model with setting values was used for modeling the hydrological balance in changed conditions - climate change.

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Picturing and modeling catchments by representative hillslopes

Hydrology and Earth System Sciences ◽

10.5194/hess-21-1225-2017 ◽

2017 ◽

Vol 21 (2) ◽

pp. 1225-1249 ◽

Cited By ~ 19

Author(s):

Ralf Loritz ◽

Sibylle K. Hassler ◽

Conrad Jackisch ◽

Niklas Allroggen ◽

Loes van Schaik ◽

...

Keyword(s):

Expert Knowledge ◽

Water Dynamics ◽

Rainfall Runoff ◽

Data Set ◽

Physically Based Model ◽

Optimal Representation ◽

Physically Based ◽

Physically Based Models ◽

Streamflow Data ◽

Runoff Events

Abstract. This study explores the suitability of a single hillslope as a parsimonious representation of a catchment in a physically based model. We test this hypothesis by picturing two distinctly different catchments in perceptual models and translating these pictures into parametric setups of 2-D physically based hillslope models. The model parametrizations are based on a comprehensive field data set, expert knowledge and process-based reasoning. Evaluation against streamflow data highlights that both models predicted the annual pattern of streamflow generation as well as the hydrographs acceptably. However, a look beyond performance measures revealed deficiencies in streamflow simulations during the summer season and during individual rainfall–runoff events as well as a mismatch between observed and simulated soil water dynamics. Some of these shortcomings can be related to our perception of the systems and to the chosen hydrological model, while others point to limitations of the representative hillslope concept itself. Nevertheless, our results confirm that representative hillslope models are a suitable tool to assess the importance of different data sources as well as to challenge our perception of the dominant hydrological processes we want to represent therein. Consequently, these models are a promising step forward in the search for the optimal representation of catchments in physically based models.

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A Physically Based Runoff Model Analysis of the Querétaro River Basin

Journal of Applied Mathematics ◽

10.1155/2014/586872 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Carlos Javier Villa Alvarado ◽

Eladio Delgadillo-Ruiz ◽

Carlos Alberto Mastachi-Loza ◽

Enrique González-Sosa ◽

Ramos Salinas Norma Maricela

Keyword(s):

Land Use ◽

Water Balance ◽

River Basin ◽

Land Use Changes ◽

Geographical Location ◽

Physical Parameters ◽

Rainfall Runoff ◽

Strong Base ◽

Physically Based ◽

Runoff Model

Today the knowledge of physical parameters of a basin is essential to know adequately the rainfall-runoff process; it is well known that the specific characteristics of each basin such as temperature, geographical location, and elevation above sea level affect the maximum discharge and the basin time response. In this paper a physically based model has been applied, to analyze water balance by evaluating the volume rainfall-runoff using SHETRAN and hydrometric data measurements in 2003. The results have been compared with five ETp different methodologies in the Querétaro river basin in central Mexico. With these results the main effort of the authorities should be directed to better control of land-use changes and to working permanently in the analysis of the related parameters, which will have a similar behavior to changes currently being introduced and presented in observed values in this basin. This methodology can be a strong base for sustainable water management in a basin, the prognosis and effect of land-use changes, and availability of water and also can be used to determine application of known basin parameters, basically depending on land-use, land-use changes, and climatological database to determine the water balance in a basin.

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The Hydrologic Response of The Eaton River Basin, Quebec

Canadian Journal of Earth Sciences ◽

10.1139/e71-007 ◽

1971 ◽

Vol 8 (1) ◽

pp. 102-115 ◽

Cited By ~ 6

Author(s):

M. A. Carson ◽

E. A. Sutton

Keyword(s):

Surface Water ◽

River Basin ◽

Parametric Study ◽

Source Area ◽

Water Systems ◽

Hydrologic Response ◽

Rainfall Runoff ◽

Channel Network ◽

Storm Rainfall ◽

Runoff Production

This paper reports a parametric study of rainfall–runoff relations for 38 storms in the Eaton basin, southeastern Quebec, between 1950 and 1966. In addition to storm rainfall amounts, water table levels in the vicinity of the channel network, as indicated by baseflow prior to storms, appear to be very important in controlling the amount of response of the basin in different storms. Storm runoff is viewed as the product of direct interception by, and subsurface seepage into, expanded surface water systems in the valley floor areas of the basin. This is in agreement with the variable (partial) source area model developed over the last ten years by a number of hydrologists as an alternative to the Horton theory of runoff production.

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Application of Remotely Sensed Rainfall Data in Rainfall-Runoff Modelling. A Case of Pangani River Basin, Tanzania

Tanzania Journal of Engineering and Technology ◽

10.52339/tjet.v35i1.465 ◽

2014 ◽

Vol 35 (1) ◽

pp. 1-14

Author(s):

Joel Nobert ◽

Patric Kibasa

Keyword(s):

River Basin ◽

Average Method ◽

River Flow ◽

Tropical Rainfall Measuring Mission ◽

Rainfall Data ◽

Coefficient Of Determination ◽

Rainfall Runoff ◽

Data Set ◽

Satellite Rainfall ◽

Areal Rainfall

Rainfall runoff modelling in a river basin is vital for number of hydrologic applicationincluding water resources assessment. However, rainfall data from sparse gauging stationsare usually inadequate for modelling which is a major concern in Tanzania. This studypresents the results of comparison of Tropical Rainfall Measuring Mission (TRMM)satellite rainfall products at daily and monthly time-steps with ground stations rainfalldata; and explores the possibility of using satellite rainfall data for rainfall runoffmodelling in Pangani River Basin, Tanzania. Statistical analysis was carried out to find thecorrelation between the ground stations data and TRMM estimates. It was found thatTRMM estimates at monthly scale compare reasonably well with ground stations data.Time series comparison was also done at daily and annual time scales. Monthly and annualtime series compared well with coefficient of determination of 0.68 and 0.70, respectively.It was also found that areal rainfall comparison in the northern parts of the study area hadpoor results compared to the rest of areas. On the other hand, rainfall runoff modellingwith ground stations data alone and TRMM data set alone was carried out using five Real-Time River Flow Forecasting System models and then outputs combined by Models OutputsCombination Techniques. The results showed that ground stations data performed betterduring calibration period with coefficient of efficiency of 76.7%, 81.7% and 89.1% forSimple Average Method, Weight Average Method and Neural Network Method respectively.Simulation results using TRMM data were 59.8%, 73.5% and 76.8%. It can therefore beconcluded that TRMM data are adequate and promising in hydrological modelling.

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Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-4325-2007 ◽

2007 ◽

Vol 4 (6) ◽

pp. 4325-4360 ◽

Cited By ~ 2

Author(s):

A. H. te Linde ◽

J. C. J. H. Aerts ◽

R. T. W. L. Hurkmans ◽

M. Eberle

Keyword(s):

Climate Change ◽

Model Performance ◽

Change Scenario ◽

Hydrological Models ◽

Rainfall Runoff ◽

Meteorological Forcing ◽

Lumped Model ◽

Distributed Models ◽

Rhine Basin ◽

Physically Based

Abstract. Due to the growing wish and necessity to simulate the possible effects of climate change on the discharge regime on large rivers such as the Rhine in Europe, there is a need for well performing hydrological models that can be applied in climate change scenario studies. There exists large variety in available models and there is an ongoing debate in research on rainfall-runoff modelling on whether or not physically based distributed models better represent observed discharges than conceptual lumped model approaches do. In this paper, the hydrological models HBV and VIC were compared for the Rhine basin by testing their performance in simulating discharge. Overall, the semi-distributed conceptual HBV model performed much better than the distributed physically based VIC model (E=0.62, r2=0.65 vs. E=0.31, r2=0.54 at Lobith). It is argued here that even for a well-documented river basin such as the Rhine, more complex modelling does not automatically lead to better results. Moreover, it is concluded that meteorological forcing data has a considerable influence on model performance, irrespectively to the type of model structure and the need for ground-based meteorological measurements is emphasized.

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Investigation of satellite rainfall-driven rainfall–runoff model using deep learning approaches in two different catchments in India

Journal of Hydroinformatics ◽

10.2166/hydro.2021.067 ◽

2021 ◽

Author(s):

Pavan Kumar Yeditha ◽

Maheswaran Rathinasamy ◽

Sai Sumanth Neelamsetty ◽

Biswa Bhattacharya ◽

Ankit Agarwal

Keyword(s):

Deep Learning ◽

River Basin ◽

Flood Forecasting ◽

Precipitation Data ◽

Rainfall Runoff ◽

Data Set ◽

Satellite Precipitation ◽

Satellite Rainfall ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

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