scholarly journals Spatially Distributed Hydrological Modelling of a Western Africa Basin

10.29007/1xw5 ◽  
2018 ◽  
Author(s):  
Khalidou M. Bâ ◽  
Vitali Diaz ◽  
Miguel Angel Gómez-Albores ◽  
Carlos Díaz-Delgado ◽  
Nancy Nájera-Mota ◽  
...  
Keyword(s):  
Model Performance ◽  
Tropical Rainfall Measuring Mission ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Temporal Behaviour ◽  
Western Africa ◽  
Spatially Distributed ◽  
Model Efficiency ◽  
Spatio Temporal

Distributed hydrological simulations aid to investigate the spatio-temporal behaviour of hydrological variables. However, data to feed hydrological models are not always available mainly due to lack of gauges or high retrieval fees. In this research, two 0.25- degree daily precipitation databases from the Tropical Rainfall Measuring Mission (TRMM) were tested to simulate daily runoff in the basin of the main Upper Niger River tributary. Precipitation data are TRMM and TRMM Real Time (RT) 3B42V7. For runoff simulation, the grid-based hydrological model CEQUEAU was chosen. To estimate the evaporation in the model, temperatures were retrieved from the third-generation reanalysis ERA-Interim. From gauges and both TRMM data, monthly basin precipitation was calculated and compared to analyse the performance of TRMM to estimate rainfall. Runoff was simulated with each of these three precipitation products. In each case, the daily ERA-Interim temperatures were used. By Nash-Sutcliffe model Efficiency (NSE) and coefficient of determination (R2), model performance was evaluated through comparison of daily discharges with simulations for both calibration and validation periods. Results show correlation of TRMM by 0.95 and TRMMRT by 0.91 with gauge data. Both TRMM products combined with ERA-Interim temperature were found suitable for daily runoff modelling with NSE >0.835 and R2 >0.872.

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

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.

scholarly journals SWAT use of gridded observations for simulating runoff – a Vietnam river basin study

2011 ◽  
Vol 8 (6) ◽  
pp. 10679-10705 ◽  
Author(s):  
M. T. Vu ◽  
S. V. Raghavan ◽  
S. Y. Liong
Keyword(s):  
Swat Model ◽  
Model Performance ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Data Availability ◽  
Coefficient Of Determination ◽  
Reanalysis Dataset ◽  
Station Data ◽  
Rural And Urban ◽  
Grid Points

Abstract. Many research studies that focus on basin hydrology have used the SWAT model to simulate runoff. One common practice in calibrating the SWAT model is the application of station data rainfall to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) modified Global Historical Climatology Network version 2 (GHCN2) and one reanalysis dataset National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dakbla River (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Auto-calibration are performed for the SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) indices are used to benchmark the model performance. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

scholarly journals A MODIFIED EMPIRICAL MODEL FOR ESTIMATING THE WETTED ZONE DIMENSIONS UNDER DRIP IRRIGATION

2015 ◽  
Vol 76 (15) ◽  
Author(s):  
Ahmed A. M. Al-Ogaidi ◽  
Aimrun Wayayok ◽  
Md Rowshon Kamal ◽  
Ahmed Fikri Abdullah
Keyword(s):  
Empirical Model ◽  
Drip Irrigation ◽  
Irrigation System ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Irrigation Systems ◽  
Modified Model ◽  
Model Efficiency ◽  
The Mean ◽  
Statistical Criteria

Drip irrigation system has become one of the most common irrigation systems especially in arid and semi-arid regions due to its advantages in saving water. One of the most essential considerations in designing these systems is the dimensions of the wetted soil volume under emitters. These dimensions are significant in choosing the proper emitter spacing along the laterals and the suitable distance between laterals. In this study, a modified empirical equations for estimating the horizontal and vertical extend of the wetted zone under surface emitters were suggested. Data from published papers includes different conditions of soil properties and emitter discharge were used in deriving the empirical model using the nonlinear regression. The developed model has high value for coefficient of determination, R2. The results from the developed model were compared with results of other empirical models derived by other researchers. Some statistical criteria were used to evaluate the model performance which are the mean error ME, root mean square error RMSE, and model efficiency EF. The results revealed that the modified model showed good performance in predicting the wetted zone dimensions and it can be used in design and management of drip irrigation systems. 

The role of precipitation in hydrological model uncertainty

2020 ◽  
Author(s):  
András Bárdossy ◽  
Chris Kilsby ◽  
Faizan Anwar ◽  
Ning Wang
Keyword(s):  
Conceptual Models ◽  
Spatial Scales ◽  
State Variables ◽  
Rainfall Runoff ◽  
Physically Based ◽  
Spatially Distributed ◽  
Spatio Temporal ◽  
Step Care ◽  
Model Approach

<p>Rainfall-runoff models produce outputs which differ from observations due to uncertainties in process description, process parametrization, uncertainties in observations and changing spatio-temporal variability of input and state variables. Traditionally, attention has been focused mostly on process parameters to quantify runoff uncertainty using e.g. GLUE.</p><p>Here we have focused on the role of precipitation uncertainty relating to discharge. For this purpose, we used an inverse model approach. We generated time series of daily precipitation with high spatial resolution  using a modified version of Random Mixing and the Shannon-Whittaker interpolation to improve simulated runoff using the SHETRAN (physically-based) and HBV (conceptual) models, both spatially distributed for various sub-catchments of the Neckar River in Germany.  HBV was initially calibrated using interpolated precipitation, while SHETRAN uses pre-defined parameters. The modelling goal was to find a spatio-temporal series of precipitation which improved the predicted runoff,  under the constraints that the precipitation values be the same at the measurement locations and share their spatial variability with the observations at a given step. Care was taken to select subsequent days for improvement such that the previously improved step considered the effect of the previous steps.</p><p>We asked the questions: i) does improving precipitation inputs for one sub-catchment bring runoff improvement for the others? ii) Can the improved precipitation using SHETRAN be used for HBV and still get runoff improvements as compared to the interpolated precipitation and vice versa?</p><p>Results showed that overall runoff errors were reduced by 40 to 50% for all sub-catchments. For the peaks, a reduction of 70 to 90% was observed. As compared with the interpolated fields, new fields showed similar overall distribution but different details at finer spatial scales. Swapping improved precipitations between SHETRAN and HBV showed improvement as compared with the discharge from interpolated precipitation.</p>

scholarly journals SWMM-UrbanEVA: A Model for the Evapotranspiration of Urban Vegetation

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 243
Author(s):  
Birgitta Hörnschemeyer ◽  
Malte Henrichs ◽  
Mathias Uhl
Keyword(s):  
Model Performance ◽  
Soil Parameters ◽  
Suitable Model ◽  
Area Index ◽  
Fully Integrated ◽  
Model Efficiency ◽  
Spatio Temporal ◽  
Shading Effects ◽  
Moisture Conditions

Urban hydrology has so far lacked a suitable model for a precise long-term determination of evapotranspiration (ET) addressing shading and vegetation-specific dynamics. The proposed model “SWMM-UrbanEVA” is fully integrated into US EPA’s Stormwater Management Model (SWMM) and consists of two submodules. Submodule 1, “Shading”, considers the reduction in potential ET due to shading effects. Local variabilities of shading impacts can be addressed for both pervious and impervious catchments. Submodule 2, “Evapotranspiration”, allows the spatio-temporal differentiated ET simulation of vegetation and maps dependencies on vegetation, soil, and moisture conditions which are necessary for realistically modeling vegetation’s water balance. The model is tested for parameter sensitivities, validity, and plausibility of model behaviour and shows good model performance for both submodules. Depending on location and vegetation, remarkable improvements in total volume errors Vol (from Vol = 0.59 to −0.04% for coniferous) and modeling long-term dynamics, measured by the Nash–Sutcliffe model efficiency (NSE) (from NSE = 0.47 to 0.87 for coniferous) can be observed. The most sensitive model inputs to total ET are the shading factor KS and the crop factor KC. Both must be derived very carefully to minimize volume errors. Another focus must be set on the soil parameters since they define the soil volume available for ET. Process-oriented differentiation between ET fluxes interception evaporation, transpiration, and soil evaporation, using the leaf area index, behaves realistically but shows a lack in volume errors. Further investigations on process dynamics, validation, and parametrization are recommended.

scholarly journals A comparison between lumped and distributed hydrological models for daily rainfall-runoff simulation

2021 ◽  
Vol 958 (1) ◽  
pp. 012016
Author(s):  
F Vilaseca ◽  
S Narbondo ◽  
C Chreties ◽  
A Castro ◽  
A Gorgoglione
Keyword(s):  
Computational Cost ◽  
Model Performance ◽  
Daily Rainfall ◽  
Complex Model ◽  
The Other ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Distributed Hydrological Models ◽  
Social Importance

Abstract In Uruguay, the Santa Lucía Chico watershed has been studied in several hydrologic/hydraulic works due to its economic and social importance. However, few studies have been focused on water balance computation in this watershed. In this work, two daily rainfall-runoff models, a distributed (SWAT) and a lumped one (GR4J), were implemented at two subbasins of the Santa Lucía Chico watershed, with the aim of providing a thorough comparison for simulating daily hydrographs and identify possible scenarios in which each approach is more suitable than the other. Results showed that a distributed and complex model like SWAT performs better in watersheds characterized by anthropic interventions such as dams, which can be explicitly represented. On the other hand, for watersheds with no significant reservoirs, the use of a complex model may not be justified due to the higher effort required in modeling design, implementation, and computational cost, which is not reflected in a significant improvement of model performance.

scholarly journals Performance Evaluation of Mike Nam Rainfall-Runoff (R-R) Model in Daily Flow Simulation (Case Study: Gonbad Catchment in Hamedan)

2020 ◽  
Vol 10 (1) ◽  
pp. 1-6
Author(s):  
Yasamin Sajadi Bami ◽  
Jahangir Porhemmat ◽  
Hossein Sedghi ◽  
Navid Jalalkamali
Keyword(s):  
Flow Rate ◽  
Model Performance ◽  
Flow Simulation ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Mean Flow ◽  
Rainfall Runoff Model ◽  
The Right ◽  
Runoff Model

AbstractNowadays, many hydrological rainfall-runoff (R-R) models, both distributed and lumped, have been developed to simulate the catchment. However, selecting the right model to simulate a specific catchment has always been a challenge. A proper understanding of the model and its advantages and limitations is essential for selecting the appropriate model for the purpose of the study. To this end, several studies have been carried out to evaluate the performance of hydrological models for specific areas (mountainous, marshy and so on). This study was conducted aimed at evaluating the performance of MIKE11 NAM lumped conceptual hydrological rainfall-runoff model in simulation of daily flow rate in Gonbad catchment. The NAM model was calibrated and validated using flow rate data of three hydrometric stations of the Gonbad catchment. The model performance was evaluated using Percent bias (PBIAS) and the coefficient of determination or Nash-Sutcliffe coefficient. A Nash Sutcliffe efficiency (NSE) of 0.80, 0.89 and 080 were obtained during calibration, whereas, for the validation period, NSE of 0.81, 0.87 and 0.71 were obtained for Nemooneh sub catchment, Shahed sub catchment and Gonbad catchment respectively. Percent bias of -0.6, 1.5 and 6.3 were achieved for calibration, while -2.7, 7.6 and -4.2 were acquired during validation for Nemooneh sub catchment, Shahed sub catchment and Gonbad catchment respectively. Based on the results, the MIKE 11 NAM lumped conceptual model was capable of simulating daily mean flow rate and mean flow volume.

scholarly journals Investigating the Optimization Strategies on Performance of Rainfall-Runoff Modeling

10.29007/66vq ◽  
2018 ◽  
Author(s):  
Mehdi Sheikh Goodarzi ◽  
Bahman Jabbarian Amiri ◽  
Shabnam Navardi
Keyword(s):  
Model Performance ◽  
Absolute Error ◽  
Model Verification ◽  
Optimization Strategy ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Shuffled Complex Evolution ◽  
Runoff Modeling ◽  
Calibration And Verification

Regarding to importance of modeling calibration, this study will be focused on probabilistic role of different strategies in calibration and verification steps. Tank lumped conceptual model was selected as a hydrological platform to investigate the effects of each optimization strategy on model performance.However, much considerable efforts are required to calibrate a large number of parameters in conceptual models to obtain better results. With development of artificial intelligence, three probabilistic Global Search Algorithms (GSAs) including Shuffled Complex Evolution (SCE), Genetic Algorithm (GA) and Rosenbrock Multi-Start Search (RBN) and also three Objective Functions (OFs) consisted of Nash-Sutcliffe (NSE), Root Mean Square Error (RMSE) and mean absolute error (MAE) were employed for model calibration (comparing the performance of different GSAs versus OFs). The best set of parameters, which is derived from the calibration step, will be used as prediction coefficients for the model verification stage. Performance evaluation of the simulation results was undertaken using Coefficient of Correlation (r) and Descriptive Statistics.Results indicated that all of optimization strategies have a relative ability to retrieve optimal values of eighteen parameters of the Tank model. However, the best GSAs for daily runoff simulation are SCE (0.871) and GA (0.864), respectively, for calibration and verification phases. In case of the OFs result, NSE (0.763) and RMSE (0.834) are more performant for calibration and verification of the model. Finally, the best strategy was selected by combining the results of GSAs and OFs models. Finally, SCE*MAE (0.906) and GA*RMSE (0.868) were selected as a top series.

scholarly journals Improved calibration of Green-Ampt infiltration in the EROSION-2D/3D model using a rainfall-runoff experiment database

2020 ◽  
Author(s):  
Hana Beitlerová ◽  
Jonas Lenz ◽  
Jan Devátý ◽  
Martin Mistr ◽  
Jiří Kapička ◽  
...  
Keyword(s):  
Soil Erosion ◽  
3D Model ◽  
Transfer Functions ◽  
Model Performance ◽  
Percentage Error ◽  
Rainfall Runoff ◽  
Soil Infiltration ◽  
Runoff Simulation ◽  
Infiltration Process ◽  
The Impact

Abstract. Soil infiltration is one of the key factors that has an influence on soil erosion caused by rainfall. Therefore, a well-represented infiltration process is a necessary precondition for successful soil erosion modelling. Complex natural conditions do not allow the full mathematical description of the infiltration process and additional calibration parameters are required. The Green-Ampt based infiltration module in the EROSION-2D/3D model is adjusted by calibration of the skinfactor parameter. Previous studies provide skinfactor values for several combinations of soil and vegetation conditions. However, their accuracies are questionable and estimating the skinfactors for other than the measured conditions yields significant uncertainties in the model results. This study presents new empirically based transfer functions for skinfactor estimation that significantly improve the accuracy of the infiltration module and thus the overall EROSION-2D/3D model performance. The transfer functions are based on a statistical analysis of the rainfall-runoff simulation database, which contains 273 experiments compiled by two independent working groups. Linear mixed effects models, with a manual backward elimination approach for the predictor selection, were applied to derive the transfer functions. Soil moisture and bulk density were identified as the most significant predictors explaining 79 % of the skinfactor variability, followed by the soil texture and the impact of previous rainfall events. The mean absolute percentage error of the skinfactor prediction was improved from 192 % using the currently available method, to 66 % using the presented transfer functions. Error propagation of the predicted skinfactors into the surface runoff and soil loss on the hypothetical slope showed significant improvement in the EROSION-2D/3D results. A first validation of real rainfall-runoff events indicates good model performance for events with a higher total precipitation and intensity.

scholarly journals Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3528
Author(s):  
Muhammad Tariq Khan ◽  
Muhammad Shoaib ◽  
Muhammad Hammad ◽  
Hamza Salahudin ◽  
Fiaz Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
River Basin ◽  
Gene Expression Programming ◽  
Research Work ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Discrete Wavelet ◽  
Rainfall Runoff ◽  
The Impact

Rainfall–runoff modelling has been at the essence of research in hydrology for a long time. Every modern technique found its way to uncover the dynamics of rainfall–runoff relation for different basins of the world. Different techniques of machine learning have been extensively applied to understand this hydrological phenomenon. However, the literature is still scarce in cases of extensive research work on the comparison of streamline machine learning (ML) techniques and impact of wavelet pre-processing on their performance. Therefore, this study compares the performance of single decision tree (SDT), tree boost (TB), decision tree forest (DTF), multilayer perceptron (MLP), and gene expression programming (GEP) in rainfall–runoff modelling of the Soan River basin, Pakistan. Additionally, the impact of wavelet pre-processing through maximal overlap discrete wavelet transformation (MODWT) on the model performance has been assessed. Through a comprehensive comparative analysis of 110 model settings, we concluded that the MODWT-based DTF model has yielded higher Nash–Sutcliffe efficiency (NSE) of 0.90 at lag order (Lo4). The coefficient of determination for the model was also highest among all the models while least root mean square error (RMSE) value of 23.79 m3/s was also produced by MODWT-DTF at Lo4. The study also draws inter-technique comparison of the model performance as well as intra-technique differentiation of modelling accuracy.

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