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 The role of catchment classification in rainfall-runoff modeling

2011 ◽  
Vol 8 (3) ◽  
pp. 6113-6153 ◽  
Author(s):  
Y. He ◽  
A. Bárdossy ◽  
E. Zehe
Keyword(s):  
Classification Scheme ◽  
Intrinsic Value ◽  
Climatic Conditions ◽  
Ungauged Basins ◽  
Classification Methods ◽  
Rainfall Runoff ◽  
Catchment Hydrology ◽  
Natural Classification ◽  
Runoff Modeling

Abstract. A sound catchment classification scheme is a fundamental step towards improved catchment hydrology science and prediction in ungauged basins. Two categories of catchment classification methods are presented in the paper. The first one is based directly on physiographic properties and climatic conditions over a catchment and regarded as a Linnaean type or natural classification scheme. The second one is based on numerical clustering and regionalization methods and considered as a statistical or arbitrary classification scheme. This paper reviews each category including what has been done since recognition of the intrinsic value of catchment classification, what is being done in the current research, as well as what is to be done in the future.

scholarly journals ASSESSMENT OF RAINFALL-RUNOFF SIMULATION MODEL BASED ON SATELLITE ALGORITHM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 529-541
Author(s):  
A. R. Nemati ◽  
M. Zakeri Niri ◽  
S. Moazami
Keyword(s):  
Conceptual Model ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Data Driven ◽  
Support Vector ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Research Fields ◽  
Hydrology And Water Resources ◽  
Trmm 3B42

Simulation of rainfall-runoff process is one of the most important research fields in hydrology and water resources. Generally, the models used in this section are divided into two conceptual and data-driven categories. In this study, a conceptual model and two data-driven models have been used to simulate rainfall-runoff process in Tamer sub-catchment located in Gorganroud watershed in Iran. The conceptual model used is HEC-HMS, and data-driven models are neural network model of multi-layer Perceptron (MLP) and support vector regression (SVR). In addition to simulation of rainfall-runoff process using the recorded land precipitation, the performance of four satellite algorithms of precipitation, that is, CMORPH, PERSIANN, TRMM 3B42 and TRMM 3B42RT were studied. In simulation of rainfall-runoff process, calibration and accuracy of the models were done based on satellite data. The results of the research based on three criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) showed that in this part the two models of SVR and MLP could perform the simulation of runoff in a relatively appropriate way, but in simulation of the maximum values of the flow, the error of models increased.

scholarly journals Is Precipitation a Good Metric for Model Performance?

2019 ◽  
Vol 100 (2) ◽  
pp. 223-233 ◽  
Author(s):  
Francisco J. Tapiador ◽  
Rémy Roca ◽  
Anthony Del Genio ◽  
Boris Dewitte ◽  
Walt Petersen ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Climate Models ◽  
Model Performance ◽  
Model Verification ◽  
Verification And Validation ◽  
Weather And Climate ◽  
Model Verification And Validation ◽  
Global Precipitation Mission ◽  
Global Precipitation

AbstractPrecipitation has often been used to gauge the performances of numerical weather and climate models, sometimes together with other variables such as temperature, humidity, geopotential, and clouds. Precipitation, however, is singular in that it can present a high spatial variability and probably the sharpest gradients among all meteorological fields. Moreover, its quantitative measurement is plagued with difficulties, and there are even notable differences among different reference datasets. Several additional issues sometimes lead to questions about its usefulness in model validation. This essay discusses the use of precipitation for model verification and validation and the crucial role of highly precise and reliable satellite estimates, such as those from NASA’s Global Precipitation Mission Core Observatory.

scholarly journals The effects of spatial discretization on performances and parameters of urban hydrological model

2019 ◽  
Vol 80 (3) ◽  
pp. 517-528 ◽  
Author(s):  
Qing Chang ◽  
So Kazama ◽  
Yoshiya Touge ◽  
Shunsuke Aita
Keyword(s):  
Spatial Resolution ◽  
Scale Effects ◽  
Model Performance ◽  
Computation Time ◽  
Rainfall Runoff ◽  
Total Runoff ◽  
Sensing Technology ◽  
Impervious Area ◽  
Runoff Modeling ◽  
The Impact

Abstract Selecting a proper spatial resolution for urban rainfall runoff modeling was not a trivial issue because it could affect the model outputs. Recently, the development of remote sensing technology and increasingly available data source had enabled rainfall runoff process to be modeled at detailed and microscales. However, the models with less complexity might have equally good performance with less model establishment and computation time. This study attempted to explore the impact of model spatial resolution on model performance and parameters. Models with different discretization degree were built up on the basis of actual drainage networks, urban parcels and specific land use. The results showed that there was very little difference in the total runoff volumes while peak flows showed obvious scale effects which could be up to 30%. Generally, model calibration could compensate the scale effect. The calibrated models with different resolution showed similar performances. The consideration of effective impervious area (EIA) as a calibration parameter marginally increased performance of the calibration period but also slightly decreased performance in the validation period which indicated the importance of detailed EIA identification.

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 Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

2022 ◽  
Author(s):  
Zhongrun Xiang ◽  
Ibrahim Demir
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Information ◽  
Model Performance ◽  
Grid Cell ◽  
Great Promise ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Runoff Modeling ◽  
Runoff Model

Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

scholarly journals MODELAGEM CHUVA-VAZÃO EM UMA BACIA TROPICAL UTILIZANDO O MODELO IPH II / RAINFALL-RUNOFF MODELING IN A TROPICAL BASIN USING THE IPH II MODEL

Geo UERJ ◽  
2018 ◽  
pp. e30557
Author(s):  
Eduardo Morgan Uliana ◽  
Donizete Dos Reis Pereira ◽  
Demetrius David da Silva ◽  
Frederico Terra de Almeida ◽  
Adilson Pacheco de Souza
Keyword(s):  
Minas Gerais ◽  
Rainfall Runoff ◽  
Shuffled Complex Evolution ◽  
Runoff Modeling

O objetivo do presente trabalho foi avaliar a aplicabilidade do modelo hidrológico IPH II para a estimativa de vazões diárias na bacia hidrográfica do rio Pomba assim como verificar a sua acurácia na simulação de eventos extremos, de forma a obter informações para o planejamento e gestão dos recursos hídricos, além da previsão e mitigação de eventos de cheia no local. A sub-bacia selecionada para o estudo teve como seção de controle a estação fluviométrica Guarani, a qual drena uma área de 1.650 km2, localizada no estado de Minas Gerais. Os dados de precipitação e evapotranspiração de referência, requeridos como dados de entrada no modelo IPH II, foram obtidos pelos métodos de Thiessen e Hargreaves-Samani, respectivamente. A calibração do modelo foi realizada de forma automática utilizando o algoritmo Shuffled Complex Evolution (SCE-UA), que possibilitou a estimativa dos parâmetros do modelo de forma rápida e eficiente. Os resultados obtidos com a utilização do modelo IPH II mostraram que as estimativas das vazões diárias foram adequadas e boas, com base no coeficiente de Nash-Sutcliffe, incluindo as máximas e mínimas diárias anuais e, também, as vazões mínimas de referência para fins de outorga, o que permite concluir que o modelo tem potencial para ser utilizado na gestão de recursos hídricos, na previsão de vazões de cheias e na mitigação de seus efeitos, assim como para análise de consistência e preenchimento de falhas nos dados de vazões.

scholarly journals Application of SAC-SMA and IPH II hydrological models in the Teles Pires River basin, Brazil

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Eduardo Morgan Uliana ◽  
Frederico Terra de Almeida ◽  
Adilson Pacheco de Souza ◽  
Ibraim Fantin da Cruz ◽  
Luana Lisboa ◽  
...  
Keyword(s):  
River Basin ◽  
Water Resource Management ◽  
Management Strategies ◽  
Rainfall Runoff ◽  
Detailed Model ◽  
Mato Grosso ◽  
Soil Moisture Accounting ◽  
Shuffled Complex Evolution ◽  
Runoff Modeling ◽  
And Performance

ABSTRACT Parameterization and performance analysis of a hydrological model allow its consolidation, so that water-resource management strategies could be evaluated and extreme events forecast. In this context, this study aimed to evaluate the performance of the Sacramento Soil Moisture Accounting (SAC-SMA) and IPH II models for runoff estimation in the Teles Pires River basin, which is located in the Amazon region, State of Mato Grosso, Brazil. Both models were automatically calibrated using Shuffled Complex Evolution algorithm (SCE-UA) and validated for five runoff monitoring units. Our results showed that both are suitable for daily runoff modeling in the Teles Pires River basin with higher performance in larger drainage area basins. We can also infer that the simple use of complex rainfall-runoff models might not provide improved estimates. Although the SAC-SMA is the most complex and detailed model for hydrological processes, it has not outperformed IPH II in any of the monitoring units in the Teles Pires River.

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 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.

Sign in / Sign up

Export Citation Format

Share Document