Rainfall Model Research Articles

Modeling of rainfall–runoff events using HEC-HMS model in southern catchments of Jerusalem Desert-Palestine

Modeling of rainfall–runoff events using HEC-HMS model in southern catchments of Jerusalem Desert-Palestine

Risk assessment for inland flooding in a small urban catchment : Focusing on the temporal distribution of rainfall and dual drainage model

Risk assessment for inland flooding in a small urban catchment : Focusing on the temporal distribution of rainfall and dual drainage model

WRF Rainfall Modeling Post-Processing by Adaptive Parameterization of Raindrop Size Distribution: A Case Study on the United Kingdom

Raindrop size distribution (RSD) is a key parameter in the Weather Research and Forecasting (WRF) model for rainfall estimation, with gamma distribution models commonly used to describe RSD under WRF microphysical parameterizations. The RSD model sets the shape parameter (μ) as a constant of gamma distribution in WRF double-moment bulk microphysics schemes. Here, we propose to improve the gamma RSD model with an adaptive value of μ based on the rainfall intensity and season, designed using a genetic algorithm (GA) and the linear least-squares method. The model can be described as a piecewise post-processing function that is constant when rainfall intensity is <1.5 mm/h and linear otherwise. Our numerical simulation uses the WRF driven by an ERA-interim dataset with three distinct double-moment bulk microphysical parameterizations, namely, the Morrison, WDM6, and Thompson aerosol-aware schemes for the period of 2013–2017 over the United Kingdom at a 5 km resolution. Observations were made using a disdrometer and 241 rain gauges, which were used for calibration and validation. The results show that the adaptive-μ model of the gamma distribution was more accurate than the gamma RSD model with a constant shape parameter, with the root-mean-square error decreasing by averages of 23.62%, 11.33%, and 22.21% for the Morrison, WDM6, and Thompson aerosol-aware schemes, respectively. This model improves the accuracy of WRF rainfall simulation by applying adaptive RSD parameterization and can be integrated into the simulation of WRF double-moment microphysics schemes. The physical mechanism of the RSD model remains to be determined to improve its performance in WRF bulk microphysics schemes.

Simulation performance of selected global and regional climate models for temperature and rainfall in some locations in India

Global and regional climate models are reported to have inherent bias in simulating the observed climatology of a region. This bias of climate models is the major source of uncertainties in climate change impact assessments. Therefore, use of bias corrected simulated climate data is important. In this study, the bias corrected climate data for 30 years’ period (1976-2005) from selected common fourGCMs and RCMs for six Indian locations are compared with the respective observed data of India Meteorological Department. The analysis indicated that the RCMs performance is much better than GCMs after bias correction for minimum and maximum temperatures. Also, RCMs performance is better than GCMs in simulating extreme temperatures. However, the selected RCMs and GCMs are found to either over estimate or under estimate the rainfall despite bias correction and also overestimated the rainfall extremes for selected Indian locations. Based on the overall performance of four models for the six locations, it was found that the GFDL_ESM2M and NORESM1-M RCMs performed comparatively better than CSIRO and IPSL models. After bias correction, the RCMs could represent the observed climatology better than the GCMs. And these RCMs viz., GFDL_ESM2M and NORESM1-M can be usedindividually after bias correction in the climate change assessment studies for the selected regions.

Reliability Assessment of Regional Climate Modeling: Case study in Johor State, Malaysia

This paper presents the work of statistically downscaling the CAN ESM 2 (Canada Earth System Model 2) climate data into regional climate data to produce the future climate scenario using the RCP (Representative Concentration Pathways) 2.6,4.5 and 8.5 green-house gas concentration trajectory suggested by Intergovernmental Panel on Climate Change Fifth Assessment Reports (IPCC-AR5). Selected location for regional climate downscaling includes Batu Pahat (1° 52’ N 102° 59’ E) and Kulai (1° 38’ N 103° 40’ E), downscaled outcome of monthly rainfall (mm), daily maximum (Tmax) and daily minimum (Tmin) temperature (°C) was produced by using SDSM (Statistical Downscaling Model) software to calibrate the CANESM2 output with the historical data. Quantile-mapping bias correction by using exponential distribution function was done to obtain bias corrected rainfall data. Reliability test using Pearson correlation coefficient was done by comparing between actual historical data. Based on Pearson correlation applied on bias corrected results, for Batu Pahat, the most suitable RCP model for both Tmax and Tmin is RCP 2.6, with correlation of 0.74 and 0.72, most suitable model for rainfall is RCP 4.5 with correlation of 0.24. For Kulai, the most suitable RCP model for Tmin is RCP 8.5, with correlation of 0.63, for Tmax and rainfall the suitable model is RCP 2.6, with correlation of 0.73 and 0.36. In overall, the more appropriate model to describe the climate for both Batu Pahat and Kulai based on Pearson correlation from year 2006 to 2019 is RCP 2.6, as the RCP 2.6 model are having higher correlation to the historical data.

Modification of basic hydrology formulation based on an approach of the rational method at field measurement

The rational method was a simple technique for estimating the design discharge of a small watershed and can be used in a probabilistic approach. The main parameter in the rational method formula was the C coefficient. The rational method was the primary method for determining the peak discharge from surface runoff flow. At the end of the dry season and the beginning of the rainy season, it placed an automatic water level recorder and automatic rain gauges for the initial water level. This field research aimed to correct a simple hydrological formulation with field measurements and build a numerical rainfall modelling based on rainfall simulation, infiltration, land use, and flow parameters according to the relationship model of precipitation. The hypothesis in this study, the basic formulation of hydrology, is simple: it requires experience to fit the theoretical hydrological formula so simply (simplicity). The theory needs to be simplified because it is an experience of field necessary to explain.

Estimation Models for Soil Water Content and Rainfall in Andong

Estimation Models for Soil Water Content and Rainfall in Andong

Assessment of mudflow risk in Uzbekistan using CMIP5 models

Precipitation induced mudflows are a major and longstanding threat in Uzbekistan, impacting on many properties and livelihoods. In this paper, the role of large-scale atmospheric circulation in producing the conditions necessary to initiate mudflows in piedmont areas of Uzbekistan have been evaluated based on historical and scenario (Representative Concentration Pathways; RCP8.5) experiments along from 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Applying the well-established circulation weather type (CWT) technique, and CMIP5 models reveal that mudflow generating large-scale circulation flows will increase by up to 5% to the end of the century. Considering the historical simulations over 1979–2005 and following the projections of RCP8.5 emission scenario for the target period of 2071–2100, precipitation climatology has been evaluated using bias correction techniques. By this way, the synthetic rainfall series were linked to a central proxy – a mudflow generating weather types, such as cyclonic (C), westerly (W) and south-westerly (SW) in order to diagnose potential changes in mudflow occurrences given the changed CWT characteristics by running the statistical-empirical algorithm of antecedent daily rainfall model (ADRM) and statistical logistic regression (LRM). Results for the important weather types (C, W and SW) confirm that mudflow activity will increase in the selected region as precipitation values associated with the CWT C and W flows in CMIP5 projections are expected to increase in the warm season for the target period of 2071–2100.The research focuses on piedmont areas of Uzbekistan as it has remained poorly understood due to limited climate research, particularly, in mountain areas. This is important in the face of climate change, which is likely to increase pressure upon high mountain areas that may need to investigate more frequent mudflow occurrences.

Accounting for satellite rainfall uncertainty in rainfall-triggered landslide forecasting

With an increasing spatial/temporal resolution and accuracy of satellite rainfall products, their potential in landslide forecasting has gradually been recognized. Despite this, few studies have evaluated the effect of satellite rainfall uncertainty on landslide forecasting. In this study, a specific procedure is developed to account for the satellite rainfall uncertainty in rainfall-triggered landslide forecasting. We select the Emilia-Romagna region in northern Italy as the study area, and the NASA GPM-based IMERG Early Run product as the representative of satellite rainfall estimates. An error model for satellite rainfall is first constructed based on the distribution of GR|SR (gauge rainfall | satellite rainfall), which consists of a deterministic distortion component and a stochastic component. With the error model, the satellite rainfall uncertainty is characterized by generating the ensemble of probable “true rainfall” for each rainfall condition responsible for landslides. Then the generated rainfall ensembles are applied to the definition of rainfall thresholds using the bootstrap technique. Finally, the landslide forecasting performance of rainfall thresholds is evaluated with the criteria of hit rate, false alarm rate and the Euclidean distance to the perfect point. Results show that the simulated uncertainty band of the IMERG product encompasses most of the rain gauge measurements. The IMERG uncertainty also propagates to the rainfall thresholds, especially when rainfall events have long durations, the cumulated event rainfall that is likely to trigger landslides has a larger variation range. Besides, the landslide forecasting performance for thresholds that account for IMERG uncertainty varies a lot in terms of false alarm rate from 0.50 to 0.75, demonstrating that it is necessary to take into account the uncertainty associated with the IMERG Early Run product when using it to construct rainfall thresholds for landslide forecasting.

Biases in sea surface temperature and the annual cycle of Greater Horn of Africa rainfall in CMIP6

AbstractClimatological rainfall across much of the Greater Horn of Africa has a bimodal annual cycle characterized by the short rains from October to December and the long rains from March to May. Previous generations of climate models from the Coupled Model Intercomparison Project (CMIP3 and CMIP5) generally misrepresented the bimodal rainfall distribution in this region by generating too much rainfall during the short rains and too little during the long rains. The peak of the long rains in these models also typically showed a pronounced 1‐month lag relative to observations. Here, the ability of 21 CMIP6 models to properly simulate the observed, climatological annual cycle of Greater Horn rainfall is examined, comparing results with CMIP5 and CMIP3. As previous work has shown a connection between Greater Horn climatological rainfall biases and model biases in sea surface temperatures (SSTs), pattern correlations of climatological SST biases are also analysed. For the multi‐model mean, it is found that the earlier biases in Greater Horn rainfall and associated SSTs persist in CMIP6. Examining only the three best performing models in each CMIP group reveals the CMIP6 models outperform those in CMIP3, with mixed results regarding improvements over CMIP5. For the best performing CMIP6 models, the SST and 850 hPa wind biases are reduced over the Indian Ocean relative to the other CMIP6 models examined. No statistically significant relationship was identified between CMIP6 model performance and the horizontal resolution of the model. Combined, these results indicate the importance of properly simulating the annual cycle of SSTs in order to successfully model the observed rainfall annual cycle in the Greater Horn.

Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk.

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

AbstractRainfall–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 Nash-Sutcliffe Efficiency Coefficient (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.

Optimal Stochastic Forecast Models of Rainfall in South-West Region of Nigeria

Rainfall estimates are important components of water resources applications, especially in agriculture, transport constructing irrigation and drainage systems. This paper aims to stochastically model and forecast the rainfall trend and pattern for a city, each purposively selected in five states of the South-Western Region of Nigeria. The data collected from Nigerian Meteorological Agency (NIMET) website are captured with fractional autoregressive integrated moving average (ARFIMA) and seasonal autoregressive integrated moving average (SARIMA) models. The autocorrelation function (ACF) and partial autocorrelation function (PACF) are used for model identification, the models selected are subjected to diagnostic checks for the models adequacy. Several tests: Augmented Dickey Fuller (ADF), Ljung Box and Jarque Bera tests are used for investigating unit root, serial autocorrelation and normality of residuals, respectively; the mean square error, root mean square error and mean absolute error are employed in validating the optimal stochastic model for each city in all states, in which the model with the lowest error of forecasting of all competing models is suggested as the best. The analyses and findings suggest SARIMA(1,0,1)(1,1,0) [12], SARIMA(3,0,2)(1,0,0) [12], SARIMA(1,0,0)(1,1,0) [12], SARIMA(2,0,2)(2,1,0) [12] and SARIMA(0,0,1)(1,1,0) [12] for (Ibadan) Oyo State, (Ikorodu) Lagos State, (Osogbo) Osun State, (Abeokuta) Ogun State and (Akure) Ondo state, respectively. The seasonal ARIMA (SARIMA) model was proven to be the best optimal stochastic forecast model for forecasting rainfall in the selected cities. The SARIMA model was, therefore, recommended as a veritable technique that will assist decision makers (Government, Farmers, and Policymakers) to establish better strategies “aprior” on the management of rainfall against upcoming weather changes to ensure increase in agricultural yields for the betterment of the citizenry and general economic growth.

The role of land use and morphology representation in the setup and calibration of the conceptual TUW model

The processes of the transformation of rainfall to runoff are highly complicated, and the proper characterisation of these processes with conceptual hydrological models is a very challenging task. Morphology and land cover have a significant influence on a river basin’s hydrologic response. Thus, catchment characteristics of the topography and land use play an essential role in parametrising the runoff concentration processes in hydrological models. In the study, our goal was to detect which characteristics and their spatial distribution influence the efficiency of a conceptual rainfall-runoff model efficiency most. The spatially lumped and semi-distributed versions of the TUW conceptual rainfall model, which is an HBV type model, were compared. Both models use the concept of lumped storages associated with the surface and subsurface, interconnected by thresholds and links to simulate the runoff transformation. We focused on two land-use characteristics, the percentage cover of the agricultural land and percentage cover of the forests, and the mean slope of the terrain as a topography characteristic. The differences between runoff model efficiencies both in the calibration and validation periods were evaluated. Based on which version of the model was more effective in the simulation of the runoff, it was detected which types of catchment land use, and morphology were better represented by using the lumped or semi-distributed version of the TUW model, respectively. The analysis aimed to improve the understanding of the influence of spatial representation morphology and land cover in conceptual models on model efficiency and may help to improve model setup and calibration.

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have demonstrated the applicability of LSTM-based models for rainfall–runoff modelling; however, LSTMs have not been tested on catchments in Great Britain (GB). Moreover, opportunities exist to use spatial and seasonal patterns in model performances to improve our understanding of hydrological processes and to examine the advantages and disadvantages of LSTM-based models for hydrological simulation. By training two LSTM architectures across a large sample of 669 catchments in GB, we demonstrate that the LSTM and the Entity Aware LSTM (EA LSTM) models simulate discharge with median Nash–Sutcliffe efficiency (NSE) scores of 0.88 and 0.86 respectively. We find that the LSTM-based models outperform a suite of benchmark conceptual models, suggesting an opportunity to use additional data to refine conceptual models. In summary, the LSTM-based models show the largest performance improvements in the north-east of Scotland and in south-east of England. The south-east of England remained difficult to model, however, in part due to the inability of the LSTMs configured in this study to learn groundwater processes, human abstractions and complex percolation properties from the hydro-meteorological variables typically employed for hydrological modelling.

Assessment of the groundwater yield and sustainability of the transboundary Dibdibba aquifer using numerical modelling approach

This study investigates the sustainable yield of a shared/regional aquifer that covers a hydrogeological basin, Dibdibba Delta Aquifer Basin (DDAB). The aquifer extends over an area of 60,000 km2 in three countries: Iraq, Kuwait, and Saudi Arabia. The groundwater resources of DDAB are used by these countries for agricultural and industrial purposes. The study aims at assessing the sustainable yield of DDAB accounting its recharge, discharge rates including submarine groundwater discharge (SGD) rates, and trans-boundary fluxes. The methodology adopted in this study is based on hydrogeological investigations, modified Chloride Mass Balance (CMB) for brackish aquifers to approximate recharge rates from rainfall, HEC-GeoHMS–SCS–CN model with suitable GIS tools to determine runoff rates from rainfall, numerical flow and salinity modeling. These calibrated numerical models were used to simulate the effect of utilization plan of brackish groundwater from the DDAB from 1975 until 2030. The results of the study show that aquifer recharge from rainfall is on average 9.6% and runoff rate from rainfall is about 10%. The simulation results show that SGD rates from Kuwait and Iraq to the Arabian Gulf were 184 and 43 Mm3/yr, respectively. The current pumping activities in the DDAB can be increased from the existing utilization level at 425 Mm3/yr to the sustainable yield level at 515 Mm3/yr with a consequence of increasing the drawdown to 12 m and salinity deterioration of 205% in some areas.

Temporal change of extreme precipitation intensity–duration–frequency relationships in Thailand

Abstract Due to climate change, many research studies have derived the updated extreme precipitation intensity–duration–frequency relationship (IDF curve) from forecasted sub-hourly rainfall intensity time series, which is one of the most important tools for the planning and designing of hydraulic infrastructures. In this study, the IDF curves (1990–2016) of the six regions and procedures are used in accordance with those of the Royal Irrigation Department (RID)’s study (1950–1988). Each set of IDF relationships consists of 81 intensity values which are the combination of nine durations and nine return periods. The intensity ratios of this study and RID are compared. A greater-than-1 ratio indicates extreme intensity increment from the past to the present. Considering 81 ratios for each region, the number of greater-than-1 ratios for the North, Northeast, Central, East, West, and South regions are 8, 2, 31, 34, 6, and 7, respectively. These ratio numbers are far below 81 which means that the majority of extreme rainfall intensities do not increase from the past to the present. The study found that using accurate historical sub-hourly rainfall time series to create a set of IDF curves would be more reliable than using forecasted rainfall modeling.

The impacts of climate change on rainfall modeling in the Pantanal of Mato Grosso do Sul

The most significant and influential meteorological element in environmental conditions and human activities is precipitation. The objective of this study was to adjust eight probability distributions to monthly, seasonal and annual rainfall data in the Pantanal of Mato Grosso do Sul, Brazil, using a time series of data (1983-2013) by the National Meteorological Water Agency (ANA). The performance evaluation of different probability distribution models was assessed by the quality of fit of the selected probability distributions for precipitation data. Quality tests as chi-square, Kolmogorov-Smirnov (KS) and Anderson-Darling (AD), the information criteria as Akaike (AIC) and the Bayesian criterion (BIC) were used. Then the mean root square error (RMSE) and the coefficient of determination (R2) were applied. The analyzes were made monthly, annually and by seasons. The 3-parameter Lognormal distribution performs the best for all twelve months and provides the best-fit to the monthly rainfall data. Thus characterizing a dry period that runs from May to September and a rainy period between the months of October and April, it was observed that the 3-parameter Lognormal distribution has best adjustment for spring and summer, and for winter and autumn the 2-parameter Gamma and 3-parameter Gamma distribution performed better. For annual observations, the function that best fits is 3-parameter Weibull distribution.

How suitable are satellite rainfall estimates in simulating high flows and actual evapotranspiration in MelkaKunitre catchment, Upper Awash Basin, Ethiopia?

Understanding the suitability of Satellite Rainfall Estimates (SREs) in simulating high flows and Actual Evapotranspiration (AET) is crucial for developing flood monitoring systems. Therefore, this study aims to assess i) the suitability of SREs in simulating both high flows and AET for different levels of model complexity, and ii) the effect of streamflow calibration on simulating AET for different rainfall inputs in Melkakunitre catchment, Upper Awash Basin, Ethiopia. Three state-of-the-art SREs (TRMM 3B42v7, IMERG v06B, and TAMSAT v3) were used and their usefulness in simulating high flows (Q5), daily streamflow, and wet season flows (from June to September) was assessed using the HBV-light model for the period 2003–2015. The model was set up for two levels of complexity: with and without considering the effect of orography on rainfall and temperature. Moreover, the water balance derived AET was compared against three remotely sensed AET products, MOD 16A2, GLEAM v3, and SSEBob, so as to examine the effect of streamflow calibration on AET simulation. Results show that rainfall inputs and model complexity have a strong impact on simulating streamflow and AET. For all rainfall forcing datasets, the performance of the hydrological model improves when we consider the effects of orography on rainfall and temperature. The IMERG v06B and TAMSAT v3 products showed the highest and least performances in simulating all the three flow conditions, respectively. Moreover, the MODIS-AET is the best remotely sensed AET product in reproducing the water balance-derived AET for all rainfall inputs except TAMSAT v3. The HBV-light model parameters calibrated with streamflow provided better results for simulating AET as well. On average, the usefulness of the IMERG v06B product for simulating high flows and AET is outstanding and can be thus used for developing flood monitoring and management systems in the study catchment.

Hydrological modeling of rainfall-runoff of the semi-arid Aguibat Ezziar watershed through the GR4J model

Abstract The management of water resources requires as a first step the modeling of rainfall-runoff. It allows simulating the hydrological behavior of the basin for a good evaluation of the potentiality of this in terms of water production. There are different hydrological models used for water resource assessment, but conceptual models are still the most used due to their simple structure and satisfactory performance. In this study, t he performances of the conceptual model of rainfall and runoff (GR4J) modeled under R with the AirGR package, are used to Aguibat Ezziar the subbasin of the Bouregreg basin in Morocco. The enormous amount of data required and the uncertainty of some of the m makes these models of limited usefulness. The GR4J model allows evaluation of the runoff rates and describes the hydrological behavior of the Aguibat Ezziar watershed, which presents the aim behind writing this paper. A period from 2003 to 2017 has been selected. This period has been divided into two parts: one for calibration (2003-2006), and one for validation (2013-2016). After the calibration of the model and following the performance obtained (Nash higher than 0.72) we can say that the GR4J model behaves well in the Aguibat Ezziar catchment area.