Hydrological Rainfall-runoff Model Research Articles

Analysing Urban Flooding Risk with CMIP5 and CMIP6 Climate Projections

Fitting probability distribution functions to observed data is the standard way to compute future design floods, but may not accurately reflect the projected future pattern of extreme events related to climate change. In applying the latest coupled model intercomparison project (CMIP5 and CMIP6), this research investigates how likely it is that precipitation changes in CMIP5 and CMIP6 will affect both the magnitude and frequency of flood analysis. GCM output from four modelling institutes in CMIP5, with representative pathway concentration (RCP8.5) and the corresponding CMIP6 shared socioeconomic pathways (SSP585), were selected for historical and future periods, before the project precipitation was statistically downscaled for selected cities by using delta, quantile mapping (QM), and empirical quantile mapping (EQM). On the basis of performance evaluation, a rainfall-runoff hydrological model was developed by using the stormwater management model (SWMM) for CMIPs (CMIP5 and CMIP6) in historical and future horizons. The results reveal an unprecedented increase in extreme events, for both CMIP5 (historical) and CMIP6 (future) projections. The years 2070–2080 were identified by both CMIP5 and CMIP6 as experiencing the most severe flooding.

Effect of Surface Characteristics and Rainfall in Infiltration Rate: A Case Study of Kaligandaki River Basin

Study of infiltration characteristics is crucial for effective watershed management, as it helps determine the water availability for crops, the need of irrigation, groundwater recharge, and surface runoff. This study is aimed at identifying the relation between infiltration rate and surface characteristics as well as rainfall rate in the Kaligandaki River basin of Nepal using Hydrologic Modelling System (HEC-HMS). HEC-HMS is a physically based, semi-distributed model that uses the weather and spatial information of soil and landuse of the basin for deriving the inputs for the model. A hydrological rainfall-runoff model is developed for the Kaligandaki River basin, with outlet at hydrological station 420, Kotgau, of Department of Hydrology and Meteorology (DHM). The model was then validated at DHM station 420, Angsing, of Kaligandaki River. Model generated subbasin wise infiltration rates and existing landuse were analysed and it was found that sub-basins with dominant landuse as forest had high infiltration rate among prevailing landuse types. Conversely, low infiltration rate was observed for barren area, snow/glacier and grassland landcover type dominated subbasins. A positive correlation among rainfall rate and infiltration rate was seen with linear increase in infiltration rate for an exponential increase of average rainfall intensity. Infiltration rate was found to have positive correlation with the Hydrologic Soil Group types as suggested by NRCS. Moreover, the study signified a greater impact of landcover over soil types on infiltration rates. Whereas, no significant impact of basin slope on infiltration rate could be identified, which may be due to preclusion of multiple topographic features. Understanding the impacts of rainfall intensity and surface characteristics on infiltration enables proactive measures to mitigate the issues like landslide and reduce flooding and soil erosion by improving infiltration rates through surface condition assessment.

Applicabilty of TRMM Precipitation for Hydrological Modeling Using HEC-HMS in Karnali River Basin in Nepal

This study aims to validate the HEC-HMS model in the Karnali river basin of Nepal, while also physically verifying the model parameters for the area. The HEC-HMS model is a semi-distributed physically based model that requires site-specific information about the weather, as well as spatial information on soil and land use. Using Arc-GIS, HEC-HMS, and HEC DSS, a hydrological rainfall runoff model was developed for the Karnali River basin at Asaraghat (DHM station 240) for the period of 1998-2005. Calibration for the time period of 1998-2001 resulted in an NSE value of 73.2% and an R2 value of 84%. Validation for the time period of 2003-2005 resulted in an NSE value of 72.4% and an R2 value of 82.02%. To evaluate the performance of the TRMM 3B42V7 rainfall product, statistical indicators such as NSE and R2 were used. Results showed that the TRMM rainfall data was reliable and had good precision in application to the Karnali River Basin. This study concludes that the TRMM rainfall data can be used as an alternative data source for basins with sparse rain gauge stations in Nepal. The results also suggest that the HEC-HMS modeling tool is good enough to relate rainfall and runoff. The model parameters for each sub-basin and reaches were analyzed with the physical characteristics of the watershed. Minor variations were found within calibrated and calculated Initial abstraction and Muskingum K, x, leading to the verification of the model parameters physically.

Prospective analysis of low flows in a climate change context in West Africa: case of the N’zo-Sassandra watershed (West of Côte d’Ivoire)

The objective of this study is to analyze the prospective of the low water flows of the N'zo-Sassandra River located in the West of Ivory Coast. The hydroclimatic data used (rainfall, ETP, flow) extend on the one hand over the historical period (1961-2017) and on the other hand over the future period 2071-2100 (CORDEX data). The methodological approach adopted is based on the analysis of past low flows, the climate prospective analysis and the hydrological prospective analysis. The hydrological impact variable used is the annual minimum monthly mean flow (MAMF). Thus, a pessimistic climate forecast scenario was used (RCP 8.5.). This climate scenario was then fed into a rainfall-runoff hydrological model (GR2M model) calibrated to the 1961-1990 reference period and validated over the 1991-2017 period. In response to the climate forcing, future monthly low-flow rates (MAMF) were simulated. An increase in the number of flows in the class ]0-4] m3/s was observed, reflecting low flows in the 2071–2100-time frame. The best statistical law retained following the frequency analysis of the low-water flows is the lognormal law. By 2071-2100, a decrease in rainfall (-19.2%) and an increase in temperature (+3.9°C) are predicted in the N'zo-Sassandra watershed. Frequent low-water flows estimated from the lognormal law have shown a bias of -50.38%, which translates into a decrease of half of the frequent low-water flows by the end of the 21st century compared to the current period. This decrease in flows will have consequences on the availability of water resources for irrigation, fishing, breeding, drinking water supply, navigation, etc. This is why we propose as alternative resources for agricultural needs, groundwater which is less impacted by global changes.

Basin Runoff Responses to Climate Change Using a Rainfall-Runoff Hydrological Model in Southeast Australia

The effects of climate change have been observed in the Murrumbidgee River basin, which is one of the main river basins in the southeast region of Australia. The study area is the largest and most important agricultural production area within the Murray Darling Basin (MDB). It produces more than AUD 1.9 billion of agricultural products annually and accounts for about 46% of Australia’s total agricultural production. Since Australia’s economy largely depends on its natural resources, climate change adversely impacts the economy in various ways. According to the Intergovernmental Panel on Climate Change’s fifth assessment report (IPCC, AR5), the adaptive capacity and adaptation processes have increased in Australia. The country has implemented policies and management changes in both rural and urban water systems to adapt to future drought, unexpected floods, and other climatic changes. In this study, future catchment runoff has been estimated using the hydrological model, Simplified Hydrolog (SIMHYD), which is integrated with data from three different General Circulation Models (GCMs) and future emission scenarios. Two different representative concentration pathway (RCP) emission scenarios, RCP 4.5 and RCP 8.5, have been used to obtain downscaled future precipitation and evapotranspiration data for the period of 2016 to 2100. Modeling results from the two emission scenarios showed an anticipated warmer and drier climate for the Murrumbidgee River catchment. Runoff in the Murrumbidgee catchment is affected by various dams and weirs, which yields positive results in runoff even when the monthly rainfall trend decreases. The overall runoff simulation result indicated that the impact of climate change is short and intense. The result of the Simplified Hydrolog (SIMHYD) modeling tool used in this study under the RCP 4.5 scenario for the period 2016 to 2045 indicates a significant future impact from climate change on the volumes of runoff in the Murrumbidgee River catchment. For the same period, the climate change prediction showed a decrease in total annual rainfall within the range of 2% to 62%. This reduction in rainfall is projected to decrease river runoff in the upper catchments (e.g., Tharwa, and Yass) by 17% to 58% over the projected periods. However, the runoff trends in the lower sub-catchments (e.g., Borambola) have increased by 137% to 87% under RCP 4.5 and RCP 8.5, respectively. This increasing potential runoff trend in the lower Murrumbidgee catchments gives an indication to build irrigation dams for dry season irrigation management.

Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa

Flood events occurrences and frequencies in the world are of immense worry for the stability of the economy and life safety. Africa continent is the third continent the most negatively affected by the flood events after Asia and Europe. Eastern Africa is the most hit in Africa. However, Africa continent is at the early stage in term of flood forecasting models development and implementation. Very few hydrological models for flood forecasting are available and implemented in Africa for the flood mitigation. And for the majority of the cases, they need to be improved because of the time evolution. Flash flood in Bamako (Mali) has been putting both human life and the economy in jeopardy. Studying this phenomenon, as to propose applicable solutions for its alleviation in Bamako is a great concern. Therefore, it is of upmost importance to know the existing scientific works related to this situation in Mali and elsewhere. The main aim was to point out the various solutions implemented by various local and international institutions, in order to fight against the flood events. Two types of methods are used for the flood events adaptation: the structural and non-structural methods. The structural methods are essentially based on the implementation of the structures like the dams, dykes, levees, etc. The problem of these methods is that they may reduce the volume of water that will inundate the area but are not efficient for the prediction of the coming floods and cannot alert the population with any lead time in advance. The non-structural methods are the one allowing to perform the prediction with acceptable lead time. They used the hydrological rainfall-runoff models and are the widely methods used for the flood adaptation. This review is more accentuated on the various types non-structural methods and their application in African countries in general and West African countries in particular with their strengths and weaknesses. Hydrologiska Byråns Vattenbalansavdelning (HBV), Hydrologic Engineer Center Hydrologic Model System (HEC-HMS) and Soil and Water Assessment Tool (SWAT) are the hydrological models that are the most widely used in West Africa for the purpose of flood forecasting. The easily way of calibration and the weak number of input data make these models appropriate for the West Africa region where the data are scarce and often with bad quality. These models when implemented and applied, can predict the coming floods, allow the population to adapt and mitigate the flood events and reduce considerably the impacts of floods especially in terms of loss of life.

Aprimorando a previsão de vazão para o setor elétrico brasileiro: uma estratégia baseada em um hiper-multimodelo

ABSTRACT Streamflow forecasting plays an important role in ensuring the reliable supply of electricity in countries heavily reliant on hydropower. This paper proposes a novel framework that integrates various hydrological models, climate models, and observational data to develop a comprehensive forecasting system. Three families of models were employed: seasonal forecasting climate models integrated with hydrological rainfall-runoff models; stochastic or machine learning models utilizing endogenous variables, and stochastic or machine learning models that consider exogenous variables. The hyper-multimodel framework could successfully increase the overall performance of the scenarios generated through the use of the individual models. The quality of the final scenarios generated was directly connected to the performance of the individual models. Therefore, the proposed framework has potential to improve hydrological forecast for the Brazilian electricity sector with the use of more refined and calibrated individual models.

Evaluation of Hydrological Rainfall Loss Methods Using Small-Scale Physical Landslide Model

An adequate representation of the relationship between effective rainfall and rainfall losses is required in hydrological rainfall–runoff models to reduce the uncertainty of the modelling results. This study evaluates the performance of several hydrological rainfall loss methods using the experimental data obtained from a laboratory small-scale physical landslide model with variable slope inclination, homogenous material and no vegetation effects. Three different experiments were selected and five rainfall loss methods were tested to evaluate their performance in reproducing the experimental results from the perspective of the surface runoff formation on the experimental slope. Initial and calibrated parameters were used to test the performance of these hydrological rainfall loss methods. The results indicate that the initial parameters of the rainfall loss model can satisfactorily reproduce the experimental results in some cases. Despite the fact that the slope material characteristics used in the laboratory experiments were relatively homogenous, some well-known methods yielded inaccurate results. Hence, calibration of the rainfall loss model proved to be essential. It should also be noted that, in some cases, the calibrated model parameters were relatively different from the initial model parameters estimated from the literature. None of the tested hydrological rainfall loss methods proved to be superior to the others. Therefore, in the case of natural environments with heterogeneous soil characteristics, multiple rainfall loss methods should be tested and the most suitable method should be selected only after cross-validation or a similar evaluation of the tested methods.

Soil Moisture and Black Truffle Production Variability in the Iberian Peninsula

The relationship between modelled root zone soil moisture (SM) and black truffle production in the Iberian Peninsula was studied. Previous works have investigated the influence that precipitation exerts on truffle yield highlighting the importance of water for the growth of black truffle. However, SM had not been used until now due to the lack of suitable databases. The SM series from the LISFLOOD hydrological rainfall–runoff model was used in this study. Annual black truffle yield series from 175 locations in Spain was correlated with SM for the period 1991–2012. For this, different approaches were applied considering daily, weekly and monthly temporal scales. The same analysis was carried out using precipitation data to compare the behaviors of both variables related to truffle production variability. The results obtained show critical periods in terms of soil water content in summer (June–September) and during October–November months. Moreover, a clear delay between precipitation and SM influence on black truffle was observed. The results obtained in this study highlight the importance of SM for black truffle production, since this variable truly expresses the available water for this fungus, which completes its entire life cycle living below ground.

SOL40: Forty Years of Simulations under Climate and Land Use Change

Nowadays, a major issue is land-use change by urban development that alters the catchment response to meteorological events. Urban basins have less storage capacity and more rapid runoff, so urban rivers rise more quickly during storms and have higher peak discharge rates than rural catchments. An exemplary case of this situation is the city of Milan (northern Italy) and its whole territory that extends towards the north collecting meteoric precipitation through the Seveso, Olona and Lambro (SOL) rivers. To assess the impact of anthropogenic development on urban catchment scale hydrology, a reanalysis of 40 years of simulations was carried out with the Curve Number (CN) map based on current land use and compared to simulations using the CN maps based on past land use. A coupled hydro-meteorological system was built that combined a physically based rainfall-runoff hydrological model FEST-WB, developed by the Politecnico di Milano, with the ERA5-Land hourly dataset for the period 1981 to 2020, that was provided by the ECMWF under the framework of the Copernicus Climate Change Service Programme. The study (named SOL40) analyses 40 year trends in the main meteorological (air temperature, precipitation) and hydrological variables (runoff) over the SOL area and tried to quantify and separate the impact of land use change from that of climate change.

Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile

We used the lumped rainfall–runoff hydrologic models Génie Rural à 4, 5, 6 paramètres Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating discharge on four forested small catchments (<40 ha) in south-central Chile. Different evapotranspiration methods were evaluated: Oudin, Hargreaves–Samani and Priestley–Taylor. Oudin’s model allows the achievement of the highest efficiencies in the flow simulation. The more sensitive parameters for each model were identified through a Generalized Probability Uncertainty Estimation (GLUE) model. Our results demonstrate that the three hydrological models were capable of efficiently simulating flow in the four study catchments. However, the GR6J model obtained the most satisfactory results in terms of simulated to measured streamflow closeness. In general, the three models tended to underestimate peak flow, as well as underestimate and overestimate flow events in most of the in situ observations, according to the probability of non-exceedance. We also evaluated the models’ performance in a simulation of summer discharge due to the importance of downstream water supply in the months of greatest scarcity. Again, we found that GR6J obtained the most efficient simulations.

Estimating the impact of climate change on water levels in a data-poor river basin in southeastern Brazil

Abstract In basins with multiple water uses, it is possible a conflict over water use may occur. This probability increases under a water scarcity scenario. Therefore, it is important to estimate the possible impact of climate change on water availability in this type of basin, to support the decision-making of its users. The Ribeirão do Cipó is an example of a Brazilian watershed susceptible to this situation. Besides having poor hydrological data, it is used for public water supply, electricity generation and recreation. The present work developed a methodology for estimating water availability impacted by climate change, which was particularly applied to this watershed. The methodology consisted of feeding the rainfall–runoff hydrological model called soil moisture accounting procedure with precipitation and evapotranspiration data projected by the Eta-CPTEC regional climate model and nested to three global climate models. The outputs methods were obtained in terms of average monthly flow, for 2010–2039, 2040–2069 and 2070–2099 for the representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios. Despite the small amount of hydrological data available on the basin, the results were similar to those of the methods used as reference, thus demonstrating that the methodology used can be an alternative in estimating flow for climate change scenarios.

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

Origin and variability of statistical dependencies between peak, volume, and duration of rainfall-driven flood events

Floods are among the most common and impactful natural events. The hazard of a flood event depends on its peak (Q), volume (V) and duration (D), which are interconnected to each other. Here, we used a worldwide dataset of daily discharge, two statistics (Kendall’s tau and Spearman’s rho) and a conceptual hydrological rainfall-runoff model as model-dependent realism, to investigate the factors controlling and the origin of the dependence between each couple of flood characteristics, with the focus to rainfall-driven events. From the statistical analysis of worldwide dataset, we found that the catchment area is ineffective in controlling the dependence between Q and V, while the dependencies between Q and D, and V and D show an increasing behavior with the catchment area. From the modeling activity, on the U.S. subdataset, we obtained that the conceptual hydrological model is able to represent the observed dependencies between each couple of variables for rainfall-driven flood events, and for such events, the pairwise dependence of each couple is not causal, is of spurious kind, coming from the “Principle of Common Cause”.

An Automatic Optimization Technique for the Calibration of a Physically Based Hydrological Rainfall-Runoff Model

Models are tools widely used in the prediction of hydrological phenomena. The present study aims to contribute to the implementation of an automatic optimization strategy of parameters for the calibration of a hydrological model based on the least action principle (HyMoLAP). The Downhill Simplex method is also known as the Nelder-Mead algorithm, which is a heuristic research method, is used to optimize the cost function on a given domain. The performance of the model is evaluated by the Nash Stucliffe Efficiency Index (NSE), the Root Mean Square Error (RMSE), the coefficient of determination (R2), the Mean Absolute Error (MAE). A comparative estimation is conducted using the Nash-Sutcliffe Modeling Efficiency Index and the mean relative error to evaluate the performance of the optimization method. It appears that the variation in water balance parameter values is acceptable. The simulated optimization method appears to be the best in terms of lower variability of parameter values during successive tests. The quality of the parameter sets obtained is good enough to impact the performance of the objective functions in a minimum number of iterations. We have analyzed the algorithm from a technical point of view, and we have carried out an experimental comparison between specific factors such as the model structure and the parameter’s values. The results obtained confirm the quality of the model (NSE = 0.90 and 0.75 respectively in calibration and validation) and allow us to evaluate the efficiency of the Nelder-Mead algorithm in the automatic calibration of the HyMoLAP model. The developed hybrid automatic calibration approach is therefore one of the promising ways to reduce computational time in rainfall-runoff modeling.

Soil Moisture Simulation in Selected Austrian Catchements With Use of the Tuw Conceptual Semi-Distributed Rainfall Runoff Model

Nowadays, hydrological rainfall-runoff models are routinely used for modelling hydrological balances, the generation of floods, or droughts. For decades numerous rainfallrunoff models have been tested with many calibration procedures and different model structures. However, for rainfall-runoff, it is challenging to find a particular model structure and parameter set that can correctly describe the complicated flow formation processes in diverse physiographic conditions. Improvements to existing rainfall-runoff modelling concepts and data assimilation are therefore continuously being tested. In recent years, remote sensing has played an increasing role in the surveying of hydrological phenomena. Remote sensing of soil moisture data can be very helpful because soil moisture measurements in field conditions are not always straightforward. The quality of remotely sensed data is rising; nowadays, we can routinely start using data with proper spatial and temporal resolutions. In this paper, we have focused on an evaluation of the parametrisation of the soil moisture submodel of the TUW rainfall-runoff model by remotely sensed soil moisture data. We calibrated the TUW model for three selected catchments in Austria with flat hypsometric characteristics using discharges as a criterion. For the calibration, we used both the lumped and semi-distributed model versions of the model and compared the quality of the soil moisture of both versions. Both the lumped and semi-distributed versions performed well in the discharge simulation. In the case of the soil moisture simulation, we achieved slightly better results with the semi-distributed version of the model. Difficulties with accessing the data from the remote sensing are discussed since remote sensing sensors still have problems when clouds and snow cover the catchments.

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

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

Abstract Nowadays, 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.

Calibration and Validation of the SWAT Model on the Watershed of Bafing River, Main Upstream Tributary of Senegal River: Checking for the Influence of the Period of Study

Management of reservoir water resources requires the knowledge of flow inputs in this reservoir. Hydrological rainfall-runoff model is used for this purpose. There are several types of hydrological model according the description of the hydrological processes: black-box models, conceptual models, deterministic physical based model. SWAT is a semi-distributed hydrological model designed for water quality and quantity. This versatile tool has been used all around the world to assess and manage water resources. The main objective of the paper is to calibrate and validate the SWAT model on the watershed of Bafing located between 10°30' and 12°30' north latitude and between 12°30' and 9°30' west longitude to assess climate change on this river flows. A DEM with a resolution of 12.5 m × 12.5 m, the daily average flows and the daily observed precipitations on the period 1979-1986 (long period) are used as inputs for the calibration, while precipitations for the period 1988-1994 are used for the validation. The sensitivity analysis was done to detect the most determining coefficients during the calibration step. It shows that 19 parameters are required. Then, the effect of the period on the parameters calibration is checked by considering first the whole period of study and then each year of the period of study. The Nash criterion was used to compare the calculated and the observed hygrographs in each case. The results showed that the longer is the period of calibration, the more accurate is the Nash criterion. The calibration per year gave a best Nash criterion except for a single year. During the validation, the parameters calculated on the long period lead to the best Nash criterion. The values of the Nash criterion calibration and validation are very suitable. These results of calibration can be used to study the long-term evolution of flow at Senegal River on Bafing Makana.

A Hybrid model to assess the hydropower potential for rivers

A computer program called hydropower plant site assessment program (HPSAP) was developed by using Visual Basic programming to assess the hydropower potential in river basins. A methodology to select the best site to install the hydropower plant was developed according to meteorological, hydrological and economical design criteria. HPSAP has several options to analyse watersheds for different head or power criteria. HPSAP estimates the locations coordinate and upstream drainage area for powerhouse and weir locations, elevation of powerhouse and weir, the distance between weir site and powerhouse site, penstock length, design head and power for proposed plants. The design discharge is estimated by using a flow duration curve regression equation developed by group method of data handling (GMDH) based on rainfall-runoff model outputs. The hydrological engineering centre-hydrological modelling system (HEC-HMS) model was used as the hydrological rainfall-runoff model in this study. The results have proven the ability of the program to assess the hydropower potential in river basins.