scholarly journals Assessment of the Performance of Rainfall-Runoff Model GR4J to Simulate Streamflow in Ouémé Watershed at Bonou’s outlet (West Africa)

2018 ◽  
Author(s):  
Domiho Japhet Kodja ◽  
Gil Mahé ◽  
Ernest Amoussou ◽  
Michel Boko ◽  
Jean-Emmanuel Paturel
Keyword(s):  
Climate Change ◽  
High Water ◽  
High Flow ◽  
Performance Criteria ◽  
Rainfall Runoff ◽  
Flow Rates ◽  
Water Flows ◽  
Rainfall Runoff Model ◽  
And Performance ◽  
Runoff Model

The study aims to analyze the performance criteria of the GR4J model to reproduce high water flows in the Ouémé watershed at Bonou's outlet which has been vulnerable to climate change in recent decades. The methodology focused on the use of daily climatological and hydrometric data extracted from files of National Directorate of Meteorology, and General Directorate of Water; they were supplemented by those of SIEREM/HSM dataset over the period 1961-2015. The rainfall was regionalized using Thiessen method. The performance of the GR4J model was assessed with NSE, RMSE and KGE criteria. The results indicate that the study area is marked by rainfall variabilities and detection of two breakpoints (1968 and 1987) which divide the series into three sub-periods; these discontinuities have repercussions on the streamflow. It's found that GR4J model overestimates the streamflow during the low water period and underestimates them in high water. However, the efficiency and performance criteria NSE, RMSE and KGE calculated on high water flow rates are better in calibration than in validation. The KGE values are range between 83-85% in calibration and 56-68% during validation, which gives to GR4J model the efficiency and performance to reproduce high flow rates in the study area

scholarly journals Climate change model as a decision support tool for water resources management in northern Iraq: a case study of Greater Zab River

2017 ◽  
Vol 10 (1) ◽  
pp. 197-209 ◽  
Author(s):  
Y. Osman ◽  
N. Al-Ansari ◽  
M. Abdellatif
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Daily Rainfall ◽  
Decision Support Tool ◽  
Northern Region ◽  
Future Climate Change ◽  
Rainfall Runoff ◽  
Support Tool ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract The northern region of Iraq heavily depends on rivers, such as the Greater Zab, for water supply and irrigation. Thus, river water management in light of future climate change is of paramount importance in the region. In this study, daily rainfall and temperature obtained from the Greater Zab catchment, for 1961–2008, were used in building rainfall and evapotranspiration models using LARS-WG and multiple linear regressions, respectively. A rainfall–runoff model, in the form of autoregressive model with exogenous factors, has been developed using observed flow, rainfall and evapotranspiration data. The calibrated rainfall–runoff model was subsequently used to investigate the impacts of climate change on the Greater Zab flows for the near (2011–2030), medium (2046–2065), and far (2080–2099) futures. Results from the impacts model showed that the catchment is projected to suffer a significant reduction in total annual flow in the far future; with more severe drop during the winter and spring seasons in the range of 25 to 65%. This would have serious ramifications for the current agricultural activities in the catchment. The results could be of significant benefits for water management planners in the catchment as they can be used in allocating water for different users in the catchment.

scholarly journals Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

2010 ◽  
Vol 11 (2) ◽  
pp. 482-495 ◽  
Author(s):  
Mohammad Sajjad Khan ◽  
Paulin Coulibaly
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Rainfall Runoff ◽  
Bayesian Neural Network ◽  
Uncertainty Estimates ◽  
Change Impact ◽  
Climate Change Impact Study ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Different Sources

Abstract A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall–runoff modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network–based rainfall–runoff model—namely, Bayesian neural network (BNN)—is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The study is undertaken to simulate daily river flow and daily reservoir inflow in the Serpent and the Chute-du-Diable watersheds, respectively, in northeastern Canada. It is found that the uncertainty bands of the mean ensemble flow (i.e., flow simulated using the mean of the ensemble members of downscaled meteorological variables) is able to mostly encompass all other flows simulated with various individual downscaled meteorological ensemble members whichever CGCM or emission scenario is used. In addition, the uncertainty bands are also able to typically encompass most of the flows simulated with another rainfall–runoff model, namely, Hydrologiska Byråns Vattenbalansavdelning (HBV). The study results suggest that the BNN model could be used as an effective hydrological modeling tool in assessing the hydrologic effect of climate change with uncertainty estimates in the form of confidence intervals. It could be a good alternative method where resources are not available to implement the general multimodel ensembles approach. The BNN approach makes the climate change impact study on water resources with uncertainty estimate relatively simple, cost effective, and time efficient.

scholarly journals Quantitative analysis of runoff reduction in the Laohahe basin

2012 ◽  
Vol 43 (1-2) ◽  
pp. 38-47 ◽  
Author(s):  
Liliang Ren ◽  
Xiaofan Liu ◽  
Fei Yuan ◽  
Jing Xu ◽  
Wei Liu
Keyword(s):  
Climate Change ◽  
Human Activity ◽  
Hydrological Model ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Runoff Reduction ◽  
Runoff Series ◽  
Drastic Effect ◽  
Rainfall Runoff Model ◽  
Runoff Model

In order to determine the reason for runoff reduction, daily natural runoff series were restored using a conceptual rainfall–runoff model. The period of 1970–1979 was regarded as a base period with little human activity; model parameters for each subcatchment within the Laohahe basin were calibrated for this period. The effects of human activity and climate change on runoff were quantified by comparing the observed runoff and the natural runoff simulated by the hydrological model. The results show that the observed annual mean runoffs in the 1980s and especially in the 2000s are smaller than those of the 1970s. Although runoff reduction in the 1980s and 2000s is mainly caused by climate change, human activity also plays an important role on the runoff reduction. Taking the 2000 as an example, human activity and climate change are responsible for 45.6 and 54.4% of the runoff reduction in Laohahe basin, respectively. The effect of human activity on runoff reduction in the Laohahe basin is increasingly intensive from the 1980s to the 2000s. Human activity in the Dianzi catchment has the most drastic effect within the Laohahe basin.

scholarly journals Coupling Tank Model and Lars-Weather Generator in Assessments of the Impacts of Climate Change on Water Resources

2019 ◽  
Vol 27 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Naser Mohammadzadeh ◽  
Bahman Jabbarian Amiri ◽  
Leila Eslami Endergoli ◽  
Shirin Karimi
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Weather Generator ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
The Caspian Sea ◽  
Tank Model ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract With the aim of assessing the impact of climate change on surface water resources, a conceptual rainfall-runoff model (the tank model) was coupled with LARS-WG as a weather generator model. The downscaled daily rainfall, temperature, and evaporation from LARS-WG under various IPCC climate change scenarios were used to simulate the runoff through the calibrated Tank model. A catchment (4648 ha) located in the southern basin of the Caspian Sea was chosen for this research study. The results showed that this model has a reasonable predictive capability in simulating minimum and maximum temperatures at a level of 99%, rainfall at a level of 93%, and radiation at a level of 97% under various scenarios in agreement with the observed data. Moreover, the results of the rainfall-runoff model indicated an increase in the flow rate of about 108% under the A1B scenario, 101% under the A2 scenario, and 93% under the B1 scenario over the 30-year time period of the discharge prediction.

scholarly journals A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments

2011 ◽  
Vol 59 (3) ◽  
pp. 145-156 ◽  
Author(s):  
Marco Vinagre ◽  
Claudio Blanco ◽  
André Amarante Mesquita
Keyword(s):  
Frequency Distribution ◽  
Power Plants ◽  
Gain Factor ◽  
Performance Criteria ◽  
Rainfall Runoff ◽  
Process Data ◽  
Non Linear ◽  
Flow Frequency ◽  
Rainfall Runoff Model ◽  
Runoff Model

A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments The objective of this paper is to simulate flow frequency distribution curves for Amazon catchments with the aim of scaling power generation from small hydroelectric power plants. Thus, a simple nonlinear rainfall-runoff model was developed with sigmoid-variable gain factor due to the moisture status of the catchment, which depends on infiltration, and is considered a factor responsible for the nonlinearity of the rainfall-runoff process. Data for a catchment in the Amazon was used to calibrate and validate the model. The performance criteria adopted were the Nash-Sutcliffe coefficient (R2), the RMS, the Q95% frequencyc flow percentage error, and the mean percentage errors ranging from Q5% to Q95%.. Calibration and validation showed that the model satisfactorily simulates the flow frequency distribution curves. In order to find the shortest period of rainfall-runoff data, which is required for applying the model, a sensitivity analysis was performed whereby rainfall and runoff data was successively reduced by 1 year until a 1.5-year model application minimum period was found. This corresponds to one hydrological year plus the 6-month long "memory". This analysis evaluates field work in the ungauged sites of the region.

scholarly journals Estimation of the impact of climate change-induced extreme precipitation events on floods

2015 ◽  
Vol 45 (3) ◽  
pp. 173-192 ◽  
Author(s):  
Kamila Hlavčová ◽  
Milan Lapin ◽  
Peter Valent ◽  
Ján Szolgay ◽  
Silvia Kohnová ◽  
...  
Keyword(s):  
Climate Change ◽  
Rainfall Runoff ◽  
Mountainous Regions ◽  
Global Circulation Models ◽  
Extreme Precipitation Events ◽  
Air Temperatures ◽  
Physically Based ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract In order to estimate possible changes in the flood regime in the mountainous regions of Slovakia, a simple physically-based concept for climate change-induced changes in extreme 5-day precipitation totals is proposed in the paper. It utilizes regionally downscaled scenarios of the long-term monthly means of the air temperature, specific air humidity and precipitation projected for Central Slovakia by two regional (RCM) and two global circulation models (GCM). A simplified physically-based model for the calculation of short-term precipitation totals over the course of changing air temperatures, which is used to drive a conceptual rainfall-runoff model, was proposed. In the paper a case study of this approach in the upper Hron river basin in Central Slovakia is presented. From the 1981–2010 period, 20 events of the basin’s most extreme average of 5-day precipitation totals were selected. Only events with continual precipitation during 5 days were considered. These 5-day precipitation totals were modified according to the RCM and GCM-based scenarios for the future time horizons of 2025, 2050 and 2075. For modelling runoff under changed 5-day precipitation totals, a conceptual rainfall-runoff model developed at the Slovak University of Technology was used. Changes in extreme mean daily discharges due to climate change were compared with the original flood events and discussed.

Parameterization of the rainfall-runoff model in changing climate

2021 ◽  
Author(s):  
Milica Aleksić ◽  
Patrik Sleziak ◽  
Kamila Hlavčová
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Climate Scenario ◽  
Future Climate ◽  
Future Climate Change ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

AbstractA conceptual rainfall-runoff model was used for estimating the impact of climate change on the runoff regime in the Myjava River basin. Changes in climatic characteristics for future decades were expressed by a regional climate model using the A1B emission scenario. The model was calibrated for 1981–1990, 1991–2000, 2001–2010, 2011–2019. The best set of model parameters selected from the recent calibration period was used to simulate runoff for three periods, which should reflect the level of future climate change. The results show that the runoff should increase in the winter months (December and January) and decrease in the summer months (June to August). An evaluation of the long-term mean monthly runoff for the future climate scenario indicates that the highest runoff will occur in March.

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