scholarly journals Estimation of groundwater recharge using multiple climate models in Bayesian frameworks

2021 ◽  
Author(s):  
Kevin O. Achieng ◽  
Jianting Zhu
Keyword(s):  
Groundwater Recharge ◽  
River Basin ◽  
Empirical Bayes ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Bayesian Framework ◽  
Vital Role ◽  
Bias Error ◽  
Bayesian Priors

Abstract Groundwater recharge plays a vital role in replenishing aquifers, sustaining demand, and reducing adverse effects (e.g. land subsidence). In order to manage climate change-induced effects on groundwater dynamics, climate models are increasingly being used to predict current and future recharges. Even though there has been a number of hydrological studies that have averaged climate models’ predictions in a Bayesian framework, few studies have been related to the groundwater recharge. In this study, groundwater recharge estimates from 10 regional climate models (RCMs) are averaged in 12 different Bayesian frameworks with variations of priors. A recession-curve-displacement method was used to compute recharge from measured streamflow data. Two basins of different sizes located in the same water resource region in the USA, the Cedar River Basin and the Rainy River Basin, are selected to illustrate the approach and conduct quantitative analysis. It has been shown that groundwater recharge prediction is affected by the Bayesian priors. The non-Empirical Bayes g-Local-based Bayesian priors result in posterior inclusion probability values that are consistent with the performance of the climate models outside the Bayesian framework. With the proper choice of priors, the Bayesian frameworks can produce good results of groundwater recharge with R2, percent bias error, and Willmott's index of agreement of >0.97, <2%, and >0.97, respectively, in the two basins. The Bayesian framework with an appropriate prior provides opportunity to estimate recharge from multiple climate models.

scholarly journals Hydrological Modelling of the Mono River Basin at Athiémé

2021 ◽  
Vol 384 ◽  
pp. 57-62
Author(s):  
Eliézer Iboukoun Biao ◽  
Ezéchiel Obada ◽  
Eric Adéchina Alamou ◽  
Josué Esdras Zandagba ◽  
Amédée Chabi ◽  
...  
Keyword(s):  
River Basin ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Stochastic Approach ◽  
Projection Data ◽  
Rainfall Runoff ◽  
Future Projection ◽  
The Individual ◽  
Modelling Approach

Abstract. The objective of this study is to model the Mono River basin at Athiémé using stochastic approach for a better knowledge of the hydrological functioning of the basin. Data used in this study consist of observed precipitation and temperature data over the period 1961–2012 and future projection data from two regional climate models (HIRHAM5 and REMO) over the period 2016–2100. Simulation of the river discharge was made using ModHyPMA, GR4J, HBV, AWBM models and uncertainties analysis were performed by a stochastic approach. Results showed that the different rainfall-runoff models used reproduce well the observed hydrographs. However, the multi-modelling approach has improved the performance of the individual models. The Hermite orthogonal polynomials of order 4 are well suited for the prediction of flood flows in this basin. This stochastic modeling approach allowed us to deduce that extreme events would therefore increase in the middle of the century under RCP8.5 scenario and towards the end of the century under RCP4.5 scenario.

scholarly journals Using a Hydrologic Model to Assess the Performance of Regional Climate Models in a Semi-Arid Watershed in Brazil

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 170 ◽  
Author(s):  
Carlos Santos ◽  
Felizardo. Rocha ◽  
Tiago Ramos ◽  
Lincoln Alves ◽  
Marcos Mateus ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Northeastern Brazil ◽  
Impact Of Climate Change ◽  
Monthly Streamflow ◽  
The Impact

This study assessed the impact of climate change on the hydrological regime of the Paraguaçu river basin, northeastern Brazil. Hydrological impact simulations were conducted using the Soil and Water Assessment Tool (SWAT) for 2020–2040. Precipitation and surface air temperature projections from two Regional Climate Models (Eta-HadGEM2-ES and Eta-MIROC5) based on IPCC5—RCP 4.5 and 8.5 scenarios were used as inputs after first applying two bias correction methods (linear scaling—LS and distribution mapping—DM). The analysis of the impact of climate change on streamflow was done by comparing the maximum, average and reference (Q90) flows of the simulated and observed streamflow records. This study found that both methods were able to correct the climate projection bias, but the DM method showed larger distortion when applied to future scenarios. Climate projections from the Eta-HadGEM2-ES (LS) model showed significant reductions of mean monthly streamflow for all time periods under both RCP 4.5 and 8.5. The Eta-MIROC5 (LS) model showed a lower reduction of the simulated mean monthly streamflow under RCP 4.5 and a decrease of streamflow under RCP 8.5, similar to the Eta-HadGEM2-ES model results. The results of this study provide information for guiding future water resource management in the Paraguaçu River Basin and show that the bias correction algorithm also plays a significant role when assessing climate model estimates and their applicability to hydrological modelling.

scholarly journals Multi-Criteria Decision Framework to Evaluate Bias Corrected Climate Change Projections in the Piracicaba River Basin

2021 ◽  
Author(s):  
Camila Billerbeck ◽  
Ligia Monteiro da Silva ◽  
Silvana Susko Marcellini ◽  
Arisvaldo Méllo Junior
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Annual Rainfall ◽  
Monthly Precipitation ◽  
Climate Change Effects ◽  
Piracicaba River

Abstract Regional climate models (RCM) are the main tools for climate change impacts assessment in hydrological studies. These models, however, often show biases when compared to historical observations. Bias Correction (BC) are useful techniques to improve climate projection outputs. This study presents a multi-criteria decision analysis (MCDA) framework to compare combinations of RCM with selected BC methods. The comparison was based on the modified Kling-Gupta efficiency (KGE’). The criteria evaluated the general capability of models in reproducing the observed data main statistics. Other criteria evaluated were the relevant aspects for hydrological studies, such as seasonality, dry and wet periods. We applied four BC methods in four RCM monthly rainfall outputs from 1961 to 2005 in the Piracicaba river basin. The Linear Scaling (LS) method showed higher improvements in the general performance of the models. The RCM Eta-HadGEM2-ES, corrected with Standardized Reconstruction (SdRc) method, achieved the best results when compared to the observed precipitation. The bias corrected projected monthly precipitation (2006-2098) preserved the main signal of climate change effects when compared to the original outputs regarding annual rainfall. However, SdRc produced significant decrease in monthly average rainfall, higher than 45% for July, August and September for RCP4.5 and RCP8.5 scenarios.

scholarly journals Use of regional climate model simulations as input for hydrological models for the Hindukush–Karakorum–Himalaya region

2008 ◽  
Vol 5 (2) ◽  
pp. 865-902 ◽  
Author(s):  
M. Akhtar ◽  
N. Ahmad ◽  
M. J. Booij
Keyword(s):  
River Basin ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Meteorological Station ◽  
Hydrological Models ◽  
Calibration And Validation ◽  
Model Experiments ◽  
Hbv Model ◽  
Hunza River

Abstract. The most important climatological inputs required for the calibration and validation of hydrological models are temperature and precipitation that can be derived from observational records or alternatively from regional climate models (RCMs). In this paper, meteorological station observations and results of the PRECIS (Providing REgional Climate for Impact Studies) RCM driven by the outputs of reanalysis ERA-40 data and HadAM3P general circulation model (GCM) results are used as input in the hydrological model. The objective is to investigate the effect of precipitation and temperature simulated with the PRECIS RCM nested in these two data sets on discharge simulated with the HBV model for three river basins in the Hindukush-Karakorum-Himalaya (HKH) region. Three HBV model experiments are designed: HBV-Met, HBV-ERA and HBV-Had where HBV is driven by meteorological station data and by the outputs from PRECIS nested with ERA-40 and HadAM3P data, respectively. Present day PRECIS simulations possess strong capacity to simulate spatial patterns of present day climate characteristics. However, there also exist some quantitative biases in the HKH region, where PRECIS RCM simulations underestimate temperature and overestimate precipitation with respect to CRU observations. The calibration and validation results of the HBV model experiments show that the performance of HBV-Met is better than the HBV models driven by the PRECIS outputs. However, using input data series from sources different from the data used in the model calibration shows that HBV models driven by the PRECIS outputs are more robust compared to HBV-Met. The Gilgit and Astore river basin, which discharges are depending on the preceding winter precipitation, have higher uncertainties compared to the Hunza river basin which discharge is driven by the energy inputs. The smaller uncertainties in the Hunza river basin may be because of the stable behavior of the input temperature series compared to the precipitation series. The resulting robustness and uncertainty ranges of the HBV models suggest that in data sparse regions such as the HKH region data from regional climate models may be used as input in hydrological models for climate scenarios studies.

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