High-Resolution, Integrated Hydrological Modeling of Climate Change Impacts on a Semi-Arid Urban Watershed in Niamey, Niger

This study evaluates the impact of climate change on water resources in a large, semi-arid urban watershed located in the Niamey Republic of Niger, West Africa. The watershed was modeled using the fully integrated surface–subsurface HydroGeoSphere model at a high spatial resolution. Historical (1980–2005) and projected (2020–2050) climate scenarios, derived from the outputs of three regional climate models (RCMs) under the regional climate projection (RCP) 4.5 scenario, were statistically downscaled using the multiscale quantile mapping bias correction method. Results show that the bias correction method is optimum at daily and monthly scales, and increased RCM resolution does not improve the performance of the model. The three RCMs predicted increases of up to 1.6% in annual rainfall and of 1.58 °C for mean annual temperatures between the historical and projected periods. The durations of the minimum environmental flow (MEF) conditions, required to supply drinking and agricultural water, were found to be sensitive to changes in runoff resulting from climate change. MEF occurrences and durations are likely to be greater from 2020–2030, and then they will be reduced for the 2030–2050 statistical periods. All three RCMs consistently project a rise in groundwater table of more than 10 m in topographically high zones, where the groundwater table is deep, and an increase of 2 m in the shallow groundwater table.

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High-Resolution Integrated Hydrological Modeling of Climate Change Impacts on a Semi-arid Urban Watershed in Niamey, Niger

10.20944/preprints201912.0329.v1 ◽

2019 ◽

Author(s):

Abdou Boko Boubacar ◽

Konaté Moussa ◽

Nicaise Yalo ◽

Steven J. Berg ◽

Andre R. Erler ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Hydrological Modeling ◽

Correction Method ◽

Groundwater Table ◽

Annual Rainfall ◽

Urban Watershed ◽

Bias Correction Method ◽

The Impact ◽

Semi Arid

This study evaluated the impact of climate change on water resources in a large semi-arid urban watershed located in Niamey Republic of Niger, West Africa. The watershed was modeled using the fully integrated surface-subsurface HydroGeoSpheremodel at a high spatial resolution. Historical (1980-2005) and projected (2020-2050) climate scenario derived from the outputs of three Regional Climate Models (RCM) under the RCP 4.5 scenario were statistically downscaled using the multiscale quantile mapping bias correction method. Results show that the bias correction method is optimum at daily and monthly scales, and increased RCM resolution does not improve the performance of the model. The three RCMs predict increases of up to 1.6% in annual rainfall and of 1.58°C for mean annual temperatures between the historical and projected periods. The durations of the Minimum Environmental Flow (MEF) conditions, required to supply drinking and agriculture water, were found to be sensitive to changes in runoff resulting from climate change. MEF occurrences and durations are likely to be greater for (2020-2030), and then they will be reduced for (2030-2050). All three RCMs consistently project a rise in groundwater table of more than 10 meters in topographically high zones where the groundwater table is deep and an increase of 2 meters in the shallow groundwater table.

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Estimating monthly rainfall in rural river basins under climate change: an improved bias-correcting statistical downscaling approach

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-6847-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 6847-6896

Author(s):

D. L. Jayasekera ◽

J. J. Kaluarachchi

Keyword(s):

Climate Change ◽

General Circulation Model ◽

River Basin ◽

Bias Correction ◽

General Circulation ◽

Circulation Model ◽

Correction Method ◽

Annual Rainfall ◽

Bias Correction Method ◽

Time Space

Abstract. This study extended the work of Kim et al. (2008) to generate future rainfall under climate change using a discrete-time/space Markov chain based on historical conditional probabilities. A bias-correction method is proposed by fitting suitable statistical distributions to transform rainfall from the general circulation model (GCM) scale to watershed scale. The demonstration example used the Nam Ngum River Basin (NNRB) in Laos which is a rural river basin with high potential for hydropower generation and significant rain-fed agriculture supporting rural livelihoods. This work generated weekly rainfall for a 100 yr period using historical rainfall data from 1961 to 2000 for ten selected weather stations. The bias-correction method showed the ability to reduce bias of the mean values of GCMs when compared to the observed mean amount at each station. The simulated rainfall series is perturbed using the delta change estimated at each station to project future rainfall for the Special Report on Emission Scenarios (SRES) A2. GCMs consisting of third generation coupled general circulation model (CGCM3.1 T63) and European center Hamburg model (ECHAM5) projected an increasing trend of mean annual rainfall in the NNRB. Seasonal rainfall percent changes showed an increase in the wet and dry seasons with the highest increase in the dry season mean rainfall of about 31% from 2051 to 2090. While the GCM projections showed good results with appropriate bias corrections, the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model significantly underestimated historical behavior and produced higher mean absolute errors compared to the corresponding GCM predictions.

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Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Proceedings ◽

10.3390/ecws-3-05809 ◽

2018 ◽

Vol 7 (1) ◽

pp. 14 ◽

Cited By ~ 1

Author(s):

Enrique Soriano ◽

Luis Mediero ◽

Carlos Garijo

Keyword(s):

Climate Change ◽

Bias Correction ◽

Correction Method ◽

Flood Frequency ◽

Precipitation Extremes ◽

Bias Correction Method ◽

Simulated Precipitation ◽

The Future ◽

The Impact ◽

Frequency Curves

Annual maximum daily rainfalls will change in the future because of climate change, according to climate projections provided by EURO-CORDEX. This study aims at understanding how the expected changes in precipitation extremes will affect the flood behavior in the future. Hydrological modeling is required to characterize the rainfall-runoff process adequately in a changing climate to estimate flood changes. Precipitation and temperature projections given by climate models in the control period usually do not fit the observations in the same period exactly from a statistical point of view. To correct such errors, bias correction methods are used. This paper aims at finding the most adequate bias correction method for both temperature and precipitation projections, minimizing the errors between observed and simulated precipitation and flood frequency curves. Four catchments located in central western Spain have been selected as case studies. The HBV hydrological model has been calibrated, using the observed precipitation, temperature, and streamflow data available at a daily scale. Expected changes in precipitation extremes are usually smoothed by the reduction of soil moisture content due to expected increases in temperatures and decreases in mean annual precipitation. Consequently, rainfall is the most significant input to the model and polynomial quantile mapping is the best bias correction method.

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Impact of Bias Correction on Present and Mid-Future Rainfall Projections over Senegal

European Scientific Journal ESJ ◽

10.19044/esj.2021.v17n37p137 ◽

2021 ◽

Vol 17 (37) ◽

pp. 137

Author(s):

Sarr Alioune Badara ◽

Diatta Samo ◽

Kébé Ibourahima ◽

Sultan Benjamin ◽

Camara Moctar

Keyword(s):

Bias Correction ◽

Regional Climate ◽

Correction Method ◽

Climate Change Signal ◽

Linear Scaling ◽

Rainfall Events ◽

Mean Intensity ◽

Bias Correction Method ◽

The Mean ◽

The Impact

In this study, we analyze the impact of bias correction models on present and future precipitation and extremes rainfall events over Senegal. The commonly used linear scaling (LS) bias correction method has been applied on four (4) regional climate models (RCMs) of the Coordinated Regional Climate Downscaling Experiment (CORDEX) program. The linear scaling bias correction method was firstly calibrated and validated during the 1976-1990 and 1991-2005 periods, respectively. The comparison with the observed data revealed that the linear scaling method significantly improves the mean and the extreme precipitations during the validation period. The RCMs generally simulate a decrease of rainfall in the mid-twenty-first century under the RCP8.5 greenhouse gas concentration pathway compared to the reference period (1976-2005), except for the CCLM4 and the RCA4 models which show respectively a slight increase overall Senegal and the east of the country. The changes in precipitation indices such as the number of wet days (R1mm) and mean frequency of heavy rainfall events (R20mm) follows that mean precipitation change distribution. Almost uncorrected RCMs (except RCA4) predict during the near future an increase in of the mean intensity of daily rainfall events (SDII), the mean intensity of precipitation events above the 95th Percentile (R95PTOT) and the mean maximum dry spells length (CDD), whereas a decrease in the mean maximum wet spells length (CWD) is projected. After applying the LS bias correction, the spatial distribution patterns are not so much modified in all the models but the magnitude of the climate change signal is either amplified or moderated depending on the considered variables.

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Analysis of Rainfall-Runoff Characteristics on Bias Correction Method of Climate Change Scenarios

Journal of Korean Society on Water Environment ◽

10.15681/kswe.2015.31.3.241 ◽

2015 ◽

Vol 31 (3) ◽

pp. 241-252 ◽

Cited By ~ 1

Author(s):

Donghyuk Kum ◽

Younsik Park ◽

Young Hun Jung ◽

Min Hwan Shin ◽

Jichul Ryu ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Correction Method ◽

Climate Change Scenarios ◽

Rainfall Runoff ◽

Bias Correction Method

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

Water ◽

10.3390/w11010170 ◽

2019 ◽

Vol 11 (1) ◽

pp. 170 ◽

Cited By ~ 4

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.

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Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction

Agronomy ◽

10.3390/agronomy11050990 ◽

2021 ◽

Vol 11 (5) ◽

pp. 990

Author(s):

Joana Martins ◽

Helder Fraga ◽

André Fonseca ◽

João Andrade Santos

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Model ◽

Correction Method ◽

Maximum Temperature ◽

Lower Sensitivity ◽

Climate Projections ◽

Quantile Mapping ◽

Bias Correction Method ◽

Wine Region

The implications of weather and climate extremes on the viticulture and winemaking sector can be particularly detrimental and acquire more relevance under a climate change context. A four-member ensemble of the Regional Climate Model-Global Climate Model chain simulations is used to evaluate the potential impacts of climate change on indices of extreme temperature and precipitation, as well as on agroclimatic indices of viticultural suitability in the Douro Wine Region, Portugal, under current and future climate conditions, following the RCP8.5 anthropogenic radiative forcing scenario. Historical (1989–2005) and future (2051–2080) periods are considered for this purpose. Although model outputs are bias-corrected to improve the accuracy of the results, owing to the sensitivity of the climatic indicators to the specific bias correction method, the performance of the linear and quantile mapping methods are compared. The results hint at the importance of choosing the most accurate method (quantile mapping), not only in replicating extremes events but also in reproducing the accumulated agroclimatic indices. Significant differences between the bias correction methods are indeed found for the number of extremely warm days (maximum temperature > 35 °C), number of warm spells, number of warm spell days, number of consecutive dry days, the Dryness Index, and growing season precipitation. The Huglin Index reveals lower sensitivity, thus being more robust to the choice of the method. Hence, an unsuitable bias correction method may hinder the accuracy of climate change projections in studies heavily relying on derived extreme indices and agroclimatic indicators, such as in viticulture. Regarding the climate change signal, significant warming and drying trends are projected throughout the target region, which is supported by previous studies, but also accompanied by an increase of intensity, frequency, and duration of extreme events, namely heatwaves and dry spells. These findings thereby corroborate the need to adopt timely and effective adaptation strategies by the regional winemaking sector to warrant its future sustainability and enhance climate resilience.

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Assessment of Streamflow from EURO-CORDEX Regional Climate Simulations in Semi-Arid Catchments Using the SWAT Model

Sustainability ◽

10.3390/su13137120 ◽

2021 ◽

Vol 13 (13) ◽

pp. 7120

Author(s):

Alberto Martínez-Salvador ◽

Agustín Millares ◽

Joris P. C. Eekhout ◽

Carmelo Conesa-García

Keyword(s):

Climate Change ◽

Assessment Tool ◽

Swat Model ◽

Regional Climate ◽

Future Climate Change ◽

Historical Period ◽

Climate Change Scenarios ◽

Rcp 8.5 ◽

The Impact ◽

Semi Arid

This research studies the effect of climate change on the hydrological behavior of two semi-arid basins. For this purpose, the Soil and Water Assessment Tool (SWAT) model was used with the simulation of two future climate change scenarios, one Representative Concentration Pathway moderate (RCP 4.5) and the other extreme (RCP 8.5). Three future periods were considered: close (2019–2040), medium (2041–2070), and distant (2071–2100). In addition, several climatic projections of the EURO-CORDEX model were selected, to which different bias correction methods were applied before incorporation into the SWAT model. The statistical indices for the monthly flow simulations showed a very good fit in the calibration and validation phases in the Upper Mula stream (NS = 0.79–0.87; PBIAS = −4.00–0.70%; RSR = 0.44–0.46) and the ephemeral Algeciras stream (NS = 0.78–0.82; PBIAS = −8.10–−8.20%; RSR = 0.4–0.42). Subsequently, the impact of climate change in both basins was evaluated by comparing future flows with those of the historical period. In the RCP 4.5 and RCP 8.5 scenarios, by the end of the 2071–2100 period, the flows of the Upper Mula stream and the ephemeral Algeciras stream will have decreased by between 46.3% and 52.4% and between 46.6% and 55.8%, respectively.

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