Local climate change projections and impact on the surface hydrology in the Vea catchment, West Africa

Larbi, Isaac; Hountondji, Fabien C. C.; Dotse, Sam-Quarcoo; Mama, Daouda; Nyamekye, Clement; Adeyeri, Oluwafemi E.; Koubodana, Houteta Djan’na; Odoom, Peter Rock Ebo; Asare, Yaw Mensah

doi:10.2166/nh.2021.096

Cited by 14 publications

(4 citation statements)

References 52 publications

(64 reference statements)

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“…Using SDSM, daily precipitation datasets were generated at a 0.002 km resolution. The model generated dataset according to the following steps: (1) control data quality, (2) screen predictor factor, (3) calibrate and validate the model, (4) generate weather generator, and (5) generate future scenario (Larbi et al, 2021).…”

Section: Wrf Model Temperature Simulationmentioning

confidence: 99%

High-resolution downscaled climate variables spatiotemporal variation and drought projected in the Sanjiang Plain, Northeast China

Huang

Xie

et al. 2023

Preprint

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Drought is greatly impacted by climate variables, and high-resolution downscaled climate variables are valuable for precisely identifying drought characteristics. Due to Sanjiang Plain’s crucial strategic position and drastic climate changes, we analyze its spatiotemporal variation in climate variables and standardized precipitation evapotranspiration index (SPEI). Two sharing economy pathway scenarios (SSP245 and SSP585) during the early (2023–2030), middle (2050–2060), and late periods (2090–2100) are projected. The Weather Research and Forecasting (WRF) and Statistical Downscaling Model (SDSM) are used for downscaling to simulate temperature and precipitation, respectively. WRF model is driven by the bias-corrected CMIP6 dataset, the ensemble of CMIP6 daily predictor variables are applied to SDSM, which generate high-resolution downscaled data named SSP-DS scenario. The SPEI computed from precipitation and reference evapotranspiration (ET0) is adopted to identify drought characteristics. The results indicate that downscaled results accurately reflect the CMIP6 original outputs change trend, but increase ET0 and reduce precipitation. The average temperature, total ET0, total precipitation manifests an increasing trend over time, and SSP585-DS scenario increases more significantly. High radiative forcing contributes to increasing temperature and ET0. Seven stations dry and wet characteristics have no obvious spatial heterogeneity; accumulated16 to 23 (17 to 24) drought events are captured, mild drought is the most frequent and extreme drought is the least under the SSP245-DS and SSP585-DS scenario. This study predicts the spatiotemporal variation in climate variables and drought characteristics based on high-resolution downscaled data, which contributes to Sanjiang Plain management strategy against drought risk and climate change.

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Section: Wrf Model Temperature Simulationmentioning

confidence: 99%

High-resolution downscaled climate variables spatiotemporal variation and drought projected in the Sanjiang Plain, Northeast China

Huang

Xie

et al. 2023

Preprint

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“…As the application of statistical downscaling tools continues to increase, Ghana's contribution remains limited, particularly in coastal zones. Few research studies, such as [44,[69][70][71], have utilized the SDSM in Ghana. It should be noted that these studies are confined to the river catchments and basin areas (Vea Catchment, White and Black Volta Basins) of the country, to the exclusion of other equally important regions, such as the coastal zones.…”

Section: Introductionmentioning

confidence: 99%

Extreme Temperature and Rainfall Events and Future Climate Change Projections in the Coastal Savannah Agroecological Zone of Ghana

2023

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The global climate has changed, and there are concerns about the effects on both humans and the environment, necessitating more research for improved adaptation. In this study, we analyzed extreme temperature and rainfall events and projected future climate change scenarios for the coastal Savannah agroecological zone (CSAZ) of Ghana. We utilized the ETCCDI, the RClimDex software (version 1.0), the Mann–Kendall test, Sen’s slope estimator, and standardized anomalies to analyze homogeneity, trends, magnitude, and seasonal variations in temperature (Tmax and Tmin) and rainfall datasets for the zone. The SDSM was also used to downscale future climate change scenarios based on the CanESM2 (RCP 2.6, 4.5, and 8.5 scenarios) and HadCM3 (A2 and B2 scenarios) models for the zone. Model performance was evaluated using statistical methods such as R2, RMSE, and PBIAS. Results revealed more changepoints in Tmin than in Tmax and rainfall. Results again showed that the CSAZ has warmed over the last four decades. The SU25, TXn, and TN90p have increased significantly in the zone, and the opposite is the case for the TN10p and DTR. Spatially varied trends were observed for the TXx, TNx, TNn, TX10p, TX90p, and the CSDI across the zone. The decrease in RX1day, RX5day, SDII, R10, R95p, and R99p was significant in most parts of the central region compared to the Greater Accra and Volta regions, while the CDD significantly decreased in the latter two regions than in the former. The trends in CWD and PRCPTOT were insignificant throughout the zone. The overall performance of both models during calibration and validation was good and ranged from 58–99%, 0.01–1.02 °C, and 0.42–11.79 °C for R2, RMSE, and PBIAS, respectively. Tmax is expected to be the highest (1.6 °C) and lowest (−1.6 °C) across the three regions, as well as the highest (1.5 °C) and lowest (−1.6 °C) for the entire zone, according to both models. Tmin is projected to be the highest (1.4 °C) and lowest (−2.1 °C) across the three regions, as well as the highest (1.4 °C) and lowest (−2.3 °C) for the entire zone. The greatest (1.6 ℃) change in mean annual Tmax is expected to occur in the 2080s under RCP8.5, while that of the Tmin (3.2 ℃) is expected to occur in the 2050s under the same scenario. Monthly rainfall is expected to change between −98.4 and 247.7% across the three regions and −29.0 and 148.0% for the entire zone under all scenarios. The lowest (0.8%) and highest (79%) changes in mean annual rainfall are expected to occur in the 2030s and 2080s. The findings of this study could be helpful for the development of appropriate adaptation plans to safeguard the livelihoods of people in the zone.

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“…Statistical downscaling could be appropriate for this study as it is located in heterogeneous areas where point scale information is required. However, this study opted to assess rainfall and temperature projections and its impact on the water Resources using a Hybrid of dynamically and statistically downscaled data (Adeyeri et al, 2020;Larbi et al, 2021). The study used an ensemble mean of four bias-corrected four CORDEX RCMS and output from SDSM and LARS-WG simulations for climate change analysis and hydrological model input.…”

Section: Climate Change and Climate Downscalingmentioning

confidence: 99%

“…Few studies have analysed the impacts of changing climate on hydrologic processes using the downscaled data from Regional Climate Models (RCM) as input in the hydrological models (Hyandye et al, 2018;Näschen et al, 2019). The RCMs at their finer resolution simulates the detailed local climate conditions and provide future prediction, however, RCMs perform different from one location to another (Larbi et al, 2021). Other Studies also applied the land cover classifications and modelling techniques to assess the hydrologic responses on impact of land use/cover change in various parts Tanzania (Hyandye et al, 2018;Näschen et al, 2019;.…”

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confidence: 99%

Hydrologic responses to climate and land use/cover changes in world heritage site of Ngorongoro conservation area and surrounding catchments, northern Tanzania

Mwabumba

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In Tanzania, various studies have analyzed the impact of climate and land use/cover changes on water resources. However, information on the interactions between climate and land use/cover change, temporal and spatial variability of hydrological components and water quality at the local scale is insufficient. The objective of this study was to evaluate the hydrological response to climate and land use/cover changes in Ngorongoro Conservation Area (NCA) and surroundings. The study performed climate change analysis using outputs from a multi-model ensemble of Regional Climate Models (RCMs) and statistically downscaled Global Climate Models (GCMs). The CA–Markov model applied to project Land use/cover for the future 2025 and 2035. This study further used the Soil Water Assessment Tool (SWAT) modelling approaches to analyse the hydrological responses and HYDRUS 1D to determine the change in Groundwater quality due to climate and land use/cover changes. The analysis of climate change between historical period (1982-2011) and future period (2021-2050) indicated an increase in the mean annual rainfall and temperature, seasonal rainfall except June to September (JJAS) season which showed a decreasing trend. Spatially, rainfall and temperatures would increase over the entire area. The projected Land use/cover change for the period 2025 to 2035 compared to the baseline 2016, showed a reduction in bushland, forest, water, and woodland, but an intensification in cultivated land, grassland, bare land, and the built-up area. The surface runoff, evapotranspiration, lateral flow, and water yield would significantly increase in the future, while groundwater would decrease under combined climate and land use/cover change. It is predicted that two anions (Cl− and PO4 −3 ) and two cations (Na+ and K+ ) would exceed the permissible limits for the drinking water set by the World Health organisation (WHO) and Tanzania Bureau of Standards (TBS), from 2036 to 2050. Changes in groundwater quality due to major cations and anions is significantly correlated to evapotranspiration and temperature with Pearson correlation (r) between 0.35 and 0.85. Furthermore, correlate to the changes in all land use/ cover types with Pearson correlation (r) between 0.56 and 0.96. The results obtained provide further insight into future water resources management planning and adaptation strategie

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Local climate change projections and impact on the surface hydrology in the Vea catchment, West Africa

Cited by 14 publications

References 52 publications

High-resolution downscaled climate variables spatiotemporal variation and drought projected in the Sanjiang Plain, Northeast China

High-resolution downscaled climate variables spatiotemporal variation and drought projected in the Sanjiang Plain, Northeast China

Extreme Temperature and Rainfall Events and Future Climate Change Projections in the Coastal Savannah Agroecological Zone of Ghana

Hydrologic responses to climate and land use/cover changes in world heritage site of Ngorongoro conservation area and surrounding catchments, northern Tanzania

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