Tag: Climate Change Downscaling Techniques

Abstract

The frequency of extreme hydrologic events such as floods, storm surges, droughts, heat waves, extreme precipitation, and other similar occurrences has been increasing in Bangladesh due to the impact of climate change. Therefore, the assessment of changes in future climates is essential for climate-induced risk management in the country to safeguard natural resources and human lives. The main purpose of the current study is to assess the trend of maximum temperature (T_max), minimum temperature (T_min), and precipitation for the north-west region of Bangladesh in seasonal and annual scales for three future periods, including 2025–2050, 2051–2080, and 2081–2100, respectively. In order to achieve this goal, a large-scale atmospheric dataset obtained from the well-known general circulation model (GCM), CanESM2, is downscaled to finer scales at the local level using the widely used statistical downscaling model (SDSM). The downscaling of local climate variables is carried out using daily observed climate data under three representative concentration pathways (RCP) scenarios, including RCP2.6, RCP4.5, and RCP8.5, respectively. Correlation matrices with p-values have been utilized to select the most suitable predictors from NCEP/NCAR reanalysis data. Both the calibration (0.87 < R2 < 0.98, 0.87 < EV < 0.99, 19.24 > SE < 0.12) and validation findings demonstrate that the model performs satisfactorily. The bias correction approach is also adopted to achieve more consistent results. Seasonally, the mean seasonal temperature and precipitation are projected to rise in all seasons (except winter for precipitation). Annually, T_max and T_min have grown by 0.49 °C and 1.36 °C, respectively, whereas precipitation has increased by 49% up to the next century considering the RCP8.5 scenario (worst case). Overall, the outcome of the current study is expected to be supportive to policymakers and water managers in planning climate-resilient agricultural and infrastructure development activities for managing climate-induced disastrous events in the north-west region of Bangladesh.

Abstract

Under global warming, extreme events have been increasing in the last decade and are projected to increase in the future with every increment of global warming. The potential increase in compound drought and hot events may induce a complex web of impacts on societies, ecosystems, and economies, including crop failure, wildfires, and water scarcity. This is particularly concerning for Brazil, where it has been demonstrated to be vulnerable to recent extreme climate events. Using an ensemble of CORDEX-CORE simulations over Tropical Brazil, we investigate changes in compound events in response to changes in radiative forcing and their impact on climate extreme events, including drought and extreme heat. The simulations are conducted at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Each model covers the period from 1980 to 2100 under two Radiative Concentration Pathways (RCP2.6 and RCP8.5) in the 21st-century projection period. We used observed data from the Brazilian Daily Weather Gridded Data (BR-DWGD) to evaluate the simulations and perform a quantitative assessment of areas affected by these compound events during the present day. The study finds a generally good agreement between RCM simulations and observed data, with moderate to high correlation coefficients for precipitation, though the strength of these correlations varies across different regions and seasons. The analysis emphasizes the prevalence of compound climate events during the Austral summer season and projects a significant increase in both extreme heat and drought events in the coming decades. These findings underscore Brazil’s vulnerability to compound climate events, highlighting the need for adaptive strategies and policy interventions to mitigate the socio-economic and environmental impacts across various sectors.

Abstract

Little is known about the effect of future weather and climate on municipal water demand in coastal communities with tourist-centric economies. To address this knowledge gap, we used an econometric model of monthly water demand that allowed for non-linear responses to weather variables to estimate temperature-response functions for demand from a sample of communities in the Oregon Mid-Coast. A main result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley—the source of most tourists to the Oregon coast—was. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourists escaping the heat in the Willamette Valley for cooler conditions on the coast. Applying the temperature response functions to scenarios of future climate to the year 2070 led to projected increases in water demand independent of other factors. Whether future tourism is either constrained by the local resident population that serves tourism or is constrained by the potential tourist population in the Willamette Valley, the climate-change contribution to projected water demand is generally of comparable magnitude to—if not greater than—the contribution from resident population change alone over the next 50 years. For communities where the population is projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand.

Abstract

Droughts and floods are common in the Baro basin and climate change may exacerbate them. This study aimed to investigate the hydrological response to climate change’s impact in the Baro River basin. Four climate models namely, Hadley Centre Global Environmental Model, version 2 (HadGEM2-ES), Max Planck Institute Earth System Model—Low Resolution (MPI-ESM-LR), Coupled Model Version 5, Medium Resolution (CM5A-MR) and European Community Earth System Model (EC-Earth) dynamically downscaled outputs were obtained from Africa coordinated regional downscaling experiment program. The four climate models were evaluated using a suite of statistical measures such as bias, Root Mean Squared Error, and Coefficient of Variation. The bias of the simulated rainfall varies between − 4.20% and − 25.39% suggesting underestimation. The performance of the models differs subject to the performance measures used for evaluation. Before being used in the climate impact analysis, the climate model data was heavily skewed and needed correction. In terms of bias, HadGEM2-ES performed the worst while EC-Earth performed the best. MPI-ESM-LR was the worst performer in terms of RMSE and CM5A-MR was the best. Changes in the hydrological response to climate change were compared to the baseline scenario (1971–2000) under the Representative Concentration Pathway Scenarios (RCP 4.5) for the medium term (2041–2070). The GCM predictions for the RCP 4.5 scenarios suggested that, in the medium period (2041–2070) the maximum temperature in the Baro River basin will probably rise by 2.1 °C for MPI-ESM-LR and 2.49 °C for CM5A-MR, while the minimum temperature would likely climb by 1.7 °C to EC-Earth and 2.8 °C for HadGEM2-ES. Annual rainfall is expected to fall by 7.02% for CM5A-MR and 17.01% for HadGEM2-ES, while annual evapotranspiration potential is likely to rise. Except from March to May CM5A-MR consistently generated the greatest amount of streamflow change, while MPI-ESM-LR consistently generated the highest magnitude of streamflow change. The annual streamflow reduction is consistent with the annual precipitation reduction and increased annual potential evapotranspiration. Generally, climate change is predicted to have a significant impact on the hydrological response in the Baro River basin under the RCP 4.5 scenario.

Abstract

The scarcity of surface water resources has a significant impact on Mediterranean basin. This study aims to assess the climate change impacts on surface water resources in the Mitidja plain in Algeria. Two pre-calibrated monthly water balance models, namely, the GR2M model and the abcd model, were used. These models were driven by bias-corrected datasets from the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) under two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) and two Shared Socioeconomic Pathways (SSP2 and SSP5). The combined Box–Cox transformation and bootstrapping procedure was used to aggregate the multiple runoff projections generated. The results revealed significant variations in the runoff patterns across the different sub-basins. In addition, all scenarios indicated a reduction in projected runoff across all sub-basins of the Mitidja plain, spanning from 26 to 74.32%. Furthermore, CMIP6 simulations showed more intense changes over the Mitidja basin.

Abstract

Climate change presents significant challenges to the demand and availability of irrigation water, resulting in profound consequences for the long-term viability of development that can be sustained. This study utilized a thorough bibliometric analysis to examine the patterns, development, and possible future research paths in this crucial field. The investigation, conducted using 2,211 documents from the Scopus database, demonstrated a steady and rising trajectory in publications. This pattern indicates the growing importance of this subject matter and its worldwide focus. The results emphasized the various topics and subjects investigated, such as climate modeling, water resource management, agricultural practices, and policy consequences. The study identified significant works, industrious nations, institutions, authors, and patterns of collaboration and occurrence. Thematic evolution maps and factorial analyses have identified new research areas, including incorporating advanced technologies like remote sensing, machine learning, and the Internet of Things. Additionally, there is a focus on developing adaptation techniques to improve resilience. Proposed future research areas highlight the importance of multidisciplinary collaboration, integrated modeling frameworks, and holistic approaches to effectively tackle the complex difficulties arising from climate change’s impact on water demand and availability.

Abstract

The Euphrates-Tigris River Basin, which spans Turkey, Syria, Iraq, and Iran, is one of the most vulnerable zones to climate change. This study quantifies the impacts of changing climate on streamflow and suspended sediment load rates in the most threatened highlands region of the Euphrates-Tigris Basin, with the case of Bitlis Creek. In this evaluation, the multi-model ensemble approach is utilized to produce precipitation and temperature projections by analyzing the simulation performances of 24 global circulation models (GCMs) from the coupled model intercomparison project phase 6 (CMIP6). The Soil and Water Assessment Tool (SWAT) is used to estimate future streamflow and suspended sediment load rates over 25-year periods under the medium- and high-forcing shared socio-economic pathway (SSP) scenarios of SSP2-4.5 and SSP5-8.5. The results illustrate that the mean annual streamflow and suspended sediment load rates are expected to decrease by up to 8.5 and 21.4% under the SSP2-4.5 scenario, and by up to 20.9 and 40.7% under the SSP5-8.5 scenario, respectively. The projected shift from snowy to rainy winters leads to significant increases in winter streamflow and suspended sediment load rates, anticipated to reach 39.1 and 73.5%, respectively, during the 2075–2099 period for the SSP5-8.5 scenario. In contrast, declines in spring streamflow and suspended sediment load rates are projected to reach 40.9 and 60.0%, respectively, during the same period under the SSP5-8.5 scenario. These results suggest that the riparian countries should incorporate adaptive measures into their water resources management plans to ensure a sustained water supply in the coming decades.

Abstract

The study investigates the interplay of land use dynamics and climate change on the hydrological regime of the Ravi River using a comprehensive approach integrating Geographic Information System (GIS), remote sensing, and hydrological modeling at the catchment scale. Employing the Soil and Water Assessment Tool (SWAT) model, simulations were conducted to evaluate the hydrological response of the Ravi River to both current conditions and projected future scenarios of land use and climate change. This study differs from previous ones by simulating future land use and climate scenarios, offering a solid framework for understanding their impact on river flow dynamics. Model calibration and validation were performed for distinct periods (1999–2002 and 2003–2005), yielding satisfactory performance indicators (NSE, R², PBIAS = 0.85, 0.83, and 10.01 in calibration and 0.87, 0.89, and 7.2 in validation). Through supervised classification techniques on Landsat imagery and TerrSet modeling, current and future land use maps were generated, revealing a notable increase in built-up areas from 1990 to 2020 and projections indicating further expansion by 31.7% from 2020 to 2100. Climate change projections under different socioeconomic pathways (SSP2 and SSP5) were derived for precipitation and temperature, with statistical downscaling applied using the CMhyd model. Results suggest substantial increases in precipitation (10.9 − 14.9%) and temperature (12.2 − 15.9%) across the SSP scenarios by the end of the century. Two scenarios, considering future climate conditions with current and future land use patterns, were analyzed to understand their combined impact on hydrological responses. In both scenarios, inflows to the Ravi River are projected to rise significantly (19.4 − 28.4%) from 2016 to 2100, indicating a considerable alteration in seasonal flow patterns. Additionally, historical data indicate a concerning trend of annual groundwater depth decline (0.8 m/year) from 1996 to 2020, attributed to land use and climate changes. The findings underscore the urgency for planners and managers to incorporate climate and land cover considerations into their strategies, given the potential implications for water resource management and environmental sustainability.

Abstract

This study analyzed long-term hydro-metrological drought under climate change in the Kesem sub-basin, Middle Awash basin, Ethiopia. The comparative analysis was employed using three drought indices (the streamflow drought index, standard precipitation index, and reconnaissance drought index). These indices were evaluated using the ordinal by ordinal Spearman’s correlation, interval by interval Pearson, and kappa measure of agreement. The three drought indices have statistically significant (α < 0.01) strong correlation (> 0.78) and degree of agreement (0.2 fair agreement to 0.8 near-perfect agreement) tested at 99% confidence  interval. The potential evapotranspiration (PET) estimation shows an increase of + 25.9 mm (1.6%) from the base period to RCP 4.5 (2020) and + 26.7 mm (1.67%) to RCP 8.5 (2020), and + 55 mm (3.4%) to RCP 4.5 (2050) and + 56.8 mm (3.5%) to RCP 8.5 (2050). This increase in PET is an indication that the watershed is very susceptible to water deficit and drought in the coming periods. Mild to extreme hydro-metrological drought was experienced during the baseline period (1984–2010) and is projected to occur in the current (2011–2044) and future (2045–2075) periods under both RCP 4.5 and 8.5 emission scenarios at 6- and 12-month timescales. Droughts will likely become more frequent in the future in the study area. Currently, extreme droughts that last 6 and 12 months occur every 13 to 19 years. Under the RCP 4.5, these droughts could happen every 6–7 years by 2050. The RCP 8.5 suggests even more frequent extreme droughts every 14 years. These findings are substance information for the water users and development works in the basin including the Kesem dam reservoir.

Abstract

Earth system models have been continously improved over the past decades, but systematic errors compared with observations and uncertainties in climate projections remain. This is due mainly to the imperfect representation of subgrid-scale or unknown processes. Here we propose a next-generation Earth system modelling approach with artificial intelligence that calls for accelerated models, machine-learning integration, systematic use of Earth observations and modernized infrastructures. The synergistic approach will allow faster and more accurate policy-relevant climate information delivery. We argue a multiscale approach is needed, making use of kilometre-scale climate models and improved coarser-resolution hybrid Earth system models that include essential Earth system processes and feedbacks yet are still fast enough to deliver large ensembles for better quantification of internal variability and extremes. Together, these can form a step change in the accuracy and utility of climate projections, meeting urgent mitigation and adaptation needs of society and ecosystems in a rapidly changing world.