Tag: Climate Change Downscaling Techniques

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.

Abstract

Impact-based nowcasting systems at detailed scales, to the street level, have become essential in flood risk management. This is achieved by focusing on predicting the impacts of flood events rather than merely forecasting weather conditions. This approach leverages advancements in 2D hydrodynamic modelling, high-performance computing (HPC), and detailed rainfall forecasting to improve the precision of early warning systems. However, its real-world implementation is hindered by challenges such as the coarse temporal resolution of weather forecasts and inherent modelling uncertainties. This study investigates the uncertainties and challenges associated with impact-based nowcasting systems, using the Mandra town (Greece) as a case study. We demonstrate the feasibility of applying a comprehensive framework that integrates 2D hydrodynamic modelling, HPC, and temporally disaggregated rainfall forecasting. Our findings show that the Alternating Block Method (ABM) effectively captures storm dynamics, mitigating significant underestimations that arise from coarser forecast inputs. Additionally, we assess various flood impact indices to manage modelling uncertainties. Our results highlight that similarities exist in the flood indices when storms are mild with short return periods. However, discrepancies between indices increase with storms of longer return periods, underscoring the critical need for careful index selection. This research provides new insights into enhancing flood nowcasting accuracy and effectiveness, particularly in small to medium-sized catchments. Moreover, it offers evidence that the scientific community along with the stakeholders such as Civil Protection, local governments, and others should focus orient their efforts on more reliable flood indices, as the discrepancies between the methodologies investigated increase with the severity of the events.

Abstract

Soil erosion is very hazardous to the global ecosystem. Government aided soil erosion control schemes happen dilatorily with minimal resources. Recognition and identifying the scale and the area of eroded land can be extremely time-consuming and difficult as well. To overcome this problem, a real-time Soil erosion detection system is introduced. The real-time part has been implemented using satellite imagery with the use of RUSLE modelling considering various factors. This was generated with the help of Google Earth Engine (GEE) interface. The RUSLE model offers a straightforward approach to assess soil erosion. By using remote sensing data and GIS, RUSLE effectively evaluates erosion. Researchers have developed various equations to model the five factors of the RUSLE model, considering the diverse variations in the soil erosion process. The system also includes the analysis of satellite imagery with a mapped view of soil erosion. Here, the Unet (EfficientNetb3) model is used giving optimal accuracy for the detection of soil erosion.

Abstract

Reference evapotranspiration (ET0), as one of the main components of the hydrological cycle, plays an important role in water resources management and agricultural planning. This study was conducted with the aim of predicting the temporal and spatial changes of ET0 in the Gorganroud watershed in northern Iran. The minimum and maximum temperatures were predicted using the output of five CMIP6 climate models under two climate scenarios of SSP2-4.5 and SSP5-8.5 for the historical base (1985–2014), near future (2025–2054) and far future (2071–2100) periods. The bias correction of the simulation data was performed using the linear scaling method. To reduce the uncertainty of climate models, a multi-model ensemble based on the application of Bayesian Model Averaging (BMA) was created and the reference evapotranspiration was calculated using the Hargreaves-Samani method. The results showed that under the SSP2-4.5 scenario, the minimum and maximum temperatures will increase by 1.65 and 1.8 ºC, respectively, whereas under the SSP5-8.5 scenario, the minimum and maximum temperatures will increase by 2.5 and 2.7 ºC, respectively. Similarly, the projections show that the reference evapotranspiration will increase on seasonal and annual scales in the future climate compared to the base period. The largest increase in ET0 is estimated to be 12.4% under the SSP5-8.5 scenario in the period 2071–2100 compared to the base period. The largest increase in evapotranspiration is in summer with values of 5.8–8% and 7.8–13.3% for the SSP2-4.5 and SSP5-8.5 scenarios, respectively. Analysis of the zonation of changes in evapotranspiration showed that most of the changes occur in the eastern regions and at Gharehbil and Cheshmehkhan stations. Our results indicate that future climate change will cause a significant increase in ET0 at high altitudes.

Abstract

As a result of economic development, population growth, accelerated urbanization and the frequent occurrence of extreme weather events, the contradiction between the supply and demand for water resources between regions has become increasingly acute. In order to solve the problem of regional water shortage and irrational utilization, the optimal allocation of water resources has become one of the research hotspots in recent years. In this study, firstly a multi-objective integrated allocation model of regional water resources is constructed by introducing social, economic, and environmental objective functions to address the complex uncertainties in the water resources system. Secondly, the standard whale algorithm is optimized and improved by introducing chaotic population initialization, chaotic convergence factor, adaptive Lévy flight and improved positive cosine mechanism. The model parameters, including the 2025 water resource demand and supply, pollutant discharge content, and unit water supply cost coefficients, are set by consulting the Shanxi Water Resources Bulletin 2022, the Shanxi Provincial Department of Water Resources, and the Report on the Work of the Shanxi Provincial Government 2023. Subsequently, the improved whale algorithm is utilized for the optimization of the predicted water resources for various target years in the future in the lower reaches of the Fen River in Shanxi Province, China. This ultimately yields optimized allocation results independently from both supply and demand sides. The experimental results demonstrate that the framework for water resource optimization using the improved whale algorithm is feasible, providing a reference scheme for regional multi-objective water resource optimization. Finally, the proposed policy recommendations emphasize the necessity of strengthening water diversion planning and management, promoting virtual water and water-saving initiatives, and highlighting water recycling and environmental protection in order to ensure the sustainable allocation of water resources in the downstream Fen River basin.