Tag: Climate Change Downscaling Techniques

Abstract

The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress on freshwater resources. Ensuring that increases in irrigated production occur only in places where water is relatively abundant is a key objective of sustainable agriculture and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet, a spatially detailed understanding of the evolution of the global area equipped for irrigation (AEI) is missing. In this study, we used the latest subnational irrigation statistics (covering 17,298 administrative units) from various official sources to develop a gridded (5 arcmin resolution) global product of AEI for the years 2000, 2005, 2010 and 2015. We found that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha), with areas of both substantial expansion, such as northwest India and northeast China, and decline, such as Russia. Combining these outputs with information on green (that is, rainfall) and blue (that is, surface and ground) water stress, we also examined to what extent irrigation has expanded unsustainably in places already experiencing water stress. We found that more than half (52%) of the irrigation expansion has taken place in areas that were already water-stressed in the year 2000, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security.

Abstract

Ensemble analysis is proven to provide advantages in climate change impact assessment based on outputs from climate models. Ensembled series are shown to outperform single-model assessments through increased consistency and stability. This study aims to test the improvement of precipitation estimates through the use of ensemble analysis for south and southwestern Turkey which is known to have complex climatic features due to varying topography and interacting climate forcings. The analysis covers an evaluation of the performance of eight regional climate models (RCMs) from the EUR-11 domain available from the CORDEX database. The historical outputs are evaluated for their representativeness of the current climate of the Mediterranean region and its surroundings in Turkey through a comparison with long-term monthly precipitation time series obtained from ground-based precipitation observations by the use of statistical performance indicators and Taylor diagrams. This is followed by a comparative evaluation of three ensemble methodologies, simple average of the models, multiple linear regression for superensemble, and artificial neural networks (ANN). The analysis results show that the overall performance of ensembled time series is better compared to individual RCMs. ANN generally provided the best performance when all RCMs are used as inputs. Improvement in the performance of ensembling due to the use of nonlinear models is further confirmed by fuzzy inference systems (FIS). Both ANN and FIS generated monthly precipitation time series with higher correlations with those of observations. However, extreme events are poorly represented in the ensembled time series, and this may result in inefficiency in the design of various water structures such as spillways and storm water drainage systems that are based on high return period events.

Abstract

Understanding how precipitation fluctuates geographically and temporally over a specific place due to climate change is critical. Generally, simulations of general circulation models (GCM) under different scenarios are downscaled to the local scale to study the impact of climate change on precipitation. However, selecting suitable GCMs for the given study area is one of the most hectic tasks, as the performance of GCMs may vary with respect to space and timescale. Therefore, the current study ranks twenty-seven CMIP 6 (Coupled Modelled Intercomparison Project Phase 6) GCMs in simulating precipitation over India for nine times series, including daily, monthly, yearly, and six extreme series extracted with annual maximum and peak over threshold methods. The gridded daily rainfall data provided by the India Meteorological Department (IMD) are used as the observed data. The GCMs' outputs are corrected for the systematic bias using the linear scaling method. The performance of a GCM is assessed with three statistical performance metrics, namely NSE, RMSE, and R². The GCMs' ranks are determined using a multi-criterion decision-making technique named the modified technique of order preference by similarity to an ideal solution (mTOPSIS) for every grid point and nine timescales (i.e., daily, monthly, yearly, and six extreme series). From the results, for the entire India, the top ten recommended CMIP 6 GCMs are FGOALS-g3, HadGEM3-GC31-MM, EC-Earth3, BCC-CSM2-MR, CNRM-CM6-1-HR, CanESM5, AWI-ESM-1-1-LR, MPI-ESM-1-2-HR, IITM-ESM, and INM-CM5-0. The identified best-performing models provide insightful information for better regional climate projections and underscore the necessity of considering multiple model outputs for reliable climate change impact assessments and adaptation strategies in the region.

Abstract

Climate change is a pressing global issue that has far-reaching effects on the environment, including its impact on infrastructure such as roads and pavements. As temperatures rise, it is essential to consider selecting asphalt binder performance grades to ensure the long-lasting and reliable performance of asphalt pavements. In Sharjah, the penetration grading system guides the selection of asphalt binder grade (grade 60/70). However, this grading system is empirical in nature and doesn’t take into account local temperature fluctuations. The superpave performance grade (PG) system considers explicitly local temperature records while allowing the incorporation of future temperature changes. The main goal of this study project is to assess the impact of future temperature changes on the required asphalt binder PG in Sharjah and the consequent impact on pavement performance simulated by AASHTOWare Pavement ME Design. Future temperature records were predicted via the MRI-CGCM3 model considering two RCPs (4.5 and 8.5) and two future periods (near and far). The historical temperature records revealed a PG 70–10 at a 98% reliability level for Sharjah compared to a PG 64–16, equivalent to the currently used 60/70 penetration grade. Future temperature changes yielded a higher performance grade of PG 76–10 for the far future, considering RCP 8.5. The performance simulations emphasized the importance of carefully selecting the asphalt binder PG, as this decision has been shown to increase the life span of asphalt pavement.

Abstract

Climate change is a pressing global issue that has far-reaching effects on the environment, including its impact on infrastructure such as roads and pavements. As temperatures rise, it is essential to consider selecting asphalt binder performance grades to ensure the long-lasting and reliable performance of asphalt pavements. In Sharjah, the penetration grading system guides the selection of asphalt binder grade (grade 60/70). However, this grading system is empirical in nature and doesn’t take into account local temperature fluctuations. The superpave performance grade (PG) system considers explicitly local temperature records while allowing the incorporation of future temperature changes. The main goal of this study project is to assess the impact of future temperature changes on the required asphalt binder PG in Sharjah and the consequent impact on pavement performance simulated by AASHTOWare Pavement ME Design. Future temperature records were predicted via the MRI-CGCM3 model considering two RCPs (4.5 and 8.5) and two future periods (near and far). The historical temperature records revealed a PG 70–10 at a 98% reliability level for Sharjah compared to a PG 64–16, equivalent to the currently used 60/70 penetration grade. Future temperature changes yielded a higher performance grade of PG 76–10 for the far future, considering RCP 8.5. The performance simulations emphasized the importance of carefully selecting the asphalt binder PG, as this decision has been shown to increase the life span of asphalt pavement.

Abstract

Hydrological models serve as valuable instruments for assessing the impact of climate change on water resources and agriculture as well as for developing adaptation measures. In Yemen, climate change and variability are imposing a significant impact on the most important sectors such as agriculture and economy. The current study evaluates the influence of future climate on hydrology and water balance components in Yemen’s highlands using a semi-distributed physical-based hydrologic model Soil Water Assessment Tool (SWAT) and employing high-resolution climate projections. The SWAT was calibrated and verified using observed streamflow data from 1982 to 2000 in three large catchments. Ground data from 24 stations and statistically downscaled future climate data for the period 2010–2100 under RCP2.6 and RCP8.5 are used. SWAT performance was assessed using multiple statistical methods, which revealed the commendable performance of SWAT during the calibration (average NSE = 0.80) and validation (NSE = 0.72) periods. The outcome indicates an increase in future seasonal and annual rainfall, maximum temperature, and minimum temperature in the 2020s and the 2080s under both RCP2.6 and RCP8.5 scenarios. This projected increase in the rainfall and the local temperature will result in increased averages of surface runoff, evapotranspiration, soil water, and groundwater recharge in the representative three catchments up to 6.5%, 21.1%, 7.6%, and 6.4%, respectively. Although, the projected increase in the water balance components will benefit the agriculture and water sector, specific adaptation measures will be crucial to mitigate potential flood impacts arising from the increased precipitations as well as to minimize the consequences of the increased temperature. Likewise, demand for supplementary irrigation is expected to increase to offset the higher evapotranspiration rates in the future.

Abstract

Global wheat production has faced, and will persist in encountering many challenges. Therefore, developing a dynamic cultivation approach generated through modeling is crucial to coping with the challenges in specific districts. The modeling can contribute to achieving global objectives of farmers’ financial independence and food security by enhancing the cropping systems. The current study aims to assess the effects of cultivars and sowing windows intricately on irrigated wheat production using the two models from Coupled Model Intercomparison Project Phase 6 (CMIP6), including ACCES-CM2 and HadGEM31-LL under two shared socioeconomic pathways (SSP245, and SSP585). A two-year on-farm experiment was conducted for parametrization and validation of the APSIM-Wheat model at two locations. The model reasonably simulated the days to anthesis, maturity, biomass production, and yield within all cultivars. The normalized root-mean-square error (RMSE) of the phenological stages was simulated and measured values were 5% and 2–4%, while the index of agreement (IOA) was in the range of 0.84–0.88 and 0.95–0.97. An acceptable agreement of the simulated biomass (RMSE = 5–7% and 0.91–0.78) and yield (RMSE = 6–11% and IOA = 0.70–0.94) was identified in the model. Afterward, the LARS-WG model generated the baseline (2000–2014) based on the weather data at the sites and projected the models for the near (2030–2049) and remote future (2050–2070). The models revealed that not only the average maximum and minimum temperatures will rise by 1.85 °C and 1.62 °C which will exacerbate the reference evapotranspiration (ET₀), but also the precipitation and solar radiation will reach + 58%, and + 0.25 Mj m⁻². Our results clearly showed that precipitation volume over the growing seasons would elevate approximately two times as much as the baseline in the future, while there is a significant decrease in water productivity (WP) and yield from the intensive ET₀. Based on the wheat simulation, the short-duration cultivar (Kalate) combined with the postponed planting (16-Dec) was determined as a practical alternative; nonetheless, both WP and yield significantly decreased by 40% and 7%, respectively (p < 0.05). In conclusion, identifying and analyzing future farming conditions (e.g., agro-climate, soil and crop management data) would provide a perception of the forthcoming scenarios. When applied, this knowledge can potentially mitigate the adverse impacts of climate change on global wheat production.

Abstract

Argentina is a country with a variety of climates, where an increase in mean and extreme temperatures is currently on-going, demanding regional climate information to design and implement effective strategies for climate change adaptation. In this regard, the use of empirical statistical downscaling (ESD) procedures can help provide tailored climate information. In this work, a set of ESD models were tested and applied to generate plausible regional climate projections for daily maximum and minimum temperatures (Tx, Tn) in Argentina. ESD models were applied to an ensemble of CMIP5 and CMIP6 global circulation models (GCMs) to downscale historical and future RCP8.5 and SSP585 scenarios. The plausibility of the ESD projections was analysed by comparing them with their driving GCMs and with CORDEX regional climate models (RCMs). Generally, all ESD models added value during the historical period, in mean values as well as in extreme indices, especially for Tx. The climate projections depicted an extended signal of warming (both in the mean and in the frequency of extremes), consistent between all simulations (GCMs, RCMs and ESD) and strongest over northern Argentina. ESD models showed potential to produce plausible projections, although, depending on the technique considered (for Tx) and the predictor configurations (for Tn), differences in the change rates were identified. Nevertheless, the uncertainty in future changes was considerably reduced by RCMs and ESD when compared to their driving GCMs. Overall, this study evidences the potential of ESD in a climate change context and contributes to the assessment of the uncertainty on the future Argentine climate.

Abstract

The climate change assessment in the context of urban areas is very crucial for policy making regarding hazard mitigation and citizen’s health, especially for the smart cities. The past climate assessment and present climate monitoring is somewhere easy, but the projection of future climate at city level is very difficult as most climate models fail to resolve the cities spatially. Over the highly populated areas like the region of Gangetic Plains the precise city specific climate projection becomes more important. The present study aims to project the future temperature, covering both minimum temperature (Tmin) and maximum temperature (Tmax) and analyse the extreme indices over smart cities in the Gangetic Plains, as one of the pioneer works using CMIP6 model’s downscaled outputs using SDSM model. The study reveals that these smart cities are likely to experience warmer and more extreme temperatures in the upcoming decades. The future temperature projections were generated under two emission scenarios (SSP245 and SSP585), and for near future (2030–2065) and far future (2066–2100) periods. The drastic change in minimum temperature (Tmin) was observed over New Delhi, Prayagraj, Kolkata, and Lucknow by the end of the century under SSP585. Four extreme temperature indices were also analyzed for future time series: (1) TXgt40(No. of days Tmax > 40ºC); (2) TNlt10(No. of days when Tmin < 10ºC); (3) TX90p (Percentage of days when Tmax > 90th percentile); and (4) TN10P (Percentage of days when Tmin < 10th percentile). The increasing trend of warm temperature Indices and decreasing trend of cool temperature indices were observed over all the stations. The drastic change in extreme temperature indices may have a significant effect on urban climate, it could impact public health by increasing the incidence of heat-related illnesses such as heat stress or heat exhaustion. The present study can be also utilized as a probable baseline for assessing the extreme climate conditions in the future. As this study is one of the very first attempts under the aspect of smart cities and thus it may help in developing early warning systems for smart cities in the Gangetic Plain.

Abstract

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district’s land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation.