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Urbanization has made significant changes in Land Use Land Cover (LULC) patterns and these changes have a considerable impact on Land Surface Temperature (LST) in highly populated and rapidly growing cities. The current study aims to analyze the past and future LULC changes and seasonal (summer and winter) LST patterns in Herat City, Afghanistan. Random Forest (RF) algorithm and Landsat thermal bands were applied to retrieve LULC changes and associated LST patterns during 2000 and 2020. In addition, the Cellular Automata-Markov (CA-Markov) and the CA-Logistic Regression (CA-LR) models were used to predict the future LULC and seasonal (summer and winter) LST patterns, respectively for the year 2030. The CA-Markov and CA-LR models’ validation for LULC and LST were assured using the predicted and actual data for the year 2020. Results showed a significant increase in the built-up area about 48.06 km2 (42.53%), while the vegetation 27.65 km2 (24.48%) and bare soil 20.45 km2 (18.11%) reduced significantly, however, the water bodies decreased slightly in Herat City. The seasonal (summer and winter) LST patterns revealed higher temperatures in built-up area followed by the bare soil, vegetation and water bodies. Predicted LULC patterns showed a considerable increase in the built-up area of about 16.69 km2 (14.77%) for the year 2030 as compared to 2020. Seasonal analysis predicts that by 2030, around 51.84 km2 (45.88%) of Herat City may experience summer LST > 45 °C, while 90.15 km2 (79.78%) could have winter LST ranging from 10–20 °C. The results indicated a direct relation between rapid urbanization and rising LST, which negatively impacts both the local population and the environment. To ensure sustainable urban growth and reduce the impact of LST in Herat City, the future city master plans should prioritize urban greenery and the protection of natural resources. [ABSTRACT FROM AUTHOR]

This study evaluates hydrology and hydrological extremes under future climate change scenarios. The climate change scenarios were developed from multiple Global Circulation Models (GCMs), Representative Concentration Pathway (RCP) scenarios, and statistical downscaling techniques. To ensure hydrological model robustness, the Soil Water Assessment Tool (SWAT) was calibrated and validated using the Differential Split Sample Test (DSST) approach. The model was also calibrated and validated at the multi-gauges of the watershed. Future climate change scenarios revealed a reduction in precipitation (in the order of −9.1% to 4.9%) and a consistent increase in maximum temperature (0.34°C to 4.10°C) and minimum temperature (−0.15 °C to 3.7°C) in different climate model simulations. The climate change scenarios triggered a reduction of surface runoff and streamflow and a moderate increase in evapotranspiration. Future climate change scenarios projected a decrease in high flow (Q5) and low flow (Q95). A higher reduction of Q5 and annual minimum flow is also simulated in future climate scenarios, whereas an increase in annual maximum flow is simulated in climate change scenarios developed from the RCP8.5 emission scenario. The study suggests optimal water management structures which can reduce the effect of change in high and low flows. [ABSTRACT FROM AUTHOR]

The increase in computational resources has enabled the emergence of multi-model ensembles of convection-permitting regional climate model (CPRCM) simulations at very high horizontal resolutions. An example is the CORDEX Flagship Pilot Study on "Convective phenomena at high resolution over Europe and the Mediterranean", a set of kilometre-scale simulations over an extended Alpine domain. This first-of-its-kind multi-model ensemble, forced by the ERA-Interim reanalysis, can be considered a benchmark dataset. This study uses a recently proposed metric to determine the added value of all the available Flagship Pilot Study hindcast kilometre-scale simulations for maximum and minimum temperature. The analysis is performed using state-of-the-art gridded and station observations as ground truth. This approach directly assesses the added value between the high-resolution CPRCMs against their driving global simulations and coarser resolution RCM counterparts. Overall, models display some modest gains, but also considerable shortcomings are exhibited. In part, these deficiencies can be attributed to the assimilation of temperature observations into ERA-Interim. Although the gains for the use of kilometre-scale resolution for temperature are limited, the improvement of the spatial representation of local atmospheric circulations and land–atmosphere interactions can ultimately lead to gains, particularly in coastal areas. [ABSTRACT FROM AUTHOR]

Climate models are fundamental tools to estimates the reliable future climate change and its effects on the water resources and agriculture in basins. However, all climate models are not equally performed for all areas. Therefore, determining the most appropriate climate models for a specific study area is essential. The focus of this study was to evaluate the performance of the regional climate models with regard to simulating precipitation, and temperatures at Katar watershed. This study examines the performance of fourteen CORDEX-AFRICA-220 Regional Climate Models (RCMs) for the period of 1984–2005 using statistical metrics such as Pearson correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and bias. The findings indicated that GERICS-MPI was better performed in representing Areta, and Bokoji station, GERICS-IPSL was better representing in Assela, Ketergenet, and Sagure station, CCCma-CanESM2-AFR22, and RCA4-ICHEC performed relatively better in representing the mean annual observed rainfall at the Kulumsa, and Ogolcho station respectively. However, RCA4-CSIRO performed weakly in estimation of annual rainfall at all stations. RCM model such as GERICS-MPI was relatively better than the others in replicating the annual pattern of the maximum temperature at Areta, Bokoji, and Ketergenet stations. Similarly, GERICS-IPSL were relatively better in replicating the annual maximum temperature at Assela, and Sagure stations, CCCma-CanESM2-AFR22 at Kulumsa station, and RCA4-ICHEC at Ogolcho station performed well in capturing the observed and simulated annual maximum temperature. Better performance was observed on minimum temperature at CCCma-CanESM2-AFR22 at Areta, Assela, and Ketergenet stations, GERICS-MOHE-AFR-22 at Bokoji station, GERICS-MPI at Kulumsa, and Ogolcho stations, RAC4-NOAA-2G at Sagure stations. However, weak performance was observed RCA4-CSIRO at all stations. RCM models of GERICS-MPI, and CCLM4-NCC-AFR-22 performed better than the other RCM models for correction of annual rainfall in Katar watershed. However, poor performance was observed at RCA4-ICHEC model on Katar watershed. The GERICS-MPI model performed well. However, poor performance was observed at RCA4-ICHEC on maximum temperature, and GERICS-NOAA-2M on minimum temperature in Katar watershed. [ABSTRACT FROM AUTHOR]

Engineering biology (EngBio) is a dynamic field that uses gene editing, synthesis, assembly, and engineering to design new or modified biological systems. EngBio applications could make a significant contribution to achieving net zero greenhouse gas emissions. Yet, policy support will be needed if EngBio is to fulfil its climate mitigation potential. What form should such policies take, and what EngBio applications should they target? This paper reviews EngBio's potential climate contributions to assist policymakers shape regulations and target resources and, in so doing, to facilitate democratic deliberation on desirable futures. Engineering biology is a dynamic field that uses gene editing, synthesis, assembly, and engineering to design new or modified biological systems. Here the authors discuss the policy considerations and interventions needed to support a role for engineering biology in climate change mitigation. [ABSTRACT FROM AUTHOR]

The current study aimed to assess the ability of Global Climate Models (GCMs) from the third, fifth, and sixth phases of the Coupled Model Intercomparison Project (CMIP) to reproduce observed rainfall over the Western Himalaya Region (WHR) between 1901 and 2000. A probabilistic approach was adopted to evaluate the models' skill in capturing the observed precipitation from 22 India Meteorological Department stations by comparing their probability distribution function (PDF) with that of the observed rainfall. The results indicated that all three generations of climate models could adequately reproduce the PDF of observed precipitation, surpassing the observed PDFs in both annual and monsoon seasons. Subsequently, each model was assigned a rank based on its skill score. The Geophysical Fluid Dynamics Laboratory model from CMIP3, the community Earth system model from CMIP5, and the CMIP6 model from the Met Office Hadley Centre achieved the highest ranks, accurately capturing the distribution of observed precipitation over WHR. Remarkably, the Japanese model from the Model for Interdisciplinary Research and the UK model from Hadley Centre exhibited consistently high performance across all three CMIP generations. Overall, the top-ranked models from each generation can be deemed reliable for use by researchers and decision-makers concerned with the impact of climate change on the Western Himalaya Region. [ABSTRACT FROM AUTHOR]

The intensification of heatwaves dues to climate change is a significant concern, with substantial impacts on ecosystems and human health, particularly in developing countries. This study utilizes NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) and projected population data accounting for China's population policies to project changes in various grades of heatwaves (light, moderate, and severe) and the population exposure to heatwaves (PEH) in Xinjiang under three shared socioeconomic pathways (SSP1–2.6, SSP2-4.5, and SSP5-8.5). The results show that the number of days and intensity of heatwaves in Xinjiang are projected to increase. Heatwaves occurring in Xinjiang will predominantly be severe heatwaves (SHW) in the long-term under the SSP5-8.5 scenario, and the number of SHW days projected to increase by 62 ± 18.4 days compared to the reference period. Changes in heatwaves are anticipated to influence PEH, estimating population exposure to light, moderate, and severe heatwaves (LPEH, MPEH, and SPEH) at 534.6 ± 64 million, 496.2 ± 43.5 million, and 1602.4 ± 562.5 million person-days, respectively, in the long-term under the SSP5-8.5 scenario. The spatial distribution of PEH is projected to be consistent with that of the reference period, with high values persisting in Urumqi, Kashgar and Hotan. Changes in PEH are primarily driven by climate effects, followed by interactive effects, while population effects contribute the least. Therefore, mitigating climate change is crucial to reduce the PEH in Xinjiang. [ABSTRACT FROM AUTHOR]

In this study, the existing empirical models for sediment management for ungauged watersheds in South Korea developed in 2019 were updated. The existing models were developed based on the Universal Soil Loss Equation structure with domestic river sediment data obtained from 2005 to 2014 and watershed characteristic values. To update the models, the specific degradation (SD) value was calculated first in the same way as the existing method by adding sediment measurement and daily discharge data obtained from 2015 to 2020. The calculated SD values with recent data decreased compared with the past and differed from the results predicted using the existing models and the watershed characteristics investigated in this study. For this reason, the existing models were updated using the latest watershed characteristics and SD values. The root-mean-square error values of the existing models, which ranged from 131 to 90 tons/km2·year, decreased to 102 to 62 tons/km2·year in the updated models with the same structures. Multicollinearity was tested to evaluate the reliability of the updated models, and a new 95% prediction interval for usability was estimated. In addition, quantitative changes in the future sediment load of domestic rivers were analyzed using the future rainfall data obtained from climatic change scenarios and the watershed area. The results of this study reconfirmed that periodic updates are required for the existing empirical models. It is expected that the empirical models will be used in various ways, such as for the prediction of future sediment loads for river sediment management. [ABSTRACT FROM AUTHOR]

Evidence is mounting that the 1.5 °C goal of the Paris Agreement is unrealistic. But, contrary to many in the scientific community, major NGOs have remained steadfast in their support for this target. We argue that this is because "unrealistic" or aspirational targets can serve important political objectives, including becoming focal points for coalition-building and broad-based mobilization and allowing for effective naming and shaming. [ABSTRACT FROM AUTHOR]