Climate change and hydropower resilience: a CMIP6-SWAT analysis of the Kankai River Basin's energy landscape

We present the climate change impact on the annual and seasonal precipitation over Rajang River Basin (RRB) in Sarawak by employing a set of models from Coupled Model Intercomparison Project Phase 5 (CMIP5). Based on the capability to simulate the historical precipitation, we selected the three most suitable GCMs (i.e. ACCESS1.0, ACCESS1.3, and GFDL-ESM2M) and their mean ensemble (B3MMM) was used to project the future precipitation over the RRB. Historical (1976–2005) and future (2011–2100) precipitation ensembles of B3MMM were used to perturb the stochastically generated future precipitation over 25 rainfall stations in the river basin. The B3MMM exhibited a significant increase in precipitation during 2080s, up to 12 and 8% increase in annual precipitation over upper and lower RRB, respectively, under RCP8.5, and up to 7% increase in annual precipitation under RCP4.5. On the seasonal scale, Mann-Kendal trend test estimated statistically significant positive trend in the future precipitation during all seasons; except September to November when we only noted significant positive trend for the lower RRB under RCP4.5. Overall, at the end of the twenty-first century, an increase in annual precipitation is noteworthy in the whole RRB, with 7 and 10% increase in annual precipitation under the RCP4.5 and the RCP8.5, respectively.

ABSTRACTThe Coupled Model Inter‐comparison Project phase 6 (CMIP6) provides a suite of general circulation models (GCMs) and Socioeconomic Shared Pathways (SSPs) primarily for continental‐scale climate assessments. However, adapting these models for sub‐national assessments, particularly in countries with varied geography like Ecuador, and for complex variables such as precipitation, introduces challenges, including uncertainties in selecting appropriate GCMs and SSPs. To address these issues, we adopt a biogeographical approach that integrates regional climatic variations. Our analysis explores 26 GCMs, four SSP scenarios and four 20‐year time frames from WorldClim to evaluate discrepancies between the GCM precipitation projections, historical data and national climate projections across five Ecuadorian bioregions. This approach enabled us to sort the GCMs by annual precipitation medians, classify their monthly precipitation using Dynamic Time Warping (DTW) clustering, and develop ensembles highlighting both the largest and average precipitation anomalies within and beyond the bioregions. Among the 26 models examined, 16 projected an increase in annual precipitation in Ecuador, especially during the wet seasons, with the BCC‐CSM2‐MR model showing peak values, notably in the Choco region and eastern Amazon basin. Conversely, 10 models, with CMCC‐ESM2 showing the largest decreases, projected reduced precipitation across almost all Ecuadorian territories, except the Choco region. The largest reductions were in the Amazon basin, raising concerns about reduced precipitation. Discrepancies, primarily in the Andes and Galapagos bioregions, reveal the challenges posed by their complex topography and insular environments. While the GCMs captured spatial patterns of ENSO, our research was constrained to 20‐year averages, making direct comparison with historical records infeasible, highlighting the need for further research with shorter time frames and finer spatial resolutions. The variability in precipitation was linked to geographical factors, GCM configurations and unexpected SSP outcomes. Therefore, selecting GCMs and climatic indices tailored to specific bioregions is recommended for effective climate change impact assessments.

Radley Horton,1,a Daniel Bader,1,a Yochanan Kushnir,2 Christopher Little,3 Reginald Blake,4 and Cynthia Rosenzweig5 1Columbia University Center for Climate Systems Research, New York, NY. 2Ocean and Climate Physics Department, Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY. 3Atmospheric and Environmental Research, Lexington, MA. 4Physics Department, New York City College of Technology, CUNY, Brooklyn, NY. 5Climate Impacts Group, NASA Goddard Institute for Space Studies; Center for Climate Systems Research, Columbia University Earth Institute, New York, NY

Climate change scenarios were created for Estonia by employing the SRES (= Special Report on Emission Scenarios) emission scenarios and general circulation model (GCM) outputs used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4) and presented at the IPCC Data Distribution Centre. Control simulations were explored for the estimation of the suitability of different GCMs to describe climatic conditions in Estonia. Comparing the modelled and observed monthly mean temperatures and precipitation during 1961–1990, better-quality GCM outputs were selected for further analysis. Climate change scenarios based on GCMs were created for Estonia for the period 2070–2099. The mean projected increase in air temperature was 3–4 K; it was slightly higher in winter than in summer. All models revealed some warming in all months. The projections of precipitation were more variable. The mean increase in annual precipitation was estimated to be mostly between 10% and 20%. An increase in precipitation was uniformly predicted for the cold season, while a variety of possible changes existed in summer. Some models projected even a decrease in precipitation in July, August and September.

Climate change is affecting the agriculture, water, and energy sectors in East Africa and the impact is projected to increase in the future. To allow adaptation and mitigation of the impacts, we assessed the changes in climate and their impacts on hydrology and hydrological extremes in East Africa. We used outputs from seven CMIP‐6 Global Climate Models (GCMs) and 1981–2010 is used as a reference period. The output from GCMs are statistically downscaled using the Bias Correction‐Constructed Analogs with Quantile mapping reordering method to drive a high‐resolution hydrological model. The Variable Infiltration Capacity and vector‐based routing models are used to simulate runoff and streamflow across 68,300 river reaches in East Africa. The results show an increase in annual precipitation (up to 35%) in Ethiopia, Uganda, and Kenya and a decrease (up to 4.5%) in Southern Tanzania in the 2050s (2041–2070) and 2080s (2071–2100). During the long rainy season (March–May), precipitation is projected to be higher (up to 43%) than the reference period in Southern Ethiopia, Kenya, and Uganda but lower (up to −20%) in Tanzania. Large parts of Kenya, Uganda, Tanzania, and Southern Ethiopia show an increase in precipitation (up to 38%) during the short rainy season (October–December). Temperature and evapotranspiration will continue to increase in the future. Further, annual and seasonal streamflow and hydrological extremes (droughts and floods) are projected to increase in large parts of the region throughout the 21st century calling for site‐specific adaptation.

Concentration of atmospheric greenhouse gas (GHG) has been increasing since the middle of 19th century. The Intergovernmental Panel on Climate Change (IPCC) estimated an increase of global GHG emissions by 25 to 90 % between 2000 and 2030. The global average surface temperature is expected to increase between 1.8 and 4.0 °C and precipitation by 5 to 20 % from 1990 to 2100. Understanding potential hydrologic influences of projected climate change is important for management of water resources. The objective of this study was to assess the impact of climate change on hydrologic processes of Bagmati basin in Nepal using the Soil and Water Assessment Tool (SWAT). A SWAT model was calibrated and validated based on observed flow for 2000–2006. The temperature and precipitation outputs from a global climate model (GCM) were used to drive the calibrated SWAT model in order to study the impacts of climate change. The GCM used in this study is from IPCC Fifth Assessment report. This study demonstrates the temporal differences in hydrologic responses to future climate changes in Bagmati basin, Nepal. The climate projection indicates an increase in annual precipitation in the basin, even though most of the precipitation will be concentrated within the summer monsoon. No appreciable change in the seasonality of rainfall was observed. The increase in precipitation results in an increase in annual water yield in future. Evapotranspiration is modeled to increase during the pre-monsoon dry summer, possibly indicating longer dry periods.

Basin-scale projections of river runoff at different warming levels provide useful information for climate change adaptation. In this study, we investigated changes in the projected climate and simulated runoff under 1.5°C and 2.0°C global warming of three inland rivers in the Hexi Corridor: the Shiyang River (SYR), the Heihe River (HHR), and the Shule River (SLR). The change in climate was projected based on five global climate models (GCMs) under three representative concentration pathways (RCPs), and the change in runoff was simulated based on the Soil and Water Assessment Tool (SWAT) hydrological model. Furthermore, the uncertainties in projected climate change and simulated runoff constrained by the GCMs and RCPs were quantified. The results indicate that, compared with the baseline period (1976–2005), there is a 1.42–1.54°C increase in annual air temperature and 4%–12% increase in annual mean precipitation in the three river basins under 1.5°C global warming, while there is a 2.09–2.36°C increase in annual air temperature and 5%–11% increase in annual mean precipitation under 2.0°C global warming. The simulated annual runoff of the SYR decreases by 4% under 1.5°C global warming, that of the HHR decreases by 3% and 4%, while that of the SLR increases considerably by 10% and 11% under 1.5°C and 2.0°C global warming, respectively. The additional 0.5°C global warming results in an annual air temperature increase of 0.67–0.82°C, a change of −1% to 1% in annual mean precipitation, and a change of −1% to 5% in simulated runoff. The simulated annual runoff has greater uncertainty. The simulations indicate substantial and consistent warming in autumn and winter in the three basins, relatively drier summer and autumn in the SYR and HHR basins, and a relatively drier autumn in the SLR basin. The simulated monthly runoff shows more complex changes with large uncertainties constrained mainly by the GCMs.

Impact of climate change and water use policies on hydropower potential in the south-eastern Alpine region

Climate change significantly affects the environmental and socioeconomic conditions in northwest China. Here we evaluate the ability of five general circulation models (GCMs) from 6th phase of the Coupled Model Inter-comparison Project (CMIP6) to reproduce regional temperature and precipitation over northwest China from 1961 to 2014, and project the future temperature and precipitation during 2021 to 2100 under SSPs-RCPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5). The results show that the CMIP6 models can simulate temperature better than precipitation. Projections show that the annual mean temperature will further increase under different SSPs-RCPs scenarios in the 21st century. Future climate changes in the near-term (2021–2040), mid-term (2041–2060) and long-term (2081–2100) are analyzed relative to the reference period (1995–2014). In the long term, warming will be significantly higher than the near and mid-terms. In the long term, annual mean temperature will increase by 1.4°C, 1.9°C, 3.3°C, 5.5°C, 2.7°C, 3.8°C and 6.0°C under SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5, respectively. Spatially, warming in the Junggar Basin will be higher than those in the Tarim Basin. Seasonally, the maximum warming zone will be in the mountainous areas of Tarim Basin during spring and autumn, in the southern basin during winter, and in the east during summer. Precipitation shows an increasing trend under different SSPs-RCPs in the 21st century. In the long term, increase in precipitation will be significantly higher than in the near and mid-terms. Increase in annual precipitation in the long term will be 4.1% under SSP1-1.9, 13.9% under SSP1-2.6, 28.4% under SSP2-4.5, 35.2% under SSP3-7.0, 6.9% under SSP4-3.4, 8.9% under SSP4-6.0, and 27.3% under SSP5-8.5 relative to the reference period of 1995–2014. Spatially, precipitation increase will be higher in the south than the north, especially higher in mountainous regions than the basin under SSP2-4.5, SSP3-7.0, and SSP5-8.5. Seasonally, highest increase can be expected for winter, followed by spring, with significant increase in mountainous regions of southern Tarim Basin. Summer precipitation will reduce in Tian Shan and basins but will significantly increase in the northern margin of the Kunlun Mountain.

The increasing impacts of climate change pose significant challenges for urban watersheds, necessitating effective strategies for stormwater management. This study evaluates the impacts of climate change on stormwater runoff and nutrient loads in the Sweetwater Creek Watershed, employing an integrated approach combining climate modeling, hydrological simulations, and green infrastructure (GI) optimization. Utilizing downscaled and bias-corrected data from eight General Circulation Models (GCMs) under two emission scenarios (SSP245 and SSP585), we project future changes in precipitation, temperature, and potential evapotranspiration (PET) for three timeframes: historical (1985–2014), near-future (2020–2049), and far-future (2070–2099). Projections indicate a 15%–25% increase in annual precipitation and a 2°C–4°C rise in average temperature under SSP245, with more extreme changes under SSP585, including up to a 40% increase in precipitation and a 5°C–7°C rise in temperature by the far-future period. These changes are expected to drive a 30%–45% increase in annual runoff volume and a 20%–35% rise in nutrient loads (e.g., nitrogen and phosphorus) under SSP585. The Storm Water Management Model (SWMM) was calibrated (NSE = 0.82) and validated (NSE = 0.79) using historical data to simulate hydrological processes and nutrient transport within the watershed. Using iPlantGreenS², a web-based GI planning tool, optimal GI locations and configurations were identified based on cost-effectiveness and nutrient removal efficiency. GI solutions, such as bioretention cells and vegetative swales, reduced runoff volume by 20%–35% and nutrient loads by 25%–40% in the near-future scenarios, with cost-effectiveness ratios ranging from $50–$150 per kilogram of nutrient removed. However, GI effectiveness declined by 10%–20% under extreme far-future climate conditions, emphasizing the need for adaptive designs to accommodate higher variability in precipitation and temperature. These findings highlight the critical role of GI in enhancing urban water management resilience and provide actionable insights for policymakers and urban planners.

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). The explicit method, in contrast to the implicit method, explicitly considers spatial differences of climate scenarios and variability during downscaling. Monthly projections of precipitation and temperature during 1950–2039 were used in the implicit and explicit spatial downscaling. A stochastic weather generator (CLIGEN) was then used to disaggregate monthly values to daily weather series following the spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat–wheat–maize rotation under conventional tillage at the 8.7 and 17.6% slopes in southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010–2039 at the Changwu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more dynamic or variable. Consequently, the responses to climate change, simulated with the explicit method, seemed more dynamic and sensitive. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3–6 (4–10) times greater with the explicit method than those with the implicit method. Differences in grain yield were also found between the two methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscore the importance of proper spatial treatments of climate change scenarios, and especially climate variability, prior to impact simulation. The implicit method, which applies aggregated climate changes at the GCM grid scale directly to a target station, is more appropriate for simulating a first-order regional response of nature resources to climate change. But for the site-specific impact assessments, especially for entities that are heavily influenced by local conditions such as soil loss and crop yield, the explicit method must be used.

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project (CMIP2) are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.

Climate change in Ireland from precipitation and streamflow observations

Arthropods, with over a million species described, are ubiquitous throughout different environments. Knowledge of their responses to human impact is crucial for understanding and predicting changes in ecosystem structure and functions. Our aim was to investigate the general patterns and to identify sources of variation in changes of the diversity, abundance and fitness of terrestrial arthropods (including Arachnida, Collembola and Insecta) in habitats affected by point polluters. We found 134 suitable studies which were published between 1965 and 2007. These data came from impact zones of 74 polluters in 20 countries with the largest representation from Russia (28 polluters), Poland (12 polluters) and the USA (six polluters). The database allowed calculation of 448 effect sizes (i.e. relative differences between measurements taken from polluted and control sites) on the effects of various point polluters like non-ferrous industries, aluminium plants, cement, magnezite, fertilising and chemical plants, power plants, iron- and steel-producing factories. We used meta-analysis to search for general effects and to compare between polluter types and arthropod groups, and linear regression to describe the latitudinal gradient and to quantify relationships between pollution and arthropod responses. The overall effect of pollution on arthropod diversity did not differ from zero. However, species richness of soil arthropods (both living on the soil surface and within the soil) tended to decrease, and species richness of herbivores to increase, near point polluters. Abundance of terrestrial arthropods near point polluters decreased in general. This decrease resulted from strong adverse effects on soil arthropods, especially on decomposers and predators. Densities of herbivores increased, but a number of research biases that we discovered in published data may have led to overestimation of the latter effect. The dome-shaped density pattern along pollution gradients was discovered only in 5% of data sets. Among herbivores, only free-living defoliators and sap-feeders demonstrated higher densities in polluted sites; the effects of pollution on other guilds were not significant. Near the polluters, conifers suffered higher increase in damage from herbivores than deciduous woody plants and herbs. Overall effect of pollution on arthropod performance was negative; in particular, individuals from polluted sites were generally smaller than individuals from control sites. This negative effect weakened with increase in duration of the pollution impact, hinting evolution of pollution resistance in populations inhabiting polluted sites. Stepwise regression analysis demonstrated that pollution-induced changes in both the density and performance of arthropods depended on climate of the locality. Negative effects on soil fauna increased with increase in annual precipitation; positive effects on herbivore population density increased with increases in both mean July temperature and annual precipitation. We detected effects of research methodology on the outcome of published studies. Many of them suffer from research bias-the tendency to collect data on organisms or under conditions in which one has an expectation of detecting significant effects. Pseudoreplicated studies (one polluted site contrasted to one control site) frequently reported larger effects than replicated studies (several polluted sites contrasted with several control sites). These methodological flaws especially influenced herbivory studies; we conclude that increase in herbivory in both heavily and moderately polluted habitats is not as frequent as it was earlier suggested. In contrast, the decrease in abundance of predators is likely to be a widespread phenomenon. Thus, our analysis supports the hypothesis that pollution may favour herbivore populations by creating an enemy-free space. Consistent declines in abundance of soil arthropods in impact zones of different polluters suggest that this group can potentially be used in bioindication of pollution-induced changes in terrestrial ecosystems. Main effects of pollution on arthropod communities (decreased abundance of decomposers and predators and increased herbivory) may have negative consequences for structure and services of entire ecosystems. Responses of arthropods to pollution depend on both temperature and precipitation in such a way that ecosystem-wide adverse effects are likely to increase under predicted climate change. Our analysis confirmed that local severe impacts of industrial enterprises on biota are well-suited to reveal the direction and magnitude of the biotic effects of aerial pollution, as well as to explore the sources of variation in responses of organisms and communities. Although we analysed the effects of point polluters, our conclusions can be applied to predict consequences of pollution impacts on regional and even global scales. We argue that possible interactions between pollution and climate should be accounted for in the analyses of global change impacts on biota.

The purpose of the research is to establish trends in climatic factors during 1974-2023 according to data from Rivne and Poltava weather stations and to compare them for 1974–1998 and 1999–2023. Meteorological databases (average annual, minimum, maximum temperature, annual precipitation, relative humidity) have been created for the cities of Rivne and Poltava, which are located in the forest and forest-steppe natural zones. When comparing climatic factors for 1974–1998 and 1999–2023 for the weather stations of the cities of Rivne and Poltava, it was found that for Poltava in the second period compared to the first, the average annual temperature increased by 1.7◦С (18.2%), for the city of Rivne the temperature increase was 1.6◦С (18.2%), The rates of increase in maximum temperature were recorded for the forest zone, where the maximum temperature increased by 2.0◦С (15.4%), for the forest-steppe zone, respectively, by 1.8◦С (13.1%). The minimum temperature for Rivne weather station increased in 1999-2023 compared to 1974-1998 by 0.8◦С (20.1%), while for Poltava the corresponding value was 1.7◦С (32.97%). That is, the increase in the minimum temperature was greater for the forest-steppe zone compared to the forest zone. A slight increase in annual precipitation was observed for both weather stations: for the Rivne weather station, this difference for the two periods was 17.7 mm (2.9%), and for the Poltava weather station – 13.9 mm (2.4%). Relative humidity, on the contrary, decreased in the second period for both weather stations: for Rivne - by 2.7% (3.5%), and for Poltava – by 2.3% (3.2%). Hydrothermal coefficients reflecting the hydrological conditions of the growing season were higher in Rivne than in Poltava and changed at a faster rate. Conclusions. When comparing climatic factors for 1974–1998 and 1999–2023 for the weather stations of Rivne and Poltava, it was found that the increase in average, maximum and minimum temperatures in the second period occurred in the range of 13-33%. In the second period, the increase in precipitation for the Rivne weather station fell mainly on the cold period (from December of the previous year to April of the current year) in contrast to the warm period (from May to September), the amount of precipitation decreased. During October-November, the amount of precipitation was almost the same for both periods. For the Poltava weather station, the increase in precipitation in the second period compared to the first period also occurred during the cold period (from November of the previous year to February of the current year), and during March-May (the exception is June) the opposite was observed - a decrease in precipitation. Amount precipitation also increased in the second period by 2.4-3.5%, in contrast to relative humidity, which decreased by 2.7–3.2% for both weather stations in the second period. This indicates that a slight increase in precipitation could not compensate for a significant increase in temperatures, which led to a decrease in relative humidity for the Rivne weather station. Hydrothermal coefficients (de Martonne coefficient and Selyaninov hydrothermal coefficient) were higher in Rivne than in Poltava and changed at a faster rate. There was increasing aridity during the growing season in both natural zones (forest and forest-steppe), with this process occurring more rapidly in the forest zone. There is an increase in aridity during the growing season in both natural zones (forest and forest-steppe), with this process occurring faster in the forest zone. Keywords: average annual, maximum and minimum temperatures, precipitation, relative humidity, hydrothermal indicators, Rivne weather station, Poltava weather.