Representative Concentration Pathways Research Articles

Diagnosing the causes of AMOC slowdown in a coupled model: a cautionary tale

Abstract. It is now established that the increase in atmospheric CO2 is likely to cause a weakening, or perhaps a collapse, of the Atlantic Meridional Overturning Circulation (AMOC). To investigate the mechanisms of this response in CMIP5 models, Levang and Schmitt (2020) have estimated the geostrophic streamfunction in these models offline and have decomposed the simulated changes into a contribution caused by the variations in temperature and salinity. They concluded that under a warming scenario, and for most models, the weakening of the AMOC is fundamentally driven by temperature anomalies, while freshwater forcing actually acts to stabilise it. However, given that both 3-D fields of ocean temperature and salinity are expected to respond to a forcing at the ocean surface, it is unclear to what extent the diagnostic is informative about the nature of the forcing. To clarify this question, we used the Earth system Model of Intermediate Complexity (EMIC), cGENIE, which is equipped with the C-GOLDSTEIN friction-geostrophic model. First, we reproduced the experiments simulating the Representative Concentration Pathway 8.5 (RCP8.5) warming scenario and observed that cGENIE behaves similarly to the majority of the CMIP5 models considered by Levang and Schmitt (2020), with the response dominated by the changes in the thermal structure of the ocean. Next, we considered hysteresis experiments associated with (1) water hosing and (2) CO2 increase and decrease. In all experiments, initial changes in the ocean streamfunction appear to be primarily caused by the changes in the temperature distribution, with variations in the 3-D distribution of salinity only partly compensating for the temperature contribution. These experiments also reveal limited sensitivity to changes in the ocean's salinity inventory. That the diagnostics behave similarly in CO2 and freshwater forcing scenarios suggests that the output of the diagnostic proposed in Levang and Schmitt (2020) is mainly determined by the internal structure of the ocean circulation rather than by the forcing applied to it. Our results illustrate the difficulty of inferring any information about the applied forcing from the thermal wind diagnostic and raise questions about the feasibility of designing a diagnostic or experiment that could identify which aspect of the forcing (thermal or haline) is driving the weakening of the AMOC.

An analysis of the expected wave conditions in the Mediterranean Sea in the context of global warming

The main objective of the work proposed herewith is to assess the climate change impact on sea state conditions in the Mediterranean Sea under Representative Concentration Pathways and Shared Socio-economic Pathways scenarios and its expected dynamics in the near future. To perform this, the analysis is structured in two-time intervals, each one covering 30 years. The first period represents the recent past, while the second comprises the near future (2041–2070) under the RCP8.5 and SSP5-8.5 scenarios. A wave modelling system based on the SWAN (Simulating WAves Nearshore) model is implemented and validated in the target area using ERA5 wind data to drive the wave model for the recent past. Then, for the same period, simulations with the wave modelling system forced by the Regional Climate Model wind fields are carried out. For the near future, wind fields under the above-mentioned scenarios are considered to drive the wave model. Various analyses and comparisons are performed to identify the climate change impacts on the wave climate and wave power, to evaluate the differences between the results obtained for each scenario, and also the evolution of the characteristics of the extreme values in the Mediterranean Sea basin. No high differences between the average values of significant wave height and wave power obtained under the two scenarios along the entire basin of the Mediterranean Sea were found. In the marginal seas sometimes different trends appear, especially in the Aegean and Tyrrhenian Seas. Regarding the 99th percentile of Hs, under the SSP5-8.5 scenario, they are slightly higher than those under the RCP8.5 scenario.

A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan

This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season.

Characterization of temperature and humidity effects on extreme heat stress under global warming and urban growth in the Pearl and Yangtze River Deltas of China

With global warming accelerating, the heavily populated and rapidly urbanized coastal regions of the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) stand as representative areas with mounting concerns about extreme heat stress. This study analyzes differentiated effects of temperature (TAS) and relative humidity (RH) on human heat stress measured by wet-bulb globe temperature (WBGT) in those urban regions based on machine learning and mathematical derivation, while also examining the impacts of global warming and urbanization on prospective heat risks. To generate fine-scale climate projections targeted at the PRD and YRD, two global projections forced by Representative Concentration Pathway (RCP) 8.5 scenario are dynamically downscaled using non-hydrostatic Regional Climate Model version 4.7 (RegCM4), with the urban density and extent updated every year based on Shared Socioeconomic Pathways 5-8.5 (SSP5) scenario, thereby incorporating the transient urban growth into future projections. The bias-corrected downscaled simulations effectively capture the distinct interdependencies between TAS and RH on WBGT across different regions, similar to the observed patterns during the historical period. While the absolute contribution of TAS to WBGT is larger than RH regardless of warming levels and regions, the relative increase in RH becomes more pronounced with warming. Under RCP8.5 scenario, unprecedentedly extreme WBGT is projected to emerge in the far future (2080–2099). In contrast, the effect of urbanization appears to be more dominant in the near future (2030–2049) as urban density under SSP5 scenario is projected to peak around the 2040s and gradually decrease afterwards. The reduction of RH is found in the intensely urbanized areas locally, but it does not significantly lower WBGT because the positive contribution of increased TAS is more dominant. As a result, highly urbanized regions still exhibit higher WBGT compared to other areas. In addition, urban heat island effect is more pronounced for compact areas with high urban density (i.e., PRD) and at night. Despite the smaller temperature increase from urban heat island effect compared to global warming, it can play a critical role in exacerbating heat stress, adding to the already dangerous humid and hot conditions.

Assessing the impacts of climate change on crop yields, soil organic carbon sequestration and N2O emissions in wheat–maize rotation systems

Straw retention has been widely implemented to increase soil organic carbon (SOC) sequestration and greenhouse gas (GHG) mitigation for global agriculture. However, the combined effects of long-term straw retention on crop production, SOC sequestration and GHG emissions response to climate change remain unknown. Two nearby wheat–maize rotation field experiments in the North China Plain, combined with local weather, soil and agronomic measurements, were used to evaluate the applicability of the SPACSYS model. The model was then applied to assess the response of crop yield, SOC storage and nitrous oxide (N2O) emissions to three nitrogen fertilizer application levels (100, 200, 400 kg N ha−1) and three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) under straw retention for the wheat–maize rotation systems during 2021–2100. In general, the model was reasonably accurate with R2 and model efficiency (EF) ranging from 0.25 to 0.96 and 0.32–0.94, respectively. Climate change decreased wheat yield by 4–39%, while maize showed a slight increase (3%) under RCP2.6 and a decrease of 1–15% under RCP4.5 and RCP8.5. The SOC storage in the 0–20 cm soil increased at a rate of 73–195 kg C ha−1 yr−1 but decreased within the upper 1 m soil at 280–390 kg C ha−1 yr−1. Climate change reduced the positive effect of SOC sequestration except in RCP2.6 and stimulated substantial N2O emissions ranging from 1–7.5 to 2.9–23.2 kg N ha−1 yr−1, consequently, the global warming potential increased from –79–2555 to 548–9357 kg CO2-eq ha−1 yr−1 under various N fertilizer application levels. This study reveals that the negative feedback under climate change with modelling approach, and extensive efforts are needed to make adaptations to ensure food production and reduce GHG emissions in the future.

Assessment of Water Resources under Climate Change in Western Hindukush Region: A Case Study of the Upper Kabul River Basin

This study aims to estimate the surface runoff and examine the impact of climate change on water resources in the Upper Kabul River Basin (UKRB). A hydrological model was developed using the Soil and Water Assessment Tool (SWAT) from 2009 to 2019. The monthly calibration was conducted on streamflow in six stations for the period from 2010 to 2016, and the results were validated from 2017 to 2018 based on available observed data. The hydrological sensitivity parameters were further prioritized using SWAT-CUP. The uncertainty of the model was analyzed by the 95% Prediction Uncertainty (95PPU). Future projections were analyzed for the 2040s (2030–2049) and 2090s (2080–2099) compared to the baseline period (1986–2005) under two representation concentration pathways (RCP4.5, RCP8.5). Four Regional Climate Models (RCMs) were bias-corrected using the linear scaling bias correction method. The modeling results exhibited a very reasonable fit between the estimated and observed runoff in different stations, with NS values ranging from 0.54 to 0.91 in the calibration period. The future mean annual surface runoff exhibited an increase in the 2040s and 2090s compared to the baseline under both RCPs of 4.5 and 8.5 due to an increase in annual precipitation. The annual precipitation is projected to increase by 5% in the 2040s, 1% in the 2090s under RCP4.5, and by 9% in the 2040s and 2% in the 2090s under RCP8.5. The future temperature is also projected to increase and consequently lead to earlier snowmelt, resulting in a shift in the seasonal runoff peak to earlier months in the UKRB. However, the shifts in the timing of runoff could lead to significant impacts on water availability and exacerbate the water stress in this region, decreasing in summer runoff and increasing in the winter and spring runoffs. The future annual evapotranspiration is projected to increase under both scenarios; however, decreases in annual snowfall, snowmelt, sublimation, and groundwater recharge are predicted in the UKRB.

Trend Projections of Potential Evapotranspiration in Yangtze River Delta and the Uncertainty

Global warming may increase potential evapotranspiration (ETp), reducing the water resources in Yangzi River Delta. Therefore, it is important to investigate the trend of ETp there under the background of climate change. To this purpose, the systematic biases in temperature outputs of 24 global climate models (GCMs) under 3 shared socioeconomic pathways—representative concentration pathways (SSPs) emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5)—are first corrected by using 8 bias correction methods. Then, the trend of ETp in Yangtze River Delta is projected by using 4 ETp calculation formulas (Blaney–Criddle, Hargreaves–Samani, Makkink, and Priestley–Taylor). The uncertainty of the projections is estimated and decomposed by using multi-way analysis of variance frameworks. The influence of uncertainty on the projected change signal is quantified by using the signal-to-noise ratio. The results show that all emission scenarios indicate robust increments of ETp. Specifically, relative to 1971~2000, ETp will increase by 0.14~0.17 mm d−1 (5.7~6.8%) during 2021~2050 and by 0.21~0.41 mm d−1 (8.5~16.7%) during 2061~2090, respectively. During 2021~2050, the uncertainty of ETp projections is dominantly contributed by the main effects of GCM (63%) and the ETp calculation formula (24%). During 2061~2090, it is mainly contributed by the main effect of GCM (36%), followed by the main effects of the emission scenario (34%) and the ETp calculation formula (18%). The ETp projections are generally reliable and robust during the two projection periods.

Projected Heat Waves in Ecuador under Climate Change: Insights from HadGEM-RegCM4 Coupled Model

This study examines heat wave projections across Ecuador’s Coastal, Highlands, and Amazon regions for 1975–2004 and 2070–2099 under Representative Concentration Pathways (RCP) scenarios 2.6, 4.5, and 8.5. Employing dynamic downscaling, we identify significant increases in heatwave intensity and maximum air temperatures (Tmax), particularly under RCP 8.5, with the Coastal region facing the most severe impacts. A moderate positive correlation between Tmax and climate indices such as the Pacific Decadal Oscillation (PDO) and the Oceanic Niño Index (ONI) suggests regional climatic influences on heatwave trends. These findings highlight the critical need for integrated climate adaptation strategies in Ecuador, focusing on mitigating risks to health, agriculture, and ecosystems. Proposed measures include urban forestry initiatives and the promotion of cool surfaces, alongside enhancing public awareness and access to cooling resources. This research contributes to the understanding of climate change impacts in Latin America, underscoring the urgency of adopting targeted adaptation and resilience strategies against urban heat island effects in Ecuador’s urban centers.

The future of Chinese rivers: Increasing plastics, nutrients and Cryptosporidium pollution in half of the basins

Many Chinese rivers are polluted with multiple pollutants. Therefore, this study aims to quantify the annual inputs of nutrients, macro-and microplastics, and Cryptosporidium (a pathogen) to 395 Chinese rivers from agriculture and urbanization-related sources during 2010–2050 following the storyline of Shared Socio-economic Pathway 3 and Representative Concentration Pathway 8.5. Our model estimates that multiple pollutants in Chinese rivers are projected to increase by 41–88 % between 2010 and 2050. Central, eastern, and southern Chinese sub-basins may be more polluted in the future. In half of the sub-basins, at least two types of pollutants are expected to increase by over 50 % during 2010–2050. By 2050, nutrients and plastics in rivers are projected to increase by 70 % due to urbanization. For nutrients and Cryptosporidium in rivers increases of 49–88 % are projected, due to agricultural activities. In contrast, water is expected to get cleaner in some western sub-basins by 2050 because of fewer human activities. Our insights about multiple pollutants in Chinese rivers could help prioritize water pollution reduction strategies for sub-basins.

Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios

One of the main challenges regarding the prediction of groundwater resource changes is the climate change phenomenon and its impacts on quantitative variations of such resources. Groundwater resources are treated as one of the main strategic resources of any region. Given the climate change phenomenon and its impacts on hydrological parameters, it is necessary to evaluate and predict future changes to achieve an appropriate plan to maintain and preserve water resources. In this regard, the present study is put forward by utilizing the Statistical Down-Scaling Model (SDSM) to forecast the main climate variables (i.e., temperature and precipitation) based on new Rcp scenarios for greenhouse gas emissions within a period from 2020 to 2060. The results obtained from the prediction of climate parameters indicate different values in each emission scenario, so the limit, minimum and maximum values occur in the Rcp8.5, Rcp2.6 and Rcp4.5 scenarios, respectively. Also, a model is developed by utilizing the GMDH artificial neural network technique. The developed model predicts the average groundwater level based on the climate variables in such a way that by implementing the climate parameters forecasted by the SDSM model, the groundwater level within a time period from 2020 to 2060 is predicted. The results obtained from the verification and validation of the model imply its proper performance and reasonable accuracy in predicating groundwater level based on the climate variables. The findings derived from the present paper indicate that compared to the years prior to the prediction period, the groundwater level of the Sahneh Plain has dramatically dropped so that based on the Rcp scenarios, the groundwater level values are in their lowest state within the period from 2046 to 2056. The findings of this paper can be used by managers and decision makers as a layout for evaluating climate change effects in the Sahneh Plain.

Common agronomic adaptation strategies to climate change may increase soil greenhouse gas emission in Northern Europe

Climate change poses a significant threat to agriculture, highlighting the need for adaptation strategies to reduce its impacts. Agronomic adaptation strategies, such as changes in planting dates, fertilization, and irrigation, might sustain crop yield. However, their impact on soil greenhouse gas (GHG) emission is unknown under future climate scenarios. Using the LandscapeDNDC model, we assessed the effect of agronomic adaptation strategies (early sowing, increased fertilization dose, and increased irrigation amount) on soil GHG emission, yield, and yield-scaled GHG emission. A diversified crop rotation (potato – winter wheat – spring barley – faba bean) of a long-term experiment in Denmark was used for model validation. The adaptation practices to climate change were implemented for two representative concentration pathways (RCPs; 4.5 and 8.5) and five coupled global circulation and regional climate models. The adaptation scenarios were contrasted against a baseline scenario under current management practices. Soil-related variables showed better model fit (refined index of agreement ≥ 0.38) and lower errors (mean absolute error ≤ 8.18) than crop-based outputs for model validation. A total yield of ∼29 (± 3) t DW ha−1, and soil GHG emission of ∼3.02 (± 1.39) t CO2e ha−1 (RCP8.5) were obtained for the crop rotation system under the baseline for 2071–2100. Early sowing and its combination with increased fertilization decreased the yield compared to the baseline by 6.1 and 4.8 %, respectively (RCP8.5). Conversely, early sowing with increased irrigation, and early sowing with increased fertilization and irrigation, produced higher yields by 2.3 and 4.0 %, respectively (RCP8.5). All the agronomic adaptation strategies increased soil GHG emissions (ranging from 4.1 to 17.8 %) as well as yield-scaled GHG emissions (varying from 3.0 to 12.9 %) (RCP8.5). The highest soil GHG emission was simulated for early sowing in combination with increased fertilization and irrigation. Our study indicates that soil GHG emission will increase in the coming decades and that the agronomic adaptation strategies needed to sustain food production may further exacerbate this emission.

Effects of grazing and climate change on aboveground standing biomass and sheep live weight changes in the desert steppe in Inner Mongolia, China

CONTEXTThe Stipa breviflora desert steppe ecosystem is fragile and sensitive to climate change and grazing disturbance. Previous studies have reported the effects of climate change and grazing on aboveground standing biomass of the plant community and sheep live weight, however, the interaction between climate change and grazing remains unclear. Process models have become ideal tools for the evaluation of the effects of grazing management practices under climate change. OBJECTIVEWe used the Soil-Plant-Atmosphere Continuum System (SPACSYS) model to investigate aboveground standing biomass and the live weight of sheep in the desert steppe of Inner Mongolia under future climate change scenarios and different grazing management. The results will be used to inform adaptive management strategies. METHODSThe grazing experiment consisted of four treatments: no grazing (0 sheep ha−1 half year−1), light stocking rate (0.91 sheep ha−1 half year−1), moderate stocking rate (1.82 sheep ha−1 half year−1), and high stocking rate (2.71 sheep ha−1 half year−1). We used observed data on soil temperature, soil volumetric water content, changes to sheep live weight, and aboveground standing biomass of plant community to provide parameterization and validation for the SPACSYS model. We then predicted aboveground standing biomass of the plant community and sheep live weight changes for different grazing management under three representative concentration pathways (RCP) scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) from 2021 to 2100. RESULTS AND CONCLUSIONSModerate and high stocking rates decreased aboveground standing biomass and sheep live weight changes more than the light stocking rate. A light stocking rate can maintain higher aboveground standing biomass and sheep live weight as well as meet production requirements. Therefore, a light stocking rate is a potentially effective management approach to improve food production security and combat global climate change in the desert steppe. SIGNIFICANCEThe model can inform management strategies for grazing in the desert steppe under climate change, supporting efforts to maintain the stability of the steppe ecosystem and increase economic benefits, while also providing a theoretical basis for adaptive management in the desert steppe.

Future Projection and Uncertainty Analysis of Wind and Solar Energy in China Based on an Ensemble of CORDEX‐EA‐II Regional Climate Simulations

AbstractWind and solar energy are crucial for meeting the growing energy demand and mitigating the impact of climate change, and their sources show a climate‐dependence. Here, based on the outputs from two regional climate models (RCMs) driven by three global climate models within the Coordinated Regional Climate Downscaling Experiments‐East Asia (CORDEX‐EA‐II), the effects of future climate change on wind power density (WPD) and photovoltaic power potential (PVP) in China under the Representative Concentration Pathway (RCP) 2.6 scenario and RCP8.5 scenario are comprehensively investigated, and the sources of uncertainty are also quantified. Results show that all RCMs can reproduce the observed WPD and PVP in China by employing the Quantile Delta Mapping method for wind speed simulations. For the future projections, the annual averages of WPD and PVP in China tend to decrease by −11.67% to −1.7% and −4.6% to −1.12%, respectively, with more significant reduction under the RCP8.5 than under the RCP2.6. Note that uncertainties exist among the RCMs' simulations in terms of future projections and long‐term trends. Further analysis reveals that the uncertainty in both WPD and PVP projections are primarily driven by the internal variability in most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. This study highlights the potential benefits of controlling greenhouse gas emissions in enhancing and stabilizing renewable energy in China.

Climate change influences on the streamflow and sediment supply to the Chao Phraya River basin, Thailand

This study investigates the effects of climate change on the sediment loads of the Ping and Wang River basins and their contribution to the sediment dynamics of the lower Chao Phraya River basin in Thailand. The various climate models under different Representative Concentration Pathways (RCPs) scenarios are employed to project sediment loads in future. The findings indicate a significant increase in river flow approximately 20% in the Ping River (PR) and 35% in the Wang River (WR) by the mid-21st century and continuing into the distant future. Consequently, this is expected to result in sediment loads up to 0.33 × 106 t/y in the PR and 0.28 × 106 t/y in the WR. This escalation is particularly notable under the RCP 8.5 scenario, which assumes higher greenhouse gas emissions. Additionally, the research provides insights into the potential positive implications for the Chao Phraya Delta's coastal management. Without further damming in the Ping and Wang River basins, the anticipated rise in sediment supply could aid in mitigating the adverse effects of land subsidence and sea-level rise, which have historically caused extensive shoreline retreat in the delta region, particularly around Bangkok Metropolis. The paper concludes that proactive adaptation strategies are required to manage the expected changes in the hydrological and sediment regimes to protect vulnerable coastal zones and ensure the sustainable management of the Chao Phraya River Basin in the face of climate change.

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Uncertainties related to runout distances in shallow landslide analyses may not only affect lives but may also result in economic losses. Owing to the increase in shallow landslides, which are especially triggered by heavy rainfall, runout distances have been investigated to decipher whether applications of a functional runout distance are feasible. This paper aims to give insights into the modeling of the shallow landslide runout probability in Eocene flysch facies in the Western Black Sea region of Türkiye. There are two main stages in this study—which are dominated by empirical models, the detection of initiation points, and propagation—which help us to understand and visualize the possible runout distances in the study area. Shallow landslide initiation point determination using machine learning has a critical role in the ordered tasks in this study. Modified Holmgren and simplified friction-limited model (SFLM) parameters were applied to provide a good approximation of runout distances during the propagation stage using Flow-R software. The empirical model parameters suggested for debris flows and shallow landslides were investigated comparatively. The runout distance models had approximately the same performance depending on the debris flow and shallow landslide parameters. While the impacted total runout areas for the debris flow parameters were predicted to amount to approximately 146 km2, the impacted total runout areas for the shallow landslide parameters were estimated to be about 101 km2. Considering the inclusion of the RCP 4.5 and RCP 8.5 precipitation scenarios in the analyses, this also shows that the shallow landslide and debris flow runout distance impact areas will decrease. The investigation of runout distance analyses and the inclusion of the RCP scenarios in the runout analyses are highly intriguing for landslide researchers.

Response of climate change and land use land cover change on catchment-scale water balance components: a multi-site calibration approach

Abstract The present study focused on evaluating the separate and combined response of land use land cover and climate change (CC) on future water balance components of a Subarnarekha River basin, spanning between the latitudes 21°33′N–23°18′N and longitudes 85°11′E–87°23′E, situated in the eastern India. The Soil and Water Assessment Tool is used for single-site calibration and multi-site calibration (MSC) of the model to characterize the future water balance components of the basin using the Cellular Automata-Markov model and climate projections under two representative concentration pathway (RCP) scenarios (4.5 and 8.5). The findings indicate that the model parameters obtained through MSC better represent spatial heterogeneity, making it the preferred calibration approach for model simulations. In the middle region of the basin, future annual water yield, groundwater recharge (GWR), and streamflow showed a reduction, respectively, by 46–47%, 29–30%, and 13–15%, while evapotranspiration showed an increase by 5–7% following projected CC under both RCP scenarios. The findings are relevant for policy-makers to mitigate the adverse effects of reduced GWR for sustainable water resources management. Future research may integrate reservoir operation frameworks to effectively address the water management issues of the basin.

Assessing the resilience of stormwater ponds under climate change: A case study on Grace Lake, Florida, unveiling flood control and water quality implications

Stormwater ponds have emerged as a crucial solution in Florida to accommodate excess runoff from new developments and mitigate environmental pollution. Climate change affects rainfall and evapotranspiration, likely altering the hydrologic process. It is unclear how effective stormwater ponds will be in a changing climate, especially where surface water and groundwater interaction is highly expected. To address these concerns, this study employs a fully coupled surface water and groundwater model that was calibrated for the Grace Lake, located in central Florida. Utilizing the Multi-Model Ensemble method and GCM data, future rainfall projections were generated for the mid-century (2040–2070). The results indicate a projected 13 % increase in rainfall for the mid-century under the Representative Concentration Pathway 4.5 (RCP 4.5). This increase led to a rise of 13 cm in the seasonal high water elevation of Grace Lake, reducing its reliability as a flood control mechanism by 13 %. Moreover, downstream areas from the lake are expected to experience higher flood stages in the future, with the duration of road flooding extending from less than three days (base period) to approximately seven days (future period). This increase in surface runoff raises concerns for water quality, with nitrogen and phosphorus loads projected to surge up to 67 % in the future period.

Evaluating the future total water storage change and hydrological drought under climate change over lake basins, East Africa

Climate change led an increase in the frequency of severe droughts, the effects of which are exacerbated by a lack of water storage. Despite increasing research on global-scale total water storage (TWS) change and drought prediction, basin-scale long-term hydrological drought, TWS change, and El Nino–Southern Oscillation (ENSO) influence in East Africa (EA) remain unexplored. In this paper, we used multiple approaches, of ensemble machine learning (ML) and weighted averaging, to conduct a basin-scale exploratory analysis of future hydrological drought and TWS change for the period 2025–2099. Climate change will affect future hydrological conditions in the basins, with maximum severity values of −90.3, −726.6, and −2567.7 Gt in the Tana, Turkana, and Victoria basins, respectively, under RCP6.0. Similarly, we found maximum severities of −187.5 and −277.7 Gt in the Abaya-Chamo and Tanganyika basins, respectively, under RCP 2.6. The most severe El Nino drought and exceptional La Nina conditions in the basins were found under both RCP scenarios. To ensure future water security in EA, sustainable water resource management strategies that can mitigate the impacts of extreme climate events in the future are urgently needed.

Widespread increase in plant transpiration driven by global greening

Global plant transpiration (PT) is a crucial component of the Earth's hydrological cycle and plays a significant role in regulating the exchange of water and energy between the land surface and the atmosphere. However, the long-term trend and the underlying driver of global PT remain unclear due to the significant uncertainties in estimating PT on a global scale. This study uses two sub-Mixture Density Networks (MDNc and MDNa) to predict vegetation canopy resistance (rc) and aerodynamic resistance (ra), then the predicted rc and ra are imported into the Penman-Monteith-Leuning (PML) model to simulate PT. The observed PT at 112 SAPFLUXNET sites are used to validate the performance of hybrid MDN-PML model. The verified MDN-PML model is further applied to map the spatial distribution of global PT and reconstruct a long-term (1990–2020) global PT dataset. The results indicate that the long-term average global PT is 397.2 ± 63.1 mm. During the period 1990–2020, the global PT exhibit a significant upward trend (0.79 ± 0.28 mm/year (P < 0.05)), which equates to a 6.0% increase compared with the long-term average global PT. A widespread trend of elevated PT is observed in approximately 70% of the global land surface. The trend attribution analysis results show that the change in leaf area index (LAI) can explain 66.2% of the global PT trend, indicating that elevated LAI due to global greening is the dominant factor contributing to the upward trend in global PT. The elevated LAI can be largely attributed to the CO2 fertilization effect induced by elevated atmospheric CO2 concentration. Additional analysis reveals that the increased global PT is more sensitive to CO2 fertilization effect in high LAI areas than in low LAI areas. Projected climate scenarios indicate that global land surface PT will continue to rise from 2023 to 2100, and the rate of increase in the future will be higher than in historical periods. The rising rates of global PT under the three Representative Concentration Pathway scenarios (RCP) 2.6, RCP4.5, and RCP8.5 climate change scenarios are 0.86 mm/year, 1.16 mm/year, and 1.45 mm/year, respectively, during the period 2023–2100. Our results highlight the impact of global change and vegetation greening on the global PT and hydrological cycle. This study is of great significance for the scientific response to the challenges of climate change for regional water resource management.

Detected and projected temperature changes in the area of Mediterranean Montenegro

AbstractThe goal of this research is to present the results of a detailed analysis of detected and projected temperature changes in Montenegro. A total of 10 temperature parameters from 18 meteorological stations were used. Initially, an analysis of temperature changes during the instrumental period (1961–2020) was conducted, followed by the results of high‐resolution (12.5 km) bias‐corrected projections from the regional ALADIN, REMO and CCLM4 models for the period 2021–2100, according to the RCP4.5 and RCP8.5 scenarios (representative concentration pathways). The simple difference method and trend method were employed for research purposes. Compared with the period 1961–1990, the highest temperature increase during 1991–2020 is observed in summer (TSu), while the rates of increase were slower in spring, winter and autumn (TSp, TW and TA). The average annual maximum temperature (TYx) registered a higher increase compared with the average minimum (TYn). Frosty days (FD) decreased, while the number of summer and tropical days (SU and TD) increased. The projection results indicate that the period 2021–2050 is expected to be warmer, the period 2051–2080 even warmer, and the period 2081–2100 is expected to be the warmest. In the last analysed period (2081–2100), according to the RCP8.5 scenario, the average TSu is expected to be higher by 3.7–4.6°C (CCLM4), 4.1–4.7°C (ALADIN) and 4.7–5.8°C (REMO), with an anticipated increase in TW ranging between 3.3–4.5°C (ALADIN), 3.6–5.4°C (CCLM4) and 3.4–5.6°C (REMO). In the period 2021–2100, according to the RCP4.5 scenario, the expected average trend values for TYx (TYn) are expected to be (°C/decade) 0.24 (0.26), 0.16 (0.12) and 0.20 (0.23) for the ALADIN, REMO and CCLM4 models, respectively, and according to the RCP8.5 scenario, 0.47 (0.52), 0.63 (0.57) and 0.51 (0.53), respectively. These findings have implications for planning mitigation and adaptation measures to address climate change, particularly in strategic economic sectors such as tourism, agriculture and water management in Montenegro.