Climate Models Research Articles

Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting

Abstract Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as postprocessing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initialization dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and 2-m temperature 2 weeks in advance for the continental United States. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models (a multimodel approach based on the prediction of the individual ML models). Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to using spatial information. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. In addition, we investigate feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability. Significance Statement Accurately forecasting temperature and precipitation on subseasonal time scales—2 weeks–2 months in advance—is extremely challenging. These forecasts would have immense value in agriculture, insurance, and economics. Our paper describes an application of machine learning techniques to improve forecasts of monthly average precipitation and 2-m temperature using lagged physics-based predictions and observational data 2 weeks in advance for the entire continental United States. For lagged ensembles, the proposed models outperform standard benchmarks such as historical averages and averages of physics-based predictions. Our findings suggest that utilizing the full set of physics-based predictions instead of the average enhances the accuracy of the final forecast.

Warm Jupiters around M dwarfs are great opportunities for extensive chemical, cloud, and haze characterisation with JWST

Context. The known population of short-period giant exoplanets around M-dwarf stars is slowly growing. These planets present an extraordinary opportunity for atmospheric characterisation and defy our current understanding of planetary formation. Furthermore, clouds and hazes are ubiquitous in warm exoplanets, but their behaviour is still poorly understood. Aims. We studied the case of a standard warm Jupiter around an M-dwarf star to show the opportunity of this exoplanet population for atmospheric characterisation. We aimed to derive the cloud, haze, and chemical budget of such planets using JWST. Methods. We leveraged a 3D global climate model, the generic PCM, to simulate the cloudy and cloud-free atmosphere of warm Jupiters around an M dwarf. We then post-processed our simulations to produce spectral phase curves and transit spectra as would be seen with JWST. Results. We show that, using the amplitude and offset of the spectral phase curves, we can directly infer the presence of clouds and hazes in the atmosphere of such giant planets. Chemical characterisation of multiple species is possible with an unprecedented signal- to-noise ratio, using the transit spectrum in one single visit. In such atmospheres, NH3 could be detected for the first time in a giant exoplanet. We make the case that these planets are key to understanding the cloud and haze budget in warm giants. Finally, such planets are targets of great interest for Ariel.

Considerations in designing climate change assessments for complex, non-linear hydrological systems

Bias correction of climate model simulations is vital to allow climate change impacts to be assessed for water resources systems. However, there has been limited research to date on the implications of system non-linearity on bias correction approaches. Here we bias correct regional climate model simulations of precipitation and evapotranspiration and use the output to force hydrological and water balance models of five small, interconnected lakes located south west of Sydney. We show that substantial, non-linear storage within the lakes amplifies biases that are not evident when the climate forcing or even the hydrological model simulations are evaluated using daily distributions of the climate variables and streamflow.The non-linearity in the stage-storage relationships of the lakes means that each lake responds differently to the same climate forcings. For example, ensemble mean projections for one lake suggest increases in water level across the full distribution of lake levels, whilst other lakes are projected to have decreasing water levels up to the median of the distribution, but increases during wetter conditions. These differences are explained by the varying influence of potential evapotranspiration increases depending on the surface area of the lakes at different depths. Using bottom up climate change assessments, we further explore these non-linear responses of the lakes to different climate forcings. We show that bottom up climate change assessments can provide information on the relative role of potential evapotranspiration changes compared to precipitation changes, providing more guidance to ecosystem managers than just using bias corrected climate model simulations alone. The paper discusses opportunities for future work to improve representation of climate attributes important for storage dominated water resource and natural ecosystems

Climate change impacts on seasonal runoff in the source region of the Yellow River: Insights from CORDEX experiments with uncertainty analysis

The source region of the Yellow River (SRYR) plays a crucial role in water resources to downstream areas. This study investigates the potential impacts of climate change on seasonal runoff in the SRYR using an ensemble of Regional Climate Models (RCMs) from two CORDEX experiments under RCP2.6 and RCP8.5 scenarios coupled with the WaSiM hydrological model. Two versions of the WaSiM hydrological model (WaSiM-PM and WaSiM-Hamon) are employed to validate the historical runoff, and WaSiM-PM’s superior performance make it the preferred choice for projecting climate change impacts on runoff. After bias-correction, RCM outputs show significant improvements, with correlation coefficients above 0.99 for temperature and 0.95 for precipitation. Under RCP2.6, temperature is projected to increase gradually (0.01–0.12 °C/decade), while under RCP8.5, warming is more pronounced (0.49–0.74 °C/decade). Precipitation generally shows an increasing trend, with variations between scenarios and experiments. Runoff projections indicate consistent peak flows in July across all scenarios. By the 2090 s under RCP8.5, substantial decreases in runoff are projected, especially in autumn and winter (approaching 40 %), with annual mean runoff decreasing by 30.3 %-31.5 %. Uncertainty analysis reveals that GCMs are the primary source of uncertainty (37–47 %) in the 2040 s, while emission scenarios dominate (43–58 %) in the 2090 s. Hydrological models show seasonal variability in uncertainty contribution, and downscaling experiments also play a significant role. This study highlights the potential for significant decreases in water resources in the SRYR, particularly under high emission scenarios, emphasizing the need for robust adaptation strategies in water resource management.

A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections

In cold regions, climate change, including warming and changes in snowmelt dynamics, have profound impacts on streamflow patterns, often leading to flooding events. Understanding the projected changes in hydrometeorological factors contributing to streamflow, such as snowmelt runoff and rain-on-snowmelt, is crucial for effective adaptation and mitigation strategies. It is also essential to consider uncertainty in streamflow projections and incorporate this uncertainty into flood predictions. This study presents a blueprint for calculating probabilistic future streamflow and flow duration curves in a mountainous cold region. The methodology integrated forcings from an atmospheric model (WRF) with a hydrological model (MESH) at fine spatial (4 km) and temporal (3-hourly) resolutions. To account for uncertainty, an ensemble of 15 CanRCM4 regional climate simulations with varying boundary conditions for the RCP 8.5 scenario was utilized. A novel method was developed to perturb the CanRCM4 simulations’ precipitation using a WRF Pseudo Global Warming run to incorporate uncertainty. This comprehensive approach revealed significant uncertainty in streamflow driven by internal variability. WRF-driven simulations without uncertainty showed a decrease in future high streamflows, while the perturbed simulations demonstrated the potential for substantially higher streamflows under climate change. Moreover, the study derived probabilistic flow duration curves from the streamflow projections, aiding in estimating flood frequency for floodplain mapping when driving local hydraulic models. The methodology developed in this work can be extended to other river basins in Canada and elsewhere where gridded downscaled climate model outputs are available.

Mutual impact of salinity and climate change on crop production water footprint in a semi-arid agricultural watershed: Application of SWAT-MODFLOW-Salt

Sustainable agriculture in intensively irrigated watersheds, particularly those in arid and semi-arid regions, requires enhanced management practices to maintain crop production, which depends on climate, available water resources, soil conditions, irrigation practices, and crop type. Among these factors, soil salinity and climate change are significant challenges to agricultural productivity. To investigate the long-term influence of salinity and climate change on crop production from 1999 to 2100 in irrigated semi-arid regions, we apply the water footprint (WF) concept using the hydro-chemical watershed model SWAT-MODFLOW-Salt, driven by five General Circulation Models (GCMs) and two climate scenarios (RCP4.5 and RCP8.5), to a 732 km2 irrigated stream-aquifer system within the Lower Arkansas River Valley (LARV), Colorado, USA. The study focused on calculating the green (WFgreen), blue (WFblue), and total (WFtotal) crop production WFs for 29 crops in the region, with and without including salinity effect on crop yield. Results reveal that during the baseline period (1999–2009), the total annual average WFgreen, WFblue, and WFtotal increased by 7.6 %, 4.4 %, and 6.5 %, respectively, under salinity stress, as crops experienced reductions of up to 4.6 %, 1.6 %, and 2.3 % in green, blue, and total crop yield. The mutual impact of salinity and the worst-case climate model (IPSL_CM5A_MR) under the higher emission scenario (RCP8.5) led to a 3.3 %, 1.9 %, and 3 % increase in green, blue, and total crop production WFs. Furthermore, the study highlighted that the proportion of green, blue, and total crop production WFs in the LARV exceeded the world average. This discrepancy was attributed to various factors, including different spatial and temporal crop distribution, irrigation practices, soil types, and climate conditions. Notably, salinity stress affected green crop yield and green WF more significantly than blue crop yield and blue WF across all GCM models. This finding underscores the importance of prioritizing management practices to control salinity-associated challenges within the region.

Phenological Adaptation Is Insufficient to Offset Climate Change-Induced Yield Losses in US Hybrid Maize.

Climate change is projected to decrease maize yields due to warmer temperatures and their consequences. Studies using crop growth models (CGMs), however, have predicted that, through a combination of alterations to planting date, flowering time, and maturity, these yield losses can be mitigated or even reversed. Here, we examine three assumptions of such studies: (1) that climate has driven historical phenological trends, (2) that CGM ensembles provide unbiased estimates of yields under high temperatures, and (3) that the effects of temperature on yields are an emergent property of interactions between phenology and environment. We used data on maize phenology from the United States Department of Agriculture, a statistical model of maize hybrid heat tolerance derived from 80 years of public yield trial records across four US states, and outputs of an ensemble of CMIP6 climate models. While planting dates have advanced historically, we found a trend toward later planting dates after 2005 and no trend for silking or maturity, shifting more time into the reproductive period. We then projected maize yields using the historical model and crop calendars devised using three previously proposed adaptation strategies. In contrast to studies using CGMs, our statistical yield model projected severe yield losses under all three strategies. Finally, we projected maize yields accounting for historical genetic variability for heat tolerance, discovering that it was insufficient to overcome the negative effects of projected warming. These projections are driven by greater heat stress exposure under all crop calendars and climate scenarios. Combined with analysis of the internal sensitivities of CGMs to temperature, our results suggest that current projections do not adequately account for the effects of increasing temperatures on maize yields. Climate adaptation in the US Midwest must utilize a richer set of strategies than phenological adaptation, including improvements to heat tolerance and crop diversification.

Internal variability of Arctic liquid freshwater content in a coupled climate model large ensemble

Internal variability of Arctic liquid freshwater content in a coupled climate model large ensemble

Indian Summer Monsoon Precipitation Dominates the Reproduction of Circumglobal Teleconnection Pattern: A Comparison of CMIP5 and CMIP6 Models

Abstract The zonal wavenumber-5 circumglobal teleconnection (CGT) pattern is one of the most critical atmospheric teleconnection patterns during boreal summer over the Northern Hemisphere (NH). CGT can exert significant climatic impact across NH including Europe, East Asia, and North America, but how reliable coupled climate models simulate the characteristics of CGT is poorly understood. Here, 20 coupled models with their respective versions in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and CMIP6 are selected to evaluate their performance on CGT simulation. We find that while both CMIP5 and CMIP6 models are able to capture the basic features of CGT in multimodel mean (MMM), there are large intermodel discrepancies in the simulation of CGT pattern among CMIP5 and CMIP6 models. High-skill models exhibit strong action center over west-central Asia, coinciding with the pattern derived from reanalysis, while the corresponding action center in low-skill models is weaker. Further analyses demonstrate that high-skill models are capable of simulating more realistic Indian summer monsoon (ISM) precipitation anomalies related to CGT. The resultant anomalous upper-tropospheric divergence over west-central Asia, acting as a Rossby wave source, can therefore excite the abovementioned action center. This high- and low-skill model difference on CGT–ISM relationship is consistent in both CMIP5 and CMIP6. It is also found that high-skill models tend to simulate more realistic CGT–ENSO relationship. The relationship between simulation skills of CGT–ENSO correlation and CGT spatial pattern is attributed to the remote impact of ENSO on CGT wave train through affecting ISM precipitation anomalies.

Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere

Abstract The deep atmosphere of Venus remains mysterious because of the planet’s high, optically thick cloud decks. While phenomena such as the observed decadal fluctuations in sulfur dioxide abundance above the clouds could shed light on conditions below, poor understanding of vertical and horizontal transport limits such an approach. Nightside spectral windows permit observation of trace gas species in the lower atmosphere, but incomplete understanding of the circulation makes the distribution of these species challenging to interpret. We performed two simulations with the Venus Planetary Climate Model including an age of air calculation to investigate tracer transport (a) between the surface and the stagnant lower haze layer and (b) between the cloud deck and the observable upper atmosphere. We find a timescale on the order of many decades for surface-to-lower haze layer transport and ∼1.4 yr from the lowest cloud deck to 101 km. The extreme slowness of transport from the surface to the clouds makes it unlikely that compositional variability at the surface could affect the upper atmosphere sulfur dioxide abundance on observed timescales. Planetary-scale Rossby waves with a zonal wavenumber of 1 in both hemispheres are found to circumnavigate the planet in the deep atmosphere in 36 Earth days. These waves are associated with gyres that collect tracers and areas of upwelling that transport them to higher altitudes, leading to significantly younger air at polar latitudes in the altitude range of 25–45 km. The existence of chemically enhanced traveling Rossby gyres could explain the observed deep atmosphere carbon monoxide variability.

Future aridity and drought risk for traditional and super-intensive olive orchards in Portugal

Portugal, a leading olive oil producer, boasts six Protected Denomination of Origin (PDO) regions, with distinct olive orchard (OR) densities (traditional rainfed to super-intensive irrigated). This study aimed to assess future drought and aridity conditions and the impacts on ORs located in the PDOs. Therefore, drought and aridity indicators were considered for the historical (ERA5: 1981–2000) and future periods (2041–2060; 2081–2100), and anthropogenic forcing scenarios (RCP4.5 and RCP8.5), using a 7-member ensemble of global climate models. From Spearman’s correlation analysis, Annual Mean Aridity (AIA) was selected as the most representative indicator of the climate conditions, to which the ORs were exposed. Readily Available Soil Water (RAW; mm) was considered to represent the available soil water reservoir for olive trees. Moreover, the Olive Drought and Aridity Risk Index (ODAR) was developed to determine each OR's future risks. This index considered that the AIA and RAW were weighted by OR density fractions. In the future, southern Portugal will be more arid (0.69) than northern and central (0.60). ORs soil shows lower RAW in southern PDOs (< 60 mm) than in central and northern regions (> 90 mm). These results suggest that the south of ORs will be more exposed to water stress than the northern regions. According to ODAR, the ORs exposed to low and high risk will be mainly located in the central parts of the PDOs. In northern ORs, moderate to high risk will predominate. In the south, however, the risk will be very high, which means that the olive tree growth, fruit development, and olive oil quality could be negatively affected. Implementation of tailored adaptation measures will be required to improve the climate resiliency of the sector.

Customized Statistically Downscaled CMIP5 and CMIP6 Projections: Application in the Edwards Aquifer Region in South‐Central Texas

AbstractClimate projections are being used for decision‐making related to climate mitigation and adaptation and as inputs for impacts modeling related to climate change. The plethora of available projections presents end users with the challenge of how to select climate projections, known as the “practitioner's dilemma.” In addition, if an end‐user determines that existing projections cannot be used, then they face the additional challenge of producing climate projections for their region that are useful for their needs. We present a methodology with novel features to address the “practitioner's dilemma” for generating downscaled climate projections for specific applications. We use the Edwards Aquifer region (EAR) in south‐central Texas to demonstrate a process to select a subset of global climate models from both the CMIP5 and CMIP6 ensembles, followed by downscaling and verification of the accuracy of downscaled data against historical data. The results show that average precipitation changes range from a decrease of 10.4 mm to an increase of 25.6 mm, average temperature increases from 2.0°C to 4.3°C, and the number of days exceeding 37.8°C (100°F) increase by 35–70 days annually by the end of century. The findings enhance our understanding of the potential impacts of climate change on the EAR, essential for developing effective regional management strategies. Additionally, the results provide valuable scenario‐based projected data to be used for groundwater and spring flow modeling and present a clearly documented example addressing the “practitioner's dilemma” in the EAR.

Global-scale multidecadal variability in climate models and observations, part II: The stadium wave

Global-scale multidecadal variability in climate models and observations, part II: The stadium wave

Are convection‐permitting climate projections reliable for urban planning over Africa? A case study of Johannesburg

AbstractCities are particularly vulnerable to surface water flooding. It is also well‐known that they influence local rainfall themselves, which has important implications for climate change adaptation planning for cities. At km‐scale resolution, convection‐permitting climate models (CPCMs) better resolve cities and should better represent local urban temperature and rainfall modifications. However, using state‐of‐the‐art pan‐African CPCM simulations with the Met Office Unified Model (CP4), we show that for the city of Johannesburg, South Africa, this is not the case. A significant enhancement of rainfall occurs over the city compared with surrounding rural areas, which is not seen in available observations. We demonstrate this is associated with an overestimated urban heat island effect, which leads to additional triggering of rainfall. Urban signals in future rainfall change are small compared with changes in the wider surroundings, the latter of which we expect to be more reliable than in models with parameterized convection. This suggests that deficiencies in representation of urban processes are of secondary importance in terms of future percentage change in rainfall. We recommend urban planners apply relative changes in CP4 as an uplift to observations, where available, or treat absolute future rainfall as an upper estimate if used directly.

Examining the impact of climate change on local ecosystems and communities

We examine the particular impacts of rising temperatures, changed precipitation patterns, and an increase in the frequency of extreme weather events using climate models, field data, and socioeconomic analysis. The results of our investigation show that phenology, biodiversity, and species distributions are all significantly changing in the local ecosystems. Ecosystem services including pollination, water purification, and carbon sequestration are being hampered by these changes. One of the most important environmental issues facing humanity in the twenty-first century is climate change. The world's wealthiest and poorest nations are both impacted by climate change. In areas that are susceptible, for instance, shorter growing seasons and more frequent droughts are reducing agricultural productivity and leading to food insecurity. In addition to the environmental effects, local residents have substantial financial challenges. According to economic assessments, climate-related calamities such as floods and droughts have a substantial financial impact on both infrastructure and people. Particularly impacted are communities that depend on agriculture and natural resources, which has a big impact on their standard of living and economic stability. Our research also demonstrates how different adaptation tactics, including as water conservation and sustainable land management, can improve community resilience. Another important component of our research is the effects on health. We see a rise in climate-sensitive health outcomes, especially in areas with poor access to healthcare, such as vector-borne infections and heat-related illnesses. The mediating function of socioeconomic factors, such as income and education levels, on these health outcomes is significant. Our results highlight the necessity of focused interventions and policies to improve adaptive ability and reduce health risks. The study's finding highlights how intricately local ecosystems and communities are impacted by climate change. By providing a comprehensive analysis, our aim is to inform stakeholders and policymakers about workable mitigation and adaptation strategies. Future research should focus on longer observation periods and developing comprehensive approaches to address the complex issues caused by climate change. The paper concludes that although policy frameworks are necessary, their implementation and enforcement are hampered by Africa's poor governance structure and lack of political will.

A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China.

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.

Pronounced spatial disparity of projected heatwave changes linked to heat domes and land-atmosphere coupling

Heatwaves are projected to substantially increase at a global scale, exacerbating worldwide heat-related risks in the future. However, understanding future heterogeneous heatwave changes and their origins remains challenging. By analyzing the output of various climate models from the Coupled Model Intercomparison Project Phase 6, we found pronounced spatial disparity of projected heatwave increases in the Northern Hemisphere, even outstretching seven-fold inter-regional differences in extreme heatwave occurrences, attributed primarily to future changes in heat-dome-like circulations and soil moisture–temperature coupling. Specifically, we found that by the end of the 21st century, the modulations of combined Pacific El Niño and positive Pacific Meridional Mode on magnified heat-dome-like circulations would be translated into summertime hotspots over western Asia and western North America. Amplified soil moisture–temperature couplings then further aggravate the heatwave intensity over these two hotspots. This study provides support for formulating impact-based mitigation strategies and efficiently addressing the potential future risks of heatwaves.

ВЛИЯНИЕ СОВРЕМЕННОГО ИЗМЕНЕНИЯ КЛИМАТА НА ИСПАРЕНИЕ С ВОДНОЙ ПОВЕРХНОСТИ В ИЛЕ-БАЛКАШСКОМ БАССЕЙНЕ

Using the Ile-Balkash basin as an example, changes in evaporation from the water surface over the last 40 years, as well as changes in the main meteorological elements that affect evaporation, are considered. Within the framework of this work, the changes in evaporation from the water surface for 1996-2020 in relation to the previous period of 1980-1995 were assessed. It was established that over the last 20 years, there was a slight increase in evaporation at many lowland stations of the Ile-Balkash basin and it is about 1-10 %. In mountainous areas and near large water areas, such as Balkash and Ulken Almaty Lakes, there is a decrease in evaporation by about 2-9 %. The main factors influencing the change in evaporation values from the water surface were analyzed. It was concluded that the increase in evaporation from the water surface at the lowland stations in the Ile-Balkash basin is mainly due to the increase in air temperature and some increase in precipitation, and the decrease in evaporation in mountainous areas and near a water area is caused by the decrease of average wind speed and an insignificant increase in air humidity. This study can help support decision making in the agriculture and water resources sector, since evaporation from the water surface is involved in many hydrological, climatic and hydrodynamic models. Therefore, the obtained results can be used in many scientific calculations.

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.

Climate change impacts on the hydrological behaviour of watershed in northeastern Tunisia (Oued El Abid watershed)

Climate change significantly impacts watershed hydrology, altering water supplies, natural disasters, and eco-hydrologic processes. These effects vary geographically, with some regions facing severe droughts while others experience increased precipitation and flooding. Climate change influences atmospheric evaporation demand, precipitation patterns, vegetation composition, streamflow characteristics, and groundwater dynamics. The temperature increases and rainfall changes in some watersheds can lead to increased potential evapotranspiration and decreased streamflow and soil water. Flood frequency analyses indicate an overall increasing trend in the latter half of the 21st century. The Oued El Abid catchment area, as like all catchments in northern Tunisia, is likely to be subject to major climatic and hydrological variations as a result of climate change. Advanced hydrological modelling using the HBV-Light model was set up to simulate current conditions and future scenarios based on Afric-Cordex climate models. The model demonstrates robust performance, achieving Nash-Sutcliffe Efficiency (NSE) values of 0.71 in calibration (1972-1992), and 0.66 in validation from 1993-2001. This climate change has a direct effect on the hydrological projections, which reveal significant changes, with annual flow reductions ranging from 35% to 43%, with more severe impacts under the RCP8.5 scenario for 2069-2099. Spring and summer seasons are particularly vulnerable, showing flow decreases of up to 68% and over 40%, respectively, extreme monthly variability is projected, reaching 88% reduction in July under RCP8.5. This research provides important results for water resource managers, highlighting the necessity of long-term planning and sustainable water management practices in the face of climate change.