Ensemble Of Climate Projections Research Articles

Increasing importance of heat stress for cattle farming under future global climate scenarios

In the last decades, livestock species have been severely affected by heat stress because of increasing temperatures, which has threatened animal welfare and decreased production. Based on thermal comfort indices and ensemble climate projections, we analyzed the current and future global spatiotemporal patterns of the heat exposure of cattle in 10 agroclimatic zones. The results show that ~7% of the global cattle population is currently exposed to dangerous heat conditions. This percentage is projected to increase to ~48% before 2100 under a scenario of growing emissions. Tropical agroclimatic zones are expected to face an early increase in the exposure to intense heat before 2050. Heat exposure was negatively correlated with the socioeconomic variables, showing that poor and livestock-dependent tropical countries are the most affected. Our results demonstrate the near-future consequences of heat stress on livestock, emphasizing the limited time available to implement effective abatement strategies.

Dryness decreases average growth rate and increases drought sensitivity of Mongolia oak trees in North China

Mongolian oak is a widely distributed tree species in broadleaf forests of North China, which are susceptible to increasing drought. Expansion of oak species ranges has been reported in Europe, America, and Asia with drought and heat stress, and thus, oak species are often deemed to be drought-resistant. As an expanding species in our study region, the Mongolian oak (Quercus mongolica Fisch. ex Ledeb) was used to investigate how tree growth rate and drought resistance change along a moisture gradient. In this study, we used a network of Mongolian oak tree-ring samples across a wide range of moisture gradients in North China to assess oak tree growth and its response to drought under different moisture conditions. Our results revealed that spatial variation in tree radial growth rate was highly related to the regions’ moisture index, an index indicating moisture condition. Oak trees in drier portions grew slower than those in wetter portions and their growth was more sensitive to drought events. We used an ensemble of climate projections from general circulation models to predict future changes in the Ivanov moisture index, and then estimated the tree growth rate and drought-growth correlation for the period 2021–2100. Increased dryness is likely to reduce tree average growth rate markedly and increase the drought sensitivity of radial growth in the expanding dry portions of our study region, leading to substantial overall drought suppression of growth. Our results suggest that dryness will present an increasing challenge for the continuing expansion of Mongolia oak in already dry areas of North China, whereas oak trees will thrive better in high moisture conditions. The distribution of Mongolia oak trees will likely shift to regions with increased moisture availability, especially under the increasing drought.

Projections of Directional Spectra Help to Unravel the Future Behavior of Wind Waves

Based on a novel approach, present-day and future spectral wind-wave conditions in a high-emission scenario from a seven-member wave climate projection ensemble are compared. The spectral analysis at the selected locations aids in understanding the propagation of swell projected changes from the generation areas across the ocean basins. For example, a projected increase in the energy from Southern Ocean swells can be observed in all ocean basins and both hemispheres, which is especially relevant in the west coast of North America due to the penetration of these swells beyond 30°N. Similarly, a consistent decrease in the energy of large northern Atlantic swells is noted close to the equator. This work provides evidence that assessments based on only integrated wave parameters (e.g., significant wave height and mean wave period) can mask information about the sign, magnitude, and robustness of the actual wave climate changes due to the offset of positive and negative variations within the spectrum, leading to a significant underestimation of the change associated with certain wave systems.

Greater vulnerability of snowmelt-fed river thermal regimes to a warming climate

While the advective flux from cool melt runoff can be a significant source of thermal energy to mountainous rivers, it has been a much less addressed process in river temperature modeling and thus our understanding is limited with respect to the spatiotemporal effect of melt on river temperatures at the watershed scale. In particular, the extent and magnitude of the melt cooling effect in the context of a warming climate are not yet well understood. To address this knowledge gap, we improved a coupled hydrology and stream temperature modeling system, distributed hydrology soil vegetation model and river basin model (DHSVM-RBM), to account for the thermal effect of cool snowmelt runoff on river temperatures. The model was applied to a snow-fed river basin in the Pacific Northwest to evaluate the responses of snow, hydrology, stream temperatures, and fish growth potential to future climates. Historical simulations suggest that snowmelt can notably reduce the basin-wide peak summer temperatures particularly at high-elevation tributaries, while the thermal impacts of melt water can persist through the summer along the mainstem. Ensemble climate projections suggested that a warming climate will decrease basin mean peak snow and summer streamflow by 92% and 60% by the end of the century. Due to the compounded influences of warmer temperatures, lower flows and diminished cooling from melt, river reaches in high elevation snow-dominated areas were projected to be most vulnerable to future climate change, showing the largest increases in summer peak temperatures. As a result, thermal habitat used by anadromous Pacific salmon was projected to exhibit substantially lower growth potential during summer in the future. These results have demonstrated the necessity of accounting for snowmelt influence on stream temperature modeling in mountainous watersheds.

Evaluation of CMIP5 models and ensemble climate projections using a Bayesian approach: a case study of the Upper Indus Basin, Pakistan

The availability of a variety of Global Climate Models (GCMs) has increased the importance of the selection of suitable GCMs for impact assessment studies. In this study, we have used Bayesian Model Averaging (BMA) for GCM(s) selection and ensemble climate projection from the output of thirteen CMIP5 GCMs for the Upper Indus Basin (UIB), Pakistan. The results show that the ranking of the top best models among thirteen GCMs is not uniform regarding maximum, minimum temperature, and precipitation. However, some models showed the best performance for all three variables. The selected GCMs were used to produce ensemble projections via BMA for maximum, minimum temperature and precipitation under RCP4.5 and RCP8.5 scenarios for the duration of 2011–2040. The ensemble projections show a higher correlation with observed data than individual GCM’s output, and the BMA’s prediction well captured the trend of observed data. Furthermore, the 90% prediction intervals of BMA’s output closely captured the extreme values of observed data. The projected results of both RCPs were compared with the climatology of baseline duration (1981–2010) and it was noted that RCP8.5 show more changes in future temperature and precipitation compared to RCP4.5. For maximum temperature, there is more variation in monthly climatology for the duration of 2011–2040 in the first half of the year; however, under the RCP8.5, higher variation was noted during the winter season. A decrease in precipitation is projected during the months of January and August under the RCP4.5 while under RCP8.5, decrease in precipitation was noted during the months of March, May, July, August, September, and October; however, the changes (decrease/increase) are higher than under the RCP4.5.

Potential shifts in climate zones under a future global warming scenario using soil moisture classification

Climate zones fundamentally shape the patterns of the terrestrial environment and human habitation. How global warming alters their current distribution is an important question that has yet to be properly addressed. Using root-layer soil moisture as an indicator, this study investigates potential future changes in climate zones with the perturbed parameter ensemble of climate projections by the HadGEM3-GC3.05 model under the CMIP5 RCP8.5 scenario. The total area of global drylands (including arid, semiarid, and subhumid zones) can potentially expand by 10.5% (ensemble range is 0.6–19.0%) relative to the historical period of 1976–2005 by the end of the 21st century. This global rate of dryland expansion is smaller than the estimate using the ratio between annual precipitation total and potential evapotranspiration (19.2%, with an ensemble range of 6.7–33.1%). However, regional expansion rates over the mid-high latitudes can be much greater using soil moisture than using atmospheric indicators alone. This result is mainly because of frozen soil thawing and accelerated evapotranspiration with Arctic greening and polar warming, which can be detected in soil moisture but not from atmosphere-only indices. The areal expansion consists of 7.7% (–8.3 to 23.6%) semiarid zone growth and 9.5% (3.1–20.0%) subhumid growth at the expense of the 2.3% (–10.4 to 7.4%) and 12.6% (–29.5 to 2.0%) contraction of arid and humid zones. Climate risks appear in the peripheries of subtype zones across drylands. Potential alteration of the traditional humid zone, such as those in the mid-high latitudes and the Amazon region, highlights the accompanying vulnerability for local ecosystems.

Increased risk of extreme heat to European roads and railways with global warming

Changing climatic conditions pose a risk to existing transport infrastructures, which are generally built based on historical climate variations. When temperatures exceed built operating conditions, roads may require greater maintenance due to rutting, while railways are susceptible to buckling. We quantify the additional operation and maintenance (O&M) costs to railway and road infrastructures across the 27 Member States of the EU and the United Kingdom (EU + UK) due to the projected rise in extreme heat under different levels of global warming. Our risk assessment integrates an ensemble of climate projections under RCP4.5 and RCP8.5, a high-resolution spatial representation of the rail and road network, and asset valuation and maintenance information derived from multi-country databases, with a road pavement damage model and a railway buckling simulation model. Under a 4 °C global warming level, increased levels of extreme heat in EU + UK cause annual transport O&M costs to rise by €4.8 billion, corresponding to an overall 6.9% rise compared to current values. Mitigating emissions to comply with a 1.5 °C, 2 °C, and 3 °C rise in global average temperature limits the increase to €0.9 billion, €1.3 billion, and €2.8 billion, corresponding to O&M increases of 1.3%, 1.5%, 4.1% respectively. Depending on the value of the stock, replacement costs, and maintenance cycles in a country, the increase in risk can be much stronger. To pre-emptively limit risk, EU + UK road pavement construction standards and railway stress free temperatures should be updated to account for future changes in temperatures.

Vulnerabilities of irrigated and rainfed corn to climate change in a humid climate in the Lower Mississippi Delta

The use of fossil fuels for energy needs increases atmospheric greenhouse gas (GHG) concentrations to levels that can significantly exacerbate the climate on earth. Assessing the vulnerability of regional crop production systems to such an altered climate in the future is essential for implementing appropriate adaptation and mitigation strategies for sustainable agriculture. We investigated the possible impacts of climate change (CC) projected by multiple global climate models (GCMs) on rainfed and irrigated corn (Zea mays L., a C4 plant) in the Lower Mississippi Delta region (LMD), USA. The CSM-CROPGRO-Maize v4.6 module in the RZWQM2 model (hereafter referred to as the “corn model”) was previously calibrated and validated for modeling corn at Stoneville, Mississippi, a representative location in the LMD was used. The CC scenarios considered in this study were ensembles of climate projections of multiple GCMs (97 ensemble members) that participated in the Climate Model Inter-comparison and Improvement Program 5. These CC scenarios were bias-corrected and spatially downscaled (BCSD) at the location for the years 2050 and 2080. Four representative GHG concentration pathways (RCP) 2.6, 4.5, 6.0, and 8.5 drove these CC scenarios. Under both irrigated and rainfed conditions, corn yield responses to enhanced CO2 were weak; thus, yield declined significantly in response to the enhanced air temperatures under all the RCP scenarios in both 2050 and 2080. The yield declines across RCPs ranged between 10 and 62% under irrigated conditions, and between 9 and 60% under rainfed conditions, mainly due to increased frequency of extreme temperatures and reduced crop durations. Water use efficiency declined between 22 and 150% under irrigated, and 8 and 54% under rainfed management. As an adaptation measure, planting corn up to 9 weeks earlier in the season, in general, failed to boost yields from increased crop duration and reduction in upper extreme air temperatures, as incidences of lower extreme temperatures also increased alarmingly. Development of cultivars that are more heat tolerant and produce higher yields under extreme temperatures would be required to combat corn yield decline in the region from climate change.

High-resolution Ensemble Climate Projections of Water Deficit in Arid Regions by the Mid-21st Century

High-resolution Ensemble Climate Projections of Water Deficit in Arid Regions by the Mid-21st Century

Calibrating large-ensemble European climate projections using observational data

Abstract. This study examines methods of calibrating projections of future regional climate for the next 40–50 years using large single-model ensembles (the Community Earth System Model (CESM) Large Ensemble and Max Planck Institute (MPI) Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an “imperfect model” approach using the historical/representative concentration pathway 8.5 (RCP8.5) simulations from the Coupled Model Intercomparison Project 5 (CMIP5) archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely homogeneous Gaussian regression (HGR), is used for the subsequent analysis. As an extension to the HGR calibration method it is applied to dynamically decomposed data, in which the underlying data are separated into dynamical and residual components (HGR-decomp). Based on the verification results obtained using the imperfect model approach, the HGR-decomp method is found to produce more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes.

Will Evolving Climate Conditions Increase the Risk of Floods of the Large U.S.‐Canada Transboundary Richelieu River Basin?

AbstractIn spring 2011, an unprecedented flood hit the complex eastern United States (U.S.)–Canada transboundary Lake Champlain–Richelieu River (LCRR) Basin, destructing properties and inducing negative impacts on agriculture and fish habitats. The damages, covered by the Governments of Canada and the U.S., were estimated to C$90M. This natural disaster motivated the study of mitigation measures to prevent such disasters from reoccurring. When evaluating flood risks, long‐term evolving climate change should be taken into account to adopt mitigation measures that will remain relevant in the future. To assess the impacts of climate change on flood risks of the LCRR basin, three bias‐corrected multi‐resolution ensembles of climate projections for two greenhouse gas concentration scenarios were used to force a state‐of‐the‐art, high‐resolution, distributed hydrological model. The analysis of the hydrological simulations indicates that the 20‐year return period flood (corresponding to a medium flood) should decrease between 8% and 35% for the end of the 21st Century (2070–2099) time horizon and for the high‐emission scenario representative concentration pathway (RCP) 8.5. The reduction in flood risks is explained by a decrease in snow accumulation and an increase in evapotranspiration expected with the future warming of the region. Nevertheless, due to the large climate inter‐annual variability, short‐term flood probabilities should remain similar to those experienced in the recent past.

The CORDEX-Australasia ensemble: evaluation and future projections

A new regional climate projection ensemble has been created for the Australasia region as part of the World Climate Research Programs Coordinated Regional Downscaling Experiment (CORDEX). The CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia.

Differential Credibility Assessment for Statistical Downscaling

AbstractClimate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. The approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact-relevant Climate Extreme (CLIMDEX) temperature indices that are assigned credibility based on 1) the reproduction of relevant large-scale predictors by the GCMs (i.e., fraction of regression beta weights derived from predictors that are well reproduced) and 2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations and between the two GCM and is generally higher for minimum temperature than for maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision-makers about projection confidence and/or can be extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

Climate Change Significantly Alters Future Wildfire Mitigation Opportunities in Southeastern Australia

AbstractPrescribed burning is used globally to mitigate the risks of wildfires, with severe wildfires increasing in frequency in recent decades. Despite their importance in wildfire management, the nature of future changes to prescribed burn windows under global warming remains uncertain. We use a regional climate projection ensemble to provide a robust spatiotemporal quantification of statistically significant future changes in prescribed burn windows for southeastern Australia. There are significant decreases during months presently used for prescribed burning, that is, in March to May in 2060–2079 versus 1990–2009 across several temperate regions. Conversely, burn windows show widespread significant increases in June to August, that is, months when burns have rarely occurred historically, and also in spring (September–October). Overall, projected changes in temperature and fuel moisture show the most widespread and largest decreases (or increases) in the number of days within their respective ranges suitable for conducting burns. These results support wildfire risk mitigation planning.

Extreme-value analysis for the characterization of extremes in water resources: A generalized workflow and case study on New Mexico monsoon precipitation

Water managers need non-stationary tools to better characterize precipitation extremes. Statistical approaches based on extreme value theory (EVT) are increasingly being used, but few end-to-end generalized workflows are available. In this paper, a step-by-step framework is demonstrated for developing an EVT model that considers the influence of dominant weather patterns on precipitation extremes in a watershed. Specifically, the Point Process (PP) model is utilized, which is a unified statistical framework for modeling the frequency and magnitude of extremes above a threshold. Because threshold selection can be subjective, a demonstration of how to go about selecting a threshold is provided; in particular, by examining a range of thresholds. The workflow is applied to daily precipitation from the Rio Grande watershed in New Mexico. In this arid watershed, extreme precipitation events substantially contribute to total runoff. An improved understanding of the drivers and extent of changes in extreme precipitation is essential for water resource and risk management. In addition to a stationary PP model without covariates, several covariates are examined for inclusion in the location and scale parameters. The significance of including the covariates is assessed, as well as several additional criteria, including if the covariate(s) make intuitive sense and if it is a good candidate for statistical downscaling (i.e., methods that relate large-scale variables to the local scale). A final PP model is selected that includes the wet weather types in the location and scale parameters. This model is applied in a downscaling context using a large ensemble of climate projections, which shows that the frequency of exceeding a high threshold increases after 2050, but the conditional likelihood of exceeding the maximum observed precipitation stays relatively constant.

Optimal configuration and resolution for the first convection‐permitting ensemble of climate projections over the United Kingdom

AbstractConvection‐permitting models (CPMs) provide a better representation of sub‐daily precipitation statistics and convective processes on both climate and weather forecasting timescales. This study aims to establish what horizontal resolution and physical process settings are suitable for the first ensemble of convection‐permitting climate projections for the United Kingdom. For this purpose, 12‐year‐long simulations were run using three different convection‐permitting resolutions (grid spacing of 4 km, 2.2 km and 1.5 km) each with several configurations. The focus is on the ability to represent sub‐daily precipitation due to its potential for high impacts on society through flooding. The analysis shows that the use of a finer grid spacing within the convection‐permitting regime improves the representation of sub‐daily precipitation but the added value decreases as the grid becomes increasingly finer, while the computational costs substantially increase. Changes in the representation of physical processes can have as much impact as the grid spacing and can lead to similar differences in the diurnal cycle as between convection‐parametrized models and CPMs. Although the 4km model shows some key deficiencies that make it unreliable for climate projection, the 2.2km model performs as well as the 1.5km model for most of the metrics examined with considerable benefits in terms of computer cost. The optimal configuration and resolution identified here is being used for an ensemble of simulations run within the UK Climate Projections 2018 (UKCP18) project, which allows, for the first time, an estimate of uncertainty in future changes at convection‐permitting scale and providing more reliable climate change projections at local and hourly scales.

Quels futurs possibles pour les débits des affluents français du Rhin (Moselle, Sarre, Ill) ?

Le projet MOSARH21 a réalisé une évaluation des effets du changement climatique anthropique sur les débits des affluents français du Rhin grâce à des projections climatiques récentes (expérience CMIP5). La démarche s'appuie sur deux modèles hydrologiques, GRSD et LARSIM, utilisés conjointement avec un ensemble de projections climatiques désagrégées. Les effets sont quantifiés à l'aide de divers indicateurs relatifs aux régimes des cours d'eau, aux crues et aux étiages. Les projections hydrologiques indiquent que les débits moyens interannuels seraient en légère augmentation, celle-ci pouvant même devenir importante pour le scénario radiatif RCP 8.5 (scénario le plus pessimiste en termes de réchauffement climatique). L'aléa de crue devrait s'intensifier dans un futur proche (2021–2050). En revanche, dans un futur lointain (2071–2100), l'évolution des indicateurs de crue est incertaine, les projections hydrologiques étant divergentes. Les débits d'étiages diminueraient dans un futur proche. Leur évolution pour la fin du siècle est plus incertaine, allant de la baisse drastique à une augmentation sensible selon le scénario choisi. Les résultats ont été comparés à ceux obtenus dans le cadre d'études d'impact antérieures réalisées dans la partie française du bassin du Rhin. Compte tenu des différences méthodologiques et des résultats obtenus, on peut conclure que la base de connaissances actualisée produite dans le cadre du projet MOSARH21 valide les tendances des études précédentes.

Response of southern African vegetation to climate change at 1.5 and 2.0° global warming above the pre-industrial level

Climate change has severely impacted southern African vegetation over the last decades. Recent studies have shown that limiting the global warming level to well below 2 °C above pre-industrial level may reduce the catastrophic effects of anthropogenic climate change. However, there is limited knowledge on the response of vegetation to climate change at different global warming levels. This study examines how southern African vegetation may respond to climate change at 1.5 °C and 2 °C warming. An ensemble of climate projections from the CESM model were analyzed to evaluate the potential changes in timing and magnitude of vegetation index and parameters across southern African biomes for a global warming of 1.5 °C and 2 °C under the RCP8.5 scenario. The results show that the spatial pattern of the Normalized Difference Vegetation Index (NDVI) are well captured by the ensemble mean. However, the model overestimates NDVI over the east coast of South Africa while it underestimates it over Angola. The strong relationship between the NDVI and other vegetation parameters (e.g. net primary production, leaf area cover) over southern Africa, suggests that vegetation productivity is projected to decline over Mozambique, Namibia, Botswana, Zimbabwe and Zambia at 1.5 °C and 2 °C warming. However, vegetation productivity is projected to likely increase over some parts of Madagascar and South Africa. The results further show that there is a similarity in the response of vegetation parameters at 1.5 °C and 2 °C warming. The difference in projected changes of the above ground net primary production, soil carbon and surface run-off is negligible between 1.5 °C and 2 °C warming. While the model ensemble members show a range in the magnitude of changes, they all project an increase in below ground net primary production, soil carbon and photosynthesis over the southern African biomes while projecting a decline in canopy transpiration over all the biomes. Furthermore, the CESM projects an increase in drought intensity as temperature increases over the southern African biomes. The projected changes in the drought intensity appears more pronounced with the Standardized Precipitation Evapotranspiration Index (SPEI) than with the Standardized Precipitation Index (SPI). The results of the study is important in informing policy makers for climate change mitigation and adaptation efforts in southern Africa. For effective mitigation and adaptation strategy, there is a need to translate the results to make them more accessible to inform policy through the use of appropriate engagement platforms such as multi-stakeholder partnerships and workshops.

Groundwater contamination risks from conservative point source pollutants in a future climate

ABSTRACT The groundwater contamination risk in future climates was investigated at three locations in Sweden. Solute transport penetration depths were simulated using the HYDRUS-1D model using historical data and an ensemble of climate projections including two global climate models (GCMs), three emission scenarios and one regional climate model. Most projections indicated increasing precipitation and evapotranspiration until mid-century with a further increase at end-century. Results showed both increasing and decreasing groundwater contamination risks depending on emission scenario and GCM. Generally, the groundwater contamination risk is likely to be unchanged until mid-century, but higher at the end of the century. Soil and site specific relationships between Δ(P – PET) (i.e. change in the difference between precipitation, P, and potential evapotranspiration, PET) and changes in solute transport depths were determined. Using this, changes in solute transport depths for other climate projections can be assessed.

Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 different combinations of a global climate model (GCM)–regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041–2070) and long-term (2071–2100) periods with respect to the baseline (1983–2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between different GCM–RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.