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Global warming is expected to cause significant changes in the pattern of precipitation minus evaporation (P − E), which represents the net flux of water from the atmosphere to the surface or, equivalently, the convergence of moisture transport within the atmosphere. In most global climate model simulations, the pattern of P − E change resembles an amplification of the historical pattern—a tendency known as "wet gets wetter, dry gets drier." However, models also predict significant departures from this approximation that are not well understood. Here, we introduce a new method of decomposing the pattern of P − E change into contributions from various dynamic and thermodynamic mechanisms and use it to investigate the response of P − E to global warming within the CESM1 Large Ensemble. In contrast to previous decompositions of P − E change, ours incorporates changes not only in the monthly means of atmospheric winds and moisture, but also in their temporal variability, allowing us to isolate the hydrologic impacts of changes in the mean circulation, transient eddies, relative humidity, and the spatial and temporal distributions of temperature. In general, we find that changes in the mean circulation primarily control the P − E response in the tropics, while temperature changes dominate at higher latitudes. Although the relative importance of specific mechanisms varies by region, at the global scale departures from the wet-gets-wetter approximation over land are primarily due to changes in the temperature lapse rate, while changes in the mean circulation, relative humidity, and horizontal temperature gradients play a secondary role. [ABSTRACT FROM AUTHOR]

Groundwater and climate interact in a two-way manner. Precipitation ultimately controls groundwater recharge and, conversely, groundwater may influence climate through evapotranspiration. Yet very few global climate models or Earth system models actually simulate groundwater flows. And while the expected impacts of climate change on groundwater resources are the subject of a growing concern, global-scale groundwater–climate feedbacks have received very little attention so far. Here we show that the integration of unconfined aquifers in a global climate model can regionally affect the climate change signal on temperatures and precipitation. We assess the impact of groundwater under preindustrial and 4xCO2 conditions (after climate stabilization). In both cases, we find that groundwater has a cooling and a wetting effect in certain regions of the world. In eastern Europe, both these impacts are stronger in the warmer climate (4xCO2 forcing) where the presence of groundwater reduces the frequency of summer heatwaves by 40%, compared to a 15% reduction in the preindustrial world. This work constitutes one of the very first global assessments of the potential feedbacks of groundwater on climate change. Our results support the idea that groundwater should be represented in global climate models and Earth system models, as it does indeed play an active role in the climate system. [ABSTRACT FROM AUTHOR]

Despite multiple advances in the understanding of the water cycle intensification in a warmer climate, climate models still diverge in their hydrological projections. Here we constrain annual runoff projections over individual and aggregated Arctic river basins. For this purpose, we use two ensembles of global climate models and two statistical methods: a regression scheme assuming similar runoff sensitivities at interannual versus climate change timescales, and a Bayesian method where models are used to derive a posterior runoff response conditioned on historical observations. While both techniques are shown to narrow model uncertainties, more or less substantially depending on rivers, the Bayesian method is less sensitive to the choice of the model ensemble and is more skillful when tested with synthetic observations. It has also been applied over the whole Arctic watershed, showing so far a limited narrowing of the inter‐model spread, but its skill will further improve with increasing climate change. Plain Language Summary: Despite considerable progress in understanding the intensification of the water cycle in response to global warming, projections of changes in the various components of the continental water balance remain highly model‐dependent. This is particularly the case for the evolution of runoff, which remains very sensitive to the way atmospheric and continental processes are represented. Here we evaluate and compare two statistical methods to constrain climate projections of annual runoff at high latitudes via available or synthetic observations. Our Bayesian method, based on historical observations, is found to be more robust than a previous method based on observed interannual climate variability. It underlines the importance of having reliable flow reconstructions to constrain runoff projections and of using empirical observational constraints with caution, as they are likely to lead to over‐confident projections. Key Points: Runoff sensitivity to temperature depends on the considered timescaleObservational constraints based on interannual variability can thus lead to overconfident projectionsObservational constraints based on historical trends can be effective if such trends are driven by human emissions of greenhouse gasses [ABSTRACT FROM AUTHOR]

Persistent precipitation deficits are among the most impactful consequences of global warming. Here we focus on changes in the annual number of dry days (NDD) and in the annual maximum length of dry spells due to a quadrupling of atmospheric CO2. We use atmosphere‐only simulations to decompose the projected changes into additive contributions. A fast adjustment leads to a global increase in NDD despite notable regional exceptions (e.g., South Asia and Sahel). The effect of the uniform component of the surface ocean warming is model‐dependent but shapes the regional distribution of the NDD response in each model. Finally, the ocean warming pattern also contributes to large uncertainties, likely through contrasting changes in large‐scale circulation. Our results thus highlight the complexity of the NDD response, with policy‐relevant practical implications for mitigation and adaptation strategies. Plain Language Summary: Global warming is expected to intensify the global water cycle, including the intensity and frequency of precipitation extremes. Yet, the response of the dry side of the daily precipitation distribution to increased atmospheric CO2 has received so far less attention. Here we show that this response remains highly model‐dependent across the latest generation of global climate models. Furthermore, we use atmosphere‐only simulations to isolate different drivers of the precipitation response. A fast radiative and vegetation adjustment to increased CO2 leads to an overall increase in the mean annual number of dry days (NDD) despite some regional exceptions. The effect of the ocean surface warming is highly model‐dependent, only partly due to the diversity of the simulated patterns of sea surface temperature anomalies. The regional response of NDD and of the maximum length of dry spells thus does not simply scale with global warming across the different models. Key Points: AGCM simulations are used to split the dry day response to increased CO2 into fast adjustment versus slower uniform and patterned SST effectsNone of the three components consistently dominates the global land response of dry days across the selected CMIP6 modelsThe uniform SST warming dominates the regional anomalies, which however do not scale with global warming across the multi‐model ensemble [ABSTRACT FROM AUTHOR]

Climate change is expected to have major impacts on land surface and subsurface processes through its expression on the hydrological cycle, but the impacts to any particular basin or region are highly uncertain. Non-stationarities in the frequency, magnitude, duration, and timing of rainfall events have important implications for human societies, water resources, and ecosystems. The conventional approach for assessing the impacts of climate change is to downscale global climate model output and use it to drive regional and local models that express the climate within hydrology near the land surface. While this approach may be useful for linking global general circulation models to regional hydrological cycle, it is limited for examining the details of hydrological response to climate forcing for a specific location over timescales relevant to decision makers. For example, management of flood or drought hazard requires detailed information that includes uncertainty based on variability in storm characteristics, rather than on differences between models within an ensemble. To fill this gap, we present the second version of our STOchastic Rainfall Model (STORM), an open-source, parsimonious and user-friendly modeling framework for simulating climatic expression as rainfall fields over a basin. This work showcases the use of STORM in simulating ensembles of realistic sequences, and spatial patterns of rainstorms for current climate conditions, and bespoke climate change scenarios that are likely to affect the water balance near the Earth's surface. We outline, and detail STORM's new approaches: one copula for linking marginal distributions of storm intensity and duration; orographic stratification of rainfall using the copula approach; a radial decay-rate for rainfall intensity which takes into consideration potential, but unrecorded, maximum storm intensities; an optional component to simulate storm start date-times via circular/directional statistics; and a simple implementation for modelling future climate scenarios. We also introduce a new pre-processing module that facilitates the generation of model input in the form of probability density functions (PDFs) from historical data for subsequent stochastic sampling. Independent validation showed that the average performance of STORM falls within a 5.5% of the historical seasonal total rainfall in the Walnut Gulch Experimental Watershed (Arizona, USA) that occurred in the current century. [ABSTRACT FROM AUTHOR]

This study develops a convolutional recurrent deep learning model to accurately predict fine‐resolution spatiotemporal changes in grass coverage in arid regions. Applying the model to the Gobi Desert reveals that ecological flow regulation contributes to 61.8% of the total increase in grass cover (130.6 km2) in the study area (40,423 km2) over 2005–2015, nearly triple the contribution of local climate change (+23.0%). The transboundary hydrological impact (+32.4%) and interactions between drivers (−17.2%) are also significant. In an intermediate future climate change scenario, we found no statistically significant trend for the total grass‐covered area due to the counteracting effects among different drivers. The study findings suggest that timely, adaptive and spatially heterogeneous ecological flow management is crucial for addressing grassland degradation in arid regions. This study provides a promising approach to land surface modeling under climate change and human disturbance and expands the existing understanding of the global greening process. Plain Language Summary: The Earth's recent greening (i.e., vegetation increase) has boosted the terrestrial carbon sink, intensified the hydrologic cycle, and contributed to mitigating global warming. CO2 fertilization has been identified as the primary cause at the global scale, while climate change, land use changes, and nitrogen deposition also contribute. However, there are still many gaps in our understanding of how grasslands in arid areas are being affected. Studies in Central Asia identified precipitation as the dominant factor affecting grassland change and grazing as the main influence from humans. Based on satellite data, we employ a novel deep learning (DL) approach to emulate spatiotemporal changes in grass coverage at a 1‐km resolution over an area of 40,423 km2 in the Gobi Desert in northwestern China. Our study identified previously overlooked mechanisms contributing to the increase in grass coverage, including ecological flow management and transboundary hydrological impact. Ecological flow management was found to be responsible for almost three times the increase in grass coverage over 2005–2015 than that of local climate changes. Our findings suggest that timely, adaptive, and spatially targeted ecological flow management is urgently needed to prevent further grassland degradation in arid regions, and DL‐based artificial intelligence can offer valuable management suggestions. Key Points: Deep learning (DL) precisely emulates spatiotemporal changes in grass coverage at a 1‐km resolution over an area of 40,423 km2DL interprets ecological flow regulation (EFR) as the leading cause of grassland greening in the Gobi DesertTimely, adaptive and spatially heterogeneous ecological flow management is desired to prevent grassland degradation in arid regions [ABSTRACT FROM AUTHOR]

The response of zonal-mean precipitation minus evaporation (P 2 E) to global warming is investigated using a moist energy balance model (MEBM) with a simple Hadley cell parameterization. The MEBM accurately emulates zonal-mean P 2 E change simulated by a suite of global climate models (GCMs) under greenhouse gas forcing. The MEBM also accounts for most of the intermodel differences in GCM P 2 E change and better emulates GCM P 2 E change when compared to the "wet-gets-wetter, dry-gets-drier" thermodynamic mechanism. The intermodel spread in P 2 E change is attributed to intermodel differences in radiative feedbacks, which account for 60%-70%of the intermodel variance, with smaller contributions from radiative forcing and ocean heat uptake. Isolating the intermodel spread of feedbacks to specific regions shows that tropical feedbacks are the primary source of intermodel spread in zonal-mean P 2 E change. The ability of the MEBM to emulate GCM P 2 E change is further investigated using idealized feedback patterns. A less negative and narrowly peaked feedback pattern near the equator results in more atmospheric heating, which strengthens the Hadley cell circulation in the deep tropics through an enhanced poleward heat flux. This pattern also increases gross moist stability, which weakens the subtropical Hadley cell circulation. These two processes in unison increase P 2 E in the deep tropics, decrease P 2 E in the subtropics, and narrow the intertropical convergence zone. Additionally, a feedback pattern that produces polar-amplified warming partially reduces the poleward moisture flux by weakening the meridional temperature gradient. It is shown that changes to the Hadley cell circulation and the poleward moisture flux are crucial for understanding the pattern of GCM P 2 E change under warming. [ABSTRACT FROM AUTHOR]

Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we use output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. Our framework is developed using synthetic data in a Pearl-causal perspective wherein causality can be identified using intervention-based simulations. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming. [ABSTRACT FROM AUTHOR]

Changes in the integral characteristics of the global water exchange, at climatic time scales, are considered as random functions (processes). “Trajectories” obtained as the results of numerical calculations on various, from 34 to 43, climate models (participating at the CMIP-6 “historical” experiment covering the period from 1850 through 2014) are taken as realizations of these processes. Temporal variations of following annually averaged parameters are studied: (1) average evaporation from the ocean surface, (2) precipitation over the ocean, (3) “effective evaporation” from the ocean (difference “evaporation minus precipitation”, on average equal to the water transport from the ocean to land), (4) precipitation over land, (5) evaporation (evapotranspiration) from the land surface, (6) “effective precipitation” over land (or “climatic runoff”: precipitation minus evaporation), and (7) river runoff. It is shown that precipitation over the ocean and evaporation from land largely suppress the monotonous trends in the mean values of evaporation from the ocean and precipitation over land, respectively, at secular time scales. At the same time, this damping does not extend to the trends of the last few decades, which may be due to a combination of a sharp increase in global temperature with explosive volcanic eruptions that preceded this period. An analysis of the time divergence in the model trajectories of each of the components of the global water exchange, as well as the very existence of such divergences, indicates an increase in the uncertainty of processes that is not associated with anthropogenic impact on the climate system. [ABSTRACT FROM AUTHOR]

• Schemes projecting groundwater extraction under various scenarios were developed and integrated into the global climate model CAS-FGOALS-g3. • Climate change-induced changes in terrestrial hydrological variables are most pronounced under medium and high emission scenarios. • Major irrigated areas experience excessive groundwater extraction, thereby declining groundwater storage. Human groundwater extraction alters hydrological processes, thereby influencing climate through land–atmosphere interactions. As socioeconomic development progresses, the escalating water demand exacerbates conflicts with limited water resources. Nevertheless, potential response of the global terrestrial water cycle to the combined impact of climate change and groundwater extraction under various future scenarios remain vaguely understood. Therefore, this study developed human water regulation projection schemes based on Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) integrated into a global climate model (CAS-FGOALS-g3). The results show that global water demand in 2100 under the SSP370 scenario almost doubled relative to 2010. The proportion of surface soil drying due to warming is 3%-11% more in the medium- and high-emission scenarios than in the low one. In major irrigated areas, excessive groundwater extraction changed the groundwater storage trend, especially in the Southern Plains of the United States, where the increasing groundwater storage trend became negative compared to scenarios without groundwater extraction. This study underscores the complex interplay between human water use and hydroclimate within an evolving environment, emphasizing the critical need for effective water resource management to mitigate future water stress. [ABSTRACT FROM AUTHOR]

• The global average FR was 23.94, with 82.8% of the observations greater than 1. • The global average ER for all selected ions was 79.63. • DBH, stand density, and height explained nearly 60% of relative importance for FR. • Biotic variables were also the dominant predictors for selected ions of ER. Stemflow has been increasingly recognized as an important ecohydrological process altering the water cycle and biogeochemistry processes within vegetated ecosystems. The funneling ratio (FR) and enrichment ratio (ER) of stemflow are two common metrics to express the funneling efficiency of water and solute compared with bulk precipitation on an equivalent area. However, variation in patterns and drivers of FR and ER for stemflow across global biomes remains poorly understood, which constrain predictions of biome hydrological cycle by climate models under global climate change. Here, we compiled data from 477 peer-reviewed publications with 1040 observations to calculate the FR and ER of stemflow at global scale. The boosted regression trees (BRT) model was employed to quantify the relative importance of biotic and abiotic variables on FR and ER of stemflow. The results indicated that the average value of FR across global biomes was 23.94, with 82.8 % of the observations greater than 1. The FR in different biomes displayed a significant variability, with relatively higher values in water limited ecosystems but lower values in humid rain forests. The BRT model showed that biotic variables including diameter at breast height (DBH), plant height, and stem density were the dominant predictors for FR, accounting nearly 60 % of the relative importance for FR. For ER, the global average values for all selected ions, cations, and anions were 79.63, 89.05, and 46.33, respectively, with relatively higher ER values in base cations (e.g. K+, Mg2+, and Ca2+). Biotic variables, such as stem density, DBH, and plant height, were found to be the dominant predictors for the selected base cations of ER. To conclude, although stemflow is much smaller than throughfall, its utility in providing water and nutrients for vegetation growth is much greater than that of throughfall and precipitation, and has important implications for the understanding of ecohydrological processes in the changing global environments. [ABSTRACT FROM AUTHOR]

• Hydrologic regimes over western Himalayan basin were investigated. • The peak flow is projected to shift 47 days earlier in the future. • Uncertainty in future hydrologic projections varies seasonally. • Model parameters are influential in hydrological variations of glaciated regions. Melt water from snow and glaciers is a major contributor to the streamflow over the Himalayan mountainous region. However, rising temperatures and uncertain precipitation trends may change the timing of snow melt and snow accumulation, altering the hydrological cycle. This study investigated future hydrological changes and the associated uncertainties over a snow-fed catchment of the western Himalayan River, Astore. A process-based hydrological model (WRF-Hydro/Glacier) was used with meteorological forcing from global climate models (GCMs), bias correction methods (BCMs), and multiple optimized hydrological parameters (MPs). The simulated results of streamflow, the snow-covered area (SCA), and evapotranspiration (ET) were examined over three periods: baseline (BL; 2000–2019), mid-future (MF; 2040–2059), and far-future (FF; 2080–2099). With early snow melt and late accumulation of snow, the peak streamflow is projected to shift from July during the BL, to mid-June during MF, and to mid-May during FF. Moreover, a decrease in the SCA and an increase in ET was projected during both MF and FF periods, leading to reduced solid water reserves in snow and glaciers. Furthermore, by quantifying the uncertainty contribution of GCMs, BCMs, and MPs in projecting future hydrological components, it was determined that more emphasis should be placed on the selection of an appropriate GCM and MP than that of a BCM in projecting streamflow and SCA. When projecting SCA, the GCM and MP are major contributors to uncertainty in fall–winter and spring–summer, respectively. Conversely, when projecting ET, GCM and BCM have more influence than MP. Overall, this study highlights the importance of uncertainty assessments to develop robust adaptation plans in ensemble-based climate change projections over the western Himalayan region. [ABSTRACT FROM AUTHOR]

• Extreme rainfall events that lead to floods in large rivers are expected to decrease. • Antecedent soil moisture is expected to be reduced in most of South America. • Soil moisture seems more impactful than precipitation regarding ordinary floods. A warmer atmosphere is able to hold more water which consequently intensifies the hydrological cycle. The projected increase in extreme precipitation has been associated with greater floods; however, most recent studies have argued that the reduced soil moisture could be causing the opposite effect. We aim to understand how the hydrometeorological variables affect flood discharge and what the projections are for South America, a vulnerable continent that has been barely studied regarding flood trends. We used climate data from Eta simulations nested in 4 global climate models (BESM, CanESM2, HadGEM2-ES, MIROC5) as input for the MGB-SA hydrological model to yield flood discharge estimates. Then we were able to project the climate impacts on extreme precipitation, antecedent soil moisture, and flood discharge for large rivers (>1,000 km2) and understand how these variables are related. Our results showed a strong sign that antecedent soil moisture is expected to be reduced in most of the continent except in Southeastern South America (SESA). On the other hand, there are mixed signs for rarer precipitation and a clear spatial pattern for 2-year precipitations (RP2), which is expected to increase on the SESA and west Amazon and decrease on Central South America (CSA). For basins>100,000 km2, results indicate a negative change sign for 2-year precipitations, meaning that rainfall events that generate ordinary floods in large South American rivers are expected to decrease in the XXI century due to climate change. The change signs for flood discharge and extreme precipitation are spatially similar but more basins show a decrease for flooding than for rainfall. While only half of the South American basins are expected to present reduced 2-year precipitations, nearly 70 % of the rivers present a negative sign for 2-year floods, which can be attributed to the reduced antecedent soil moisture. [ABSTRACT FROM AUTHOR]

• Model agreement and uncertainty in global TWSA simulations within the ISIMIP are evaluated. • The ensembles agree with GRACE better in drying trend than wetting trend. • The ensemble members show better agreement in the interannual TWS variations in the Northern Hemisphere. • Most members show higher (lower) seasonal TWS ranges in North America, Europe and Asia (South America, Africa and Australia). • GWM (GCM) dominates uncertainty in the Northern Hemisphere (Southern Hemisphere). Terrestrial water storage (TWS) is a vital component in global hydrologic cycle with direct linkage to water resources availability and hydrologic extremes. Assessing the uncertainty in TWS projections are fundamentally important for developing counter-measures against impacts of climate change on water resources and disasters. This study presents a global-scale evaluation of model agreement and uncertainty in TWS anomaly (TWSA) simulations within the ISIMIP 2b framework by comparison with the 2006–2016 Gravity Recovery and Climate Experiment (GRACE) data. The total 24 members of ISIMIP2b ensembles considered here include the permutations of 6 global water models (GWMs: CLM4.5, H08, LPLmL, MATSIRO, PCR-GLOBWB, WaterGAP2) simulations forced by 4 global climate models (GCMs: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) under RCP2.6 and RCP6.0 scenarios. Results show that the fraction of global land area with the same sign between the ISIMIP2b ensemble mean and GRACE TWSA is 47.5 % and 47.3 % under RCP2.6 and RCP6.0, respectively. The ensemble mean reproduces the negative (positive) TWSA in southern North America, southwestern Asia, northern and central Africa (central South America and northern Europe) under both scenarios. More than 50 % of ensemble members agree with GRACE TWS better in the sign of drying trend than wetting trend over the global land. The interannual TWS variations among ensemble members show better agreement in North America and Europe than other continents. More than 80 % of ensemble members simulate larger (smaller) TWS seasonal amplitude than GRACE in North America, Europe, and Asia (South America, Africa, and Australia). GWM is found to be the main source of uncertainty in TWS simulations in North America, Europe, and Asia (>55 %), while GCM uncertainty is the dominant uncertainty source in South America, Africa, and Australia (>45 %). [ABSTRACT FROM AUTHOR]

• Isotope-based fog classification is consistent with meteorological-based fog classification. • Isotope hydrology suggests fog formation is dynamic and can include isotopic evolution. • Line conditioned excess is a useful tool in differentiating fog types. • Fog type should be taken into account for accurate modelling of global climate change impacts on fog dependent ecosystems. Although fog is an important component of the hydrological cycle of drylands, its formation mechanisms are not fully understood leading to discrepancy of fog type classifications using different methodologies. Such discrepancies may result in under or over estimation of the potential impacts of global climate change on the ecohydrology of fog dependent ecosystems. To fill this knowledge gap, this study applies hydrogen and oxygen stable isotopes to objectively classify fog and compares this classification to meteorological conditions defined for the formation of these fog types. Results suggest that this isotope based fog classification method is consistent with expected meteorological conditions for the different fog types such as radiation fog, advection fog and mixed fog. Wind speeds decreased from advection, to radiation fog while estimated fog (cloud) height was lowest during radiation fog and highest during advection fog. In addition, precipitation-offset (lc-excess) suggests that radiation and advection fog had different moisture sources, with radiation fog being locally sourced and advection fog originating from the ocean. This study suggests isotope-based fog classification is an objective method that could be applied to other coastal fog dependent ecosystems to help assess the potential impact of climate change on these ecosystems. [ABSTRACT FROM AUTHOR]

Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (>60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6-14 days yr−1. This reduces California's mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (>60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions. [ABSTRACT FROM AUTHOR]

• Catchment model experiments assessed performance shifts under climatic change. • We tested parameter transferability, model weighting and dynamic parameters. • Transferability depended more on validation than calibration conditions. • Approaches tested improved average validation performance, but results varied. It has been repeatedly shown that conceptual hydrologic model performance degrades under conditions that deviate from those of the calibration period. In this study, we describe three experiments that aim to understand and address this problem using the conceptual model GR4J over 164 Australian catchments. The first is an investigation of model transferability, where parameters calibrated under certain conditions are applied to simulate both similar and contrasting conditions. We find that model performance degradation is more dependent on the conditions under which the model is tested than the conditions under which it is calibrated. Because dry periods are typically more difficult to simulate than wet periods, this means that transferring dry-calibrated parameters to wet periods is more successful than transferring wet-calibrated parameters to dry periods. For both wet and dry periods the best results were obtained when climatically similar calibration periods were used, suggesting that targeted use of climatically similar calibration data could improve predictive capacity. To this end, a second experiment was designed that preferentially weights modeled series calibrated under different conditions, with series associated with more climatically similar calibration periods weighted more heavily. While this improved model performance in most cases, the success was variable across the different catchments. Given that the model weighting scheme could not easily be generalized to all catchments in the sample, a third experiment was conducted where each model parameter was defined dynamically as a function of climate. The dynamic parameters were calibrated separately for each model, so the individual sensitivities of each catchment to climate conditions could be captured. While this also gave performance improvements, especially under drier testing conditions, the results continued to vary between catchments and there was no clear pattern in the parameter variation. This suggests that nonstationarity can be captured in different parameters for models of different catchments. While both model weighting and dynamic parameters can benefit overall conceptual model performance, it seems that reliable improvements across large samples of catchments may be difficult to achieve without more physically realistic model structures. [ABSTRACT FROM AUTHOR]

In the context of climate change, more severe and extended periods of low flows are expected, which will affect the quantity and quality of water sources. Evapotranspiration is an important process in the water cycle and is particularly significant during low-flow events. Therefore, it is important to study the effects of uncertainty in evapotranspiration modelling in the hydroclimatic modelling chain to understand the impacts of climate change on the water availability. As evapotranspiration is a rather hard and expensive process to measure, formulas are used to determine potential evapotranspiration (PET), which is the maximum water quantity that can be evaporated and transpired without any limiting factors. The objective is to determine the contribution of PET formulas to uncertainty in the full modelling chain in the context of climate change. Eleven simple PET formulas that only require minimum and maximum temperatures as well as the latitude are used. These formulas are applied to 2080 watersheds in North America (235 in Canada, 1825 in United States, and 20 in Mexico). As PET is directly related to climatic conditions, studying a large quantity of watersheds that cover many climatic zones increases the robustness of the conclusions. Three global hydrological models (GR4J, HMETS and Mohyse) calibrated with the Nash-Sutcliffe criterion are employed for the hydrologic modelling aspect of the work. Subsequently, the study of transferability under future climate is conducted with simulations from ten climate models and two different greenhouse gas emission scenarios. These simulations outputs (precipitations and temperatures) are post processed with two bias correction methods and are then introduced into the three hydrological models. The PET contribution in the global modelling chain in climate change and its uncertainty are estimated with different analyses such as variance decomposition. The analysis of different low-flow indices allows studying the climate change trends as expressed through the PET formulas. For instance, low-flow values over a period of several consecutive days for a certain recurrence period are compared to see if changes in the PET affect low-flows. Results show that the PET increases and the low-flow decreases are different according to climatic zones. Despite a great variability between the different formulas, results show similar trends under climate change. This project also shows that some specific formulas do not adapt well to future climate. This project results in a better comprehension and an increase in knowledge regarding the impacts of the PET formula choice in hydroclimatic modelling chain under climate change. The conclusions can be used for water resources management to identify adequate adaptation measures, such as preventing the drying up of water resources during low-flow periods. [ABSTRACT FROM AUTHOR]

Assessment of the impacts of climate change on hydrological regime is important for sustainable water resources management. The objective of this study is to assess the impacts of future climate changes on the hydrological regime of the headwater catchment of the Vistucky Creek (area 9.8 km2) in south-western Slovakia. Changes in climatic characteristics (i.e., precipitation and air temperature) for periods 2022-2060 and 2062-2100 were prepared by two regional climate models KNMI and MPI using the A1B emission scenario (average related to fossil carbon production). Both climatic scenarios assume increase in the air temperature and precipitation (higher in winter than in summer). A lumped conceptual rainfall-runoff model (the HBV-based TUW model) was used to simulate the catchment hydrological behaviour. The TUW model was calibrated for the reference period of 1982 – 2008. The calibration of the model was performed 50 times with a differential evolution algorithm. After obtaining the collection of the 50 parameter sets, the best set (in terms of Nash-Sutcliffe efficiency and the volume error) was chosen. This set of model parameters was used for the simulation of long-term mean monthly runoff for the three periods (i.e., 1982-2008, 2022-2060, and 2062-2100).The results show that changes in the long-term runoff seasonality and extremality of hydrological cycle could be expected in the future if the climate changes as the scenarios assume. The runoff should increase in autumn and winter months (i.e., from September to February) and decrease in spring and summer months (i.e, from April to August) compared to the reference period. Peakflows should increase in period 2062-2100 while discharge minima should slightly decrease (only for the climatic data from the KNMI model). It indicates possible increase in flow extremality. Catchment water storage as expressed by the soil moisture index and baseflow should decrease in period 2062-2100, especially according to climatic data from the KNMI model. Our contribution will discuss these changes in hydrological regime in the climate change context.AcknowledgmentThis work was supported by the Slovak Research and Development Agency under the contract No. APVV-15-0497. [ABSTRACT FROM AUTHOR]

Climate change jeopardizes the future of hydropower across the world. Precipitation patterns evolve, glacier retreat affects sedimentation, and rising temperatures perturb both the water cycle and the energy-price seasonality. The energy sector also faces an energy turnaround. The growing penetration of intermittent wind and solar energy increases supply variability, thus price volatility. These trends raise concerns about the future profitability of traditional hydropower. This work quantifies these impacts with an integrated, transferable, and parsimonious model. It integrates a semi-distributed hydrological with hydropower management models to translate climate change and energy scenarios into future energy generation and revenue. We simulated nine future streamflow scenarios for an Italian and a Swiss case study. These inputs fueled a hydropower management model, which maximizes revenue accordingly to five Italian and twenty-eight Swiss electricity prices scenarios. The simulations couple both types of scenario for the period 2016-2045. This time horizon especially matters for current decisions in climate and energy policy. Results show that variations of climate regime adversely influence the hydroelectric production. Climate change will modify the seasonality of inflows and volumes that are exploitable for hydropower generation. However, smart hydropower management could mitigate revenue losses. This research represents a toolkit for dam operators and decision makers to assess future generation capacity in a warming climate. Key words: Climate Change, Hydropower, Electricity Market, Water-Energy Nexus, Modelling [ABSTRACT FROM AUTHOR]

This study presents the first insights into the intensifying impact of global warming on extreme hydrological shifts between wet and dry conditions at the sub-seasonal to seasonal scale. For instance, the flood in England during the summer of 2007 was followed by heavy drought conditions, and a switch took place in the recent past from extreme drought to severe flooding in California. Most recently, the 2018 flood to drought transition in Japan brought on one of the most intense heatwaves the country has ever faced. These are just a few real-world examples. Considering the interconnected nature of the processes governing the increases in wet and dry events, we introduce a new index called 'Event to event variability (E2E)' as a proxy of the hydrological cycle, taking the aforementioned shifts in wet to dry (or dry to wet) transition into account. Based on multi-model large ensemble climate simulations, we assessed the impact of half a degree of additional warming from 1.5°C to 2°C for the global mean temperature, reflecting the warming targets of the Paris agreement of 2015 that were adopted at the 21st Conference of the Parties (COP) of the United Nations Framework Convention on Climate Change (UNFCC). Our results show that for an additional 0.5°C warming, mean intensification of the wet-dry (or dry-wet) transition will be significant on a global scale. The E2E index was further utilized to investigate the extreme conditions of said intensification, which showed extremes will increase 10 times compared to the mean state intensification. We found that event-to-event variability increases with warming for Western North America, Japan, and the UK, exhibiting intensification in both wet and dry conditions, which is potentially attributable to the increased occurrence of the aforementioned extreme events. We believe that our study will provide a new perspective into hydroclimatic stress under the future climate, in particular, for the additional 0.5°C warming and the importance of adhering to the Paris Agreement. [ABSTRACT FROM AUTHOR]

High quality climate data at a high spatial and temporal resolution are essential to develop and provide adequate climate services for Africa. Rainfall characteristics such as the number of dry and wet days, extreme rainfall or the length of rainy seasons are derived from regional climate data to create climate service products, e.g. for the agricultural sector. A common method to produce regional climate data is to dynamically downscale global climate change simulations by means of regional climate models (RCMs). These RCMs have to be able to simulate realistically the hydrological cycle in the different African climate zones including the movement of the intertropical convergence zone (ITCZ) over the continent. Deficiencies in the representation of the sea surface temperatures (SSTs) in global ocean models due to a too coarse spatial resolution and missing atmosphere-ocean interactions in RCMs contribute to the rainfall bias over regions affected by the moisture transport from the ocean. The application of a regionally coupled atmosphere-ocean model may reduce the rainfall biases over the African continent.In our work, we analyze the potential impact of the atmosphere-ocean coupling on the simulation of rainfall characteristics over Africa and identify those regions providing an added value using the regionally coupled atmosphere-ocean model ROM. For this purpose, we downscaled the ERA-Interim dataset and the MPI-ESM-LR historical simulation for present climate and the MPI-ESM-LR RCP8.5 simulation for future climate with ROM. Each simulation was downscaled for a time slice of at least 30 years at a spatial resolution of 25 x 25 km² for the whole African continent and the West Atlantic Ocean. In detail, we will show the potential impact of the atmosphere-ocean coupling on droughts and extreme rainfall using different rainfall indices such as consecutive dry days (CDDs), consecutive wet days (CWDs) and very wet days (R95p). Moreover, the effect of the atmosphere-ocean coupling on rainy season parameters will also be analyzed. Finally, a comparison between the bandwidths of the rainfall parameters derived from the uncoupled/coupled projections and the bandwidths of the parameters from the uncoupled CORDEX-CORE Africa ensemble will be shown. [ABSTRACT FROM AUTHOR]