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Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the understanding of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest (RF) feature importance to detect the input variables that most influence simulated hydrological fluxes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX, and PCR‐GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoff. RF models functioned as a metamodel and could reproduce GHM/LSMs outputs with a coefficient of determination (R2) over 0.85 in all cases and often considerably better. The GHM/LSMs agreed that precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff, while topographic slope and soil texture have no influence on the total variance of the water balance. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regard to the relative importance of soil texture and topographic slope. Finally, the selection of soil maps was only important for target variables of which soil is a relevant predictor. We conclude that estimating feature importance is a useful diagnostic approach for model intercomparison projects. Plain Language Summary: Simulations of hydrological fluxes such as evaporation and runoff at a global scale are uncertain. This happens because the models that produce global simulations are different in terms of structure, parametrization and meteorological data. So, several model intercomparison projects (MIP) have tried to identify where the hydrological fluxes estimates are most discrepant. In order to make MIPs even more useful, we are proposing an additional method focusing on understanding why the models disagree. This method consists of replacing the original global model with a random forest model and then identifying which input variables are more relevant using the feature importance functionality. More specifically, we detected how important meteorological variables, soil properties, land cover and topography are for each global model. We observed that the models agree that precipitation, climate and land cover are equally important for evaporation and that precipitation is the most important feature for estimating runoff. When partitioning runoff into quick and slow flow, we observed that the models disagree on the importance of features, especially topographic slope and soil. Key Points: Detecting the predictors importance can be an additional approach for Model Intercomparison ProjectsGlobal models agree about the features importance for water balance components but disagree for surface and subsurface runoffSelecting the soil database only matters when soil is a relevant predictor, which is not the case for all models and target variables [ABSTRACT FROM AUTHOR]

Flood event set generation, as employed in catastrophe risk models, relies on gauge information that is not available in data‐scarce regions. To overcome this limitation, we develop a stochastic fluvial and pluvial flood model of Southeast Asia, using freely and globally available discharge data from the global hydrological model GloFAS and rainfall from the ERA5 reanalysis. We use a conditional multivariate statistical model to produce a synthetic catalog of 10,000 years of flood events. We calculate the flood population exposure associated with each flood event using freely available population data from WorldPop and generate exposure probability exceedance curves. We validate the population exposure curves against observed flood disaster data from EM‐DAT, showing that our methodology provides exposure estimates that are in line with historical observations. We find that there is a 1% probability that more than 30 million people will be exposed to flooding in a given year according to our event set. This number is roughly half the population living in the 100‐year return period flood zone of Fathom's hazard maps, suggesting most studies based on static flood maps overestimate exposure. This analysis provides significant progress over previous non‐stochastic studies which are only able to compute total or average exposure within a given floodplain area and demonstrates that a reanalysis‐based stochastic flood model can be designed to generate reliable estimates of population exposure probability exceedance. This study is a step toward a fully global catastrophe model for floods capable of providing exposure and loss estimates worldwide. Key Points: Global hydrological models can be used to drive a large‐scale stochastic flood inundation model in Southeast AsiaA reanalysis‐based stochastic flood model generates realistic flood eventsThe computed flood exposure exceedance curve for Southeast Asia compares well to the EM‐DAT database [ABSTRACT FROM AUTHOR]

Streamflow simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b) were analyzed to evaluate future changes in surface water resources over northern Patagonia, a region that contributes significantly to the total hydropower production of Argentina. Ten global hydrological models (GHMs), forced by four general circulation models, effectively capture the winter streamflow maximum in the Negro river basin. However, most of them face challenges in simulating the late-spring pulse due to a misrepresentation of temperature over the higher elevations of the Andes. We quantified the future streamflow evolution using a multi-model ensemble from a subset of the best-performing GHMs under the RCP2.6 and RCP6.0 emission scenarios for two temporal horizons. According to the multi-model ensemble, there is a projected decrease in the annual streamflow of the analyzed rivers, which is more important considering the RCP6.0 scenario during the late 21st century, reaching up to −40% relative to the 1979–2005 reference period. This reduction is attributed to the projected precipitation decline in the headwaters of the Negro river basin in response to changes in the surface pressure patterns. These results have implications for regional water authorities for the development of adaptation plans considering future demand projections. HIGHLIGHTS A set of global hydrological models (GHMs) from the ISIMIP2b project was used to characterize streamflow changes in the Comahue region.; A decrease in the annual streamflow is projected along the 21st century, particularly for the Neuquén river.; Uncertainty is mainly linked to the global climate models used to drive the GHMs.; Hydropower generation and irrigation for agriculture are expected to face future reductions.;

Study region: Senegal River Basin in West Africa Study focus: This paper aims to evaluate the sensitivity of global hydrological models to potential evapotranspiration (PET) methods in the Senegal River Basin. Potential evapotranspiration is estimated using 21 methods and its influence on the performance of three GR models (GR4J, GR5J and GR6J) is investigated in five catchments. The data used are mean rainfall, discharge and observed and calculated PET over the period 1984–1995. PET is calculated based on observed climate data and those from NASA POWER reanalysis data. The methodology consists in: (i) comparing the consistency of reanalysis data with respect to the observed PET, (ii) assessing the robustness of GR models and their sensitivity to different PET estimation methods. The evaluation criteria used to assess the performance of the hydrological model are KGE and PBIAS. New hydrological insights for the region: Good consistency is obtained between PET calculated with observed and reanalysis data. The GR4J and GR5J are more efficient to simulate flows in the Senegal River sub-basins. The aerodynamic PET methods perform well with the three hydrological models. However, in this context where data are scarce, temperature methods such as Droogers and Allen are a good choice for hydrological modeling. The results also show that the GR models have the ability to adapt to poorly estimated PET.

Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short‐Term Memory (LSTM) is widely used to for such predictions, owing to its excellent migration performance. Traditional LSTM forced by meteorological data and catchment attribute data barely highlight the optimum data integration strategy for LSTM and its migration from data‐rich basins to ungauged ones. In this study, we experimented with 1,897 global catchments and found that LSTM‐corrected Global Hydrological Models (GHMs) outperformed uncorrected GHMs, improving the median Nash‐Sutcliff efficiency (NSE) from 0.03 to 0.66. Notably, there was a large gap between traditional LSTM modeling in ungauged basins and autoregressive modeling in data‐rich basins, and GHM‐forced LSTM were an effective way to close this gap in ungauged basins. The spatial heterogeneity of the performance of GHM‐forced LSTM was mainly influenced by three metrics (dryness, the leaf area index and latitude), which described the hydrological similarity among catchments. Weaker hydrological similarity among continental catchments results in larger variability in GHM‐forced LSTM, with the best performance in Siberia (NSE, 0.54) and the worst in North America (NSE, 0.10). However, the migration performance of GHM‐forced LSTM was significantly improved (NSE, 0.63) in ungauged basins when hydrological similarity was considered. This study stressed the advantages of GHM‐forced LSTM and due significance should be attached to hydrological similarities among catchments to improve hydrological prediction in ungauged catchments. Key Points: Compared to Global Hydrological Models, GHM‐forced LSTM improved the median Nash‐Sutcliff efficiency (NSE) from 0.03 to 0.66Three metrics (dryness, the leaf area index, and latitude) helped to quickly identify hydrological similarities between catchmentsGHM‐forced LSTM reached a high NSE value of 0.63 for prediction in ungauged basins when hydrological similarity is considered [ABSTRACT FROM AUTHOR]

Typical flood models do not take into consideration the spatial structure of flood events, which can lead to errors in the estimation of flood risk at regional to continental scales. Large‐scale stochastic flood models can simulate synthetic flood events with a realistic spatial structure, although this method is limited by the availability of gauge data. Simulated discharge from global hydrological models has been successfully used to drive stochastic modeling in data‐rich areas. This research evaluates the use of discharge hindcasts from global hydrological models in building stochastic river flood models globally: synthetic flood events in different regions of the world (Australia, South Africa, South America, Malaysia, Thailand, and Europe) are simulated using both gauged and modeled discharge. By comparing them, we analyze how a model‐based approach can simulate spatial dependency in large‐scale flood modeling. The results show a promising performance of the model‐based approach, with errors comparable to those obtained over data‐rich sites: a model‐based approach simulates the joint occurrence of relative flow exceedances at two given locations similarly to when a gauge‐based statistical model is used. This suggests that a network of synthetic gauge data derived from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data‐scarce regions and loss exceedance curves where exposure data are available. Key Points: Large scale stochastic flood models can simulate synthetic flood events with a realistic spatial structure even in data‐poor regionsUsing a model‐based multivariate extreme model provides more robust dependence estimates than empirical distance decay functionsModeled flow can be used in data poor‐regions to characterize dependence in large‐scale stochastic flood models and estimate flood risk [ABSTRACT FROM AUTHOR]

Study region: Eight main river basins covering the major part of Ukraine. Study focus: The main aim of this study was to provide an assessment of climate change impacts on water availability across Ukraine using global hydrological models. Six global hydrological models were evaluated for their performance in the historical period in the basins under study. Future river discharge was simulated by using the best performing model and all available models driven by bias-corrected GCM projections from the ISIMIP project under the RCP 2.6 and RCP 8.5 scenarios. New hydrological insights for the region: The results show precipitation increase up to 10 % under RCP 2.6, and variable changes from -14 % to +10 % under RCP 8.5 by the end of the century. The projections show the decreasing mean annual river discharge in the majority of basins for the middle (2040–2070) and far future (2071–2100) periods under both RCPs, and the decrease is stronger under RCP 8.5. The seasonal changes are characterised by a decrease in summer and a small to moderate increase in winter months in most of the basins. The highest reduction of mean annual discharge was projected for the Pripyat, Southern Bug and Dniester basins, reaching up to -30 % to the end of the century under RCP 8.5.

Importance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections. [ABSTRACT FROM AUTHOR]

• RHMs better characterizes the dynamics of local scale hydrology more than GHMs. • Probabilistic framework of the GPR improves its ability in hydrological modelling. • In-situ precip. provides a key metric for local-scale water storage assessments. • Regression downscaling effectively enhances GRACE-TWS res. for local evaluations. The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) data has limited its application in the management of local-scale water resources. To address this limitation, we developed a new downscaling approach using predictors from regional and global hydrological models for a 15-year period (2002–2017) and tested it in the northern Great Artesian Basin, Australia. We used four different machine learning algorithms (support vector machine, partial least squares, gaussian process and random forest) to downscale the original GRACE estimate of 0.5° to a spatial grain size of 0.1° (global) and 0.05° (regional). This was based on precipitation, evapotranspiration and runoff estimates from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and Australian Water Outlook (AWO) hydrological models, respectively. The downscaled products were validated using 42 in-situ precipitation observations spread across the test region. We further evaluated which of the downscaled products best mimicked local-scale hydrology using a range of statistical metrics. Our results showed that regional hydrological models best characterized the dynamics of local scale hydrology (rainfall v. downscaled product), and the gaussian process regression algorithm made the best predictions for both models. The correlation coefficients for the raw values varied from 0.45 to 0.49 while that of the standardized values varied from 0.46 to 0.52 with the random forest model providing the best fitting for the regional-based products. The regional downscaling approach employed in this study may be readily integrated into local water resources planning programs. [ABSTRACT FROM AUTHOR]

Abstract This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained. Highlights • We present the first use of machine learning-based model combination applied to a global hydrological model ensemble. • The multi-model combination (MMC) performs in most cases better than any individual input model and the ensemble mean. • MMC is not always able to out-perform model combination based on multiple linear regression. • The physical interpretation of the MMC solutions is limited by the complexity of their non-linear weighting schemes. [ABSTRACT FROM AUTHOR]

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002-2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH,MOSAIC, VIC, CLM and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (⩽-0.5 km3/y) and increasing (⩾0.5 km3/y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km3/y, whereas most models estimate decreasing trends (-71 to 11 km3/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71-82 km3/y) but negative for models (-450 to -12 km3/y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and humaninduced water storage changes may be underestimated. [ABSTRACT FROM AUTHOR]

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models’ ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

Worldwide, 98% of total electricity is currently produced by thermoelectric power and hydropower. Climate change is expected to directly impact electricity supply, in terms of both water availability for hydropower generation and cooling water usage for thermoelectric power. Improved understanding of how climate change may impact the availability and temperature of water resources is therefore of major importance. Here we use a multi-model ensemble to show the potential impacts of climate change on global hydropower and cooling water discharge potential. For the first time, combined projections of streamflow and water temperature were produced with three global hydrological models (GHMs) to account for uncertainties in the structure and parametrization of these GHMs in both water availability and water temperature. The GHMs were forced with bias-corrected output of five general circulation models (GCMs) for both the lowest and highest representative concentration pathways (RCP2.6 and RCP8.5). The ensemble projections of streamflow and water temperature were then used to quantify impacts on gross hydropower potential and cooling water discharge capacity of rivers worldwide. We show that global gross hydropower potential is expected to increase between +2.4% (GCM-GHM ensemble mean for RCP 2.6) and +6.3% (RCP 8.5) for the 2080s compared to 1971–2000. The strongest increases in hydropower potential are expected for Central Africa, India, central Asia and the northern high-latitudes, with 18–33% of the world population living in these areas by the 2080s. Global mean cooling water discharge capacity is projected to decrease by 4.5-15% (2080s). The largest reductions are found for the United States, Europe, eastern Asia, and southern parts of South America, Africa and Australia, where strong water temperature increases are projected combined with reductions in mean annual streamflow. These regions are expected to affect 11–14% (for RCP2.6 and the shared socio-economic pathway (SSP)1, SSP2, SSP4) and 41–51% (RCP8.5–SSP3, SSP5) of the world population by the 2080s. [ABSTRACT FROM AUTHOR]

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Global hydrological models (GHMs) have effectively become a separate research field in the last two decades. The paper reviews and compares 12 known global modelling efforts since 1989, the year the first GHM was published. Structure, strengths and weaknesses of individual models are examined, and the objectives of model development and their initial applications are documented. Issues such as model uncertainty, data scarcity, integration with remote sensing data and spatial resolution are discussed. Editor D. Koutsoyiannis [ABSTRACT FROM AUTHOR]

• Comparably lower performance of GHMs in simulating monthly runoff-deficits. • CHMs and GHMs were similar in estimating SRI. • Ensemble models performed better than the individual models. • Ensemble models simulated drought frequency well. • Differences in ability to simulate the occurrence of observed drought event. Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated simulations of hydrological droughts from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed hydrological droughts of at least one month duration, over 31 years (1971–2001). Hydrological drought events were identified from runoff-deficits and the Standardised Runoff Index (SRI). In all catchments, the CHMs performed relatively better than the GHMs, for simulating monthly runoff-deficits. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means, have limited abilities to accurately simulate drought events in all eight catchments, in terms of their occurrence and magnitude. Overall, there are opportunities to improve both CHMs and GHMs for better characterisation of hydrological droughts. [ABSTRACT FROM AUTHOR]

The transboundary Tigris-Euphrates Basin (TEB) is prone to water-scarcity disputes. Water scarcity is related to aridity, climate extremes, limited supplies, upstream reservoir storage, rising water demand, and population growth. Understanding the water budget and storage changes in the basin in relation to hydrological extremes is fundamental to mitigate the drought and flood impacts and the key to efficient water resources management. This study evaluated the water budget related to drought occurrences in the TEB over four decades (1979–2020) based on GRACE/GRACE-FO, and altimetry satellites data, in situ observations, and hydrological modeling using a Bayesian model averaging (BMA) approach. Results show that severe droughts occurred at about decadal timescales with increasing recovery times. Severe and exceptional droughts dominated from (1998 to 2000, 2007 to 2009). Mild to moderate droughts occurred in 1983–1984, 1989–1992, 2011–2013, and 2018. The most severe drought occurred in 2007–2009, with the largest decline (−80 km3) in GRACE total water storage (TWS). Depletion in TWS was dominated by depletion in reservoir storage. In contrast, groundwater (GW) depletion accounted for only 25–30% of TWS decline. Storage depletion was amplified by human intervention (e.g., irrigation and GW abstraction) by at least 50% during drought. Marked recovery in TWS occurred in 2019 and 2020 (totaling ~144 km3 by July 2020, representing ~2× total depletion between 2007 and 2018) in response to regional flooding. Applying the BMA approach to the estimates of water cycle fluxes improved the accuracy and similarity of storage change, but not variability relative to GRACE. In summary, prolonged droughts are the norm rather than the exception in the TEB over the past four decades. The frequency and severity of droughts have substantial implications for water scarcity for countries sharing the TEB and underscore riparian countries' needs to expand their water management portfolio to mitigate drought impacts. Unlabelled Image • Tigris-Euphrates Basin has been subjected to multiple droughts since the 1980s. • Global hydrological models underestimate water storage depletion relative to GRACE. • Irrigation water use amplified drought impacts on water storages. • Management options should be taken to build resilience to the extremes. [ABSTRACT FROM AUTHOR]

Understanding the competing pressures on water resources requires a detailed knowledge of the future water balance under uncertain environmental change. The need for a robust, scientifically rigorous evidence base for effective policy planning and practice has never been greater. Environmental change includes, but is not limited to, climate change; it also includes land-use and land-cover change, including deforestation for agriculture, and occurs alongside changes in anthropogenic interventions that are used in natural resource management such as the regulation of river flows using dams, which can have impacts that frequently exceed those arising in the natural system. In this paper, we examine the role that land surface models can play in providing a robust scientific basis for making resource management decisions against a background of environmental change. We provide some perspectives on recent developments in modelling in land surface hydrology. Among the range of current land surface and hydrology models, there is a large range of variability, which indicates that the specification and parametrization of several basic processes in the models can be improved. Key areas that require improvement in order to address hydrological applications include (i) the representation of groundwater in models, particularly at the scales relevant to land surface modelling, (ii) the representation of human interventions such as dams and irrigation in the hydrological system, (iii) the quantification and communication of uncertainty, and (iv) improved understanding of the impact on water resources availability of multiple use through treatment, recycling and return flows (and the balance of consumptive and conservative uses). Through a series of examples, we demonstrate that changes in water use could have important reciprocal impacts on climate over a wide area. The effects of water management decisions on climate feedbacks are only beginning to be investigated--they are still only rarely included in climate impact assessments--and the links between the hydrological system and climate are rarely acknowledged in studies of ecosystem services. Nevertheless, because water is essential not only for its direct uses but also for the indirect functions that it serves (including food production, fisheries and industry), it is vital that these connected systems are studied. Building on the examples above, we highlight recent research showing that assessment of these trade-offs is particularly complex in wetland areas, especially in situations where these trade-offs play to the advantage of different communities. [ABSTRACT FROM AUTHOR]