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Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modeling methods, this paper presents a new global ∼30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies—ranging from locally collected event water levels, to national inventories of engineering flood maps—we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ∼0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ∼0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1‐2.6), we show end‐of‐century global flood hazard (average annual inundation volume) increases are limited to 9% (likely range ‐6%–29%); this is within the likely climatological uncertainty of −8%–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5‐8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7%–109%) increase by 2100. This work verifies the fitness‐for‐purpose of this new‐generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment. Plain Language Summary: Computer models use a variety of data and physical equations to estimate the extent and depth of possible flood events. Global applications of these tools have been developed over the past decade, but they are not very good at simulating the behavior of real floods. In this paper, we address some key problems to make a global model that does a lot better than past ones. We apply new techniques to better understand how much water we need to put into the model for a given flood probability. This movement of water is simulated by the model over a more accurate map of the Earth's terrain than has been available previously, with river channels represented in a smarter way. We look at the projected changes in rainfall, river discharge, and sea levels for given levels of warming simulated by available climate models and adjust the probabilities of a given magnitude flood accordingly. The model results suggest that the effect of future climate change might be small relative to our ability to understand flood hazards today, but this depends heavily on how much carbon we emit in the coming decades. Key Points: New climate‐conditioned model framework represents fluvial, pluvial, and coastal flood hazards at high‐resolution globallyComprehensive validation studies suggest that the model is approaching local model skill in many casesEmissions reduction can hold flood hazards largely constant this century, though coastal flooding will increase drastically regardless [ABSTRACT FROM AUTHOR]

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

In a study from 2008, Larivière and colleagues showed, for the field of natural sciences and engineering, that the median age of cited references is increasing over time. This result was considered counterintuitive: with the advent of electronic search engines, online journal issues and open access publications, one could have expected that cited literature is becoming younger. That study has motivated us to take a closer look at the changes in the age distribution of references that have been cited in water resources journals since 1965. Not only could we confirm the findings of Larivière and colleagues. We were also able to show that the aging is mainly happening in the oldest 10-25% of an average reference list. This is consistent with our analysis of top-cited papers in the field of water resources. Rankings based on total citations since 1965 consistently show the dominance of old literature, including text books and research papers in equal shares. For most top-cited old-timers, citations are still growing exponentially. There is strong evidence that most citations are attracted by publications that introduced methods which meanwhile belong to the standard toolset of researchers and practitioners in the field of water resources. Although we think that this trend should not be overinterpreted as a sign of stagnancy, there might be cause for concern regarding how authors select their references. We question the increasing citation of textbook knowledge as it holds the risk that reference lists become overcrowded, and that the readability of papers deteriorates. [ABSTRACT FROM AUTHOR]

Water insecurity poses a complex challenge for the western United States. Large populations are exposed and susceptible to physical and social factors that can leave them with precarious access to sufficient water supplies. Consideration of social issues by water managers can help ensure equitable supply. However, how social factors affect water insecurity conditions remains unclear. This paper reviews literature on how social vulnerability influences water insecurity in the western United States. Through a meta‐analysis, indicators measuring how dimensions of social vulnerability influence water insecurity were classified and hierarchical clustering was used to characterize the relationships among these vulnerability dimensions for the largest water‐users—the agricultural and municipal sectors. The study then assessed uncertainty associated with social vulnerability dimensions and their indicators. There is greatest evidence for the influence of demographic characteristics, socioeconomic status, and exposure. Indicators of these determinants were mainly significant and exacerbated conditions of water insecurity. Evidence for indicators of social dependence and special needs populations was limited, although studies assessing these factors showed significant agreement on their influence on water insecurity. Conceptual framings of social vulnerability and water security determined which indicators were measured, whereas studies of the water‐use sectors focused on differing associations of social vulnerability. These findings indicate the importance of recognizing the different contexts posed by water‐use sectors and diverse conceptual framings. Further, some determinants such as living conditions remain important but underexplored drivers of a community's experience of water insecurity. Understanding the uncertainty associated with these measures has implications to equitable decision making. Plain Language Summary: Water security ensures sustainable access to adequate quantities of safe water to sustain livelihoods and economic and social well‐being. Rapidly growing populations and changing climatic conditions make this a challenging task for the western United States. This study reviews previous academic literature to identify how they measured socioeconomic factors that impact water access, quality, and quantity for the largest water‐users ‐ the agricultural and municipal sectors ‐ in the western US. Which social factors were measured depended on which water‐use sector was studied, and whether the authors viewed vulnerability and water security as preexisting or emergent conditions. We found most studies measured characteristics of demographics, socioeconomics, or exposure. Characteristics of risk perception or health were studied less often, with less agreement on their relationship to conditions of water security. There are therefore different levels of surety associated with the available measures of social vulnerability to water insecurity. Water resources managers need to be aware of these differing amounts of surety in the measures used to assist their decision making. Our study explores what is known about different indicators of social vulnerability to water security. Further, we provide a framework comparing otherwise disparate research on the complex subject of water insecurity. Key Points: This study determines the influence and uncertainty associated with indicators of social vulnerability to water insecurityThere is substantial uncertainty about the strength and/or direction of many indicator relationships to water insecurity conditionsIntegrated studies are vital as conceptual framing of social vulnerability and water security define which indicators are measured [ABSTRACT FROM AUTHOR]

Hypoxic blackwater events occur worldwide, affecting inland and coastal waters. These events have been exacerbated by man‐made floodplain drainage, leading to large‐scale fish kills and ecological degradation. This paper presents a new method to identify estuarine catchment areas that are most likely to generate hypoxic conditions. The method uses established risk factors, including vegetation type, inundation extent and duration, ground‐truthed in eastern Australia. A catchment is at higher risk of hypoxic blackwater generation if (a) it is located where floodwaters are high and/or drainage is impeded, (b) the site topography includes an extensive, low‐lying floodplain; and/or (c) the land‐use and environmental characteristics have a high deoxygenation potential. Consequent impacts to an estuary are determined by the floodplain connectivity with the estuary, and the discharge characteristics of the catchment drainage system. Where multiple, proximate catchments have similar drainage conditions, compounding plumes of hypoxic blackwater can overwhelm the assimilation capacity of the estuary. Climate change may significantly increase the volume and frequency of blackwater events in estuarine environments as a result of reduced drainage due to sea level rise, higher temperatures, and more intense and sporadic rainfall events. It is recommended that management measures be introduced to mitigate the effects of climate change and avoid further widespread hypoxic blackwater events. Key Points: A methodology is presented to differentiate estuarine catchments by their potential to generate hypoxic blackwater eventsLocal topographic and hydrodynamic conditions strongly influence the potential volume of hypoxic blackwater generated over a catchmentClimate change may increase the frequency and severity and change the distribution of blackwater risk throughout an estuary [ABSTRACT FROM AUTHOR]

Currently, climate change is considered as an important factor affecting nutrient loads introduced through riverine systems into the Baltic Sea. Although the prospect of a large increase in pollution has long seemed very real, it still does not translate into planning of effective remedial actions. One of the factors limiting the development of such activities is the scale of simulations, focusing generally on catchment outlet profiles. To fill this gap and enable a step forward in understanding responses toward future predictions in a higher resolution scale (subcatchment), we assessed nutrient load contribution using calculation profiles localized along a main watercourse and its tributaries. To track spatial and seasonal changes of total nitrogen and phosphorus for the Wełna River (central Poland), we used climate change data and the SWAT model. Having at our disposal a catchment model with a good performance we could follow not only total load changes in particular subcatchments, but also track localization of the pollution sources and their direct impact on load estimations. Our results showed an increase of the loads, especially from the agricultural land use type, up to 34% for TN and 85% for TP, in the most extreme scenario. Moreover, forest areas have been noted as highly reactive to climate change, and through their localisation are able to distinctly alter nutrient outflow. Finally, the contribution of urban areas should be further investigated since the dynamics of nitrogen and phosphorus release from impervious surfaces is noticeably different here than from the other diffuse sources. Plain Language Summary: This paper describes how climate change will affect the amount of nutrients in a small river catchment in the Baltic Sea region. While it is known that climate change can increase nutrient loads, effective actions to prevent them are still lacking. The "big picture" based on a whole catchment is still poor in terms of finding nutrient vulnerable areas. In this research, we looked at a more detailed scale to see where the nutrients are coming from and how they are changing over time. We used computer modeling to show that the amount of nutrients coming from agriculture, forests, and city areas will increase due to climate change. Overall, the amount of nitrogen can raise by 34% and the amount of phosphorus by 85%. Our results can be the basis for making decisions regarding actions aimed at improving the condition of surface waters and counteracting climate change effects. Key Points: Spatio‐temporal trends of nutrient loads have been tracked in a Baltic Sea region catchment under climate change scenariosClimate change suggests a potential load increase from the whole catchment by 34% for total nitrogen and 85% for total phosphorusOutputs from individual nutrient sources could grow by even 187% for total nitrogen and 302% for total phosphorus [ABSTRACT FROM AUTHOR]

To address groundwater issues, it is often necessary to develop geological and hydrogeological models. Combining geological, geophysical and hydrogeological data available on a site to build such models is often a challenge. This paper presents a methodology to integrate such data within a geologically consistent model with robust error estimation. The methodology combines the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm with a hierarchical geological modeling approach (ArchPy). Geophysical and hydrogeological field data are jointly assimilated in a stochastic ESMDA framework. To speed up the inversion process, forward responses are computed in lower‐dimensional spaces relevant to each physical problem. By doing so, the final models take into account multiple data sources and regional conceptual geological knowledge. This study illustrates the applicability of this novel approach using actual data from the upper Aare Valley, Switzerland. The results of cross‐validation show that the combination of different data types, each sensitive to different spatial dimensions, enhances the quality of the model within a reasonable computing time. The proposed methodology allows the automatic generation of groundwater models with robust uncertainty estimation and could be applied to a wide variety of hydrogeological issues. Plain Language Summary: When dealing with groundwater, it is necessary to develop underground models. However, taking into account all the different data types on a site is time‐consuming, and there is a lack of uncertainty quantification. In this study, we develop an approach that automatically combines different types of data, including boreholes, geophysical data, and hydrogeological measurements. All data are combined using a stochastic algorithm and produce an ensemble of plausible and data‐compatible models. These models can be used, for example, to forecast groundwater availability, pollutant distribution, or the effect of climate change on groundwater. Key Points: A methodology is proposed to assimilate geological, hydrogeological, and geophysical data in consistent stochastic modelsThe methodology combines a hierarchical geological modeling technique with the ensemble smoother with multiple data assimilationThe applicability of the methodology is demonstrated using actual field data from the upper Aare valley aquifer in Switzerland [ABSTRACT FROM AUTHOR]

This study investigates how environmental flows (e‐flows) can be designed as dynamic operating policies to optimize long‐term economic and ecosystem performance in reservoir systems. The main goal is to provide e‐flow solutions that contribute to better preparedness and flexibility of hydro‐systems to face multiyear stress periods, reducing the impact of water crises. The methodology framework combines a fish‐flow model with a multi‐objective evolutionary algorithm to construct multiple environmental water demand curves and capture the opportunity cost of different levels of ecosystem preservation. The water demand curves applied to a stochastic dynamic hydro‐economic model then derive dynamic e‐flow policies that balance immediate and future water use tradeoffs. The approach, termed dynamically adaptive environmental flows (DAE‐flows), is demonstrated on the Paraná River Basin, Brazil, a large‐scale hydropower system. Results show that the approach can adjust e‐flows (coordinated with other hydro‐system releases) over the time horizon, sacrificing them at certain times at the expense of some ecosystem loss, but improving long‐term ecosystem functioning. A long‐term approach to adaptation also yields better results for the environment without imposing a hard constraint to hydropower during droughts. Even under a drier climate change scenario, this allowed maintenance and improvement of environmental performance in most years, so during severe droughts the water could still be reallocated to hydropower but at a lesser cost to the environment. Plain Language Summary: The environment provides us with important resources (e.g., clean water, fish, livelihood and recreation). However, in order to thrive, the environment also needs water, which has specific patterns of flow in time depending on the species that live in the rivers and lakes. As we divert and store water in reservoirs to meet other important economic demands (e.g., hydropower) those flow patterns are disturbed, along with the species relying on it. This paper investigates how we can better represent flow patterns necessary to sustain fish life in large river systems, so we can identify and implement new strategies to achieve those flows downstream of reservoirs while still maintaining good performance to other economic demands. Results show that those strategies can not only be created by combining different patterns of flow but can also be updated and adjusted as conditions in the river change in the future, resulting from drought events and even different climates. To implement those strategies however, we need operations that are flexible and aimed at the future, besides the present. This is a key learning that helps us find better ways to adapt to future challenges, preserving our environmental assets while also meeting other important demands. Key Points: Ecological‐flow relationships, hydrological conditions and long‐term ecosystem performance are applied as drivers of dynamic e‐flowsDynamic e‐flows conserve water in some periods at the expense of some environment loss to improve long‐term ecosystem functioningThe operation of hydro‐systems can be adapted to reconcile long‐term economic and environmental demands [ABSTRACT FROM AUTHOR]

Climate change and the predicted warmer temperatures and more extreme hydrological regimes could affect freshwater ecosystems and their energy pathways. To appreciate the complex spatial and temporal interactions of carbon cycling in flowing waters, ecosystem metabolism (gross primary production [GPP] and ecosystem respiration [ER]) must be resolved at the scale of an entire river network. Here, we propose a meta‐ecosystem framework that couples light and temperature regimes with a reach‐scale ecosystem model and integrates network structure, catchment land cover, and the hydrologic regime. The model simulates the distributed functioning of dissolved and particulate organic carbon, autotrophic biomass, and thus ecosystem metabolism, and reproduces fairly well the metabolic regimes observed in 12 reaches of the Ybbs River network, Austria. Results show that the annual network–scale metabolism was heterotrophic, yet with a clear peak of autotrophy in spring. Autochthonous energy sources contributed 43% of the total ER. We further investigated the effect of altered thermal and hydrologic regimes on metabolism and ecosystem efficiency. We predicted that an increase of 2.5°C in average stream water temperature could boost ER and GPP by 31% (24%–57%) and 28% (5%–57%), respectively. The effect of flashier hydrologic regimes is more complex and depends on autotrophic biomass density. The analysis shows the complex interactions between environmental conditions and biota in shaping stream metabolism and highlights the existing knowledge gaps for reliable predictions of the effects of climate change in these ecosystems. Plain Language Summary: Organic carbon is one of the main source of energy sustaining riverine ecosystems. It can be internally produced by autotrophic organisms, or subsidized from the adjacent terrestrial ecosystem. Understanding the fraction of organic carbon which is metabolized within a stream network rather than exported to downstream ecosystems, and how such fraction is controlled by the thermal and hydrologic regime, is crucial to predict the whole ecosystem function, the ecosystem services it can provide, and the regional (and global) carbon cycling. Through the development of a mathematical model applied to a well‐monitored river network, the paper addresses this fundamental question and attempts to predict how these energetic fluxes might be affected by climate change. Key Points: The framework simulates temporal dynamics of ecosystem metabolism at the stream network scaleThe effect of climate change on network‐scale metabolism and carbon spiraling is investigatedA temperature increase of 2.5 C will boost ecosystem respiration and gross primary production by 31% (24%–57%) and 28% (5%–57%), respectively [ABSTRACT FROM AUTHOR]

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Key Points: Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation techniqueDynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposedOverall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty [ABSTRACT FROM AUTHOR]

Uncertainty arising from climate change poses a central challenge to the long‐term performance of many engineered water systems. Water supply infrastructure projects can leverage different types of flexibility, in planning, design, or operations, to adapt infrastructure systems in response to climate change over time. Both flexible planning and design enable future capacity expansion if‐and‐when needed, with flexible design proactively incorporating physical design changes that enable retrofits. All three forms of flexibility have not previously been analyzed together to explicitly assess their relative value in mitigating cost and water supply reliability risk. In this paper, we propose a new framework to evaluate combinations of flexible planning, design, and operations. We develop a nested stochastic dynamic optimization approach that jointly optimizes dam development and operating policies under dynamic climate uncertainty. We demonstrate this approach on a reservoir project near Mombasa, Kenya. Our results find that flexible operations have the greatest potential to reduce costs. Flexible design and flexible planning can amplify the value of flexible operations under higher discounting scenarios and when initial infrastructure capacities are undersized. This approach provides insight on the climate change and techno‐economic conditions under which flexible planning, design, and operations can be best leveraged individually or in combination to reduce climate change uncertainty risks in water supply infrastructure projects. Key Points: We develop a framework to compare the value of combinations of flexibility in water supply infrastructure planning, design, and operationsFlexible operations are more effective than flexible planning or design in addressing climate uncertainty in water‐limited environmentsFlexible planning and design are more useful when initial reservoir capacity is low, discounting is high, or the cost of shortages is high [ABSTRACT FROM AUTHOR]

Flood events are the most commonly occurring natural disaster, with over 5 million properties at risk in the UK alone. Changes in the global climate are expected to increase the frequency and magnitude of flood events. Flood hazard assessments, using climate projections as input, guide policy decisions and engineering projects to reduce the impact of large return period events. Probabilistic flood modeling is required to take into account uncertainties in climate model projections. However, the dichotomous relationship between probabilistic modeling, computational cost and model resolution limits the applicability of such techniques. This paper examines improvements to traditional Monte Carlo methods using Latin hypercube sampling (LHS) and Multi‐level Monte Carlo (MLMC) to quantify the uncertainty in flood extent resulting from input hydrograph uncertainty. The results demonstrate that MLMC is a more efficient modeling strategy than current methods (i.e., traditional Monte Carlo) with high resolution outputs produced in less time than previously possible. The novel application of MLMC technique to three Scottish case studies, demonstrating a variety of river characteristics, domain sizes and computational costs, using a high resolution 5 m grid resulted in a 99.2% reduction in computational cost compared to traditional Monte Carlo methods and up to 2.3 times speedup over Latin Hypercube Sampling. Key Points: Multi‐level Monte Carlo (MLMC) reduces the computational cost by 99.2% over traditional full Monte Carlo; tested across a range of case studiesProbabilistic flood hazard assessment accuracy can be increased by incorporating higher resolution models with minimal increase in costsMLMC allows realistic uncertainty quantification at previously unfeasible 5 m grid resolution, yielding converged estimates of flooded area [ABSTRACT FROM AUTHOR]

Future water availability is threatened by changes in both climate and water demand. Water rights with differing priorities are an important foundation of demand‐side tools (e.g., buyback, water pricing, and water market) to improve water use efficiency and reduce water scarcity, especially in highly regulated river systems. This paper assesses the impact of climate change on water yields from carryover storage with dual‐priority (high/low) water rights allocation systems using a simple and rapid analytical method. The method characterizes reservoir inflows using readily available flow characteristics (annual mean and Cv). We evaluate this method against a water resource simulation model in the Goulburn River basin, Australia. In general, our analytical "dual‐priority" Gould‐Dincer model reproduces water allocation estimates from the simulation model. We further demonstrate this method across 12 Australian catchments to investigate the climate change impact on "dual‐priority" water rights yield at the catchment scale. The hydrological projections show decreasing mean annual runoff and increasing annual runoff variability, except for some catchments in northern Australia. Water yield for high‐priority water rights (HPWRs) and low‐priority water rights (LPWRs) decreases for most catchments except for some catchments in northern Australia. South Dandalup in the 2070s (RCP8.5) shows the largest percentage decrease in HPWR and LPWR yield (about −53.53% and −56.81%, respectively). Our results show that changes in mean annual inflow have a more significant influence on water yield of HPWR and LPWR than Cv. Overall, the simple method provides a rapid assessment of water yields with "dual‐priority" water rights which is applicable across multiple sites at regional or even global scale. Key Points: A simple analytical method is used to evaluate the impact of climate change on the yield of "dual‐priority" water rightsWater yield for both high‐ and low‐priority water rights generally decreases under future climate in AustraliaFuture change in mean annual inflow has a more significant influence on water yield than change in annual flow variability [ABSTRACT FROM AUTHOR]

Groundwater response to climate variations is often pivotal to managing groundwater sustainably. However, this relationship is rarely explicitly examined because of the complexity of surface to subsurface processes and the diverse impacts of multiple drivers, such as groundwater pumping and land use changes. In this paper, we address this challenge by proposing methods to quantify the sensitivity of groundwater level and recharge to temporal climate variability across Australia. Using the HydroSight groundwater hydrograph toolbox we first identify 1,143 out of a total of 4,350 bores as climate‐driven, where historically, head was primarily driven by climate variations. Streamflow elasticity measures are then adapted to groundwater to quantify the long‐term head and recharge sensitivity. We find that the national median sensitivity of head and recharge to precipitation change are 42 and 0.43 mm mm−1, respectively (interquartiles: 20–77 and 0.30–0.55 mm mm−1); both of which are ∼8 times that of potential evapotranspiration. Nationally, the results are spatially correlated, suggestive of large‐scale effects. The responses of head and recharge appear to be primarily related to climate type and hydrogeology. The more arid the climate, the higher the head sensitivity but the lower the recharge sensitivity. Porous media generally show higher head sensitivity than fractured media due to smaller aquifer specific yield, and again contrarily for that of recharge. These findings contribute to understanding the long‐term impact of climate change on groundwater and thus provide valuable insights for sustainable groundwater management. Plain Language Summary: In this study, we assess the response of groundwater to meteorological variations by using long‐term groundwater level records across Australia. We first identify the sites where the groundwater level has been primarily impacted by climate variations alone. The changes in groundwater level and replenishment rate (also called "recharge") at the natural sites are simulated under a range of precipitation and evapotranspiration shifts and their relationships are statistically quantified. Results show that the national median groundwater level changes by 42 mm and the recharge changes by 0.43 mm per 1 mm change in precipitation. The response of groundwater level and recharge are found to be primarily governed by the inherent properties of the sites, such as climate type and hydrogeology. This study provides valuable insights into climate change impacts on groundwater availability and contributes to developing adaptive water management strategies. Key Points: Climate‐dominated groundwater bores throughout Australia are statistically identified (n = 1,143, 26%)Median head and recharge sensitivity to precipitation are 42 and 0.43 mm mm−1Head and recharge sensitivity are primarily controlled by climate type and hydrogeology [ABSTRACT FROM AUTHOR]

Drought and water scarcity pose a risk to the economy, particularly sectors where water is a crucial input to the production process. This paper describes integrated simulations of water shortages from a dynamic national water resource systems model with a static economic input‐output (I‐O) model to assess total drought risk to the economy. To quantify the economic risks of drought and water scarcity, the analysis: (a) integrates a large ensemble of simulated meteorological droughts, propagated through a national hydrological model and water resource systems model to simulate water availability from reservoirs and groundwater; (b) assesses implications of restrictions on water availability for multiple water user categories, representing natural and human contributions to water scarcity; (c) investigates how water shortages propagate into economy‐wide direct and indirect impacts. The study focuses on England and Wales where the water supply system is considered under strain, with growing recognition of the economic risk of prolonged and widespread shortages. The direct Expected Annual Loss (EAL) to water users, averaged over an ensemble equivalent to 2800 years of synthetic daily weather, is estimated to be £11.7 million in the 2011 base‐year. Accounting for indirect economic losses results in a total EAL of £30.2 million in 2011. The most severe event simulated results in a total loss of £1.4 billion in 2011, equivalent to 0.11% of GVA. The analysis provides a framework to assess what the economically efficient level of system reliability should be, and for deciding upon appropriate water management regulations and strategic investments. Key Points: Simulated water availability from a national water resource system model are integrated with a macroeconomic model to assess drought riskThe use of a large ensemble of synthetic drought simulations supports the risk‐based analysis of direct and indirect economic lossesThe method supports national economic assessment of the implications of climate change and different water management strategies [ABSTRACT FROM AUTHOR]

Eutrophication of rivers and receiving waters has become a major environmental issue worldwide, which may further intensify due to projected climate changes and expansion of urbanization or agricultural development to meet the needs of increased human population. However, our understanding of climatic and land use change impacts on riverine nutrient retention and export to receiving waters is limited due to lack of empirical measurements and uncertainties about the nature of future climate changes. The aim of this paper is to analyze the response of nitrate transport and transformation in river systems to potential changes in climate and land use management, individually, and in combination. A coupled hydrological and biogeochemical model is implemented in a hypothetical river basin, with realistic inputs and parameters, for the simulation of nitrate dynamics in river networks. Results indicate that for the same annual rainfall, increased within‐year rainfall variability propagates to increased streamflow variability and a significant reduction in nitrate retention. Analysis of the effects of the spatial distribution of nitrate inputs around the river network showed that for the same total nitrate input from land, nitrate retention increased considerably through locating land use types with high nitrate input away from the river outlet. Simulation results also indicated that even without a reduction in total nitrogen input from land, optimized land use distribution around the river network can compensate the enhanced nitrogen input that may arise from increased rainfall variability. These findings offer viable strategies for catchment management to mitigate increased water quality degradation caused by expected future climate changes. Key Points: The increase in rainfall variability could lead to significant reduction in the overall nitrate removal by increasing streamflow variabilitySpatial heterogeneity in lateral input could have impact on overall nitrate removal when they are scaled with distance to outletBetter land use management is capable to compensate the increasing risk of eutrophication caused by climate change induced rainfall change [ABSTRACT FROM AUTHOR]

As urban droughts make headlines across the globe, it is increasingly relevant to critically evaluate the long‐term sustainability of both water supply and demand in the world's cities. This is the case even in water‐rich regions, where upward swings in water demands during periods of hot, dry weather can aggravate already strained water supplies and increase cities' vulnerability to water shortage. Summer spikes in water demand have motivated several cities to impose permanent restrictions on outdoor water uses; however, little is yet known about their effectiveness. This paper examines daily water production data from 15 Canadian cities to (1) quantify how overall and seasonal demands are evolving over time across humid and semiarid settings and (2) determine whether permanent water use restrictions have been effective in curbing summer water demands both seasonally and during specific hot and dry periods. Results show that while per‐capita water demand is declining in all cities studied, the seasonal distribution of that demand has remained largely stable in all but a few cases. While average demands in the summer months remain largely unaffected by the imposition of permanent restrictions, cities that enforce stringent limits on outdoor water use have seen a reduction in the variability of daily demands and a decline in peak demands following their implementation. During short‐term periods of exceptionally hot and dry weather when vulnerability to water shortage is most acute, cities with strict restrictions also see smaller surges in demand than those with weaker or no restrictions in place. Key Points: Water production records from 15 Canadian cities show a decline in per‐capita demand, with rate of decline greater in semiarid cities with high seasonal variabilityPermanent water use restrictions have only minor impacts on mean or median summer day water demandsHowever, stringent restrictions are shown to reduce demand variability at the daily scale, reduce peaks, and suppress demand surges during heat events [ABSTRACT FROM AUTHOR]

Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic conditions in past years or even decades. Here we demonstrate that five commonly used conceptual "bucket" rainfall‐runoff models are unable to replicate multiyear trends exhibited by natural systems during the "Millennium Drought" in south‐east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13‐year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model "buckets" are already "emptying" on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall‐runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall‐runoff relationships in the context of a changing climate. Key Points: Environmental state variables such as groundwater display multiyear trends in response to sustained climate anomaliesWe show that five commonly used conceptual "bucket" rainfall runoff models are unable to replicate such trendsBecause of this, the models fail to extrapolate realistically to different climatic conditions, compromising runoff projections Plain Language Summary: It is common in science to use a mental picture or metaphor that simplifies a complex phenomenon. A common metaphor used for a water supply catchment is that of a leaky bucket. When it rains, the bucket fills up; when it does not rain for a while, the bucket empties due to evaporation and water used by trees; and leaking water is like river flow. Computer models based on variants of this metaphor are common and can provide predictions of how much streamflow might occur under future scenarios. This paper explores limitations of the bucket metaphor and associated models. Recently, during a 13‐year drought in Australia, river catchments gradually started to dry up. With each passing year, the depth to groundwater increased gradually as the water used by trees was not replenished by rainfall. We compare this long, slow behavior to that of five commonly used "bucket" models. The models do not show the long, slow drying up—they only show the seasonal ups and downs, and their predictions of streamflow over the drought are poor. This is surprising, and it means we should choose our models carefully and seek out models that can simulate this behavior and its impact on streamflow. [ABSTRACT FROM AUTHOR]

The increasing expression of human activity, climate variability, and climate change on humid, terrestrial hydrologic systems has made the integrated nature of large river basins more apparent. However, to date, there is no instrument platform sufficient to characterize river basins' hydrologic couplings and feedbacks, with many processes and impacts left almost entirely unobserved (e.g., snowmelt floods). Characterization at the river basin scale will require a more holistic vision and a far greater commitment from the environmental science community. It will require new designs and implementation of integrated instrumentation, a new generation of models, and a management framework that clearly addresses the human-climate-terrestrial interactions impacting our watersheds and river basins. Initially, we propose that existing 'similarity classifications' (e.g., regional soil, geologic, ecologic, hydrographic digital products) can provide a starting point for organizing historical data and initiating a long-term adaptive, multiscale observing strategy. This vision paper outlines instrumentation platforms for point, plot, reach, and hillslope scales that could be located within the 'characteristic' landscapes of river basins. The network of observing platforms then forms the basis of a 'Hydro-Mesonet' that can potentially support multiscale, multiprocess scientific studies necessary to understand and improve forecasts of our water resources at the river basin scale. This paper concludes with a discussion of how a network of such sites can support research at the level of the individual researcher and scale to the level of community-wide initiatives. [ABSTRACT FROM AUTHOR]

Soil moisture data are critical to understanding biophysical and societal impacts of climate change. However, soil moisture data availability is limited due to sparse in situ monitoring, particularly in mountain regions. Here we present methods, specifications, and initial results from the interactive Roaring Fork Observation Network (iRON), a soil, weather, and ecological monitoring system in the Southern Rocky Mountains of Colorado. Initiated in 2012, the network is currently composed of nine stations, distributed in elevation from 1,890 to 3,680 m, that continually collect and transmit measurements of soil moisture at three depths (5, 20, and 50 cm), soil temperature (20 cm), and meteorological conditions. Time‐lapse cameras for phenological observations, snow depth sensors, and periodic co‐located vegetation surveys complement selected stations. iRON was conceived and designed with the joint purpose of supporting bioclimatic research and resource management objectives in a snow‐dominated watershed. In the short term, iRON data can be applied to assessing the impact of temperature and precipitation on seasonal soil moisture conditions and trends. As more data are collected over time, iRON will help improve understanding of climate‐driven changes to soil, vegetation, and hydrologic conditions. In presenting this network and its initial data, we hope that the network's elevational gradient will contribute to bioclimatic mountain research, while active collaboration with partners in resource management may provide a model for science‐practice interaction in support of long‐term monitoring. Plain Language Summary: As climate change drives shifts in temperature and precipitation, researchers and resource managers can benefit from improved monitoring of soil moisture. Understanding the relationship between soil moisture and other system components is crucial to improving water availability projections and understanding ecosystem responses to climate change. Despite their significance, in‐ground soil‐moisture measurements are often not available across multiple elevations within a single watershed. This paper presents a network in the Southern Rocky Mountains intended to help address this data gap and compliment data from other networks. The interactive Roaring Fork Observation Network consists of nine locations across an 1,800‐m change in elevation. Each station measures soil moisture at three depths, soil temperature, air temperature, humidity, and precipitation. Some stations are equipped with cameras or snow depth gauges, and for eight sites vegetation surveys are conducted. The data are available through a simple data portal. The network was established with local resource manager support, and one of its guiding purposes is to support management and restoration planning efforts. Because of the network's ongoing monitoring across multiple elevations and habitats, interactive Roaring Fork Observation Network will provide researchers and resource managers with access to valuable information about changes in soil conditions in a changing climate. Key Points: Soil moisture is key to understanding and predicting change in hydrology and ecology amid climate variability and changeIn situ soil moisture and weather monitoring data are now available across an 1,800‐m elevation span in a mountain watershedThe network is supported and guided by resource managers and supports both research and resource management goals [ABSTRACT FROM AUTHOR]

This paper presents a short history of water resources systems analysis from its beginnings in the Harvard Water Program, through its continuing evolution toward a general field of water resources systems science. Current systems analysis practice is widespread and addresses the most challenging water issues of our times, including water scarcity and drought, climate change, providing water for food and energy production, decision making amid competing objectives, and bringing economic incentives to bear on water use. The emergence of public recognition and concern for the state of water resources provides an opportune moment for the field to reorient to meet the complex, interdependent, interdisciplinary, and global nature of today's water challenges. At present, water resources systems analysis is limited by low scientific and academic visibility relative to its influence in practice and bridled by localized findings that are difficult to generalize. The evident success of water resource systems analysis in practice (which is set out in this paper) needs in future to be strengthened by substantiating the field as the science of water resources that seeks to predict the water resources variables and outcomes that are important to governments, industries, and the public the world over. Doing so promotes the scientific credibility of the field, provides understanding of the state of water resources and furnishes the basis for predicting the impacts of our water choices. [ABSTRACT FROM AUTHOR]

Rainfall‐runoff models are often deficient under changing climatic conditions, yet almost no recent studies propose new or improved model structures, instead focusing on model intercomparison, input sensitivity, and/or quantification of uncertainty. This paucity of progress in model development is (in part) due to the difficulty of distinguishing which cases of model failure are truly caused by structural inadequacy. Here we propose a new framework to diagnose the salient cause of poor model performance in changing climate conditions, be it structural inadequacy, poor parameterization, or data errors. The framework can be applied to a single catchment, although larger samples of catchments are helpful to generalize and/or cross‐check results. To generate a diagnosis, multiple historic periods with contrasting climate are defined, and the limits of model robustness and flexibility are explored over each period separately and for all periods together. Numerous data‐based checks also supplement the results. Using a case study catchment from Australia, improved inference of structural failure and clearer evaluation of model structural improvements are demonstrated. This framework enables future studies to (i) identify cases where poor simulations are due to poor calibration methods or data errors, remediating these cases without recourse to structural changes; and (ii) use the remaining cases to gain greater clarity into what structural changes are needed to improve model performance in changing climate. Plain Language Summary: Rainfall runoff models are tools used by hydrologists in climate change assessments to estimate how future streamflow might change in response to a given (often hypothetical) climate scenario. For example, suppose we can assume that rainfall in a particular location is going to reduce by 20% in the future. Does this mean that streamflow will also reduce by 20%? Or will it be 10% less or 40% less? Although rainfall runoff models are among the best tools available, they are often not very good at answering this question. When tested on historical multiyear droughts, they often perform poorly, and we are unsure why. One problem is that when a model fails in this task, it is difficult to know what went wrong. Perhaps there was a problem with the data, since environmental monitoring is often subject to large errors. Perhaps the problem lay not with the model itself but with the way it was trained, or calibrated, to the data. Lastly, perhaps the model itself—its mathematical equations—need to be changed. To improve our estimates, we need a method to test which cause is behind the model failure; otherwise, we might make changes where none are warranted. This paper proposes such a method, in the form of a multistep framework that can isolate the causes of model failures. By ensuring that our attention is focused in the correct direction, this framework will help us to understand and make better estimates of how river flow will be altered by a changing climate. Key Points: Diagnosing model deficiency is difficult as there are many possible causes of poor simulationsSplit sample failure does not mean model structural inadequacy—further tests are requiredThis framework diagnoses the cause of poor performance and prioritizes remedial action [ABSTRACT FROM AUTHOR]

Accurately estimating actual evapotranspiration (ET) across global land surface is one of the key challenges in terrestrial hydrological cycles and energy flux balance studies. Gridded ET products have the potential for application in ungauged basins, but their uncertainties are possibly large and it remains unclear which one is best for a given basin. The water balance (WB) method provides a direct estimate of basin scale ET, but it cannot be used in ungauged basins where streamflow data are unavailable. Here, we first assess the performance of ET from 10 global ET products against WB ET estimates in 43 large river basins. The paper then uses three indirect evaluation methods [Three Cornered Hat (TCH), Arithmetic Average (AA), and Bayesian Three Cornered Hat] to identify the optimal ET products without the need of prior information, and to generate fusion products combining the ET from multiple products. Using the evaluation results derived from the WB method as the reference, the results show that the three methods have great success in identifying poorer products, suggesting that they are useful in filtering poor ET products in applications. However, the ability of such methods in identifying better ET products degrades slightly. The AA fusion product, which combines ET outputs from multiple products, is marginally better than the best single ET product in many of the 43 basins. Because of its simplicity, it could be used to reduce the uncertainty in ET estimates from multiple products for ungauged basins and regions. Key Points: A comprehensive study on reducing the uncertainty in evapotranspiration (ET) estimates from 10 global ET products for application in ungauged river basinsThe indirect evaluation methods without using prior information, particularly Arithmetic Average (AA) and Bayesian Three Cornered Hat can successfully identify the poor ET productsThe AA fusion product, which combines ET outputs from multiple products, is generally marginally better than the best single ET product [ABSTRACT FROM AUTHOR]

Abstract: Planning water supply infrastructure includes identifying interventions that cost‐effectively secure an acceptably reliable water supply. Climate change is a source of uncertainty for water supply developments as its impact on source yields is uncertain. Adaptability to changing future conditions is increasingly viewed as a valuable design principle of strategic water planning. Because present decisions impact a system's ability to adapt to future needs, flexibility in activating, delaying, and replacing engineering projects should be considered in least‐cost water supply intervention scheduling. This is a principle of Real Options Analysis, which this paper applies to least‐cost capacity expansion scheduling via multistage stochastic mathematical programming. We apply the proposed model to a real‐world utility with many investment decision stages using a generalized scenario tree construction algorithm to efficiently approximate the probabilistic uncertainty. To evaluate the implementation of Real Options Analysis, the use of two metrics is proposed: the value of the stochastic solution and the expected value of perfect information that quantify the value of adopting adaptive and flexible plans, respectively. An application to London's water system demonstrates the generalized approach. The investment decisions results are a mixture of long‐term and contingency schemes that are optimally chosen considering different futures. The value of the stochastic solution shows that by considering uncertainty, adaptive investment decisions avoid £100 million net present value (NPV) cost, 15% of the total NPV. The expected value of perfect information demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Sensitivity of results to the characteristics of the scenario tree and uncertainty set is assessed. [ABSTRACT FROM AUTHOR]

Abstract: Assessing the accuracy of gridded climate data sets is highly relevant to climate change impact studies, since evaluation, bias correction, and statistical downscaling of climate models commonly use these products as reference. Among all impact studies those addressing hydrological fluxes are the most affected by errors and biases plaguing these data. This paper introduces a framework, coined Hydrological Coherence Test (HyCoT), for assessing the hydrological coherence of gridded data sets with hydrological observations. HyCoT provides a framework for excluding meteorological forcing data sets not complying with observations, as function of the particular goal at hand. The proposed methodology allows falsifying the hypothesis that a given data set is coherent with hydrological observations on the basis of the performance of hydrological modeling measured by a metric selected by the modeler. HyCoT is demonstrated in the Adige catchment (southeastern Alps, Italy) for streamflow analysis, using a distributed hydrological model. The comparison covers the period 1989–2008 and includes five gridded daily meteorological data sets: E‐OBS, MSWEP, MESAN, APGD, and ADIGE. The analysis highlights that APGD and ADIGE, the data sets with highest effective resolution, display similar spatiotemporal precipitation patterns and produce the largest hydrological efficiency indices. Lower performances are observed for E‐OBS, MESAN, and MSWEP, especially in small catchments. HyCoT reveals deficiencies in the representation of spatiotemporal patterns of gridded climate data sets, which cannot be corrected by simply rescaling the meteorological forcing fields, as often done in bias correction of climate model outputs. We recommend this framework to assess the hydrological coherence of gridded data sets to be used in large‐scale hydroclimatic studies. [ABSTRACT FROM AUTHOR]

Hydrological connectivity has emerged as an effective means to understand and manage fluxes of water and transport of nutrients and sediment at the catchment scale, especially as fluxes are modified by changing climate and land use. However, hydrology has not yet adopted it as a unifying concept given uncertainties regarding different conceptions and gaps in understanding of how connectivity functions at different temporal and spatial scales. This paper outlines a conceptual model of hydrological connectivity for semiarid hillslopes and highlights the direction that future attempts to quantify dynamic hydrological connectivity might take. Rainfall-runoff analysis emphasizes the influence of antecedent moisture and temporal storm structure on hillslope-scale flood generation. Plot-scale field flume experiments demonstrate the spatial and temporal variability of flow resistance. The morphological runoff zone framework is presented as a method to upscale such results to the hillslope and incorporate the broader-scale issue of hillslope form. The need to design field experiments to inform attempts to model feedbacks between runoff depth and flow resistance forms the central argument of this paper. Patterns of infiltration and resistance across entire flow paths and their variability throughout a storm event are the key to understanding dynamic hydrological connectivity at the hillslope scale. [ABSTRACT FROM AUTHOR]

Although information about climate change and its implications is becoming increasingly available to water utility managers, additional tools are needed to translate this information into secondary products useful for local assessments. The anticipated intensification of the hydrologic cycle makes quantifying changes to hydrologic extremes, as well as associated water quality effects, of particular concern. To this end, this paper focuses on using extreme value statistics to describe maximum monthly flow distributions at a given site, where the nonstationarity is derived from concurrent climate information. From these statistics, flow quantiles are reconstructed over the historic record and then projected to 2100. This paper extends this analysis to an associated source water quality impact, whereby the corresponding risk of exceeding a water quality threshold is examined. The approach is applied to a drinking water source in the Pacific Northwest United States that has experienced elevated turbidity values correlated with high streamflow. Results demonstrate that based on climate change information from the most recent Intergovernmental Panel on Climate Change assessment report, the variability and magnitude of extreme streamflows substantially increase over the 21st century. Consequently, the likelihood of a turbidity exceedance increases, as do the associated relative costs. The framework is general and could be applied to estimate extreme streamflow under climate change at other locations, with straightforward extensions to other water quality variables that depend on extreme hydroclimate. [ABSTRACT FROM AUTHOR]

While past strategies for agricultural water management have focused on irrigation (use of blue water), this paper demonstrates the dominance of green water in food production. A global, yet spatially disaggregated, green-blue analysis of water availability and requirement, using the LPJmL dynamic vegetation and water balance model, indicates that many countries currently assessed as severely water short are able to produce enough food for their populations if green water is considered and is managed well. The need to integrate green and blue water management is highlighted in a future scenario of water availability under climate change and population growth (HadCM2 A2). For 2050, the scenario indicates that 59% of the world population will face blue water shortage, and 36% will face green and blue water shortage. Even under climate change, good options to build water resilience exist without further expansion of cropland, particularly through management of local green water resources that reduces risks for dry spells and agricultural droughts. [ABSTRACT FROM AUTHOR]

This paper explores the impacts of a water right's allocative priority-as an indicator of farmers' risk-bearing ability-on land irrigation under water supply uncertainty. We develop and use an economic model to simulate farmers' land irrigation decision and associated economic returns in eastern Idaho. Results indicate that the optimal acreage of land irrigated increases with water right priority when hydroclimate risk exhibits a negatively skewed or right-truncated distribution. Simulation results suggest that prior appropriation enables senior water rights holders to allocate a higher proportion of their land to irrigation, 6 times as much as junior rights holders do, creating a gap in the annual expected net revenue reaching up to $141.4 acre−1 or $55,800 per farm between the two groups. The optimal irrigated acreage, expected net revenue, and shadow value of a water right's priority are subject to substantial changes under a changing climate in the future, where temporal variation in water supply risks significantly affects the profitability of agricultural land use under the priority-based water sharing mechanism. [ABSTRACT FROM AUTHOR]

Permafrost in high alpine catchments is expected to disappear in future warmer climates, but the hydrological impact of such changes is poorly understood. This paper investigates the flow paths and the hydrological response in a 5 km2 high alpine catchment in the Ötztal Alps, Austria, and their changes resulting from a loss of permafrost. Spatial permafrost distribution, depth to the permafrost table, and depth to the bedrock were mapped by geophysical methods. Catchment runoff and meteorological variables were monitored from June 2008 to December 2011. These data were used along with field experience to infer conceptual schemes of the dominant flow paths in four types of hillslopes that differ in terms of their unconsolidated sediment characteristics and the presence of permafrost. The four types are: talus fans, rock glaciers, Little Ice Age (LIA) till, and pre-LIA till. Permafrost tends to occur in the first three types, but is absent from pre-LIA till. Based on these flow path concepts, runoff was simulated for present conditions and for future conditions when permafrost has completely disappeared. The simulations indicate that complete disappearance of permafrost will reduce flood peaks by up to 17% and increase runoff during recession by up to 19%. It is argued that change modeling needs to account for flow path types and their changes based on geophysical surveys and field investigations. [ABSTRACT FROM AUTHOR]

Many water resource systems have been designed assuming that the statistical characteristics of future inflows are similar to those of the historical record. This assumption is no longer valid due to large-scale changes in the global climate, potentially causing declines in water resource system performance, or even complete system failure. Upgrading system infrastructure to cope with climate change can require substantial financial outlay, so it might be preferable to optimize existing system performance when possible. This paper builds on decision scaling theory by proposing a bottom-up approach to designing optimal feedback control policies for a water system exposed to a changing climate. This approach not only describes optimal operational policies for a range of potential climatic changes but also enables an assessment of a system's upper limit of its operational adaptive capacity, beyond which upgrades to infrastructure become unavoidable. The approach is illustrated using the Lake Como system in Northern Italy-a regulated system with a complex relationship between climate and system performance. By optimizing system operation under different hydrometeorological states, it is shown that the system can continue to meet its minimum performance requirements for more than three times as many states as it can under current operations. Importantly, a single management policy, no matter how robust, cannot fully utilize existing infrastructure as effectively as an ensemble of flexible management policies that are updated as the climate changes. [ABSTRACT FROM AUTHOR]

This paper proposes a new classification scheme of atmospheric cyclone tracks over Europe. The cyclones are classified into nine types, based on the geographic regions, the cyclones traverse before entering central Europe. The method is applied to ERA-40 data for 1961-2002, considering all significant cyclones above a relative vorticity threshold. About 120 and 80 cyclone tracks per year are identified at sea level pressure and 700 hPa geopotential height, respectively. About 25% are Atlantic type cyclones, 25% emerge directly over central Europe, and another 25% originate from the lee of the Alps. The other types are less frequent (Mediterranean 12%, Polar 7%, Continental 2%, and Vb 4%). The track types show distinct characteristics in terms of cyclone intensity and cyclone life stage when entering central Europe. Cyclones of type Vb are, on average, the most intense cyclones over central Europe and even more intense than Atlantic cyclones in summer, pointing to their potential for generating extreme precipitation. The identified cyclones account for 46%-76% of long-term precipitation in a focus region in central Europe. Precipitation differs significantly between cyclones, with Atlantic and Vb cyclones producing the highest and Continental and Polar cyclones producing the lowest long-term precipitation totals. The contributions of cyclone types to total precipitation show distinct spatial patterns within central Europe. The new cyclone type catalog will be useful for identifying the relevance of specific track types for precipitation extremes in central Europe and analyze their temporal behavior in the context of climate change. [ABSTRACT FROM AUTHOR]

In this paper we combined a multimodel ensemble based on 15 regional climate models with a multicatchment approach to explore the hydrologic sensitivity of 14 neighboring and rather similar catchments to changing climate conditions. Current (1982-2010) and future (2062-2090) streamflow was simulated with the HBV model. A diagnostic approach was used, which considered major behavioral catchment functions by using hydrologically relevant signatures related to overall water balance, flow duration curves and hydrograph attributes. Projected increases in temperature and precipitation resulted in increased total available streamflow, with lower spring and summer flows, but substantially higher winter streamflow. Furthermore, significant changes in flow durations with lower chances of both high and low flows can be expected in boreal Sweden in the future. This overall trend in projected streamflow pattern changes was comparable among the analyzed catchments but the magnitude of change differed considerably. This suggests that catchments belonging to the same region can show distinctly different degrees of hydrological responses to the same external climate change signal. We reason that differences in spatially distributed physical catchment properties within catchments are not only of great importance for current streamflow behavior, but also play a major role in the sensitivity of catchments to changing climate conditions. [ABSTRACT FROM AUTHOR]

River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure. [ABSTRACT FROM AUTHOR]

In many partially glacierized watersheds glacier recession driven by a warming climate could lead to complex patterns of streamflow response over time, often marked with rapid increases followed by sharp declines, depending on initial glacier ice cover and rate of climate change. Capturing such ''phases'' of hydrologic response is critical in regions where communities rely on glacier meltwater, particularly during low flows. In this paper, we investigate glacio-hydrologic response in the headwaters of the Zongo River, Bolivia, under climate change using a distributed glacio-hydrological model over the period of 1987-2100. Model predictions are evaluated through comparisons with satellite-derived glacier extent estimates, glacier surface velocity, in situ glacier mass balance, surface energy flux, and stream discharge measurements. Historically (1987-2010) modeled glacier melt accounts for 27% of annual runoff, and 61% of dry season (JJA) runoff on average. During this period the relative glacier cover was observed to decline from 35 to 21% of the watershed. In the future, annual and dry season discharge is projected to decrease by 4% and 27% by midcentury and 25% and 57% by the end of the century, respectively, following the loss of 81% of the ice in the watershed. Modeled runoff patterns evolve through the interplay of positive and negative trends in glacier melt and increased evapotranspiration as the climate warms. Sensitivity analyses demonstrate that the selection of model surface energy balance parameters greatly influences the trajectory of hydrological change projected during the first half of the 21st century. These model results underscore the importance of coupled glacio-hydrology modeling. [ABSTRACT FROM AUTHOR]

Global climate models suggest an increase in evapotranspiration, changing storm tracks, and moisture delivery in many parts of the world, which are likely to cause more prolonged and severe drought, yet the weakness of climate models in modeling persistence of hydroclimatic variables and the uncertainties associated with regional climate projections mean that impact assessments based on climate model output may underestimate the risk of multiyear droughts. In this paper, we propose a vulnerability-based approach to test water resource system response to drought. We generate a large number of synthetic streamflow series with different drought durations and deficits and use them as input to a water resource system model. Marginal distributions of the streamflow for each month are generated by bootstrapping the historical data, while the joint probability distributions of consecutive months are constructed using a copula-based method. Droughts with longer durations and larger deficits than the observed record are generated by perturbing the copula parameter and by adopting an importance sampling strategy for low flows. In this way, potential climate-induced changes in monthly hydrological persistence are factored into the vulnerability analysis. The method is applied to the London water system (England) to investigate under which drought conditions severe water use restrictions would need to be imposed. Results indicate that the water system is vulnerable to drought conditions outside the range of historical events. The vulnerability assessment results were coupled with climate model information to compare alternative water management options with respect to their vulnerability to increasingly long and severe drought. [ABSTRACT FROM AUTHOR]

The vulnerability of water supplies to shortage depends on the complex interplay between streamflow variability and the management and demands of the water system. Assessments of water supply vulnerability to potential changes in streamflow require methods capable of generating a wide range of possible streamflow sequences. This paper presents a method to generate synthetic monthly streamflow sequences that reproduce the statistics of the historical record and that can express climate-induced changes in user-specified streamflow characteristics. The streamflow sequences are numerically simulated through random sampling from a parametric or a nonparametric distribution fitted to the historical data while shuffling the values in the time series until a sequence matching a set of desired temporal properties is generated. The desired properties are specified in an objective function which is optimized using simulated annealing. The properties in the objective function can be manipulated to generate streamflow sequences that exhibit climate-induced changes in streamflow characteristics such as interannual variability or persistence. The method is applied to monthly streamflow data from the Thames River at Kingston (UK) to generate sequences that reproduce historical streamflow statistics at the monthly and annual time scales and to generate perturbed synthetic sequences expressing changes in short-term persistence and interannual variability. [ABSTRACT FROM AUTHOR]

Natural proxy records of hydroclimatic behavior, such as tree ring chronologies, are a rich source of information of past climate-driven nonstationarities in hydrologic variables. In this study, we investigate tree ring chronologies that demonstrate significant correlations with streamflows, with the objective of identifying the spatiotemporal patterns and extents of nonstationarities in climate and hydrology, which are essentially representations of past 'climate changes.' First and second-order nonstationarities are of particular interest in this study. As a prerequisite, we develop a methodology to assess the consistency and credibility of a regional network of tree ring chronologies as proxies for hydrologic regime. This methodology involves a cluster analysis of available tree ring data to understand and evaluate their dependence structure, and a regional temporal-consistency plot to assess the consistency of different chronologies over time. The major headwater tributaries of the Saskatchewan River basin (SaskRB), the main source of surface water in the Canadian Prairie Provinces, are used as the case study. Results indicate that stationarity might never have existed in the hydrology of the region, as the statistical properties of annual paleo-hydrologic proxy records across the basin, i.e., the mean and autocorrelation structure, have consistently undergone significant changes (nonstationarities) at different points in the history of the region. The spatial pattern of the changes in the mean statistic has been variable with time, indicating a time-varying cross-correlation structure across the tributaries of the SaskRB. Conversely, the changes in the autocorrelation structure across the basin have been in harmony over time. The results demonstrate that the 89 year period of observational record in this region is a poor representation of the long-term properties of the hydrologic regime, and shorter periods, e.g., 30 year periods, are by no means representative. This paper highlights the need to broaden the understanding of hydrologic characteristics in any basin beyond the limited observational records, as an improved understanding is essential for more reliable assessment and management of available water resources. [ABSTRACT FROM AUTHOR]

Many studies report that hydrologic regimes are modulated by large-scale modes of climate variability such as the El Niño Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO). Climate-informed frequency analysis models have therefore been proposed to condition the distribution of hydrologic variables on climate indices. However, standard climate indices may be poor predictors in some regions. This paper therefore describes a regional frequency analysis framework that conditions the distribution of hydrologic variables directly on atmospheric or oceanic fields, as opposed to predefined climate indices. This framework is based on a two-level probabilistic model describing both climate and hydrologic data. The climate data set (predictor) is typically a time series of atmospheric of oceanic fields defined on a grid over some area, while the hydrologic data set (predictand) is typically a regional data set of station data (e.g., annual average flow at several gauging stations). A Bayesian estimation framework is used, so that a natural quantification of uncertainties affecting hydrologic predictions is available. A case study aimed at predicting the number of autumn flood events in 16 catchments located in Mediterranean France using geopotential heights at 500 hPa over the North-Atlantic region is presented. The temporal variability of hydrologic data is shown to be associated with a particular spatial pattern in the geopotential heights. A cross-validation experiment indicates that the resulting probabilistic climate-informed predictions are skillful: their reliability is acceptable and they are much sharper than predictions based on standard climate indices and baseline predictions that ignore climate information. [ABSTRACT FROM AUTHOR]

Input uncertainty and deficiencies in the key elements of hydrologic models are fundamental challenges to improving model performance. Evapotranspiration (ET) models are sensitive to energy imbalance and energy interaction between the canopy and surface, and these errors can bias model simulation. This paper presents a Bayesian framework that accounts for energy imbalance and energy interaction uncertainty in calibration of the Shuttleworth‐Wallace (SW) model and discusses whether these efforts can improve the SW model performance. Specifically, the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm is used to analyze the energy imbalance error and energy interaction correction during SW model calibration, and Bayesian model evidence (BME) is used to scrutinize multiple hypotheses about alternative representations of energy. The results indicate that explicit treatment of the energy imbalance and conceptual corrections of the energy interaction significantly reduce the uncertainty of model parameters and provide energy corrections that are more appropriate and largely improve the SW model performance. Moreover, the optimal model selected by BME is mostly consistent with that evaluated using traditional statistical criteria (TSC) during a validation period. This result shows that the improved SW model also provides the best predictions, and thus consistent model selection should also be asymptotically efficient. The findings may allow improvement of ET model performance, and hence the accuracy of hydrologic and climatic simulations, toward fully understanding the key process underlying hydrologic and climatic variability and change over land. [ABSTRACT FROM AUTHOR]

Climate change introduces substantial uncertainty to water resources planning and raises the key question: when, or under what conditions, should adaptation occur? A number of recent studies aim to identify policies mapping future observations to actions—in other words, framing climate adaptation as an optimal control problem. This paper uses the control paradigm to review and classify recent dynamic planning studies according to their approaches to uncertainty characterization, policy structure, and solution methods. We propose a set of research gaps and opportunities in this area centered on the challenge of characterizing uncertainty, which prevents the unambiguous application of control methods to this problem. These include exogenous uncertainty in forcing, model structure, and parameters propagated through a chain of climate and hydrologic models; endogenous uncertainty in human‐environmental system dynamics across multiple scales; and sampling uncertainty due to the finite length of historical observations and future projections. Recognizing these challenges, several opportunities exist to improve the use of control methods for climate adaptation, namely, how problem context and understanding of climate processes might assist with uncertainty quantification and experimental design, out‐of‐sample validation and robustness of optimized adaptation policies, and monitoring and data assimilation, including trend detection, Bayesian inference, and indicator variable selection. We conclude with a summary of recommendations for dynamic water resources planning under climate change through the lens of optimal control. [ABSTRACT FROM AUTHOR]

As a direct measure of watershed resilience, watershed storage is important for understanding climate change impacts on water resources. In this paper we quantify water budget components and storage changes for two of the largest watersheds in the State of Michigan, USA (the Grand River and the Saginaw Bay watersheds) using remotely sensed data and a process-based hydrologic model (PAWS) that includes detailed representations of subsurface and land surface processes. Model performance is evaluated using ground-based observations (streamflows, groundwater heads, soil moisture, and soil temperature) as well as satellite-based estimates of evapotranspiration (Moderate-resolution Imaging Spectroradiometer, MODIS) and watershed storage changes (Gravity Recovery and Climate Experiment, GRACE). We use the model to compute annual-average fluxes due to evapotranspiration, surface runoff, recharge and groundwater contribution to streams and analyze the impacts of land use and land cover (LULC) and soil types on annual hydrologic budgets using correlation analysis. Watershed storage changes based on GRACE data and model results showed similar patterns. Storage was dominated by subsurface components and showed an increasing trend over the past decade. This work provides new estimates of water budgets and storage changes in Great Lakes watersheds and the results are expected to aid in the analysis and interpretation of the current trend of declining lake levels, in understanding projected future impacts of climate change as well as in identifying appropriate climate adaptation strategies. [ABSTRACT FROM AUTHOR]

Cities around the world are increasingly involved in climate action and mitigating greenhouse gas (GHG) emissions. However, in the context of responding to climate pressures in the water sector, very few studies have investigated the impacts of changing water use on GHG emissions, even though water resource adaptation often requires greater energy use. Consequently, reducing GHG emissions, and thus focusing on both mitigation and adaptation responses to climate change in planning and managing urban water supply systems, is necessary. Furthermore, the minimization of GHG emissions is likely to conflict with other objectives. Thus, applying a multiobjective evolutionary algorithm (MOEA), which can evolve an approximation of entire trade-off (Pareto) fronts of multiple objectives in a single run, would be beneficial. Consequently, the main aim of this paper is to incorporate GHG emissions into a MOEA framework to take into consideration both adaptation and mitigation responses to climate change for a city's water supply system. The approach is applied to a case study based on Adelaide's southern water supply system to demonstrate the framework's practical management implications. Results indicate that trade-offs exist between GHG emissions and risk-based performance, as well as GHG emissions and economic cost. Solutions containing rainwater tanks are expensive, while GHG emissions greatly increase with increased desalinated water supply. Consequently, while desalination plants may be good adaptation options to climate change due to their climate-independence, rainwater may be a better mitigation response, albeit more expensive. [ABSTRACT FROM AUTHOR]

Model studies suggest that semiarid ecosystems with patterned vegetation can respond in a nonlinear way to climate change. This means that gradual changes can result in a rapid transition to a desertified state. Previous model studies focused on the response of patterned semiarid ecosystems to changes in mean annual rainfall. The intensity of rain events, however, is projected to change as well in the coming decades. In this paper, we study the effect of changes in rainfall intensity on the functioning of patterned semiarid ecosystems with a spatially explicit model that captures rainwater partitioning and runoff-runon processes with simple event-based process descriptions. Analytical and numerical analyses of the model revealed that rainfall intensity is a key parameter in explaining patterning of vegetation in semiarid ecosystems as low mean rainfall intensities do not allow for vegetation patterning to occur. Surprisingly, we found that, for a constant annual rainfall rate, both an increase and a decrease in mean rainfall intensity can trigger desertification. An increase negatively affects productivity as a greater fraction of the rainwater is lost as runoff. This can result in a shift to a bare desert state only if the mean rainfall intensity exceeds the infiltration capacity of bare soil. On the other hand, a decrease in mean rainfall intensity leads to an increased fraction of rainwater infiltrating in bare soils, remaining unavailable to plants. Our findings suggest that considering rainfall intensity as a variable may help in assessing the proximity to regime shifts in patterned semiarid ecosystems and that monitoring losses of resource through runoff and bare soil infiltration could be used to determine ecosystem resilience. [ABSTRACT FROM AUTHOR]

Despite a decade of research into climate change impacts on water resources, the scientific community has delivered relatively few practical methodological developments for integrating uncertainty into water resources system design. This paper presents an application of the 'decision scaling' methodology for assessing climate change impacts on water resources system performance and asks how such an approach might inform planning decisions. The decision scaling method reverses the conventional ethos of climate impact assessment by first establishing the climate conditions that would compel planners to intervene. Climate model projections are introduced at the end of the process to characterize climate risk in such a way that avoids the process of propagating those projections through hydrological models. Here we simulated 1000 multisite synthetic monthly streamflow traces in a model of the Melbourne bulk supply system to test the sensitivity of system performance to variations in streamflow statistics. An empirical relation was derived to convert decision-critical flow statistics to climatic units, against which 138 alternative climate projections were plotted and compared. We defined the decision threshold in terms of a system yield metric constrained by multiple performance criteria. Our approach allows for fast and simple incorporation of demand forecast uncertainty and demonstrates the reach of the decision scaling method through successful execution in a large and complex water resources system. Scope for wider application in urban water resources planning is discussed. [ABSTRACT FROM AUTHOR]

The consensus in the scientific community is that the intensity of extreme precipitation will increase in a warmer climate. However, as there is limited observational evidence to this effect, there is a growing body of research which focuses on directly investigating the relationship between temperature and precipitation. This is currently performed by binning precipitation data in temperature bins and then investigating the trend in the precipitation percentiles in each bin with temperature. In this paper, we highlight limitations in the binning approach and present quantile regression as an alternative to the above process. Quantile regression allows estimation of this scaling directly and, unlike binning, is unbiased with sample size. Moreover, quantile regression presents a natural framework for investigation into other factors (covariates) that may be affecting the nature of the scaling relationship. Results using subdaily rainfall data for Australia show the efficacy of the proposed quantile regression method, as well as the presence of season indicators as significant covariates that affect the scaling relationship of precipitation with temperature. A general increase in the scaling coefficient in winter versus summer is observed. [ABSTRACT FROM AUTHOR]

Climate reconstructions using tree rings and lake sediments have contributed significantly to the understanding of Holocene climates. Approaches focused specifically on reconstructing the temporal water-level response of lakes, however, are much less developed. This paper describes a statistical correlation approach based on time series with Palmer Drought Severity Index (PDSI) values derived from instrumental records or tree rings as a basis for reconstructing stage hydrographs for closed-basin lakes. We use a distributed lag correlation model to calculate a variable, ωt that represents the water level of a lake at any time t as a result of integrated climatic forcing from preceding years. The method was validated using both synthetic and measured lake-stage data and the study found that a lake's 'memory' of climate fades as time passes, following an exponential-decay function at rates determined by the correlation time lag. Calculated trends in ωt for Moon Lake, Rice Lake, and Lake Mina from A.D. 1401 to 1860 compared well with the established chronologies (salinity, moisture, and Mg/Ca ratios) reconstructed from sediments. This method provides an independent approach for developing high-resolution information on lake behaviors in preinstrumental times and has been able to identify problems of climate signal deterioration in sediment-based climate reconstructions in lakes with a long time lag. [ABSTRACT FROM AUTHOR]

Alpine lakes on the Tibetan Plateau are minimally disturbed by human activities and are sensitive indicators of climate variability. Accelerated lake expansion in the 2000s has been confirmed by both dramatic lake-area increases (for 312 lakes larger than 10 km2) derived from optical images, and rapid water-level rises (for 117 lakes with water-level data) measured by satellite altimetry. However, the underlying climate causes remain unclear. This paper analyzes the relationship between the water-level changes of lakes on the plateau and the potential driving factors, such as the glacier meltwater supply and a dependency on precipitation and runoff over the whole plateau and in each zone. The results show that the rates of change of non-glacier-fed lakes in the 2000s were as high as those of glacier-fed lakes across the whole plateau and the lake-level changes were closely associated with the lake supply coefficients (the basin/lake area ratio). The lake variations agreed well with the spatial pattern of precipitation changes. However, in different zones, especially at around 33°N north of the plateau, glacier-fed lakes did exhibit faster lake level increases than no-glacier-fed lakes, indicating that the presence of a glacier meltwater supply augmented the precipitation-driven lake expansions in these areas. Despite the absence of quantitative modeling due to limited data availability, this study provides qualitative support that the lake expansions on the Tibetan Plateau in the 2000s have been driven primarily by changes in precipitation and evapotranspiration and not solely by the effect of glacier wastage. [ABSTRACT FROM AUTHOR]

The simulation of space-time precipitation has been studied since the late 1980s. However, there are still many open issues concerning the most appropriate approach to simulate it, specially in highly heterogeneous areas, such as in mountain environments. For this reason, we present here a comprehensive investigation of the Space-Time Neyman-Scott Rectangular Pulses model, with the purpose of analyzing its performance in a challenging Alpine environment of Switzerland and identifying weaknesses that can drive future improvements. The results point at the suitability of the model in reproducing not only the basic statistics at different temporal aggregations, but also the more challenging distributional and scaling properties. The intrinsic stationarity of the model in space, induced by the parameter estimation procedure, poses occasional limitations with regard to the accurate simulation of the variability of the observed climate characteristics, which are strongly influenced by local microclimates. However, the model is able, even in the complex Alpine environment, to preserve the spatial patterns observed in the actual precipitation process. The study allowed (i) to conclude about the robustness of the model and its suitability for multisite downscaling of precipitation estimated from climate model simulations, as reported in the companion paper, and (ii) to put in evidence some limitations that require further consideration to improve space-time rainfall generation. [ABSTRACT FROM AUTHOR]

Metamodeling uses computationally efficient models to emulate the outputs of complex models, trading off computational time against prediction accuracy and/or precision. Although potentially powerful, there is limited understanding of the uncertainty introduced by the metamodeling procedure. In particular, the errors associated with transformations of the predictions, such as aggregations during upscaling or differences between results used for impacts analysis, have not been explored in the metamodeling literature. We present an application of metamodeling that upscales physics-based model predictions to make catchment scale predictions of land management change impacts on peak flows. Two parallel sets of simulations are conducted, one with the original physics-based models and the other with metamodels. Despite good performance in emulating the local scale physics-based model simulations, once incorporated into a catchment scale model and especially once impacts of change are calculated, errors associated with the metamodeling procedure alone become significant, accounting for almost half of the prediction uncertainty in peak flows. The additional (metamodel-contributed) uncertainty is introduced both through biases in peak flows and through increases in peak flow variance. In the context of land management impacts, the results demonstrate the importance of tracking propagation of errors during upscaling, and of evaluating a model's ability to predict change, as well as independent observations. Despite these errors, the predictions of land management impacts from both physics-based models and metamodels are broadly consistent between each other, and in accordance with expectations from the literature. [ABSTRACT FROM AUTHOR]