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As the climate warms, snow‐dominated regions are increasingly susceptible to precipitation phase changes and earlier snowmelt. In alpine catchments, snowmelt is the majority of runoff, governed by heterogeneous interactions among snowfall, wind, topography and vegetation. The role of the alpine snowpack in delaying the downstream release of accumulated snowfall remains understudied, despite its fundamental role in sustaining lower‐elevation ecosystems and populations. We evaluated fine‐scale changes in the magnitude and duration of alpine snow water storage and release by comparing effective precipitation seasonality to that of surface water inputs using a Snow Storage Index (SSI). We forced the Distributed Hydrology Soil Vegetation Model with historical climate conditions and an end‐of‐century warming signal down‐scaled from a 4‐km regional climate model, to simulate future changes in snow water storage and partitioning of precipitation to runoff and evapotranspiration. The hydrological modelling was conducted on a 2‐m spatial grid to resolve fine scale processes over a 0.6‐km2 headwater catchment within the Niwot Ridge Long‐Term Ecological Research site located in the Rocky Mountains, Colorado, USA. Proportionally more rainfall, reduced snowpack size and extent, and earlier snowmelt occurred in the future scenario with decreased catchment average SSI and thus snow water storing capabilities, with inordinately large decreases occurring in areas of the catchment with reconstructed snowdrifts (−57% average and −100% maximum). Using the Budyko framework to evaluate annual hydrologic partitioning, precipitation inputs to streamflow increased by a maximum 10% in large SSI regions with snowdrifts under warming conditions, signifying a potential buffer for total catchment and annual streamflow loss in a future climate. The fine‐scale analysis of hydrologic sensitivities to warming presented herein are important because climate conditions are expected to further reduce alpine snow water storage, alter hydrologic partitioning, and runoff generation for ecosystems and people living downstream. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance. Plain Language Summary: As global temperatures rise, there's growing concern about an increase in the frequency of extreme precipitation events. However, projections of future changes to large‐magnitude precipitation events often rely on coarse‐resolution models which simulate meteorology on a spatial scale larger than most storms. To address this, we explored the potential of higher resolution models from the Coupled Model Intercomparison Project HighResMIP ensemble to improve the fidelity of simulated precipitation variability across the western United States. We compared four HighResMIP models with their coarse‐resolution counterparts from 1950 to 2014 and found that coarse‐resolution models consistently underestimated the dominance of large storms compared to observed data. While some HighResMIP models showed improvement, others remained unchanged or even showed increased bias. This suggests that besides spatial resolution alone, factors like model structure and numerical representation of fine scale precipitation processes play a role in accurately simulating intense storms. These findings highlight the importance of considering both resolution and climate model structure in predicting future extreme precipitation events, especially in regions where precipitation is generated by processes that operate on a wide range of spatial scales. Key Points: Increasing spatial resolution of general circulation models (GCMs) does not consistently improve simulation of large‐storm dominance across the western United StatesCoarse resolution GCMs simulate large‐storm dominance well in regions where the largest daily precipitation events are due to synoptic scale precipitationModel structure and parameterization of precipitation generation appear to be equally as important as resolution in the simulation of large‐storm dominance [ABSTRACT FROM AUTHOR]

Terrestrial hydrology is altered by fires, particularly in snow‐dominated catchments. However, fire impacts on catchment hydrology are often neglected from land surface model (LSM) simulations. Western U.S. wildfire activity has been increasing in recent decades and is projected to continue increasing over at least the next three decades, and thus it is important to evaluate if neglecting fire impacts in operational land surface models (LSMs) is a significant error source that has a noticeable signal among other sources of uncertainty. We evaluate a widely used state‐of‐the‐art LSM (Noah‐MP) in runoff and snowpack simulations at two representative fire‐affected snow‐dominated catchments in the Pacific Northwest: Andrew's Creek in Washington and Johnson Creek in Idaho. These two catchments are selected across all western U.S. fire‐affected catchments because they are snow‐dominated and experienced more than 50% burning in a single fire event with minimal burning outside of this event, which allows analyses of distinct pre‐ and post‐fire periods. There are statistically significant shifts in model skills from pre‐to post‐fire years in simulating runoff and snowpack. At both study catchments, simulations miss enhancements in early‐spring runoff and annual runoff efficiency during post‐fire years, resulting in persistent underestimates of annual runoff anomalies throughout the 12‐year post‐fire analysis periods. Enhanced post‐fire snow accumulation and melt contributes to observed but unmodeled increases of spring runoff and annual runoff efficiency at these catchments. Informing simulations with satellite observed land cover classifications, leaf area index, and green fraction do not consistently improve the model ability to simulate hydrologic responses to fire disturbances. Plain Language Summary: Western U.S. fire activity has been increasing in recent decades and is expected to continue to increase in coming decades. Fires remove vegetation and alter soils which in turn alters terrestrial hydrology. Fire effects on hydrology are particularly significant over snowy catchments that serve as natural water towers for major western U.S. rivers. Sophisticated models often neglect or underrepresent fire effects on land surface properties, and thus are susceptible to larger errors after fires. This study compares runoff and snow simulations from a widely used land surface model (LSM) with observations to quantify fire‐induced changes in model accuracy over two snow dominated catchments in the Pacific Northwest. Simulations persistently underestimate enhanced spring runoff and annual runoff anomalies in post‐fire years. These underestimates are consistent with observed enhancements in post‐fire snow accumulation and melt, which the model mostly failed to capture. The finding that post‐fire model errors are consistent with previously published fire impacts on hydrology supports that fire is an important error source in LSM simulations that should be accounted for. Key Points: Noah‐MP underestimates annual runoff anomalies relative to observations following fires in the Pacific‐NorthwestIn post‐fire years, Noah‐MP fails to simulate deeper snowpacks that ablate faster and the associated enhanced spring runoffInforming the model with satellite‐observed vegetation characteristics (including fire effect) did not resolve post‐fire model deficiencies [ABSTRACT FROM AUTHOR]

Lakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record‐low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high‐quality hypsometry data. Here, we propose a novel proxy‐based method to reconstruct multi‐decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003–2009) and recently launched ICESat‐2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km2. Reconstructed water levels have a median root‐mean‐square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%–32%. The improvement is attributable to the new method's robust construction of high‐quality hypsometry, with a median R2 value of 0.92. Most reconstructed water level time series have a bi‐monthly or higher frequency. Given that ICESat‐2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time‐varying lake levels and thus inform water resource management more broadly than existing methods. Key Points: Landsat images and laser altimeters were leveraged to reconstruct multi‐decadal lake levels of both large and small lakesReconstructed water levels were validated against observed levels on 342 global lakes with a median error of 0.66 mMost of the reconstructed lake level time series have a bi‐monthly or higher frequency [ABSTRACT FROM AUTHOR]

In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters. Plain Language Summary: Reservoir storage capacity is steadily lost due to sediment filling, which threatens freshwater supplies both now and in the future. Yet, lost reservoir storage capacities to sediment are largely unknown. Here, we develop a generic method to estimate capacity losses and reservoir sedimentation rates by leveraging remote sensing techniques. We tested on eight reservoirs across the central and western United States and found capacity losses and sedimentation rates vary across reservoirs. The proposed method offers a promising alternative to evaluate and predict capacity losses in reservoirs nationwide and globally, and thus supports effective water managements and planning for sustainable freshwater supplies in the future. Key Points: High‐resolution Sentinel‐2 images and daily in situ water levels were used to estimate reservoir sedimentation rates and capacity lossesEstimated reservoir sedimentation rates and storage capacity losses have a mean error of 0.05% yr−1 of full storage capacityPotential applications of this method to ungauged reservoirs are feasible with sub‐monthly level data from recent satellite altimeters [ABSTRACT FROM AUTHOR]

In the Upper Colorado River Basin (UCRB), there is a deep reliance on seasonal snowpack for maintenance of water resources. The term "snow drought" has recently emerged to describe periods of anomalously low snowpack. Unique seasonal patterns in precipitation and temperature that drive snow drought can have distinct hydrologic signatures, and these relationships have not been carefully studied in the UCRB. Here we examine snow drought with a new classification scheme using peak snow water equivalent (SWE) and the ratio of basin‐wide modeled peak SWE to accumulated (onset to peak) precipitation (SWE/P) that clusters snow drought years into three distinct groups—"warm," "dry," and "warm & dry"—that minimize within‐group variance. Over the period 1916–2018, we identify 14 warm years (P¯ = 160 mm; SWE/P¯ = 0.24), 24 dry years (P¯ = 117 mm; SWE/P¯ = 0.35), and 21 warm & dry years (P¯ = 94 mm; SWE/P¯ = 0.23). An elevation‐based analysis reveals two distinct patterns: warm snow droughts see severe SWE reductions primarily at lower (<2600 m) elevations (65% at lower elevations, 37% overall), whereas "dry" scenarios exhibit a consistent reduction across all elevations (39% overall). Using naturalized streamflow data, we also differentiate snow droughts by their earlier streamflow timing and decreased peakedness (warm: 7 days, 2%; dry: 7 days, 2%; warm & dry: 13 days, 5%). This research provides new insights into snow drought patterns relevant for regional water management. [ABSTRACT FROM AUTHOR]

The Niwot Ridge and Green Lakes Valley (NWT) long‐term ecological research (LTER) site collects environmental observations spanning both alpine and subalpine regimes. The first observations began in 1952 and have since expanded to nearly 300 available datasets over an area of 99 km2 within the north‐central Colorado Rocky Mountains that include hydrological (n = 101), biological (n = 79), biogeochemical (n = 62), and geographical (n = 56) observations. The NWT LTER database is well suited to support hydrologic investigations that require long‐term and interdisciplinary data sets. Experimentation and data collection at the NWT LTER are designed to characterize ecological responses of high‐mountain environments to changes in climate, nutrients, and water availability. In addition to the continuation of the many legacy NWT datasets, expansion of the breadth and utility of the NWT LTER database is driven by new initiatives including (a) a catchment‐scale sensor network of soil moisture, temperature, humidity, and snow‐depth observations to understand hydrologic connectivity and (b) snow‐albedo alteration experiments using black sand to evaluate the effects of snow‐disappearance on ecosystems. Together, these observational and experimental datasets provide a substantial foundation for hydrologic studies seeking to understand and predict changes to catchment and local‐scale process interactions. [ABSTRACT FROM AUTHOR]

Seasonal water storage in high‐elevation alpine catchments are critical sources of water for mountainous regions like the western U.S. The spatial distribution of snow in these topographically complex catchments is primarily governed by orography, solar radiation, and wind redistribution. While the effect of solar shading is relatively consistent from year‐to‐year, the redistribution of snow due to wind is more variable – capable of producing snowpacks that have varying degrees of uniformity across these hydrologically‐important catchments. A reasonable hypothesis is that a warmer climate will cause snowfall to become more dense (i.e. wetter and heavier), possibly leading to less wind redistribution and thus produce a more uniformly distributed snowpack across the landscape. In this study, we investigate the role of increasingly uniform spatial snowpack distributions on streamflow generation in the Green Lakes Valley Niwot Ridge Long Term Ecological Research station, within the headwaters of the Boulder Creek watershed in Colorado. A set of idealized hydrologic simulation experiments driven by reconstructed snowpacks spanning 2001–2014 show that more a more uniform spatial snowpack distribution leads to an earlier melt‐out of 31 days on average and tends to produce less total streamflow, with maximum decreases as large as 7.5%. Isolating the role of snowpack heterogeneity from melt‐season precipitation, we find that snowpack uniformity reduces total streamflow by as much as 13.2%. Reductions in streamflow are largely explained by greater exposure to solar radiation in the uniformly distributed case relative to a more heterogeneous snowpack, with this exposure driving shifts towards earlier snowmelt and changes in soil water storage. Overall, we find that the runoff efficiency from shallower snowpacks is more sensitive to the effects of uniformity than deeper snowpacks, which has potential implications for a warming climate where shallower snowpacks and enhanced sensitivities may be present. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall‐triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall‐triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select from a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide‐triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity–duration thresholds. The average intensity, peak intensity, duration, and NOAA‐Atlas return periods are compared ahead of 177 reported landslides across the continental United States and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real‐time and post‐processed satellite (IMERG), radar (MRMS), and gauge‐based (NLDAS‐2). Landslide‐triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296 mm with an average range of 51 mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8 mm/h and as much as 57 mm/h. The two products more reliant upon ground‐based observations (MRMS and NLDAS‐2) performed better at identifying landslides according to published intensity–duration storm thresholds, but all products exhibited hit ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually verified landslide locations. We recommend practitioners consider low‐latency products like MRMS for investigating landslides, given their near‐real time data availability and good performance in detecting landslides. Practitioners would be well‐served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms. [ABSTRACT FROM AUTHOR]

Future reduction in mountain snowpack due to anthropogenic climate change poses a threat to many snow‐adapted species worldwide. Mountain topography exerts a strong control on snowpack not only due to elevation but also through the effect of slope and aspect on the surface energy balance. We develop high‐resolution projections of snowpack in order to provide improved, physically based estimates of the spatial distribution of future snowpack to inform species conservation efforts for the wolverine (Gulo gulo) in two study areas in the Rocky Mountains: one in Montana with known den sites and one in Colorado with recent wolverine activity and potential for reintroduction. Here we assess springtime snowpack loss in actual and potential denning areas under five future climate scenarios for the mid‐21st century. Snowpack in April and May is likely to persist into the mid‐21st century in the upper half of current denning elevations in all but the warmest future climate scenario, while large declines are projected for the lower half of the denning elevations. We gain new insight into the influence of topographical aspect on future snowpack and quantify the potential for enhanced snow persistence on north and east facing slopes under future scenarios that is only revealed in simulations where terrain slopes are resolved. Plain Language Summary: Climate change and its effect on snow are a threat to high mountain ecosystems and species worldwide. The future of mountain snowpack is complex, with multiple drivers, and with a strong elevation dependence. What has received much less attention is the dependence on topographical aspect—how will the snowpack on north facing versus south facing slopes respond differently under climate change. In this paper we develop snow projections motivated by a conservation issue for the wolverine: the future of the springtime snowpack at elevations of observed and potential wolverine denning for two study areas in the Rocky Mountains by the mid‐21st century. While there is significant snowpack loss in the lower half of denning elevations, the upper denning elevations retain springtime snowpack, supporting conservation actions through midcentury in these regions. Key Points: Early spring snowpack declines sharply in the lower half of wolverine den elevations studied but persists through midcentury higher upSpringtime snowpack is less affected by climate change on north and east facing slopes leading to potential for climate refugiaHigh‐resolution modeling that resolves topographical aspect may provide useful information to support conservation decisions [ABSTRACT FROM AUTHOR]

Climate‐change impacts are among the most serious and complex challenges facing society, affecting both natural and social systems. Addressing these requires a new paradigm of interdisciplinary collaboration which incorporates tools, techniques, and insights from across the social, natural, and engineering sciences. Yet, a wide range of intrinsic and extrinsic hurdles need to be overcome to conduct successful, integrated interdisciplinary research. The results of a bibliometric analysis and survey of early to mid‐career scientists from 56 countries who were involved with the interdisciplinary DISsertations initiative for the advancement of Climate Change ReSearch (DISCCRS) emphasize the particular challenges faced by early career researchers. Survey respondents perceive conflict between the need for interdisciplinary climate‐change research and its potential detriment to career advancement. However, participation in interventions for early career scientists, such as networking and training symposia, had both perceived and measurable impacts on the likelihood of engagement in climate‐centric interdisciplinary research. Respondents also ranked alternative mechanisms for encouraging incorporation of interdisciplinary science at early career stages, prioritizing funding of interdisciplinary seed grants, fellowships, and junior faculty networks, interdisciplinary teamwork and communication training, and interdepartmental symposia. To this we add the suggestion that interdisciplinarity be incorporated into tenure and promotion evaluations through the use of exploratory science mapping tools. Despite the need to foster interdisciplinary research and the availability of multiple prospective solutions, there remain expansive structural challenges to its promotion and recognition which, unless collectively addressed, will continue to hinder its potential growth and application to climate‐change science. This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice Integrated Assessment of Climate Change > Methods of Integrated Assessment of Climate Change [ABSTRACT FROM AUTHOR]

Hydrological simulation of states and fluxes represents a viable approach for predicting the occurrence of extreme events and diagnosing their underlying causes. In recent decades, databases of soil textural properties have been published that provide a basis for estimating hydrologic model parameters. The aim of this study is to quantify the impact of soil textural assumptions on hydrologic states and fluxes: an often overlooked source of hydrologic modelling uncertainty. Mesoscale hydrologic model simulations driven by the digital general soil map of the USA (STATSGO2) soil database (1 : 250 000) were compared with those using the Food and Agriculture Organization (FAO)-based harmonized world soil database (1 : 5 000 000) over the Mississippi river basin. Annual and seasonal differences in modelled water fluxes and states were explored, in addition to major historical flood and drought events during 1988, 1993 and 2012. Model simulations using STATSGO2 consistently led to greater separation between fast and slow runoff responses (by approximately 24-39%) and, generally, more extreme responses to historic flood and drought events. Simulated soil moisture and runoff from the coarser harmonized world soil database produced greater persistence by roughly 0.10 autocorrelation points at 1-, 3- and 6-month lags, due to larger active water storage capacity. Overall, the choice of soil database altered the partitioning of precipitation between evapotranspiration and runoff, and affected the correlation structure between forcing and modelled fluxes by up to 0.2 points. As compared with other decisions needed to make hydrologic predictions, this analysis demonstrates that choice of soil textural properties for a single model can be an appreciable source of uncertainty and therefore warrants careful consideration. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In recent decades, computational hydrology has trended toward higher-resolution distributed models of the land surface. The accuracy of these models is limited, by uncertainty in distributed precipitation forcings. In this research, different precipitation distribution schemes were compared through inter-station transfer experiments, as well as within a distributed hydrologic model applied at ≤150 m resolution over four study catchments in complex terrain. Distributed precipitation estimates were derived using multiplicative spatial scaling (MSS) and map-based precipitation (MBP) techniques including both climatological and time-varying spatial information from a range of native spatial resolutions (500 m-4 km). The primary interest was to evaluate a novel application of satellite-based snow water equivalent (SWE) reconstruction (RSWE) as a surrogate for cold season precipitation against a common source of spatial precipitation information: the Parameter-elevation Regressions on Independent Slopes Model (PRISM). An elevation-based orographic precipitation gradient and simple inverse-distance interpolation were also included as a baseline. For the case of RSWE, MSS was very sensitive to differences between observed SWE and reconstructed SWE, producing positive biases in the catchment water balance. Over 12 year simulations, daily streamflow correlations from the uncalibrated model were highest for RSWE when adjusted for accumulation-season sublimation, and for monthly PRISM, both achieving R=0.8, where the former performed better during anomalous years for both MSS and MBP. Annual water balance ratios were much more sensitive to the choice of distribution scheme, with large overestimates, > 30%, for RSWE products using the MSS techniques versus MBP (< 15%). Overall, PRISM performed best using MSS, while RSWE performed best using MBP. [ABSTRACT FROM AUTHOR]

[i] We describe a regional parameter estimation scheme for the unified land model developed using a set of 220 river basins (10²-104 km²) distributed across the conterminous United States. We evaluate predictive relationships between geographically varying catchment features and the model's soil parameters using principal components analysis. In addition to commonly used catchment descriptors (meteorological, geomorphic, and land-cover characteristics), we used satellite remote-sensing products and the United States Geologic Survey Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II) database. In a series of regionalization experiments, we contrast the more conventional procedure of using locally optimized parameters as predict ands, with an approach that searches for zonally representative parameter values, using limited additional simulations. Parameters were evaluated through hydrologic model simulations in which daily flows were compared with observations over a 20 year period. We show that the penalty in streamflow prediction skill for using zonal parameters at a given basin (i.e., locally) is comparatively smaller than the penalty for using local parameters zonally. Regionalizations using zonal parameters and local catchment descriptors had the best model performance for both training and validation periods. Finally, we investigate the potential for transferring parameters globally by repeating the regionalization using only catchment attributes derived from globally available data and show that for the United States, model performance is only slightly worse than when U.S.-specific data area used. [ABSTRACT FROM AUTHOR]

Global water systems are facing unprecedented pressures, including climate change‐driven drought and escalating flood risk, environmental contamination, and over allocation. Water management and governance typically lack integration across spatial scales, including relationships between surface and ground water systems. They also routinely ignore connectivity across temporal scales, including the need for intergenerational water planning. As a global and interdisciplinary group of scientists, we seek to highlight how power and scale dynamics influence and determine water outcomes. We argue that attending to complex water systems challenges requires understanding the function and influence of power at different temporal and spatial scales. Building this understanding is key to designing multi‐scalar, reflexive, and pluralistic policy solutions that avoid ineffective or unintended outcomes. We use a co‐learning process to reveal important lessons for the challenge of interdisciplinary research and set a pluralist agenda for understanding power and scale in future water governance.This article is categorized under: Human Water > Water Governance Human Water > Water as Imagined and Represented Human Water > Methods [ABSTRACT FROM AUTHOR]