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The year 2015 marks the 50th anniversary of Water Resources Research (WRR), which was founded in 1965. More than 15,000 papers have been published in WRR since its inception, and these papers have been cited more than 430,000 times. The history of hydrology and the water sciences are also reflected in WRR, which has served as a premier publication outlet and instigator of scientific growth over the last 50 years. The legacy of WRR provides a strong scientific foundation for the hydrology community to rise to the challenges of sustainable water resources management in a future where dramatic environmental change and increasing human population are expected to stress the world's water resources from local to global scales. [ABSTRACT FROM AUTHOR]

A paper has recently been published in Water Resources Research, which explored the influence of smallholder's perceptions regarding water availability on crop choice and water allocation through social‐hydrological modeling. The present paper attempts to discuss and comment on that paper. The authors have developed a social‐hydrological model, behind which some assumptions of the hydrological aspects (including the omission of deep percolation and the complete separation of transpiration and evaporation for the growing season and the off season) are not well justified. Further, a few parameter values seem to be mistakenly presented by the authors. Most importantly, the equation used for calculating the irrigation depth for crop 1 was incorrectly derived, which could lead to apparent biases in the simulated soil moisture, drought memories, and crop fractions. Hence, the key findings of Kuil et al. (2018, https://doi.org/10.1002/2017WR021420) might be associated with large uncertainties and should be interpreted with much caution. Key Points: The present paper attempts to discuss and comment on a paper recently published in Water Resources ResearchSome assumptions of the hydrological aspects within the social‐hydrological model are not well justifiedThe irrigation depth for crop 1 was incorrectly estimated, which could bring significant biases to the simulations [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these "gage‐constrained" discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science. Plain Language Summary: The Surface Water and Ocean Topography (SWOT) satellite mission was launched on 15 December 2022. SWOT is designed to produce estimates of river discharge on many rivers where no in situ discharge measurements are currently available. This paper describes how SWOT discharge estimates will be created, and their expected accuracy. SWOT discharge will be estimated using simple flow laws that combine SWOT measurements of river water elevation above sea level, river width, and river slope, with ancillary data such as river bathymetry. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by a systematic bias. Temporal variations in river discharge time series are expected to be estimated within 15% for nearly all reaches, thus capturing the response of river discharge to rainfall and snowmelt events, including in basins that are currently ungaged, and providing a new capability for scientists to better track the flows of freshwater water through the Earth system. Key Points: The Surface Water and Ocean Topography satellite mission is designed to enable the estimation of discharge for global rivers wider than 100 mWhen unconstrained by in situ data, discharge uncertainty is expected to be <30% for most reaches, and to be dominated by systematic biasWe expect discharge temporal variations to be estimated to within 15% for nearly all reaches globally [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the Assembly of the International Association of Hydrological Sciences-IAHS (within the Assembly of the International Union of Geodesy and Geophysics-IUGG) in Prague in June 2015. This event was a follow-up of a similar meeting held in July 2013 in Gothenburg (as reported by Blöschl et al. [2014]). These meetings enable the group of editors to review the current status of the journals and the publication process, and share thoughts on future strategies. Journals were represented in the 2015 meeting through their editors, as shown in the list of authors. The main points on fostering innovation and improving impact assessment in journal publications in hydrology are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

The findings of hydrological modeling studies depend on which model was used. Although hydrological model selection is a crucial step, experience suggests that hydrologists tend to stick to the model they have experience with, and rarely switch to competing models, although these models might be more adequate given the study objectives. To gain quantitative insights into model selection, we explored the use of seven rainfall‐runoff models based on the abstract of 1,529 peer‐reviewed papers published between 1991 and 2018. The models selected were the Hydrologiska Byråns Vattenbalansavdelning model (HBV), the Variable Infiltration Capacity model (VIC), the mesoscale Hydrological model (mHM), the TOPography‐based hydrologic model (TOPMODEL), the Precipitation Runoff Modelling System (PRMS), the Génie Rural model à 4 paramètres Journaliers (GR4J), and the Sacramento soil moisture accounting model. We provide quantitative evidence of regional preferences in model use across the world and demonstrate that specific models are consistently preferred by certain institutes. Model attachment is particularly strong. In ~74% of the studies, the model selected can be predicted solely based on the affiliation of the first author. The influence of adequacy on the model selection process is less clear. Our data reveal that each model is used across a wide range of purposes, landscapes, and temporal and spatial scales (i.e., as a model of everything and everywhere). Model intercomparisons can provide guidance for model selection and improve model adequacy, but they are still rare (because each model must usually be setup individually) and the insights they provide are currently limited (because they are rarely controlled experiments). We suggest that moving from fixed‐structure models to modular modeling frameworks (master templates for model generation) can overcome these issues, enable a more collaborative and responsive model development environment, and result in improved model adequacy. Key Points: Text mining of 1,500+ peer‐reviewed articles enabled us to relate hydrological models to institutes, regions, and research topicsWe provide evidence of regional preferences in model use across the world and underline the decisive influence of legacy on model selectionWe reflect on current tendencies and future model development strategies and advocate for a broader use of modular modeling frameworks [ABSTRACT FROM AUTHOR]

We present an overview of the contributions collected to celebrate the fiftieth anniversary of Water Resources Research along with a critical discussion of the legacy and perspectives for the science of hydrology in the 21st century. This collection of papers highlights exciting pathways to the future of water sciences. New monitoring and modeling techniques and increasing opportunities for data and knowledge sharing from hydrological research will provide innovative means to improve water management and to ensure a sustainable development to society. We believe that this set of papers will provide valuable inspiration for future hydrologists, and will support the intensification of international cooperation among scientists. [ABSTRACT FROM AUTHOR]

Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation. Key Points: Weighted modified Fractional Gaussian Noise model improves spatial correlations without significantly sacrificing temporal correlationThe method searches for an optimal convex combination of the spatial and temporal correlation matrices according on the user's priorityOur results in several Chilean basins demonstrate that Weighted‐modified Fractional Gaussian Noise (WmFGN) represents a significant improvement over the mFGN in preserving correlations [ABSTRACT FROM AUTHOR]

The basic structure of the scientific method-at least in its idealized form-is widely championed as a recipe for scientific progress, but the day-to-day practice may be different. Here, we explore the spectrum of current practice in hypothesis formulation and testing in hydrology, based on a random sample of recent research papers. This analysis suggests that in hydrology, as in other fields, hypothesis formulation and testing rarely correspond to the idealized model of the scientific method. Practices such as 'p-hacking' or 'HARKing' (Hypothesizing After the Results are Known) are major obstacles to more rigorous hypothesis testing in hydrology, along with the well-known problem of confirmation bias-the tendency to value and trust confirmations more than refutations-among both researchers and reviewers. Nonetheless, as several examples illustrate, hypothesis tests have played an essential role in spurring major advances in hydrological theory. Hypothesis testing is not the only recipe for scientific progress, however. Exploratory research, driven by innovations in measurement and observation, has also underlain many key advances. Further improvements in observation and measurement will be vital to both exploratory research and hypothesis testing, and thus to advancing the science of hydrology. [ABSTRACT FROM AUTHOR]

Field hydrology is on the decline. Meanwhile, the need for new field-derived insight into the age, origin and pathway of water in the headwaters, where most runoff is generated, is more needed than ever. Water Resources Research (WRR) has included some of the most influential papers in field-based runoff process understanding, particularly in the formative years when the knowledge base was developing rapidly. Here we take advantage of this 50th anniversary of the journal to highlight a few of these important field-based papers and show how field scientists have posed strong and sometimes outrageous hypotheses-approaches so needed in an era of largely model-only research. We chronicle the decline in field work and note that it is not only the quantity of field work that is diminishing but its character is changing too: from discovery science to data collection for model parameterization. While the latter is a necessary activity, the loss of the former is a major concern if we are to advance the science of watershed hydrology. We outline a vision for field research to seek new fundamental understanding, new mechanistic explanations of how watershed systems work, particularly outside the regions of traditional focus. [ABSTRACT FROM AUTHOR]

Inspired by the work of Newton, Darwin, and Wegener, this paper tracks the drivers and dynamics that have shaped the growth of hydrological understanding over the last century. On the basis of an interpretation of this history, the paper then speculates about what kind of future is in store for hydrology and how we can better prepare for it. The historical narrative underpinning this analysis indicates that progress in hydrological understanding is brought about by changing societal needs and technological opportunities: new ideas are generated by hydrologists through addressing societal needs with the technologies of their time. We suggest that progress in hydrological understanding over the last century has expressed itself through repeated cycles of euphoria and disenchantment, which have served as stimuli for the progress. The progress, for it to happen, also needed inspirational leaders as well as a supportive scientific community that provided the backdrop to major advances in the field. The paper concludes that, in a similar way to how Newton, Darwin, and Wegener conducted their research, hydrology too can benefit from synthesis activities aimed at 'connecting the dots.' [ABSTRACT FROM AUTHOR]

Water Resources Research published nine commentaries in the AGU 'Science is Essential' collection. The goal of these papers is to celebrate the advances in hydrologic science, to show how hydrologic science is essential for society, and to illustrate how hydrologic science has influenced policies. Here we provide a brief introduction to these papers, to encourage you to explore them in full. [ABSTRACT FROM AUTHOR]

The Surface Water Ocean Topography (SWOT) mission will launch in early 2022 to provide the first global inventory of terrestrial surface water. Although SWOT is primarily a research mission with key science objectives in both the oceanography and hydrology domains, SWOT data are expected to have application potential to address many societal needs. To identify SWOT applications, prepare for the use of SWOT data, and quantify SWOT impacts prior to launch, realistic proxy SWOT observations with representative measurement errors are required. This paper provides a step‐by‐step description of two methods for deriving proxy SWOT water surface elevations (WSEs) from an Observing System Simulation Experiment (OSSE) using the Weather Research and Forecasting hydrological extension package (WRF‐Hydro). The first, a basic method, provides a simple and efficient way to sample WRF‐Hydro output according to the SWOT orbit and add random white noise to simulate measurement error, similar to many previous approaches. An alternate method using the Centre National d'Etudes Spatiales (CNES) Large‐Scale SWOT Hydrology Simulator accounts for additional sources of measurement error and produces output in formats comparable to that expected from official SWOT products. The basic method is ideal for river hydrology applications in which a full representation of SWOT measurement errors and spatial resolution is unnecessary, whereas the CNES simulator approach is better‐suited for more rigorous scientific studies that require a comprehensive error budget. Plain Language Summary: The Surface Water Ocean Topography (SWOT) mission is a multinational satellite mission that is expected to launch in 2022 to observe global rivers, lakes, reservoirs, and wetlands. As the first of its kind to measure inland water, SWOT is expected to address many societal needs. To identify SWOT applications, prepare for the use of SWOT data, and quantify SWOT impacts prior to launch, realistic sample SWOT observations are needed. This paper provides a step‐by‐step description for deriving proxy SWOT measurements using a hydrologic model and a SWOT observation simulator. Key Points: Two approaches for generating proxy SWOT water surface elevations from a hydrology modeling framework are providedProxy SWOT data are well‐suited for societal applications and scientific studies prior to and following launchThe Large‐Scale SWOT Hydrology Simulator provides realistic proxy SWOT data for error budget studies [ABSTRACT FROM AUTHOR]

Because of the possibility of getting the right answers for the wrong reasons, the predictive performance of a complex systems model is not by itself a reliable indicator of hypothesis quality for the purposes of scientific learning about processes. The predictive performance of a structurally adequate model should be an emergent property of its functional performance. In this context, any Pareto trade‐off between measures of predictive performance versus functional performance indicates process‐level error in the model; this trade‐off, if it exists, indicates that the model's predictions are right for the wrong functional reasons. This paper demonstrates a novel concept based on information theory that is capable of attributing observed errors to specific processes. To demonstrate that the concept and method hold true for models and observations of real systems, we employ a minimal single‐parameter‐variation sensitivity analysis using a sophisticated ecohydrology model, MLCan, for a well‐monitored field site (Bondville IL Ameriflux Soybean). We identify both functional and predictive error in MLCan, and also evidence of the hypothesized tradeoffs between the two. This trade‐off indicates structural error within MLCan. For example, the sensible heat flux process can be calibrated to achieve good predictive performance at the cost of poor functional performance. In contrast, we find little structural error for processes driven by solar radiation, which appear "right for the right reasons." This method could be applied broadly to pinpoint process error and structural error in a wide range of system models, beyond the ecohydrological scope demonstrated here. Key Points: Information theory can attribute model error to structural and process‐level sourcesPareto‐style trade‐offs exist between functional and predictive performanceThe MLCan ecohydrology model demonstrates both well‐posed and ill‐posed trade‐offs between different processes [ABSTRACT FROM AUTHOR]

The special section on socio‐hydrology is one of most successful in Water Resources Research, containing more than 30 articles, with many more submitted. As we would hope for a special section, this is just the start, and we continue to receive a flow of papers that either explicitly describe themselves as socio‐hydrology or are addressing the challenges of coupled human and water systems. In this Editorial, we reflect upon the scope and nature of those articles and make some tentative suggestions about future directions and the realistic ambition for socio‐hydrology. Key Points: This Commentary is adapted from the invited talk by Jim Hall at the 2018 AGU Fall Meeting in Washington DCThe current state of socio-hydrology is reviewed from a personal perspective by one of the editors of Water Resources ResearchThe review is based upon the recent Special Section on socio-hydrology in WRR and other influential publications on the topic [ABSTRACT FROM AUTHOR]

The paper presents a thermodynamic basis for water balance partitioning at the catchment scale, through formulation of flux-force relationships for the constituent hydrological processes, leading to the derivation of optimality conditions that satisfy the principle of Maximum Entropy Production (MEP). Application of these optimality principles at three different time scales leads to the derivation of water balance equations that mimic widely used, empirical models, i.e., Budyko-type model over long-term scale, the 'abcd' model at monthly scale, and the SCS model at the event scale. The applicability of MEP in each case helps to draw connections between the water balances at the three different time scales, and to demonstrate a common thermodynamic basis for the otherwise empirical water balance models. In particular, it is concluded that the long time scale Budyko-type model and the event scale SCS model are both special cases of the monthly 'abcd' model. [ABSTRACT FROM AUTHOR]

The article presents the authors' reply to S. Neuman's comments which advocates the need for an adequate hydrogeological characterization of aquifers. It provides a brief summary of their work that was overlooked by Neuman's comment which include a critical reexamination of the field data collected by L.W. Gelhar et al especially those referred to as of medium or low reliability and the addition of new data what were again screened for reliability.

This paper presents a two‐dimensional shallow water equations code coupled with a physically based erosion model, able to predict the opening and evolution of breaches forming in levees built with either cohesive or noncohesive material. The bottom elevation change is evaluated using an excess shear‐stress equation, which accounts for the hydrodynamic conditions and for the material characteristics. The proposed model modifies the local topography at runtime wherever the levee is overtopped without having to predefine the position and shape of the breach. The model is implemented in CUDA programming language, so that simulations can be run on graphics processing units, guaranteeing fast execution times even for high‐resolution meshes and large domains. The validation is performed based on several experimental tests, and numerical predictions are in good agreement with the measurements. The strengths and weaknesses of the proposed approach are also discussed by comparison with a sediment transport model based on the Exner equation: While the latter gives good results only for breaches forming in levees built with noncohesive material, the proposed model can also be applied to cohesive embankments. The application to a historical flood event is also presented, showing that the model can effectively be employed for real field simulations also in the case of multiple breaches. Key Points: The paper presents a 2D SWE model able to simulate the opening of a levee breach due to overtopping and the subsequent floodingThe physically based erosion model allows predicting the breach evolution in levees made of cohesive or noncohesive materialThe model can be applied to real cases, thanks to the fast execution times guaranteed by the GPU parallelization of computations [ABSTRACT FROM AUTHOR]

The first issue of WRR appeared eight years after the launch of Sputnik, but by WRR's 25th anniversary, only seven papers that used remote sensing had appeared. Over the journal's second 25 years, that changed remarkably, and remote sensing is now widely used in hydrology and other geophysical sciences. We attribute this evolution to production of data sets that scientists not well versed in remote sensing can use, and to educational initiatives like NASA's Earth System Science Fellowship program that has supported over a thousand scientists, many in hydrology. We review progress in remote sensing in hydrology from a water balance perspective. We argue that progress is primarily attributable to a creative use of existing and past satellite sensors to estimate such variables as evapotranspiration rates or water storage in lakes and reservoirs and to new and planned missions. Recent transforming technologies include the Gravity Recovery and Climate Experiment (GRACE), the European Soil Moisture and Ocean Salinity (SMOS) and U.S. Soil Moisture Active Passive (SMAP) missions, and the Global Precipitation Measurement (GPM) mission. Future missions include Surface Water and Ocean Topography (SWOT) to measure river discharge and lake, reservoir, and wetland storage. Measurement of some important hydrologic variables remains problematic: retrieval of snow water equivalent (SWE) from space remains elusive especially in mountain areas, even though snow cover extent is well observed, and was the topic of 4 of the first 5 remote sensing papers published in WRR. We argue that this area deserves more strategic thinking from the hydrology community. [ABSTRACT FROM AUTHOR]

Global hydrology has come a long way since the first introduction of the primitive land surface model of Manabe (1969) and the declaration of the 'Emergence of Global Hydrology' by Eagleson (1986). Hydrological submodels of varying complexity are now part of global climate models, of models calculating global terrestrial carbon sequestration, of earth system models, and even of integrated assessment models. This paper reviews the current state of global hydrological modeling, discusses past and recent developments, and extrapolates these to future challenges and directions. First, established domains of global hydrological model applications are discussed, in terms of societal and science questions posed, the type of models developed, and recent advances therein. Next, a genealogy of global hydrological models is given. After reviewing recent efforts to connect model components from different domains, new domains are identified where global hydrology is now starting to become an integral part of the analyses. Finally, inspired by these new domains of application, persistent and emerging challenges are identified as well as the directions global hydrology is likely to take in the coming decade and beyond. [ABSTRACT FROM AUTHOR]

A theoretical analysis of transport in a controlled hydrologic volume, inclusive of two willow trees and forced by erratic water inputs, is carried out contrasting the experimental data described in a companion paper. The data refer to the hydrologic transport in a large lysimeter of different fluorobenzoic acids seen as tracers. Export of solute is modeled through a recently developed framework which accounts for nonstationary travel time distributions where we parameterize how output fluxes (namely, discharge and evapotranspiration) sample the available water ages in storage. The relevance of this work lies in the study of hydrologic drivers of the nonstationary character of residence and travel time distributions, whose definition and computation shape this theoretical transport study. Our results show that a large fraction of the different behaviors exhibited by the tracers may be charged to the variability of the hydrologic forcings experienced after the injection. Moreover, the results highlight the crucial, and often overlooked, role of evapotranspiration and plant uptake in determining the transport of water and solutes. This application also suggests that the ways evapotranspiration selects water with different ages in storage can be inferred through model calibration contrasting only tracer concentrations in the discharge. A view on upscaled transport volumes like hillslopes or catchments is maintained throughout the paper. [ABSTRACT FROM AUTHOR]

This work advances a unified approach to process-based hydrologic modeling, which we term the 'Structure for Unifying Multiple Modeling Alternatives (SUMMA).' The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty. [ABSTRACT FROM AUTHOR]

The article discusses the status and future of hydrological journal publications. It notes the major increase in the number of submissions which challenged editors to find a sufficient number of competent reviewers. It also cites other developments and changes in the way hydrological science is undertaken including complexity of models and data sets, shift of journals towards open access publication, and the increasing pressure to publish which led to increasing number of cases of plagiarism.

While current efforts to model sociohydrologic phenomena provide crucial insight, critics argue that these do not fully reflect the complexity one observes empirically in the real world. The policy sciences, with its focus on the interaction between human agency and the institutions that constrain public choice, can complement such efforts by providing a narrative approach. This paper demonstrates this complementarity by investigating the idea of resilience in a community response to floods. Using the quantitative Q methodology, we trace the dynamics of a common sociohydrologic hypothesis--the "memory effect" and how it decreases vulnerability and, more crucially, the instances when such memory effects do not obtain. Our analysis of a floodprone maladaptive community in Assam, India, finds four distinct narrative types: the Hardened Preparer, the Engineer, Discontent, and the Pessimist. This paper put forward an explicitly sociohydrological conception of resilience which takes into account the role of sociological indicators such as narrative types and perceptions. Such contextual understandings and narrative types can form the basis of generic resilience indicators which complement the anticipated outcomes of sociohydrologic models generally. [ABSTRACT FROM AUTHOR]

The most important drawback of standard fuzzy arithmetic is unrealistic accumulation of input uncertainties which results in divergence of fuzzy outputs. Some of the currently available methods of simulating fuzzy systems provide the results which tend to naive large values after several time steps of the system simulation. In this paper, a new fuzzy arithmetic operator based on fuzzy extension principle has been proposed for simulation and assessment of uncertainty in hydrological systems. Implementing the concept of fuzzy approximate reasoning in the proposed approach in this study represents acceptable behavior in uncertainty propagation from the parameters and structures of the models to the outputs. To show the efficiency of the proposed fuzzy arithmetic operator in the context of hydrologic modeling, two nonlinear monthly water balance models have been examined and their outputs have been compared with the results obtained by standard fuzzy arithmetic and the Vertex method. One small humid basin in France and a middle size basin in a semiarid region in Iran have been the case studies of this research. In this paper, the lower and upper bounds and the most frequent values of the model parameters inferred from the sampling-simulation procedure have been used to define triangular fuzzy membership functions. Three statistical indicators have been used to evaluate efficiency of the methods based on the bracket observations and coverage of the uncertainty bounds. The estimated values of these indicators have shown that both Vertex and the proposed methods outperform standard fuzzy arithmetic. Also, the proposed method has provided better or roughly equal efficiencies compared with Vertex method over both basins. [ABSTRACT FROM AUTHOR]

The paper compares a theory for immiscible displacement based on distinguishing percolating and nonpercolating fluid parts with experimental observations from multistep outflow experiments. The theory was published in 2006 in Physica A, volume 371, pages 209-225; the experiments were published in 1991 in Water Resources Research, volume 27, pages 2113. The present paper focuses on hysteretic phenomena resulting from repeated cycling between drainage and imbibition processes in multistep pressure experiments. Taking into account, the hydraulic differences between percolating and nonpercolating fluid parts provides a physical basis to predict quantitatively the hysteretic phenomena observed in the experiment. While standard hysteretic extensions of the traditional theory are nonlocal in time the theory used in this paper is local in time. Instead of storing the pressure and saturation history, it requires only the current state of the system to reach the same quantitative agreement. [ABSTRACT FROM AUTHOR]

Key to model development is the selection of an appropriate representational system, including both the representation of what is observed (the data), and the formal mathematical structure used to construct the input-state-output mapping. These choices are critical, because they completely determine the questions we can ask, the nature of the analyses and inferences we can perform, and the answers we can obtain. Accordingly, a representation that is suitable for one kind of investigation might be limited in its ability to support some other kind. Arguably, how different representational approaches affect what we can learn from data is poorly understood. This paper explores three representational strategies as vehicles for understanding how catchment scale hydrological processes vary across hydro-geo-climatologically diverse Chile. Specifically, we test a lumped water-balance model (GR4J), a data-based dynamical systems model (LSTM), and a data-based regression tree model (Random Forest). Insights were obtained regarding system memory encoded in data, spatial transferability by use of surrogate attributes, and informational deficiencies of the data set that limit our ability to learn an adequate input-output relationship. As expected, each approach exhibits specific strengths, with LSTM providing the best characterization of dynamics, GR4J being the most robust under informationally deficient conditions, and Random Forest regression-tree method being most supportive of interpretation. Overall, the contrasting nature of the three approaches suggests the value of adopting a multi-representational framework to more fully extract information from the data and, by doing so, find information that better facilities the goals of robust prediction and improved understanding, ultimately supporting enhanced scientific discovery. [ABSTRACT FROM AUTHOR]

A seepage face is a nonlinear dynamic boundary that strongly affects pressure head distributions, water table fluctuations, and flow patterns. Its handling in hydrological models, especially under complex conditions such as heterogeneity and coupled surface/subsurface flow, has not been extensively studied. In this paper, we compare the treatment of the seepage face as a static (Dirichlet) versus dynamic boundary condition, we assess its resolution under conditions of layered heterogeneity, we examine its interaction with a catchment outlet boundary, and we investigate the effects of surface/subsurface exchanges on seepage faces forming at the land surface. The analyses are carried out with an integrated catchment hydrological model. Numerical simulations are performed for a synthetic rectangular sloping aquifer and for an experimental hillslope from the Landscape Evolution Observatory. The results show that the static boundary condition is not always an adequate stand-in for a dynamic seepage face boundary condition, especially under conditions of high rainfall, steep slope, or heterogeneity; that hillslopes with layered heterogeneity give rise to multiple seepage faces that can be highly dynamic; that seepage face and outlet boundaries can coexist in an integrated hydrological model and both play an important role; and that seepage faces at the land surface are not always controlled by subsurface flow. The paper also presents a generalized algorithm for resolving seepage face outflow that handles heterogeneity in a simple way, is applicable to unstructured grids, and is shown experimentally to be equivalent to the treatment of atmospheric boundary conditions in subsurface flow models. [ABSTRACT FROM AUTHOR]

Training image-based geostatistical methods are increasingly popular in groundwater hydrology even if existing algorithms present limitations that often make real-world applications difficult. These limitations include a computational cost that can be prohibitive for high-resolution 3-D applications, the presence of visual artifacts in the model realizations, and a low variability between model realizations due to the limited pool of patterns available in a finite-size training image. In this paper, we address these issues by proposing an iterative patch-based algorithm which adapts a graph cuts methodology that is widely used in computer graphics. Our adapted graph cuts method optimally cuts patches of pixel values borrowed from the training image and assembles them successively, each time accounting for the information of previously stitched patches. The initial simulation result might display artifacts, which are identified as regions of high cost. These artifacts are reduced by iteratively placing new patches in high-cost regions. In contrast to most patch-based algorithms, the proposed scheme can also efficiently address point conditioning. An advantage of the method is that the cut process results in the creation of new patterns that are not present in the training image, thereby increasing pattern variability. To quantify this effect, a new measure of variability is developed, the merging index, quantifies the pattern variability in the realizations with respect to the training image. A series of sensitivity analyses demonstrates the stability of the proposed graph cuts approach, which produces satisfying simulations for a wide range of parameters values. Applications to 2-D and 3-D cases are compared to state-of-the-art multiple-point methods. The results show that the proposed approach obtains significant speedups and increases variability between realizations. Connectivity functions applied to 2-D models transport simulations in 3-D models are used to demonstrate that pattern continuity is preserved. [ABSTRACT FROM AUTHOR]

It is increasingly acknowledged that effective management of water resources requires a holistic understanding of the coevolving dynamics inherent in the coupled human-hydrology system. One of the fundamental information gaps concerns the sensitivity of coupled system feedbacks to various endogenous system properties and exogenous societal contexts. This paper takes a previously calibrated sociohydrology model and applies an idealized implementation, in order to: (i) explore the sensitivity of emergent dynamics resulting from bidirectional feedbacks to assumptions regarding (a) internal system properties that control the internal dynamics of the coupled system and (b) the external sociopolitical context; and (ii) interpret the results within the context of water resource management decision making. The analysis investigates feedback behavior in three ways, (a) via a global sensitivity analysis on key parameters and assessment of relevant model outputs, (b) through a comparative analysis based on hypothetical placement of the catchment along various points on the international sociopolitical gradient, and (c) by assessing the effects of various direct management intervention scenarios. Results indicate the presence of optimum windows that might offer the greatest positive impact per unit of management effort. Results further advocate management tools that encourage an adaptive learning, community-based approach with respect to water management, which are found to enhance centralized policy measures. This paper demonstrates that it is possible to use a place-based sociohydrology model to make abstractions as to the dynamics of bidirectional feedback behavior, and provide insights as to the efficacy of water management tools under different circumstances. [ABSTRACT FROM AUTHOR]

Global hydrological models (GHMs) have become an established tool to simulate water resources worldwide. Most of the GHMs are however uncalibrated and typically use a set of basic hydrological parameters, that could potentially lead to unrealistic projections of the terrestrial water cycle. The calibration of hydrological models is usually performed by using and comparing modeled to observed discharge. Accurate station data and reliable time series data of discharge are, however, often not available for many parts of the world and classic calibration approaches are therefore not feasible. In this paper, we aim to develop a new calibration approach that requires no additional data, is easy to implement, and substantially improves model performance, especially in regions where uncalibrated model performance is rather poor. This is achieved by using the Budyko framework, which provides a conceptual representation of the long‐term water and energy balance. We use a state‐of‐the‐art GHM and calibrate the model within nine river catchments of different sizes and characteristics. Since observed river discharge is available for these catchments, we are able to compare the Budyko‐based calibration approach to a classic discharge‐based calibration scheme and the uncalibrated model version. In all catchments, the Budyko‐based calibration approach decreases biases and increases model performance compared to the uncalibrated model version although performance improvements obtained through a classic calibration approach are greater. Nonetheless, a Budyko‐based calibration is a valuable, intermediate approach between use of a basic set of a priori hydrological parameters and classical calibration against discharge data. Key Points: Most global hydrological models are uncalibrated using predefined sets of basic hydrological parametersWe developed a simple calibration approach that requires no additional data and substantially improves model performanceThe new approach is based on the Budyko framework providing a conceptual representation of the long‐term water and energy balance [ABSTRACT FROM AUTHOR]

This paper introduces the Water Resources Research special section on Uncertainty Assessment in Surface and Subsurface Hydrology. Over the past years, hydrological literature has seen a large increase in the number of papers dealing with uncertainty. In this article, we present an overview of the different sources of uncertainty and the different types of problems associated with uncertainty assessment. It is argued here that clarity about which part of the large field of uncertainty research is addressed by a given research activity would already help guide discussions within the hydrological community. We present an introduction to the differences between the more classical frequentist approach to uncertainty and Bayesian approaches and between probabilistic and nonprobabilistic approaches. Bayesian approaches allow for inclusion of more subjective expert knowledge and would be more appropriate where less 'hard' data are available. Any underlying assumptions need to be made very clear to the end user. Finally, a brief classification of the articles of the special section is presented. [ABSTRACT FROM AUTHOR]

A large number of models in hydrology and climate sciences rely on multiple linear regression to explain the link between key variables. The relationship in the physical world may experiment sudden changes because of climatic, environmental, or anthropogenic perturbations. To deal with this issue, a Bayesian method of multiple changepoint detection in multiple linear regression is proposed in this paper. It is an adaptation of the recursion-based multiple changepoint method of Fearnhead (2005, 2006) to the classical multiple linear model. A new class of priors for the parameters of the multiple linear model is introduced, and useful formulas are derived that permit straightforward computation of the posterior distribution of the changepoints. The proposed method is numerically efficient and does not involve time consuming Monte-Carlo Markov Chain simulation as opposed to other Bayesian changepoint methods. It allows fast and straightforward simulation of the probability of each possible number of changepoints as well as the posterior probability distribution of each changepoint conditional on the number of changes. The approach is validated on simulated data sets and then compared to the methodology of Seidou et al. (2006) on two practical problems, as follows: (1) the changepoint detection in the multiple linear relationship between mean basin scale precipitation at different periods of the year and the summer-autumn flood peaks of the Broadback River located in Northern Quebec, Canada; and (b) the detection of trend variations in the streamflows of the Ogoki River located in the province of Ontario, Canada. [ABSTRACT FROM AUTHOR]

This paper introduces the special issue by first reviewing the cultural climate and the scientific ancestors that suggested title and contents. These are aimed at the core of the dynamic origins of natural form and function (or, as in often abused words, 'how nature works'), seen through the perspective of hydrologic research. An overview of the invited contributions follows, which includes a subjective explanation for the conceptual thread that joins them. Key to the contents of the invited papers is the future role of hydrology within the geosciences. Such a role is argued to be of growing importance owing, on one side, to the objective social and economic relevance of hydrologic matters (a fair distribution of water resources, flood and droughts among them) and, on the other, to the quality and the spatial/temporal extent of hydrologic data, which have been indeed instrumental in the radical change we recently experienced in the way we perceive and measure natural phenomena. [ABSTRACT FROM AUTHOR]

Snow surveys in two Arctic watersheds located in Alaska, USA, provide 32 years of spatially distributed snow water equivalent (SWE) and snow depth observations. Annual snow surveys from the Imnavait Creek (20,036 measurements from 1985 to 2017) and Upper Kuparuk River (5,804 measurements from 1997 to 2017) watersheds were conducted to capture end‐of‐winter snow accumulation. The average end‐of‐winter SWE in the Upper Kuparuk River watershed (102 ± 29 mm) is consistently less than the Imnavait Creek watershed (130 ± 34 mm) during the common period of record (1997–2017). The average end‐of‐winter SWE in both watersheds indicates a positive trend. Comparison of SWE records with cumulative solid precipitation measured at the Imnaviat [sic] SNOTEL site highlights the undercatch of gauge precipitation and difference in long‐term trends. In this paper, we present a historic overview of data collection, discuss data accuracy, and point out advantages and limitations associated with ground‐based snow measurements in remote Arctic locations. As new methods and techniques of measuring SWE and solid precipitation become available, the presented data set will provide a historic perspective for new observations and will quantitatively relate current or future snow conditions to those that have occurred since the late twentieth century. Key Points: Repeated snow surveys (25,840 measurements over 32 years) are conducted in two Arctic watersheds for hydrologic applicationsAnnual snow water equivalent range is 73–207 mm, with average SWE of 124 ± 34 mm in the Imnavait Creek watershed (1985–2017)Annual snow water equivalent range is 51–169 mm with average SWE of 102 ± 29 mm in the Upper Kuparuk River watershed (1997–2017) [ABSTRACT FROM AUTHOR]

Integrated, process-based numerical models in hydrology are rapidly evolving, spurred by novel theories in mathematical physics, advances in computational methods, insights from laboratory and field experiments, and the need to better understand and predict the potential impacts of population, land use, and climate change on our water resources. At the catchment scale, these simulation models are commonly based on conservation principles for surface and subsurface water flow and solute transport (e.g., the Richards, shallow water, and advection-dispersion equations), and they require robust numerical techniques for their resolution. Traditional (and still open) challenges in developing reliable and efficient models are associated with heterogeneity and variability in parameters and state variables; nonlinearities and scale effects in process dynamics; and complex or poorly known boundary conditions and initial system states. As catchment modeling enters a highly interdisciplinary era, new challenges arise from the need to maintain physical and numerical consistency in the description of multiple processes that interact over a range of scales and across different compartments of an overall system. This paper first gives an historical overview (past 50 years) of some of the key developments in physically based hydrological modeling, emphasizing how the interplay between theory, experiments, and modeling has contributed to advancing the state of the art. The second part of the paper examines some outstanding problems in integrated catchment modeling from the perspective of recent developments in mathematical and computational science. [ABSTRACT FROM AUTHOR]

Significant progress in hydrology, especially in subsurface flow and solute transport, has been made over the last 35 years because of sustained interest in underground nuclear waste repositories. The present paper provides an overview of the key hydrologic issues involved, and to highlight advances in their understanding and treatment because of these efforts. The focus is not on the development of radioactive waste repositories and their safety assessment, but instead on the advances in hydrologic science that have emerged from such studies. Work and results associated with three rock types, which are being considered to host the repositories, are reviewed, with a different emphasis for each rock type. The first rock type is fractured crystalline rock, for which the discussion will be mainly on flow and transport in saturated fractured rock. The second rock type is unsaturated tuff, for which the emphasis will be on flow from the shallow subsurface through the unsaturated zone to the repository. The third rock type is clay-rich formations, whose permeability is very low in an undisturbed state. In this case, the emphasis will be on hydrologic issues that arise from mechanical and thermal disturbances; i.e., on the relevant coupled thermo-hydro-mechanical processes. The extensive research results, especially those from multiyear large-scale underground research laboratory investigations, represent a rich body of information and data that can form the basis for further development in the related areas of hydrologic research. [ABSTRACT FROM AUTHOR]

The challenge of sustainable freshwater management requires identification and characterization of the underlying components and dynamic interactions within the coupled human-hydrology system. This paper builds a model that captures the dynamic water balance evolution and coupled human response within the Lake Toolibin catchment in West Australia's wheatbelt region. Two subcatchments in different parts of the landscape were selected to examine the key emergent properties of the coupled socio-hydrology system over a 100 year period, by analyzing the two-way feedbacks of land use management (human system feedback) and land degradation (natural system feedback). Using a relatively simple parameterization of community sensitivity to land degradation within the model, we identified positive and negative feedbacks, the presence of threshold behavior, time scale differences between fast and slow moving variables, differences in time lags resulting from disparate resistance levels of the natural system, and the degree of adaptive learning inherent in the human system. Specifically, the valley floor subcatchment transitioned through four phases-expansion, contraction, recession, and recovery-demonstrating a threshold shift in the human feedback after 60 years, while the upslope subcatchment appears to still be in the contraction phase, with no sign of reaching a threshold shift in 100 years. These results demonstrate that the model is capable of isolating the two-way feedbacks of the coupled system and has implications for resilience theory, suggesting that greater resistance in the underlying natural system counteracts the onset of a negative feedback loop and instigation of adaptive behaviors in the human system. [ABSTRACT FROM AUTHOR]

The hyporheic zone, defined by shallow subsurface pathways through river beds and banks beginning and ending at the river, is an integral and unique component of fluvial systems. It hosts myriad hydrologically controlled processes that are potentially coupled in complex ways. Understanding these processes and the connections between them is critical since these processes are not only important locally but integrate to impact increasingly larger scale biogeochemical functioning of the river corridor up to the river network scale. Thus, the hyporheic zone continues to be a growing research focus for many hydrologists for more than half the history of Water Resources Research. This manuscript partly summarizes the historical development of hyporheic zone hydrologic science as gleaned from papers published in Water Resources Research, from the birth of the concept of the hyporheic zone as a hydrologic black box (sometimes referred to as transient storage zone), to its adolescent years of being torn between occasionally competing research perspectives of interrogating the hyporheic zone from a surface or subsurface view, to its mature emergence as an interdisciplinary research field that employs the wide array of state-of-the-art tools available to the modern hydrologist. The field is vibrant and moving in the right direction of addressing critical fundamental and applied questions with no clear end in sight in its growth. There are exciting opportunities for scientists that are able to tightly link the allied fields of geology, geomorphology, hydrology, geochemistry, and ecology to tackle the many open problems in hyporheic zone science. [ABSTRACT FROM AUTHOR]

This paper examines the impact of short-term operation on long-term energy production. We propose a multiobjective optimization model for the short-term, daily operation of a system of cascade reservoirs. The two objectives considered in the daily model are: (1) minimizing the total amount of water released and (2) maximizing the stored energy in the system. Optimizing short-term operation without considering its impact on long-term energy production does not guarantee maximum energy production in the system. Therefore, a major goal of this paper is to identify desirable short-term operation strategies that, at the same time, optimize long-term energy production. First, we solve the daily model for 1 month (30 days) using a nondominated genetic algorithm (NSGAII). We then use the nondominated solutions obtained by NSGAII to assess the impact on long-term energy production using a monthly model. We use historical monthly inflows to characterize the inflow variability. We apply the proposed methodology to the Qingjiang cascade system of reservoirs in China. The results show: (1) in average hydrology scenarios, the solution maximizing stored energy produces the most overall long-term energy production; (2) in moderately wet hydrology scenarios, the solution minimizing water released outperforms the maximizing stored energy solution; and (3) when extremely wet hydrology scenarios are expected, a compromise solution is the best strategy. [ABSTRACT FROM AUTHOR]

Prolonged periods of low reservoir inflows (droughts) significantly reduce a hydropower producer's ability to generate both electricity and revenues. Given the capital intensive nature of the electric power industry, this can impact hydropower producers' ability to pay down outstanding debt, leading to credit rating downgrades, higher interests rates on new debt, and ultimately, greater infrastructure costs. One potential tool for reducing the financial exposure of hydropower producers to drought is hydrologic index insurance, in particular, contracts structured to payout when streamflows drop below a specified level. An ongoing challenge in developing this type of insurance, however, is minimizing contracts' 'basis risk,' that is, the degree to which contract payouts deviate in timing and/or amount from actual damages experienced by policyholders. In this paper, we show that consideration of year-to-year changes in the value of hydropower (i.e., the cost of replacing it with an alternative energy source during droughts) is critical to reducing contract basis risk. In particular, we find that volatility in the price of natural gas, a key driver of peak electricity prices, can significantly degrade the performance of index insurance unless contracts are designed to explicitly consider natural gas prices when determining payouts. Results show that a combined index whose value is derived from both seasonal streamflows and the spot price of natural gas yields contracts that exhibit both lower basis risk and greater effectiveness in terms of reducing financial exposure. [ABSTRACT FROM AUTHOR]

This paper reports about the experimental evidence collected on the transport of five fluorobenzoate tracers injected under controlled conditions in a vegetated hydrologic volume, a large lysimeter (fitted with load cells, sampling ports, and an underground chamber) where two willows prompting large evapotranspiration fluxes had been grown. The relevance of the study lies in the direct and indirect measures of the ways in which hydrologic fluxes, in this case, evapotranspiration from the upper surface and discharge from the bottom drainage, sample water and solutes in storage at different times under variable hydrologic forcings. Methods involve the accurate control of hydrologic inputs and outputs and a large number of suitable chemical analyses of water samples in discharge waters. Mass extraction from biomass has also been performed ex post. The results of the 2 year long experiment established that our initial premises on the tracers' behavior, known to be sorption-free under saturated conditions which we verified in column leaching tests, were unsuitable as large differences in mass recovery appeared. Issues on reactivity thus arose and were addressed in the paper, in this case attributed to microbial degradation and solute plant uptake. Our results suggest previously unknown features of fluorobenzoate compounds as hydrologic tracers, potentially interesting for catchment studies owing to their suitability for distinguishable multiple injections, and an outlook on direct experimental closures of mass balance in hydrologic transport volumes involving fluxes that are likely to sample differently stored water and solutes. [ABSTRACT FROM AUTHOR]

This work advances a unified approach to process-based hydrologic modeling to enable controlled and systematic evaluation of multiple model representations (hypotheses) of hydrologic processes and scaling behavior. Our approach, which we term the Structure for Unifying Multiple Modeling Alternatives (SUMMA), formulates a general set of conservation equations, providing the flexibility to experiment with different spatial representations, different flux parameterizations, different model parameter values, and different time stepping schemes. In this paper, we introduce the general approach used in SUMMA, detailing the spatial organization and model simplifications, and how different representations of multiple physical processes can be combined within a single modeling framework. We discuss how SUMMA can be used to systematically pursue the method of multiple working hypotheses in hydrology. In particular, we discuss how SUMMA can help tackle major hydrologic modeling challenges, including defining the appropriate complexity of a model, selecting among competing flux parameterizations, representing spatial variability across a hierarchy of scales, identifying potential improvements in computational efficiency and numerical accuracy as part of the numerical solver, and improving understanding of the various sources of model uncertainty. [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]

Hillslope hydrological investigations in humid regions to date have focused mostly on runoff generation during events. The few papers that have also examined groundwater recharge processes associated with subsurface stormflow production have found strong linkages between episodes of runoff and recharge to the aquifer. But the range of climate, vegetation, and geological conditions examined thus far has been limited. Here we explore how geologic characteristics, timing of subsurface stormflow, and hydroclimatic conditions relate to the timing of bedrock groundwater recharge at the well‐studied Maimai watershed. We hypothesized that recharge would be determined by subsurface stormflow frequency in this system with high rainfall and little seasonality of the hydrologic response. Unexpectedly, isotopic analysis and noble gas measurements indicated that recharge occurred almost exclusively during winter months despite previous work at Maimai showing subsurface stormflow occurs in all seasons and rainfall‐runoff ratios are high year‐round. A sprinkler and dye experiment conducted directly on open bedrock identified groundwater recharge mechanisms and rates, and a simple empirical recharge model suggests almost 90% of recharge occurred from only 55% of annual precipitation. We found no correlation between the timing and magnitude of groundwater recharge and total precipitation, direct runoff or subsurface stormflow. The catchment effectively converted rainfall to runoff during all seasons, but the unfractured low permeability bedrock (2.3 × 10−8 m/s) required long durations of extended catchment wetness for appreciable recharge to occur—a condition satisfied only during winter months with lower evapotranspiration. These findings suggest the need to better understand the geologic controls of recharge in headwaters. Key Points: Despite minimal seasonality in rainfall, runoff generation, or the rainfall‐runoff ratio, there is strong seasonality in groundwater rechargeExtended periods of high catchment wetness, rather than storm events, were the primary driver of groundwater recharge at the Maimai watershedUnfractured low permeability bedrock and an efficient preferential flow network explain why runoff generation and recharge were uncorrelated [ABSTRACT FROM AUTHOR]

Model calibration using optimization algorithms is a perennial challenge in hydrological modeling. This study explores opportunities to improve the efficiency of a Newton‐type method by making it more robust against problematic features in models' objective functions, including local optima and other noise. We introduce the robust Gauss‐Newton (RGN) algorithm for least squares optimization, which employs three heuristic schemes to enhance its exploratory abilities while keeping costs low. The large sampling scale (LSS) scheme is a central difference approximation with perturbation (sampling scale) made as large as possible to capture the overall objective function shape; the best‐sampling point (BSP) scheme exploits known function values to detect better parameter locations; and the null‐space jump (NSJ) scheme attempts to escape near‐flat regions. The RGN heuristics are evaluated using a case study comprising four hydrological models and three catchments. The heuristics make synergistic contributions to overall efficiency: the LSS scheme substantially improves reliability albeit at the expense of increased costs, and scenarios where LSS on its own is ineffective are bolstered by the BSP and NSJ schemes. In 11 of 12 modeling scenarios, RGN is 1.4–18 times more efficient in finding the global optimum than the standard Gauss‐Newton algorithm; similar gains are made in finding tolerable optima. Importantly, RGN offers its largest gains when working with difficult objective functions. The empirical analysis provides insights into tradeoffs between robustness versus cost, exploration versus exploitation, and how to manage these tradeoffs to maximize optimization efficiency. In the companion paper, the RGN algorithm is benchmarked against industry standard optimization algorithms. Key Points: Robust Gauss‐Newton (RGN) optimization algorithm is introduced for least squares model calibrationRGN employs several heuristics including coarse gradient approximations to favor exploration over exploitationRGN heuristics provide synergistic benefits, increasing overall efficiency by factors of 1.4–18 in 11 of 12 hydrological modeling scenarios [ABSTRACT FROM AUTHOR]

The editors of Water Resources Research express their appreciation to the reviewers of articles submitted to the journal in 2017. Key Point: WRR editors thank the reviewers of the journal in 2017 [ABSTRACT FROM AUTHOR]

In a recent WRR paper, Yan et al. (2018, https://doi.org/10.1002/2017WR021290) have derived a simple, elegant, and useful way of reanalyzing quality‐controlled SNOTEL data sets to produce quantitative estimates of water reaching the land surface during rain, snowmelt, and rain‐on‐snow events, and use these data to generate "next‐generation" intensity duration, and frequency curves for these quantities at SNOTEL measurement locations. These new data sets may prove useful as direct inputs to design processes in some cases, but I argue in this commentary that they will probably have much more important application to the validation and refinement of off‐line hydrology models and land surface schemes embedded in climate models, which can then be used to extend these data in space and time to create more comprehensive products to guide infrastructure design. If well validated, such tools can be used to extend backward in time to make detailed hindcasts of the historical record with more complete spatial and temporal coverage (which also facilitates more accurate estimation of extremes with longer return intervals) and forward in time to project future conditions that are needed to design long‐lived infrastructure in what will likely be a highly nonstationary environment. In addition, there is a need to extend these data sets to include lower elevation areas in mountain environments, and other areas of the United States and Canada with cold winter climates. Key Point: A new data set produced by Yan et al. 2018 will likely be an important validation resource for hydrologic and atmospheric modelers [ABSTRACT FROM AUTHOR]