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The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order "n" is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2). Plain Language Summary: In hydrology, as in other endeavors, location matters. This study presents a new type of data that describes the location of a point on the landscape in the context of the network of streams that are present across the continental United States. The new data are presented as maps, and different patterns can be recognized in different areas of the United States. The patterns that can be seen also vary as one looks more or less closely at an area. The patterns that are present in these maps are shown to be useful for the purposes of mapping water resources. Key Points: Multiorder hydrologic position (lateral position and distance from stream to divide) quantifies the location of a point on the landscapeMultiorder hydrologic position is mapped for nine hydrologic orders at a 30‐m resolution for the conterminous United StatesMultiorder hydrologic position is used in machine learning models for the purposes of physiographic and hydrologic mapping [ABSTRACT FROM AUTHOR]

The ensemble Kalman filter (EnKF) is a widely used data assimilation method that has the capacity to sequentially update system parameters and states as new observations become available. One noticeable feature of the EnKF is that it not only can provide real-time updates of model parameters and state variables, but also can give the uncertainty associated with them in each assimilation step. The natural system is open and complex, rendering it prone to multiple interpretations and mathematical descriptions. In this paper, a multimodel data assimilation method is proposed by embedding the EnKF into the Bayesian model averaging framework to account for the uncertainty stemming from the model itself. An illustrative example, considering both hydrogeological and groundwater flow uncertainties, is employed to demonstrate the proposed multimodel data assimilation approach via the EnKF. Results show that statistical bias and uncertainty underestimation can occur when the data assimilation process relies on a single postulated model. The posterior model weight can adjust itself dynamically in time according to its consistency with observations. The performances of log conductivity estimation and head prediction are compared to the standard EnKF method based on the postulated single model and the proposed multimodel EnKF method. Comparisons show that the multimodel EnKF performs better in terms of statistical measures, such as log score and coverage, when sufficient observations have been assimilated in this case. [ABSTRACT FROM AUTHOR]

The spatial and temporal dynamics of seasonal snow covers play a critical role for many hydrological, ecological, and climatic processes. This paper presents a new, innovative approach to continuously monitor these dynamics using numerous low-cost, standalone snow monitoring stations (SnoMoS). These stations provide snow and related meteorological data with a high temporal and spatial resolution. Data collected by SnoMoS include: snow depth, surface temperature, air temperature and humidity, total precipitation, global radiation, wind speed, and barometric pressure. A total of 99 sensors were placed over the winters 2010/2011 and 2011/2012 at multiple locations within three 40-180 km2 basins in the Black Forest region of Southern Germany. The locations were chosen to cover a wide range of slopes, elevations, and expositions in a stratified sampling design. Furthermore, 'paired stations' located in close proximity to each other, one in the open and one underneath various forest canopies, were set up to investigate the influence of vegetation on snow dynamics. The results showed that considerable differences in snow depth and, therefore, snow water equivalent (SWE) are present within the study area despite its moderate temperatures and medium elevation range (400-1500 m). The relative impact of topographical factors like elevation, aspect, and of different types of forest vegetation were quantified continuously and were found to change considerably over the winter period. The recorded differences in SWE and snow cover duration were large enough that they should be considered in hydrologic and climate models. [ABSTRACT FROM AUTHOR]

Temporal shifts in precipitation and runoff regime curves appear throughout the continental United States, but differ from region to region. This paper explores these regime shifts by building upon a hydroclimatic classification system that partitions the United States into clusters of similarly behaved catchments using four simple hydroclimatic indicators. Hydroclimate data from over four hundred catchments over a 55 year period (belonging to the MOPEX data set) are analyzed to reveal how the indicators have shifted before and after 1970, before and after 1975, and before and after 1980. Statistically significant hydroclimatic changes in these indicators are explored qualitatively, suggesting which catchments today might resemble other catchments tomorrow. Thus, a preview of current locations in one class under future conditions is provided by observing existing locations of another class. The classification system structure enables organization of these data, allowing patterns of regime change to emerge without highly specified models at each individual site. Regional analyses explore changes in mean seasonal precipitation/runoff regimes, including shifts in the daily variability of precipitation and runoff. Additionally, changes in regime curves of minimum and maximum precipitation/runoff observations are analyzed and discussed. Results indicate that after 1980, classifications typically found in the southeastern quarter of the United States have expanded northward and westward. Regionally, the Midwest and Rocky Mountains seem to demonstrate more frequent, but less intense storms after 1980, while southeastern catchments receive much less water in the form of precipitation and runoff than in previous years. [ABSTRACT FROM AUTHOR]

This paper presents the results of a comprehensive model-based analysis of a uranyl [U(VI)] tracer test conducted at the U.S. DOE Hanford 300 Area (300A) IFRC. Despite the highly complex field conditions the numerical three-dimensional multicomponent reactive transport model was able to capture most of the spatiotemporal variations of the observed U(VI) concentrations. A multimodel analysis was performed to interrogate the relative importance of various processes and factors for controlling field-scale reactive transport during the uranyl tracer test. The results indicate that multirate sorption/desorption, surface complexation reactions, and initial concentration distributions were the most important processes and factors controlling U(VI) migration. On the other hand, cation exchange reactions, the choice of the surface complexation model, and dual-domain mass transfer processes played less important roles under the prevailing field-test conditions. Further analysis of the modeling results demonstrates that these findings are conditioned to the relatively stable groundwater chemistry and the selected length of the field experimental duration (16 days). The model analysis also revealed the crucial role of the intraborehole flow that occurred within the long-screened monitoring wells and thus affected both field measurements and simulated U(VI) concentrations as a combined effect of aquifer heterogeneity and dynamic flow conditions. This study provides the first highly data-constrained uranium transport simulations under highly dynamic flow conditions. It illustrates the value of reactive transport modeling for elucidating the relative importance of individual processes in controlling uranium transport under specific field-scale conditions. [ABSTRACT FROM AUTHOR]

This paper experimentally investigates the role of shear processes on the variation of critical Reynolds number and nonlinear flow through rough-walled rock fractures. A quantitative criterion was developed to quantify the onset of nonlinear flow by comprehensive combination of Forchheimer's law and Reynolds number. At each shear displacement, several high-precision water flow tests were carried out with different hydraulic gradients then the critical Reynolds number was determined based on the developed criterion. The results show that (i) the Forchheimer's law was fitted very well to experimental results of nonlinear fluid flow through rough-walled fractures, (ii) the coefficients of viscous and inertial pressure drops experience 4 and 7 orders of magnitude reduction during shear displacement, respectively, and (iii) the critical Reynolds number varies from 0.001 to 25 and experiences 4 orders of magnitude enlargement by increasing shear displacement from 0 to 20 mm. These findings may prove useful in proper understanding of fluid flow through rock fractures, or inclusions in computational studies of large-scale nonlinear flow in fractured rocks. [ABSTRACT FROM AUTHOR]

Gaps in hydrological information of the Congo Basin increase uncertainties in understanding hydroclimatic processes in the basin, and consequently the risks associated with decision making for major water resources development plans. There is also uncertainty about the predictions of future climate and land use change. These challenges make it essential to explore possible approaches to close the information gaps. Some of the gaps can be filled using hydrological simulation models, which if they prove practical, can be established with available data, but generate sufficiently reliable information for management purposes. This paper discusses the results of applying a semidistributed rainfall-runoff model which was established for the whole Congo Basin, using the available historical data, with an ultimate goal of understanding processes of runoff generation as well as assessing the impacts of future climate and land use changes on water resources availability, including options for water resources development in the basin. Issues of water resources assessment in the basin, approaches used to address them and some directions for future research are discussed. It is noted that the hydrological model applied in this study for the Congo Basin is able to capture the timing and magnitude of high and low flows satisfactorily, irrespective of the subbasins are located in headwater areas, downstream areas or at the outlets of regions strongly affected by wetlands and lakes. There remain a number of opportunities to improve the methods used for water resources assessment within the basin. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based 'local' methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative 'bucket-style' hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times. A postprocessing approach is used, and a Gaussian model is applied for transformed variables. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross-validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. [ABSTRACT FROM AUTHOR]

Recent focus of flood frequency analysis (FFA) studies has been on development of methods to model joint distributions of variables such as peak flow, volume, and duration that characterize a flood event, as comprehensive knowledge of flood event is often necessary in hydrological applications. Diffusion process based adaptive kernel (D-kernel) is suggested in this paper for this purpose. It is data driven, flexible and unlike most kernel density estimators, always yields a bona fide probability density function. It overcomes shortcomings associated with the use of conventional kernel density estimators in FFA, such as boundary leakage problem and normal reference rule. The potential of the D-kernel is demonstrated by application to synthetic samples of various sizes drawn from known unimodal and bimodal populations, and five typical peak flow records from different parts of the world. It is shown to be effective when compared to conventional Gaussian kernel and the best of seven commonly used copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and Student's T) in estimating joint distribution of peak flow characteristics and extrapolating beyond historical maxima. Selection of optimum number of bins is found to be critical in modeling with D-kernel. [ABSTRACT FROM AUTHOR]

This paper presents a multicontinuum approach to model fractal temporal scaling of catchment response in hydrological systems. The temporal scaling of discharge is quantified in frequency domain by the transfer function [ABSTRACT FROM AUTHOR]

Regional flood frequency analysis aims to estimate flood risk at sites where little or no hydrological data are available. The index-flood model is one of the commonly employed models for this purpose. In this model, the predicted value depends on the growth curve and its regional parameters. The latter are estimated as weighted averages of the at-site parameters. Traditional approaches are mainly based on site record lengths or region size to define these weights. Hence, they are not representative of the hydrological similarity between sites within a region. In addition, they are not defined to reach optimality in terms of model performance. To overcome these limitations, the present paper aims to propose a new optimal iterative weighting scheme to the index-flood model. The proposed approach is based on a number of elements: a statistical depth function to introduce similarity between sites, a weight function to amplify and control the depth values, an iterative procedure to improve estimation accuracy, and an optimization algorithm to objectively automate the choice of the weight function. A data set from the Island of Sicily (Italy) is used to compare the proposed approach with traditional ones. On the basis of the L-moments and using cluster analysis techniques, the studied region is subdivided into three homogeneous subregions. The results indicate that the proposed approach performs significantly better than traditional ones both in terms of relative bias and relative root mean squares error. The proposed approach allows identification of cross correlation in the region and provides a significant performance improvement. [ABSTRACT FROM AUTHOR]

Sequential data assimilation methods, such as the ensemble Kalman filter (EnKF), provide a general framework to account for various uncertainties in hydrologic modeling, simultaneously estimating dynamic states and model parameters with a state augmentation technique. But this technique suffers from spurious correlation for impulse responses, such as the rainfall-runoff process, especially in the case of high-dimensional state spaces containing various parameters. This paper presents a partitioned forecast-update scheme based on the EnKF to reduce the degree of freedom of the high-dimensional state space and to correctly capture covariances between states and parameters. In this update scheme, the parameter set is partitioned into several types according to their sensitivities, and each type of sensitive parameter is estimated in an individual loop by repeated forecast and assimilation. We test this scheme with a synthetic case and a distributed hydrologic model concerning the real case of the Zhanghe river basin in China. The results from the synthetic experiments show that this new scheme can retrieve optimal parameter values and represent the correlations in a more stable manner when compared with the standard state augmentation technique. The real case further demonstrates the robustness of the partitioned update scheme for state and parameter estimation owing to the low estimation errors of streamflow in the assimilation and the prediction periods. [ABSTRACT FROM AUTHOR]

Knowledge of hydrological model complexity can aid selection of an optimal prediction model out of a set of available models. Optimal model selection is formalized as selection of the least complex model out of a subset of models that have lower empirical risk. This may be considered equivalent to minimizing an upper bound on prediction error, defined here as the mathematical expectation of empirical risk. In this paper, we derive an upper bound that is free from assumptions on data and underlying process distribution as well as on independence of model predictions over time. We demonstrate that hydrological model complexity, as defined in the presented theoretical framework, plays an important role in determining the upper bound. The model complexity also acts as a stabilizer to a hydrological model selection problem if it is deemed ill-posed. We provide an algorithm for computing complexity of any arbitrary hydrological model. We also demonstrate that hydrological model complexity has a geometric interpretation as the size of model output space. The presented theory is applied to quantify complexities of two hydrological model structures: SAC-SMA and SIXPAR. It detects that SAC-SMA is indeed more complex than SIXPAR. We also develop an algorithm to estimate the upper bound on prediction error, which is applied on five different rainfall-runoff model structures that vary in complexity. We show that a model selection problem is stabilized by regularizing it with model complexity. Complexity regularized model selection yields models that are robust in predicting future but yet unseen data. [ABSTRACT FROM AUTHOR]

Estimation of parameter values in hydrological models has gradually moved from subjective, trial-and-error methods into objective estimation methods. Translation of nature's complexity to bit operations is an uncertain process as a result of data errors, epistemic gaps, computational deficiencies, and other limitations, and relies on calibration to fit model output to observed data. The robustness of the calibrated parameter values to these types of uncertainties is therefore an important concern. In this study, we investigated how the hydrological robustness of the model-parameter values varied within the geometric structure of the behavioral (well-performing) parameter space with a depth function based on α shapes and an in-depth posterior performance analysis of the simulations in relation to the observed discharge uncertainty. The α shape depth is a nonconvex measure that may provide an accurate and tight delimitation of the geometric structure of the behavioral space for both unimodal and multimodal parameter-value distributions. WASMOD, a parsimonious rainfall-runoff model, was applied to six Honduran and one UK catchment, with differing data quality and hydrological characteristics. Model evaluation was done with two performance measures, the Nash-Sutcliffe efficiency and one based on flow-duration curves. Deep parameter vectors were in general found to be more hydrologically robust than shallow ones in the analyses we performed; model-performance values increased with depth, deviations to the observed data for the high-flow aspects of the hydrograph generally decreased with increasing depth, deep parameter vectors generally transferred in time with maintained high performance values, and the model had a low sensitivity to small changes in the parameter values. The tight delimitation of the behavioral space provided by the α shapes depth function showed a potential to improve the efficiency of calibration techniques that require further exploration. For computational reasons only a three-parameter model could be used, which limited the applicability of this depth measure and the conclusions drawn in this paper, especially concerning hydrological robustness at low flows. [ABSTRACT FROM AUTHOR]

Stormwater runoff causes environmental problems such as flooding, soil erosion, and water pollution. Conventional stormwater management has focused primarily on flood reduction, while a new generation of decentralized stormwater solutions yields ancillary benefits such as healthier aquatic habitat, improved surface water quality, and increased water table recharge. Previous research has estimated values for flood reduction from stormwater management, but no estimates exist for the willingness to pay (WTP) for some of the other environmental benefits of alternative approaches to stormwater control. This paper uses a choice experiment survey of households in Champaign-Urbana, Illinois, to estimate the values of several attributes of stormwater management outcomes. We analyzed data from 131 surveyed households in randomly selected neighborhoods. We find that people value reduced basement flooding more than reductions in yard or street flooding, but WTP for basement flood reduction in the area only exists if individuals are currently experiencing significant flooding themselves. Citizens value both improved water quality and improved hydrologic function and aquatic habitat from runoff reduction. Thus, widespread investment in low impact development stormwater solutions could have very large total benefits, and stormwater managers should be wary of policies and infrastructure plans that reduce flooding at the expense of water quality and aquatic habitat. [ABSTRACT FROM AUTHOR]

In recent years a number of papers have quantitatively explored multiple steady states and resilience within a wide range of hydrological systems. Many have identified multiple steady states by conducting simulations from different initial state variables and a few have used the more advanced technique of equilibrium or limit cycle continuation analysis to quantify how the number of steady states may change with a single model parameter. However, like resilience investigations into other natural systems, these studies often omit explanation of these fundamental resilience science techniques; rely on complex numerical methods rather than analytical methods; and overlook use of more advanced techniques from nonlinear systems mathematics. In the interests of wider adoption of advanced resilience techniques within hydrology, and advancing resilience science more broadly, this paper details fundamental methods for quantitative resilience investigations. Using a simple model of a spatially lumped unconfined aquifer, one and two parameter continuation analysis was undertaken algebraically. The shape of each steady state attractor basin was then quantified using Lyapunov stability curves derived at a range of precipitation rates, but was found to be inconsistent with the resilience behavior demonstrated by stochastic simulations. Most notably, and contrary to standard resilience concepts, the switching between steady states from wet or dry periods (and vice versa) did not occur by crossing of the threshold between the steady states. It occurred by exceedance of the two steady-state domain, producing a counterclockwise hysteresis loop. Additionally, temporary steady states were identified that could not have been detected using equilibrium continuation with a constant forcing rate. By combining these findings with the Lyapunov stability curves, new measures of resilience were developed for endogenous disturbances to the model and for the recovery from disturbances exogenous to the model. [ABSTRACT FROM AUTHOR]

The distinction between the channel network, the headwater subcatchments, and the lateral subcatchments plays an important role in distributed hydrological and ecohydrological applications. This paper presents some newly found invariance properties of headwater and upstream subcatchments and shows that the invariant morphometric properties characterize only natural networks and virtual networks verifying optimal channel networks (OCN) properties but are not verified for virtual non-OCNs. A model based on self-affine properties was developed in order to calculate the number of headwater catchments N and the total upstream area of headwater catchments U as a function of the cutoff area A used to delineate streams. For 18 French catchments between 43 and 116,450 km2 and for 4 virtual OCNs, results show that U(A)/A0 (with A0 being the catchment area) is independent of A for 0.5 < A < 5 km2 and seems to be constant (0.29 ± 0.03) for various shapes and sizes of channel networks and, consequently, can be considered as an invariant general descriptor of natural channel and virtual OCN networks. On the contrary, this is not the case when the approach is applied on six virtual non-OCNs. Moreover, results show that the knowledge of six morphometric indices enable us to calculate both functions N(A) and U(A) for all values of A < A0. These indices can be considered as geometric and topological properties of headwater and upstream subcatchments and are useful for studying the effects of cutoffs on self-affine river networks or as similarity indices for channel network comparison. [ABSTRACT FROM AUTHOR]

Assessing health risk in hydrological systems is an interdisciplinary field. It relies on the expertise in the fields of hydrology and public health and needs powerful translation concepts to provide decision support and policy making. Reliable health risk estimates need to account for the uncertainties and variabilities present in hydrological, physiological, and human behavioral parameters. Despite significant theoretical advancements in stochastic hydrology, there is still a dire need to further propagate these concepts to practical problems and to society in general. Following a recent line of work, we use fault trees to address the task of probabilistic risk analysis and to support related decision and management problems. Fault trees allow us to decompose the assessment of health risk into individual manageable modules, thus tackling a complex system by a structural divide and conquer approach. The complexity within each module can be chosen individually according to data availability, parsimony, relative importance, and stage of analysis. Three differences are highlighted in this paper when compared to previous works: (1) The fault tree proposed here accounts for the uncertainty in both hydrological and health components, (2) system failure within the fault tree is defined in terms of risk being above a threshold value, whereas previous studies that used fault trees used auxiliary events such as exceedance of critical concentration levels, and (3) we introduce a new form of stochastic fault tree that allows us to weaken the assumption of independent subsystems that is required by a classical fault tree approach. We illustrate our concept in a simple groundwater-related setting. [ABSTRACT FROM AUTHOR]

This paper deals with modeling the mutual feedbacks between runoff production and geomorphological processes and attributes that lead to patterns of equilibrium soil depth. Our primary goal is an attempt to describe spatial patterns of soil depth resulting from long-term interactions between hydrologic forcings and soil production, erosion, and sediment transport processes under the framework of landscape dynamic equilibrium. Another goal is to set the premises for exploiting the role of soil depths in shaping the hydrologic response of a catchment. The relevance of the study stems from the massive improvement in hydrologic predictions for ungauged basins that would be achieved by using directly soil depths derived from geomorphic features remotely measured and objectively manipulated. Hydrological processes are here described by explicitly accounting for local soil depths and detailed catchment topography. Geomorphological processes are described by means of well-studied geomorphic transport laws. The modeling approach is applied to the semiarid Dry Creek Experimental Watershed, located near Boise, Idaho. Modeled soil depths are compared with field data obtained from an extensive survey of the catchment. Our results show the ability of the model to describe properly the mean soil depth and the broad features of the distribution of measured data. However, local comparisons show significant scatter whose origins are discussed. [ABSTRACT FROM AUTHOR]

This paper describes the use of satellite-based estimates of rainfall to force the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a hydrology-based mechanistic model of malaria transmission. We first examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 h resolution. We then investigated whether HYDREMATS could be effectively forced by satellite-based estimates of rainfall instead of ground-based observations. The Climate Prediction Center morphing technique (CMORPH) precipitation estimates distributed by the National Oceanic and Atmospheric Administration are available at a 30 min temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results demonstrate that adjusted rainfall estimates from satellites can be used with a mechanistic model to accurately simulate the dynamics of mosquito populations. [ABSTRACT FROM AUTHOR]

The $40 'Wiimote' (an input device belonging with the Nintendo® Wii™ game system) can be used by hydrologists as a sensor. The device contains three accelerometers and an infrared camera with built-in source tracking. It communicates by Bluetooth®. Because of the efforts of the hacking community it is now easy to let the Wiimote communicate with a standard personal computer. Using a floating evaporation pan as an example, we show that the Wiimote, although it may have potential drawbacks when used in field campaigns, is a good addition to the hydrologist's bag of tools, especially for proof of concept testing. [ABSTRACT FROM AUTHOR]

Peatlands are carbon-rich ecosystems that are extensive in the northern high latitudes where significant 21st century climate changes are expected. In response to climate change, peatlands may become a net source of greenhouse gases, thereby inducing a positive climate feedback effect. In this paper, the impact of precipitation variability and the mean climate state on long-term peat accumulation is investigated with model simulations. The models couple peat accumulation with the hydrological cycle, which results in peatland bistability, where peatlands may take the physical characteristics from one of two possible alternative stable states. The models consider precipitation as a stochastic forcing variable, temperature-dependent functions and are parameterized with climatology and peat characteristics to represent the West Siberian Lowlands (WSL) between 55°N and 60°N. Observed WSL peat depths statistically imply bistability. Peatland bistability, however, is eliminated in model simulations with moderate-to-large precipitation variability and warmer and wetter climates. This suggests that projected late 21st century climate change would put the thick peatlands in WSL on a transition to thin peatlands. The loss of thick peatlands could significantly increase atmospheric carbon dioxide and provide a positive climate feedback effect. However, the impacts depend on the importance of unaccounted stabilizing factors. The study also shows that precipitation variability induces peatlands to switch between extended periods of accumulation and depletion even if the peatlands are in long-term equilibrium. Thus, short-term observations may see only natural fluctuations and new, longer-term observational strategies are necessary to diagnose if peatlands are undergoing fundamental changes. [ABSTRACT FROM AUTHOR]

When plants are both sensitive to salt levels in the root zone and able to modify the soil salt balance, changes in vegetation cover may affect the local hydrologic conditions and favor the accumulation of salt within different parts of the soil profile. In such cases a salt-vegetation feedback may exist, whereby both a state with vegetation cover, deep water table, and low salinity and a state with sparse or no vegetation, shallow water table, and high salinity can be stable. In this paper, we develop a modeling framework to relate vegetation-soil salinity feedbacks to the emergence of multiple stable states in the underlying dynamics. This model is used to simulate various scenarios involving changes in forcing parameters of salinity-vegetation dynamics using data from the Murray-Darling Basin. Results show the presence of a strong feedback resulting in bistable dynamics for a wide range of environmental conditions, which has the effect of reducing the resilience of plant ecosystems and the productivity of agricultural systems for areas where such a feedback can occur. [ABSTRACT FROM AUTHOR]

Uncertainty analysis has become an important topic in environmental research. Uncertainty in hydrological modeling, in general, has been studied by investigating mainly the influence of the parameter uncertainty on the uncertainty of the simulated outputs. This paper focuses essentially on the impact of point input uncertainty on fully distributed hydrological modeling and proposes an integrated approach to cope with input and parameter uncertainty. The approach uses Bayesian theory in two steps: first, to compute the uncertainty in input forcing data, and second, to compute the hydrological parameter uncertainty and the uncertainties of a lumped-error term that include other sources of errors: response errors and model error. The method is applied with a fully distributed model: WaSiM-ETH. The results show that the methodology proposed here is a valuable tool to assess different sources of uncertainty in hydrological modeling and show the effects of uncertainty in the input forcing when a fully distributed, physically based hydrological model is used. [ABSTRACT FROM AUTHOR]

One approach to predicting streamflow in an ungauged catchment is to select an ensemble of hydrological models previously identified for similar gauged catchments, where the similarity is based on some combination of important physical catchment attributes. The focus of this paper is the identification of catchment attributes and optimization of a similarity measure to produce the best possible ungauged streamflow predictions given a data set and a conceptual model structure. As a case study, the SimHyd rainfall-runoff model is applied to simulate monthly streamflow in 184 Australian catchments. Initial results show that none of 27 catchment attributes can be safely said to consistently give a better ensemble of models than random selection when used independently of other attributes. This is contrary to prior expectations and indicates the sparseness of information within our database of catchments, the importance in this case of prior knowledge for defining important attributes, and the potential importance of combining multiple attributes in order to usefully gauge similarity. Seven relatively independent attributes are then selected on the basis of prior knowledge. The weight with which each of these attributes contributes to the similarity measure is optimized to maximize streamflow prediction performance across a set of 95 catchments. The other 89 catchments are used to independently test the accuracy of streamflow predictions. Using the optimal set of weights led to marked improvement in the accuracy of predictions, showing that the method, while inferior to local calibration, is superior to alternative methods of model regionalization based on regression and spatial proximity. However, there is evidence of nonuniqueness in the optimal solution and the possibility that the attribute weights are somewhat dependent on the catchments used. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are powerful tools for the modeling and forecasting of complex engineering systems and have been exploited by researchers to solve a variety of problems over the last couple of decades. In spite of their proven ability to provide superior model performance compared to traditional modeling approaches, they have not become popular among decision makers for operational use. It is probably because of their perceived black box nature that does not explain or consider the underlying physical processes involved. This paper presents the results of a study aimed at a systematic dissection of the massively parallel architectures of trained ANN hydrologic models to determine if they learn the underlying physical subprocesses during training. This has been achieved using simple qualitative and quantitative techniques. The data derived from three contrasting catchments at two different time scales were employed to develop ANN models and test the methodologies employed for knowledge extraction. The results obtained in this study indicate that the number of hidden neurons determined during training for a particular data set correspond to certain subprocesses of the overall physical process being modeled. It has been found that the time scale of the data employed has an effect on optimum ANN architecture and knowledge extracted. [ABSTRACT FROM AUTHOR]

This paper presents the results of statistical investigations for the evaluation of soil water retention models (SWRMs). We employed three different methods developed for model selection in the field of nonlinear regression, namely, simulation studies, analysis of nonlinearity measures, and resampling strategies such as cross validation and bootstrap methods. Using these methods together with small data sets, we evaluated the performance of three exemplarily chosen types of SWRMs with respect to their parameter properties and the reliability of model predictions. The resulting rankings of models show that the favorable models are characterized by few parameters with an almost linear estimation behavior and close to symmetric distributions. To further demonstrate the potential of the statistical methods in the field of model selection, a modification of the four-parameter van Genuchten model is proposed which shows significantly improved and robust statistical properties. [ABSTRACT FROM AUTHOR]

Climate change may impact water resources management conditions in difficult-to-predict ways. A key challenge for water managers is how to incorporate highly uncertain information about potential climate change from global models into local- and regional-scale water management models and tools to support local planning. This paper presents a new method for developing large ensembles of local daily weather that reflect a wide range of plausible future climate change scenarios while preserving many statistical properties of local historical weather patterns. This method is demonstrated by evaluating the possible impact of climate change on the Inland Empire Utilities Agency service area in southern California. The analysis shows that climate change could impact the region, increasing outdoor water demand by up to 10% by 2040, decreasing local water supply by up to 40% by 2040, and decreasing sustainable groundwater yields by up to 15% by 2040. The range of plausible climate projections suggests the need for the region to augment its long-range water management plans to reduce its vulnerability to climate change. [ABSTRACT FROM AUTHOR]

The Little River Experimental Watershed located in the headwaters of the Upper Suwannee River basin is one of twelve national benchmark watersheds participating in the U.S. Department of Agriculture (USDA) Conservation Effects Assessment Project-Watershed Assessment Studies (CEAP-WAS). Historical paper files and maps (circa 1980-2006) were collected and used to develop a geographic database of conservation practices supported by the USDA Natural Resource Conservation Service. The CEAP-WAS database can be queried by conservation practice, total acreages enrolled, year of implementation, and location. The CEAP-WAS database is integral to understanding the links between conservation practice implementation and placement with observed changes in hydrologic processes within a small southern Coastal Plain watershed. All associated geographic information has been provided in shapefile format and has been projected into universal transverse Mercator coordinates (zone 17), using NAD83 as the datum and GRS80 as the ellipsoid. Data may be accessed via ftp://www.tiftonars.org/, archived in a folder named ceap_data. [ABSTRACT FROM AUTHOR]

Effective control of nonpoint source contaminants in runoff from urbanized watersheds requires knowledge about the locations of runoff source areas under a variety of conditions. Physical monitoring of spatially variant processes, such as runoff production form variable source areas, is time-consuming and expensive. Thus modeling the processes may provide a valuable cost-effective alternative. In this paper we adapt and validate a variable source model for an urban watershed to predict areas of the landscape prone to elevated soil moisture levels and saturation excess runoff. We modified the soil moisture distribution and routing (SMDR) model to simulate hydrologic processes in an urban upstate New York watershed by considering the impact of impervious surfaces, hydraulic control structures (detention basins), and land use on the water balance. In our model, infiltration excess runoff from impervious surfaces infiltrates in the surrounding soils. Simulated and observed streamflow agreed well, and more importantly, the distribution of soil moisture levels and overland flow throughout the watershed were well predicted. Removing urban features from the model resulted in substantially lower peak stormflows than observed, and the base and interflows increased accordingly. Both modeled and measured distributed results indicated that the more urbanized areas of the study site had both higher soil moistures and runoff losses due to shallower soils, greater upslope contributing areas, and a larger area of impervious surfaces generating runoff. The model results helped identify processes that describe how urbanization impacts integrated and distributed hydrology, which provides useful information for targeted water quality management. [ABSTRACT FROM AUTHOR]

Peatlands respond to natural hydrologic cycles of precipitation and evapotranspiration with reversible deformations due to variations of water content in both the unsaturated and saturated zone. This phenomenon results in short-term vertical displacements of the soil surface that superimpose to the irreversible long-term subsidence naturally occurring in drained cropped peatlands because of bio-oxidation of the organic matter. These processes cause changes in the peat structure, in particular, soil density and void ratio. The consequential changes in the hydrological parameters need to be incorporated in water flow dynamical models. In this paper, we present a new constitutive relationship for the soil shrinkage characteristic (SSC) in peats by describing the variation of porosity with moisture content. This model, based on simple physical considerations, is valid for both anisotropic and isotropic three-dimensional peat deformations. The capability of the proposed SSC to accurately describe the deformation dynamics has been assessed by comparison against a set of laboratory experimental results recently published. The constitutive relationship has been implemented into a Richards' equation-based numerical code and applied for the simulation of the peat soil dynamics as observed in a peatland south of the Venice Lagoon, Italy, in an ad hoc field experiment where the relevant parameters are continuously measured. The modeling results match well a large set of field data encompassing a period of more than 50 days and demonstrate that the proposed approach allows for a reliable reproduction of the soil vertical displacement dynamics as well as the hydrological behavior in terms of, for example, water flow, moisture content, and suction. [ABSTRACT FROM AUTHOR]

The Earth's surface is shaped by the interaction of tectonics, water, sediment, solutes, and biota over a wide range of spatial and temporal scales and across diverse environments. Development of a predictive science of Earth surface dynamics integrates many disciplines and approaches, including hydrology, geomorphology, ocean and atmospheric science, sedimentary and structural geology, geochemistry, and ecology. This paper discusses challenges, opportunities, and a few example problems that can serve as pathways toward this integration. [ABSTRACT FROM AUTHOR]

Integrated assessment is a discipline that takes a holistic, problem-oriented approach to scientific assessment. It is increasingly being applied to consider a range of environmental issues, including water resource management problems. This paper considers lessons for integrated assessment arising from a project focused on water allocation options in the Namoi River catchment, Australia. It is seen how the problem focus of this work prescribes the requirements placed on the structure, components and conceptualisation of the integrated assessment model. A brief description of the model illustrates the ways in which these requirements are addressed. Lessons for integrated assessment arising from this work are then described. These relate to the importance of identifying the modeling problem and objectives, balancing disciplinary perspectives, selecting appropriate spatial and temporal scales, and development of trust with stakeholder groups. [ABSTRACT FROM AUTHOR]

Drought monitoring and forecasting systems are used in the United States (U.S.) to inform drought management decisions. Drought forecasting efforts have often been conducted and evaluated at coarse spatial resolutions (i.e., >10‐km), which miss key local drought information at higher resolutions. Addressing the importance of forecasting drought at high resolutions, this study develops statistical models to evaluate 1‐ to 3‐month lead time predictability of meteorological and agricultural summer drought across the western U.S. at a 4‐km resolution. Our high‐resolution drought predictions have statistically significant skill (p ≤ 0.05) across 70%–100% of the western U.S., varying by evaluation metric and lead time. 1‐ to 3‐month lead time drought forecasts accurately represent monitored summer drought spatial patterns during major drought events, the interannual variability of drought area from 1982 to 2020 (r = 0.84–0.93), and drought trends (r = 0.94–0.97). 71% of western U.S summer drought area interannual variability can be explained by cold‐season (November–February) climate conditions alone allowing skillful 3‐month lead time predictions. Pre‐summer drought conditions (represented by drought indices) are the most important predictors for summer drought. Thus, the statistical models developed in this study heavily rely on the autocorrelation of chosen agricultural and meteorological drought indices which estimate land surface moisture memory. Indeed, prediction skill strongly correlates with persistence of drought conditions (r ≥ 0.73). This study is intended to support future development of operational drought early warning systems that inform drought management. Plain Language Summary: Droughts are complex and devastating natural disasters with severe implications in the western U.S. for energy production, agricultural yields, and wildfires, particularly during summer months. Drought monitoring and forecasting systems are used in the U.S. to inform drought management decisions. However, existing products and analyses are typically performed at coarse spatial resolutions (>10‐km). Because summer drought is spatially variable at finer resolutions than current drought monitoring and forecasting systems, it is valuable to develop seasonal drought forecasting systems at higher spatial resolutions. In this study we present a novel statistical modeling framework to forecast summer drought conditions at a high resolution (4‐km) at 1‐ to 3‐month lead times across the western U.S. Our models provide skillful predictions of drought across most of the western U.S., but there is degraded skill in areas where drought tends to be less persistent. Future research can expand on our work by predicting drought at finer temporal resolutions while maintaining the high spatial resolution used in this study's methodology. This research is intended to support development of future operational drought early warning systems that provide high spatial resolution forecasts. Key Points: Statistical models accurately predict summer meteorological and agricultural drought at 1‐, 2‐, and 3‐month lead times at a 4‐km resolutionMost of the interannual variability in summer drought area can be explained by preceding cold‐season climate conditionsStatistical models rely heavily on the autocorrelation of drought indices in drought forecasts [ABSTRACT FROM AUTHOR]

The extent and mechanisms by which precipitation extremes affect hydrological processes in the critical zone (CZ) of loess possessing a thick unsaturated zone remains poorly understood. To this end, we here employed a coupled liquid‒vapor‒heat‒airflow STEMMUS (simultaneous transfer of energy, mass, and momentum in unsaturated soil) model to investigate the impact of extreme precipitation, at both event and annual scales, on CZ hydrological processes within a semiarid loess site vegetated by apple trees on China's Loess Plateau. At the event scale, the vapor flux was two orders of magnitude lower than the liquid water flux. However, the thermal vapor flux penetrated to depths of 200 cm, whereas the isothermal liquid water flux only infiltrated to 100 cm during the study period, implying that thermal‐gradient‐driven vapor transfer is an important mechanism for deep‐layer recharge (DLR). At the annual scale, the DLR below 200 cm during extremely wet years was 6.5 times larger than that during extremely dry years. In contrast, extreme changes in climate had only limited impacts on evapotranspiration; the difference between extremely wet and extremely dry years averaged only 35 mm, much less than the difference in precipitation, which averaged 310 mm. However, the extremely dry years showed a higher ratio (0.58) between transpiration and evapotranspiration than did the extremely wet years (0.51). The findings reported here improve our understanding of CZ hydrological processes related to precipitation extremes in semiarid loess regions. Key Points: Thermal‐gradient‐driven vapor transfer is an important mechanism for deep‐layer recharge (DLR) in loessThe DLR below 200 cm during extremely wet years was 6.5 times larger than that during extremely dry yearsExtreme changes in precipitation had only limited impacts on evapotranspiration [ABSTRACT FROM AUTHOR]

Subsurface conditions may differ considerably from conditions indicated by surficial geologic maps, particularly in glaciated areas. Therefore, it is advantageous to use a series of lithofacies maps as a sequence of overlays plotted on transparent paper. Such maps permit direct visual determination of the lithology and areal extent of aquifers and indicate areas of possible stream losses or gains. They indicate areas and boundaries of confining layers and, in some places, they may be used to delineate areas of water-table and artesian conditions. They are especially useful in determining buried channels in glaciated areas. The porosity of rock units making up an aquifer may be plotted on a lithofacies map, and the product of the volume of each lithologic type and the porosity provides an estimate of the total water in storage. In addition, if the water-bearing properties are known, the quantity of water moving through an aquifer and the amount available to wells may be determined. Lithofacies maps also aid in the explanation of complex changes in water quality. [ABSTRACT FROM AUTHOR]

Co‐loading of mercury (Hg) with dissolved organic carbon (DOC) is a key driver of the observed spatial and temporal Hg patterns among aquatic ecosystems. Their strong biogeochemical coupling has spurred the use of DOC as a predictor of Hg concentrations and exports in boreal regions where sampling logistics for Hg are costly and complex. Yet relationships between Hg and methylmercury (MeHg) with DOC have recently been shown to be highly variable in terms of slope and strength, suggesting that mechanisms other than co‐transport along the land‐water continuum may drive the relationship between Hg and DOC across landscapes. In this study, we explore the relationship between Hg and MeHg with DOC across 18 boreal rivers collectively draining over 350,000 km2 of the eastern James Bay territory (Québec), comprising watersheds with a wide range of vegetation, water residence time and riverine DOC concentrations and optical properties. Our results show that although a large portion of the variation in Hg and MeHg is explained by concentrations of DOC, Hg‐DOC and MeHg‐DOC relationships and ratios vary greatly both spatially and temporally. We show that ratios and strength of the coupling can be predicted from system hydrology, with declines in Hg:DOC and increase in MeHg:DOC ratios and stronger coupling during the seasonal progression to warmer temperatures, higher water evaporation, and longer residence time. Our study highlights the role of seasonal hydrology and biogeochemical processing in governing Hg, MeHg and DOC patterns in boreal rivers. Plain Language Summary: Different forms of mercury and organic carbon may have similar behaviors in rivers, which facilitates understanding of the processes in the landscapes and in the water that controls the inputs and the losses of these elements. Seasonal and geographic differences in the factors behind this behavior, however, may break this similarity and highlight key processes of environmental importance. Here we show how organic carbon and mercury loaded from land tend to be strongly associated across 18 rivers found across a >350,000 km2 boreal landscape, especially during periods of high flow and low temperature that favor passive transport of these elements. Higher concentrations and proportions of biomagnifiable neurotoxic methylated mercury relative to total mercury and organic carbon concentrations, were found during warm and low flow seasons that favored high biological transformation of the mercury and carbon pools during the riverine transit from land to ocean. The amount and nature of the mercury and carbon that is loaded from land to water, and that is transformed in rivers thus greatly varies across seasons, with implications for future trends in climate and hydrology. Key Points: Hg:DOC and MeHg:DOC ratios were as variable over seasons than spatially across a 350,000 km2 boreal landscapeHg and dissolved organic carbon (DOC) were mostly coupled under conditions of high flow and low biogeochemical processingHg and colored DOC were preferentially lost during high processing seasons compared to bulk DOC and MeHg [ABSTRACT FROM AUTHOR]

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation‐phase partitioning methods (PPMs) that use near‐surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric‐based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5‐km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric‐based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post‐processing algorithm determining the snow level and the associated precipitation phase. Two ground‐based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet‐bulb temperature. Compared to the upper benchmark, the snow‐level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post‐processed snow level. The limitations to drive snowpack models at slope scale are also discussed. Plain Language Summary: The partitioning of precipitation between rainfall and snowfall is a crucial component of the evolution of the snowpack in mountains. Most snowpack models use the air temperature and humidity near the surface to derive the precipitation phase. However, the phase at the surface is strongly influenced by processes such as melting and refreezing of falling hydrometeors that occur above the surface. Atmospheric models simulate these processes and the corresponding phase at the surface. However, snowpack models rarely use this information. In this study, we considered two estimates of precipitation phase from an atmospheric model and tested them with a physically‐based snow model over the mountains of southwestern Canada and northwestern United States. The results were compared with traditional approaches using the air temperature and humidity near the surface to derive the precipitation phase. Our results showed that the precipitation phase associated with the snow level obtained from the atmospheric model improved snowfall estimate and snowpack prediction compared to the traditional approaches. In contrast, the cloud/precipitation scheme of the atmospheric model decreased performance in phase estimate and snow simulations due to missing physical processes. Our study highlights that snowpack predictions in the mountains can be improved if valuable information is obtained from atmospheric models. Key Points: Estimates of precipitation phase from an atmospheric model were used to drive snow simulations with a detailed snowpack modelSnowfall prediction and snowpack modeling are improved by using the snow level from post‐processing of the atmospheric modelDirect precipitation phase from the microphysics scheme does not improve snow simulations compared to simpler rain‐snow partitioning schemes [ABSTRACT FROM AUTHOR]

Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness of river corridors in removing excess nitrogen via denitrification were often limited to the reach‐scale and low‐order stream watersheds. We developed a basin‐scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC‐related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible. Key Points: Hyporheic exchange flux controls the spatial variation of denitrification across reaches with different sizes and land usesThe combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) explains the differences in denitrification for different land use streamsD50, stream slope, precipitation, evapotranspiration, and shrub area can explain most of the spatial variability in denitrification [ABSTRACT FROM AUTHOR]

Reproducibility is a foundational principle in scientific research. Yet in computational hydrology the code and data that actually produces published results are not regularly made available, inhibiting the ability of the community to reproduce and verify previous findings. In order to overcome this problem we recommend that reuseable code and formal workflows, which unambiguously reproduce published scientific results, are made available for the community alongside data, so that we can verify previous findings, and build directly from previous work. In cases where reproducing large-scale hydrologic studies is computationally very expensive and time-consuming, new processes are required to ensure scientific rigor. Such changes will strongly improve the transparency of hydrological research, and thus provide a more credible foundation for scientific advancement and policy support. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) provides a robust connection between hydrological cycles and surface energy balance. Accurate and near‐daily ET estimation has utility in water resources, agricultural management applications, crop yields and drought monitoring. This study describes the implementation of an ET modeling system based on a Priestley‐Taylor version of the Two‐Source (soil and vegetation) Energy Balance Model (TSEB‐PT) within Google Earth Engine environment. TSEB‐PT performance was compared with the simpler single‐source HSEB (Hybrid Surface Energy Balance) ET model to assess relative advantages and disadvantages for operational application. Results were evaluated across multiple biomes and climatic zones across the US, Europe, and Australia in comparison with eddy covariance data from 30 flux tower sites. Both models produced similar results when considering all biomes at daily, weekly, and monthly timescales. Daily ET metrics for all sites combined yielded comparable results for both models, with a slightly lower root‐mean‐square error for TSEB‐PT (HSEB) of 1.2 (1.3) mm/d and a higher correlation (r) of 0.83 (0.80), but a larger mean percent bias error (MPBE = −9%) than HSEB (MPBE = 1%). TSEB‐PT performance was lowest for sites in warm summer humid continental and hot semi‐arid climates and in evergreen broadleaf forest cover, while HSEB showed lowest performance in tropical savanna hot semi‐arid climates and in savanna covers. Model performance was improved for both cropland and non‐cropland sites when TSEB‐PT and HSEB ET estimates were combined through simple averaging due to cancellation of opposing errors, showing a promise as potential tools for water resource management on a global scale. Key Points: A Novel 30 m actual evapotranspiration mapping based on the dual (Priestley‐Taylor version of the Two‐Source Energy Balance model, TSEB‐PT) and single (hybrid single‐source energy balance model, HSEB) source energy balance models is presentedModels leverage globally available state‐of‐the‐art thermal, surface reflectance, reanalysis, and land cover data in Google Earth EngineModels are comparable in performance, and HSEB is more computationally efficient than TSEB but is slightly less accurate in croplands [ABSTRACT FROM AUTHOR]

Temperature logs are an important tool in the geothermal industry. Temperature measurements from boreholes are used for exploration, system design, and monitoring. The number of observations, however, is not always sufficient to fully determine the temperature field or explore the entire parameter space of interest. Drilling in the best locations is still difficult and expensive. It is therefore critical to optimize the number and location of boreholes. Due to its higher spatial resolution and lower cost, four‐dimensional (4D) temperature field monitoring via time‐lapse Electrical Resistivity Tomography has been investigated as a potential alternative. We use Bayesian Evidential Learning (BEL), a Monte Carlo‐based training approach, to optimize the design of a 4D temperature field monitoring experiment. We demonstrate how BEL can take into account various data source combinations (temperature logs combined with geophysical data) in the Bayesian optimal experimental design (BOED). To determine the optimal data source combination, we use the Root Mean Squared Error of the predicted target in the low dimensional latent space where BEL is solving the prediction problem. The parameter estimates are accurate enough to use in BOED. Furthermore, the method is not limited to monitoring temperature fields and can be applied to other similar experimental design problems. The method is computationally efficient and requires little training data. For the considered optimal design problem, a training set of only 200 samples and a test set of 50 samples is sufficient. Plain Language Summary: The design of experiments is a critical step in scientific research to ensure that the data collected is of sufficient quality to answer the research question. The design of an experiment is often optimized to minimize operational costs while still fulfilling the desired accuracy in the prediction. In this contribution, we use an approach called Bayesian Evidential Learning to optimize the design of an experiment. The Bayesian philosophy is used to incorporate all available information into the design of the experiment and to quantify the uncertainty in the prediction. In particular, our approach makes it straightforward to use different data sources (sensors), and different types of data (e.g., temperature, pressure, and concentration measurements). We demonstrate the method in the design of a temperature field monitoring experiment to characterize the geothermal energy storage potential of an aquifer, a key step in the development of geothermal energy projects. This framework makes experiment design easier and faster, which is required for sound risk analysis and decision‐making. The method is not limited to temperature fields and can be applied to other similar experimental problems, such as the monitoring of a contaminant plume or saltwater intrusion. Key Points: We propose a method for optimizing the design of a four‐dimensional (4D) temperature field monitoring experimentWe use Bayesian Evidential Learning (BEL) to infer the posterior distribution of the temperature field for the experiment designWe demonstrate how BEL can combine temperature logs and Electrical Resistivity Tomography data to assist in the design of experiments for a 4D temperature monitoring [ABSTRACT FROM AUTHOR]

Despite the proliferation of computer‐based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor reused. Given the commonalities between existing process‐based hydrologic models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here, we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model‐agnostic preprocessing of specific data sets from the model‐specific requirements that models impose on their input files. We use this workflow to create large‐domain (global and continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the "Community Workflows to Advance Reproducibility in Hydrologic Modeling" (CWARHM; pronounced "swarm"). Key Points: Reproducible, transparent modeling increases confidence in model simulations and requires careful tracking of all model configuration stepsWe show an example of model configuration code applied globally that is traced and shared through a version control systemStandardizing file formats and sharing of code can increase efficiency and reproducibility of modeling studies [ABSTRACT FROM AUTHOR]

The Columbia River basin is a large transboundary basin located in the Pacific Northwest. The basin spans seven US states and one Canadian province, encompassing a diverse range of hydroclimates. Strong seasonality and complex topography are projected to give rise to spatially heterogeneous climate effects on unregulated streamflow. The basin's water resources are economically critical, and regulation across the domain is extensive. Many sensitivity studies have investigated climate impacts on the basin's naturalized hydrology; however, few have considered the large role of regulation. This study investigates where and when regulation affects projected changes in streamflow by comparing climate outcomes across 80‐member ensembles of unregulated and regulated streamflow projections at 75 sites across the basin. Unregulated streamflow projections are taken from an existing data set of climate projections derived from Coupled Model Intercomparison Project version 5 Global Climate Models. Regulated streamflow projections were modeled by the US Army Corps of Engineers and the US Bureau of Reclamation by using these unregulated flows as input to hydro‐regulation models that simulate operations based on current and historical water demands. Regulation dampens shifts in winter and summer streamflow volumes. Regulation generally attenuates changes in cool‐season high flow extremes but amplifies shifts in warm‐season and annual high flow extremes at historically snow‐dominant headwater reservoirs. Regulation reduces dry‐season low flow changes in headwater tributaries where regulation is large but elsewhere has little effect on changes in low flows. Results highlight the importance of accounting for water management in climate sensitivity analysis particularly in snow‐dominant basins. Key Points: Regulation dampens future winter and summer volume changes where the degree of upstream regulation is largeRegulation dampens cool‐season high flow extreme increases and amplifies warm‐season increases at snow‐dominant headwater basinsRegulation dampens low flow changes in tributaries with a large degree of upstream regulation but has little to no effect elsewhere [ABSTRACT FROM AUTHOR]

Flow resistance in open channels determines the average flow velocity in a river, with important implications for downstream and at‐a‐station hydraulic geometry and sediment transport in natural channels. However, flow resistance in steep mountain channels is challenging to understand and predict due to heterogeneous channel morphology and spatially variable flow. Using a double‐averaging approach appropriate for very rough channels, the width, reach, and double‐averaged variables for steady, uniform flow in an open channel are derived. Using an empirical model for the coefficient of form drag, a flow resistance model is derived that compares well to a large data set of flow velocity in natural channels. Several parameters that together describe bed morphology are observed to be relatively consistent in the data set used and compare well to previous estimates of their values. This implies a degree of self‐organization in bed morphology that may simplify the challenging problem of predicting flow resistance in steep mountain rivers. Key Points: A semiempirical model of form drag on boulders is shown to be consistent with flow resistance in rough channelsData are suggestive that geometry and flow resistance in rough channels are conditioned to maximize flow resistance, leading to convergence in behavior [ABSTRACT FROM AUTHOR]

Land Surface Models (LSMs) are key components of Earth System Models, which the Intergovernmental Panel on Climate Change relies on in many of their studies. However, these models either neglect or oversimplify basin‐scale hydrological processes that produce the land surface water balance. Bringing the dominant physical processes from the local scale up to the LSMs presents a significant challenge for improving the models. This article presents an upscaling (or bottom‐up) approach to identify the basin‐scale driving variables that need to be exported into LSMs. The approach is developed on the Little Washita Watershed (OK, USA) using 20‐year hydrology (1993–2013). A physically based 3D model built with HydroGeoSphere first produces a reference simulation. An equivalent hillslope model is then able to capture both 3D simulated water balance and local water table dynamics with reasonable accuracy. The physical analysis of the water balance in the different hillslope compartments leads to the identification of two driving variables: seepage face extension and water table slope. The two variables are then implemented in a conceptual model. Results show a good capacity of this model to capture the water balance of hillslopes having different lengths and slopes. Moreover, the model is able to capture the water balance of the reference simulation with reasonable accuracy. The proposed approach thus reduces the 3D watershed model to a two‐variable conceptual model that constitutes a basis for developing an improved LSM hydrology. Key Points: An upscaling approach reduces model dimensionality from 3D watershed to 2D equivalent hillslope, and then to a two‐hydrologic‐variable modelThe seepage face extension and the water table slope are identified as two key variablesThe approach is validated on the 20‐year hydrology (1993–2013) of the Little Washita Watershed (Ok, USA) [ABSTRACT FROM AUTHOR]