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Showing papers in "Water Resources Research in 2016"


Journal ArticleDOI
TL;DR: In this article, a Coastline Resolution Improvement (CRI) filter is developed to reduce leakage errors across coastlines, and a set of gain factors is derived to reduce the leakage errors for continental hydrology applications.
Abstract: Recent advances in processing data from the Gravity Recovery and Climate Experiment (GRACE) have led to a new generation of gravity solutions constrained within a Bayesian framework to remove correlated errors rather than relying on empirical filters. The JPL RL05M mascon solution is one such solution, solving for mass variations using spherical cap mass concentration elements (mascons), while relying on external information provided by near-global geophysical models to constrain the solution. This new gravity solution is fundamentally different than the traditional spherical harmonic gravity solution, and as such, requires different care when postprocessing. Here, we discuss two classes of postprocessing considerations for the JPL RL05M GRACE mascon solution: (1) reducing leakage errors across land/ocean boundaries, and (2) scaling the solutions to account for leakage errors introduced through parameterizing the gravity solution in terms of mascons. A Coastline Resolution Improvement (CRI) filter is developed to reduce leakage errors across coastlines. Synthetic simulations reveal a reduction in leakage errors of ∼50%, such that residual leakage errors are ∼1 cm equivalent water height (EWH) averaged globally. A set of gain factors is derived to reduce leakage errors for continental hydrology applications. The combined effect of the CRI filter coupled with application of the gain factors, is shown to reduce leakage errors when determining the mass balance of large (>160,000 km2) hydrological basins from 11% - 30% (0.6-1.5 mm EWH) averaged globally, with local improvements up to 8% - 81% (9-19 mm EWH). This article is protected by copyright. All rights reserved.

382 citations


Journal ArticleDOI
TL;DR: In this paper, the authors compare GRACE mascons from the Center for Space Research (CSR-M) with NASA JPL mascons (JPL-M), and with CSR Tellus gridded spherical harmonics rescaled (sf) (CSRT-GSH).
Abstract: Recent developments in mascon (mass concentration) solutions for GRACE (Gravity Recovery and Climate Experiment) satellite data have significantly increased the spatial localization and amplitude of recovered terrestrial Total Water Storage anomalies (TWSA); however, land hydrology applications have been limited. Here we compare TWSA from April 2002 through March 2015 from (1) newly released GRACE mascons from the Center for Space Research (CSR-M) with (2) NASA JPL mascons (JPL-M), and with (3) CSR Tellus gridded spherical harmonics rescaled (sf) (CSRT-GSH.sf) in 176 river basins, ∼60% of the global land area. Time series in TWSA mascons (CSR-M and JPL-M) and spherical harmonics are highly correlated (rank correlation coefficients mostly >0.9). The signal from long-term trends (up to ±20 mm/yr) is much less than that from seasonal amplitudes (up to 250 mm). Net long-term trends, summed over all 176 basins, are similar for CSR and JPL mascons (66–69 km3/yr) but are lower for spherical harmonics (∼14 km3/yr). Long-term TWSA declines are found mostly in irrigated basins (−41 to −69 km3/yr). Seasonal amplitudes agree among GRACE solutions, increasing confidence in GRACE-based seasonal fluctuations. Rescaling spherical harmonics significantly increases agreement with mascons for seasonal fluctuations, but less for long-term trends. Mascons provide advantages relative to spherical harmonics, including (1) reduced leakage from land to ocean increasing signal amplitude, and (2) application of geophysical data constraints during processing with little empirical postprocessing requirements, making it easier for nongeodetic users. Results of this product intercomparison should allow hydrologists to better select suitable GRACE solutions for hydrologic applications.

317 citations


Journal ArticleDOI
TL;DR: In this article, a scheme for regionalization of model parameters at the global scale was developed for streamflow simulation, using data from a diverse set of small-to-medium sized catchments (10-10,000 km).
Abstract: Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10-10,000 km

256 citations


Journal ArticleDOI
TL;DR: In this paper, the authors provide a global parameterization of an analytical Zr model that balances the marginal carbon cost and benefit of deeper roots, and produce a climatological (i.e., 1982-2010 average) global Zr map.
Abstract: Plant rooting depth (Zr) is a key parameter in hydrological and biogeochemical models, yet the global spatial distribution of Zr is largely unknown due to the difficulties in its direct measurement. Additionally, Zr observations are usually only representative of a single plant or several plants, which can differ greatly from the effective Zr over a modelling unit (e.g., catchment or grid-box). Here, we provide a global parameterization of an analytical Zr model that balances the marginal carbon cost and benefit of deeper roots, and produce a climatological (i.e., 1982-2010 average) global Zr map. To test the Zr estimates, we apply the estimated Zr in a highly transparent hydrological model (i.e., the Budyko-Choudhury-Porporato (BCP) model) to estimate mean annual actual evapotranspiration (E) across the globe. We then compare the estimated E with both water balance-based E observations at 32 major catchments and satellite grid-box retrievals across the globe. Our results show that the BCP model, when implemented with Zr estimated herein, optimally reproduced the spatial pattern of E at both scales (i.e., R2=0.94, RMSD=74 mm yr−1 for catchments, and R2=0.90, RMSD=125 mm yr−1 for grid-boxes) and provides improved model outputs when compared to BCP model results from two already existing global Zr datasets. These results suggest that our Zr estimates can be effectively used in state-of-the-art hydrological models, and potentially biogeochemical models, where the determination of Zr currently largely relies on biome type-based look-up tables. This article is protected by copyright. All rights reserved.

165 citations


Journal ArticleDOI
TL;DR: In this article, the authors evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily "remote sensing" measurements derived from hydraulic models corrupted with minimal observational errors, and found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-ofbank flows, multichannel planforms, and backwater effects.
Abstract: The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 125% SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results

164 citations


Journal ArticleDOI
TL;DR: It is shown how a Hazard Scenario can be identified in terms of a specific geometry and a suitable probability level, and how this approach is well suited to cope with the notion of Failure Probability, a tool traditionally used for design and risk assessment in engineering practice.
Abstract: This paper is of methodological nature, and deals with the foundations of Risk Assessment. Several international guidelines have recently recommended to select appropriate/relevant Hazard Scenarios in order to tame the consequences of (extreme) natural phenomena. In particular, the scenarios should be multivariate, i.e., they should take into account the fact that several variables, generally not independent, may be of interest. In this work, it is shown how a Hazard Scenario can be identified in terms of (i) a specific geometry and (ii) a suitable probability level. Several scenarios, as well as a Structural approach, are presented, and due comparisons are carried out. In addition, it is shown how the Hazard Scenario approach illustrated here is well suited to cope with the notion of Failure Probability, a tool traditionally used for design and risk assessment in engineering practice. All the results outlined throughout the work are based on the Copula Theory, which turns out to be a fundamental theoretical apparatus for doing multivariate risk assessment: formulas for the calculation of the probability of Hazard Scenarios in the general multidimensional case ( d≥2) are derived, and worthy analytical relationships among the probabilities of occurrence of Hazard Scenarios are presented. In addition, the Extreme Value and Archimedean special cases are dealt with, relationships between dependence ordering and scenario levels are studied, and a counter-example concerning Tail Dependence is shown. Suitable indications for the practical application of the techniques outlined in the work are given, and two case studies illustrate the procedures discussed in the paper.

158 citations


Journal ArticleDOI
TL;DR: In this paper, the concept of underlying water use efficiency (uWUE) was used to develop a new method for ET partitioning by assuming that the maximum, or the potential uWUE is related to transpiration while the averaged or apparent UWUE was related to evapotranspiration.
Abstract: Evapotranspiration (ET) is dominated by transpiration (T) in the terrestrial water cycle. However, continuous measurement of transpiration is still difficult, and the effect of vegetation on ET partitioning is unclear. The concept of underlying water use efficiency (uWUE) was used to develop a new method for ET partitioning by assuming that the maximum, or the potential uWUE is related to T while the averaged or apparent uWUE is related to ET. T/ET was thus estimated as the ratio of the apparent over the potential uWUE using half-hourly flux data from 17 AmeriFlux sites. The estimated potential uWUE was shown to be essentially constant for 14 of the 17 sites, and was broadly consistent with the uWUE evaluated at the leaf scale. The annual T/ET was the highest for croplands, i.e., 0.69 for corn and 0.62 for soybean, followed by grasslands (0.60) and evergreen needle leaf forests (0.56), and was the lowest for deciduous broadleaf forests (0.52). The enhanced vegetation index (EVI) was shown to be significantly correlated with T/ET and could explain about 75% of the variation in T/ET among the 71 site-years. The coefficients of determination between EVI and T/ET were 0.84 and 0.82 for corn and soybean, respectively, and 0.77 for deciduous broadleaf forests and grasslands, but only 0.37 for evergreen needle leaf forests. This ET partitioning method is sound in principle and simple to apply in practice, and would enhance the value and role of global FLUXNET in estimating T/ET variations and monitoring ecosystem dynamics.

155 citations


Journal ArticleDOI
TL;DR: In this paper, the authors present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations, which is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons.
Abstract: We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20–60% of the total forecast value.

148 citations


Journal ArticleDOI
TL;DR: In this paper, the Euler characteristic of the nonwetting phase (χn) is used as a shape measure of displacement fronts to characterize the hysteretic behavior of two-phase flow in porous media.
Abstract: The macroscopic description of the hysteretic behavior of two-phase flow in porous media remains a challenge. It is not obvious how to represent the underlying pore-scale processes at the Darcy-scale in a consistent way. Darcy-scale thermodynamic models do not completely eliminate hysteresis and our findings indicate that the shape of displacement fronts is an additional source of hysteresis that has not been considered before. This is a shortcoming because effective process behavior such as trapping efficiency of CO2 or oil production during water flooding are directly linked to pore-scale displacement mechanisms with very different front shape such as capillary fingering, flat frontal displacement, or cluster growth. Here we introduce fluid topology, expressed by the Euler characteristic of the nonwetting phase (χn), as a shape measure of displacement fronts. Using two high-quality data sets obtained by fast X-ray tomography, we show that χn is hysteretic between drainage and imbibition and characteristic for the underlying displacement pattern. In a more physical sense, the Euler characteristic can be interpreted as a parameter describing local fluid connectedness. It may provide the closing link between a topological characterization and macroscopic formulations of two-phase immiscible displacement in porous rock. Since fast X-ray tomography is currently becoming a mature technique, we expect a significant growth in high-quality data sets of real time fluid displacement processes in the future. The novel measures of fluid topology presented here have the potential to become standard metrics needed to fully explore them.

145 citations


Journal ArticleDOI
TL;DR: Prec-DWARF (Precipitation Downscaling with Adaptable Random Forests) as mentioned in this paper is a machine learning based method for statistical downscaling of precipitation.
Abstract: This paper introduces Prec-DWARF (Precipitation Downscaling With Adaptable Random Forests), a novel machine-learning based method for statistical downscaling of precipitation. Prec-DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge-radar precipitation data at 0.125° from NLDAS-2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5° and 1°). Quantitative evaluation of these experiments demonstrates that Prec-DWARF consistently outperforms the baseline (i.e., bi-linear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec-DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine-scale spatial structure, especially for the 1° experiments. Prec-DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate feature importance analysis shows that the most important predictors for the downscaling are the coarse scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec-DWARF and machine-learning based techniques in general for the statistical downscaling of precipitation. This article is protected by copyright. All rights reserved.

142 citations


Journal ArticleDOI
TL;DR: It is recommended that reuseable code and formal workflows, which unambiguously reproduce published scientific results, are made available for the community alongside data, so that the community can verify previous findings, and build directly from previous work.
Abstract: 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.

Journal ArticleDOI
TL;DR: A new and general sensitivity analysis framework (called VARS), based on an analogy to “variogram analysis,” that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space is presented.
Abstract: Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role and importance of these factors in producing model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to “variogram analysis,” that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. Synthetic functions that resemble actual model response surfaces are used to illustrate the concepts, and show VARS to be as much as two orders of magnitude more computationally efficient than the state-of-the-art Sobol approach. In a companion paper, we propose a practical implementation strategy, and demonstrate the effectiveness, efficiency, and reliability (robustness) of the VARS framework on real-data case studies.

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the effect of wettability of carbonate rocks on the morphologies of remaining oil after sequential oil and brine injection in a capillary-dominated flow regime at elevated pressure.
Abstract: We have investigated the effect of wettability of carbonate rocks on the morphologies of remaining oil after sequential oil and brine injection in a capillary-dominated flow regime at elevated pressure. The wettability of Ketton limestone was altered in situ using an oil phase doped with fatty acid which produced mixed-wet conditions (the contact angle where oil contacted the solid surface, measured directly from the images, θ=180°, while brine-filled regions remained water-wet), whereas the untreated rock (without doped oil) was weakly water-wet (θ=47 ± 9°). Using X-ray micro-tomography, we show that the brine displaces oil in larger pores during brine injection in the mixed-wet system, leaving oil layers in the pore corners or sandwiched between two brine interfaces. These oil layers, with an average thickness of 47 ± 17 µm, may provide a conductive flow path for slow oil drainage. In contrast, the oil fragments into isolated oil clusters/ganglia during brine injection under water-wet conditions. Although the remaining oil saturation in a water-wet system is about a factor of two larger than that obtained in the mixed-wet rock, the measured brine-oil interfacial area of the disconnected ganglia is a factor of three smaller than that of oil layers.

Journal ArticleDOI
TL;DR: A robust reservoir outflow simulation model is presented, which incorporates one of the well‐developed data‐mining models (Classification and Regression Tree) to predict the complicated human‐controlled reservoir outflows and extract the reservoir operation patterns.
Abstract: Author(s): Yang, T; Gao, X; Sorooshian, S; Li, X | Abstract: The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others.

Journal ArticleDOI
TL;DR: In this article, the authors applied an approach based on Pareto optimality to explore trade-offs between model performance in different climatic conditions, and found that the latter often missed potentially promising parameter sets within a given model structure, giving a false negative impression of the capabilities of the model.
Abstract: Hydrologic models have potential to be useful tools in planning for future climate variability. However, recent literature suggests that the current generation of conceptual rainfall runoff models tend to underestimate the sensitivity of runoff to a given change in rainfall, leading to poor performance when evaluated over multiyear droughts. This research revisited this conclusion, investigating whether the observed poor performance could be due to insufficient model calibration and evaluation techniques. We applied an approach based on Pareto optimality to explore trade-offs between model performance in different climatic conditions. Five conceptual rainfall runoff model structures were tested in 86 catchments in Australia, for a total of 430 Pareto analyses. The Pareto results were then compared with results from a commonly used model calibration and evaluation method, the Differential Split Sample Test. We found that the latter often missed potentially promising parameter sets within a given model structure, giving a false negative impression of the capabilities of the model. This suggests that models may be more capable under changing climatic conditions than previously thought. Of the 282[347] cases of apparent model failure under the split sample test using the lower [higher] of two model performance criteria trialed, 155[120] were false negatives. We discuss potential causes of remaining model failures, including the role of data errors. Although the Pareto approach proved useful, our aim was not to suggest an alternative calibration strategy, but to critically assess existing methods of model calibration and evaluation. We recommend caution when interpreting split sample results.

Journal ArticleDOI
TL;DR: In this paper, a spatially referenced regression on watershed attributes (SPARROW) water quality model was applied to assess the spatial distribution of base flow, the fraction of streamflow supported by base flow and estimates of and potential processes contributing to the amount of baseflow that is lost during in-stream transport in the Upper Colorado River Basin (UCRB).
Abstract: The Colorado River has been identified as the most overallocated river in the world. Considering predicted future imbalances between water supply and demand and the growing recognition that base flow (a proxy for groundwater discharge to streams) is critical for sustaining flow in streams and rivers, there is a need to develop methods to better quantify present-day base flow across large regions. We adapted and applied the spatially referenced regression on watershed attributes (SPARROW) water quality model to assess the spatial distribution of base flow, the fraction of streamflow supported by base flow, and estimates of and potential processes contributing to the amount of base flow that is lost during in-stream transport in the Upper Colorado River Basin (UCRB). On average, 56% of the streamflow in the UCRB originated as base flow, and precipitation was identified as the dominant driver of spatial variability in base flow at the scale of the UCRB, with the majority of base flow discharge to streams occurring in upper elevation watersheds. The model estimates an average of 1.8 × 1010 m3/yr of base flow in the UCRB; greater than 80% of which is lost during in-stream transport to the Lower Colorado River Basin via processes including evapotranspiration and water diversion for irrigation. Our results indicate that surface waters in the Colorado River Basin are dependent on base flow, and that management approaches that consider groundwater and surface water as a joint resource will be needed to effectively manage current and future water resources in the Basin.

Journal ArticleDOI
TL;DR: In this article, the authors compare the predictions of Poisson DFN to new DFN models where fractures result from a growth process defined by simplified kinematic rules for nucleation, growth and fracture arrest.
Abstract: A major use of DFN models for industrial applications is to evaluate permeability and flow structure in hardrock aquifers from geological observations of fracture networks. The relationship between the statistical fracture density distributions and permeability has been extensively studied, but there has been little interest in the spatial structure of DFN models, which is generally assumed to be spatially random (i.e. Poisson). In this paper, we compare the predictions of Poisson DFNs to new DFN models where fractures result from a growth process defined by simplified kinematic rules for nucleation, growth and fracture arrest (Davy et al, 2010, 2013). This so-called ‘kinematic fracture model' is characterized by a large proportion of T-intersections, and a smaller number of intersections per fracture. Several kinematic models were tested and compared with Poisson DFN models with the same density, length and orientation distributions. Connectivity, permeability and flow distribution were calculated for 3D networks with a self-similar power-law fracture length distribution. For the same statistical properties in orientation and density, the permeability is systematically and significantly smaller by a factor of 1.5 to 10 for kinematic than for Poisson models. In both cases, the permeability is well described by a linear relationship with the areal density p32, but the threshold of kinematic models is 50% larger than of Poisson models. Flow channeling is also enhanced in kinematic DFN models. This analysis demonstrates the importance of choosing an appropriate DFN organization for predicting flow properties from fracture network parameters.

Journal ArticleDOI
TL;DR: In this paper, the authors quantified signature uncertainty stemming from discharge data uncertainty for 43 UK catchments and propagated these uncertainties in signature regionalization, while accounting for regionalization uncertainty with a weighted-pooling-group approach.
Abstract: Reliable information about hydrological behavior is needed for water-resource management and scientific investigations. Hydrological signatures quantify catchment behavior as index values, and can be predicted for ungauged catchments using a regionalization procedure. The prediction reliability is affected by data uncertainties for the gauged catchments used in prediction and by uncertainties in the regionalization procedure. We quantified signature uncertainty stemming from discharge data uncertainty for 43 UK catchments and propagated these uncertainties in signature regionalization, while accounting for regionalization uncertainty with a weighted-pooling-group approach. Discharge uncertainty was estimated using Monte Carlo sampling of multiple feasible rating curves. For each sampled rating curve, a discharge time series was calculated and used in deriving the gauged signature uncertainty distribution. We found that the gauged uncertainty varied with signature type, local measurement conditions and catchment behavior, with the highest uncertainties (median relative uncertainty ±30–40% across all catchments) for signatures measuring high- and low-flow magnitude and dynamics. Our regionalization method allowed assessing the role and relative magnitudes of the gauged and regionalized uncertainty sources in shaping the signature uncertainty distributions predicted for catchments treated as ungauged. We found that 1) if the gauged uncertainties were neglected there was a clear risk of over-conditioning the regionalization inference, e.g. by attributing catchment differences resulting from gauged uncertainty to differences in catchment behavior, and 2) uncertainty in the regionalization results was lower for signatures measuring flow distribution (e.g. mean flow) than flow dynamics (e.g. autocorrelation), and for average flows (and then high flows) compared to low flows. This article is protected by copyright. All rights reserved.

Journal ArticleDOI
TL;DR: In this article, the authors used NASA's Gravity Recovery and Climate Experiment (GRACE) satellites for the period of January 2003 to May 2014 to characterize spatiotemporal variations of terrestrial water storage and groundwater storage (GWS).
Abstract: Investigating changes in terrestrial water storage (TWS) is important for understanding response of the hydrological cycle to recent climate variability worldwide. This is particularly critical in India where the current economic development and food security greatly depend on its water resources. We use 129 monthly gravity solutions from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites for the period of January 2003 to May 2014 to characterize spatiotemporal variations of TWS and groundwater storage (GWS). The spatiotemporal evolution of GRACE data reflects consistent patterns with that of several hydroclimatic variables and also shows that most of the water loss has occurred in the northern parts of India. Substantial GWS depletion at the rate of 1.25 and 2.1 cm yr−1 has taken place, respectively in the Ganges Basin and Punjab state, which are known as the India's grain bowl. Of particular concern is the Ganges Basin's storage loss in drought years, primarily due to anthropogenic groundwater withdrawals that sustain rice and wheat cultivation. We estimate these losses to be approximately 41, 44, and 42 km3 in 2004, 2009, and 2012, respectively. The GWS depletions that constitute about 90% of the observed TWS loss are also influenced by a marked rise in temperatures since 2008. A high degree of correspondence between GRACE-derived GWS and in situ groundwater levels from observation well validates the results. This validation increases confidence level in the application of GRACE observations in monitoring large-scale storage changes in intensely irrigated areas in India and other regions around the world.

Journal ArticleDOI
TL;DR: In this paper, the authors describe how channel velocity and stem-generated turbulence influence the deposition within and around an emergent patch of model vegetation, with a particular focus on deposition within the patch.
Abstract: The interaction between flow and vegetation creates feedbacks to deposition that vary with channel velocity. This experimental study describes how channel velocity and stem-generated turbulence influence the deposition within and around an emergent patch of model vegetation, with a particular focus on deposition within the patch. The Reynolds number threshold for stem-scale turbulence generation was determined using velocity spectra and flow visualization. At high channel velocity resuspension occurred in the bare regions of the channel and a nonuniform spatial distribution of net deposition was observed around and within the patch. In contrast, at low channel velocity there was no (or limited) resuspension and a uniform distribution of net deposition was observed around and within the patch. The deposition inside the patch was enhanced, relative to a bare-channel control, only when the following two criteria were met: (1) the absence of stem turbulence, and (2) the presence of sediment resuspension in the bare channel. Comparison to previous lab and field studies further support these criteria.

Journal ArticleDOI
TL;DR: In this article, the authors investigated fundamental processes that contribute to permafrost thaw by comparing observed and simulated thaw development and landscape transition of a peat plateau-wetland complex in the Northwest Territories, Canada from 1970 to 2012.
Abstract: Recent climate change has reduced the spatial extent and thickness of permafrost in many discontinuous permafrost regions. Rapid permafrost thaw is producing distinct landscape changes in the Taiga Plains of the Northwest Territories, Canada. As permafrost bodies underlying forested peat plateaus shrink, the landscape slowly transitions into unforested wetlands. The expansion of wetlands has enhanced the hydrologic connectivity of many watersheds via new surface and near-surface flow paths, and increased streamflow has been observed. Furthermore, the decrease in forested peat plateaus results in a net loss of boreal forest and associated ecosystems. This study investigates fundamental processes that contribute to permafrost thaw by comparing observed and simulated thaw development and landscape transition of a peat plateau-wetland complex in the Northwest Territories, Canada from 1970 to 2012. Measured climate data are first used to drive surface energy balance simulations for the wetland and peat plateau. Near-surface soil temperatures simulated in the surface energy balance model are then applied as the upper boundary condition to a three-dimensional model of subsurface water flow and coupled energy transport with freeze-thaw. Simulation results demonstrate that lateral heat transfer, which is not considered in many permafrost models, can influence permafrost thaw rates. Furthermore, the simulations indicate that landscape evolution arising from permafrost thaw acts as a positive feedback mechanism that increases the energy absorbed at the land surface and produces additional permafrost thaw. The modeling results also demonstrate that flow rates in local groundwater flow systems may be enhanced by the degradation of isolated permafrost bodies.

Journal ArticleDOI
TL;DR: In this article, the authors quantify event water delivery to tile drains via macropore flow paths during storm events and determine the effect of tillage practices on event water and P delivery to tiles.
Abstract: Elevated phosphorus (P) concentrations in subsurface drainage water are thought to be the result of P bypassing the soil matrix via macropore flow. The objectives of this study were to quantify event water delivery to tile drains via macropore flow paths during storm events and to determine the effect of tillage practices on event water and P delivery to tiles. Tile discharge, total dissolved P (DP) and total P (TP) concentrations, and stable oxygen and deuterium isotopic signatures were measured from two adjacent tile-drained fields in Ohio, USA during seven spring storms. Fertilizer was surface-applied to both fields and disk tillage was used to incorporate the fertilizer on one field while the other remained in no-till. Median DP concentration in tile discharge prior to fertilizer application was 0.08 mg L−1 in both fields. Following fertilizer application, median DP concentration was significantly greater in the no-tilled field (1.19 mg L−1) compared to the tilled field (0.66 mg L−1), with concentrations remaining significantly greater in the no-till field for the remainder of the monitored storms. Both DP and TP concentrations in the no-till field were significantly related to event water contributions to tile discharge, while only TP concentration was significantly related to event water in the tilled field. Event water accounted for between 26 and 69% of total tile discharge from both fields, but tillage substantially reduced maximum contributions of event water. Collectively, these results suggest that incorporating surface-applied fertilizers has the potential to substantially reduce the risk of P transport from tile-drained fields.

Journal ArticleDOI
TL;DR: In this article, the authors derived time variable travel time distributions for 35 study sites within the Attert catchment in Luxembourg using the soil physical model HYDRUS-1D, and tracked the water parcels introduced with each rainfall event over a period of several years.
Abstract: Water travel times reflect hydrological processes, yet we know little about how travel times in the unsaturated zone vary with time. Using the soil physical model HYDRUS-1D, we derived time variable travel time distributions for 35 study sites within the Attert catchment in Luxembourg. While all sites experience similar climatic forcing, they differ with regard to soil types (16 Cambisols, 12 Arenosols, and 7 Stagnosols) and the vegetation cover (29 forest and 6 grassland). We estimated site specific water flow and transport parameters by fitting the model simulations to observed soil moisture time series and depth profiles of pore water stable isotopes. With the calibrated model, we tracked the water parcels introduced with each rainfall event over a period of several years. Our results show that the median travel time of water from the soil surface to depths down to 200 cm is mainly driven by the subsequent rainfall amounts. The median time until precipitation is taken up by roots is governed by the seasonality of evapotranspiration rates. The ratio between the amount of water that leaves the soil profile by on the one hand and evaporation and transpiration on the other hand also shows an annual cycle. This time variable response due to climatic forcing is furthermore visible in the multimodal nature of the site specific master transit time distribution representing the flow-averaged probability density for rainwater to become recharge. The spatial variability of travel times is mainly driven by soil texture and structure, with significant longer travel times for the clayey Stagnosols than for the loamy to sandy Cambisols and Arenosols.

Journal ArticleDOI
TL;DR: In this article, a Bayesian approach to a nonstationary, bivariate probabilistic model, including the estimation of Copula parameters, is used to assess the time varying return period of the current drought.
Abstract: Using a multi-century reconstruction of drought, we investigate how rare the 2012-2015 California drought is. A Bayesian approach to a nonstationary, bivariate probabilistic model, including the estimation of Copula parameters is used to assess the time varying return period of the current drought. Both the duration and severity of drought exhibit similar multi-century trends. The period from 800-1200 AD was perhaps more similar to the recent period than the period from 1200 to 1800 AD. The median return period of the recent drought accounting for both duration and severity, varies from approximately 667 to 2652 years, if the model parameters from the different time periods are considered. However, we find that the recent California drought is of unprecedented severity, especially given the relatively modest duration of the drought. The return period of the severity of the recent drought given its 4-year duration is estimated to be nearly 21,000 years. This article is protected by copyright. All rights reserved.

Journal ArticleDOI
TL;DR: In this article, the authors demonstrate the synergistic application of remote sensing and watershed modeling to capture the dynamics and quantity of flow in the Amazon River Basin, respectively, by applying satellite altimetry to a poorly gauged basin.
Abstract: In this study, rating curves (RCs) were determined by applying satellite altimetry to a poorly gauged basin. This study demonstrates the synergistic application of remote sensing and watershed modeling to capture the dynamics and quantity of flow in the Amazon River Basin, respectively. Three major advancements for estimating basin‐scale patterns in river discharge are described. The first advancement is the preservation of the hydrological meanings of the parameters expressed by Manning's equation to obtain a data set containing the elevations of the river beds throughout the basin. The second advancement is the provision of parameter uncertainties and, therefore, the uncertainties in the rated discharge. The third advancement concerns estimating the discharge while considering backwater effects. We analyzed the Amazon Basin using nearly one thousand series that were obtained from ENVISAT and Jason‐2 altimetry for more than 100 tributaries. Discharge values and related uncertainties were obtained from the rain‐discharge MGB‐IPH model. We used a global optimization algorithm based on the Monte Carlo Markov Chain and Bayesian framework to determine the rating curves. The data were randomly allocated into 80% calibration and 20% validation subsets. A comparison with the validation samples produced a Nash‐Sutcliffe efficiency ( urn:x-wiley:00431397:media:wrcr22024:wrcr22024-math-0001) of 0.68. When the MGB discharge uncertainties were less than 5%, the urn:x-wiley:00431397:media:wrcr22024:wrcr22024-math-0002 value increased to 0.81 (mean). A comparison with the in situ discharge resulted in an urn:x-wiley:00431397:media:wrcr22024:wrcr22024-math-0003 value of 0.71 for the validation samples (and 0.77 for calibration). The urn:x-wiley:00431397:media:wrcr22024:wrcr22024-math-0004 values at the mouths of the rivers that experienced backwater effects significantly improved when the mean monthly slope was included in the RC. Our RCs were not mission‐dependent, and the urn:x-wiley:00431397:media:wrcr22024:wrcr22024-math-0005 value was preserved when applying ENVISAT rating curves to Jason‐2 altimetry at crossovers. The cease‐to‐flow parameter of our RCs provided a good proxy for determining river bed elevation. This proxy was validated against Acoustic Doppler current profiler (ADCP) cross sections with an accuracy of more than 90%. Altimetry measurements are routinely delivered within a few days, and this RC data set provides a simple and cost‐effective tool for predicting discharge throughout the basin in nearly real time.

Journal ArticleDOI
TL;DR: In this paper, the authors developed and implemented a practical "star-based" sampling strategy (called STAR-VARS), for the application of VARS to real-world problems and also developed a bootstrap approach to provide confidence level estimates for the VARS sensitivity metrics and evaluate the reliability of inferred factor rankings.
Abstract: Based on the theoretical framework for sensitivity analysis called “Variogram Analysis of Response Surfaces” (VARS), developed in the companion paper, we develop and implement a practical “star-based” sampling strategy (called STAR-VARS), for the application of VARS to real-world problems. We also develop a bootstrap approach to provide confidence level estimates for the VARS sensitivity metrics and to evaluate the reliability of inferred factor rankings. The effectiveness, efficiency, and robustness of STAR-VARS are demonstrated via two real-data hydrological case studies (a 5-parameter conceptual rainfall-runoff model and a 45-parameter land surface scheme hydrology model), and a comparison with the “derivative-based” Morris and “variance-based” Sobol approaches are provided. Our results show that STAR-VARS provides reliable and stable assessments of “global” sensitivity across the full range of scales in the factor space, while being 1–2 orders of magnitude more efficient than the Morris or Sobol approaches.

Journal ArticleDOI
TL;DR: In this article, the authors developed an approach that eliminates most of these limitations to produce an approximately 3 day temporal resolution water level time series from the original typically (sub)monthly data sets for the Po River in detail, and for Congo, Mississippi, and Danube Rivers.
Abstract: Limitations of satellite radar altimetry for operational hydrology include its spatial and temporal sampling as well as measurement problems caused by local topography and heterogeneity of the reflecting surface. In this study, we develop an approach that eliminates most of these limitations to produce an approximately 3 day temporal resolution water level time series from the original typically (sub)monthly data sets for the Po River in detail, and for Congo, Mississippi, and Danube Rivers. We follow a geodetic approach by which, after estimating and removing intersatellite biases, all virtual stations of several satellite altimeters are connected hydraulically and statistically to produce water level time series at any location along the river. We test different data-selection strategies and validate our method against the extensive available in situ data over the Po River, resulting in an average correlation of 0.7, Root-Mean-Square Error of 0.8 m, bias of −0.4 m, and Nash-Sutcliffe Efficiency coefficient of 0.5. We validate the transferability of our method by applying it to the Congo, Mississippi, and Danube Rivers, which have very different geomorphological and climatic conditions. The methodology yields correlations above 0.75 and Nash-Sutcliffe coefficients of 0.84 (Congo), 0.34 (Mississippi), and 0.35 (Danube).

Journal ArticleDOI
TL;DR: A statistical model in which EVT results apply not only to heavy, but also to low precipitation amounts (zeros excluded), in compliance with EVT on both ends of the spectrum and allows a smooth transition between the two tails, while keeping a low number of parameters.
Abstract: In statistics, extreme events are often defined as excesses above a given large threshold. This definition allows hydrologists and flood planners to apply Extreme-Value Theory (EVT) to their time series of interest. Even in the stationary univariate context, this approach has at least two main drawbacks. First, working with excesses implies that a lot of observations (those below the chosen threshold) are completely disregarded. The range of precipitation is artificially shopped down into two pieces, namely large intensities and the rest, which necessarily imposes different statistical models for each piece. Second, this strategy raises a nontrivial and very practical difficultly: how to choose the optimal threshold which correctly discriminates between low and heavy rainfall intensities. To address these issues, we propose a statistical model in which EVT results apply not only to heavy, but also to low precipitation amounts (zeros excluded). Our model is in compliance with EVT on both ends of the spectrum and allows a smooth transition between the two tails, while keeping a low number of parameters. In terms of inference, we have implemented and tested two classical methods of estimation: likelihood maximization and probability weighed moments. Last but not least, there is no need to choose a threshold to define low and high excesses. The performance and flexibility of this approach are illustrated on simulated and hourly precipitation recorded in Lyon, France.

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TL;DR: In this paper, the authors investigated the variability in the magnitude of shift in rainfall-runoff partitioning observed during the Millennium drought in south-eastern Australia and found that the shifts were mostly influenced by catchment characteristics related to predrought climate (aridity index and rainfall seasonality) and soil and groundwater storage dynamics.
Abstract: While the majority of hydrological prediction methods assume that observed interannual variability explores the full range of catchment response dynamics, recent cases of prolonged climate drying suggest otherwise. During the ∼decade-long Millennium drought in south-eastern Australia significant shifts in hydrologic behavior were reported. Catchment rainfall-runoff partitioning changed from what was previously encountered during shorter droughts, with significantly less runoff than expected occurring in many catchments. In this article, we investigate the variability in the magnitude of shift in rainfall-runoff partitioning observed during the Millennium drought. We re-evaluate a large range of factors suggested to be responsible for the additional runoff reductions. Our results suggest that the shifts were mostly influenced by catchment characteristics related to predrought climate (aridity index and rainfall seasonality) and soil and groundwater storage dynamics (predrought interannual variability of groundwater storage and mean solum thickness). The shifts were amplified by seasonal rainfall changes during the drought (spring rainfall deficits). We discuss the physical mechanisms that are likely to be associated with these factors. Our results confirm that shifts in the annual rainfall-runoff relationship represent changes in internal catchment functioning, and emphasize the importance of cumulative multiyear changes in the catchment storage for runoff generation. Prolonged drying in some regions can be expected in the future, and our results provide an indication of which catchments characteristics are associated with catchments more susceptible to a shift in their runoff response behavior.

Journal ArticleDOI
TL;DR: In this article, the authors proposed a variant of existing ensemble-based GRACE-TWS data assimilation schemes, which differs in how the analysis increments are computed and applied.
Abstract: Observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have a coarse resolution in time (monthly) and space (roughly 150,000 km(sup 2) at midlatitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This work proposes a variant of existing ensemble-based GRACE-TWS data assimilation schemes. The new algorithm differs in how the analysis increments are computed and applied. Existing schemes correlate the uncertainty in the modeled monthly TWS estimates with errors in the soil moisture profile state variables at a single instant in the month and then apply the increment either at the end of the month or gradually throughout the month. The proposed new scheme first computes increments for each day of the month and then applies the average of those increments at the beginning of the month. The new scheme therefore better reflects submonthly variations in TWS errors. The new and existing schemes are investigated here using gridded GRACE-TWS observations. The assimilation results are validated at the monthly time scale, using in situ measurements of groundwater depth and soil moisture across the U.S. The new assimilation scheme yields improved (although not in a statistically significant sense) skill metrics for groundwater compared to the open-loop (no assimilation) simulations and compared to the existing assimilation schemes. A smaller impact is seen for surface and root-zone soil moisture, which have a shorter memory and receive smaller increments from TWS assimilation than groundwater. These results motivate future efforts to combine GRACE-TWS observations with observations that are more sensitive to surface soil moisture, such as L-band brightness temperature observations from Soil Moisture Ocean Salinity (SMOS) or Soil Moisture Active Passive (SMAP). Finally, we demonstrate that the scaling parameters that are applied to the GRACE observations prior to assimilation should be consistent with the land surface model that is used within the assimilation system.