Showing papers in "Water Resources Research in 2012"
TL;DR: In this paper, the accuracy of global-gridded terrestrial water storage (TWS) estimates derived from temporal gravity field variations observed by the Gravity Recovery and Climate Experiment (GRACE) satellites is assessed.
Abstract: [1] We assess the accuracy of global-gridded terrestrial water storage (TWS) estimates derived from temporal gravity field variations observed by the Gravity Recovery and Climate Experiment (GRACE) satellites. The TWS data set has been corrected for signal modification due to filtering and truncation. Simulations of terrestrial water storage variations from land-hydrology models are used to infer relationships between regional time series representing different spatial scales. These relationships, which are independent of the actual GRACE data, are used to extrapolate the GRACE TWS estimates from their effective spatial resolution (length scales of a few hundred kilometers) to finer spatial scales (∼100 km). Gridded, scaled data like these enable users who lack expertise in processing and filtering the standard GRACE spherical harmonic geopotential coefficients to estimate the time series of TWS over arbitrarily shaped regions. In addition, we provide gridded fields of leakage and GRACE measurement errors that allow users to rigorously estimate the associated regional TWS uncertainties. These fields are available for download from the GRACE project website (available at http://grace.jpl.nasa.gov). Three scaling relationships are examined: a single gain factor based on regionally averaged time series, spatially distributed (i.e., gridded) gain factors based on time series at each grid point, and gridded-gain factors estimated as a function of temporal frequency. While regional gain factors have typically been used in previously published studies, we find that comparable accuracies can be obtained from scaled time series based on gridded gain factors. In regions where different temporal modes of TWS variability have significantly different spatial scales, gain factors based on the first two methods may reduce the accuracy of the scaled time series. In these cases, gain factors estimated separately as a function of frequency may be necessary to achieve accurate results.
1,043 citations
TL;DR: Two broad families of surrogates namely response surface surrogates, which are statistical or empirical data‐driven models emulating the high‐fidelity model responses, and lower‐f fidelity physically based surrogates which are simplified models of the original system are detailed in this paper.
Abstract: [1] Surrogate modeling, also called metamodeling, has evolved and been extensively used over the past decades. A wide variety of methods and tools have been introduced for surrogate modeling aiming to develop and utilize computationally more efficient surrogates of high-fidelity models mostly in optimization frameworks. This paper reviews, analyzes, and categorizes research efforts on surrogate modeling and applications with an emphasis on the research accomplished in the water resources field. The review analyzes 48 references on surrogate modeling arising from water resources and also screens out more than 100 references from the broader research community. Two broad families of surrogates namely response surface surrogates, which are statistical or empirical data-driven models emulating the high-fidelity model responses, and lower-fidelity physically based surrogates, which are simplified models of the original system, are detailed in this paper. Taxonomies on surrogate modeling frameworks, practical details, advances, challenges, and limitations are outlined. Important observations and some guidance for surrogate modeling decisions are provided along with a list of important future research directions that would benefit the common sampling and search (optimization) analyses found in water resources.
663 citations
TL;DR: In this article, the authors quantify globally the amount of non-renewable or nonsustainable groundwater abstraction to sustain current irrigation practice and show that non-rechargeable groundwater abstraction contributes approximately 20% to the global gross irrigation water demand for the year 2000.
Abstract: [1] Water used by irrigated crops is obtained from three sources: local precipitation contributing to soil moisture available for root water uptake (i.e., green water), irrigation water taken from rivers, lakes, reservoirs, wetlands, and renewable groundwater (i.e., blue water), and irrigation water abstracted from nonrenewable groundwater and nonlocal water resources. Here we quantify globally the amount of nonrenewable or nonsustainable groundwater abstraction to sustain current irrigation practice. We use the global hydrological model PCR-GLOBWB to simulate gross crop water demand for irrigated crops and available blue and green water to meet this demand. We downscale country statistics of groundwater abstraction by considering the part of net total water demand that cannot be met by surface freshwater. We subsequently confront these with simulated groundwater recharge, including return flow from irrigation to estimate nonrenewable groundwater abstraction. Results show that nonrenewable groundwater abstraction contributes approximately 20% to the global gross irrigation water demand for the year 2000. The contribution of nonrenewable groundwater abstraction to irrigation is largest in India (68 km3 yr−1) followed by Pakistan (35 km3 yr−1), the United States (30 km3 yr−1), Iran (20 km3 yr−1), China (20 km3 yr−1), Mexico (10 km3 yr−1), and Saudi Arabia (10 km3 yr−1). Results also show that globally, this contribution more than tripled from 75 to 234 km3 yr−1 over the period 1960–2000.
510 citations
TL;DR: In this article, the authors report the results of an experimental investigation into the multiphase flow properties of CO2 and water in four distinct sandstone rocks: a Berea sandstone and three reservoir rocks from formations into which CO2 injection is either currently taking place or is planned.
Abstract: [1] We report the results of an experimental investigation into the multiphase flow properties of CO2 and water in four distinct sandstone rocks: a Berea sandstone and three reservoir rocks from formations into which CO2 injection is either currently taking place or is planned. Drainage relative permeability and residual gas saturations were measured at 50 � C and 9 MPa pore pressure using the steady state method in a horizontal core flooding apparatus with fluid distributions observed using x-ray computed tomography. Absolute permeability, capillary pressure curves, and petrological studies were performed on each sample. Relative permeability in the four samples is consistent with general characteristics of drainage in strongly water-wet rocks. Measurements in the Berea sample are also consistent with past measurements in Berea sandstones using both CO2/brine and oil/water fluid systems. Maximum observed saturations and permeabilities are limited by the capillary pressure that can be achieved in the experiment and do not represent endpoint values. It is likely that maximum saturations observed in other studies are limited in the same way and there is no indication that low endpoint relative permeabilities are a characteristic of the CO2/water system. Residual trapping in three of the rocks is consistent with trapping in strongly water-wet systems, and the results from the Berea sample are again consistent with observations in past studies. This confirms that residual trapping can play a major role in the immobilization of CO2 injected into the subsurface. In the Mt. Simon sandstone, a nonmonotonic relationship between initial and residual CO2 saturations is indicative of a rock that is mixed or intermediate wet, and further investigations should be performed to establish the wetting properties of illite-rich rocks. The combined results suggest that the petrophysical properties of the multiphase flow of CO2/water through siliciclastic rocks is for the most part typical of a strongly water-wet system and that analog fluids and conditions may be used to characterize these properties. Further investigation is required to identify the wetting properties of illite-rich rocks during imbibition processes.
466 citations
TL;DR: MT‐DREAM(ZS), which combines the strengths of multiple‐try sampling, snooker updating, and sampling from an archive of past states is introduced, which is especially designed to solve high‐dimensional search problems and receives particularly spectacular performance improvement over other adaptive MCMC approaches when using distributed computing.
Abstract: [1] Spatially distributed hydrologic models are increasingly being used to study and predict soil moisture flow, groundwater recharge, surface runoff, and river discharge. The usefulness and applicability of such complex models is increasingly held back by the potentially many hundreds (thousands) of parameters that require calibration against some historical record of data. The current generation of search and optimization algorithms is typically not powerful enough to deal with a very large number of variables and summarize parameter and predictive uncertainty. We have previously presented a general-purpose Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference of the posterior probability density function of hydrologic model parameters. This method, entitled differential evolution adaptive Metropolis (DREAM), runs multiple different Markov chains in parallel and uses a discrete proposal distribution to evolve the sampler to the posterior distribution. The DREAM approach maintains detailed balance and shows excellent performance on complex, multimodal search problems. Here we present our latest algorithmic developments and introduce MT-DREAM(ZS), which combines the strengths of multiple-try sampling, snooker updating, and sampling from an archive of past states. This new code is especially designed to solve high-dimensional search problems and receives particularly spectacular performance improvement over other adaptive MCMC approaches when using distributed computing. Four different case studies with increasing dimensionality up to 241 parameters are used to illustrate the advantages of MT-DREAM(ZS).
456 citations
TL;DR: In this paper, the authors presented a new computationally efficient hydraulic model for simulating the spatially distributed dynamics of water surface elevation, wave speed, and inundation extent over large data sparse domains.
Abstract: [1] This paper presents a new computationally efficient hydraulic model for simulating the spatially distributed dynamics of water surface elevation, wave speed, and inundation extent over large data sparse domains. The numerical scheme is based on an extension of the hydraulic model LISFLOOD-FP to include a subgrid-scale representation of channelized flows, which allows river channels with any width below that of the grid resolution to be simulated. The scheme is shown to be numerically stable and scalable, before being applied to an 800 km reach of the river Niger in Mali. The Niger application focused on the performance of four different model structures: a model without channels (two-dimensional (2-D) model), a model without a floodplain (one-dimensional (1-D) model), a model of the main channels and floodplain (1-D/2-D model), and the subgrid approach developed here. Inclusion of both the channel network and the floodplain was shown to be essential, meaning that large scale models of this region, including routing models for land surface schemes, will require a floodplain component. Including subgrid-scale channels on the floodplain changed inundation dynamics over the delta significantly and increased simulation accuracy in terms of water level, wave propagation speed, and inundation extent. Furthermore, only the subgrid model showed a consistent parameterization when calibrated against either gauge or ICESat water level data, suggesting that connectivity provided by small channels is a strong control on the hydraulics of the floodplain, or, at the very least, that low resolution gridded hydraulic models require additional connectivity to represent the delta flow dynamics.
370 citations
TL;DR: In this article, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested, and the results indicate that coupled wavelet-neural network models are a potentially promising new method of urban water forecast that merit further study.
Abstract: [1] Daily water demand forecasts are an important component of cost-effective and sustainable management and optimization of urban water supply systems. In this study, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested. Multiple linear regression (MLR), multiple nonlinear regression (MNLR), autoregressive integrated moving average (ARIMA), ANN and WA-ANN models for urban water demand forecasting at lead times of one day for the summer months (May to August) were developed, and their relative performance was compared using the coefficient of determination, root mean square error, relative root mean square error, and efficiency index. The key variables used to develop and validate the models were daily total precipitation, daily maximum temperature, and daily water demand data from 2001 to 2009 in the city of Montreal, Canada. The WA-ANN models were found to provide more accurate urban water demand forecasts than the MLR, MNLR, ARIMA, and ANN models. The results of this study indicate that coupled wavelet-neural network models are a potentially promising new method of urban water demand forecasting that merit further study.
369 citations
TL;DR: This paper presents an approach for climate risk assessment that links bottom-up vulnerability assessment with multiple sources of climate information and couples the benefits of stochastic assessment of risks with the potential insight from climate projections.
Abstract: [1] There are few methodologies for the use of climate change projections in decision making or risk assessment processes. In this paper we present an approach for climate risk assessment that links bottom-up vulnerability assessment with multiple sources of climate information. The three step process begins with modeling of the decision and identification of thresholds. Through stochastic analysis and the creation of a climate response function, climate states associated with risk are specified. Climate information such as available from multi-GCM, multirun ensembles, is tailored to estimate probabilities associated with these climate states. The process is designed to maximize the utility of climate information in the decision process and to allow the use of many climate projections to produce best estimates of future climate risks. It couples the benefits of stochastic assessment of risks with the potential insight from climate projections. The method is an attempt to make the best use of uncertain but potentially useful climate information. An example application to an urban water supply system is presented to illustrate the process.
350 citations
TL;DR: In this paper, a generalized split-sample test (GSST) was proposed to evaluate the robustness of three hydrological models over a set of 216 catchments in southeast Australia.
Abstract: [1] This paper investigates the actual extrapolation capacity of three hydrological models in differing climate conditions. We propose a general testing framework, in which we perform series of split-sample tests, testing all possible combinations of calibration-validation periods using a 10 year sliding window. This methodology, which we have called the generalized split-sample test (GSST), provides insights into the model's transposability over time under various climatic conditions. The three conceptual rainfall-runoff models yielded similar results over a set of 216 catchments in southeast Australia. First, we assessed the model's efficiency in validation using a criterion combining the root-mean-square error and bias. A relation was found between this efficiency and the changes in mean rainfall (P) but not with changes in mean potential evapotranspiration (PE) or air temperature (T). Second, we focused on average runoff volumes and found that simulation biases are greatly affected by changes in P. Calibration over a wetter (drier) climate than the validation climate leads to an overestimation (underestimation) of the mean simulated runoff. We observed different magnitudes of these models deficiencies depending on the catchment considered. Results indicate that the transfer of model parameters in time may introduce a significant level of errors in simulations, meaning increased uncertainty in the various practical applications of these models (flow simulation, forecasting, design, reservoir management, climate change impact assessments, etc.). Testing model robustness with respect to this issue should help better quantify these uncertainties.
348 citations
TL;DR: In this article, a unified conceptual framework for modeling the terrestrial hydrosphere is proposed, based on philosophical perspectives from the groundwater, unsaturated zone, terrestrial hydrometeorology, and surface water communities.
Abstract: [1] The past decade has seen significant progress in characterizing uncertainty in environmental systems models, through statistical treatment of incomplete knowledge regarding parameters, model structure, and observational data. Attention has now turned to the issue of model structural adequacy (MSA, a term we prefer over model structure “error”). In reviewing philosophical perspectives from the groundwater, unsaturated zone, terrestrial hydrometeorology, and surface water communities about how to model the terrestrial hydrosphere, we identify several areas where different subcommunities can learn from each other. In this paper, we (a) propose a consistent and systematic “unifying conceptual framework” consisting of five formal steps for comprehensive assessment of MSA; (b) discuss the need for a pluralistic definition of adequacy; (c) investigate how MSA has been addressed in the literature; and (d) identify four important issues that require detailed attention—structured model evaluation, diagnosis of epistemic cause, attention to appropriate model complexity, and a multihypothesis approach to inference. We believe that there exists tremendous scope to collectively improve the scientific fidelity of our models and that the proposed framework can help to overcome barriers to communication. By doing so, we can make better progress toward addressing the question “How can we use data to detect, characterize, and resolve model structural inadequacies?”
333 citations
TL;DR: In this article, the authors describe a soil moisture data set from the 82,000 km2 Murrumbidgee River Catchment in southern New South Wales, Australia, which is archived from the MSMMN since its inception in September 2001.
Abstract: [1] This paper describes a soil moisture data set from the 82,000 km2 Murrumbidgee River Catchment in southern New South Wales, Australia. Data have been archived from the Murrumbidgee Soil Moisture Monitoring Network (MSMMN) since its inception in September 2001. The Murrumbidgee Catchment represents a range of conditions typical of much of temperate Australia, with climate ranging from semiarid to humid and land use including dry land and irrigated agriculture, remnant native vegetation, and urban areas. There are a total of 38 soil moisture-monitoring sites across the Murrumbidgee Catchment, with a concentration of sites in three subareas. The data set is composed of 0–5 (or 0–8), 0–30, 30–60, and 60–90 cm average soil moisture, soil temperature, precipitation, and other land surface model forcing at all sites, together with other ancillary data. These data are available on the World Wide Web at http://www.oznet.org.au.
TL;DR: In this article, the authors compare updated estimates of groundwater storage changes in the California Central Valley using GRACE satellites with storage changes from groundwater level data, and apply a new processing approach that optimally uses available GRACE and water balance component data to extract changes in groundwater storage.
Abstract: [1] There is increasing interest in using Gravity Recovery and Climate Experiment (GRACE) satellite data to remotely monitor groundwater storage variations; however, comparisons with ground-based well data are limited but necessary to validate satellite data processing, especially when the study area is close to or below the GRACE footprint. The Central Valley is a heavily irrigated region with large-scale groundwater depletion during droughts. Here we compare updated estimates of groundwater storage changes in the California Central Valley using GRACE satellites with storage changes from groundwater level data. A new processing approach was applied that optimally uses available GRACE and water balance component data to extract changes in groundwater storage. GRACE satellites show that groundwater depletion totaled $31.0 6 3.0 km 3 for Groupe de Recherche de Geodesie Spatiale (GRGS) satellite data during the drought from October 2006 through March 2010. Groundwater storage changes from GRACE agreed with those from well data for the overlap period (April 2006 through September 2009) (27 km 3 for both). General correspondence between GRACE and groundwater level data validates the methodology and increases confidence in use of GRACE satellites to monitor groundwater storage changes.
TL;DR: In this paper, the authors investigated the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally, and vertically disaggregated GRACE data into the US and North American Drought Monitors.
Abstract: [1] The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS) GRACE is characterized by low spatial (>150,000 km2) and temporal (>10 days) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously The GRACE Data Assimilation System (DAS), based on the Catchment Land Surface Model (CLSM), enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture components (ie, groundwater, soil moisture, and snow), which individually are more useful for scientific applications In this study, GRACE DAS was applied to North America, and GRACE-based drought indicators were developed as part of a larger effort to investigate the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally, and vertically disaggregated GRACE data into the US and North American Drought Monitors Previously, the drought monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought Extensive data sets of groundwater storage from US Geological Survey monitoring wells and soil moisture from the Soil Climate Analysis Network were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of a GRACE-assimilated water storage field for improving drought detection
TL;DR: In this paper, the authors explore the magnitude of such departures as detected from flux tower measurements of ecosystem-scale evapotranspiration, and investigate their attribution to site characteristics (biome, seasonal rainfall distribution, and frozen precipitation).
Abstract: [1] The Budyko framework elegantly reduces the complex spatial patterns of actual evapotranspiration and runoff to a general function of two variables: mean annual precipitation (MAP) and net radiation. While the methodology has first-order skill, departures from a globally averaged curve can be significant and may be usefully attributed to additional controls such as vegetation type. This paper explores the magnitude of such departures as detected from flux tower measurements of ecosystem-scale evapotranspiration, and investigates their attribution to site characteristics (biome, seasonal rainfall distribution, and frozen precipitation). The global synthesis (based on 167 sites with 764 tower-years) shows smooth transition from water-limited to energy-limited control, broadly consistent with catchment-scale relations and explaining 62% of the across site variation in evaporative index (the fraction of MAP consumed by evapotranspiration). Climate and vegetation types act as additional controls, combining to explain an additional 13% of the variation in evaporative index. Warm temperate winter wet sites (Mediterranean) exhibit a reduced evaporative index, 9% lower than the average value expected based on dryness index, implying elevated runoff. Seasonal hydrologic surplus explains a small but significant fraction of variance in departures of evaporative index from that expected for a given dryness index. Surprisingly, grasslands on average have a higher evaporative index than forested landscapes, with 9% more annual precipitation consumed by annual evapotranspiration compared to forests. In sum, the simple framework of supply- or demand-limited evapotranspiration is supported by global FLUXNET observations but climate type and vegetation type are seen to exert sizeable additional controls.
TL;DR: In this article, the authors used an unsupervised classification approach using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 250 m vegetation product to estimate the surface water areas over the MODIS period of record (2000 to 2010).
Abstract: [1] We studied 34 global reservoirs for which good quality surface elevation data could be obtained from a combination of five satellite altimeters for the period from 1992 to 2010. For each of these reservoirs, we used an unsupervised classification approach using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 250 m vegetation product to estimate the surface water areas over the MODIS period of record (2000 to 2010). We then derived elevation-area relationships for each of the reservoirs by combining the MODIS-based estimates with satellite altimeter-based estimates of reservoir water elevations. Through a combination of direct observations of elevation and surface area along with documented reservoir configurations at capacity, we estimated storage time histories for each reservoir from 1992 to 2010. We evaluated these satellite-based data products in comparison with gauge observations for the five largest reservoirs in the United States (Lakes Mead, Powell, Sakakawea, Oahe, and Fort Peck Reservoir). The storage estimates were highly correlated with observations (R = 0.92 to 0.99), with values for the normalized root mean square error (NRMSE) ranging from 3% to 15%. The storage mean absolute error (expressed as a percentage of reservoir capacity) for the reservoirs in this study was 4%. The multidecadal reconstructed reservoir storage variations are in accordance with known droughts and high flow periods on each of the five continents represented in the data set.
TL;DR: Molins et al. as mentioned in this paper investigated the effect of pore scale flow on average geochemical reaction rates using direct numerical simulation and found that the effect depends on the pore size.
Abstract: An Investigation of the Effect of Pore Scale Flow on Average Geochemical Reaction Rates Using Direct Numerical Simulation Sergi Molins 1 David Trebotich 2 Carl I. Steefel 1 Chaopeng Shen 2 Earth Sciences Division Lawrence Berkeley National Laboratory One Cyclotron Road, Mail Stop 90R1116, Berkeley, California 94720, USA Computational Research Division Lawrence Berkeley National Laboratory One Cyclotron Road, Mail Stop 50A-1148, Berkeley, California 94720, USA
TL;DR: In this paper, the authors examined seasonal and event-scale spatial soil moisture dynamics in the topsoil and subsoil of the small spruce-covered Wustebach catchment, Germany.
Abstract: [1] Our understanding of short- and long-term dynamics of spatial soil moisture patterns is limited due to measurement constraints. Using new highly detailed data, this research aims to examine seasonal and event-scale spatial soil moisture dynamics in the topsoil and subsoil of the small spruce-covered Wustebach catchment, Germany. To accomplish this, univariate and geo-statistical analyses were performed for a 1 year long 4-D data set obtained with the wireless sensor network SoilNet. We found large variations in spatial soil moisture patterns in the topsoil, mostly related to meteorological forcing. In the subsoil, temporal dynamics were diminished due to soil water redistribution processes and root water uptake. Topsoil range generally increased with decreasing soil moisture. The relationship between the spatial standard deviation of the topsoil soil moisture (SD� ) and mean water content (� ) showed a convex shape, as has often been found in humid temperate climate conditions. Observed scatter in topsoil SD� (� ) was explained by seasonal and event-scale SD� (� ) dynamics, possibly involving hysteresis at both time scales. Clockwise hysteretic SD� (� ) dynamics at the event scale were generated under moderate soil moisture conditions only for intense precipitation that rapidly wetted the topsoil and increased soil moisture variability controlled by spruce throughfall patterns. This hysteretic effect increased with increasing precipitation, reduced root water uptake, and high groundwater
TL;DR: In this article, the geomorphological integrity of multiscale terrain models rendered from a TLS survey of the braided River Feshie, Scotland is analyzed using a new, computationally efficient geospatial toolkit.
Abstract: [1] Recent advances in technology have revolutionized the acquisition of topographic data, offering new perspectives on the structure and morphology of the Earth's surface. These developments have had a profound impact on the practice of river science, creating a step change in the dimensionality, resolution, and precision of fluvial terrain models. The emergence of “hyperscale” survey methods, including structure from motion photogrammetry and terrestrial laser scanning (TLS), now presents the opportunity to acquire 3-D point cloud data that capture grain-scale detail over reach-scale extents. Translating these data into geomorphologically relevant products is, however, not straightforward. Unlike traditional survey methods, TLS acquires observations rapidly and automatically, but unselectively. This results in considerable “noise” associated with backscatter from vegetation and other artifacts. Moreover, the large data volumes are difficult to visualize; require very high capacity storage; and are not incorporated readily into GIS and simulation models. In this paper we analyze the geomorphological integrity of multiscale terrain models rendered from a TLS survey of the braided River Feshie, Scotland. These raster terrain models are generated using a new, computationally efficient geospatial toolkit: the topographic point cloud analysis toolkit (ToPCAT). This performs an intelligent decimation of point cloud data into a set of 2.5-D terrain models that retain information on the high-frequency subgrid topography, as the moments of the locally detrended elevation distribution. The results quantify the degree of terrain generalization inherent in conventional fluvial DEMs and illustrate how subgrid topographic statistics can be used to map the spatial pattern of particle size, grain roughness, and sedimentary facies at the reach scale.
TL;DR: In this paper, the authors investigate the role of permafrost in controlling groundwater flow paths and fluxes in interior Alaska and evaluate potential hydrologic consequences of permfrost degradation in the Yukon Flats Basin (YFB).
Abstract: [1] Understanding the role of permafrost in controlling groundwater flow paths and fluxes is central in studies aimed at assessing potential climate change impacts on vegetation, species habitat, biogeochemical cycling, and biodiversity. Recent field studies in interior Alaska show evidence of hydrologic changes hypothesized to result from permafrost degradation. This study assesses the hydrologic control exerted by permafrost, elucidates modes of regional groundwater flow for various spatial permafrost patterns, and evaluates potential hydrologic consequences of permafrost degradation. The Yukon Flats Basin (YFB), a large (118,340 km2) subbasin within the Yukon River Basin, provides the basis for this investigation. Model simulations that represent an assumed permafrost thaw sequence reveal the following trends with decreasing permafrost coverage: (1) increased groundwater discharge to rivers, consistent with historical trends in base flow observations in the Yukon River Basin, (2) potential for increased overall groundwater flux, (3) increased spatial extent of groundwater discharge in lowlands, and (4) decreased proportion of suprapermafrost (shallow) groundwater contribution to total base flow. These trends directly affect the chemical composition and residence time of riverine exports, the state of groundwater-influenced lakes and wetlands, seasonal river-ice thickness, and stream temperatures. Presently, the YFB is coarsely mapped as spanning the continuous-discontinuous permafrost transition that model analysis shows to be a critical threshold; thus, the YFB may be on the verge of major hydrologic change should the current permafrost extent decrease. This possibility underscores the need for improved characterization of permafrost and other hydrogeologic information in the region via geophysical techniques, remote sensing, and ground-based observations.
TL;DR: In this paper, the authors identify the key determinants of household water use, with a view to identifying those factors that could be targeted in water demand management campaigns, and highlight the value of policies that support long-term cultural shifts in the way people think about and use water.
Abstract: Securing water supplies in urban areas is a major challenge for policy makers, both now and into the future. This study aimed to identify the key determinants of household water use, with a view to identifying those factors that could be targeted in water demand management campaigns. Objective water use data and surveys were collected from 1008 households in four local government areas of southeast Queensland, Australia. Results showed that demographic, psychosocial, behavioral, and infrastructure variables all have a role to play in determining household water use. Consistent with past research, household occupancy was the most important predictor of water use. Households in regions recently exposed to drought conditions and higher-level restrictions also used less water than those who had less experience with drought. The effect of water efficient technology was mixed: some water efficient appliances were associated with less water use, while others were associated with more water use. Results also demonstrated the importance of considering water use as a collective behavior that is influenced by household dynamics. Households who reported a stronger culture of water conservation used less water. These findings, along with evidence that good water-saving habits are linked to water conservation, highlight the value of policies that support long-term cultural shifts in the way people think about and use water.
TL;DR: In this article, a multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update for anomaly assimilation of the satellite data.
Abstract: [1] Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR-E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high-elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower-elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root-mean-square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR-E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts.
TL;DR: This article conducted a meta-analysis of 22 coupled human-water system case studies, using qualitative comparison analysis (QCA) to identify water resource system outcomes and the factors that drive them.
Abstract: Freshwater scarcity has been cited as the major crisis of the 21st century, but it is surprisingly hard to describe the nature of the global water crisis. We conducted a meta- analysis of 22 coupled human– water system case studies, using qualitative comparison analysis (QCA) to identify water resource system outcomes and the factors that drive them. The cases exhibited different outcomes for human wellbeing that could be grouped into a six “syndromes ”: groundwater depletion, ecological destruction, drought-driven conflicts, unmet subsistence needs, resource capture by elite, and water reallocation to nature. For syndromes that were not successful adaptations, three characteristics gave cause for concern: (1) unsustainability —a decline in the water stock or ecosystem function that could result in a long-term steep decline in future human wellbeing; (2) vulnerability —high variability in water resource availability combined with inadequate coping capacity, leading to temporary drops in human wellbeing; (3) chronic scarcity —persistent inadequate access and hence low conditions of human wellbeing. All syndromes could be explained by a limited set of causal factors that fell into four categories: demand changes, supply changes, governance systems, and infrastructure/technology. By considering basins as members of syndrome classes and tracing common causal pathways of water crises, water resource analysts and planners might develop improved water policies aimed at reducing vulnerability, inequity, and unsustainability of freshwater systems.
TL;DR: In this article, an improved particle filter with variable variance multipliers and Markov Chain Monte Carlo (MCMC) methods is proposed to improve the reliability of the particle filter for hydrologic models.
Abstract: [1] Particle filters (PFs) have become popular for assimilation of a wide range of hydrologic variables in recent years. With this increased use, it has become necessary to increase the applicability of this technique for use in complex hydrologic/land surface models and to make these methods more viable for operational probabilistic prediction. To make the PF a more suitable option in these scenarios, it is necessary to improve the reliability of these techniques. Improved reliability in the PF is achieved in this work through an improved parameter search, with the use of variable variance multipliers and Markov Chain Monte Carlo methods. Application of these methods to the PF allows for greater search of the posterior distribution, leading to more complete characterization of the posterior distribution and reducing risk of sample impoverishment. This leads to a PF that is more efficient and provides more reliable predictions. This study introduces the theory behind the proposed algorithm, with application on a hydrologic model. Results from both real and synthetic studies suggest that the proposed filter significantly increases the effectiveness of the PF, with marginal increase in the computational demand for hydrologic prediction.
TL;DR: In this article, the authors developed a new model called POLARIS to describe the complex conductivity of (pyrite-free) shaly poorly sorted sands, based on the solution given by the effective medium theory for grains coated by an electrical double layer and immersed in a background electrolyte.
Abstract: [1] I developed a new model named POLARIS describing the complex conductivity of (pyrite-free) shaly poorly sorted sands. This model is based on the solution given by the effective medium theory for grains coated by an electrical double layer and immersed in a background electrolyte. The electrical double layer comprises the Stern layer and the diffuse layer. Both layers play very distinct roles in the in-phase and quadrature conductivities. The polarization of the shaly sands is mainly controlled by the polarization of the Stern layer (except at very high salinities) with a very small mobility of the counterions contained in this layer. The in-phase component of the conductivity is controlled by the conductivity of the pore water with a contribution associated with the diffuse layer (the contribution of the Stern layer seems negligible). The fraction of counterions in the Stern layer is computed from a simple sorption isotherm and is used to infer the quadrature conductivity. The quadrature conductivity is assumed to be frequency independent, which is a reasonable approximation in clayey sands and sandstones, in agreement with observations. The polarization model is also based on the assumption that the Stern layer is discontinuous between grains, an assumption that is consistent with recent models of ionic transport in clayey sands. POLARIS explains the dependence of the quadrature conductivity on the salinity, cation exchange capacity, specific surface area (or specific surface per unit pore volume), and temperature. It can be used to predict the saturation and the permeability (inside 1 order of magnitude).
TL;DR: In this paper, Wu et al. developed a hierarchical Dominant River Tracing (DRT) algorithm for automated extraction and spatial upscaling of basin flow directions and river networks using fine-scale hydrography inputs (e.g., flow direction, river networks, and flow accumulation).
Abstract: Coarse resolution (upscaled) river networks are critical inputs for runoff routing in macroscale hydrologic models. Recently, Wu et al. (2011) developed a hierarchical Dominant River Tracing (DRT) algorithm for automated extraction and spatial upscaling of basin flow directions and river networks using fine-scale hydrography inputs (e.g., flow direction, river networks, and flow accumulation). The DRT was initially applied using HYDRO1K baseline fine-scale hydrography inputs and the resulting upscaled global hydrography maps were produced at several spatial scales, and verified against other available regional and global datasets. New baseline fine-scale hydrography data from HydroSHEDS are now available for many regions and provide superior scale and quality relative to HYDRO1K. However, HydroSHEDS does not cover regions above 60°N. In this study, we applied the DRT algorithms using combined HydroSHEDS and HYDRO1K global fine-scale hydrography inputs, and produced a new series of upscaled global river network data at multiple (1/16° to 2°) spatial resolutions in a consistent (WGS84) projection. The new upscaled river networks are internally consistent and congruent with the baseline fine-scale inputs. The DRT results preserve baseline fine-scale river networks independent of spatial scales, with consistency in river network, basin shape, basin area, river length, and basin internal drainage structure betweenmore » upscaled and baseline fine-scale hydrography. These digital data are available online for public access (ftp://ftp.ntsg.umt.edu/pub/data/DRT/) and should facilitate improved regional to global scale hydrological simulations, including runoff routing and river discharge calculations.« less
TL;DR: In this paper, high-resolution thermal sensors were used to describe the vertical component of hyporheic flux at high spatial resolution across vertical profiles in the streambed, which can be applied to one-dimensional conduction-advection-dispersion models.
Abstract: [1] Hyporheic flow can be extremely variable in space and time, and our understanding of complicated flow systems, such as exchange around small dams, has generally been limited to reach-averaged parameters or discrete point measurements Emerging techniques are starting to fill the void between these disparate scales, increasing the utility of hyporheic research When ambient diurnal temperature patterns are collected at high spatial resolution across vertical profiles in the streambed, the data can be applied to one-dimensional conduction-advection-dispersion models to quantitatively describe the vertical component of hyporheic flux at the same high spatial resolution We have built on recent work by constructing custom fiber-optic distributed temperature sensors with 0014 m spatial resolution that are robust enough to be installed by hand into the streambed, maintain high signal strength, and permit several sensors to be run in series off a single distributed temperature sensing unit Data were collected continuously for 1 month above two beaver dams in a Wyoming stream to determine the spatial and temporal nature of vertical flux induced by the dams Flux was organized by streambed morphology with strong, variable gradients with depth indicating a transition to horizontal flow across a spectrum of hyporheic flow paths Several profiles showed contrasting temporal trends as discharge decreased by 45% The high-resolution thermal sensors, combined with powerful analytical techniques, allowed a distributed quantitative description of the morphology-driven hyporheic system not previously possible
TL;DR: In this article, an advection, dispersion, and residence time model with a multiple Monod kinetics model simulating the concentrations of oxygen (O2), ammonium (NH4), nitrate (NO3), and dissolved organic carbon (DOC) was used to examine coupled nitrification-denitrification dynamics across many scales of transport and reaction conditions.
Abstract: [1] The fate of biologically available nitrogen (N) and carbon (C) in stream ecosystems is controlled by the coupling of physical transport and biogeochemical reaction kinetics. However, determining the relative role of physical and biogeochemical controls at different temporal and spatial scales is difficult. The hyporheic zone (HZ), where groundwater–stream water mix, can be an important location controlling N and C transformations because it creates strong gradients in both the physical and biogeochemical conditions that control redox biogeochemistry. We evaluated the coupling of physical transport and biogeochemical redox reactions by linking an advection, dispersion, and residence time model with a multiple Monod kinetics model simulating the concentrations of oxygen (O2), ammonium (NH4), nitrate (NO3), and dissolved organic carbon (DOC). We used global Monte Carlo sensitivity analyses with a nondimensional form of the model to examine coupled nitrification-denitrification dynamics across many scales of transport and reaction conditions. Results demonstrated that the residence time of water in the HZ and the uptake rate of O2 from either respiration and/or nitrification determined whether the HZ was a source or a sink of NO3 to the stream. We further show that whether the HZ is a net NO3 source or net NO3 sink is determined by the ratio of the characteristic transport time to the characteristic reaction time of O2 (i.e., the Damkohler number, DaO2), where HZs with DaO2 < 1 will be net nitrification environments and HZs with DaO2 ≪ 1 will be net denitrification environments. Our coupling of the hydrologic and biogeochemical limitations of N transformations across different temporal and spatial scales within the HZ allows us to explain the widely contrasting results of previous investigations of HZ N dynamics which variously identify the HZ as either a net source or sink of NO3. Our model results suggest that only estimates of residence times and O2uptake rates are necessary to predict this nitrification-denitrification threshold and, ultimately, whether a HZ will be either a net source or sink of NO3.
TL;DR: In this paper, a method to postprocess GCM precipitation simulations by imparting correct distributional and persistence attributes, resulting in sequences that are representative of observed records across a range of time scales is presented.
Abstract: [1] Climate change impact assessments of water resources systems require simulations of precipitation and evaporation that exhibit distributional and persistence attributes similar to the historical record. Specifically, there is a need to ensure general circulation model (GCM) simulations of rainfall for the current climate exhibit low-frequency variability that is consistent with observed data. Inability to represent low-frequency variability in precipitation and flow leads to biased estimates of the security offered by water resources systems in a warmer climate. This paper presents a method to postprocess GCM precipitation simulations by imparting correct distributional and persistence attributes, resulting in sequences that are representative of observed records across a range of time scales. The proposed approach is named nesting bias correction (NBC), the rationale being to correct distributional and persistence bias from fine to progressively longer time scales. In the results presented here, distributional attributes have been represented by order 1 and 2 moments with persistence represented by lag 1 autocorrelation coefficients at monthly and annual time scales. The NBC method was applied to the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mk3.5 and MIROC 3.2 hires rainfall simulations for Australia. It was found that the nesting method worked well to correct means, standard deviations, and lag 1 autocorrelations when the biases in the raw GCM outputs were not too large. While the bias correction improves the representation of distributional and persistence attributes at the time scales considered, there is room for representation of longer-term persistence by extending to time scales longer than a year.
TL;DR: In this article, the authors apply nonlinear, monotonic transformations to the observed states, rendering them Gaussian (Gaussian anamorphosis, GA) to improve EnKF for parameter estimation in groundwater applications.
Abstract: [1] Ensemble Kalman filters (EnKFs) are a successful tool for estimating state variables in atmospheric and oceanic sciences. Recent research has prepared the EnKF for parameter estimation in groundwater applications. EnKFs are optimal in the sense of Bayesian updating only if all involved variables are multivariate Gaussian. Subsurface flow and transport state variables, however, generally do not show Gaussian dependence on hydraulic log conductivity and among each other, even if log conductivity is multi-Gaussian. To improve EnKFs in this context, we apply nonlinear, monotonic transformations to the observed states, rendering them Gaussian (Gaussian anamorphosis, GA). Similar ideas have recently been presented by Beal et al. (2010) in the context of state estimation. Our work transfers and adapts this methodology to parameter estimation. Additionally, we address the treatment of measurement errors in the transformation and provide several multivariate analysis tools to evaluate the expected usefulness of GA beforehand. For illustration, we present a first-time application of an EnKF to parameter estimation from 3-D hydraulic tomography in multi-Gaussian log conductivity fields. Results show that (1) GA achieves an implicit pseudolinearization of drawdown data as a function of log conductivity and (2) this makes both parameter identification and prediction of flow and transport more accurate. Combining EnKFs with GA yields a computationally efficient tool for nonlinear inversion of data with improved accuracy. This is an attractive benefit, given that linearization-free methods such as particle filters are computationally extremely demanding.
TL;DR: In this paper, the authors present a 6-year record of energy balance and detailed radiation measurements in the Senator Beck Basin Study Area, San Juan Mountains, Colorado, USA, including broadband and visible albedo, longwave irradiance, wind speed, relative humidity, and air temperatures.
Abstract: [1] Dust in snow accelerates snowmelt through its direct reduction of snow albedo and its further indirect reduction of albedo by accelerating the growth of snow grains. Since the westward expansion of the United States that began in the mid-19th century, the mountain snow cover of the Colorado River Basin has been subject to five-fold greater dust loading, largely from the Colorado Plateau and Great Basin. Radiative forcing of snowmelt by dust is not captured by conventional micrometeorological measurements, and must be monitored by a more comprehensive suite of radiation instruments. Here we present a 6 year record of energy balance and detailed radiation measurements in the Senator Beck Basin Study Area, San Juan Mountains, Colorado, USA. Data include broadband irradiance, filtered irradiance, broadband reflected flux, filtered reflected flux, broadband and visible albedo, longwave irradiance, wind speed, relative humidity, and air temperatures. The gradient of the snow surface is monitored weekly and used to correct albedo measurements for geometric effects. The snow is sampled weekly for dust concentrations in plots immediately adjacent to each tower over the melt season. Broadband albedo in the last weeks of snow cover ranged from 0.33 to 0.55 across the 6 years and two sites. Total end of year dust concentration in the top 3 cm of the snow column ranged from 0.23 mg g−1 to 4.16 mg g−1. These measurements enable monitoring and modeling of dust and climate-driven snowmelt forcings in the Upper Colorado River Basin.