ALBERT

Description: At present continental to global scale flood forecasting predicts at a point discharge, with little attention to detail and accuracy of local scale inundation predictions. Yet, inundation variables are of interest and all flood impacts are inherently local in nature. This paper proposes a large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas. The model was built for the Lower Zambezi River to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. ECMWF ensemble forecast (ENS) data were used to force the VIC (Variable Infiltration Capacity) hydrological model, which simulated and routed daily flows to the input boundary locations of a 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst representing the floodplain at an appropriate scale. The modeling system was calibrated using channel water levels from satellite laser altimetry and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of between one and two model resolutions compared to an observed flood edge and inundation area agreement was on average 86%. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km 2 and at model grid resolutions up to several km 2 .

Description: A class of capillary flows in unsaturated porous media is characterized by quasi-steady viscous flow confined behind curved air-water interfaces and within liquid bodies held by capillary forces along crevices and grain contacts. The geometry of the connected capillary liquid network within the pore space resembles channels that form between adjacent bubbles in foam (Plateau borders) with solid grains representing gas bubbles in foam. For simplified channel geometry we combine expressions for viscous flow with continuity considerations to describe the evolution of the channels cross-sectional area during gravity drainage. This formulation enables modeling of unsaturated flow without invoking the Richards equation and associated hydraulic functions. We adapt a formalism originally developed for foam “free drainage” (drainage under gravity) or “forced drainage” (infiltration front motion) to a class of unsaturated flows in porous media that require a few input parameters only (mean channel corner angle, air entry value and porosity) for certain initial and boundary conditions. We demonstrate that the reduction in capillary channel cross section yields a consistent description of self-regulating internal fluxes towards attainment of the so-called “field capacity” in soil and provides an alternative method for interpretation of outflow experiments for prescribed pressure boundary conditions. Additionally, the geometrically-explicit formulation provides a more intuitive picture of capillary flows across textural boundaries (changes in channel cross-section and number of channels). The foam drainage methodology expands the range of tools available for analyses of unsaturated flow processes and offers more realistic links between liquid configuration and flow dynamics in unsaturated porous media.

Description: Synthetic streamflows at different sites in a river basin are needed for planning, operation and management of water resources projects. Modeling the temporal and spatial dependence structure of monthly streamflow at different sites is generally required. In this study, the maximum entropy copula method is proposed for multisite monthly streamflow simulation, in which the temporal and spatial dependence structure is imposed as constraints to derive the maximum entropy copula. The monthly streamflows at different sites are then generated by sampling from the conditional distribution. A case study for the generation of monthly streamflow at three sites in the Colorado River basin illustrates the application of the proposed method. Simulated streamflow from the maximum entropy copula is in satisfactory agreement with observed streamflow.

Description: The Krycklan Catchment Study (KCS) provides a unique field infrastructure for hillslope to landscape-scale research on short and long-term ecosystem dynamics in boreal landscapes. The site is designed for process-based research assessing the role of external drivers including forest management, climate change, and long-range pollutant transport on forests, mires, soils, streams, lakes and groundwater. The over-arching objectives of KCS are to (1) provide a state-of-the-art infrastructure for experimental and hypothesis driven research, (2) maintain a collection of high quality, long-term climatic, biogeochemical, hydrological and environmental data, and (3) support the development of models and guidelines for research, policy and management.

Description: 3-D Hydraulic Tomography (3-D HT) is a method for aquifer characterization whereby the 3-D spatial distribution of aquifer flow parameters (primarily hydraulic conductivity, K) is estimated by joint inversion of head change data from multiple partially-penetrating pumping tests. While performance of 3-D HT has been studied extensively in numerical experiments, few field studies have demonstrated the real-world performance of 3-D HT. Here we report on a 3-D transient hydraulic tomography (3-D THT) field experiment at the Boise Hydrogeophysical Research Site which is different from prior approaches in that it represents a “baseline” analysis of 3-D THT performance using only a single arrangement of a central pumping well and 5 observation wells with nearly complete pumping and observation coverage at 1m intervals. We jointly analyze all pumping tests using a geostatistical approach based on the quasi-linear estimator of kitanidis [1995]. We reanalyze the system after progressively removing pumping and/or observation intervals; significant progressive loss of information about heterogeneity is quantified as reduced variance of the K field overall, reduced correlation with slug test K estimates at wells, and reduced ability to accurately predict independent pumping tests. We verify that imaging accuracy is strongly improved by pumping and observational densities comparable to the aquifer heterogeneity geostatistical correlation lengths. Discrepancies between K profiles at wells, as obtained from HT and slug tests, are greatest at the tops and bottoms of wells where HT observation coverage was lacking.

Description: Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical post-processing is required. In this work Bayesian model averaging (BMA) is applied to statistically post-process ensemble runoff raw forecasts for a catchment in Switzerland, at lead-times ranging from 1 to 240 hours. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead-time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead-times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead-times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the post-processed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

Description: We derive a series solution for the nonlinear Boussinesq equation in terms of the similarity variable of the Boltzmann transformation in a semi-infinite domain. The first few coefficients of the series have been known for a long time, having been obtained by a truncated inversion of the series solution of the Blasius equation, but no direct recurrence relation was known for the complete series representing the solution of the Boussinesq equation. The series turns out to have a finite radius of convergence, which we estimate with a numerical complex-plane integration method that identifies the singularities of the solution when the equation is extended to the complex plane. The homogeneous condition at the origin produces a singularity which complicates numerical solutions with Runge-Kutta methods. We present two variable transformations that circumvent the problem and that are best suited to the complex-variable and the real-variable versions of the equation, respectively. Using those tools, an approximate solution accurate to 1.75 × 10 -10 and valid for the entire positive real axis is then developed by matching a Padé approximant of the exact series and an asymptotic solution (to overcome the restriction imposed by the finite radius of convergence of the series), along the same lines of the expression proposed by Hogarth and Parlange [1999]. The accuracies of all of the existing and the newly proposed solutions are obtained.

Description: Field sampling in unwadeable and flashy flood events encounters the problem that lateral variability of flow hydraulics and sediment transport cannot be captured adequately, and there is also an accuracy problem because parameters change while being measured. Moreover, event based gravel-sand mixed transport data in rapidly changing conditions are largely missing, in particular for gravel-bed rivers in small catchments. In this study, field measurements of bed load, suspended load, flow velocities, water depths and cross section geometry were collected during flood events at a monitoring station near the mouth of the Versilia river, Italy. Since the observed hydrographs are characterized by short durations, to the order of a few hours, an analysis of the lateral and temporal flow variability was carried out to enable the design of a sampling strategy and to minimize the errors created by the time variations of discharge associated with unsteady flow conditions. The measurements were interpreted using a 1D hydro-morphodynamic numerical model simulating the dynamics of flow and sediment discharges during a flood event for a given return period. The flow and sediment rating curves were then developed through an integrated approach combining different methodologies: field measurements, laboratory analyses and mathematical modeling. The developed approach allows one to capture the main physical mechanisms associated to the transport of sand–gravel mixtures, such as selective transport, and the hysteretic behaviour of sediment transport produced by rapid and intense flood events.

Description: ABSTRACT A study was performed to characterize over land precipitation associated with tropical cyclones (TCs) for basins around the world based upon the International Best Track Archive for Climate Stewardship (IBTrACS). From 1998 to 2009, data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product 3B42, showed that TCs accounted for 5.5%, 7.5%, 6%, 9.5%, and 8.9% of the annual precipitation for impacted over land areas of the Americas, East Asia, South and West Asia, Oceania, and East Africa respectively, and that TC contribution decreased significantly within the first 150-km from the coast. Locally, TCs contributed on average to more than 25% and up to 61% of the annual precipitation budget over very different climatic areas with arid or tropical characteristics. East Asia represented the higher and most constant TC rain (118±19% mm y -1 ) normalized over the area impacted, while East Africa presented the highest variability (108±60% mm y -1 ), and the Americas displayed the lowest average TC rain (65±24% mm y -1 ) despite a higher TC activity. Furthermore, the maximum monthly TC contribution (8-11%) was found later in the TC season and depended on the peak of TC activity, TC rainfall, and the domain transition between dry and wet regimes if any. Finally, because of their importance in terms of rainfall amount, the contribution of TCs was provided for a selection of 50 urban areas experiencing cyclonic activity. Results showed that for particularly intense years, urban areas prone to cyclonic activity received more than half of their annual rainfall from TCs.

Description: A multivariate, multi-site daily weather generator is presented for use in decision-centric vulnerability assessments under climate change. The tool is envisioned useful for a wide range of socioeconomic and biophysical systems sensitive to different aspects of climate variability and change. The proposed stochastic model has several components, including 1) a wavelet decomposition coupled to an autoregressive model to account for structured, low-frequency climate oscillations, 2) a Markov Chain and k-nearest-neighbor (KNN) resampling scheme to simulate spatially-distributed, multivariate weather variables over a region, and 3) a quantile mapping procedure to enforce long-term distributional shifts in weather variables that result from prescribed climate changes. The Markov Chain is used to better represent wet and dry spell statistics while the KNN bootstrap resampler preserves the covariance structure between the weather variables and across space. The wavelet-based autoregressive model is applied to annual climate over the region and used to modulate the Markov Chain and KNN resampling, embedding appropriate low-frequency structure within the daily weather generation process. Parameters can be altered in any of the components of the proposed model to enable the generation of realistic time series of climate variables that exhibit changes to both lower-order and higher-order statistics at long-term (inter-annual), mid-term (seasonal), and short-term (daily) timescales. The tool can be coupled with impact models in a bottom-up risk assessment to efficiently and exhaustively explore the potential climate changes under which a system is most vulnerable. An application of the weather generator is presented for the Connecticut River basin to demonstrate the tool's ability to generate a wide range of possible climate sequences over an extensive spatial domain.

Description: The objective of the least cost design problem of a water distribution system is to find its minimum cost with discrete diameters as decision variables and hydraulic controls as constraints. The goal of a robust least cost design is to find solutions which guarantee its feasibility independent of the data (i.e., under model uncertainty). A robust counterpart approach for linear uncertain problems is adopted in this study, which represents the uncertain stochastic problem as its deterministic equivalent. Robustness is controlled by a single parameter providing a trade-off between the probability of constraint violation and the objective cost. Two principal models are developed-uncorrelated uncertainty model with implicit design reliability, and correlated uncertainty model with explicit design reliability. The models are tested on three example applications and compared for uncertainty in consumers’ demands. The main contribution of this study is the inclusion of the ability to explicitly account for different correlations between water distribution systems demand nodes. In particular it is shown that including correlation information in the design phase has a substantial advantage in seeking more efficient robust solutions.

Description: Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p -level probability regions of a joint distribution returns a set of events that is a subset of the p -level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p -level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyse two case studies already presented in the literature.

Description: Sparse geologic dictionaries provide a novel approach for subsurface flow model representation and calibration. Learning sparse dictionaries from prior training datasets is an effective approach to describe complex geologic connectivity patterns in subsurface imaging applications. However, the computational cost of sparse learning algorithms becomes prohibitive for large models. Performing the sparse dictionary learning process on smaller image patches (segments) provides a simple approach to address this problem in image processing applications. However, in underdetermined subsurface flow model calibration inverse problems, reconstruction of a segmented image can introduce significant structural distortion and discontinuity at the boundaries of the segments. This paper proposes an alternative sparse learning approach where the sparse dictionaries are learned from low-rank representations of the large-scale training dataset in spectral domains (e.g., frequency domain). The objective is to develop a computationally efficient dictionary learning approach that emphasizes large-scale spatial connectivity patterns. This is achieved by removing the strong spatial correlations in the training data, thereby eliminating a large number of insignificant components from the sparse learning computation. In addition to improving the computational complexity, sparse learning from low-rank training datasets suppresses the small-scale details from entering the reconstruction of large-scale connectivity patterns, and providing a regularization effect in solving the resulting ill-posed inverse problems. We apply the proposed approach to travel-time tomography inversion and nonlinear subsurface flow model calibration inverse problems to demonstrate its effectiveness and practicality.

Description: A new hybrid wavelet–bootstrap–neural network (WBNN) model is proposed in this study for short term (1, 3 and 5 day; 1 and 2 week; and 1 and 2 month) urban water demand forecasting. The new method was tested using data from the city of Montreal in Canada. The performance of the WBNN method was compared with the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average model with exogenous input variables (ARIMAX), traditional NNs, wavelet analysis based NNs (WNN), bootstrap based NNs (BNN), and a simple naïve persistence index model. The WBNN model was developed as an ensemble of several NNs built using bootstrap resamples of wavelet sub-time series instead of raw datasets. The results demonstrated that the hybrid WBNN and WNN models produced significantly more accurate forecasting results than the traditional NN, BNN, ARIMA and ARIMAX models. It was also found that the WBNN model reduces the uncertainty associated with the forecasts, and the performance of WBNN forecasted confidence bands were found to be more accurate and reliable than BNN forecasted confidence bands. It was found in this study that maximum temperature and total precipitation improved the accuracy of water demand forecasts using wavelet analysis. The performance of WBNN models was also compared for different numbers of bootstrap resamples (i.e., 25, 50, 100, 200, and 500) and it was found that WBNN models produced optimum results with different numbers of bootstrap resamples for different lead time forecasts with considerable variability.

Description: ABSTRACT Although mechanistic reaction networks have been developed to quantify the biogeochemical evolution of subsurface systems associated with bioremediation, it is difficult in practice to quantify the onset and distribution of these transitions at the field scale using commonly collected wellbore datasets. As an alternative approach to the mechanistic methods, we develop a data-driven, statistical model to identify biogeochemical transitions using various time-lapse aqueous geochemical data (e.g., Fe(II), sulfate, sulfide, acetate, and uranium concentrations) and induced polarization (IP) data. We assume that the biogeochemical transitions can be classified as several dominant states that correspond to redox transitions and test the method at a uranium-contaminated site. The relationships between the geophysical observations and geochemical time-series vary depending upon the unknown underlying redox status, which is modeled as a hidden Markov random field. We estimate unknown parameters by maximizing the joint likelihood function using the maximization-expectation algorithm. The case study results show that when considered together aqueous geochemical data and IP imaginary conductivity provide a key diagnostic signature of biogeochemical stages. The developed method provides useful information for evaluating the effectiveness of bioremediation, such as the probability of being in specific redox stages following biostimulation where desirable pathways (e.g., uranium removal) are more highly favored. The use of geophysical data in the approach advances the possibility of using non-invasive methods to monitor critical biogeochemical system stages and transitions remotely and over field relevant scales (e.g., from square meters to several hectares).

Description: In recent years, a number of numerical modelling studies of transient sea-level rise (SLR) and seawater intrusion (SWI) in flux-controlled systems have reported an overshoot phenomenon, whereby the freshwater-saltwater interface temporarily extends further inland than the eventual steady-state position. In this study, we have carried out physical sand tank modelling of SLR-SWI in a flux-controlled unconfined aquifer setting to test if SWI overshoot is a measurable physical process. Photographs of the physical SLR experiments show, for the first time, that an overshoot occurs under controlled laboratory conditions. A sea-level drop (SLD) experiment was also carried out, and overshoot was again observed, whereby the interface was temporarily closer to the coast than the eventual steady-state position. This shows that an overshoot can occur for the case of a retreating interface. Numerical modelling corroborated the physical SLR and SLD experiments. The magnitude of the overshoot for SLR and SLD in the physical experiments was 24% of the change in steady-state interface position, albeit the laboratory setting is designed to maximise overshoot extent by adopting high groundwater flow gradients and large and rapid sea-level changes. While the likelihood of overshoot at the field scale appears to be low, this work has shown that it can be observed under controlled laboratory conditions.

Description: A new analytical solution of the flow equation has been developed to estimate the time to reach a near-equilibrium state in mixed aquifers, i.e. having unconfined and confined portions, following a large hydraulic perturbation. Near-equilibrium is defined as the time for an initial aquifer perturbation to dissipate by an average 95% across the aquifer.The new solution has been obtained by solving the flow system of a simplified conceptual model of a mixed aquifer using Laplace transforms. The conceptual model is based on two assumptions: 1) the groundwater flow can be reduced to a horizontal 1D problem; and 2) the transmissivity, a function of the saturated thickness, is assumed constant on the unconfined portion. This new solution depends on the storativity of the unconfined portion, the lengths of the unconfined and confined portions and the transmissivity, assumed to be constant and equal in both portions of the mixed aquifer. This solution was then tested and validated against a numerical flow model, where the variations of the saturated thickness and therefore variations of the transmissivity were either ignored, or properly modeled. The agreement between the results from the new solution and those from the numerical model is good, validating the use of this new solution to estimate the time to reach near-equilibrium in mixed aquifers. This solution for mixed aquifers, as well as the solutions for a fully confined or fully unconfined aquifer, have been used to estimate the time to reach near-equilibrium in thirteen large aquifers in the world. For those different aquifers, the time to reach near-equilibrium ranges between 0.7 ky to 2.4x10 7 ky. These results suggest that the present hydraulic heads in these aquifers are typically a mixture of responses induced from current and past hydrologic conditions and thus climate conditions. For some aquifers, the modern hydraulic heads may in fact depend upon hydrologic conditions resulting from several past climate cycles.

Description: The impact of contact angle on 2D spatial and temporal water content distribution during infiltration and drainage was experimentally studied. The 0.3-0.5 mm fraction of a quartz dune sand was treated and turned sub-critically repellent (contact angle of 33 0 , 48 0 , 56 0 , and 75 0 for S33, S48, S56, and S75, respectively). The media were packed uniformly in transparent flow chambers and water was supplied to the surface as a point source at different rates (1 to 20 ml/min). A sequence of grey-value images was taken by CCD camera during infiltration and subsequent drainage; grey values were converted to volumetric water content by water volume balance. Narrow and long plumes with water accumulation behind the downward moving wetting front (tip) and negative water gradient above it (tail) developed in the S56 and S75 media during infiltration at lower water application rates. The plumes became bulbous with spatially uniform water content distribution as water application rates increased. All plumes in these media propagated downward at a constant rate during infiltration and were frozen during drainage. In contrast, regular plume shapes were observed in the S33 and S48 media at all flow rates, and drainage profiles were non-monotonic with a transition plane at the depth that water reached during infiltration. Given that the studied media have similar pore-size distributions, the conclusion is that imbibition hindered by the non-zero contact angle induced pressure buildup at the wetting front (dynamic water entry value) that controlled the plume shape and internal water-content distribution during infiltration and drainage.

Description: Estimation of design quantiles of hydro-meteorological variables at critical locations in river basins is necessary for hydrological applications. To arrive at reliable estimates for locations (sites) where no or limited records are available, various regional frequency analysis (RFA) procedures have been developed over the past five decades. The most widely used procedure is based on Index-flood approach and L -moments. It assumes that values of scale and shape parameters of frequency distribution are identical across all the sites in a homogeneous region. In real world scenario, this assumption may not be valid even if a region is statistically homogeneous. To address this issue, a novel mathematical approach is proposed. It involves (i) identification of an appropriate frequency distribution to fit the random variable being analyzed for homogeneous region, (ii) use of a proposed transformation mechanism to map observations of the variable from original space to a dimensionless space where the form of distribution does not change, and variation in values of its parameters is minimal across sites, (iii) construction of a growth curve in the dimensionless space, and (iv) mapping the curve to the original space for the target site by applying inverse transformation to arrive at required quantile(s) for the site. Effectiveness of the proposed approach in predicting quantiles for ungauged sites is demonstrated through Monte-Carlo simulation experiments considering five frequency distributions that are widely used in RFA, and by case study on watersheds in conterminous United States. Results indicate that the proposed approach outperforms methods based on index-flood approach.

Description: Prediction of microbial surface water contamination is a formidable task because of the inherent randomness of environmental processes driving microbial fate and transport. In this article we develop a theoretical framework of a fully stochastic model of microbial transport in watersheds, and apply the theory to a simple flow network to demonstrate its use. The framework bridges the gap between microscopic behavior of individual microorganisms and macroscopic ensemble dynamics. This scaling is accomplished within a single mathematical framework, where each microorganism behaves according to a continuous-time discrete-space Markov process, and the Markov behavior of individual microbes gives rise to a non-homogeneous Poisson random field that describes microbial population dynamics. Mean value functions are derived, and the spatial and temporal distribution of water contamination risk is computed in a straightforward manner.

Description: The anomalously snowy winter season of 2010/11 in the Sierra Nevada is analyzed in terms of snow water equivalent (SWE) anomalies and the role of atmospheric rivers (ARs)―narrow channels of enhanced meridional water vapor transport between the tropics and extratropics. Mean April 1 SWE was 0.44 m (56%) above normal averaged over 100 snow sensors. AR occurrence was anomalously high during the period, with 20 AR dates during the season and 14 in the month of December 2010, compared to the mean occurrence of 9 dates per season. Fifteen out of the 20 AR dates were associated with the negative phases of the Arctic Oscillation (AO) and the Pacific-North American (PNA) teleconnection pattern. Analysis of all winter ARs in California during water years 1998–2011 indicates more ARs occur during the negative phase of AO and PNA, with the increase between positive and negative phases being ˜90% for AO, and ˜50% for PNA. The circulation pattern associated with concurrent negative phases of AO and PNA, characterized by cyclonic anomalies centered northwest of California, provides a favorable dynamical condition for ARs. The analysis suggests that the massive Sierra Nevada snowpack during the 2010/11 winter season is primarily related to anomalously high frequency of ARs favored by the joint phasing of −AO and −PNA, and that a secondary contribution is from increased snow accumulation during these ARs favored by colder air temperatures associated with −AO, −PNA and La Niña.

Description: The baseflow recession constant, K b , is used to characterize the interaction of groundwater and surface water systems. Estimation of K b is critical in many studies including rainfall-runoff modeling, estimation of low flow statistics at ungaged locations and baseflow separation methods. The performance of several estimators of K b are compared, including several new approaches which account for the impact of human withdrawals. A traditional semi-log estimation approach adapted to incorporate the influence of human withdrawals was preferred over other derivative-based estimators. Human withdrawals are shown to have a significant impact on the estimation of baseflow recessions, even when withdrawals are relatively small. Regional regression models are developed to relate seasonal estimates of K b to physical, climatic, and anthropogenic characteristics of stream-aquifer systems. Among the factors considered for explaining the behavior of K b , both drainage density and human withdrawals have significant and similar explanatory power. We document the importance of incorporating human withdrawals into models of the baseflow recession response of a watershed and the systemic downward bias associated with estimates of K b obtained without consideration of human withdrawals.

Description: There are significant uncertainties inherent in precipitation forecasts and these uncertainties can be communicated to users via large ensembles that are generated using stochastic models of forecast error. The Met Office and the Australian Bureau of Meteorology developed the Short Term Ensemble Prediction System (STEPS) which has been operational for a number of years. The initial formulation of Bowler et al. [2006] has been revised and extended to improve the performance over large domains, to include radar observation errors, and to facilitate the combination of forecasts from a number of sources. This paper reviews the formulation of STEPS, discusses those aspects of the formulation that have proved most problematic and presents some solutions. The performance of STEPS nowcasts is evaluated using a combination of case study examples and statistical verification from the UK. Routine forecast verification demonstrates that STEPS is capable of producing near optimal blends of a rainfall nowcast and high resolution NWP forecast. It also shows that the spread of STEPS nowcast ensembles are a good predictor of the error in the control member (unperturbed) nowcast.

Description: In this study seasonal and interannual variability of the main atmospheric moisture sources over eight regions in the Mediterranean basin were investigated along a twenty one year period. The Lagrangian dispersion model FLEXPART, developed by Stohl and James [2004, 2005], was applied to identify the contribution of humidity to the moisture budget of each region. This methodology is used to compute budgets of evaporation minus precipitation (E-P) by calculating changes in the specific humidity along backward trajectories, for the preceding ten-day periods. The results show clear seasonal differences in the moisture sources between wet and dry seasons. The Western Mediterranean Sea is the dominant moisture source for almost all the regions in the Mediterranean basin during the wet season, while the local net evaporation dominates during the dry season. The highest interannual variability is found in contributions to the Iberian Peninsula, Italy and the Eastern Mediterranean. It is seen that the role of teleconnections is more limited than for the precipitation recorded in the region.

Description: We present a new hydrologic model based on the frequency distribution of hillslope landscape elements along the stream network as a basis for simulating landscape-scale hydrologic connectivity and catchment runoff. Hydrologic connectivity describes shallow water table continuity between upland and stream elements of the catchment and is important for the movement of water and solutes to streams. This concept has gained traction in physical hydrology but has received less attention in rainfall-runoff modeling. Our model is based on the empirical studies of Jencso et al. [2009; 2010], who found a strong correlation between the duration of shallow groundwater connectivity across hillslope, riparian, and stream zones and upslope accumulated area. We explored the relationship between catchment form and function by testing the extent to which streamflow generation could be predicted by a model based on the topographic form (distribution of landscape elements) of the catchment. We applied the model to the Stringer Creek catchment of the Tenderfoot Creek Experimental Forest, located in Montana, USA. Detailed field observations collected by Jencso et al. [2009] were used to inform the underpinnings of the model and to corroborate internal consistency of the model simulations. The model demonstrated good agreement between the observed and predicted streamflow and connectivity duration curves. The ability of this model to simulate internal dynamics without conditioning the parameters on these data suggests that it has the potential to be more confidently extrapolated to other shallow, topographically driven catchments than hydrologic models that fail to consistently reproduce internal variables.

Description: Knowledge of hydrological model complexity can aid selection of an optimal prediction model out of a set of available models. Optimal model selection is formalized as selection of the least complex model out of a subset of models that have lower empirical risk. This may be considered equivalent to minimizing an upper bound on prediction error, defined here as the mathematical expectation of empirical risk. In this paper we derive an upper bound that is free from assumptions on data and underlying process distribution as well as on independence of model predictions over time. We demonstrate that hydrological model complexity, as defined in the presented theoretical framework, plays an important role in determining the upper bound. The model complexity also acts as a stabilizer to a hydrological model selection problem if it is deemed ill-posed. We provide an algorithm for computing complexity of any arbitrary hydrological model. We also demonstrate that hydrological model complexity has a geometric interpretation as the size of model output space. The presented theory is applied to quantify complexities of two hydrological model structures: SAC-SMA and SIXPAR. It detects that SAC-SMA is indeed more complex than SIXPAR. We also develop an algorithm to estimate the upper bound on prediction error, which is applied on 5 different rainfall-runoff model structures that vary in complexity. We show that a model selection problem is stabilized by regularizing it with model complexity. Complexity regularized model selection yields models that are robust in predicting future but yet unseen data.

Description: The temporal dynamics and spatial distribution of the concentrations of dissolved gases (He, Ar, Kr, N 2 , O 2 and CO 2 ) in an infiltrating groundwater system fed by the peri-alpine River Thur (Switzerland) were analysed before, during and after a single, well-defined flood event. The analysis was based on measurements taken in five different groundwater observation wells that were located approximately 10 m apart and tapped the same groundwater body, but were situated in three different riparian zones. The input of O 2 into the groundwater as a result of the formation of excess air was found to be of the same order of magnitude as that resulting from the advection of river water, although the amount of excess air formed and the amount of O 2 delivered varied significantly among the riparian zones. The results suggest that the input of O 2 into groundwater as a result of excess air formation is controlled not only by the hydraulic conditions prevailing in the river and the groundwater, but also by the thickness of the confining bed at the top of the aquifer. The sandy gravel aquifer itself is too coarse to trap a significant amount of air during the water level rise. The clay layer confining the aquifer, however, acts as a barrier hindering the escape of air from the subsoil to the surface, and hence is likely to be a key factor controlling the trapping and dissolution of air in groundwater.

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

Description: The cosmic-ray neutron probe measures soil moisture over tens of hectares, thus averaging spatially variable soil moisture fields. A previous paper described how variable soil moisture profiles affect the integrated cosmic-ray neutron signal from which depth-average soil moisture is computed. Here, we investigate the effect of horizontal heterogeneity on the relationship between neutron counts and average soil moisture. Observations from a distributed sensor network at a site in southern Arizona indicate that the horizontal component of the total variance of the soil moisture field is less variably in time than the vertical component. Using results from neutron particle transport simulations we show that 1-D binary distributions of soil moisture may affect both the mean and variance of neutron counts of a cosmic-ray neutron detector placed arbitrarily in a soil moisture field, potentially giving rise to an underestimate of the footprint average soil moisture. Similar simulations that used 1 and 2-D Gaussian soil moisture fields indicate consistent mean and variances of a randomly placed detector if the correlation length scales are short (〈˜30 m) and/or the soil moisture field variance is small (〈0.032 m 6 m -6 ). Taken together, these soil moisture observations and neutron transport simulations show that horizontal heterogeneity likely has a small effect on the relationship between mean neutron counts and average soil moisture for soils under natural conditions.

Description: Widespread disturbance within forested catchments typically increases runoff. However, following widespread fire in 1939 throughout south-east Australia Kuczera [1987] reported persistent reductions in runoff that were attributed to increased evapotranspiration from regenerating ‘ash’ forests. Kuczera projected ongoing reductions of water yield for ~150 years. In 2003 widespread fire in the headwaters of the Murray-Darling Basin (MDB) again stimulated extensive regeneration of ash forests, raising the prospect of subsequent water yield reductions. To understand the potential impact of the 2003 bushfires we re-evaluated yield reductions from three of the catchments originally studied by Kuczera using the same calibration period. We also used an expanded pre-fire calibration period (1908-1938) based on data not originally available to Kuczera. The trend of post-fire water yield that we observed in 1939-affected catchments is qualitatively consistent with Kuczera's projections, but the quantitative details were, as expected, sensitive to the pre-fire calibration period used. We then used a simplified method to examine a further five ash-dominated catchments affected by the 2003 fires. We report relative reductions in mean annual stream flow in all five catchments and a statistically significant (α=0.05) post-fire reduction in one of five catchments. Post-fire yield reductions during the austral summer (October-April) were greater in relative magnitude in all five catchments and were statistically significant (α=0.05) in three of five catchments. We conclude that a post-bushfire Kuczera-type response may be widespread in regenerating ash forests. On that basis we anticipate post-fire yield reductions in ash forests elsewhere and conclude that further reductions in stream flow are likely in the MDB for at least another decade.

Description: Drought triggers are patterns in hydro-climatic variables that herald upcoming droughts and form the basis for mitigation plans. This study develops a new method for identification of triggers for hydrologic droughts by examining the association between the various hydro-climatic variables and streamflows. Since numerous variables influence streamflows to varying degrees, Principal Component Analysis (PCA) is utilized for dimensionality reduction in predictor hydro-climatic variables. The joint dependence between the first two principal components, that explain over 98% of the variability in the predictor set, and streamflows is computed by a scale-free measure of association using asymmetric Archimedean copulas over two study watersheds in Indiana, USA, with unregulated streamflows. The M6 copula model is found to be suitable for the data and is utilized to find expected values and ranges of predictor hydro-climatic variables for different streamflow quantiles. This information is utilized to develop drought triggers for one-month lead time over the study areas. For the two study watersheds, soil moisture, precipitation and runoff are found to provide the fidelity to resolve amongst different drought classes. Combining the strengths of PCA for dimensionality reduction and copulas for building joint dependence allows the development of hydrologic drought triggers in an efficient manner.

Description: We present a unified asymptotic theory of rainfall extremes including annual maxima, excesses above high thresholds, and intensity-duration-frequency (IDF) curves that builds on previous findings and derive new non-asymptotic results. The analysis is based on stationary multifractal representations of rainfall and produces extensions of the familiar results from extreme value (EV) and extreme excess (EE) theories. The latter results apply to the T -yr maximum as and the excess above z as . By exploiting the scaling relationship among the distributions of rainfall intensity for different averaging durations d , the multifractal asymptotics include, in addition, results in the small-scale limits and with α 〉 0. In all cases the maximum distributions are of the generalized extreme value (GEV) type, but the index k depends on the limit considered. Multifractal models produce also asymptotic scaling results for the IDF curves. For the non-asymptotic case ( d and T finite), we obtain accurate approximations of the IDF curves and derive a semi-theoretical formula for the index k of the GEV model that best approximates the distribution of the annual maximum over a finite range of return-period intensities. The non-asymptotic analysis explains several observed deviations of rainfall extremes from the asymptotic predictions, such as the tendency of k to decrease as the averaging duration d increases and the tendency of the IDF curves to converge as d or the return period T increase.

Description: Remaining oil saturation established by waterflooding was measured in Indiana limestone in its original, water-wet state and under mixed-wet conditions established by adding organic acid to the oil phase. The porous plate technique was used to establish initial oil saturations ranging from S nwi = 0.23 to 0.93 under capillary-dominated conditions. For water-wet conditions, the residual oil saturation increased linearly with its initial saturation. In contrast, the remaining oil saturation under mixed-wet conditions, S nw , displayed three distinct regimes. First, S nw increased with its initial saturation up to S nwi = 0.58. Next, S nw decreased from S nwi = 0.58 to 0.76. Finally, S nw increased again as S nwi approached one. The non-monotonic dependence of S nw on S nwi at S nwi 〉 0.5 is well described by a concave-up quadratic function, and may be a salient feature of mixed-wet rocks.

Description: We present a general phenomenological formalism for the modeling of hydraulic head behaviour in naturally fractured aquifers. A non local in time version of the double porosity model is developed for Euclidean and fractal reservoirs. In the fractal case, time non-locality allows to find the geometric and topological factors responsible for subdiffusive behaviour in such heterogeneous environments. Opposite to other fractal models presented in the literature Chang and Yortsos [1990], our model include dead-ends-backbone interactions instead of matrix-fracture interactions with clear and well defined scaling exponents, thus giving a better characterization of the reservoir after such parameters are estimated. Applications to field tests are discussed. In particular, a distinctive short time head behaviour during well tests is found.

Description: The publicly available global discharge database is limited in spatial and temporal coverage. Although regional exceptions exist, the population of the database has declined over the past several years. As discharge is one of the most important parameters for modeling hydrological interactions, alternative measuring techniques must be sought. In the recent past, satellite altimetry has been investigated as an alternative for monitoring inland water level. In the present study, altimetry footprints in the vicinity of river gauging stations for the Amazon, Amur, Brahmaputra, Danube, Don, Mekong, Niger, Ob and Vistula rivers are analyzed for a functional relationship between the water level measurements from altimetry and discharge from the gauging stations. Such a functional relationship is conventionally established via a rating curve computed using simultaneous data. This study proposes a statistical approach based on quantile functions to infer this functional relation without the need for having synchronous datasets. The statistical approach provides the opportunity of extracting discharge values from altimetry data for rivers like the Mekong, Brahmaputra, Don and Vistula for which the discharge measurements at the selected gauges were made before the age of satellites. The algorithm is then validated over those rivers which do have discharge measurements available within periods of altimetry. Our validation shows that our algorithm is in the same quality range as the conventional approach. We are thus able to salvage pre-satellite altimetry discharge data and turn them into active use for the satellite altimetry time frame.

Description: While satellite based remote-sensing has provided hydrologists with valuable new datasets, integration of such datasets in operational modeling systems is usually not straightforward due to spatial or temporal resolution issues or because remote sensing does not directly measure the hydrological quantities of interest. This is the case for satellite based radar-altimetry. River level variations can be tracked using radar altimetry at a temporal resolution between 10 and 35 days, depending on the satellite, but hydrologists are typically interested in river flows rather than levels and require predictions at daily or even sub-daily temporal resolutions. One way to exploit satellite radar altimetry is therefore to combine the data with hydrological models in a data assimilation framework. In this study, radar altimetry data from 6 ENVISAT virtual stations were assimilated to a routing model of the main reach of the Brahmaputra River driven by the outputs of a calibrated rainfall runoff model. The Extended Kalman Filter was used to update the routed water volumes for the years 2008 to 2010. Model performance was improved with the Nash-Sutcliffe model efficiency for daily discharge increasing from 0.78 to 0.84. The method uses very little in situ data and is easily implemented as an add-on to hydrological models and it therefore has the potential for large scale application to improve hydrological predictions in many river basins.

Description: Management of water temperatures in the Columbia River Basin (Washington) is critical because water projects have substantially altered the habitat of Endangered Species Act (ESA) listed species, such as salmon, throughout the basin. This is most important in tributaries to the Columbia, such as the Methow River, where the spawning and rearing life stages of these cold water fishes occurs. Climate change projections generally predict increasing air temperatures across the western United States, with less confidence regarding shifts in precipitation. As air temperatures rise, we anticipate a corresponding increase in water temperatures, which may alter the timing and availability of habitat for fish reproduction and growth. To assess the impact of future climate change in the Methow River, we couple historical climate and future climate projections with a statistical modeling framework to predict daily mean stream temperatures. A K -nearest neighbor algorithm is also employed to: (i) adjust the climate projections for biases compared to the observed record and (ii) provide a reference for performing spatiotemporal disaggregation in future hydraulic modeling of stream habitat. The statistical models indicate the primary drivers of stream temperature are maximum and minimum air temperature and streamflow and show reasonable skill in predictability. When compared to the historical reference time period of 1916-2006, we conclude that increases in stream temperature are expected to occur at each subsequent time horizon representative of the year 2020, 2040, and 2080, with an increase of 0.8 ± 1.9 °C by the year 2080.

Description: Thermal remote sensing methods for mapping evapotranspiration (ET) exploit the physical interconnection that exists between land-surface temperature (LST) and evaporative cooling, employing principles of surface energy balance (SEB). Unfortunately, while many applications in water resource management require ET information at daily and field spatial scales, current satellite-based thermal sensors are characterized by either low spatial resolution and high repeatability or by moderate/high spatial resolution and low frequency. Here we introduce a novel approach to ET mapping that fuses characteristics of both classes of sensors to provide optimal spatiotemporal coverage. In this approach, coarse resolution daily ET maps generated with a SEB model using geostationary satellite data are spatially disaggregated using daily MODIS (MODerate resolution Imaging Spectroradiometer) 1-km and bi-weekly Landsat LST imagery sharpened to 30-m. These ET fields are then fused to obtain daily ET maps at 30-m spatial resolution. The accuracy of the fused Landsat-MODIS daily ET maps was evaluated over Iowa using observations collected at 8 flux towers sited in corn and soybean fields during the Soil Moisture Experiment of 2002 (SMEX02), as well as in comparison with a Landsat-only retrieval. A significant improvement in ET accuracy (reducing errors from 0.75 to 0.58 mm/d on average) was obtained by fusing MODIS and Landsat data in comparison with the Landsat-only case, with most notable improvements when a rainfall event occurred between two successive Landsat acquisitions. The improvements are further evident at the seasonal timescale, where a 3% error is obtained using Landsat-MODIS fusion vs. a 9% Landsat-only systematic underestimation.

Description: This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches [e.g., Marzouk and Xiu , 2009; Marzouk and Najm , 2009] and uses generalized polynomial chaos (gPC) theory to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU-efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a 2-5 times speed up in sampling efficiency.

Description: An earlier infiltration equation relied on curve fitting of infiltration data for the determination of one of the parameters, which limits its usefulness in practice. This handicap is removed here and the parameter is now evaluated by linking it directly to soil-water properties. The new predictions of infiltration using this evaluation are quite accurate. Positions and shapes of soil-water profiles are also examined in detail and found to be predicted analytically with great precision.

Description: Fibre-optic Distributed Temperature Sensing (FO-DTS) has been frequently applied for analysing thermal patterns, including the identification of groundwater-surface water exchange fluxes across aquifer-river interfaces. However, the impacts of a) seasonal variability in signal strength (given by the difference between groundwater and surface water temperatures) and b) monitoring modes on the accuracy of FO-DTS surveys has not yet been determined. This study uses a well investigated field site as model system for quantifying the accuracy and uncertainty of FO-DTS surveys in dependency of seasonal signal variation and monitoring mode. The analysis of the relationship between seasonal variability in signal strength and diurnal oscillations in end-member temperatures at the study site revealed that winter conditions, with substantially lower diurnal temperature oscillations provide the highest temporal stability in signal strength. The choice of monitoring mode proved to have significant impact on the accuracy of FO-DTS surveys. The proposed two-way single-ended averaging of FO-DTS surveys had significant advantages compared to single-ended or double-ended surveys, with a higher accuracy in signal detection in particular for small-scale temperature variations. Since FO-DTS surveys in two-way single-ended averaging mode were better suited for detecting the full complexity of spatial temperature patterns for the investigated aquifer-river interface, we recommend its wider application in similarly complex systems with small-scale thermal patterns.

Description: The paper presents a semianalytical method to solve the multispecies reactive solute-transport equation coupled with a sequential first-order reaction network under spatially or temporally varying flow velocities and dispersion coefficients. This method employs the generalized integral transform technique (GITT) and general linear transformation method by Clement [2001] to transform the set of coupled multispecies reactive transport equations into a set of independent uncoupled equations and to solve these independent equations for spatially or temporally varying flow velocities and dispersion coefficients, as well for temporally varying inlet concentration. The proposed semianalytical solution is compared against previously published analytical solutions of Srinivasan and Clement [2008b] and van Genuchten [1985]. An example is used to show application of the solution to a hypothetical multilayered medium. The solution of proposed approach is compared also with a numerical solution using the 2DFATMIC. Three scenarios are illustrated to show the capabilities of the proposed semianalytical method to deal with aquifer heterogeneity and transient situations. We also show a practical implementation of the solution to an actual field, single-well push-pull test (PPT) example designed to obtain the concentration distribution of reactants consumed and products formed at the end of the injection phase.

Description: The acquisition of reliable datasets representative of hydrological regimes and their variations is a critical concern for water resource assessment. For the subsurface, traditional approaches based on probe measurements, core analysis and well data can be laborious, expensive, and highly intrusive, while only yielding sparse data sets. For this study, an innovative field survey, merging relative microgravimetry, magnetic resonance soundings and hydrological measurements, was conducted to evaluate both surface and subsurface water storage variations in a semi-arid Sahelian area. The instrumental setup was implemented in the lower part of a typical hillslope feeding to a temporary pond. Weekly measurements were carried out using relative spring gravimeters during three months of the rainy season in 2009 over a 350 × 500 m 2 network of twelve microgravity stations. Gravity variations of small to medium amplitude (≤ 220 nm s 2 ) were measured with accuracies better than 50 nm s -2 , revealing significant variations of the water storage at small time (from one week up to three months) and space (from a couple of meters up to a few hundred meters) scales. Consistent spatial organization of the water storage variations were detected, suggesting high infiltration at the outlet of a small gully. The comparison with hydrological measurements and magnetic resonance soundings involved that most of the microgravity variations came from the heterogeneity in the vadose zone. The results highlights the potential of time lapse microgravity surveys for detecting intraseasonal water storage variations and providing rich space-time datasets for process investigation or hydrological model calibration/evaluation.

Description: The instantaneous turbulent flow fields over a smooth bed and a bed containing large-scale roughness elements are characterized by the presence of elongated low and high streamwise momentum regions, or streaks. If the bed contains large-scale roughness elements (e.g., dunes), the size of the streaks increases and is of the order of the size of these elements and the flow depth. The present large eddy simulation (LES) study focuses on the case of developing flow within wide channels containing at the bottom a long array of spanwise-oriented sinusoidal 2D dunes (2a/h=0.1, λ/h=1, λ is the wavelength, 2a is the dune height, h is the mean flow depth) and, respectively, an array of 2D asymmetric dunes (2a/h=0.25, λ/h=3.75) of closer shape to the ones observed in natural streams. For the case of an incoming steady flow, the instantaneous flow fields, in the region where the flow transitions toward a fully-developed turbulent flow regime, contain arrays of highly-organized hairpin vortices whose dimensions are larger than the dune height. LES shows that for relatively shallow channels (e.g., channels with 2a/h=0.25), the large-scale hairpins and the streaks penetrate regularly up to the free surface, thus affecting mass transport and mixing over the whole water column. The paper explains the mechanism for the formation of these arrays of hairpin vortices and discusses changes between a case with asymmetric dunes that are characterized by a large value of λ/2a (=15) and a long upslope face, and a case with symmetric dunes for which λ/2a=10, the upslope face is relatively short and the rate of change of the bed curvature around the dune's crest is relatively small. The study discusses the main mechanisms through which large-scale hairpin form and how these mechanisms change between two dune geometries (sinusoidal vs. asymmetric dunes). We also show that hairpin eddies play the primary role in the formation of the streaks over the region containing dunes and we characterize the average dimensions of these streaks. The presence of resolved turbulence in the incoming flow reduces the streamwise distance needed for the streaks to develop over region containing dunes, but does not affect qualitatively the transition process toward the fully-developed flow regime, nor the spacing of the streaks in the fully-developed flow region.

Description: Parlange and Brutsaert [1987] derived a modified Boussinesq equation to account for the capillary effect on watertable dynamics in unconfined aquifers. Barry et al . [1996] solved this equation subject to a periodic boundary condition. Their solution shows significant influence of capillarity on watertable fluctuations, which evolve to finite-amplitude standing waves at the high frequency limit. Here, we propose a new governing equation for the watertable, which considers both horizontal and vertical flows in an unsaturated zone of finite thickness. An approximate analytical solution for periodic watertable fluctuations based on the new equation was derived. In agreement with previous results, the analytical solution shows that the unsaturated zone's storage capacity permits watertable fluctuations to propagate more readily than predicted by the Boussinesq equation. Furthermore, the new solution reveals a capping effect of the unsaturated zone on both the amplitude and phase of the watertable fluctuations as well as the watertable overheight. Due to the finite thickness of the unsaturated zone, the capillary effect on watertable fluctuations is modified mainly with reduced amplitude damping and phase shift.

Description: Impacts of rising sea level on the hydraulic balance between aquifers and the ocean threaten fresh water resources and aquatic ecosystems along many world coastlines. Understanding the vulnerability of groundwater systems to these changes and the primary factors that determine the magnitude of system response is critical to developing effective management and adaptation plans in coastal zones. We assessed the vulnerability of two types of groundwater systems, recharge-limited and topography-limited, to changes caused by sea-level rise over a range of hydrogeologic settings. Vulnerability in this context is defined by the rate and magnitude of salinization of coastal aquifers and changes in groundwater flow to the sea. Two-dimensional variable-density groundwater flow and salt transport simulations indicate that the response of recharge-limited systems is largely minimal, whereas topography-limited systems are vulnerable for various combinations of permeability, vertical anisotropy in permeability, and recharge. World coastlines were classified according to system type as a vulnerability indicator. Results indicate that ~70% of world coastlines may be topography-limited, though variability in hydrogeologic conditions strongly affects classification. Future recharge and sea-level rise scenarios have much less influence on the proportion of vulnerable coastlines than differences in permeability, distance to a hydraulic divide, and recharge, indicating that hydrogeologic properties and setting are more important factors to consider in determining vulnerability than uncertainties in the magnitude of sea-level rise and hydrologic shifts associated with future climate change.

Description: ABSTRACT Given the range of future uncertainty, there is increasing interest in developing and evaluating water management strategies that are robust to an uncertain future. As part of a process termed “decision scaling”, a climate response function was developed to isolate the impact of climate change on a water system in terms of hazards identified by stakeholders. The climate response function was then used to evaluate system performance over a wide range of climate conditions and to define robustness indicators. The robustness indicators, which measure system performance as a function of climate state, are conditioned on explicit assumptions about climate variable probability distributions. To illustrate this process, it is applied to the Upper Great Lakes to evaluate system robustness related to water management decisions and assess the impact of climate probability assumptions. The robustness indicators were used to identify decisions that outperformed other courses of action regardless of assumptions of future climate probabilities.

Description: Recent studies suggest that there is a strong linkage between the moisture uptake over the equatorial area of the Somali Low Level Jet (SLLJ) and the rainfall variability over most of continental India. Additionally, the Madden-Julian Oscillation (MJO) strongly modulates the intraseasonal variability of the Indian summer monsoon rainfall, since the northward propagation of the boreal summer MJO is closely associated with the active and break phases of monsoon rainfall. But a question remains open: is there a relationship between the moisture transported by the SLLJ and the MJO evolution?. In this paper a lagrangian approach is used to track the evaporation minus precipitation (E - P) evolution along trajectories of particles initially situated over the equatorial region of SLLJ. The impact of the MJO on the water budget transport of the SLLJ is examined by making composites of the obtained (E - P) fields for the different MJO phases. The spatial structures of the boreal summer intraseasonal oscillation are revealed in our results, which strongly suggest that the main responsible for the rainfall variability associated to the MJO in these regions are the changes in the moisture advected by the SLLJ. In order to assess the MJO - SLLJ interaction, an analysis of the total-column mass and the total-column specific humidity transported by the SLLJ during the MJO life cycle is performed. While a systematic difference between air mass advected to India during active and break phases of MJO is not detected, changes in the moisture of particles are found, with wet (dry) anomalies over enhanced (suppressed) convection region. This result implicitly leads to assume air-sea interaction processes.

Description: Semiarid sedimentary plains occupied by dry forest ecosystems often display low groundwater recharge rates and accumulation of salts in the soil profile. The transformation of these natural systems to rain-fed agriculture has led to raising water tables and a slow, but steady, process of groundwater and soil salinization in vast areas of Australia. In the semiarid plains of Chaco (central South America), unprecedented deforestation rates are taken place. Based on deep soil sampling (0-6 m) in seven paired stands under natural dry forest, rain-fed agriculture and pasture, with different age of clearance (〉30 years, 20 and 3 years) in Salta, Argentina, we provide evidence of groundwater recharge increase and onset of salt mobilization in areas where forests were replaced by annual croplands. Soils with higher water and lower chloride content are evidence of deep percolation and salt leaching. In Salta, stands subject to 30 years of rain fed cultivation had profiles with 30 to 46% higher moisture content and 94% lower chloride stocks compared to dry forest (0.05 ± 0.04 kg/m 2 vs. 0.77 ± 0.4 kg/m 2 ). Estimates of groundwater recharge based on the displacement of chloride peaks suggested values of 27to 87mm/yr for agricultural soybean stands, and 10.4 mm/yr for pastures. While hydrological shifts in the regional groundwater system are poorly monitored and understood, our findings show that it is potentially sensitive to land use changes and to salinization processes.

Description: In regulated river systems, such as the River Murray in Australia, the efficient use of water to preserve and restore biota in the river, wetlands and floodplains is of concern for water managers. Available management options include the timing of river flow releases and operation of wetland flow control structures. However, the optimal scheduling of these environmental flow management alternatives is a difficult task, since there are generally multiple wetlands and floodplains with a range of species, as well as a large number of management options that need to be considered. Consequently, this problem is a multi-objective optimization problem aimed at maximizing ecological benefit while minimizing water allocations within the infrastructure constraints of the system under consideration. This paper presents a multi-objective optimization framework, which is based on a multi-objective ant colony optimization approach, for developing optimal trade-offs between water allocation and ecological benefit. The framework is applied to a reach of the River Murray in South Australia. Two studies are formulated to assess the impact of (i) upstream system flow constraints and (ii) additional regulators on this trade-off. The results indicate that unless the system flow constraints are relaxed, there is limited additional ecological benefit as allocation increases. Furthermore the use of regulators can increase ecological benefits while using less water. The results illustrate the utility of the framework since the impact of flow control infrastructure on the trade-offs between water allocation and ecological benefit can be investigated, thereby providing valuable insight to managers.

Description: [1]  Crystalline rocks are often heterogeneous geological materials that contain numerous fractures of various attitudes and scales. Although considerable advances have been made in simulation of fluid flow through fractured media, our knowledge of seepage flow with free surfaces in fracture networks remains to be an outstanding issue. In this paper, the partial differential equations (PDEs) defined on the whole fracture network domain are formulated for free-surface seepage flow problems by an extension of Darcy's law. A variational inequality (VI) formulation is then presented, and the proof of the equivalence between the PDE and VI formulations is given. Since the boundary conditions involving the flux components in the PDE formulation become the natural conditions in the VI formulation, the difficulty of choosing the trial functions for numerical solutions is significantly reduced and the locations of seepage points can be easily determined. On the basis of the discrete VI, the corresponding numerical procedure for unconfined seepage analysis of discrete fracture network has been developed. The results from three typical examples demonstrate the validity and capability of the procedure for unconfined seepage problems involving complicated fracture networks.

Description: ABSTRACT Laboratory tests are commonly used to determine properties (vertical compressibility, α; specific storage, S S ; vertical hydraulic conductivity, K v ) of claystone aquitards; however, whether data representative of in situ conditions can be obtained from disturbed samples is questionable. Here, we present a method to determine the in situ α and S S of a thick sequence of Cretaceous aged claystone by estimating the loading efficiency (γ) of a formation from pore pressure responses to barometric pressure fluctuations. We installed 10 vibrating wire pressure transducers at different depths (25 to 325 m below ground, BG) in a thick claystone aquitard by placing them directly within the cement-bentonite grout. Two years of continuous transducer records using this method appeared to provide pore pressure data with a resolution of better than one part in 10 5 , equivalent to mm of hydraulic head change. Pore pressure responses to barometric pressure changes, earth tides and precipitation events can be clearly identified and the barometric responses can be easily analyzed. The resulting values of γ (0.6 to 0.93), α (2.5×10 -7 to 2.2×10 -6 kPa -1 ), and S S (2.6×10 -5 to 4.5×10 -6 m -1 ) all decrease with depth. The results are comparable with the limited existing data for in situ estimates of S S and are as much as an order of magnitude smaller than laboratory estimates of S S for similar aquitard deposits. Our findings suggest that the fully-grouted transducer method can provide an accurate and reliable means to monitor pore pressure changes and to determine in situ parameters for bedrock aquitard systems.

Description: [1]  Theoretically, if the distribution of daily rainfall is known or justifiably assumed, then one could argue, based on extreme value theory, that the distribution of the annual maxima of daily rainfall would resemble one of the three limiting types: (a) type I, known as Gumbel; (b) type II, known as Fréchet; and (c) type III, known as reversed Weibull. Yet, the parent distribution usually is not known and often only records of annual maxima are available. Thus, the question that naturally arises is which one of the three types better describes the annual maxima of daily rainfall. The question is of great importance as the naive adoption of a particular type may lead to serious underestimation or overestimation of the return period assigned to specific rainfall amounts. To answer this question, we analyze the annual maximum daily rainfall of 15,137 records from all over the world, with lengths varying from 40 to 163 years. We fit the generalized extreme value (GEV) distribution, which comprises the three limiting types as special cases for specific values of its shape parameter, and analyze the fitting results focusing on the behavior of the shape parameter. The analysis reveals that (a) the record length strongly affects the estimate of the GEV shape parameter and long records are needed for reliable estimates; (b) when the effect of the record length is corrected, the shape parameter varies in a narrow range; (c) the geographical location of the globe may affect the value of the shape parameter; and (d) the winner of this battle is the Fréchet law.

Description: The evaluation of extreme snowfalls is an important challenge for hazard management in mountaineous regions.In this paper, extreme snowfall data acquired from 40 meteorological stations in the French Alps since 1966 are analyzed using spatial extreme statistics. They are then modeled within the formal framework of max-stable processes which are the generalization of univariate extreme value theory to the spatial multivariate case. The three main max-stable processes now available are compared using composite likelihood maximisation, and the most flexible Brown-Resnick one is retained on the basis of the TIC criterion, taking into account anisotropy by space transformation. Furthermore, different models with smooth trends (linear and splines) for the spatial evolution of the GEV parameters are tested to allow snowfall maps for different return periods to be produced. After altitudinal correction that separates spatial and orographic effects, the different spatial models are fitted to the data within the max-stable framework, allowing inference of the GEV margins and the extremal dependence simultaneously. Finally a nested model selection procedure is employed to select the best linear and spline models. Results show that the best linear model produces reasonable quantile maps (assessed by cross-validation using other stations) but that it is outperformed by the best spline model which better captures the complex evolution of GEV parameters with space. For a given return period and at fixed elevation of 2000 m, extreme 3-day snowfalls are higher in the NE and SE of the French Alps. Maxima of the location parameter of the GEV margins are located in the North and South while maxima of the scale parameter are located in the SE which corresponds to the Mediterranean influence that tends to bring more variability. Besides, the dependence of extreme snowfalls is shown to be stronger on the local orientation of the Alps, an important result for meteorological variables confirming previous studies. Computations are performed for different accumulation durations which enable obtaining magnitude -- frequency curves and show that the intensity of the extremal directional dependence effect is all the more important when the duration is short. Finally, we show how the fitted model can be used to evaluate joint exceedence probabilities and conditional return level maps which can be useful for operational risk management.

Description: Conventional methods for numerical simulation of the soil evaporation process do not take into account the mechanical dispersion of vapor in the porous matrix. This omission is due to uncertainty about the flow process that generates mechanical dispersion and about the numerical value of dispersivity. In this study we assess three processes that can generate mechanical dispersion: (a) temperature variation, (b) barometric pressure variation and (c) Stefan flow. Order of magnitude estimates show that, under natural conditions, only Stefan flow can contribute significantly to the generation of mechanical dispersion. However, a simple sensitivity analysis of the effects of dispersivity on the contribution of different mechanisms to the evaporation process shows that diffusion and mechanical dispersion act in a complementary way. In simulations of a natural system, the evaporation flux increases by only 12% when dispersivity is increased from 0 m (no mechanical dispersion) to 0.078 m, although at the latter value the mechanical dispersion flux contributes up to 40% of the total evaporation flux.

Description: One-dimensional solute transport modeling to simulate experimental tracer releases in rivers is common practice. However, few studies have investigated the effect of experiment type (slug injection v. constant rate injection) on model parameter sensitivity and identifiability. We conducted slug injection and constant-rate experiments in a low-gradient, alluvial, headwater tundra stream in northern Alaska. Each experimental data set was simulated with the One-dimensional Transport with Inflow and Storage (OTIS) model, and analyzed with Monte Carlo-based techniques to investigate differences in global sensitivity and time-varying identifiability of the model parameters. We found that the longitudinal dispersion parameter exhibited relatively high sensitivity for the slug injection data, while the storage zone area parameter exhibited relatively high sensitivity for the constant rate injection data. The storage zone area and storage zone – main channel exchange rate parameters show heightened sensitivity during rising and tailing portions of the constant rate injection breakthrough curve, whereas for the slug injection data, increased identifiability for these parameters is only observed during the tailing portion of the breakthrough curve. Results demonstrate that selection of slug injection or constant rate experiment type impacts parameter sensitivity and time-varying identifiability, and that experimental data can easily be analyzed to understand the relative information content associated with each parameter of a 1D transient storage model.

Description: Rates of U(VI) release from individual dry-sieved size fractions of a field-aggregated, field-contaminated composite sediment from the seasonally saturated lower vadose zone of the Hanford 300-Area were examined in flow-through reactors to maintain quasi-constant chemical conditions. The principal source of variability in equilibrium U(VI) adsorption properties of the various size fractions was the impact of variable chemistry on adsorption. This source of variability was represented using surface complexation models with different stoichiometric coefficients with respect to hydrogen ion and carbonate concentrations for the different size fractions. A reactive transport model incorporating equilibrium expressions for cation exchange and calcite dissolution, along with rate expressions for aerobic respiration and silica dissolution, described the temporal evolution of solute concentrations observed during the flow-through reactor experiments. Kinetic U(VI) desorption was well described using a multi-rate surface complexation model with an assumed lognormal distribution for the mass-transfer rate coefficients. The estimated mean and standard deviation of the rate coefficients were the same for all 〈2mm size fractions, but differed for the 2-8mm size fraction. Micropore volumes, assessed using t -plots to analyze N 2 desorption data, were also the same for all dry-sieved 〈2mm size fractions, indicating a link between micropore volumes and mass-transfer rate properties. Pore volumes for dry-sieved size fractions exceeded values for the corresponding wet-sieved fractions. We hypothesize that repeated field wetting and drying cycles lead to the formation of aggregates and/or coatings containing (micro)pore networks which provided an additional mass-transfer resistance over that associated with individual particles. The 2-8 mm fraction exhibited a larger average and standard deviation in the distribution of mass-transfer rate coefficients, possibly caused by the abundance of microporous basaltic rock fragments.

Description: ABSTRACT Step-pool systems have been widely studied during the past few years, resulting in enhanced knowledge of mechanisms for sediment transport, energy dissipation and patterns of self-organization. We use rhodamine tracer data collected in nine step-pool reaches during high, intermediate and low flows to explore scaling of solute transport processes. Using the scaling patterns found, we propose an extension of the Aggregated Dead Zone (ADZ) approach for solute transport modeling based on the morphological features of step-pool units and their corresponding inherent variability within a stream reach. In addition to discharge, the reach-average bankfull width, mean step height, and the ratio of pool length to step-to-step length can be used as explanatory variables for the dispersion process within the studied reaches. These variables appeared to be sufficient for estimating ADZ model parameters and simulating solute transport in predictive mode for applications in reaches lacking tracer data.

Description: Infiltration is often assumed to occur with little or no impedance from the air within the vadose zone. If this assumption is not valid air counter flow may occur while the infiltration rate and degree of saturation within the transmission zone may be significantly reduced. Accurate predictions of infiltration rates are important for applications such as moisture balance calculations and predictions of pore water pressures in landslide triggering. Existing results for confined infiltration show contradictory evidence for either air pressure remaining at a threshold or continual increase of air pressure. In this paper the effect of air entrapment is investigated in the laboratory using recently developed techniques of unsaturated transparent porous media and digital photograph interpretation. These techniques enable the full saturation profile to be quantified every five seconds. This experimental data is used to quantify the decrease in infiltration rate and degree of saturation within the transmission zone in the confined infiltration, to accurately locate the wetting front, and to assess the stability of the wetting front. The results confirm previous observations in which infiltration in an open system was observed to occur significantly faster than in a closed system. However, in this study, the air pressure ahead of the wetting front was observed to reach a threshold value, which was a function of the ponding height. A Green-Ampt infiltration model based on this observation of air confinement was observed to provide a better fit to the experimental data than one based on the continual increase in air pressure assumption.

Description: An 11-year dataset of spatially distributed snow water equivalent (SWE) was used to inform a quantitative, near-optimal sensor placement methodology for real-time SWE estimation in the American River basin of California. Rank-based clustering was compared to geographically based clustering (sub-basin delineation) to determine the existence of stationary covariance structures within the overall SWE dataset. The historical SWE data, at 500 x 500 m resolution, were split into eight years of training and three years of validation data. Within each cluster, a quantitative sensor-placement algorithm, based on maximizing the metric of Mutual Information, was implemented and compared to random placement. Gaussian Process models were then built from validation data points selected by the algorithm to evaluate the efficacy of each placement approach. Rank based clusters remained stable inter-annually, suggesting that rankings of pixel-by-pixel SWE exhibit stationary features that can be exploited by a sensor-placement algorithm, yielding a 200 mm average root mean square error (RMSE) for twenty randomly selected sensing locations. This outperformed geographic and basin-wide placement approaches, which generated 460 mm and 290 mm RMSE, respectively. Mutual Information- based sampling provided the best placement strategy, improving RMSE between 0 and 100 mm compared to random placements. Increasing the number of rank-based clusters consistently lowered average RMSE from 400 mm for one cluster to 175 mm for eight clusters, for twenty total sensors placed. To optimize sensor placement we recommend a strategy that couples rank-based clustering with Mutual Information -based sampling design.

Description: We investigated the effects of different diverging-converging pore geometries and compare the micro-scale fluid flow and effective hydraulic properties from these pores with that of a pipe from viscous to inertial laminar flow regimes. The flow fields are obtained using computational fluid dynamics and the comparative analysis is based on a dimensionless hydraulic shape factor β which is the ‘specific surface’ scaled by the length of pores. Results from all diverging-converging pores show an inverse pattern in velocity and vorticity distributions relative to the pipe flow. The hydraulic conductivity K of all pores we considered is dependent on and can be predicted from β with a power function with an exponent of 3/2. The differences in K are due to the differences in distribution of local friction drag on the pore walls. At Reynolds number ( Re ) ∼ 0 flows, viscous eddies are found to exist almost in all pores in different sizes, but not in the pipe. Eddies grow when Re →1 and leads to the failure of Darcy's Law. During non-Darcy or Forchheimer flows, the apparent hydraulic conductivity K a decreases due to the growth of eddies which constricts the bulk flow region. At Re 〉1, the rate of decrease in K a increases, and at Re ≫1, it decreases to where the change in K a ≈ 0, and flows once again exhibits a Darcy type relationship. The degree of non-linearity during non-Darcy flow decreases for pores with increasing β . The non-linear flow behavior becomes weaker as β increases to its maximum value in the pipe which shows no non-linearity in the flow; in essence Darcy's Law stays valid in the pipe at all laminar flow conditions. The diverging-converging geometry in pores plays a critical role in modifying the intra-pore fluid flow implying that this property should be incorporated in effective larger-scale models, e.g., pore-network models.

Description: Pool-riffle sequences are geomorphological features of many streams, thought to contribute to the hydrodynamic variability necessary to support healthy habitat conditions. Due to this fact, the addition of artificial pools and riffles is a common alternative for restoration projects on channelized streams. In this paper, detailed three-dimensional (3-D) flow measurements conducted on a scale model of an existing pool-riffle design implemented as part of a restoration project is presented. The design incorporated the basic features of natural pool-riffle sequences but maintained the deepest part of the pool in the center of the cross section and away from the banks. Results showed that the 3-D flow patterns were qualitatively different for two discharge conditions tested. The lower discharge case was strongly affected by the topography, displaying a pattern consistent with a secondary flow generated by the curvature of the streamlines. The higher discharge case was less affected by the topography, presenting a secondary flow pattern similar to that observed over a flat bed and typically associated with turbulence anisotropy. Self-maintenance and flow variability were also investigated. Even though convergence of the values of bed shear stresses at pool and riffle sections with increasing discharge did take place, reversal conditions did not occur. The difference in flow structure with flow stage was also reflected in the spatial flow variability, the lower discharge displaying larger variability than the higher discharge. The higher discharge generated a level of variability comparable with the values obtained over a flat bed.

Description: [1]  Lattice Boltzmann flow simulations provide a physics-based means of estimating intrinsic permeability from pore structure and accounting for inertial flow that leads to departures from Darcy's law. Simulations were used to compute intrinsic permeability where standard measurement methods may fail and to provide better understanding of departures from Darcy's law under field conditions. Simulations also investigated resolution issues. Computed tomography (CT) images were acquired at 0.8 mm interscan spacing for seven samples characterized by centimeter-scale biogenic vuggy macroporosity from the extremely transmissive sole-source carbonate karst Biscayne aquifer in southeastern Florida. Samples were as large as 0.3 m in length; 7–9 cm-scale-length subsamples were used for lattice Boltzmann computations. Macroporosity of the subsamples was as high as 81%. Matrix porosity was ignored in the simulations. Non-Darcy behavior led to a twofold reduction in apparent hydraulic conductivity as an applied hydraulic gradient increased to levels observed at regional scale within the Biscayne aquifer; larger reductions are expected under higher gradients near wells and canals. Thus, inertial flows and departures from Darcy's law may occur under field conditions. Changes in apparent hydraulic conductivity with changes in head gradient computed with the lattice Boltzmann model closely fit the Darcy-Forchheimer equation allowing estimation of the Forchheimer parameter. CT-scan resolution appeared adequate to capture intrinsic permeability; however, departures from Darcy behavior were less detectable as resolution coarsened.

Description: Hydraulic conductivity ( K ) is one of the most important parameters of interest in groundwater applications because it quantifies the ease with which water can flow through an aquifer material. Hydraulic conductivity is typically measured by conducting aquifer tests or wellbore flow (WBF) logging. Of interest in our research is the use of proton nuclear magnetic resonance (NMR) logging to obtain information about water-filled porosity and pore space geometry, the combination of which can be used to estimate K . In this study, we acquired a suite of advanced geophysical logs, aquifer tests, WBF logs, and sidewall cores at the field site in Lexington, Nebraska, which is underlain by the High Plains aquifer. We first used two empirical equations developed for petroleum applications to predict K from NMR logging data: the Schlumberger Doll Research equation ( K SDR ) and the Timur-Coates equation ( K T-C ), with the standard empirical constants determined for consolidated materials. We upscaled our NMR-derived K estimates to the scale of the WBF-logging K ( K WBF-logging ) estimates for comparison. All the upscaled K T-C estimates were within an order of magnitude of K WBF-logging and all of the upscaled K SDR estimates were within two orders of magnitude of K WBF-logging . We optimized the fit between the upscaled NMR-derived K and K WBF-logging estimates to determine a set of site-specific empirical constants for the unconsolidated materials at our field site. We conclude that reliable estimates of K can be obtained from NMR logging data, thus providing an alternate method for obtaining estimates of K at high levels of vertical resolution.

Description: We develop a probabilistic model to estimate the rate of flood-induced losses for a set of properties distributed over a large geographical region (for example a portfolio of insured properties within a country). The use of detailed physically based models over large areas becomes difficult due to the vast amount of data needed and the high implementation cost. The proposed model allows one to incorporate results from such detailed models, but can be used also in regions that have not been studied in much detail. Minimal required information includes the rate and spatial extent of severe precipitation, the topography and river network from which regions at risk of flooding can be identified, and information on historical floods with an approximate delineation of the flooded area and associated aggregate losses for at least a few major events. An application to river flood loss from residential buildings in Belgium is presented.

Description: It is known that in variably saturated porous media, the dispersion coefficient depends on the Darcy velocity and water saturation. In one dimensional flow, it is commonly assumed that the dispersion coefficient is a linear function of velocity. The coefficient of proportionality, called the dispersivity, in considered to depend on saturation. However, there is not much known about its dependence on saturation. The purpose of this study is to investigate, using a pore network model, how the longitudinal dispersivity varies non-linearly with saturation. We schematize the porous medium as a network of pore bodies and pore throats with finite volumes. The pore space is modeled using the Multi-Directional Pore-Network (MDPN) concept, which allows for a distribution of pore coordination numbers. This topological property together with the distribution of pore sizes are used to mimic the microstructure of real porous media. The dispersivity is calculated by solving the mass balance equations for solute concentration in all network elements and averaging the concentrations over a large number of pores. We have introduced a new formulation of solute transport within pore space, where we account for different compartments of residual water within drained pores. We assign different pressures and concentrations to each compartment. This formulation makes it possible to capture the effect of limited mixing due to partial filling of the pores under variably saturated conditions. We found that dispersivity increases with the decrease in saturation, it reaches a maximum value, and then decreases with further decrease in saturation. To show the capability of our formulation to properly capture the effect of saturation on solute dispersion, we applied it to model the results of a reported experimental study. While these computations have been restricted to the calculation of flow and transport of inert solutes and determination of dispersivities, they demonstrate the significant potential of this formulation of pore-network modeling for predicting other transport properties, such as mass transfer coefficients for reactive/adsorptive solute transport considering interfaces and limited mixing within pores.

Description: The complexity of predicting surface runoff from hydrological models is compounded by uncertainties associated with the model structure, parameters and inputs. A hierarchical mixture of experts (HME) is recognised as one of the ways of incorporating model structural uncertainty into hydrological simulations. In this article, a framework capable of incorporating parameter and structural uncertainties is implemented via the use of a hierarchical mixture of experts together with sequential Monte Carlo parameter sampling. The use of a HME enables aggregation of multiple constituent models at the same instance, mixed to different extents in a dynamic manner as specified by a gating function, allowing the modeller to better characterise the uncertainty associated with the obtained predictions. This article presents a mechanism for better specifying the structure of the gating function used for combining models in a HME approach, by investigating the combination of predictor variables that allows the best model mixing. These predictors exist in various forms, each of which represents information on the catchment. We apply three different types of predictors to a case study, the Never Never River catchment in Australia. The outcomes from this case study consistently demonstrate improved Bayesian Information Criterion (BIC) readings for the HME especially when used with a combination of predictors. The predictor coefficients are further used for regionalisation with the Manning River catchment, having similar characteristics to the Never Never River catchment, and also demonstrate satisfactory improvement in BIC when compared with a single structure model.

Description: The sources of uncertainty in projecting the impacts of climate change on runoff are increasingly well recognized; however, translating these uncertainties to urban water security has received less attention in the literature. Furthermore, runoff cannot be used as a surrogate for water supply security when studying the impacts of climate change due to the non-linear transformations in modeling water supply and the effects of additional uncertainties, such as demand. Consequently, this study presents a scenario-based sensitivity analysis to qualitatively rank the relative contributions of major sources of uncertainty in projecting the impacts of climate change on water supply security through time. This can then be used by water authorities to guide water planning and management decisions. The southern system of Adelaide, South Australia, is used to illustrate the methodology, for which water supply system reliability is examined across six greenhouse gas (GHG) emissions scenarios, seven general circulation models, six demand projections, and 1000 stochastic rainfall time series. Results indicate the order of the relative contributions of uncertainty changes through time; however, demand is always the greatest source of uncertainty and GHG emission scenarios the least. In general, reliability decreases over the planning horizon illustrating the need for additional water sources or demand mitigation, while increasing uncertainty with time suggests flexible management is required to ensure future supply security with minimum regret.

Description: The original review of macropores and water flow in soils by Beven and Germann is now thirty years old and has become one of the most highly cited papers in hydrology. This paper attempts to review the progress in observations and theoretical reasoning about preferential soil water flows over the intervening period. It is suggested that the topic has still not received the attention that its importance deserves, in part because of the ready availability of software packages rooted firmly in the Richards domain, albeit that there is convincing evidence that this may be predicated on the wrong experimental method for natural conditions. There is still not an adequate physical theory linking all types of flow, and there are still not adequate observational techniques to support the scale dependent parameterisations that will be required at practical field and hillslope scales of application. Some thoughts on future needs to develop a more comprehensive representation of soil water flows are offered.

Description: Infiltration is a key process in aspects of hydrology, agricultural and civil engineering, irrigation design, and soil and water conservation. It is complex, depending on soil and rainfall properties, and initial and boundary conditions within the flow domain. During the last century, a great deal of effort has been invested to understand the physics of infiltration and to develop quantitative predictors of infiltration dynamics. Jean-Yves Parlange and Wilfried Brutsaert have made seminal contributions, especially in the area of infiltration theory and related analytical solutions to the flow equations. This review retraces the landmark discoveries and the evolution of the conceptual approaches and the mathematical solutions applied to the problem of infiltration into porous media, highlighting the pivotal contributions of Parlange and Brutsaert. A historical retrospective of physical models of infiltration is followed by the presentation of mathematical methods leading to analytical solutions of the flow equations. The review then addresses the Time Compression Approximation (TCA) developed to estimate infiltration at the transition between pre- to post-ponding conditions. Finally, the effects of special conditions, such as the presence of air and heterogeneity in soil properties, on infiltration are considered.

Description: Drinking water purveyors are increasingly relying on land conservation and management to ensure the safety of the water that they provide to consumers. To cost-effectively implement any such landscape initiatives, resources must be targeted to the appropriate spatial scale to address quality impairments of concern in a cost effective manner. Using data gathered from 40 Canadian rivers across 4 ecozones, we examined the spatial scales at which landuse was most closely associated with drinking source water quality metrics. Exploratory linear mixed effects models accounting for climatic, hydrological, and physiographic variation among sites suggested that different spatial areas of landuse influence drinking source water quality depending on the parameter and season investigated. Escherichia coli spatial variability was only associated with landuse at a local (5 km – 10 km) spatial scale. Turbidity measures exhibited a complex association with landuse suggesting that the landuse areas of greatest influence can range from a 1 km sub-catchment to the entire watershed depending on the season. Total organic carbon concentrations were only associated with landuse characterized at the entire watershed scale. The Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) was used to calculate a composite measure of seasonal drinking source water quality but did not provide additional information beyond the analyses of individual parameters. These results suggest that entire watershed management is required to safeguard drinking water sources with more focused efforts at targeted spatial scales to reduce specific risk parameters.

Description: ABSTRACT Runoff mixing patterns for baseflow and 42 storm events were investigated for a three-year period (2008-2010) in a 12 ha forested catchment in the mid-Atlantic, Piedmont region of the USA. Eleven distinct runoff sources were sampled independently and included: precipitation, throughfall, stemflow, litter leachate, wetland soil water, tension soil water, shallow groundwater, groundwater seeps, hyporheic water, riparian groundwater and deep groundwater. A rigorous end-member mixing analysis (EMMA) was implemented and all baseflow, stormflow, and end-member chemistries were evaluated in a two-dimensional mixing space. End-members enclosed stream water chemistry and displayed a systematic continuum in EMMA space. Baseflow chemistry of stream waters was similar to groundwater seeps. Storm event runoff was attributed to contributions from surficial sources (precipitation, throughfall, stemflow and litter leachate) on the rising limb of the discharge hydrograph that was followed by soil and shallow groundwater sources on the recession limb of the hydrograph. The shapes of the storm event hysteresis loops (wide versus tight, linear patterns) varied with hydrologic conditions from wet, hydrologically well-connected conditions to a dry, disconnected state. Detailed temporal data on end-member chemistry allowed us to explain the changes in stream water hysteresis patterns and runoff mixing space to shifts in end-member chemistry that occurred as the catchment became hydrologically disconnected. These results highlight the need to recognize the temporal variation in end-member chemistry as a function of catchment wetness and the need to collect high-frequency data on both - stream water as well as potential runoff end-members to better characterize catchment flow paths and mixing responses.

Description: Stochastic simulation of precipitation at multiple sites in a region can be accomplished using a latent multivariate autoregressive Gaussian process as the driver of both precipitation occurrence and precipitation amounts. This idea was proposed by Bárdossy and Plate [1992] and has been used in several subsequent studies. We investigate this modeling framework in more detail, considering both the single-site and the multi-site cases. Among other things, we demonstrate how model parameters can be conveniently estimated using the method of maximum likelihood for censored data. We explore various theoretical properties of the model, including its connection to traditional Markov chain models frequently used to model precipitation occurrence. A case study based on data from Manitoba, Canada, is used to demonstrate the range of statistics that can be reproduced by the model and to highlight potential limitations.

Description: Declining forest health, climate change and development threaten the sustainability of water supplies in the western U.S. While forest restoration may buffer threats to watershed services, funding shortfalls for landscape-scale restoration efforts limit management action. The hydrologic response and reduction in risk to watersheds following forest restoration treatments could create significant non-market benefits for downstream water users. Historic experimental watershed studies indicate a significant and positive response from forest thinning by a reallocation of water from evapotranspiration to surface-water yield. In this study, we estimate the willingness to pay (WTP) for improved watershed services for one group of downstream users, irrigators, following forest restoration activities. We find a positive and statistically significant WTP within our sample of $183.50 per household, at an aggregated benefit of over $400,000 annually for 2,181 irrigators. Our benefit estimate provides evidence that downstream irrigators may be willing to invest in landscape-scale forest restoration to maintain watershed services.

Description: There is widespread recognition that spatially-distributed information on soil surface roughness (SSR) is required for hydrological and geomorphological applications. Such information is necessary to describe variability in soil structure, which is highly heterogeneous in time and space, to parameterise hydrology and erosion models and to understand the temporal evolution of the soil surface in response to rainfall. This paper demonstrates how results from semi-variogram analysis can quantify key elements of SSR for such applications. Three soil types (silt, silt loam and silty clay) were used to show how different types of structural variance in SSR evolve during simulated rainfall events. All three soil types were progressively degraded using artificial rainfall to produce a series of roughness states. A calibrated laser profiling instrument was used to measure SSR over a 10 x 10 cm spatial extent, at a 2 mm resolution. These data were geostatistically analysed in the context of aggregate breakdown and soil crusting. The results show that such processes are represented by a quantifiable decrease in sill variance, from 8.01 (control) to 0.74 (after 60 minutes of rainfall). Soil surface features such as soil cracks, tillage lines and erosional areas were quantified by local maxima in semi-variance at a given length-scale. This research demonstrates that semi-variogram analysis can retrieve spatio-temporal variations in soil surface condition; in order to provide information on hydrological pathways. Consequently, geostatistically-derived SSR shows strong potential for inclusion as spatial information in hydrology and erosion models to represent complex surface processes at different soil structural scales.

Description: Aquifer remediation is a challenging problem with environmental, social and economic implications. As a general rule, pumping proceeds until the concentration of the target substance within the pumped water lies below a pre-specified value. In this paper we estimate the a priori potential failure of the endpoint of remediation due to a rebound of concentrations driven by back diffusion. In many cases, it has been observed that once pumping ceases, a rebound in the concentration at the well takes place. For this reason, administrative approaches are rather conservative and pumping is forced to last much longer than initially expected. While a number of physical and chemical processes might account for the presence of rebounding, we focus here on diffusion from low water mobility into high mobility zones. In this work we look specifically at the concentration rebound when pumping is discontinued while accounting for multiple mass transfer processes occurring at different time scales and parametric uncertainty. We aim to a risk-based optimal operation methodology that is capable of estimating the endpoint of remediation based on aquifer parameters characterizing the heterogeneous medium as well as pumping rate and initial size of the polluted area.

Description: Three phenomenological power law models for the permeability of porous media are derived from computational experiments with flow through explicit pore spaces. The pore spaces are represented by three dimensional pore networks in sixty-three virtual porous media along with fifteen physical pore networks. The power laws relate permeability to (i) porosity, (ii) squared mean hydraulic radius of pores, and (iii) their product. Their performance is compared to estimates derived via the Kozeny equation, which also uses the product of porosity with squared mean hydraulic pore radius to estimate permeability. The power laws provide tighter estimates than the Kozeny equation even after adjusting for the extra parameter they each require. The best fit is with the power law based on the Kozeny predictor, that is, the product of porosity with the square of mean hydraulic pore radius.

Description: Accurate estimation of the characteristics of the winter snowpack is crucial for prediction of available water supply, flooding, and climate feedbacks. Remote sensing of snow has been most successful for quantifying the spatial extent of the snowpack, although satellite estimation of snow water equivalent, fractional snow covered area, and snow depth is improving. Here we show that GPS observations of vertical land surface loading reveal seasonal responses of the land surface to the total weight of snow, providing information about the stored snow water equivalent. We demonstrate that the seasonal signal in SOPAC GPS vertical land surface position time series at six locations in the western United States is driven by elastic loading of the crust by the snowpack. GPS observations of land surface deformation are then used to predict the water load as a function of time at each location of interest and compared for validation to nearby SNOTEL observations of snow water equivalent. Estimates of soil moisture are included in the analysis and result in considerable improvement in the prediction of snow water equivalent.

Description: ABSTRACT Lumped rainfall-runoff models are widely used for flow prediction, but a long-recognized need exists for diagnostic tools to determine whether the process-level behavior of a model aligns with the expectations inherent in its formulation. To this end, we develop a comprehensive exploration of dominant parameters in the Hymod, HBV, and Sacramento Soil Moisture Accounting (SAC-SMA) model structures. Model controls are isolated using time-varying Sobol′ sensitivity analysis for twelve MOPEX watersheds in the eastern United States over a ten-year period. Sensitivity indices are visualized along gradients of observed precipitation and streamflow to identify key behavioral differences between the three models and to connect these back to the models' underlying assumptions. Results indicate that the models' dominant parameters strongly depend on time-varying hydroclimatic conditions. Parameters associated with surface processes such as evapotranspiration and runoff generally dominate under dry conditions, when high evaporative fluxes are required for accurate simulation. Parameters associated with routing processes typically dominate under high flow conditions, when performance depends on the timing of flow events. The results highlight significant inter-model differences in performance controls, even in cases where the models share similar process formulations. The dominant parameters identified can be counterintuitive; even these simple models represent complex, nonlinear systems, and the links between formulation and behavior are difficult to discern a priori as complexity increases. Scrutinizing the links between model formulation and behavior becomes an important diagnostic approach, particularly in applications such as predictions under change where dominant model controls will shift under hydrologic extremes.

Description: ABSTRACT Riparian zones in semi-arid regions often exhibit high rates of evapotranspiration (ET) in spite of low soil moisture content due to the presence of phreatophytic vegetation that is able to withdraw water from shallow aquifers. This work seeks to better define the relationship between ET, the saturated zone and the river boundary by comparing observed water table drawdown records to analytically modeled drawdown in fully penetrating wells of an unconfined aquifer in response to daily ET flux. ET at the Boise Hydrogeophysical Research Site (BHRS), a riparian zone in a temperate, semi-arid environment, is calculated using a radiation-based method to provide ET values at four different wells with different vegetation densities. Analytically modeled drawdown response to ET forcing shows that drawdown magnitude increases with increasing distance from the river edge even as the surficial ET forcing remains constant. This behavior is also observed in well hydrographs and shows the buffering effect that flow from the river has on drawdown in fully penetrating riparian wells in highly permeable, unconfined aquifers. Relative contributions of river water to aquifer storage are calculated for ET-induced diurnal fluctuations of the water table at increasing distances from the river boundary. Failure to account for these spatial differences in drawdown related to the river source may explain some errors associated with estimating ET from well hydrographs alone.

Description: The ‘Millennium Drought’ (2001–2009) can be described as the worst drought on record for southeast Australia. Adaptation to future severe droughts requires insight into the drivers of the drought and its impacts. These were analyzed using climate, water, economic and remote sensing data combined with biophysical modeling. Prevailing El Niño conditions explained about two-thirds of rainfall deficit in east Australia. Results for south Australia were inconclusive; a contribution from global climate change remains plausible but unproven. Natural processes changed the timing and magnitude of soil moisture, streamflow and groundwater deficits by up to several years, and caused the amplification of rainfall declines in streamflow to be greater than in normal dry years. By design, river management avoided impacts on some categories of water users, but did so by exacerbating the impacts on annual irrigation agriculture and, in particular, river ecosystems. Relative rainfall reductions were amplified 1.5–1.7 times in dryland wheat yields, but the impact was offset by steady increases in cropping area and crop water use efficiency (perhaps partly due to CO2 fertilization). Impacts beyond the agricultural sector occurred (e.g., forestry, tourism, utilities) but were often diffuse and not well quantified. Key causative pathways from physical drought to the degradation of ecological, economic and social health remain poorly understood and quantified. Combined with the multiple dimensions of multi-year droughts and the specter of climate change, this means future droughts may well break records in ever new ways and not necessarily be managed better than past ones.

Description: A simple yet effective method is presented to include the effects of fracture aperture variability into the modeling of solute transport in fracture networks with matrix diffusion and linear sorption. Variable apertures cause different degrees of flow channeling, which in turn influence the contact area available for these retarding processes. Our approach is based on the concept of specific flow-wetted surface (sFWS), which is the fraction of the contact area over the total fracture surface area. Larsson et al. [2012] studied the relationship between sFWS and the standard deviation σ ln K of the conductivity distribution over the fracture plane. Here an approach is presented to incorporate this into a fracture network model. With this model, solute transport through fracture networks is then analyzed. The cases of S = 0 and S = 1 correspond to those of no matrix diffusion and full matrix diffusion respectively. In between, a sFWS break point value can be defined, above which the median solute arrival time is proportional to the square of sFWS. For values below the critical sFWS (more channeled cases), the change is much slower, converging to that of no matrix diffusion. Results also indicate that details of assigning sFWS values for individual fractures in a network are not crucial; results of tracer transport are essentially identical to a case where all fractures have the mean σ ln K (or corresponding mean sFWS) value. This is obviously due to the averaging effect of the network.

Description: In this study, we developed a stochastic estimator for characterizing the hydraulic heterogeneity in both unsaturated and saturated zones of unconfined aquifers using transient drawdown data from sequential pumping tests. This estimator was built upon the successive linear estimator (SLE) by Yeh et al. [1996], the simultaneous successive linear estimator (SimSLE) by Xiang et al. [2009], and the 3-D finite element program for flow and transport through heterogeneous media by Srivastava and Yeh [1992]. The estimator was tested afterwards using simulated data sets of sequential pumping tests in a synthetic unconfined aquifer where saturated conductivity, specific storage, saturated water content, and pore-size distribution parameter vary spatially in three dimensions. Test results show that the estimator is able to produce parameter fields that capture the overall 3-D pattern of the true heterogeneous parameter fields. We subsequently validated the estimated parameter fields by assessing their ability to predict drawdowns during an independent pumping test, which was not used during the estimation phase. Results of the validation show that the predicted drawdowns based on the estimated heterogeneous parameter fields are in close agreement with the true drawdowns. In addition, predicted drawdowns based on the parameter fields from the joint interpretation are superior to those based on the parameters estimated from the homogeneous conceptual model using a single pumping test. Lastly, while many field experiments are necessary to fully assess the robustness of this estimator and sequential pumping tests, results of this study suggest they are a promising characterization technique for unconfined aquifers.

Description: A field data set collected under different conditions is analyzed to characterize the spatial arrangement of two large inflows (Ebro and Segre) with distinct physical-chemical characteristics as they join at the upstream end of Ribarroja reservoir in northern Spain. Given the short average residence time of water in the reservoir, the spatial arrangement of the rivers at their confluence and their mixing rates are likely the drivers of the stratification patterns observed near the dam. In winter, inflows have similar densities - Δ ρ/ρ 0 ≈ O(10 -5 ) - and their spatial distribution is largely determined by inertial forces, and in particular by the discharge ratio. Downstream of the confluence, both rivers flow side by side and largely un-mixed over long distances. In summer, with Δ ρ/ρ 0 of O(10 -3 ) the flow fields at the confluence are largely controlled by buoyancy forces. Atmospheric forcing during strong wind events and centrifugal forces caused by the meandering shape of the reservoir induce significant tilting of the isotherms, leading to localized high mixing rates. Mixing, in general, though is weak at this time of the year. In fall and early winter, density differences are largely controlled by conductivity differences between the incoming flows. The warmer Ebro water, with larger thermal inertia, flows beneath the colder Segre water. The spatial arrangement of the inflows is largely controlled by the discharge ratio and mixing between sources is strong, likely as a result of mixed water being denser than either of the incoming flows.

Description: A transformation of a Gaussian random field results in a change of the spatial dependence. In simple terms, the range and the shape of a variogram are different between an underlying Gaussian and a transformed Gaussian field. There is a relationship between the underlying and the transformed spatial dependence. This paper shows how this relationship can be expressed analytically. Rather than generating multi-dimensional random fields and conducting spatial statistical analysis, we develop an accurate and efficient approach based on a unique mapping of the correlation coefficients of the original multi-Gaussian fields to the transformed correlation coefficients to evaluate the spatial correlation of transformed non-Gaussian random fields for any type of geostatistical parameterization. Such a mapping can also yield accurate estimation of the spatial correaltion of the underlying Gaussian field given the spatial correlation of the transformed field. Results indicate that (1) a nonlinear transformation of spatially distributed fields usually changes the spatial dependence structure; and (2) the relationship between the dependence structures of the underlying and the transformed field can be expressed analytically, and it is sufficient to do this in one dimension. We use the developed approach to investigate the change of correlations of connected random fields generated by the absolute-value transformation. Results show (1) the correlation lengths of the underlying Gaussian fields are 1.67 and 2.64 times of those of transformed non-Gaussian fields for Gaussian and exponential covariance models, respectively; (2) the anisotropic ratio does not change; and (3) the anti-correlation in hole-effect correlation models disappear.

Description: The accumulation of discharge along a stream valley is frequently assumed to be the primary control on solute transport processes. Relationships of both increasing and decreasing transient storage, and decreased gross losses of stream water have been reported with increasing discharge; however, we have yet to validate these relationships with extensive field study. We conducted transient storage and mass recovery analysis of artificial tracer studies completed for 28 contiguous 100-m reaches along a stream valley, repeated under four baseflow conditions. We calculated net and gross gains and losses, temporal moments of tracer breakthrough curves, and best-fit transient storage model parameters (with uncertainty estimates) for 106 individual tracer injections. Results supported predictions that gross loss of channel water would decrease with increased discharge through time. However, results showed no clear relationship between discharge and transient storage, and our further analysis of solute tracer methods demonstrated that the lack of this relation may be explained by uncertainty and equifinality in the transient storage model framework. Furthermore, comparison of water balance and transient storage approaches reveals complications in clear interpretation of either method due to changes in advective transport time, which sets a the temporal boundary separating transient storage and channel water balance. We have little ability to parse this limitation of solute tracer methods from the physical processes we seek to study. We suggest the combined analysis of both transient storage and channel water balance more completely characterizes transport of solutes in stream networks than can be inferred from either method alone.

Description: Increasing numbers in interdisciplinary applications of Fibre-optic Distributed Temperature Sensing (FO-DTS) call for a quantitative assessment of the limitations and uncertainties of this new technology. This study conducts controlled laboratory experiments to analyze the qualitative (signal-size and -location) and quantitative (signal-intensity) accuracies of FO-DTS surveys of temperature signals higher and lower than ambient temperature, ranging from well above to critically below the FO-DTS sampling interval. Our results reveal that qualitative and quantitative accuracies of FO-DTS measured temperatures critically decline with decreasing signal-size, in particular for signals near the spatial sampling interval. Decreasing detection accuracy risks the masking of real temperature variation in highly dynamic systems. The resulting potential ambiguity of interpretations of signal-size, intensity and absolute location will have to be considered in future experimental design and interpretation of FO-DTS surveys.

Description: ABSTRACT We estimate seasonal drifting snow sublimation at a study site in the Swiss Alps with the numerical model Alpine3D using external wind fields from the ARPS model on a high resolution grid (10 m). Novel in the field of snow transport modeling, the transport module of Alpine3D accounts for the feedbacks between drifting snow sublimation, snow concentration, temperature and humidity of the air in three dimensions. Due to these feedbacks, drifting snow sublimation is a self-limiting process. Model results show that the domain averaged drifting snow sublimation over a season is small (about 0.1% of precipitation) but spatially highly variable. Simulation results show strongest seasonal reduction of snow amount by 1.8% due to drifting snow sublimation in a leeward slope during SE wind. This can be explained by the generally warmer and dryer conditions during events with SE wind. In the Wannengrat study area, which covers typical Alpine terrain, drifting snow sublimation is thus only significant locally or on short time scales. Note that we only consider drifting and blowing snow in the absence of concurrent precipitation. Furthermore, our results show that drifting snow sublimation is much smaller than surface sublimation in this area.

Description: High-resolution morphological modeling of fluvial processes with complex, rapidly varying flows has been limited so far by model accuracy or computational efficiency. One of the most widely used numerical algorithms is based on the Total Variation Diminishing (TVD) method, solved by either upwind or centered approaches. An upwind scheme preserves high accuracy but is complex and computationally demanding, whereas the simplicity and efficiency of a centered approach compromise the accuracy. The present paper extends a recent upwind-biased centered scheme originally developed for clear water and scalar transport over a rigid bed, to sediment-laden flows over an erodible bed. It does so by developing a fully coupled 2-D mathematical model using a finite volume method for structured grids. The complete set of non-capacity based governing equations, involving the effects of bed deformation and sediment density variation, as well as the influences of turbulence and sediment diffusion, and the temporal and spatial scales needed for sediment adaptation, is solved at one time to obtain synchronous solutions for the entire computational domain. For stability, a two-stage splitting approach together with a 2 nd order Runge-Kutta method is employed for the source terms. The model is verified in a number of tests covering a wide range of complex (sediment-laden) flows. The model is demonstrated to accurately simulate shock waves and reflection waves, but also rapid bed deformations at high sediment transport rates. The combination of high numerical accuracy and computational efficiency makes the model an important tool to forecast flood events in morphologically complex areas.

Description: Natural systems are driven by dynamic forcings that change in time as well as space, behavior that is inherited by the system flow field and results in time varying age distributions. This work presents a review of the mathematical tools and solution approaches used to model age distributions (ADs) in dynamic time-varying flow systems. A simple conceptual, numerical model is then used to explore the role of flow dynamics in age distributions for topography-driven flow systems. This model is an analog for regional groundwater systems and hyporheic zones. This model demonstrates that relatively small fluctuations in the forcing, even though importantly affecting the flow in the system, can have minimal effects in ADs. However, as the intensity of fluctuation increases, still within the bounds observed in natural systems, ADs in shallow parts of the system become highly sensitive to dynamic flow conditions, leading to considerable changes in the moments and modality of the distributions with time. In particular, transient flow can lead to emergence of new modes in the AD, which would not be present under steady flow conditions. The discrepancy observed between ADs under steady and transient flow conditions is explained by enhancement of mixing due to temporal variations in the flow field. ADs in deeper parts of the system are characterized by multimodality and tend to be more stable over time even for large forcing fluctuations.

Description: [1]  Natural mixed ecosystems (grass and woody vegetation) and managed grasslands are the dominant contrasting ecosystems of semiarid regions. These two types of land covers are known to differ in their responses to water stress, as the trees demonstrate both greater stress resistance and greater ability to tap into deeper water sources. In this study, the contrasting influences of vegetation differences (grassland versus mixed ecosystems) and soil differences (deeper alluvial valley soils versus shallow upland soils) on evapotranspiration (ET) and CO 2 exchange dynamics have been examined. Data from two representative case study sites within the Flumendosa river basin on Sardinia were obtained. At both sites, land-surface and CO 2 fluxes were estimated by eddy covariance instruments on micrometeorological towers. Fluxes at the two ecosystems were compared, and the effect of the vegetation cover was examined with the help of an ecohydrologic model to control for the different soil influences. The results show that the water and carbon fluxes in these ecosystems are more controlled by soil differences during the late spring, when the deeper soil depth leads to a doubling of the available moisture and an increase of 48% in the mixed natural vegetation transpiration. The system then switches to vegetation control in the summer as the presence or absence of drought-tolerant trees is the dominant imprint on continued transpiration and photosynthesis. In fact, total grassland ET in the summer is only 20% as large as the mixed vegetation ET in the summer.

Description: We present a model order reduction technique that overcomes the computational burden associated with the application of Monte Carlo methods to the solution of the groundwater flow equation with random hydraulic conductivity. The method is based on the Galerkin projection of the high-dimensional model equations onto a subspace, approximated by a small number of pseudo-optimally chosen basis functions (principal components). To obtain an efficient reduced order model, we develop an offline algorithm for the computation of the parameter-independent principal components. Our algorithm combines a greedy algorithm for the snapshot selection in the parameter space and an optimal distribution of the snapshots in time. Moreover, we introduce a residual-based estimation of the error associated with the reduced model. This estimation allows a considerable reduction of the number of full system model solutions required for the computation of the principal components. We demonstrate the robustness of our methodology by way of numerical examples, comparing the empirical statistics of the ensemble of the numerical solutions obtained using the traditional Monte Carlo method and our reduced model. The numerical results show that our methodology significantly reduces the computational requirements (CPU time and storage) for the solution of the Monte Carlo simulation, ensuring a good approximation of the mean and variance of the head. The analysis of the empirical probability density functions at the observation wells suggests that our reduced model produces good results and is most accurate in the regions with large drawdown.

Description: River flow synthesizing and downscaling are required for the analysis of risks associated with water resources management plans and for regional impact studies of climate change. This paper presents a probabilistic model that synthesizes and downscales monthly river flow by estimating the joint distribution of flows of two adjacent months conditional on covariates. The covariates may consist of lagged and aggregated flow variables (synthesizing), or exogenous climatic variables (downscaling), or combinations of these two types. The joint distribution is constructed by connecting two marginal distributions in terms of copulas. The relationship between covariates and distribution parameters is approximated by an artificial neural network, which is calibrated using the principle of maximum likelihood. Outputs of the neural network yield parameters of the joint distribution. From the estimated joint distribution, a conditional distribution of river flow of current month given the estimation of the previous month can be derived. Depending on the different types of covariate information, this conditional distribution may serve as the ‘engine’ for synthesizing or downscaling river flow sequences. The idea of the proposed model is illustrated using three case studies. The first case deals with synthetic data and shows that the model is capable of fitting a non-stationary joint distribution. Second, the model is utilized to synthesize monthly river flow at four sample stations on the main stream of the Colorado River. Results reveal that the model reproduces essential evaluation statistics fairly well. Third, a simple illustrative example for river flow downscaling is presented. Analysis indicates that the model can be a viable option to downscale monthly river flow as well.

Description: ABSTRACT Drought and water scarcity are keywords for river basin management in water-stressed regions. ‘Drought’ is a natural hazard, caused by large-scale climatic variability, and cannot be prevented by local water management. ‘Water scarcity’ refers to the long-term unsustainable use of water resources, which water managers can influence. Making the distinction between drought and water scarcity is not trivial, because they often occur simultaneously. In this paper, we propose an observation-modeling framework to separate natural (drought) and human (water scarcity) effects on the hydrological system. The basis of the framework is simulation of the situation that would have occurred without human influence, the ‘naturalized’ situation, using a hydrological model. The resulting time series of naturalized state variables and fluxes are then compared to observed time series. As second, more important and novel step, anomalies (i.e. deviations from a threshold) are determined from both time series and compared. We demonstrate the use of the proposed observation-modeling framework in the Upper-Guadiana catchment in Spain. Application of the framework to the period 1980-2000 shows that the impact of groundwater abstraction on the hydrological system was, on average, four times as high as the impact of drought. Water scarcity resulted in disappearance of the winter high-flow period, even in relatively wet years, and a non-linear response of groundwater. The proposed observation-modeling framework helps water managers in water-stressed regions to quantify the relative impact of drought and water scarcity on a transient basis and, consequently, to make decisions regarding adaptation to drought and combating water scarcity.

Description: Simplification is an unavoidable aspect of model usage. Even complex, physically based models are simplifications of reality. More profound simplification is required to construct the “lumped parameter” models of semi-physical basis that are often employed for simulation of large-scale processes operative over one or many watersheds. Simplification can lead to model predictive error beyond that which would be expected on the basis of study-area information deficits alone. Building on a recently developed mathematical description of the model simplification process, this work employs linear subspace methods to analyze in detail the nature and ramifications of that process when applied to a one-dimensional, Richards equation-based unsaturated zone model used to predict recharge to a groundwater system. Two simplified versions of this model are examined. The first achieves simplification through assuming vertical parameter uniformity. The second achieves simplification through use of a lumped parameter model in place of the Richards equation-based model. Relationships between parameters employed by the complex model and those used by each of the simplified models are analyzed. The nature of predictive errors incurred through simplification is explored. Also explored is the ability of the calibration process to decrease the propensity for model error in making some predictions, while increasing the propensity for model error in the making of others – an outcome that may be considered counter-intuitive from a Bayesian perspective, but which is a natural consequence of suboptimal simplification.

Description: ABSTRACT A series of community-led, large-scale laboratory experiments, termed “StreamLab”, were performed by the National Center for Earth-surface Dynamics (NCED) with the purpose of advancing multi-disciplinary research, education, and knowledge transfer at the interface of physical/chemical/biological processes in streams, science-based stream restoration practice, and environmental sensing technologies. Two series of experiments, StreamLab06 and StreamLab08, were conducted in the Main Channel of the St. Anthony Falls Laboratory at the University of Minnesota, a flume 84-m long and 2.75-m wide with water fed by the Mississippi River at a rate of up to 8.5 m 3 /s. The purpose of this paper is to share with the broader community the data collected with the hope of stimulating further analysis and future experimental campaigns towards advancing our predictive understanding of the physical, chemical, and biological processes in streams. Towards this end, a brief summary of the results to date is included and some ideas for further research are provided.

Description: ABSTRACT The impact of preferential flow on recharge and contaminant transport poses a considerable challenge to water-resources management. Typical hydrologic models require extensive site characterization, but can underestimate fluxes when preferential flow is significant. A recently developed source-responsive model incorporates film-flow theory with conservation of mass to estimate unsaturated-zone preferential fluxes with readily available data. The term source-responsive describes the sensitivity of preferential flow in response to water availability at the source of input. We present the first rigorous tests of a parsimonious formulation for simulating water-table fluctuations using two case studies, both in arid regions with thick unsaturated zones of fractured volcanic rock. Diffuse flow theory cannot adequately capture the observed water-table responses at both sites; the source-responsive model is a viable alternative. We treat the active area fraction of preferential flow paths as a scaled function of water inputs at the land surface then calibrate the macropore density to fit observed water-table rises. Unlike previous applications, we allow the characteristic film-flow velocity to vary, reflecting the lag time between source and deep water-table responses. Analysis of model performance and parameter sensitivity for the two case studies underscores the importance of identifying thresholds for initiation of film flow in unsaturated rocks, and suggests that this parsimonious approach is potentially of great practical value.

Description: The transport experiment at the MADE site (a highly heterogeneous aquifer) was investigated extensively in the last 25 years. The longitudinal mass distribution m(x,t) of the observed solute plume differed from the Gaussian shape and displayed strong asymmetry. This is in variance with the prediction of stochastic models of flow and transport in weakly heterogeneous aquifers. In the last decade we have forwarded a model coined as MIM (multi-indicator), in which the heterogeneous structure consists of blocks of different of different and independent random lognormal K . Thus the structure is completely characterized by K G (the geometric mean), (the logconducitvity variance) and the integral scale I . Flow (uniform in the mean) and advective transport were solved by the semi analytical SCA (self consistent approximation). The SCA models the travel time of a solute parcel from an injection to a control plane as a sequence of independent time steps, each resulting from the simple solution for isolated blocks surrounded by a uniform matrix. The aim of the article is to determine whether the model could predict the observed mass distribution of MADE ( based on the most recent direct-push injection logger data), by using the recently collected detailed K data and the observed mean head gradient. It was found that the agreement with the measured plume is quite satisfactory, differences related to incomplete mass recovery, injection condition and ergodicity notwithstanding. It is concluded that the physical mechanism of advection, modeled by the local ADE, and the heterogeneity of K , are able to explain the MADE plume behavior and the stochastic model could predict it.