CONTENTS:
  
   1- Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design.
   J.B. Kollat and P.M. Reed
  
   abstract:
   This study compares the performances of four state-of-the-art evolutionary multi-objective optimization (EMO) algorithms: the Non-Dominated Sorted Genetic Algorithm II (NSGAII), the Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (e-NSGAII), the Epsilon-Dominance Multi-Objective Evolutionary Algorithm (eMOEA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), on a four-objective long-term groundwater monitoring (LTM) design test case. The LTM test case objectives include: (i) minimize sampling cost, (ii) minimize contaminant concentration estimation error, (iii) minimize contaminant concentration estimation uncertainty, and (iv) minimize contaminant mass estimation error. The 25-well LTM design problem was enumerated to provide the true Pareto-optimal solution set to facilitate rigorous testing of the EMO algorithms. The performances of the four algorithms are assessed and compared using three runtime performance metrics (convergence, diversity, and e-performance), two unary metrics (the hypervolume indicator and unary e-indicator) and the first-order empirical attainment function. Results of the analyses indicate that the e-NSGAII greatly exceeds the performance of the NSGAII and the eMOEA. The e-NSGAII also achieves superior performance relative to the SPEA2 in terms of search effectiveness and efficiency. In addition, the e-NSGAII?s simplified parameterization and its ability to adaptively size its population and automatically terminate results in an algorithm which is efficient, reliable, and easy-to-use for water resources applications.
   Keywords: Long-term groundwater monitoring; Evolutionary algorithms; Multi-objective optimization; Performance metrics.
  
   2- A modified ISBA surface scheme for modeling the hydrology of Athabasca River Basin with GCM-scale data.
   Ernst Kerkhoven and Thian Yew Gan
  
   abstract:
   A soil?vegetation?atmosphere transfer model (SVAT), interactions between the soil?biosphere?atmosphere (ISBA) of M?o France, is modified and applied to the Athabasca River Basin (ARB) to model its water and energy fluxes. Two meteorological datasets are used: the archived forecasts from the Meteorological Survey of Canada?s Global Environmental Multiscale Model (GEM) and the European Centre for Mid-range Weather Forecasts global re-analysis (ERA-40), representing spatial scales typical of a weather forecasting model and a global circulation model (GCM), respectively. The original treatment of soil moisture and rainfall in ISBA (OISBA) is modified to statistically account for sub-grid heterogeneity of soil moisture and rainfall to produce new, highly non-linear formulations for surface and sub-surface runoff (MISBA). These new formulations can be readily applied to most existing SVATs. Stand alone mode simulations using the GEM data demonstrate that MISBA significantly improves streamflow predictions despite requiring two fewer parameters than OISBA. Simulations using the ERA-40 data show that it is possible to reproduce the annual variation in monthly, mean annual, and annual minimum flows at GCM scales without using downscaling techniques. Finally, simulations using a simple downscaling scheme show that the better performance of higher resolution datasets can be primarily attributed to improved representation of local variation of land cover, topography, and climate.
   Keywords: Interactions between the soil?biosphere?atmosphere; GCM and mesoscale meteorological data; Sub-grid heterogeneity; Model parameters; Downscaling; Streamflow.
  
   3- Statistical characterization of temporal structure of storms.
   G. Salvadori and C. De Michele
  
   abstract:
   The authors present a statistical procedure to estimate the probability distributions of storm characteristics. The approach uses recent advances in stochastic hydrological modeling. The temporal dynamics of rainfall are modeled via a reward alternating renewal process that describes wet and dry phases of storms. In particular, the wet phase is modeled as a rectangular pulse process with dependent random duration and intensity; the global dependence structure is described using multidimensional copulas. The marginal distributions are described by Generalized Pareto laws. The authors derive both the storm volume statistics and the rainfall volume distribution within a fixed temporal window preceding a storm. Based on these results, they calculate the antecedent moisture conditions. The paper includes a thorough discussion of the validity of the assumptions and approximations introduced, and an application to actual rainfall data. The models presented here have important implications for improved design procedures of water resources and hydrologic systems.
   Keywords: Antecedent rainfall volume; Antecedent moisture condition; Copulas; Generalized Pareto distribution; Renewal process.
  
   4- Spatial patterns in thunderstorm rainfall events and their coupling with watershed hydrological response.
   Efrat Morin, David C. Goodrich, Robert A. Maddox, Xiaogang Gao, Hoshin V. Gupta and Soroosh Sorooshian
  
   abstract:
   Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside.
   Keywords: Spatial patterns; Rainfall; Weather radar; Rain cell; Thunderstorms; Distributed hydrological models.
  
   5- Passive and active control of diversions to an off-line reservoir for flood stage reduction.
   Brett F. Sanders, John C. Pau and David A. Jaffe
  
   abstract:
   Diversion of excess streamflow to an off-line reservoir is examined as a wave interference problem that can be controlled to reduce the cresting stage of a flood. Flood diversions create depression waves in the stream channel which, superimposed upon the flood, decrease flood stage. In the context of an O(102) km2 coastal watershed in northern California, numerical modeling was performed to compare the performance of three idealized diversion control strategies including passive control, weir control, and gate control. It was found that gate control, which creates a dam-break like flow into an off-line reservoir, can be optimized to accomplish 2?3 times the flood depth reduction of passive control. Capturing 2% of the runoff, for example, the cresting depth is reduced less than 1% with passive control but 2?4% by gate control. This is particularly important in areas with little off-line or over-bank storage, such as urban watersheds. Timing of the gate action is critical. Optimal control requires gate action slightly before peak stage arrives at the diversion structure, by a duration that scales with the capacity of the reservoir. The lead time of flood forecasts appears compatible with the lead time necessary to optimize diversions, and decision support systems should compensate for forecast uncertainty by early gate action. If by poor design or operator error the gate is opened too late, gate control becomes less effective than passive control.
   Keywords: Flood control; Off-line reservoir; Flood modeling; Diversions.
  
   6- Assimilating remotely sensed snow observations into a macroscale hydrology model.
   Konstantinos M. Andreadis and Dennis P. Lettenmaier
  
   abstract:
   Accurate forecasting of snow properties is important for effective water resources management, especially in mountainous areas like the western United States. Current model-based forecasting approaches are limited by model biases and input data uncertainties. Remote sensing offers an opportunity for observation of snow properties, like areal extent and water equivalent, over larger areas. Data assimilation provides a framework for optimally merging information from remotely sensed observations and hydrologic model predictions. An ensemble Kalman filter (EnKF) was used to assimilate remotely sensed snow observations into the variable infiltration capacity (VIC) macroscale hydrologic model over the Snake River basin. The snow cover extent (SCE) product from the moderate resolution imaging spectroradiometer (MODIS) flown on the NASA Terra satellite was used to update VIC snow water equivalent (SWE), for a period of four consecutive winters (1999?2003). A simple snow depletion curve model was used for the necessary SWE?SCE inversion. The results showed that the EnKF is an effective and operationally feasible solution; the filter successfully updated model SCE predictions to better agree with the MODIS observations and ground surface measurements. Comparisons of the VIC SWE estimates following updating with surface SWE observations (from the NRCS SNOTEL network) indicated that the filter performance was a modest improvement over the open-loop (un-updated) simulations. This improvement was more evident for lower to middle elevations, and during snowmelt, while during accumulation the filter and open-loop estimates were very close on average. Subsequently, a preliminary assessment of the potential for assimilating the SWE product from the advanced microwave scanning radiometer (AMSR-E, flown on board the NASA Aqua satellite) was conducted. The results were not encouraging, and appeared to reflect large errors in the AMSR-E SWE product, which were also apparent in comparisons with SNOTEL data.
   Keywords: Data assimilation; Land surface modeling; Snow.
  
   7- Evaluation of longitudinal and transverse dispersivities/distance ratios for tracer test in a radially convergent flow field with scale-dependent dispersion.
   Jui-Sheng Chen, Chen-Wuing Liu and Ching-Ping Liang
  
   abstract:
   This study presents a novel mathematical model for analysis of non-axisymmetrical solute transport in a radially convergent flow field with scale-dependent dispersion. A two-dimensional, scale-dependent advection?dispersion equation in cylindrical coordinates is derived based on assuming that the longitudinal and transverse dispersivities increase linearly with the distance of the solute transported from its injected source. The Laplace transform finite difference technique is applied to solve the two-dimensional, scale-dependent advection?dispersion equation with variable-dependent coefficients. Concentration contours for different times, breakthrough curves of average concentration over concentric circles with a fixed radial distance, and breakthrough curves of concentration at a fixed observation point obtained using the scale-dependent dispersivity model are compared with those from the constant dispersivity model. The salient features of scale-dependent dispersion are illustrated during the non-axisymmetrical transport from the injection well into extraction well in a convergent flow field. Numerical tests show that the scale-dependent dispersivity model predicts smaller spreading than the constant-dispersivity model near the source. The results also show that the constant dispersivity model can produce breakthrough curves of averaged concentration over concentric circles with the same shape as those from the proposed scale-dependent dispersivity model at observation point near the extraction well. Far from the extracting well, the two models predict concentration contours with significantly different shapes. The breakthrough curves at observation point near the injection well from constant dispersivity model always produce lesser overall transverse dispersion than those from scale-dependent dispersivity model. Erroneous dimensionless transverse/longitudinal dispersivity ratio may result from parametric techniques which assume a constant dispersivity if the dispersion process is characterized by a distance-dependent dispersivity relationship. A curve-fitting method with an example is proposed to evaluate longitudinal and transverse scale-proportional factors of a field with scale-dependent dispersion.
   Keywords: Scale-dependent dispersion; Non-axisymmetrical solute transport; Laplace transform finite-difference method; Tracer test.
  
   8- A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty.
   Jianfeng Wu, Chunmiao Zheng, Calvin C. Chien and Li Zheng
  
   abstract:
   This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm (MCSGA) and noisy genetic algorithm (NGA), for cost-effective sampling network design in the presence of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA) with a numerical flow and transport simulator and a global plume estimator to identify the optimal sampling network for contaminant plume monitoring. The MCSGA approach yields one optimal design each for a large number of realizations generated to represent the uncertain K-field. A composite design is developed on the basis of those potential monitoring wells that are most frequently selected by the individual designs for different K-field realizations. The NGA approach relies on a much smaller sample of K-field realizations and incorporates the average of objective functions associated with all K-field realizations directly into the GA operators, leading to a single optimal design. The efficacy of the MCSGA-based composite design and the NGA-based optimal design is assessed by applying them to 1000 realizations of the K-field and evaluating the relative errors of global mass and higher moments between the plume interpolated from a sampling network and that output by the transport model without any interpolation. For the synthetic application examined in this study, the optimal sampling network obtained using NGA achieves a potential cost savings of 45% while keeping the global mass and higher moment estimation errors comparable to those errors obtained using MCSGA. The results of this study indicate that NGA can be used as a useful surrogate of MCSGA for cost-effective sampling network design under uncertainty. Compared with MCSGA, NGA reduces the optimization runtime by a factor of 6.5.
   Keywords: Contaminant transport; Monitoring network design; Spatial moment analysis; Noisy genetic algorithm; Monte Carlo analysis; Uncertainty.
  
   9- Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations.
   Luc Feyen and Jef Caers
  
   abstract:
   In this work, we address the problem of characterizing the heterogeneity and uncertainty of hydraulic properties for complex geological settings. Hereby, we distinguish between two scales of heterogeneity, namely the hydrofacies structure and the intrafacies variability of the hydraulic properties. We employ multiple-point geostatistics to characterize the hydrofacies architecture. The multiple-point statistics are borrowed from a training image that is designed to reflect the prior geological conceptualization. The intrafacies variability of the hydraulic properties is represented using conventional two-point correlation methods, more precisely, spatial covariance models under a multi-Gaussian spatial law. We address the different levels and sources of uncertainty in characterizing the subsurface heterogeneity, and explore their effect on groundwater flow and transport predictions. Typically, uncertainty is assessed by way of many images, termed realizations, of a fixed statistical model. However, in many cases, sampling from a fixed stochastic model does not adequately represent the space of uncertainty. It neglects the uncertainty related to the selection of the stochastic model and the estimation of its input parameters. We acknowledge the uncertainty inherent in the definition of the prior conceptual model of aquifer architecture and in the estimation of global statistics, anisotropy, and correlation scales. Spatial bootstrap is used to assess the uncertainty of the unknown statistical parameters. As an illustrative example, we employ a synthetic field that represents a fluvial setting consisting of an interconnected network of channel sands embedded within finer-grained floodplain material. For this highly non-stationary setting we quantify the groundwater flow and transport model prediction uncertainty for various levels of hydrogeological uncertainty. Results indicate the importance of accurately describing the facies geometry, especially for transport predictions.
   Keywords: Geostatistics; Multiple-point geostatistics; Groundwater flow and transport modelling; Uncertainty.
  
   10- Adding sediment transport to the integrated hydrology model (InHM): Development and testing.
   Christopher S. Heppner, Qihua Ran, Joel E. VanderKwaak and Keith Loague
  
   abstract:
   The addition of a sediment transport algorithm to the comprehensive hydrologic-response model known as the Integrated Hydrology Model (InHM) is discussed. The first test of the sediment transport version of InHM is reported, using field data from a series of erosion experiments conducted by Gabet and Dunne [E.J. Gabet, T. Dunne, Sediment detachment by rain power, Water Resour Res 39 (2003) 1002]. The performance of the sediment transport component of InHM, in both calibration and validation phases, is judged to be successful, based upon quantitative statistical criteria. The ability to simulate sub-plot-scale interactions between surface water hydrology and rain-induced sediment transport with InHM is demonstrated. Sensitivity analysis reveals that the rainfall intensity exponent has a substantial impact on simulated sediment discharge. Future work, related to both testing InHM and much needed field experiments for model parameterization, is discussed.
   Keywords: Hydrologic-response simulation; Sediment transport simulation; Rainsplash erosion; Hydraulic erosion; Plot-scale experiments; Model performance; Calibration; Validation; Sensitivity analysis.