Journal of Hydrologic Engineering

This study investigates capture zone uncertainty that relates to the coupled semivariogram uncertainty of hydrogeological and geophysical data. Semivariogram uncertainty is represented by the uncertainty in structural parameters (range, sill, and nugget). We used the beta distribution function to derive the prior distributions of structural parameters. The probability distributions of structural parameters were further updated through the Bayesian approach with the Gaussian likelihood functions. Cokriging of noncollocated pumping test data and electrical resistivity data was conducted to better estimate hydraulic conductivity through autosemivariograms and pseudo-cross-semivariogram. Sensitivities of capture zone variability with respect to the spatial variability of hydraulic conductivity, porosity and aquifer thickness were analyzed using ANOVA. The proposed methodology was applied to the analysis of capture zone uncertainty at the Chicot aquifer in Southwestern Louisiana, where a regional groundwater flow model was developed. MODFLOW-MODPATH was adopted to delineate the capture zone. The ANOVA results showed that both capture zone area and compactness were sensitive to hydraulic conductivity variation. We concluded that the capture zone uncertainty due to the semivariogram uncertainty is much higher than that due to the kriging uncertainty for given semivariograms. In other words, the sole use of conditional variances of kriging may greatly underestimate the flow response uncertainty. Semivariogram uncertainty should also be taken into account in the uncertainty analysis.

Flood warning systems have become increasingly important for highly urbanized areas like the Texas Medical Center (TMC) area in the Brays Bayou Watershed of southwest Houston due to the watershed’s flood characteristics and the difficulty of employing flood control measures under limited spatial conditions. An existing flood alert system (FAS2) has been developed incorporating adjusted higher-resolution next generation radar (NEXRAD) rainfall data, geographic information system, and hydrologic models to provide advanced real-time warning to TMC via the internet, e-mail, and cell phone during severe storms. This enhanced system utilizes $5\text{-}\min$ NEXRAD rainfall data as input to hydrologic models and produces outputs at control points of choice in the form of hydrograph computation as a storm passes over the watershed. In this way, lead time for emergency managers to react is increased over other manual type systems. The FAS2 system has been tested with excellent results during three rainfall events in 2006 since the flood alert system was upgraded in 2004. Because of its forecasting capability, FAS2 is being used as a platform to develop a new hydraulic prediction tool—FloodPlain Map Library (FPML) in order to provide real-time visualized data on flood inundation levels as rainfall occurs. The linkage of NEXRAD rainfall data with improved hydrologic and hydraulic models improves FAS2 with a major step forward in prediction capability and visualization. This paper provides details of the FAS2 system, evaluates the performance of the existing FAS2 system during the 2006 season, and provides the theoretical basis for the development of the FPML system with some preliminary results.

In this study, a distributed hydrological model (MIKE SHE) was employed in an integrated framework to use remote sensing data, and its advantages over using conventional data were analyzed. The command of 6 Main Canal of the Damodar Irrigation Project, West Bengal, India was chosen as the study area. IRS LISS III and RADARSAT SAR data were processed for generating soil and land use/cover maps, respectively. Thematic layers for topography, groundwater table, Thiessen polygons for rainfall and evapotranspiration, settlement area, and soil were prepared in GIS (ARC-INFO). An application program was developed to transform GIS processed ASCII data into the model input format (matrix data) and also model output to GIS. The model was subsequently calibrated and validated for monsoon season (June to October) of 1999 and 2000, respectively, considering the goodness of fit criteria between observed and simulated groundwater depths. Simulations were done with different combinations of remote sensing (RS) and conventional data setups to study the impact of using RS data on the model calibration and simulation results. The introduction of remotely sensed data shows improvements in the model calibration and eventually in its performance. Statistical results for the RS data integrated model show high Nash–Sutcliffe coefficient (0.85), lower root mean square error (0.31), and closer agreement between the mean simulated $\left(8.37\mathrm{m}\right)$ and observed $\left(8.32\mathrm{m}\right)$ groundwater depths as compared to the conventional data. Model results show little increase in the total irrigation demand by 7.7% using RS data in comparison to conventional data. In contrast, spatial distribution of irrigation demands was found to vary considerably among distributaries using RS data. The large impact of remotely sensed versus conventional data is that the RS data integrated distributed model gives more reliable and accurate results and, hence, could play a major role in irrigation studies.

A two-dimensional rainfall-runoff model is used to systematically explore the aggregate effect of spatially heterogeneous saturated hydraulic conductivity, $K_s$ , on Hortonian runoff generation. The fully dynamic model integrates overland flow and infiltration to allow for the “interactive infiltration” process (run-on). Rainfall events varying in time and intensity were simulated on synthetic hillslopes with random and spatially correlated $K_s$ fields. Model grid size discretization recommendations are developed to fully capture the variation in $K_s$ and avoid limiting the spatial interactive infiltration opportunities as found in analysis of the effects of model grid size on spatially uncorrelated hillslopes. Our results show that on highly correlated $K_s$ fields, relative to the hillslope length, the infiltration due to interaction is less than half that of uncorrelated fields for low intensity events. Previous findings, are also substantiated and further explored, with explicit consideration of model discretization, that the variation in $K_s$ increases interaction by increasing the infiltration opportunity time, simultaneously, increasing runoff. Finally we investigate the rainfall durations, which produce maximum interaction, and find interaction peaks for rainfall events approximately 1.5 times longer than the hillslope average time to ponding.

A prototype system for a Chesapeake Bay Environmental Observatory (CBEO) is under development by a multidisciplinary team of researchers from the domains of environmental engineering, marine science, hydrology, ecology, and computer science. The vision is to provide new means of coupling and synthesizing field sampled and model generated data in a way that will open up new data sources to researchers and managers interested in understanding and resolving some currently unanswered questions and problems concerning hypoxia in the Chesapeake Bay. It will do so by developing advanced cyberinfrastructure to provide uniform nationwide access to new tools and a wide variety of data of disparate type, scale, and resolution, in both spatial and temporal domains, including model-derived data from past runs of major computational models for Chesapeake Bay hydrodynamics and water quality. Some key goals of the prototype project are to resolve the existing data source heterogeneities such that all relevant data are accessible through one interface, to archive and facilitate the analysis of model input and output files, and to provide new shared tools for data analysis, all with the goal of transforming the way scientific research and science-based management is conducted on the Chesapeake Bay. There are four project teams operating separately but in close and continuous communication. The teams’ objectives are to make simultaneous and parallel advances in (1) environmental observatory network design and nationwide network access to Bay data (CBEO:N); (2) furthering the educational missions and outreach at the host institutions (CBEO:E); (3) providing a test-bed application that will allow the development and testing of new cyberinfrastructure and data analysis tools (CBEO:T); and (4) using all of the above-mentioned advances on focused science questions to demonstrate the transformative nature of the CBEO for addressing research questions and improvingmanagement approaches for large coastal systems that are heavily affected by humans. Finally, this CBEO cyberinfrastructure development is geared toward ensuring that the envisioned system is integrated into larger nationwide environmental observatory network initiatives (e.g., Water and Environmental Research System, Ocean Research Interactive Observatory Networks, National Ecological Observatory Network, and Long Term Ecological Research) thus helping to lead the way toward the development of a continental-scale environmental observatory network.

There is a great interest in understanding groundwater–surface water interactions among hydrologists and water resources planners. One of the most challenging parts of this concept is the separation and quantification of baseflow from the streamflow hydrograph. Therefore, this study is widely devoted to defining and testing these dominant forces within a watershed and incorporating their impact on streamflow components. The study area is located in the Coastal Plain of the Southeastern United States where separately measured surface and subsurface flow data are available for a field scale watershed for $9\text{years}$ (1970–1978). Sensitivity analysis was conducted with respect to the impact of different watershed characteristics on streamflow components and parameters of interest were identified. This study identified some of the key controls of streamflow partitioning by incorporating the most sensitive parameters in the streamflow partitioning approach using a regression equation based on watershed physical (e.g., land use, soils) and hydrologic characteristics (e.g. rainfall, soil moisture). Using new techniques are a step forward in understanding the streamflow partitioning process.

Quantitative assessments of the degree to which the modeled behavior of a system matches with the observations provide an evaluation of the model’s predictive abilities. In this context, the Nash–Sutcliffe efficiency index is widely used in water resources sector to assess the performance of a hydrologic model. Through a series of results, this technical note demonstrates that this index alone is not adequate in describing the performance of a model. It is shown that relatively poor models can give a high value of the index and vice-versa. Thus, it is advisable to employ the other statistical measures before arriving at a definite conclusion about the performance of a hydrologic model.

Rainfall occurrence is of a great variability in Mediterranean climate zones. This process has been studied here by applying the multifractal turbulence formalism to the daily number of rain-free hours series recorded during 23 years in Córdoba (southern Spain). A scaling break at 16 days was found from the analysis of the moments, this presumably being a manifestation of the “synoptic maximum.” A study of the empirical moments scaling exponent function for scales from 1 day to 8.5 months revealed the presence of a multifractal phase transition associated with one critical moment. The parameters of the universal multifractal model had been estimated using the double trace moment method and the simulated theoretical moments scaling exponent function showed an acceptable agreement with the corresponding empirical function. The average of the daily number of dry hours has been found to be equal at different scales meaning that the process studied was conserved or statistically stationary. The multifractal analysis overcomes the limitations of many existing deterministic and stochastic models to describe the temporal nonlinear variability of the rainfall occurrence.