Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Accuracy Assessment of NOAA Gridded Daily Reference Evapotranspiration for the Texas High Plains

The National Oceanic and Atmospheric Administration (NOAA) provides daily reference evapotranspiration (ET_ref) maps for the contiguous United States using climatic data from North American Land Data Assimilation System (NLDAS). This data provides large‐scale spatial representation of ET_ref, which is essential for regional scale water resources management. Data used in the development of NOAA daily ET_ref maps are derived from observations over surfaces that are different from short (grass — ET_os) or tall (alfalfa — ET_rs) reference crops, often in nonagricultural settings, which carries an unknown discrepancy between assumed and actual conditions. In this study, NOAA daily ET_os and ET_rs maps were evaluated for accuracy, using observed data from the Texas High Plains Evapotranspiration (TXHPET) network. Daily ET_os, ET_rs and the climatic data (air temperature, wind speed, and solar radiation) used for calculating ET_ref were extracted from the NOAA maps for TXHPET locations and compared against ground measurements on reference grass surfaces. NOAA ET_ref maps generally overestimated the TXHPET observations (1.4 and 2.2 mm/day ET_os and ET_rs, respectively), which may be attributed to errors in the NLDAS modeled air temperature and wind speed, to which reference ET_ref is most sensitive. Therefore, a bias correction to NLDAS modeled air temperature and wind speed data, or adjustment to the resulting NOAA ET_ref, may be needed to improve the accuracy of NOAA ET_ref maps.     

Identifying and Evaluating a Suitable Index for Agricultural Drought Monitoring in the Texas High Plains

Drought is a complex and highly destructive natural phenomenon that affects portions of the United States almost every year, and severe water deficiencies can often become catastrophic for agricultural production. Evapotranspiration (ET) by crops is an important component in the agricultural water budget; thus, it is advantageous to include ET in agricultural drought monitoring. The main objectives of this study were to (1) conduct a literature review of drought indices with a focus to identify a simple but simultaneously adequate drought index for monitoring agricultural drought in a semiarid region and (2) using the identified drought index method, develop and evaluate time series of that drought index for the Texas High Plains. Based on the literature review, the Standardized Precipitation‐Evapotranspiration Index (SPEI) was found to satisfy identified constraints for assessing agricultural drought. However, the SPEI was revised by replacing reference ET with potential crop ET to better represent actual water demand. Data from the Texas High Plains Evapotranspiration network was used to calculate SPEIs for the major irrigated crops. Trends and magnitudes of crop‐specific, time‐series SPEIs followed crop water demand patterns for summer crops. Such an observation suggests that a modified SPEI is an appropriate index to monitor agricultural drought for summer crops, but it was found to not account for soil water stored during the summer fallow period for winter wheat.     

Classification of Ephemeral,Intermittent, and Perennial Stream Reaches Using a TOPMODEL‐Based Approach

Whether a waterway is temporary or permanent influences regulatory protection guidelines, however, classification can be subjective due to a combination of factors, including time of year, antecedent moisture conditions, and previous experience of the field investigator. Our objective was to develop a standardized protocol using publically available spatial information to classify ephemeral, intermittent, and perennial streams. Our hypothesis was that field observations of flow along the stream channel could be compared to results from a hydrologic model, providing an objective method of how these stream reaches can be identified. Flow‐state sensors were placed at ephemeral, intermittent, and perennial stream reaches from May to December 2011 in the Appalachian coal basin of eastern Kentucky. This observed flow record was then used to calibrate the simulated saturation deficit in each channel reach based on the topographic wetness index used by TOPMODEL. Saturation deficit values were categorized as flow or no‐flow days, and the simulated record of streamflow was compared to the observed record. The hydrologic model was more accurate for simulating flow during the spring and fall seasons. However, the model effectively identified stream reaches as intermittent and perennial in each of the two basins.     

Transient population dynamics in periodic matrix models: methodology and effects of cyclic permutations

Many biological populations are subject to periodically changing environments such as years with or without fire, or rotation of crop types. The dynamics and management options for such populations are frequently investigated using periodic matrix models. However the analysis is usually limited to long-term results (asymptotic population growth rate and its sensitivity to perturbations of vital rates). In non-periodic matrix models it has been shown that long-term results may be misleading as populations are rarely in their stable structure. We therefore develop methods to analyze transient dynamics of periodic matrix models. In particular, we show how to calculate the effects of perturbations on population size within and at the end of environmental cycles. Using a model of a weed population subject to a crop rotation, we show that different cyclic permutations produce different patterns of sensitivity of population size and different population sizes. By examining how the starting environment interacts with the initial conditions, we explain how different patterns arise. Such understanding is critical to developing effective management and monitoring strategies for populations subject to periodically recurring environments.     

Evaluation of geochemical methods for discrimination of metal contamination in Antarctic marine sediments: a case study from Casey Station

Detecting anthropogenic metal contamination in regional surveys can be particularly difficult when there is a lack of pre-disturbance data, especially when trying to differentiate low to moderate levels of contamination from background values. Furthermore, comparisons with other regional studies are confounded by differing analytical methods used and variations in sediment properties such as grainsize. Several types of geochemical technique, including weak acid partial extraction, strong acid extractions and total digestion have been used. Attempts have been made to overcome the influence that grainsize has on chemical concentrations in heterogeneous environments by analysing the fines, typically the mud fraction (<63 microm), in an attempt to improve the detection of anthropogenic contamination. Here we compare a weak acid partial extraction using 1M HCl and total digestion methods for a regional survey of reference and impacted sites in Antarctica using both whole sediment (<2 mm) and mud (<63 microm) fractions. The 1M partial extraction on whole sediment (<2 mm) most closely distinguished weakly, or moderately, impacted sites from reference locations. It also identified small scale within-location spatial variation in metal contamination that the total digest did not detect. Compared with total digests or analysis of the <63 microm fraction alone, this method minimised the possibility of a Type II statistical error in the regional survey - that is, failing to identify a site as being contaminated when it has elevated metal concentrations. To allow inter-regional comparison of sediment chemistry data from elsewhere in Antarctica, and also more generally, we recommend a 1M HCl partial extraction on whole sediment (<2 mm).