Modeling of Overland Flow by the Diffusion Wave Approach

One of the major issues of present times, i.e. water quality degradation and a need for precise answers to transport of pollutants by overland flow, is addressed with special reference to the evaporator pits located adjacent to streams in the oil-producing regions of Eastern Kentucky. The practical shortcomings of the state-of-the-art kinematic wave are discussed and a new mathematical modeling-approach for overland flows using the more comprehensive diffusion wave is attempted as the first step in solving this problem. A Fourier series representation of the solution to the diffusion wave is adopted and found to perform well. The physically justified boundary conditions for steep slopes is considered and both numerical and analytical schemes are developed. The zero-depth-gradient lower condition is used and found to be adequate. The steady state analysis for mild slopes is found to be informative and both analytical and numerical solutions are found. The effect of imposing transients on the steady state solution are considered. Finally the cases for which these techniques can be used are presented

Probabilistic Assessment of Drought Characteristics using a Hidden Markov Model

Droughts are evaluated using drought indices that measure the departure of meteorological and hydrological variables such as precipitation and stream flow from their long-term averages. While there are many drought indices proposed in the literature, most of them use pre-defined thresholds for identifying drought classes ignoring the inherent uncertainties in characterizing droughts. In this study, a hidden Markov model (HMM) [1] is developed for probabilistic classification of drought states. The HMM captures space and time dependence in the data. The proposed model is applied to assess drought characteristics in Indiana using monthly precipitation and stream flow data. The comparison of HMM based drought index with standard precipitation index (SPI) [2] suggests that the HMM index provides more intuitive results

DRINET –an Online Drought Research and Collaboration Environment

DRINET is a research environment for collecting and disseminating local to regional scale drought information while interoperating with other resources and tools. The disseminated information via the DRINET will be based on a comprehensive evaluation of causal factors for short and long term droughts, as well as on a standardization of data formats and collection practices. It thus lays the foundation for investigating and providing improved drought risk and trigger indicators

Taurine: a potential marker of apoptosis in gliomas

New cancer therapies are being developed that trigger tumour apoptosis and an in vivo method of apoptotic detection and early treatment response would be of great value. Magnetic resonance spectroscopy (MRS) can determine the tumour biochemical profile in vivo, and we have investigated whether a specific spectroscopic signature exists for apoptosis in human astrocytomas. High-resolution magic angle spinning (HRMAS) 1H MRS provided detailed 1H spectra of brain tumour biopsies for direct correlation with histopathology. Metabolites, mobile lipids and macromolecules were quantified from presaturation HRMAS 1H spectra acquired from 41 biopsies of grades II (n=8), III (n=3) and IV (n=30) astrocytomas. Subsequently, TUNEL and H&E staining provided quantification of apoptosis, cell density and necrosis. Taurine was found to significantly correlate with apoptotic cell density (TUNEL) in both non-necrotic (R=0.727, P=0.003) and necrotic (R=0.626, P=0.0005) biopsies. However, the ca 2.8 p.p.m. polyunsaturated fatty acid peak, observed in other studies as a marker of apoptosis, correlated only in non-necrotic biopsies (R=0.705, P<0.005). We suggest that the taurine 1H MRS signal in astrocytomas may be a robust apoptotic biomarker that is independent of tumour necrotic status

Copula-based Approaches to Characterization of Droughts

Droughts are used to reflect water shortages. A precise mathematical definition of droughts does not exist, and a drought is loosely worded as “a prolonged absence or marked deficiency of precipitation”. Numerous drought indices have been developed using different hydrologic variables. In this study, we - explore the potential of copulas in describing the joint water deficit over multiple stations in a region. - focus on precipitation data for stations within Indiana. Goal: To develop a joint water deficit index for multiple locations to enable regional quantification of droughts. Given the high dimensional nature of the data, we combine tree structured graphical models with copulas to design a new index for drought characterization

Drought Implications on River Water Quality

Droughts–natural phenomena that reflect water deficits are expected to increase infrequency and severity based on projections of future climate change. Hydrological impacts of drought have been widely analyzed but the environmental impacts of droughts have received only perfunctory and qualitative attention

Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering

Regionalization is the procedure to find natural groups of watersheds with homogeneous hydrologic response, and finds applications in hydrologic design, planning and management of water resources systems. In regionalization studies, clustering techniques are useful to partition catchments in a region into natural groups. The linear Kohonen’s self-organizing feature map (SOFM) has been applied as a clustering technique for regionalization in several recent studies. However, SOFM is not a clustering method because it is seldom possible to interpret clusters from the output of an SOFM, irrespective of its size and dimensionality. In this study, we demonstrate that SOFMs may, however, serve as a useful precursor to clustering algorithms. We present a two-level SOFM-based clustering approach for regionalization of watersheds. In the first level, the SOFM is used to form a two-dimensional feature map. In the second level, the output nodes of SOFM are clustered by using Fuzzy c-means algorithm to form regions for flood frequency analysis (FFA). The optimal number of regions is based on fuzzy cluster validation measures. Effectiveness of the proposed approach in forming homogeneous regions for FFA is illustrated through application to data from watersheds in Indiana, USA. We find that previous indices used to decide the number of clusters are not efficient, and therefore suggest a new index that is more appropriate for this purpose. Using leave-one-out cross-validation, the performance of the proposed approach to regional FFA is compared with those from methods based on regression analysis and canonical correlation analysis. Results show that the proposed approach performs better in estimating flood quantiles at ungauged site

A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins

Effective water quality management and reliable environmental modeling depend on the availability, size, and quality of water quality (WQ) data. Observed stream water quality data are usually sparse in both time and space. Reconstruction of water quality time series using surrogate variables such as streamflow have been used to evaluate risk metrics such as reliability, resilience, vulnerability, and watershed health (WH) but only at gauged locations. Estimating these indices for ungauged watersheds has not been attempted because of the high-dimensional nature of the potential predictor space. In this study, machine learning (ML) models, namely random forest regression, AdaBoost, gradient boosting machines, and Bayesian ridge regression (along with an ensemble model), were evaluated to predict watershed health and other risk metrics at ungauged hydrologic unit code 10 (HUC-10) basins using watershed attributes, long-term climate data, soil data, land use and land cover data, fertilizer sales data, and geographic information as predictor variables. These ML models were tested over the Upper Mississippi River Basin, the Ohio River Basin, and the Maumee River Basin for water quality constituents such as suspended sediment concentration, nitrogen, and phosphorus. Random forest, AdaBoost, and gradient boosting regressors typically showed a coefficient of determination R2&gt;0.8 for suspended sediment concentration and nitrogen during the testing stage, while the ensemble model exhibited R2&gt;0.95. Watershed health values with respect to suspended sediments and nitrogen predicted by all ML models including the ensemble model were lower for areas with larger agricultural land use, moderate for areas with predominant urban land use, and higher for forested areas; the trained ML models adequately predicted WH in ungauged basins. However, low WH values (with respect to phosphorus) were predicted at some basins in the Upper Mississippi River Basin that had dominant forest land use. Results suggest that the proposed ML models provide robust estimates at ungauged locations when sufficient training data are available for a WQ constituent. ML models may be used as quick screening tools by decision makers and water quality monitoring agencies for identifying critical source areas or hotspots with respect to different water quality constituents, even for ungauged watersheds

Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering

Regionalization is the procedure to find natural groups of watersheds with homogeneous hydrologic response, and finds applications in hydrologic design, planning and management of water resources systems. In regionalization studies, clustering techniques are useful to partition catchments in a region into natural groups. The linear Kohonen’s self-organizing feature map (SOFM) has been applied as a clustering technique for regionalization in several recent studies. However, SOFM is not a clustering method because it is seldom possible to interpret clusters from the output of an SOFM, irrespective of its size and dimensionality. In this study, we demonstrate that SOFMs may, however, serve as a useful precursor to clustering algorithms. We present a two-level SOFM-based clustering approach for regionalization of watersheds. In the first level, the SOFM is used to form a two-dimensional feature map. In the second level, the output nodes of SOFM are clustered by using Fuzzy c-means algorithm to form regions for flood frequency analysis (FFA). The optimal number of regions is based on fuzzy cluster validation measures. Effectiveness of the proposed approach in forming homogeneous regions for FFA is illustrated through application to data from watersheds in Indiana, USA. We find that previous indices used to decide the number of clusters are not efficient, and therefore suggest a new index that is more appropriate for this purpose. Using leave-one-out cross-validation, the performance of the proposed approach to regional FFA is compared with those from methods based on regression analysis and canonical correlation analysis. Results show that the proposed approach performs better in estimating flood quantiles at ungauged site