Use of hydroclimatic forecasts for improved water management in central Texas

Accurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Hydrological persistence (streamflow and precipitation) and large-scale recurrent oceanic-atmospheric patterns such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific North American (PNA), and customized sea surface temperature (SST) indices were investigated for their potential to improve streamflow forecast accuracy and increase forecast lead-time in a river basin in central Texas. First, an ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distributions-oriented metrics, and implications for decision making are discussed. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. Secondly, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas. Lastly, a simplified two-stage stochastic economic-optimization model was proposed to investigate improvement in water use efficiency and the potential value of using seasonal forecasts, under the assumption of optimal decision making under uncertainty. Model results demonstrate that incorporating the probabilistic inflow forecasts into the optimization model can provide a significant improvement in seasonal water contract benefits over climatology, with lower average deficits (increased reliability) for a given average contract amount, or improved mean contract benefits for a given level of reliability compared to climatology. The results also illustrate the trade-off between the expected contract amount and reliability, i.e., larger contracts can be signed at greater risk.

Manual pumping test method for characterizing the productivity of drilled wells equipped with rope pumps

Since the introduction of the rope-pump in Nicaragua in the 1990s, the dependence on wells in rural areas has grown steadily. However, little or no attention is paid to rope-pump well performance after installation. Due to financial restraints, groundwater resource monitoring using conventional testing methods is too costly and out of reach of rural municipalities. Nonetheless, there is widespread agreement that without a way to quantify the changes in well performance over time, prioritizing regulatory actions is impossible. A manual pumping test method is presented, which at a fraction of the cost of a conventional pumping test, measures the specific capacity of rope-pump wells. The method requires only sight modifcations to the well and reasonable limitations on well useage prior to testing. The pumping test was performed a minimum of 33 times in three wells over an eight-month period in a small rural community in Chontales, Nicaragua. Data was used to measure seasonal variations in specific well capacity for three rope-pump wells completed in fractured crystalline basalt. Data collected from the tests were analyzed using four methods (equilibrium approximation, time-drawdown during pumping, time-drawdown during recovery, and time-drawdown during late-time recovery) to determine the best data-analyzing method. One conventional pumping test was performed to aid in evaluating the manual method. The equilibrim approximation can be performed while in the field with only a calculator and is the most technologically appropriate method for analyzing data. Results from this method overestimate specific capacity by 41% when compared to results from the conventional pumping test. The other analyes methods, requiring more sophisticated tools and higher-level interpretation skills, yielded results that agree to within 14% (pumping phase), 31% (recovery phase) and 133% (late-time recovery) of the conventional test productivity value. The wide variability in accuracy results principally from difficulties in achieving equilibrated pumping level and casing storage effects in the puping/recovery data. Decreases in well productivity resulting from naturally occuring seasonal water-table drops varied from insignificant in two wells to 80% in the third. Despite practical and theoretical limitations on the method, the collected data may be useful for municipal institutions to track changes in well behavior, eventually developing a database for planning future ground water development projects. Furthermore, the data could improve well-users’ abilities to self regulate well usage without expensive aquifer characterization.

Vegetation and hydrologic influences on carbon and nitrogen in subsurface water of a forested riparian wetland

Vegetation communities affect carbon and nitrogen dynamics in the subsurface water of mineral wetlands through the quality of their litter, their uptake of nutrients, root exudation and their effects on redox potential. However, vegetation influence on subsurface nutrient dynamics is often overshadowed by the influences of hydrology, soils and geology on nutrient dynamics. The effects of vegetation communities on carbon and nitrogen dynamics are important to consider when managing land that may change vegetation type or quantity so that wetland ecosystem functions can be retained. This study was established to determine the magnitude of the influences and interaction of vegetation cover and hydrology, in the form of water table fluctuations, on carbon and nitrogen dynamics in a northern forested riparian wetland. Dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), nitrate (NO3-) and ammonium (NH4+) concentrations were collected from a piezometer network in four different vegetation communities and were found to show complex responses to vegetation cover and water table fluctuations. Dissolved organic carbon, DIC, NO3- and NH4+ concentrations were influenced by forest vegetation cover. Both NO3- and NH4+ were also influenced by water table fluctuations. However, for DOC and NH4+ concentrations there appeared to be more complex interactions than were measured by this study. The results of canonical correspondence analysis (CCA) and analysis of variance (ANOVA) did not correspond in relationship to the significance of vegetation communities. Dissolved inorganic carbon was influenced by an interaction between vegetation cover and water table fluctuations. More hydrological information is needed to make stronger conclusions about the relationship between vegetation and hydrology in controlling carbon and nitrogen dynamics in a forested riparian wetland.

Using water balance models to approximate the effects of climate change on spring catchment discharge : Mt. Hanang, Tanzania

This project addresses the potential impacts of changing climate on dry-season water storage and discharge from a small, mountain catchment in Tanzania. Villagers and water managers around the catchment have experienced worsening water scarcity and attribute it to increasing population and demand, but very little has been done to understand the physical characteristics and hydrological behavior of the spring catchment. The physical nature of the aquifer was characterized and water balance models were calibrated to discharge observations so as to be able to explore relative changes in aquifer storage resulting from climate changes. To characterize the shallow aquifer supplying water to the Jandu spring, water quality and geochemistry data were analyzed, discharge recession analysis was performed, and two water balance models were developed and tested. Jandu geochemistry suggests a shallow, meteorically-recharged aquifer system with short circulation times. Baseflow recession analysis showed that the catchment behavior could be represented by a linear storage model with an average recession constant of 0.151/month from 2004-2010. Two modified Thornthwaite-Mather Water Balance (TMWB) models were calibrated using historic rainfall and discharge data and shown to reproduce dry-season flows with Nash-Sutcliffe efficiencies between 0.86 and 0.91. The modified TMWB models were then used to examine the impacts of nineteen, perturbed climate scenarios to test the potential impacts of regional climate change on catchment storage during the dry season. Forcing the models with realistic scenarios for average monthly temperature, annual precipitation, and seasonal rainfall distribution demonstrated that even small climate changes might adversely impact aquifer storage conditions at the onset of the dry season. The scale of the change was dependent on the direction (increasing vs. decreasing) and magnitude of climate change (temperature and precipitation). This study demonstrates that small, mountain aquifer characterization is possible using simple water quality parameters, recession analysis can be integrated into modeling aquifer storage parameters, and water balance models can accurately reproduce dry-season discharges and might be useful tools to assess climate change impacts. However, uncertainty in current climate projections and lack of data for testing the predictive capabilities of the model beyond the present data set, make the forecasts of changes in discharge also uncertain. The hydrologic tools used herein offer promise for future research in understanding small, shallow, mountainous aquifers and could potentially be developed and used by water resource professionals to assess climatic influences on local hydrologic systems.

REMEDIATION OF TETRACYCLINE FROM WATER SOURCES USING VETIVER GRASS (Chrysopogon zizanioides L. Nash) AND TETRACYCLINE-TOLERANT ROOT-ASSOCIATED BACTERIA

Antibiotics are emerging contaminants worldwide. Due to insufficient policy regulations, public awareness, and the constant exposure of the environment to antibiotic sources has created a major environmental concern. Wastewater treatment plants (WWTP) are not equipped to filter-out these compounds before the discharge of the disinfected effluent into water sources (e.g., lakes and streams) and current available technologies are not equipped to remediate these compounds from environmental sources. Hence, the challenge remains to establish a biological system to remove these antibiotics from wastewater. An invitro hydroponic remediation system was developed using vetiver grass (Chrysopogon zizanioides L. Nash) to remediate tetracycline (TC) from water. Comparative metabolomics studies were conducted to investigate the metabolites/pathways associated with tetracycline metabolism in plants and TC-degrading bacteria. The results show that vetiver plants effectively uptake tetracycline from water sources. Vetiver root-associated bacteria recovered during the hydroponic remediation trial were highly tolerant to TC (as high as 600 ppm) and could use TC as a sole carbon and energy source. Growth conditions (pH, temperature, and oxygen requirement) for TC-tolerant bacteria were optimized for higher TC remediation capability from water sources. The plant (roots and shoots) and bacterial species were further characterized for the metabolites produced during the TC degradation process using GC-MS to identify the possible biochemical mechanism involved. Also, the plant root zone was screened for metabolites/enzymes that were secreted during antibiotic degradation and could potentially enhance the degradation process. The root zone was selected for this analysis because this region of the plant has shown a greater capacity for antibiotic degradation compared to the shoot zone. The role of antioxidant enzymes in TC degradation process revealed glutathione-S-transferase (GSTs) as an important group of enzymes in both plant and bacteria potentially involved in TC degradation process. Metabolomics results also suggest potential GST activity in the TC remediation/ transformation process used by plants. This information could be useful in gaining insights for the application of biological remediation systems for the mitigation of antibiotics from waste-water.