Abstract: The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968). For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days), median quartile (317 days) and upper quartile (401 days) in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.
Abstract: Arundo donax (hereafter referred to as Arundo), a robust herbaceous plant, has invaded the riparian zones of the Rio Grande River and the rivers of the Texas Hill Country over the last two decades. Arundo was first observed along the Nueces River in central Texas in 1995 by the Nueces River Authority (NRA). It then spread rapidly downstream due to its fast growth rate and availability of streamflow for its consumptive use, and it completely displaced the native vegetation, primarily Panicum virgatum (hereafter referred to as switchgrass) in the riparian zone. It was hypothesized that Arundo reduced streamflows due to higher water use by Arundo when compared to switchgrass. The overall goal of this study was to assess the impacts of Arundo invasion on hydrology of the headwaters of the Nueces River through observed long-term streamflow and precipitation data analysis and simulation modeling with the Soil and Water Assessment Tool (SWAT). The observed data analysis indicated that while there was no significant change in monthly precipitation between the pre-Arundo invasion (1979–1994) and post-Arundo invasion (1995–2010) periods, streamflows changed significantly showing a positive (slightly increasing) trend during the pre-invasion period and a negative (slightly decreasing) trend during the post-invasion periods. The simulated average (1995–2010) annual evapotranspiration of Arundo in the seven Hydrologic Response Units (HRUs) in which Arundo invaded, was higher by 137 mm when compared to switchgrass. The water uptake by Arundo was therefore higher by 7.2% over switchgrass. Higher water uptake by Arundo resulted in a 93 mm higher irrigation (water use from the reach/stream) annually when compared to switchgrass. In addition, the simulated average annual water yield (net amount of water that was generated from the seven Arundo HRUs and contributed tostreamflow) under Arundo was less by about 17 mm as compared to switchgrass. In conclusion, model simulations indicated that Arundo invasion in the Nueces River has caused a statistically significant increase in water uptake and reduction in streamflow compared to the native switchgrass, which previously dominated the headwaters.
Abstract: Despite significant advances in watershed science and technology, water availability, water quality, and water related health problems remain a significant worldwide concern . While the concept of watershed-scale management to address these concerns remains intact, most scientists recognize that application of natural science concepts and advanced technologies are not sufficient to adequately address watershed-scale water management issues. There is a significant need for a paradigm shift, i.e., namely increased public interaction and participation in watershed management and decision-making. The effective application of an integrated approach requires developing new scientific concepts on integration of natural and social sciences. In recent years, concepts, such as integrated watershed management and/or holistic approaches to water resource management, have been widely promoted (e.g., [2–6]). [...]
Abstract: Water-supply outlooks that predict the April through July (snowmelt) runoff and assist in estimating the total water-year runoff, are very important to users that rely on the major contributing watersheds of the Colorado River. This study reviewed the skill level of April through July forecasts at 28 forecast points within the Colorado River basin. All the forecasts were made after 1950, with considerable variation in time period covered. Evaluations of the forecasts were made using summary measures, correlation measures and categorical measures. The summary measure, a skill score for mean absolute error, indicated a steady increase in forecast skill through the forecast season of January to May. The width of the distribution for each monthly forecast over the 28 locations remained similar through the forecast season. The Nash-Sutcliffe score, a correlation measure, showed similar results, with the Nash-Sutcliffe median showing an increase from 0.4 to 0.8 during the forecast season. The categorical measures used a three-section partition of the April through July runoff. The Probability of Detection for low and high flows showed an increase in skill from approx. 0.4 to 0.8 during the forecast season. The same score for mid-flow years showed limited increase in skill. The low False Alarm Rate illustrated the under forecast of high-flow years. The Bias of the mid-runoff forecasts indicated over forecast early in the forecast season (January to March), with lower Bias later in the forecast season (April and May), ending the forecast season at 1.0, indicating no Bias. Forecasts for both low and high runoff were under forecast early in the season with a Bias near 0.5, improving to nearly 1.0 by the end of the forecast season. The Hit Rate measure illustrated the difficulty of mid-flow forecasts, starting at 0.5 in January and increasing to 0.75 in May due to the forecasting assumption of normal climatology for the remaining forecast period. There was no relationship between basin elevation and forecast skill, reflecting the snow vs. rain dominance in all basins.
Abstract: Most models evaluated by the Intergovernmental Panel for Climate change estimate projected increases in temperature and precipitation with rising atmospheric CO2 levels. Researchers have suggested that increases in CO2 and associated increases in temperature and precipitation may stimulate vegetation growth and increase evapotranspiration (ET), which acts as a cooling mechanism, and on a global scale, may slow the climate-warming trend. This hypothesis has been modeled under increased CO2 conditions with models of different vegetation-climate dynamics. The significance of this vegetation negative feedback, however, has varied between models. Here we conduct a century-scale observational analysis of the Eastern US water balance to determine historical evapotranspiration trends and whether vegetation greening has affected these trends. We show that precipitation has increased significantly over the twentieth century while runoff has not. We also show that ET has increased and vegetation growth is partially responsible.
Abstract: It is always a dream of hydrologists to model the mystery of complex hydrological processes in a precise way. If parameterized correctly, a simple hydrological model can represent nature very accurately. In this study, a simple and effective optimization algorithm, sequential replacement of weak parameters (SRWP), is introduced for automatic calibration of hydrological models. In the SRWP algorithm, a weak parameter set is sequentially replaced with another deeper and good parameter set. The SRWP algorithm is tested on several theoretical test functions, as well as with a hydrological model. The SRWP algorithm result is compared with the shuffled complex evolution-University of Arizona (SCE-UA) algorithm and the robust parameter estimation (ROPE) algorithm. The result shows that the SRWP algorithm easily overcomes the local minima and converges to the optimal parameter space. The SRWP algorithm does not converge to a single optima; instead, it gives a convex hull of an optimal space. An ensemble of results can be generated from the optimal space for prediction purpose. The ensemble spread will account for the parameter estimation uncertainty. The methodology was demonstrated using the hydrological model (HYMOD) conceptual model on upper Neckar catchments of southwest Germany. The results show that the parameters estimated by this stepwise calibration are robust and comparable to commonly-used optimization algorithms. SRWP can be an alternative to other optimization algorithms for model calibration.