Hydrology

13557 KiB

Open AccessArticle

Optimizing Groundwater Monitoring Networks Using Integrated Statistical and Geostatistical Approaches

by Jay Krishna Thakur

Hydrology 2015, 2(3), 148-175; https://doi.org/10.3390/hydrology2030148 - 24 Aug 2015

Cited by 11 | Viewed by 6235

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. Full article

(This article belongs to the Special Issue Advancements in Data-Driven Modeling and Data-Mining Techniques in Hydrology)

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1887 KiB

Open AccessArticle

Simulating the Hydrologic Impact of Arundo donax Invasion on the Headwaters of the Nueces River in Texas

by Shailee Jain, Srinivasulu Ale, Clyde L. Munster, R. James Ansley and James R. Kiniry

Hydrology 2015, 2(3), 134-147; https://doi.org/10.3390/hydrology2030134 - 20 Aug 2015

Cited by 10 | Viewed by 5550

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 to streamflow) 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. Full article

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604 KiB

Open AccessEditorial

Introduction to the Special Issue “The Intersection of Society and Watershed Science”

by Tamim Younos, Tammy E. Parece and Alaina J. Armel

Hydrology 2015, 2(3), 132-133; https://doi.org/10.3390/hydrology2030132 - 7 Aug 2015

Viewed by 3695

Abstract

Despite significant advances in watershed science and technology, water availability, water quality, and water related health problems remain a significant worldwide concern [1]. 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]). [...] Full article

(This article belongs to the Special Issue The Intersection of Society and Watershed Science)

2088 KiB

Open AccessArticle

Skill Assessment of Water Supply Outlooks in the Colorado River Basin

by Brent Harrison and Roger Bales

Hydrology 2015, 2(3), 112-131; https://doi.org/10.3390/hydrology2030112 - 31 Jul 2015

Cited by 5 | Viewed by 5379

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. Full article

(This article belongs to the Special Issue Snow Hydrology)

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Hydrology, Volume 2, Issue 3 (September 2015) – 4 articles , Pages 112-175

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