A Decade of Measuring, Monitoring, and Studying the Fate and Transport of Triazine Herbicides and their Degradation Products in Groundwater, Surface Water, Reservoirs, and Precipitation by the US Geological Survey

2008 ◽  
pp. 451-475 ◽  
Author(s):  
E. Michael Thurman ◽  
Elisabeth A. Scribner
2019 ◽  
Vol 11 (4) ◽  
pp. 374 ◽  
Author(s):  
John Jones

In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.


2018 ◽  
Vol 488 (1) ◽  
pp. 277-289 ◽  
Author(s):  
Adebayo J. Adeloye ◽  
Bankaru-Swamy Soundharajan

AbstractHedging is universally recognized as a useful operational practice in surface water reservoirs to temporally redistribute water supplies and thereby avoid large, crippling water shortages. When based on the zones of available water in storage, hedging has traditionally involved a static rationing (i.e. supply to demand) ratio. However, given the usual seasonality of reservoir inflows, it is also possible that hedging could be dynamic with seasonally varying rationing ratios. This study examined the effect of static and dynamic hedging policies on the performance of the Pong reservoir in India during a period of climate change. The results show that the reservoir vulnerability was unacceptably high (≥60%) without hedging and that this vulnerability further deteriorated as the catchment became drier due to projected climate change. The time- and volume-based reliabilities were acceptable. The introduction of static hedging drastically reduced the vulnerability to <25%, although the hedging reduction in the water supplied during normal operational conditions was only 17%. Further analyses with dynamic hedging provided only modest improvements in vulnerability. The significance of this study is its demonstration of the effectiveness of hedging in offsetting the impact of water shortages caused by climate change and the fact that static hedging can match more complex dynamic hedging policies.


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