The use of remote-sensing techniques to monitor dense reservoir networks in the Brazilian semiarid region

Toledo, Cristian Epifanio; Araújo, José Carlos de; Almeida, Cicero Lima de

doi:10.1080/01431161.2014.915593

Cited by 24 publications

(32 citation statements)

References 38 publications

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“…While proposed thresholds are relatively stable, they still require modification for areas or places beyond those for which they were developed. Other research has applied parametric [30] or non-parametric classification techniques, such as classification and regression trees (CART) [31,32]. Although currently favored given their ability to use multi-scalar input data (such as thematic ancillary information), CART models require significant amounts of training data for calibration and validation.…”

Section: Introductionmentioning

confidence: 99%

Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network

2015

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The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an OPEN ACCESSRemote Sens. 2015, 7 12504 extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management.

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Section: Introductionmentioning

confidence: 99%

Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network

2015

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Section: Discussionmentioning

confidence: 99%

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Penny¹

2017

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The complexity and heterogeneity of human water use over large spatial areas and decadal timescales can impede the understanding of hydrological change, particularly in regions with sparse monitoring of the water cycle. In the Arkavathy watershed in southern India, surface water inflows to major reservoirs decreased over a 40-year period during which urbanization, groundwater depletion, modification of the river network, and changes in agricultural practices also occurred. These multiple, interacting drivers combined with limited hydrological monitoring make attribution of the causes of diminishing water resources in the watershed challenging and impede effective policy responses. To mitigate these challenges, we developed a novel, spatially distributed dataset to understand hydrological change by characterizing the residual trends in surface water extent that remain after controlling for precipitation variations and comparing the trends with historical land use maps to assess human drivers of change. Using an automated classification approach with subpixel unmixing, we classified water extent in nearly 1700 man-made lakes, or tanks, in Landsat images from 1973 to 2010. The classification results compared well with a reference dataset of water extent of tanks (R 2 = 0.95). We modeled the water extent of 42 clusters of tanks in a multiple regression on simple hydrological covariates (including precipitation) and time. Inter-annual variability in precipitation accounted for 63 % of the predicted variability in water extent. However, precipitation did not exhibit statistically significant trends in any part of the watershed. After controlling for precipitation variability, we found statistically sig-nificant temporal trends in water extent, both positive and negative, in 13 of the clusters. Based on a water balance argument, we inferred that these trends likely reflect a nonstationary relationship between precipitation and watershed runoff. Independently of precipitation, water extent increased in a region downstream of Bangalore, likely due to increased urban effluents, and declined in the northern portion of the Arkavathy. Comparison of the drying trends with land use indicated that they were most strongly associated with irrigated agriculture, sourced almost exclusively by groundwater. This suggests that groundwater abstraction was a major driver of hydrological change in this watershed. Disaggregating the watershed-scale hydrological response via remote sensing of surface water bodies over multiple decades yielded a spatially resolved characterization of hydrological change in an otherwise poorly monitored watershed. This approach presents an opportunity to understand hydrological change in heavily managed watersheds where surface water bodies integrate upstream runoff and can be delineated using satellite imagery.

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“…Surface networks of rainwater harvesting structures are employed in seasonal climates worldwide, whether in cascading tank systems in southern India and Sri Lanka, or hillslope farm dams in Australia (Callow and Smettem, 2009;Roohi and Webb, 2012), northeastern Brazil (Lima Neto et al, 2011;Malveira et al, 2012;de Araújo and Medeiros, 2013;de Toledo et al, 2014), South Africa (Hughes and Mantel, 2010), the US Great Plains (Womack, 2012), and China (Xiankun, 2014;Xu et al, 2013). Capitalizing on these networks as proxy indicators of rainfall and streamflow variation, as in the Arkavathy, could prove a valuable approach to circumventing problems of data scarcity and characterizing changing hydrological conditions.…”

Section: Discussionmentioning

confidence: 99%

Spatial characterization of long-term hydrological change in the Arkavathy watershed adjacent to Bangalore, India

Penny¹,

Srinivasan²,

Dronova³

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The complexity and heterogeneity of human water use over large spatial areas and decadal timescales can impede the understanding of hydrological change, particularly in regions with sparse monitoring of the water cycle. In the Arkavathy watershed in southern India, surface water inflows to major reservoirs decreased over a 40-year period during which urbanization, groundwater depletion, modification of the river network, and changes in agricultural practices also occurred. These multiple, interacting drivers combined with limited hydrological monitoring make attribution of the causes of diminishing water resources in the watershed challenging and impede effective policy responses. To mitigate these challenges, we developed a novel, spatially distributed dataset to understand hydrological change by characterizing the residual trends in surface water extent that remain after controlling for precipitation variations and comparing the trends with historical land use maps to assess human drivers of change. Using an automated classification approach with subpixel unmixing, we classified water extent in nearly 1700 man-made lakes, or tanks, in Landsat images from 1973 to 2010. The classification results compared well with a reference dataset of water extent of tanks (R 2 = 0.95). We modeled the water extent of 42 clusters of tanks in a multiple regression on simple hydrological covariates (including precipitation) and time. Inter-annual variability in precipitation accounted for 63 % of the predicted variability in water extent. However, precipitation did not exhibit statistically significant trends in any part of the watershed. After controlling for precipitation variability, we found statistically significant temporal trends in water extent, both positive and negative, in 13 of the clusters. Based on a water balance argument, we inferred that these trends likely reflect a nonstationary relationship between precipitation and watershed runoff. Independently of precipitation, water extent increased in a region downstream of Bangalore, likely due to increased urban effluents, and declined in the northern portion of the Arkavathy. Comparison of the drying trends with land use indicated that they were most strongly associated with irrigated agriculture, sourced almost exclusively by groundwater. This suggests that groundwater abstraction was a major driver of hydrological change in this watershed. Disaggregating the watershed-scale hydrological response via remote sensing of surface water bodies over multiple decades yielded a spatially resolved characterization of hydrological change in an otherwise poorly monitored watershed. This approach presents an opportunity to understand hydrological change in heavily managed watersheds where surface water bodies integrate upstream runoff and can be delineated using satellite imagery.

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The use of remote-sensing techniques to monitor dense reservoir networks in the Brazilian semiarid region

Cited by 24 publications

References 38 publications

Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network

Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network

Author comments

Spatial characterization of long-term hydrological change in the Arkavathy watershed adjacent to Bangalore, India

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