Determining watershed response in data poor environments with remotely sensed small reservoirs as runoff gauges

Abstract. In river basins with water storage facilities, the availability of regularly updated information on reservoir level and capacity is of paramount importance for the effective management of those systems. However, for the vast majority of reservoirs around the world, storage levels are either not measured or not readily available due to financial, political, or legal considerations. This paper proposes a novel approach using Landsat imagery and digital elevation models (DEMs) to retrieve information on storage variations in any inaccessible region. Unlike existing approaches, the method does not require any in situ measurement and is appropriate for monitoring small, and often undocumented, irrigation reservoirs. It consists of three recovery steps: (i) a 2-D dynamic classification of Landsat spectral band information to quantify the surface area of water, (ii) a statistical correction of DEM data to characterize the topography of each reservoir, and (iii) a 3-D reconstruction algorithm to correct for clouds and Landsat 7 Scan Line Corrector failure. The method is applied to quantify reservoir storage in the Yarmouk basin in southern Syria, where ground monitoring is impeded by the ongoing civil war. It is validated against available in situ measurements in neighbouring Jordanian reservoirs. Coefficients of determination range from 0.69 to 0.84, and the normalized root-mean-square error from 10 to 16 % for storage estimations on six Jordanian reservoirs with maximal water surface areas ranging from 0.59 to 3.79 km 2 .

Section: N Avisse Et Al: Monitoring Small Reservoirs' Storagementioning

confidence: 99%

Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas

Avisse

Tilmant

Müller³

et al. 2017

“…In India, the storage volumes of small-sized reservoirs were estimated with Landsat images (Mialhe et al, 2008). Envisat advanced synthetic aperture radar (ASAR) was used to obtain the surface areas of reservoirs (Liebe et al, 2009). At present, there are numerous approaches of extracting surface areas from remote sensing data, such as the single band threshold method (Ding, 2012;Du and Zhou, 1988), the difference of spectrum relationship method (Cao, 2006), the Normalized Difference Vegetation Index (NDVI) method (Wu et al, 2005;Li and Wu, 2008) and the Normalized Difference Water Index (NDWI) method (McFeeters, 1996;Gao, 1995;Ouma and Tateishi, 2006).…”

Section: Surface Areamentioning

confidence: 99%

Integrated hydrological modelling of small- and medium-sized water storages with application to the upper Fengman Reservoir Basin of China

Zhang

Peng

Chu

et al. 2012

Abstract. Hydrological simulation in regions with a large number of water storages is difficult due to inaccurate water storage data. To address this issue, this paper presents an improved version of SWAT2005 (Soil and Water Assessment Tool, version 2005) using Landsat, a satellite-based dataset, an empirical storage classification method and some empirical relationships to estimate water storage and release from the various sizes of flow detention and regulation facilities. The SWAT2005 is enhanced by three features: (1) a realistic representation of the relationships between the surface area and volume of each type of water storages, ranging from small-sized flow detention ponds to medium-and large-sized reservoirs with the various flow regulation functions; (2) water balance and transport through a network combining both sequential and parallel streams and storage links; and (3) calibrations for both physical and human interference parameters. Through a real-world watershed case study, it is found that the improved SWAT2005 more accurately models small-and medium-sized storages than the original model in reproducing streamflows in the watershed. The improved SWAT2005 can be an effective tool to assess the impact of water storage on hydrologic processes, which has not been well addressed in the current modelling exercises.

“…For instance, in situ measurements of tank water storage have been successfully used to calibrate and validate hydrological models in Andhra Pradesh (Perrin et al, 2012) and Tamil Nadu (Van Meter et al, 2016). Other studies in southern India (Mialhe et al, 2008), the USA (Halabisky et al, 2016), Africa (Meigh, 1995;Liebe et al, 2005;Sawunyama et al, 2006;Liebe et al, 2009;Gardelle et al, 2010), and South America (Rodrigues et al, 2012) also use surface water bodies as aggregators of streamflow.…”

mentioning

confidence: 99%

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

Penny¹,

Srinivasan²,

Dronova³

et al. 2018

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.