Published in

American Geophysical Union, Water Resources Research, 1(58), 2022

DOI: 10.1029/2021wr030799

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Drought Cycle Analysis to Evaluate the Influence of a Dense Network of Small Reservoirs on Drought Evolution

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

AbstractDrought‐affected regions often contain high densities of small reservoirs, usually informally built, as drought‐coping mechanism. These structures influence socio‐hydrological dynamics and have the potential to alter hydrological processes relevant to drought emergence and development. This study aimed to analyze the influence of a high concentration of small reservoirs on the intensification and evolution of drought events. We present an innovative method, which we call “Drought Cycle Analysis”, that tracks the concomitance of precipitation and water storage deficit and associates this with four drought stages: Wet Period, Meteorological drought, Hydro‐meteorological drought and Hydrological drought period. The methodology was tested for the Riacho do Sangue River watershed located in the semi‐arid region of northeast Brazil. We used a combination of satellite imagery (Landsat 5, 7 and 8) and an empirical equation to estimate the volume stored in the dense network of small reservoirs. Using the Drought Cycle Analysis, we show that the unmonitored small reservoirs induced and modified drought events, extending the duration of hydrological drought on average by 30%. Furthermore, this extension can double for specific drought events. The Drought Cycle Analysis method proved useful for monitoring and comparing the evolution of different drought events, in addition to being applicable as an auxiliary tool in the improvement of water resources management of large reservoirs. This study demonstrates the importance of considering small reservoirs in water resource management strategy development for drought‐prone regions.