A Decade of Hydrological Drought in Central-Western Argentina

Abstract

Most of the water used for the development of the main socio-economic activities in Central-Western Argentina (CWA), an arid to semi-arid region, home to most of the Argentinean wine production, relies on surface streamflow from several snow-fed rivers. During the last decade (2010-2020), reduced snow accumulation over the higher elevations of the Andes mountains triggered the occurrence of hydrological drought over CWA, affecting winter tourism, restricting water use for irrigation and domestic use, and leading to socio-political disputes. This study provides a detailed description of the recent hydrological drought conditions through the use of streamflow records from 15 river basins, which were complemented by precipitation, snowpack, and water equivalent thickness measurements to provide a comprehensive picture of the water losses over the last decade. Hydrological drought indices derived from the threshold level method and the standardized streamflow index allowed characterizing the unusualness of this dry period in the context of the last 49 years. The hydrological deficit over the last decade highlighted the challenges faced by the water managers to provide water for irrigation in the main agricultural oases, with a likely overexploitation of the groundwater resources to supplement the limited surface runoff. The hydrological drought severity increased since 2017, with record-breaking levels in several basins, particularly during the period between July 2019 and June 2020 for the rivers located between 35° and 36°S. We identified the main hydrological drought impacts in CWA, as well as the need for improved mitigation strategies to cope with current and future drought conditions. We also analyzed the current limitations in terms of snow and groundwater observations, highlighting the necessity for an effective hydrological drought monitoring system, together with an improved forecast of snow accumulation in the headwaters, which can contribute to better regional water management plans.

The Role of Large Dams in a Transboundary Drought Management Co-Operation Framework—Case Study of the Kabul River Basin

“…For a hydrologic period to be considered as drought, events are compared with truncation periods. To do so, they are compared with percentiles [5,33]. In statistical terms, there are benefits from the theory of runs that is a sequence of two or more types of numbers that symbolize the unbroken sequences.…”

“…The Standardized Streamflow Index (SSI) is a popular probability distribution function to determine the hydrologic drought by transforming the time series streamflow into a Gaussian distribution. In this study, the SSI and the annual cumulative deficit volume standardized by the mean flow (ACDV) [33] are used to identify the patterns of drought events in the KRB and how the proposed dams will impact water shortages during droughts. The SSI parameter illustrates the streamflow water deficit concerning the standard deviation and long-term mean of flow samples at a specific location and specific time series [33].…”

“…'Drought severity' can provide insights to decide on the amount of water to be released to mitigate drought severity. For example, the 70th percentile of the streamflow will set a threshold where the flows under the 70th percentile are to be considered as part of a drought event [33] ACDV…”

“…The SSI concept by Vicente-Serrano is based on other drought indices such as the SPI, Standardized Precipitation, and Evapotranspiration Index (SPEI) [5,33]. In this study the time-series SSI were developed to investigate if the drought is long-term or short-term as well as whether the low flow increase will impact the SSI plot trend line anomaly [66,68].…”

GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery

“…However, large bias of cropping cycles and high uncertainties were commonly observed (Figs. 4, 5), which might be attributed to the omission of the poor crops stressed by the severe drought in the 2017-2018 agricultural year (Rivera et al, 2021). Rice paddies are fundamentally different from non-flooded croplands, which affects CI mapping performance.…”