Groundwater (GW) management is an essential element in irrigated agriculture. This paper analyzes the temporal dynamics of GW table and salinity in Khorezm, a region of Uzbekistan which is situated on the lower Amu Darya River in the Aral Sea Basin and suffering from severe soil salinization. We furthermore identify the critical areas for potential soil salinization by examining GW table and salinity measured during 1990-2000 in 1,972 wells, covering the entire region. Additionally, case studies were performed to assess the contribution of the GW to the soil salinization on a field scale. Over the entire area, GW was only moderately saline averaging 1.75±0.99 g l −1 However, GW levels were generally very shallow averaging 148±57 cm below the ground surface and thus likely to prompt secondary soil salinization. Three case studies where GW table, soil and GW salinity were closely monitored at the field scale, suggested that the elevated GW levels forced soil salinization by annually adding 3.5-14 t ha −1 of salts depending on the position and salinity of the GW table. Maps interpolated from the regional dataset revealed that GW was significantly shallower and more saline in the western and southern parts of Khorezm despite the presence of a drainage network which is rather uniformly distributed throughout the region. The results of the current study will assist the development of an improved drainage management in Khorezm.
Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the Aral Sea Basin (ASB), Central Asia, where agriculture relies heavily on irrigation. Here, remote sensing is valuable to map crop types, but its quality depends on consistent ground-truth data. Yet, in the ASB, such data are missing. Addressing this issue, we collected thousands of polygons on crop types, 97.7% of which in Uzbekistan and the remaining in Tajikistan. We collected 8,196 samples between 2015 and 2018, 213 in 2011 and 26 in 2008. Our data compile samples for 40 crop types and is dominated by "cotton" (40%) and "wheat", (25%). These data were meticulously validated using expert knowledge and remote sensing data and relied on transferable, open-source workflows that will assure the consistency of future sampling campaigns.
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