“…Salinity stress affects the vegetation growth directly by reducing the plant water uptake (osmotic stress) and/or by deteriorating the transpiring leaves (specific ion effects) (11), in turn reducing organic input to the soil and ultimately leading to desertification of lands (12,13). Under extreme conditions, dispersion of saline dust (8,14), poverty, migration, and high costs of soil reclamation are long-term socioeconomic consequences of soil salinization (15).…”
Knowledge of spatiotemporal distribution and likelihood of (re)occurrence of salt-affected soils is crucial to our understanding of land degradation and for planning effective remediation strategies in face of future climatic uncertainties. However, conventional methods used for tracking the variability of soil salinity/sodicity are extensively localized, making predictions on a global scale difficult. Here, we employ machine-learning techniques and a comprehensive set of climatic, topographic, soil, and remote sensing data to develop models capable of making predictions of soil salinity (expressed as electrical conductivity of saturated soil extract) and sodicity (measured as soil exchangeable sodium percentage) at different longitudes, latitudes, soil depths, and time periods. Using these predictive models, we provide a global-scale quantitative and gridded dataset characterizing different spatiotemporal facets of soil salinity and sodicity variability over the past four decades at a ∼1-km resolution. Analysis of this dataset reveals that a soil area of 11.73 Mkm2 located in nonfrigid zones has been salt-affected with a frequency of reoccurrence in at least three-fourths of the years between 1980 and 2018, with 0.16 Mkm2 of this area being croplands. Although the net changes in soil salinity/sodicity and the total area of salt-affected soils have been geographically highly variable, the continents with the highest salt-affected areas are Asia (particularly China, Kazakhstan, and Iran), Africa, and Australia. The proposed method can also be applied for quantifying the spatiotemporal variability of other dynamic soil properties, such as soil nutrients, organic carbon content, and pH.
“…Salinity stress affects the vegetation growth directly by reducing the plant water uptake (osmotic stress) and/or by deteriorating the transpiring leaves (specific ion effects) (11), in turn reducing organic input to the soil and ultimately leading to desertification of lands (12,13). Under extreme conditions, dispersion of saline dust (8,14), poverty, migration, and high costs of soil reclamation are long-term socioeconomic consequences of soil salinization (15).…”
Knowledge of spatiotemporal distribution and likelihood of (re)occurrence of salt-affected soils is crucial to our understanding of land degradation and for planning effective remediation strategies in face of future climatic uncertainties. However, conventional methods used for tracking the variability of soil salinity/sodicity are extensively localized, making predictions on a global scale difficult. Here, we employ machine-learning techniques and a comprehensive set of climatic, topographic, soil, and remote sensing data to develop models capable of making predictions of soil salinity (expressed as electrical conductivity of saturated soil extract) and sodicity (measured as soil exchangeable sodium percentage) at different longitudes, latitudes, soil depths, and time periods. Using these predictive models, we provide a global-scale quantitative and gridded dataset characterizing different spatiotemporal facets of soil salinity and sodicity variability over the past four decades at a ∼1-km resolution. Analysis of this dataset reveals that a soil area of 11.73 Mkm2 located in nonfrigid zones has been salt-affected with a frequency of reoccurrence in at least three-fourths of the years between 1980 and 2018, with 0.16 Mkm2 of this area being croplands. Although the net changes in soil salinity/sodicity and the total area of salt-affected soils have been geographically highly variable, the continents with the highest salt-affected areas are Asia (particularly China, Kazakhstan, and Iran), Africa, and Australia. The proposed method can also be applied for quantifying the spatiotemporal variability of other dynamic soil properties, such as soil nutrients, organic carbon content, and pH.
“…51 species of 16 families visited in 2003 (Figure 3 d). They are Anatidae (6), Anhingidae (1), Ardeidae (7), Charadriidae (4), Ciconiidae (2), Glareolidae (1), Gruidae (3), Laridae (5), Pelecanidae (1), Phalacrocoracidae (2), Phoenicopteridae (2), Podicipedidae (1), Rallidae (3), Recurvirostridae (2), Scolopacidae (7), Threskiornithidae (4). Species which belong to 7 families Alaudidae, Accipitridae, Alcedinidae, Burhinidae, Cuculidae, Ibidorhynthidae, and Motacillidae were absent.…”
Section: Trend Analysismentioning
confidence: 99%
“…They show similar vertical stratification to freshwater systems but differ primarily in their ionic composition due to salinity ranging from 3 g/L to 300 g/L [3]. Due to anthropogenic pressures and climatic uncertainty, numerous lakes are rapidly drying even before we could know [4]. The recent example is 90% decline of the Aral Sea in Uzbekistan and Kazakhstan over just 50 years [5].…”
Globally, saline lakes occupying 23% by area 44% by volume among all the lakes might desiccate by 2025 due to agricultural diversion, illegal encroachment, pollution, and invasive species. India’s largest saline lake, Sambhar is currently shrinking at the rate of 4.23% due to illegal saltpan en-croachment. This research article aims to identify the trend of migratory birds and monthly wetland status. Birds survey was conducted for 2019, 2020 and 2021 and combined with literature data of 1994, 2003, and 2013 for visiting trend, feeding habit, migratory and resident ratio, and ecological diversity index analysis. Normalized Difference Water Index was scripted in Google Earth Engine. Results state that it has been suitable for 97 species. Highest NDWI values for the was whole study period was 0.71 in 2021 and lowest 0.008 in 2019 which is highly fluctuating. The decreasing trend of migratory birds coupled with decreasing water level indicates the dubious status for its existence. If the causal factors are not checked, it might completely desiccate by 2059 as per its future prediction. Certain steps are suggested that might help conservation. Least, the cost of restoration might exceed the revenue generation.
“…4 at a multi‐decadal to centurial time scale, seasonal and interannual climate variability are likely to exert a minimal influence on ∆S WS . In the context of shrinking lakes, there is a large body of evidence demonstrating that anthropogenic climate change remains a background condition to and not a primary driver of global lake shrinkage (AghaKouchak et al, 2015; Alborzi et al, 2018; Ashraf et al, 2017; Ashraf et al, 2018; Chaudhari et al, 2018; Fazel et al, 2017; Hassani et al, 2020; Khazaei et al, 2019; Madani et al, 2016; Micklin, 1988; Micklin, 2007; Moore, 2016; Morin et al, 2018; Rodell et al, 2018; Wine, Null, et al, 2019; Wine, Rimmer, & Laronne, 2019; Wine & Davison, 2019; Wurtsbaugh et al, 2017). Though not featured prominently here, those lakes influenced by enhanced glacial melt may present an exception to this generalization.…”
Lakes-quintessential features of Earth's surface prized from perspectives of water security, aquatic ecosystems, and recreation alike-are shrinking in water-limited regions of all of Earth's inhabited continents. Here we assessed Landsat-derived long-term decrease in global lake area relative to historical lake extent aiming to determine the role of recent Anthropocene levels of irrigated agriculture in the global phenomenon of lake desiccation. As of 2015, 11% (1.8 · 10 5 km 2 ) of global lake area has already been lost, primarily due to increased water consumption in support of irrigated agriculture in endorheic basins within water-limited regions. However, current levels of irrigated agriculture portend substantial additional shrinkage of global lakes before reaching new equilibria with present-day inflows, with an additional 60-130% increase in endorheic lake loss anticipated. The time required for shrinking lakes to attain new equilibria ranges from decades to centuries depending on lake hyposometry. Even a small decrease in lake area can portend lake transition from exorheic to endorheic and dramatic reductions in water quality. Thus, lake area changes severely understate the perilous condition of global lakes. The watershed area contributing to shrinking (endorheic and exorheic) lakes accounts for 18% of Earth's land area, far too large for the irrigated agriculture therein to be transferred elsewhere in order to save these lakes, though continued developments in the efficiency of water consumption in agriculture and urban areas can save significant quantities of water. This suggests that global lake shrinkage may be a harbinger signaling mankind having exceeded Earth's sustainable carrying capacity.Plain Language Summary Today, a host of processes influence the terrestrial water balance.These processes drive the observed shrinkage of lakes in water-limited regions of all inhabited continents. Typically, lake shrinkage is attributed either to climate change or unsustainable increases in human water consumption. However, while drivers of lake desiccation have been explored for individual lakes, the causes of this phenomenon have not been explored at the global scale. We therefore propose a simple conceptual model in which lake area and agricultural area are exchangeable in closed basins. We find that this simple model accurately determines the role of agriculture in loss of endorheic lakes. This model demonstrates that irrigated agriculture is the primary driver of desiccation of global lakes.
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