Series - "Irrigation agriculture and aqueous environment in arid areas" : Actualconditions of soil chlorination in large-scale irrigation farms in Central Asia.

The unique properties of ZnO nanoparticles have attracted scientists’ interest to produce on a large-scale. Household items, cosmetics, consumer products, and electric sensors are some products that utilize these ZnO nanomaterials. Eventually, ZnO nanoparticles will be released into the environment in various ways. Once released, ZnO nanoparticles would dissociate into Zn2+ ions, which are toxic to aquatic organisms. The presence of humic acid and exposure to sunlight could affect the dissolution of ZnO nanoparticles. Two sizes of commercial ZnO nanoparticles (< 50 nm and < 100 nm) were chosen to study the influence of humic acid and sunlight on the dissolution. In the presence of humic acid, the dissolution of both sizes is higher, with 67 % and 39 % Zn2+ dissolved for < 50 nm and < 100 nm, respectively. The concentration of Zn2+ ions seems to be consistent or stable when exposed to sunlight. However, the humic acid enhanced the release of Zn2+ ions. Langmuir isotherm model best fitted for the humic acid's sorption onto the ZnO nanoparticles with the process been favorable.

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Post-Soviet changes in irrigated crop production in the Amu Darya Basin

10.5194/egusphere-egu21-16266 ◽

2021 ◽

Author(s):

Daniel Müller ◽

Andrey Dara ◽

Christopher Krause ◽

Mayra Daniela Peña-Guerrero ◽

Tillman Schmitz ◽

...

Keyword(s):

Water Resources ◽

Central Asia ◽

Crop Production ◽

Large Scale ◽

Aral Sea ◽

Crop Cultivation ◽

Amu Darya ◽

Crop Type ◽

Water Scarce ◽

Hydrological Droughts

<p>Water withdrawals for irrigated crop production constitute the largest global consumer of blue water resources. Monitoring the dynamics of irrigated crop cultivation allows to track changes in water consumption of irrigated cropping, which is particularly paramount in water-scarce arid and semi-arid areas. We analyzed changes in irrigated crop cultivation along with occurrence of hydrological droughts for the Amu Darya river basin of Central Asia (534,700 km<sup>2</sup>), once the largest tributary river to the Aral Sea before large-scale irrigation projects have grossly reduced the amount of water that reaches the river delta. We used annual and seasonal spectral-temporal metrics derived from Landsat time series to quantify the three predominant cropping practices in the region (first season, second season, double cropping) for every year between 1988 and 2020. We further derived unbiased area estimates for the cropping classes at the province level based on a stratified random sample (n=2,779). Our results reveal a small yet steady decrease in irrigated second season cultivation across the basin. Regionally, we observed a gradual move away from cotton monocropping in response to the policy changes that were instigated since the mid-1990s. We compared the observed cropping dynamics to the occurrence of hydrological droughts, i.e., periods with inadequate water resources for irrigation. We find that areas with higher drought risks rely more on irrigation of the second season crops. Overall, our analysis provides the first fine-scale, annual crop type maps for the irrigated areas in the Amu Darya basin. The results shed light on how institutional changes and hydroclimatic factors that affect land-use decision-making, and thus the dynamics of crop type composition, in the vast irrigated areas of Central Asia.</p>

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Irrigation agriculture in Central Asia: a long-term perspective from Turkmenistan

The Archaeology of Drylands ◽

10.4324/9780203165737-17 ◽

2003 ◽

pp. 129-146

Keyword(s):

Central Asia ◽

Irrigation Agriculture

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Variability of the Cold Season Climate in Central Asia. Part II: Hydroclimatic Predictability

Journal of Climate ◽

10.1175/jcli-d-18-0892.1 ◽

2019 ◽

Vol 32 (18) ◽

pp. 6015-6033 ◽

Cited By ~ 5

Author(s):

Lars Gerlitz ◽

Eva Steirou ◽

Christoph Schneider ◽

Vincent Moron ◽

Sergiy Vorogushyn ◽

...

Keyword(s):

Central Asia ◽

Snow Cover ◽

Large Scale ◽

Cold Season ◽

Hadley Cell ◽

Quasi Biennial Oscillation ◽

Large Variability ◽

Forecast Performance ◽

Eurasian Snow Cover ◽

Eurasian Snow

Abstract Central Asia (CA) is subjected to a large variability of precipitation. This study presents a statistical model, relating precipitation anomalies in three subregions of CA in the cold season (November–March) with various predictors in the preceding October. Promising forecast skill is achieved for two subregions covering 1) Uzbekistan, Turkmenistan, Kyrgyzstan, Tajikistan, and southern Kazakhstan and 2) Iran, Afghanistan, and Pakistan. ENSO in October is identified as the major predictor. Eurasian snow cover and the quasi-biennial oscillation further improve the forecast performance. To understand the physical mechanisms, an analysis of teleconnections between these predictors and the wintertime circulation over CA is conducted. The correlation analysis of predictors and large-scale circulation indices suggests a seasonal persistence of tropical circulation modes and a dynamical forcing of the westerly circulation by snow cover variations over Eurasia. An EOF analysis of pressure and humidity patterns allows separating the circulation variability over CA into westerly and tropical modes and confirms that the identified predictors affect the respective circulation characteristics. Based on the previously established weather type classification for CA, the predictors are investigated with regard to their effect on the regional circulation. The results suggest a modification of the Hadley cell due to ENSO variations, with enhanced moisture supply from the Arabian Gulf during El Niño. They further indicate an influence of Eurasian snow cover on the wintertime Arctic Oscillation (AO) and Northern Hemispheric Rossby wave tracks. Positive anomalies favor weather types associated with dry conditions, while negative anomalies promote the formation of a quasi-stationary trough over CA, which typically occurs during positive AO conditions.

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Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform

Remote Sensing ◽

10.3390/rs12203430 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3430

Author(s):

Wei Wang ◽

Alim Samat ◽

Yongxiao Ge ◽

Long Ma ◽

Abula Tuheti ◽

...

Keyword(s):

Wind Speed ◽

Central Asia ◽

Wind Erosion ◽

Large Scale ◽

Google Earth ◽

Geospatial Data ◽

Aral Sea ◽

Erosion Modeling ◽

Soil Wind Erosion ◽

Google Earth Engine

A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p < 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m2/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.

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