Spatio-temporal mapping and analysis of soil salinity: An integrated approach through Electromagnetic induction (EMI), multivariate and geostatistical techniques

2021 ◽  
pp. 1-18
Author(s):  
Bhaskar Narjary ◽  
Satyendra Kumar ◽  
Murlidhar Meena ◽  
S.K. Kamra ◽  
D. K. Sharma
2021 ◽  
Vol 25 (3) ◽  
pp. 1509-1527
Author(s):  
Mohammad Farzamian ◽  
Dario Autovino ◽  
Angelo Basile ◽  
Roberto De Mascellis ◽  
Giovanna Dragonetti ◽  
...  

Abstract. Irrigated agriculture is threatened by soil salinity in numerous arid and semi-arid areas of the world, chiefly caused by the use of highly salinity irrigation water, compounded by excessive evapotranspiration. Given this threat, efficient field assessment methods are needed to monitor the dynamics of soil salinity in salt-affected irrigated lands and evaluate the performance of management strategies. In this study, we report on the results of an irrigation experiment with the main objective of evaluating time-lapse inversion of electromagnetic induction (EMI) data and hydrological modelling in field assessment of soil salinity dynamics. Four experimental plots were established and irrigated 12 times during a 2-month period, with water at four different salinity levels (1, 4, 8 and 12 dS m−1) using a drip irrigation system. Time-lapse apparent electrical conductivity (σa) data were collected four times during the experiment period using the CMD Mini-Explorer. Prior to inversion of time-lapse σa data, a numerical experiment was performed by 2D simulations of the water and solute infiltration and redistribution process in synthetic transects, generated by using the statistical distribution of the hydraulic properties in the study area. These simulations gave known spatio-temporal distribution of water contents and solute concentrations and thus of bulk electrical conductivity (σb), which in turn were used to obtain known structures of apparent electrical conductivity, σa. These synthetic distributions were used for a preliminary understanding of how the physical context may influence the EMI-based σa readings carried out in the monitored transects as well as being used to optimize the smoothing parameter to be used in the inversion of σa readings. With this prior information at hand, we inverted the time-lapse field σa data and interpreted the results in terms of concentration distributions over time. The proposed approach, using preliminary hydrological simulations to understand the potential role of the variability of the physical system to be monitored by EMI, may actually allow for a better choice of the inversion parameters and interpretation of EMI readings, thus increasing the potentiality of using the electromagnetic induction technique for rapid and non-invasive investigation of spatio-temporal variability in soil salinity over large areas.


2012 ◽  
Vol 11 (8) ◽  
pp. 1365-1376 ◽  
Author(s):  
Xiao-ming LI ◽  
Jing-song YANG ◽  
Mei-xian LIU ◽  
Guang-ming LIU ◽  
Mei YU

2021 ◽  
Author(s):  
Nima Shokri ◽  
Amirhossein Hassani ◽  
Adisa Azapagic

<p>Population growth and climate change is projected to increase the pressure on land and water resources, especially in arid and semi-arid regions. This pressure is expected to affect all driving mechanisms of soil salinization comprising alteration in soil hydrological balance, sea salt intrusion, wet/dry deposition of wind-born saline aerosols — leading to an increase in soil salinity. Soil salinity influences soil stability, bio-diversity, ecosystem functioning and soil water evaporation (1). It can be a long-term threat to agricultural activities and food security. To devise sustainable action plan investments and policy interventions, it is crucial to know when and where salt-affected soils occur. However, current estimates on spatio-temporal variability of salt-affected soils are majorly localized and future projections in response to climate change are rare. Using Machine Learning (ML) algorithms, we related the available measured soil salinity values (represented by electrical conductivity of the saturated paste soil extract, EC<sub>e</sub>) to some environmental information (or predictors including outputs of Global Circulation Models, soil, crop, topographic, climatic, vegetative, and landscape properties of the sampling locations) to develop a set of data-driven predictive tools to enable the spatio-temporal predictions of soil salinity. The outputs of these tools helped us to estimate the extent and severity of the soil salinity under current and future climatic patterns at different geographical levels and identify the salinization hotspots by the end of the 21<sup>st</sup> century in response to climate change. Our analysis suggests that a soil area of 11.73 Mkm<sup>2</sup> located in non-frigid zones has been salt-affected in at least three-fourths of the 1980 - 2018 period (2). At the country level, Brazil, Peru, Sudan, Colombia, and Namibia were estimated to have the highest rates of annual increase in the total area of soils with an EC<sub>e</sub> ≥ 4 dS m<sup>-1</sup>. Additionally, the results indicate that by the end of the 21<sup>st</sup> century, drylands of South America, southern and Western Australia, Mexico, southwest United States, and South Africa will be the salinization hotspots (compared to the 1961 - 1990 period). The results of this study could inform decision-making and contribute to attaining the United Nation’s Sustainable Development Goals for land and water resources management.</p><p>1. Shokri-Kuehni, S.M.S., Raaijmakers, B., Kurz, T., Or, D., Helmig, R., Shokri, N. (2020). Water Table Depth and Soil Salinization: From Pore-Scale Processes to Field-Scale Responses. Water Resour. Res., 56, e2019WR026707. https://doi.org/ 10.1029/2019WR026707</p><p>2. Hassani, A., Azapagic, A., Shokri, N. (2020). Predicting Long-term Dynamics of Soil Salinity and Sodicity on a Global Scale, Proc. Nat. Acad. Sci., 117, 52, 33017–33027. https://doi.org/10.1073/pnas.2013771117</p>


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