Soil Salinity Development Research Articles

The osmotic potential of soil solutions in salt tolerance studies: Following M. Th. van Genuchten's innovation

AbstractThe osmotic potential in soil solutions decreases as salinity increases, and plants cannot take up enough soil water. Therefore, the osmotic potential of soil solutions can serve as an important metric of plant growth conditions in regions affected by soil salinization. Measurements of osmotic potential are labor and time consuming. This work aimed to determine more readily available soil salinity metrics to estimate the osmotic potential in soil solutions. A model to compute the osmotic potential from soil solution composition was developed and validated with data from the U.S. states of Washington, Oregon, Colorado, and Idaho. The mean relative error was 7%. Then, this model was applied to 230 datasets on soil solutions from various salinity‐affected regions of Eurasia. The correlation coefficient between logarithms of concentration of highly soluble (not including carbonates and sulfates of calcium and magnesium) in soil solutions at saturation and logarithms of osmotic potential values was above 0.99. The concentration of highly soluble salts in soil solution at saturation was chosen as the predictor of the osmotic potential. It was used to develop nomograms for evaluating the salinity‐related yield loss for major field crops, vegetables, and fruits. This work is a part of the Vadose Zone Journal tribute to the scientific legacy of Martinus van Genuchten, who championed the use of the osmotic potential for better quantification of crop salt tolerance at the macroscale and provided invaluable contributions to modeling soil salinity development and mitigation as a part of the global struggle for food security.

Global mapping of soil salinity change

Soil salinity increase is a serious and global threat to agricultural production. The only database that currently provides soil salinity data with global coverage is the Harmonized World Soil Database, but it has several limitations when it comes to soil salinity assessment. Therefore, a new assessment is required. We hypothesized that combining soil properties maps with thermal infrared imagery and a large set of field observations within a machine learning framework will yield a global soil salinity map. The thermal infrared imagery acts as a dynamic variable and allows us to characterize the soil salinity change. For this purpose we used Google Earth Engine computational environment. The random forest classifier was trained using 7 soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. In total, six maps were produced for 1986, 2000, 2002, 2005, 2009, 2016. The validation accuracy of the resulting maps was in the range of 67–70%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Comparison with 3 studies investigating local trends of soil salinity change showed that our assessment was in correspondence with 2 of these studies. The global map of soil salinity change between 1986 and 2016 was produced to characterize the spatial distribution of the change. We conclude that combining soil properties maps and thermal infrared imagery allows mapping of soil salinity development in space and time on a global scale.

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

Soil salinity development in the yellow river delta in relation to groundwater dynamics

AbstractThe Yellow River Delta occupies an important position in the global ecosystem because of its valuable wetland habitat resources for migratory birds on the Eastern Pacific migration route. However, it has suffered from severe land degradation because of soil salinization. This paper assesses the distribution maps of saline soils during the past two decades, using field observations at three points in time using remote sensing images for the same periods, in combination with spatial models. Soil salinization appears to have expanded from the coastline to inland areas of the Yelow River Delta at a surprising speed during that period. The spatio‐temporal dynamics of the groundwater table and total dissolved solids (TDS) during the last 20 years were analyzed using maps based on Kriging interpolation. Kriging helped substantially to improve the accurateness of the predicted values of soil salt content, using a random subsample of the observation points as validation basis. Correlation analysis of the spatial data revealed that the distribution and evolution of saline soils are closely related to the dynamics of groundwater: the aggravation of soil salinization is associated with a rising groundwater table and increasing TDS. Insufficient recharge of the groundwater with fresh surface water due to reduced Yellow River discharge and subsequent seawater intrusion are therefore serious environmental problems in the Yellow River Delta ecosystem. Copyright © 2011 John Wiley & Sons, Ltd.

Assessment of alternative water management options for irrigated agriculture

A simulation study on alternative water management strategies was carried out for Sirsa Irrigation Circle in Haryana, covering an area of about 4800 km 2. Results showed that crop evapotranspiration and soil salinity development under reduction in canal water supply and increase in groundwater use, are largely influenced by the amount and distribution of rainfall. Reduction in canal water supply by 25% during the rainy season is unlikely to have any adverse effect on the salinity development in the study area. Reduction in crop evapotranspiration due to decreased canal water supply can partly be compensated by the increase in groundwater use. Leaching of salts due to monsoon rains in the study area shows that groundwater of even relatively poor quality can be used for irrigation without excessive long-term build up of soil salinity under deep groundwater depth conditions. However, increased groundwater extraction without associated actions will not be very effective to solve the problem of rising groundwater levels.

Chemistry of Groundwater of Al-Ahsa Oasis Eastern Region Saudi Arabia and Its Predictive Effects on Soil Properties

Saudi Arabia is an arid and the largest country in the middle east with a total land area of 2.253 x 10(6) km2. Recent urban and rural expansion has shown manifold increases in water use in various sectors. Water resources are limited and non-renewable coupled with unpredicted scanty rainfall. In order to meet the rising water needs, evaluation of water quality is important for allocation to various uses. A total of 101 well water samples were collected from Al-Ahsa Oasis. Water samples were analyzed for total salt concentration, pH, Ca, Mg, Na, K, HCO3, Cl, SO4, NO3, F and B contents. Soil Salinity Development (SSD), adjusted sodium adsorption ratio (adj.SAR), adjusted sodium adsorption ratio (adj. R(Na)) and Exchangeable Sodium Percentage (ESP) were calculated. The EC of groundwater ranged between 1.23 and 5.05 dS m(-1). Sodium was the most abundant cation followed by Ca, Mg and K in descending order. Chloride was the most abundant anion followed by SO4 and HCO3 in groundwater of Al-Ahsa Oasis. A significant correlation was found between Na and Cl (R2 = 0.936). Thermodynamics calculation revealed that an appreciable amount of Ca and Mg is associated with Cl and SO4 ions. The SAR and ESP values are within the permissible limits according to Ayers and Westcot, 1985. The NO3 concentration is within safe limits for drinking purpose according to WHO (1998) standards. The Saturation Indices (SI) indicated that groundwater is under-saturated (negative SI) with respect to certain minerals (for example: calcite, dolomite, gypsum, anhydrite, halite, pyrite, fluorite and aragonite) and oversaturated (positive SI) with respect to some other minerals (For example: Goethite, Siderite and hematite). The negative saturation index (SI) reveals that most of minerals are in un-saturated state and will dissolve more Ca and Mg into the soil solution after irrigation. A good relationship exists between Cl and other ions (Na, Ca and Mg) as well as between SO4 and Ca and Mg ion of groundwater. The salinity and sodicity hazards of groundwater of Al-Ahsa Oasis were classified as C3S1 and C4S2 i.e., high salinity with medium sodicity problems. The predicted soil salinity suggested application of 15-20% leaching requirements to keep soil salinity within permissible limits. Cultivation of slight to moderate salt and sodium tolerant crops is recommended for optimal agricultural production and efficient water use.

Sustaining crop production in saline groundwater areas: A case study from Pakistani Punjab

In the Indus Basin of Pakistan, multi-strainer shallow tubewells often called 'skimming wells' are used to extract groundwater from thin fresh lenses underlain by saline groundwater. Most of these wells face problems such as deteriorating water quality and reduction in discharge due to inadequate design and poor operational and management strategies. This paper evaluates the current practices of farmers in the Chaj doab area of Pakistani Punjab and suggests improvements in design and operation of skimming wells to ensure long-term sustainability of irrigated agriculture in the area. The effect of existing design and operation of skimming wells on pumped groundwater quality was evaluated using MODFLOW. To study the long-term effects of skimmed groundwater use on crop production and soil salinity development, the soil water flow and solute transport model SWAP was applied. The results revealed that farmers could reduce the number of strainers from 16 to 6 without reducing the anticipated discharges. For the conditions considered, the maximum discharge of skimming wells should be 4–8 L/s and they should not be operated for more than 2–4 h per day. Increasing discharge rate or daily operational hours can disturb the interface between fresh and saline groundwater resulting in reduced quality pumped groundwater. Weekly operational schedules together with recommended discharge rate and operational hours will be the best strategy to use skimmed groundwater for achieving optimal crop yields while maintaining root-zone salinity within acceptable limits. To avoid aquifer degradation, skimming wells should be used for supplemental irrigation rather than full irrigation of crops. Due to low discharge rates, skimming wells cannot be used to irrigate crops through surface irrigation methods. Therefore, pressurised irrigation methods should be used. The results also suggest that continuation of present irrigation practices could lead to serious problems of land and aquifer degradation. Therefore, farmers need to adjust their irrigation and leaching requirements annually considering crop evapotranspiration, precipitation, and salinity status of soils.

Metal chemistry of irrigation and drainage waters of Al-Ahsa Oasis of Saudi Arabia and its effects on soil properties

Water samples from 18 springs and 13 drainage canals and subcanals were collected from Al-Ahsa Oasis. Concentrations of total dissolved salts, Cl, SO4 HCO3, B, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, V, Ti, and Zn were determined. The soil salinity development (SSD), the adjusted Na ratio (adj. SAR), the adjusted Na adsorption ratio (adj. RNa), and exchangeable Na percentage (ESP) were calculated. Sodium was the most abundant cation in all water samples followed by Ca, Mg, Sr, and K in descending order. Concentrations of all other metals were below 0.1 mg L−1. Chloride was the most abundant anion, followed by SO4 and HCO3 in these waters. A significant correlation (P 16 meq L−1, Mg was doubled, and Na and Cl increased several times in the drainage waters. The salinity and sodicity hazards of the spring and drainage waters were classified as C4S2 i.e. high salinity with medium sodicity problems.

Field-testing of models which identify soils susceptible to salinity development

The use of a geographic information system (GIS) and automated cartographic techniques in conjunction with multiple linear regression models resulted in final map products, that accurately delineated areas having low, medium and high potentials for the development of salinity. The vector representations of the spatial data which delineated the three salinization potentials are the result of a weighted overlay of each of four soil factors believed to influence soil salinity development on irrigated soils of the arid western United States. Three multiple regression models were formulated to determine salinization potentials from four soil salinization factors: soil permeability, depth to the groundwater, groundwater quality and leaching fraction. Each model differed in its functional form in order to define a different interrelationship between the soil salinization factors. The regression models were developed from a statistical analysis of soil salinity data for a study area of 170 square miles within the Wellton-Mohawk Irrigation District of Arizona. Data for the four regressor variables were compiled for the time period 1968–1973. The best fit to the estimation data set and best predictive quality to the validation data set were for the model which indicated that salinity development in the top 61 cm (24 inches) of the soil profile is directly related to the electrical conductivity of the groundwater, inversely related to the soil permeability and inversely related to the leaching fraction, all in a linear manner: 18.03 × (1/(soil permeability in cm/h)−0.40 × (1/(leaching fraction)) +1.32 × (electrical conductivity of the groundwater in dS/m) +2.12 = final summation value; the final summation value is classified into a low, medium or high salinization potential category. A close analysis of the three models showed that irrigation management is probably the most dominant factor in the development of salinity in the Wellton-Mohawk Irrigation District for the time period studied, 1968–1973. Predicted salinization potentials for all three models were verified against measured salinities taken from a salinity traverse conducted in November of 1973. Verification results indicated a reasonably high agreement between predicted soil salinization potentials and potentials derived from measured field salinities. Approximately 86% of the salinization potentials were correctly forecast through the use of the best of the three regression models. A regression analysis of measured and predicted salinity values for the best model produced a slope and γ-intercept of 1.000 and −0.001, respectively, with an R 2 of 0.81.