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Item type:Publication, Access status: without files , Climate change and primary soil salinization : a global scale perspective for the 21st centuryPublicationtype: Conference Paper not in Proceedings45 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Global predictions of primary soil salinization under changing climate in the 21st centurySoil salinization has become one of the major environmental and socioeconomic issues globally and this is expected to be exacerbated further with projected climatic change. Determining how climate change influences the dynamics of naturally-occurring soil salinization has scarcely been addressed due to highly complex processes influencing salinization. This paper sets out to address this long-standing challenge by developing data-driven models capable of predicting primary (naturally-occurring) soil salinity and its variations in the world’s drylands up to the year 2100 under changing climate. Analysis of the future predictions made here identifies the dryland areas of South America, southern and western Australia, Mexico, southwest United States, and South Africa as the salinization hotspots. Conversely, we project a decrease in the soil salinity of the drylands in the northwest United States, the Horn of Africa, Eastern Europe, Turkmenistan, and west Kazakhstan in response to climate change over the same period.Publicationtype: Journal ArticleScopus© Citations 222 76 339 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Soil salinization under different climate change scenarios: a global scale analysis(2021-04); ; 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, ECe) 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 21st century in response to climate change. Our analysis suggests that a soil area of 11.73 Mkm2 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 ECe = 4 dS m-1. Additionally, the results indicate that by the end of the 21st 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.Publicationtype: Conference Paper70 259 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Predicting long-term dynamics of soil salinity and sodicity on a global scale(2020-12-14); ; 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.Publicationtype: Journal ArticleScopus© Citations 198 148 420 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Global-Scale Prediction of Soil Salinization under Different Climate Scenarios Using an Advanced Machine-Learning Technique(2020-12); ; Soil salinization, referring to high accumulation of soluble salts in the soil, is one of the serious land degrading threats, which can undesirably affect the soil resilience, fertility, stability, and biodiversity. Developing predictive and quantitative tools to identify the locations of saline soils and their spatio-temporal variability are crucial for devising sustainable land and water management plans. However, previous studies primarily focused on the past or current trends in spatio-temporal variability of the soil salinity. A comprehensive investigation of the future trends of soil salinization, specifically in the face of future climate uncertainty, is rare partly due to the complexity of the processes influencing soil salinization at multiple scales, which motivated the present research. In this work, we used soil profiles data, a set of purely spatial predictors stacked from the soil and land topographic properties, and a set of spatio-temporal predictors derived from the output of available Global Circulation Models (GCMs) in the Fifth and Sixth Phases of the Coupled Model Inter-comparison Projects (CMIP5 and CMIP6; Eyring et al., 2016; Taylor et al., 2012) to develop a predictive model for projecting the response of the primary (naturally occurring) soil salinity to climate change by the end of 21th century. We linked the experimentally measured soil salinity information obtained from the input soil profiles data and predictors' values (auxiliary data used for prediction of the soil salinity) through application of the tree-based Machine Learning algorithms to predict the soil electrical conductivity (ECe) as a representative of the soil salinity at different locations, times and soil depths. Overall, nearly 72% of the variability of measured ECe could be described by the prediction of the trained models (10-fold cross-validation R2 = 72.49%). The developed models were then applied to new projected spatio-temporal predictor data obtained from the output of GCMs to annually predict the average of soil ECe to the depth of one meter from the surface between 1905 and 2100 for the drylands of the world (lands with an aridity index ≤ 0.65). By comparison of the 1971 - 2100 and 1961- 1990 periods and under different greenhouse gas concentration trajectories (RCP 4.5, RCP 8.5, SSP 2-4.5, and SSP 5-8.5), our model predicts that the dryland areas of South America, southern and Western Australia, Mexico, southwest United States, and South Africa and to a lesser extent, drylands of Spain, Morocco, and northern Algeria will be the salinization hotspots in response to the key climatic drivers of primary salinization used here. On the other hand, we predict a reduction in the soil salinity of the drylands spread across the northwest United States, the Horn of Africa, Eastern Europe, Turkmenistan, and west Kazakhstan, relative to the reference period (1961 - 1990). The predictive model developed in the present study can inform land and water resources managers and policy makers to distinguish the salinisation hotspots with the priority of the required human intervention, devising viable plans for mitigation of the negative consequences of salinization, and putting those plans in action toward sustainable use of terrestrial ecosystems.Publicationtype: Conference Proceedings32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Global scale prediction of long-term variations of soil salinity and sodicity.(2020-08); ; Publicationtype: Conference Paper25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Spatio-temporal variability of global soil salinization delineated by advanced machine learning algorithms(2020-05); ; Publicationtype: Conference Presentation31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Desiccation crisis of saline lakes: a new decision-support framework for building resilience to climate change(2019-10-19); ; ; ; River flow reductions as a result of agricultural withdrawals and climate change are rapidly desiccating endorheic lakes, increasing their salinity and affecting the bio-diversity and human wellbeing in the surrounding areas. Here we present a new framework to guide eco-hydrological restoration of saline lakes and build their resilience to climate change by optimizing agricultural land use and related water withdrawals. The framework involves four steps: 1. selection of global circulation models for the basin under study; 2. establishment of a hydrological balance over the lake's area to estimate the amount of water required for its restoration; 3. water allocation modeling to determine the water available for restoration and allocation of the remaining water across different users in the lake's basin; and 4. basin-scale optimization of land use and cropping patterns subject to water availability. We illustrated the general applicability of the framework through the case of the second largest (by volume) hyper-saline lake globally, Lake Urmia, which lost 96% of its volume in only 20 years, primarily as a result of upstream water withdrawals. Through the application of the framework, we estimated the amount of water needed to restore the lake, either fully or partially, and proposed a sustainable land-use strategy, while protect farmers’ income in the basin. Considering future climate change projections under two representative concentration pathways (RCP) 4.5 and 8.5, we found that an average annual surface inflow of 3,648 Mm3 (∼70% increase in RCP 4.5) and 3,692 Mm3 (∼73% increase in RCP 8.5) would be required to restore the lake by 2050, respectively. This would require the respective conversion of 95,600 ha and 133,687 ha of irrigated land to rain-fed cropland or grassland across the basin by 2050. The proposed framework can be used for building resilience to climate change and mitigating human-induced threats to other declining saline lakes.Publicationtype: Journal ArticleScopus© Citations 36 166 256 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Declining saline lakes: Building resilience to climate changePublicationtype: Conference Presentation15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 37