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Studies examining the joint interactions and impacts of social-environmental system (SES) drivers on vegetation dynamics in Central Asia are scarce. We investigated seasonal trends and anomalies in drivers and their impacts on ecosystem structure and function (ESF). We explored the response of net primary production (NPP), evapotranspiration (ET) and normalized difference vegetation index (NDVI) to various SES drivers − climate, human influence, heat stress, water storage, and water content − and their latent relationships in Kazakhstan. We employed 13 predictor drivers from 2000 to 2016 to identify the interactions and impacts on ESF variables that reflect vegetation growth and productivity. We developed 12 models with different predictor–response variable combinations and separated them into two approaches. First, we considered the winter percent snow cover (SNOWc) and spring rainfall (P_MAM) as drivers and then as moderators in a structural equation model. SNOWc variability (SNOWcSD) as an SEM moderator exhibited superior model accuracy and explained the interactions between various predictor–response combinations. Winter SNOWcSD did not have a strong direct positive influence on summer vegetation growth and productivity; however, it was an important moderator between human influence and the ESF variables. Spring rainfall had a stronger impact on ESF variability than summer rainfall. We also found strong positive feedback between soil moisture (SM) and NDVI, as well as a strong positive influence of vegetation optical depth (VOD) and terrestrial water storage (TWS) on ESF. Livestock density exhibited a strong negative influence on ESF. Our results also showed a strong positive influence of socioeconomic drivers, including crop yield per hectare (CROPh), gross domestic product per capita (GDPca), and population density (POPD) on vegetation productivity. Finally, we found that vegetation dynamics were more sensitive to SM, VOD, LSKD and POPD than climatic drivers, suggesting that water content and human influence drivers were more critical in Kazakhstan.

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The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all other Machine Learning Models (MLMs) and two dataset integration combinations. The merged NBR product exhibited improvements in terms of continuous and categorical statistics at all temporal scales as well as in all climatic zones. Our results indicate that the SPEM2L procedure could be successfully used in any other region or basin that has a poor gauging network or where a single precipitation product performance is ineffective.

The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs. Post classification change detection, validation and accuracy assessment were executed after the preparation of the LC maps. Three evaluation indices, namely overall accuracy (OA), Kappa coefficient, and F1 score from confusion matrix reports, were calculated for all the points used for validation purposes. We have obtained an OA of 0.8792 and 0.875 for RF and 0.8926 and 0.8603 for XGBoost at the 95% confidence level for 2010 and 2020 LC maps, which are better for mountainous terrain. The applied methodology could be significant in utilizing the big earth observation data and overcoming the traditional computational challenges using GEE. In addition, the quantification of changes over time would be helpful for decision-makers to understand current environmental dynamics in the study area.