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Papers
20 resultsShowing papers similar to Identification and Prediction of Crop Waterlogging Risk Areas under the Impact of Climate Change
ClearResponse of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China
This study examined how climate change is altering the matching between precipitation timing and maize water requirements across China's monsoon region. Changes in intra-annual precipitation distribution and extreme weather frequency were found to affect crop water availability, with significant implications for food security.
Flash flood-risk areas zoning using integration of decision-making trial and evaluation laboratory, GIS-based analytic network process and satellite-derived information
Researchers developed a GIS-based decision-making model combining multiple criteria — including topography, vegetation, and soil type — to map flash flood risk zones in Golestan province, Iran, identifying 68 villages and roughly 83,595 residents at elevated risk. The framework provides local authorities with a practical tool for flood disaster planning and risk reduction.
The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed
This paper is not about microplastics; it uses satellite rainfall data, HEC-RAS flood modeling, and artificial neural networks to predict flood inundation heights in the Majalaya Watershed of Indonesia.
Multi-Criteria Analysis Approach for Potential Flood Areas Mapping in The Bedadung River Watershed, Jember Regency
A multi-criteria analysis approach was applied to map potential flood areas in the Bedadung River watershed, Indonesia, integrating spatial data on topography, land use, and drainage to prioritize flood mitigation measures. The study provided a practical tool for flood-prone area identification to inform early warning systems and emergency response planning.
Examining the Adaptation of Agriculture to Climate Change in Africa
This study examines agricultural adaptation strategies being deployed across Africa in response to climate change, focusing on how smallholder farmers and policymakers are responding to shifting precipitation patterns, temperature extremes, and degraded soil conditions.
Enhanced Entropy-Fuzzy Integration Decision Support System for Risk Assessment and Management of Hydraulic Engineering
Researchers developed an Enhanced Entropy-Fuzzy Integration Decision Support System for risk assessment and management of hydraulic engineering projects in the context of climate change and increasingly complex water resource management. The system addresses limitations of traditional probability-based risk methods by incorporating fuzzy logic to handle ambiguous and uncertain risks.
Hydrological modelling: Insights into hydrological signals and contaminant transport
Researchers modeled how future climate-driven changes in hydrological extremes — including floods and droughts — affect contaminant transport in a heavily polluted Scottish catchment, finding that traditional models calibrated on historical data perform poorly when projecting under novel climatic conditions.
Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models
Researchers applied ensemble machine learning techniques to model drought vulnerability across the state of Odisha, India, using 53 vulnerability indicators across 248 drought-prone villages. The M5P-Rotation Forest model achieved the highest accuracy with an AUC of 0.901, and the analysis found that nearly 38% of the study area showed high to very high drought vulnerability. The study demonstrates the potential of ensemble machine learning approaches for mapping regional drought risk to support better resource management.
Assessment of Uncertainties in Ecological Risk Based on the Prediction of Land Use Change and Ecosystem Service Evolution
Using the PLUS land use change model, researchers simulated future land use scenarios in southern China and evaluated how projected changes would alter ecosystem services and associated ecological risk under uncertainty.
Maximum Entropy Method for Wind Farm Site Selection: Implications for River Basin Ecosystems Under Climate Change
Researchers employed the maximum entropy (MaxEnt) spatial modeling method to identify optimal wind farm sites in Turkey, incorporating climate change scenarios and finding that 89% of currently licensed wind energy projects will remain viable in the future while overall wind energy potential is projected to increase.
Identifying hot-spots for microplastic contamination in agricultural soils—a spatial modelling approach for Germany
A spatial model was developed to identify hotspots of microplastic contamination in German agricultural soils based on plastic use in farming, sewage sludge application rates, and atmospheric deposition estimates, predicting that certain intensively farmed regions accumulate substantially more plastic than previously estimated from limited field studies.
Soil Salt and Water Regulation in Saline Agriculture Based on Physical Measures with Model Analysis
This study developed a model-based approach to optimize water and salt regulation in saline agricultural soils in the Yellow River Delta, finding that targeted irrigation management strategies can improve root zone conditions for crops in areas with shallow saline groundwater.
Competency of groundwater recharge of irrigated cotton field subjacent to sowing methods, plastic mulch, water productivity, and yield under climate change
Researchers tested different cotton planting methods with and without plastic mulch films, finding that bed planting without plastic mulch produced the highest yield and water efficiency, while climate models predict groundwater recharge will decline significantly by 2050. The study also highlights that plastic mulch films used in agriculture are a known source of microplastic contamination in soil.
Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration
Researchers used advanced topic modeling and bibliometric analysis to map global research trends in estimating soil organic carbon using remote sensing and machine learning. They identified key research clusters including satellite imagery analysis, deep learning methods, and regional carbon mapping efforts. The study provides a roadmap for future research priorities in monitoring soil carbon stocks, which is critical for understanding climate change.
Farmland Challenges in the Haor Basin of Bangladesh: Nature and Solutions
Researchers used key informant interviews and focus group discussions in the Netrokona Haor basin of Bangladesh to identify agricultural challenges facing farming communities in this flood-prone, economically disadvantaged wetland region. Irrigation system difficulties emerged as the greatest constraint, alongside issues of waterlogging, land tenure, and access to inputs that undermine food security.
Simulation of nutrient management and hydroclimatic effects on coastal water quality and ecological status—The Baltic Himmerfjärden Bay case
Researchers used computer modeling to simulate how different nutrient management scenarios and climate conditions would affect water quality and ecological status in the Baltic Sea's Himmerfjarden Bay. The study provides a tool for coastal managers to evaluate strategies for reducing eutrophication under future climate scenarios.
The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical
Researchers analyzed climate variation and human activity effects on karst spring discharge using wavelet coherence analysis, finding that anthropogenic factors including land-use changes increasingly influence groundwater dynamics alongside natural climate variability.
Evaluation of plateau wetland ecological security and its influencing factors in multi-climatic zones: A case study of Yunnan Province
Not a microplastics paper — this study assesses the ecological security of plateau wetlands across Yunnan Province, China using a pressure-state-response model based on remote sensing data, identifying climate and human activity as key threats to these fragile ecosystems.
From mapping to modelling: the evolving multidimensional microplastic risks in China's farmlands
Researchers combined a national-scale soil survey with machine learning models to map and project microplastic risks across China's farmlands through 2050, finding that agricultural film use, population density, and GDP are key drivers, and that regional risk rankings will shift counter-intuitively depending on which socioeconomic development pathway is followed.
EO-based Rice Mapping Studies in Vietnamese Mekong Delta Compared to Global Context: A Bibliometric Analysis
This bibliometric review analyzes 40 years of Earth observation-based rice mapping research globally and in Vietnam's Mekong Delta, comparing methodologies and technology trends. The study is focused on remote sensing for agricultural monitoring and is not related to microplastic research.