Unraveling the Interactions between Flooding Dynamics and Agricultural Productivity in a Changing Climate

Sustainability, Journal Year: 2024, Volume and Issue: 16(14), P. 6141 - 6141

Published: July 18, 2024

Extreme precipitation and flooding frequency associated with global climate change are expected to increase worldwide, major consequences in floodplains areas susceptible flooding. The purpose of this review was examine the effects events on changes soil properties their agricultural production. Flooding is caused by natural anthropogenic factors, can be amplified interactions between rainfall catchments. impacts structure aggregation altering resistance slaking, which occurs when aggregates not strong enough withstand internal stresses rapid water uptake. disruption enhance erosion sediment transport during contribute sedimentation bodies degradation aquatic ecosystems. Total precipitation, flood discharge, total main factors controlling suspended mineral-associated organic matter, dissolved particulate matter loads. Studies conducted paddy rice cultivation show that flooded reduced conditions neutralize pH but reversible upon draining soil. In soil, nitrogen cycling linked decreases oxygen, accumulation ammonium, volatilization ammonia. Ammonium primary form inorganic porewaters. floodplains, nitrate removal enhanced high denitrification intermittent provides necessary anaerobic conditions. soils, reductive dissolution minerals release phosphorus (P) into solution. Phosphorus mobilized events, leading increased availability first weeks waterlogging, generally time. Rainstorms promote subsurface P-enriched particles, colloidal P account for up 64% tile drainage water. Anaerobic microorganisms prevailing utilize alternate electron acceptors, such as nitrate, sulfate, carbon dioxide, energy production decomposition. metabolism leads fermentation by-products, acids, methane, hydrogen sulfide, influencing pH, redox potential, nutrient availability. Soil enzyme activity presence various microbial groups, including Gram+ Gram− bacteria mycorrhizal fungi, affected Waterlogging β-glucosidase acid phosphomonoesterase increases N-acetyl-β-glucosaminidase Since these enzymes control hydrolysis cellulose, phosphomonoesters, chitin, moisture content impact direction magnitude supply oxygen submerged plants limited because its diffusion extremely low, mitochondrial respiration plant tissues. Fermentation only viable pathway plants, which, under prolonged waterlogging conditions, inefficient results death. Seed germination also impaired stress due decreased sugar phytohormone biosynthesis. sensitivity different crops varies significantly across growth stages. Mitigation adaptation strategies, essential management agriculture, resilience through improved practices, amendments rehabilitation techniques, best zero tillage cover crops, development flood-tolerant crop varieties. Technological advances play a crucial role assessing dynamics landscapes. This embarks comprehensive journey existing research unravel intricate interplay production, environment. We synthesize available knowledge address critical gaps understanding, identify methodological challenges, propose future directions.

Language: Английский

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Flood risk in mountainous settlements: A new framework based on an interpretable NSGA-II-GB from a point-area duality perspective

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 373, P. 123842 - 123842

Published: Jan. 1, 2025

Language: Английский

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Flood susceptibility assessment using deep neural networks and open-source spatial datasets in transboundary river basin

VIETNAM JOURNAL OF EARTH SCIENCES, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

The Mekong Basin is the most critical transboundary river basin in Asia. This provides an abundant source of fresh water essential for development agriculture, domestic consumption, and industry, as well production hydroelectricity, it also contributes to ensuring food security worldwide. region often subject floods that cause significant damage human life, society, economy. However, flood risk management challenges this are increasingly substantial due conflicting objectives between several countries data sharing. study integrates deep learning with optimization algorithms, namely Grasshopper Optimisation Algorithm (GOA), Adam Stochastic Gradient Descent (SGD), open-source datasets identify probably occurring basin, covering Vietnam Cambodia. Various statistical indices, Area Under Curve (AUC), root mean square error (RMSE), absolute (MAE), coefficient determination (R²), were used evaluate susceptibility models. results show proposed models performed AUC values above 0.8, specifying DNN-Adam model achieved 0.98, outperforming DNN-GOA (AUC = 0.89), DNN-SGD 0.87), XGB 0.82. Regions very high concentrated Delta along River findings supporting decision-makers or planners proposing appropriate mitigation strategies, planning policies, particularly watershed.

Language: Английский

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High‐resolution flood probability mapping using generative machine learning with large‐scale synthetic precipitation and inundation data

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Abstract High‐resolution flood probability maps are instrumental for assessing risk but often limited by the availability of historical data. Additionally, producing simulated data needed creating probabilistic using physics‐based models involves significant computation and time effort, which inhibit its feasibility. To address this gap, study introduces Precipitation‐Flood Depth Generative Pipeline, a novel methodology that leverages generative machine learning to generate large‐scale synthetic inundation produce maps. With focus on Harris County, Texas, Pipeline begins with training cell‐wise depth estimator number precipitation‐flood events model model. This estimator, emphasizes precipitation‐based features, outperforms universal models. Subsequently, conditional adversarial network (CTGAN) is used conditionally precipitation point cloud, filtered strategic thresholds align realistic patterns. Hence, feature pool constructed each cell, enabling sampling generation events. After generating 10,000 events, created various depths. Validation similarity correlation metrics confirms accuracy distributions. The provides scalable solution high‐resolution maps, can enhance mitigation planning.

Language: Английский

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Geospatial Approach to Pluvial Flood-Risk and Vulnerability Assessment in Sunyani Municipality

Heliyon, Journal Year: 2024, Volume and Issue: 10(18), P. e38013 - e38013

Published: Sept. 1, 2024

Language: Английский

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From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(20), P. 3902 - 3902

Published: Oct. 20, 2024

Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities investigating flood in rapidly urbanising regions prone extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN), were evaluated for handling complex, nonlinear data using a dataset of 265 urban episodes. An ensemble filter feature selection (EFFS) approach was introduced overcome single-method limitations, optimising factors contributing susceptibility. Additionally, incorporates explainable artificial intelligence (XAI), namely, Shapley Additive exPlanations (SHAP) model, enhance transparency interpretability modelling results. The models’ performance various statistical measures testing dataset. ANN model demonstrated superior performance, outperforming RF SVM. SHAP analysis identified rainwater collectors, land surface temperature (LST), digital elevation (DEM), soil, river buffers, normalized difference vegetation index (NDVI) as contributors susceptibility, making them more understandable actionable stakeholders. findings highlight need tailored management strategies, offering novel forecasting that emphasises predictive power explainability.

Language: Английский

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Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods

International Journal of Disaster Risk Science, Journal Year: 2024, Volume and Issue: 15(5), P. 738 - 753

Published: Oct. 1, 2024

Abstract Floods are widespread and dangerous natural hazards worldwide. It is essential to grasp the causes of floods mitigate their severe effects on people society. The key drivers flood susceptibility in rapidly urbanizing areas can vary depending specific context require further investigation. This research developed an index system comprising 10 indicators associated with factors environments that lead disasters, used machine learning methods assess susceptibility. core urban area Yangtze River Delta served as a case study. Four scenarios depicting separate combined climate change human activity were evaluated using data from various periods, measure spatial variability findings demonstrate extreme gradient boosting model outperformed decision tree, support vector machine, stacked models evaluating Both found act catalysts for flooding region. Areas increasing mainly distributed northwest southeast Taihu Lake. increased caused by significantly larger than those activity, indicating was dominant factor influencing By comparing relationship between susceptibility, rising intensity frequency precipitation well increase impervious surface identified important reasons heightened study emphasized significance formulating adaptive strategies enhance control capabilities cope changing environment.

Language: Английский

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Intelligent Methods for Estimating the Flood Susceptibility in the Danube Delta, Romania

Water, Journal Year: 2024, Volume and Issue: 16(23), P. 3511 - 3511

Published: Dec. 6, 2024

Floods, along with other natural and anthropogenic disasters, profoundly disrupt both society the environment. Populations residing in deltaic regions worldwide are particularly vulnerable to these threats. A prime example is Danube Delta (DD), located Romanian sector of Black Sea. This research paper aims identify areas within DD that highly or very susceptible flooding. To accomplish this, we employed a combination multicriteria decision-making (AHP) artificial intelligence (AI) techniques, including deep learning neural networks (DLNNs), support vector machines (SVMs), multilayer perceptron (MLP). The input data comprised previously flooded alongside eight geographical factors. All models identified high flood potential over 65% studied area. models’ performance was assessed using receiver operating characteristic (ROC) analysis, demonstrating excellent outcomes evaluated by area under curve (AUC) exceeding 0.908. study significant as it lays groundwork for implementing measures against impacts DD.

Language: Английский

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