Soil degradation in the European Mediterranean region: Processes, status and consequences

The Science of The Total Environment, Journal Year: 2021, Volume and Issue: 805, P. 150106 - 150106

Published: Sept. 4, 2021

Soil, a non-renewable resource, sustains life on Earth by supporting around 95% of global food production and providing ecosystem services such as biomass production, filtration contaminants transfer mass energy between spheres. Unsustainable management practices climate change are threatening the natural capital soils, particularly in Mediterranean region, where increasing population, rapid land-use changes, associated socio-economic activities imposing high pressures region's shallow soils. Despite evidence soil susceptibility to degradation desertification, true extent region is unknown. This paper reviews summarises scientific literature relevant official reports, with aim advance this knowledge synthesizing, mapping, identifying gaps regarding status, causes, consequences processes European region. needed underpinning efforts counteract Three main categories then considered: physical (soil sealing, compaction, erosion), chemical organic matter, contamination, salinisation), biological. We find some be relatively well-documented (e.g. while others, loss biodiversity, remain poorly addressed, limited data availability. suggest establishment continuous, harmonised monitoring system at national regional scale provide comparable datasets chart spatial temporal changes degradation, corresponding economic implications. critical support decision-making fulfilment related sustainable development goals.

Language: Английский

---

Deep learning methods for flood mapping: a review of existing applications and future research directions

Hydrology and earth system sciences, Journal Year: 2022, Volume and Issue: 26(16), P. 4345 - 4378

Published: Aug. 25, 2022

Abstract. Deep learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models and improve results traditional methods for mapping. In this paper, we review 58 recent publications outline state art field, identify knowledge gaps, propose future research directions. The focuses on type deep various mapping applications, types considered, spatial scale studied events, data model development. show that based convolutional layers are usually more as they leverage inductive biases better process characteristics flooding events. Models fully connected layers, instead, provide accurate when coupled with other statistical models. showed increased accuracy compared approaches speed methods. While there exist several applications susceptibility, inundation, hazard mapping, work is needed understand how can assist real-time warning during an emergency it be employed estimate risk. A major challenge lies developing generalize unseen case studies. Furthermore, all reviewed their outputs deterministic, limited considerations uncertainties outcomes probabilistic predictions. authors argue these identified gaps addressed by exploiting fundamental advancements or taking inspiration from developments applied areas. graph neural networks operators arbitrarily structured thus should capable generalizing across different studies could account complex interactions natural built environment. Physics-based preserve underlying physical equations resulting reliable speed-up alternatives Similarly, resorting Gaussian processes Bayesian networks.

Language: Английский

---

Hydraulic modelling of inland urban flooding: Recent advances

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127763 - 127763

Published: March 25, 2022

Language: Английский

---

Adapting cities to the surge: A comprehensive review of climate-induced urban flooding

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102123 - 102123

Published: April 9, 2024

Climate change is a serious global issue causing more extreme weather patterns, resulting in frequent and severe events like urban flooding. This review explores the connection between climate flooding, offering statistical, scientific, advanced perspectives. Analyses of precipitation patterns show clear changes, establishing strong link heightened intensity rainfall events. Hydrological modeling case studies provide compelling scientific evidence attributing flooding to climate-induced changes. Urban infrastructure, including transportation networks critical facilities, increasingly vulnerable, worsening impact on people's lives businesses. Examining adaptation strategies, highlights need for resilient planning integration green infrastructure. Additionally, it delves into role technologies, such as artificial intelligence, remote sensing, predictive modeling, improving flood prediction, monitoring, management. The socio-economic implications are discussed, emphasizing unequal vulnerability importance inclusive policies. In conclusion, stresses urgency addressing through holistic analysis statistical trends, evidence, infrastructure vulnerabilities, adaptive measures. technologies comprehensive understanding essential developing effective, strategies. serves valuable resource, insights policymakers, researchers, practitioners striving climate-resilient futures amid escalating impacts.

Language: Английский

---

A System Dynamics Model of Urban Rainstorm and Flood Resilience to Achieve the Sustainable Development Goals

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 96, P. 104631 - 104631

Published: May 5, 2023

Language: Английский

---

Pakistan's 2022 floods: Spatial distribution, causes and future trends from Sentinel-1 SAR observations

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 304, P. 114055 - 114055

Published: Feb. 21, 2024

Language: Английский

---

Toward Explainable Flood Risk Prediction: Integrating A Novel Hybrid Machine Learning Model

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106140 - 106140

Published: Jan. 1, 2025

Language: Английский

---

A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 524 - 524

Published: Feb. 3, 2025

Climate change has led to an increase in global temperature and frequent intense precipitation, resulting a rise severe urban flooding worldwide. This growing threat is exacerbated by rapid urbanization, impervious surface expansion, overwhelmed drainage systems, particularly regions. As becomes more catastrophic causes significant environmental property damage, there urgent need understand address flood susceptibility mitigate future damage. review aims evaluate remote sensing datasets key parameters influencing provide comprehensive overview of the causative factors utilized mapping. also highlights evolution traditional, data-driven, big data, GISs (geographic information systems), machine learning approaches discusses advantages limitations different mapping approaches. By evaluating challenges associated with current practices, this paper offers insights into directions for improving management strategies. Understanding identifying foundation developing effective resilient practices will be beneficial mitigating

Language: Английский

---

Extracting the location of flooding events in urban systems and analyzing the semantic risk using social sensing data

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 603, P. 127053 - 127053

Published: Nov. 6, 2021

Language: Английский

---

Landslide susceptibility assessment in the Himalayan range based along Kasauli – Parwanoo road corridor using weight of evidence, information value, and frequency ratio

Journal of King Saud University - Science, Journal Year: 2021, Volume and Issue: 34(2), P. 101759 - 101759

Published: Dec. 9, 2021

The Himalayan region is prone to landslides. Rainfall-induced slope failure activities in the Indian Himalaya cause considerable damage, posing a serious risk life and property. This study attributes information regarding landslide triggering parameters further delineate susceptibility maps of Himachal Pradesh region. inventory map was created using from field visits, Linear Imaging Self-Scanning Sensor (LISS III), Google Earth. Thereafter, eight causative factors, viz. slope, aspect, curvature, elevation, Landuse Landcover (LULC), soil, lithology, drainage density were performed by employing weight evidence (WOE), value method (IVM) frequency ratio (FR) methods. Using ArcGIS reclassification tool, final zonation (LSZ) categorized into five zones: "very low, medium, high, very high." success rate for WOE, FR, IVM models determined as 76.27%, 78.20%, 76.09% respectively, depicting that FR model based LSZ more accurate. According map, highly susceptible classes case lithology are southeast, concave, TBS, respectively. sparsely vegetated areas landslides than other LULC areas. higher elevations, ranging 1191 1434, 1434–1655, 1655–1876 m, vulnerable compared low elevations. 30–45 45–60, medium class fine loamy soils likely prepared zone can be used future mitigation planning high zones order reduce landslide-related human economic losses.

Language: Английский

---