Estimation of Urban Imperviousness and its Impacts on Flashfloods in Gazipaşa, Turkey

Mehr, Ali Danandeh; Akdeğirmen, Özgün

doi:10.51526/kbes.2021.2.1.9-17

Cited by 20 publications

(5 citation statements)

References 10 publications

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“…Researchers like Torbick and Corbiere [55], Zhou and Wang [56], Xian et al [57], Ghazaryan et al [58], and Li et al [59], among others, have used imperviousness data to evaluate urbanization processes, while, specifically in the Mediterranean, relevant recent research includes the study of urbanization and periurbanization processes [60][61][62][63], built-up pattern analysis [15,64], polycentricity [65], and landscape conservation [66]. Imperviousness is directly related to a set of climate change impacts [67][68][69], changing microclimate conditions [70] and determining the strength of the urban heat island effect, while the sealing of the surface diminishes rainwater infiltration, reduces aquifer recharge [71], and increases water runoff [53] and subsequent soil erosion and flood risk [44].…”

Section: Introductionmentioning

confidence: 99%

Impervious Land Expansion as a Control Parameter for Climate-Resilient Planning on the Mediterranean Coast: Evidence from Greece

Lagarias

2023

Land

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Impervious land expansion is linked to ecosystem fragmentation and degradation, posing threats to nature conservation and multiplying climate change impacts. This is particularly true on the Mediterranean coast, where persistent urbanization is coupled with tourism development, further intensifying coastal erosion, flood risk, heat stress and biodiversity loss, while decreasing carbon sequestration. In this research, high-resolution imperviousness data were analyzed through a geospatial methodology to detect patterns and processes in a heavily burdened Mediterranean coastal area, namely Greece. The methodology was structured on a set of GIS tools, analyzing the distribution of new impervious cover between 2006 and 2018, to evaluate pressures exerted on coastal territories and on the environmental protection network. The results revealed relatively slow rates of impervious land expansion at a nationwide scale, mostly attributed to the economic recession period in Greece. However, certain locations exhibited continuing artificialization of land even within Natura 2000 areas, while future pressures on coastal territories are expected to increase due to the restarting of construction activity and the continuing dynamic of the mass tourism sector. The conclusions imply that controlling for imperviousness is important in order to develop spatial planning policies for climate resilience, which should be decisively enforced in the Mediterranean to prevent a business-as-usual scenario.

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Section: Introductionmentioning

confidence: 99%

Impervious Land Expansion as a Control Parameter for Climate-Resilient Planning on the Mediterranean Coast: Evidence from Greece

Lagarias

2023

Land

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“…Floods are significant natural disasters that often result in extensive property damage and human suffering worldwide [1][2][3][4]. These high-magnitude events can cause massive geomorphic changes and significant alterations to the terrain [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Flood Hazard Monitoring and Impact Assessment on Landscape: Representative Case Study in India

et al. 2023

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Currently, natural hazards are a significant concern as they contribute to increased vulnerability, environmental degradation, and loss of life, among other consequences. Climate change and human activities are key factors that contribute to various natural hazards such as floods, landslides, droughts, and deforestation. Assam state in India experiences annual floods that significantly impact the local environment. In 2022, the flooding affected approximately 1.9 million people and 2930 villages, resulting in the loss of 54 lives. This study utilized the Google Earth Engine (GEE) cloud-computing platform to investigate the extent of flood inundation and deforestation, analyzing pre-flood and post-flood C band Sentinel-1 GRD datasets. Identifying pre- and post-flood areas was conducted using Landsat 8–9 OLI/TIRS datasets and the modified and normalized difference water index (MNDWI). The districts of Cachar, Kokrajhar, Jorhat, Kamrup, and Dhubri were the most affected by floods and deforestation. The 2022 Assam flood encompassed approximately 24,507.27 km2 of vegetation loss and 33,902.49 km2 of flood inundation out of a total area of 78,438 km2. The most affected areas were the riverine regions, the capital city Dispur, Guwahati, southern parts of Assam, and certain eastern regions. Flood hazards exacerbate environmental degradation and deforestation, making satellite-based information crucial for hazard and disaster management solutions. The findings of this research can contribute to raising awareness, planning, and implementing future disaster management strategies to protect both the environment and human life.

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“…Sudden rainstorms further emphasize the need for a comprehensive understanding of primary rainfall patterns (Xie et al, 2023a;Xie et al, 2023b). The impact of urban imperviousness on runoff and flooding dynamics has also emerged as a crucial factor in recent studies (Shukla et al, 2020;Mehr and Akdegirmen, 2021).…”

Section: Introductionmentioning

confidence: 99%

A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting

Wang,

Gao,

Liu

et al. 2023

Front. Environ. Sci.

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Accurate rainfall-runoff modeling is crucial for disaster prevention, mitigation, and water resource management. This study aims to enhance precision and reliability in predicting runoff patterns by integrating physical-based models like HEC-HMS with data-driven models, such as LSTM. We present a novel hybrid model, Ia-LSTM, which combines the strengths of HEC-HMS and LSTM to improve hydrological modeling. By optimizing the “initial loss” (Ia) with HEC-HMS and utilizing LSTM to capture the effective rainfall-runoff relationship, the model achieves a substantial improvement in precision. Tested in the Yufuhe basin in Jinan City, Shandong province, the Ia-LSTM consistently outperforms individual HEC-HMS and LSTM models, achieving notable average Nash-Sutcliffe Efficiency (NSE) values of 0.873 and 0.829, and average R2 values of 0.916 and 0.870 for calibration and validation, respectively. The study shows the potential of integrating physical mechanisms to enhance the efficiency of data-driven rainfall-runoff modeling. The Ia-LSTM model holds promise for more accurate runoff estimation, with wide applications in flood forecasting, water resource management, and infrastructure planning.

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Estimation of Urban Imperviousness and its Impacts on Flashfloods in Gazipaşa, Turkey

Cited by 20 publications

References 10 publications

Impervious Land Expansion as a Control Parameter for Climate-Resilient Planning on the Mediterranean Coast: Evidence from Greece

Impervious Land Expansion as a Control Parameter for Climate-Resilient Planning on the Mediterranean Coast: Evidence from Greece

Large-Scale Flood Hazard Monitoring and Impact Assessment on Landscape: Representative Case Study in India

A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting

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