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遥感技术在水敏感城市设计中的应用

Translated by Dr. Mengyi Jin

引言

随着城市化的快速推进,城市水环境正面临前所未有的挑战 (Chen et al. 2015)。城市地表不透水面的持续扩张削弱了自然雨水循环,导致降雨径流迅速汇集,增加了内涝发生的频率,也降低了雨水对地下水的补给能力。同时,水体污染以及城市热岛效应的加剧,进一步暴露出传统城市规划对水文系统适应性的不足。在气候变化背景下,极端降雨、干旱等事件的发生频率和强度不断上升,这些现象正严峻考验着城市对水资源的调蓄、排涝、净化和生态恢复等能力。

在这个背景下,水敏感城市设计(Water Sensitive Urban Design, WSUD)作为一种新型城市发展模式被广泛关注。与“低影响开发”(Low Impact Development, LID)理念相似, 水敏感城市设计强调模拟自然水循环过程,并提升城市应对水资源相关挑战的韧性。其核心目标在于将水资源视为城市系统中不可或缺的组成部分,通过系统性地管理水的收集、输送、处理与储存过程,实现其生态功能与社会功能的有机融合(Wong 2006)。

Наводнения в Судане - Космический мониторинг наводнений для управления рисками стихийных бедствий

Translated by Marina Agarkova

В 2019 году наводнения стали причиной 43,5% всех смертей в результате стихийных бедствий и, таким образом, представляют собой самый смертоносный тип бедствия с растущим числом событий по сравнению с предыдущими годами (CRED, 2019). Кроме того, наводнения приводят к наибольшему числу пострадавших людей по сравнению с другими бедствиями, поскольку они влияют на деятельность человека и экономику (CRED, 2019; Elagib et al. 2019).

Наводнения в Судане - Космический мониторинг наводнений для управления рисками стихийных бедствий

Translated by Marina Agarkova

В 2019 году наводнения стали причиной 43,5% всех смертей в результате стихийных бедствий и, таким образом, представляют собой самый смертоносный тип бедствия с растущим числом событий по сравнению с предыдущими годами (CRED, 2019). Кроме того, наводнения приводят к наибольшему числу пострадавших людей по сравнению с другими бедствиями, поскольку они влияют на деятельность человека и экономику (CRED, 2019; Elagib et al. 2019).

Remote sensing approaches to detect and manage urban waterlogging

Urban waterlogging is an increasingly critical challenge particularly in cities where unplanned development, climate change, and inadequate drainage systems exacerbate the issue. Remote sensing provides a viable solution for detecting and managing urban waterlogging by providing real time and large-scale monitoring capabilities. Using satellite datasets such as Synthetic Aperture Radar (SAR), multispectral imaging, and thermal sensors urban planners can assess and monitor waterlogging. These datasets coupled with advanced algorithms like machine learning models allow for accurate predictions of waterlogged areas in the cities. Besides, the integration of these remote sensing tools with Geographic Information Systems (GIS) enhances the ability to manage water resources and develop sustainable urban infrastructures. Despite challenges such as data resolution and high costs of advanced imagery, remote sensing remains a key tool in addressing the socio-economic and environmental impacts of urban waterlogging, particularly in the face of climate change and urbanization.

Наводнения в Судане - Космический мониторинг наводнений для управления рисками стихийных бедствий

Translated by Marina Agarkova

В 2019 году наводнения стали причиной 43,5% всех смертей в результате стихийных бедствий и, таким образом, представляют собой самый смертоносный тип бедствия с растущим числом событий по сравнению с предыдущими годами (CRED, 2019). Кроме того, наводнения приводят к наибольшему числу пострадавших людей по сравнению с другими бедствиями, поскольку они влияют на деятельность человека и экономику (CRED, 2019; Elagib et al. 2019).

Using remote sensing to support water-sensitive urban design

Introduction

With the rapid advancement of urbanization, urban water environments are facing unprecedented challenges (Chen et al. 2015). The continuous expansion of impervious surfaces has disrupted the natural water cycle, resulting in rapid stormwater runoff, increased frequency of urban flooding, and reduced groundwater recharge. At the same time, worsening water pollution and the intensifying urban heat island effect further highlight the limitations of traditional urban planning and design in adapting to hydrological systems.

Local Perspectives Case Studies

Hydrometeorological disasters in the Indian Himalayas

Flash flood in Uttarakhand, India
Hydrometeorological disasters (HMDs) in the Hindu Kush Himalayan (HKH) area have led to multiple water-related issues that resulted from extreme rainfall, glacial melt, and changing river flows, all of which are made worse by climate change and land use changes. Accurate warnings of these disasters are difficult due to sparse gauging and rugged topography in the Garhwal Himalaya region, which increases the likelihood of disasters during the monsoon. The same region experiences water shortage and drought especially during non-monsoon periods. The use of wide coverage remote sensing data from the study region as well as from neighboring countries with access to space-based data can play a significant role in the monitoring and analysing of these challenges. This study applies spatiotemporal clustering and multi-criteria decision-making (MCDM) to map high-risk zones, which will allow policymakers to reinforce infrastructure providing disaster resilience. There is a need for a solution that uses multi-criteria decision making (MCDM) and spatiotemporal clustering to map areas in Uttarakhand, Himalaya, that are prone to disasters with the help of satellite-based data. To determine which tehsils (smaller administrative units) are vulnerable, it is suggested to examine more than 150 years of recorded disaster data with location and fatalities. Further vulnerable regions can be mapped using high-resolution satellite data (procured through Sentinel, Landsat, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Tropical Rainfall Measuring Mission (TRMM)) and analysed in the QGIS platform. This solution could use spatiotemporal clustering and MCDM to map high-risk zones, which will allow policymakers to reinforce infrastructure providing disaster resilience. Data of the Garhwal Himalayan region (India), which lies in the Hindu Kush Himalayan (HKH) region are needed. The topography of the HKH region is almost the same over eight countries, and all bear similar kinds of disasters and climate patterns. The Garhwal region occupies about 64 per cent of the area of the Uttarakhand state and is also the origin of the river Ganga.

Space-based Solution

Flood modeling for melting glacier - discontinued

Assessment of the challenge

  • Need more data about the location of the community and their usage of water
  • Split the challenge into a “glacier” and a down-stream challenge
  • No up-to-date weather data available since 2011
  • Discharge and temperature, rainfall and snow data available
  • Digital elevation surface and terrain model available

Outline steps to a solution & status

  1. Inventory of the snow cover and watershed area (completed)
  2. Build a regression model using historical data to assess the relationship

Spatiotemporal analysis of hydrometeorological disasters in the Indian Himalayas: integrating space-based techniques for enhanced disaster resilience - in development

The historical disasters of the study region, the Garhwal Himalaya, were collected, and the types of hydrometeorological disasters (HMD) were tabulated with location, attribute, morbidity, and extent from 1803 to 2025. The Garhwal region has been divided into 58 tehsils (sub-administrative regions). For analysing past HMDs and to map Multi-Hazard Susceptibility Zonation on the tehsil level, QGIS, Google Earth Engine, satellite data, k-means clustering, and AHP techniques were used.