Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data
Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data
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DOI：
10.1016/j.scitotenv.2015.08.068
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发表时间：
2015-12-15
影响因子：
9.8
通讯作者：
Ward, Philip J.
中科院分区：
文献类型：
--
作者：
Muis, Sanne;Gueneralp, Burak;Ward, Philip J.
准确了解洪水风险及其驱动因素对有效的风险管理至关重要。然而，包括不确定因素在内的详细风险预测很少，特别是在发展中国家。本文提出了一种综合全球尺度洪水灾害和土地变化建模最新进展的方法，该方法能够对国家尺度洪水风险的未来趋势进行概率分析。我们展示了它在印度尼西亚的应用。我们制定了2000年至2030年城市扩张的1000个空间明确预测，这些预测考虑了与人口和经济增长预测相关的不确定性，以及城市土地变化可能发生的不确定性。预测表明，城市范围将增加215%-357%（第5和第95百分位）。爪哇的城市扩张尤其迅速，占全国增长的79%。从2000年到2030年，暴露的增加将使河流和沿海洪水的洪水风险平均增加76%和120%。虽然海平面上升将使暴露引起的趋势进一步增加19%-37%，但河流洪水对气候变化的响应具有高度的不确定性。然而，由于城市扩张是未来风险的主要驱动因素，无论气候预测存在广泛的不确定性，实施适应措施都变得越来越紧迫。通过概率城市预测，我们发现空间规划是一种非常有效的适应策略。我们的研究强调，全球数据可以成功地用于数据稀缺国家的概率风险评估。(C) 2015 Elsevier B.V.版权所有
An accurate understanding of flood risk and its drivers is crucial for effective risk management. Detailed risk projections, including uncertainties, are however rarely available, particularly in developing countries. This paper presents a method that integrates recent advances in global-scale modeling of flood hazard and land change, which enables the probabilistic analysis of future trends in national-scale flood risk. We demonstrate its application to Indonesia. We develop 1000 spatially-explicit projections of urban expansion from 2000 to 2030 that account for uncertainty associated with population and economic growth projections, as well as uncertainty in where urban land change may occur. The projections show that the urban extent increases by 215%-357% (5th and 95th percentiles). Urban expansion is particularly rapid on Java, which accounts for 79% of the national increase. From 2000 to 2030, increases in exposure will elevate flood risk by, on average, 76% and 120% for river and coastal floods. While sea level rise will further increase the exposure-induced trend by 19%-37%, the response of river floods to climate change is highly uncertain. However, as urban expansion is the main driver of future risk, the implementation of adaptation measures is increasingly urgent, regardless of the wide uncertainty in climate projections. Using probabilistic urban projections, we show that spatial planning can be a very effective adaptation strategy. Our study emphasizes that global data can be used successfully for probabilistic risk assessment in data-scarce countries. (C) 2015 Elsevier B.V. All rights reserved.