Floods

The flooding of rivers and coastlines is the most frequent and damaging of all natural hazards. Between 1980 and 2016, total direct damages exceeded $1.6 trillion, and at least 225,000 people lost their lives. Recent events causing major economic losses include the 2011 river flooding in Thailand ($40 billion) and the 2013 coastal floods in the United States caused by Hurricane Sandy (over $50 billion). Flooding also triggers great humanitarian challenges. The 2015 Malawi floods were the worst in the country’s history and were followed by food shortage across large parts of the country.

Flood losses are increasing rapidly in some world regions, driven by economic development in floodplains and increases in the frequency of extreme precipitation events and global sea level due to climate change. The largest increase in flood losses is seen in low-income countries, where population growth is rapid and many cities are expanding quickly. At the same time, evidence shows that adaptation to flood risk is already happening, and a large proportion of losses can be contained successfully by effective risk management strategies. Such risk management strategies may include floodplain zoning, construction and maintenance of flood defenses, reforestation of land draining into rivers, and use of early warning systems.

To reduce risk effectively, it is important to know the location and impact of potential floods under current and future social and environmental conditions. In a risk assessment, models can be used to map the flow of water over land after an intense rainfall event or storm surge (the hazard). Modeled for many different potential events, this provides estimates of potential inundation depth in flood-prone areas. Such maps can be constructed for various scenarios of climate change based on specific changes in rainfall, temperature, and sea level.

To assess the impact of the modeled hazard (e.g., cost of damage or lives lost), the potential exposure (including buildings, population, and infrastructure) must be mapped using land-use and population density data and construction information. Population growth and urban expansion can be simulated by increasing the density or extent of the urban area in the model. The effects of floods on people and different types of buildings and infrastructure are determined using a vulnerability function. This indicates the damage expected to occur to a structure or group of people as a function of flood intensity (e.g., inundation depth and flow velocity).

Potential adaptation measures such as land-use change or new flood defenses can be included in the model in order to understand how effective they may be in reducing flood risk. This way, risk assessments can demonstrate the possible approaches available to policymakers to build a less risky future.

Throughout history, flood management practice has evolved in response to flood events. This heuristic approach has yielded some important incremental shifts in both policy and planning (from the need to plan at a catchment scale to the recognition that flooding arises from multiple sources and that defenses, no matter how reliable, fail). Progress, however, has been painfully slow and sporadic, but a new, more strategic, approach is now emerging.

A strategic approach does not, however, simply sustain an acceptable level of flood defence. Strategic Flood Risk Management (SFRM) is an approach that relies upon an adaptable portfolio of measures and policies to deliver outcomes that are socially just (when assessed against egalitarian, utilitarian, and Rawlsian principles), contribute positively to ecosystem services, and promote resilience. In doing so, SFRM offers a practical policy and planning framework to transform our understanding of risk and move toward a flood-resilient society. A strategic approach to flood management involves much more than simply reducing the chance of damage through the provision of “strong” structures and recognizes adaptive management as much more than simply “wait and see.” SFRM is inherently risk based and implemented through a continuous process of review and adaptation that seeks to actively manage future uncertainty, a characteristic that sets it apart from the linear flood defense planning paradigm based upon a more certain view of the future.

In doing so, SFRM accepts there is no silver bullet to flood issues and that people and economies cannot always be protected from flooding. It accepts flooding as an important ecosystem function and that a legitimate ecosystem service is its contribution to flood risk management. Perhaps most importantly, however, SFRM enables the inherent conflicts as well as opportunities that characterize flood management choices to be openly debated, priorities to be set, and difficult investment choices to be made.

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Natural Hazard Science. Please check back later for the full article.

Hindu Kush is a high mountain system located in the immediate west of Karakorum and Himalayas. It is the greatest watershed of River Kabul, River Chitral, River Swat, and River Panjkora in Pakistan and the Amu River in Central Asia. The Hindu Kush system hosts numerous glaciers, snow-clad mountains, and fertile river valleys; it also supports a large population and provides year-round water to replenish streams and rivers. The study region is vulnerable to a wide range of hazards including floods, earthquakes, landslides, drought, and desertification. However, in the Hindu Kush region, riverine and flash floods frequently occur as well as extreme hydro-meteorological events. The upper reaches experience characteristics of flash floods, whereas the lower reaches experience river floods. In the upstream areas, flash floods are sudden and more destructive in nature. Every year in summer, monsoonal rainfall, together with the heavy melting of snow, ice, and glaciers accelerates discharge in rivers. Climate change has a strong relationship with trends in temperature and resultant changes in rainfall pattern and river discharge. In the wake of observed climate change, there is a rising trend in temperature, which indicates the early and rapid melting of snow and glaciers in the catchment areas. The analysis reveals that in the late 20th and early 21st centuries a radical change in behavior of numerous valley glaciers has been noted. Similarly, a fluctuation in the amount of snowfall occurrences together with its timing and seasonality has been recorded. In addition, the spatial and temporal scales of violent weather events have grown during the past thirty years. Such changes in water regimes including the frequent but substantial increase in heavy precipitation events and rapid melting of snow in the headwater region, siltation in active channels, excessive deforestation in the past three decades, human encroachments onto the active flood channel and the bursting of temporary dams have further escalated the flooding events. Analysis reveals that the Hindu Kush region is beyond the reach of existing weather RADAR network and hence flood forecasting and early warning is ineffective. In the study region, almost every year, the floodwater overflows the levees and causes damages to standing crops, infrastructure, sources of livelihood. And worst of all, there are human casualties.

The framework presented herein supports a changing paradigm in the approaches used by coastal researchers, engineers, and social scientists to model the impacts of climate change and sea level rise (SLR) in particular along low-gradient coastal landscapes. Use of a System of Systems (SoS) approach to the coastal dynamics of SLR is encouraged to capture the nonlinear feedbacks and dynamic responses of the bio-geo-physical coastal environment to SLR, while assessing the social, economic, and ecologic impacts. The SoS approach divides the coastal environment into smaller subsystems such as morphology, ecology, and hydrodynamics. Integrated models are used to assess the dynamic responses of subsystems to SLR; these models account for complex interactions and feedbacks among individual systems, which provides a more comprehensive evaluation of the future of the coastal system as a whole. Results from the integrated models can be used to inform economic services valuations, in which economic activity is connected back to bio-geo-physical changes in the environment due to SLR by identifying changes in the coastal subsystems, linking them to the understanding of the economic system and assessing the direct and indirect impacts to the economy. These assessments can be translated from scientific data to application through various stakeholder engagement mechanisms, which provide useful feedback for accountability as well as benchmarks and diagnostic insights for future planning. This allows regional and local coastal managers to create more comprehensive policies to reduce the risks associated with future SLR and enhance coastal resilience.