Case Study: River Tees

River valley in the uk – river tees.

The River Tees is located in northeast England. The River Tees flows from the Pennine Hills to the North Sea.

Illustrative background for Landforms of erosion

Landforms of erosion

  • The High Force is a 20 m high waterfall.
  • The High Force was formed by the erosion of limestone (less resistant) which lies underneath a layer of dolerite (more resistant).

Illustrative background for Landforms of deposition

Landforms of deposition

  • Landforms of deposition are found in the lower course of the River Tees.
  • Floodplains.

1 Geography Skills

1.1 Mapping

1.1.1 Map Making

1.1.2 OS Maps

1.1.3 Grid References

1.1.4 Contour Lines

1.1.5 Symbols, Scale and Distance

1.1.6 Directions on Maps

1.1.7 Describing Routes

1.1.8 Map Projections

1.1.9 Aerial & Satellite Images

1.1.10 Using Maps to Make Decisions

1.2 Geographical Information Systems

1.2.1 Geographical Information Systems

1.2.2 How do Geographical Information Systems Work?

1.2.3 Using Geographical Information Systems

1.2.4 End of Topic Test - Geography Skills

2 Geology of the UK

2.1 The UK's Rocks

2.1.1 The UK's Main Rock Types

2.1.2 The UK's Landscape

2.1.3 Using Rocks

2.1.4 Weathering

2.2 Case Study: The Peak District

2.2.1 The Peak District

2.2.2 Limestone Landforms

2.2.3 Quarrying

3 Geography of the World

3.1 Geography of America & Europe

3.1.1 North America

3.1.2 South America

3.1.3 Europe

3.1.4 The European Union

3.1.5 The Continents

3.1.6 The Oceans

3.1.7 Longitude

3.1.8 Latitude

3.1.9 End of Topic Test - Geography of the World

4 Development

4.1 Development

4.1.1 Classifying Development

4.1.3 Evaluation of GDP

4.1.4 The Human Development Index

4.1.5 Population Structure

4.1.6 Developing Countries

4.1.7 Emerging Countries

4.1.8 Developed Countries

4.1.9 Comparing Development

4.2 Uneven Development

4.2.1 Consequences of Uneven Development

4.2.2 Physical Factors Affecting Development

4.2.3 Historic Factors Affecting Development

4.2.4 Human & Social Factors Affecting Development

4.2.5 Breaking Out of the Poverty Cycle

4.3 Case Study: Democratic Republic of Congo

4.3.1 The DRC: An Overview

4.3.2 Political & Social Factors Affecting Development

4.3.3 Environmental Factors Affecting the DRC

4.3.4 The DRC: Aid

4.3.5 The Pros & Cons of Aid in DRC

4.3.6 Top-Down vs Bottom-Up in DRC

4.3.7 The DRC: Comparison with the UK

4.3.8 The DRC: Against Malaria Foundation

4.4 Case Study: Nigeria

4.4.1 The Importance & Development of Nigeria

4.4.2 Nigeria's Relationships with the Rest of the World

4.4.3 Urban Growth in Lagos

4.4.4 Population Growth in Lagos

4.4.5 Factors influencing Nigeria's Growth

4.4.6 Nigeria: Comparison with the UK

5 Weather & Climate

5.1 Weather

5.1.1 Weather & Climate

5.1.2 Components of Weather

5.1.3 Temperature

5.1.4 Sunshine, Humidity & Air Pressure

5.1.5 Cloud Cover

5.1.6 Precipitation

5.1.7 Convectional Precipitation

5.1.8 Frontal Precipitation

5.1.9 Relief or Orographic Precipitation

5.1.10 Wind

5.1.11 Extreme Wind

5.1.12 Recording the Weather

5.1.13 Extreme Weather

5.2 Climate

5.2.1 Climate of the British Isles

5.2.2 Comparing Weather & Climate London

5.2.3 Climate of the Tropical Rainforest

5.2.4 End of Topic Test - Weather & Climate

5.3 Tropical Storms

5.3.1 Formation of Tropical Storms

5.3.2 Features of Tropical Storms

5.3.3 The Structure of Tropical Storms

5.3.4 Tropical Storms Case Study: Katrina Effects

5.3.5 Tropical Storms Case Study: Katrina Responses

6 The World of Work

6.1 Tourism

6.1.1 Landscapes

6.1.2 The Growth of Tourism

6.1.3 Benefits of Tourism

6.1.4 Economic Costs of Tourism

6.1.5 Social, Cultural & Environmental Costs of Tourism

6.1.6 Tourism Case Study: Blackpool

6.1.7 Ecotourism

6.1.8 Tourism Case Study: Kenya

7 Natural Resources

7.1.1 What are Rocks?

7.1.2 Types of Rock

7.1.4 The Rock Cycle - Weathering

7.1.5 The Rock Cycle - Erosion

7.1.6 What is Soil?

7.1.7 Soil Profiles

7.1.8 Water

7.1.9 Global Water Demand

7.2 Fossil Fuels

7.2.1 Introduction to Fossil Fuels

7.2.2 Fossil Fuels

7.2.3 The Global Energy Supply

7.2.5 What is Peak Oil?

7.2.6 End of Topic Test - Natural Resources

8.1 River Processes & Landforms

8.1.1 Overview of Rivers

8.1.2 The Bradshaw Model

8.1.3 Erosion

8.1.4 Sediment Transport

8.1.5 River Deposition

8.1.6 River Profiles: Long Profiles

8.1.7 River Profiles: Cross Profiles

8.1.8 Waterfalls & Gorges

8.1.9 Interlocking Spurs

8.1.10 Meanders

8.1.11 Floodplains

8.1.12 Levees

8.1.13 Case Study: River Tees

8.2 Rivers & Flooding

8.2.1 Flood Risk Factors

8.2.2 Flood Management: Hard Engineering

8.2.3 Flood Management: Soft Engineering

8.2.4 Flooding Case Study: Boscastle

8.2.5 Flooding Case Study: Consequences of Boscastle

8.2.6 Flooding Case Study: Responses to Boscastle

8.2.7 Flooding Case Study: Bangladesh

8.2.8 End of Topic Test - Rivers

8.2.9 Rivers Case Study: The Nile

8.2.10 Rivers Case Study: The Mississippi

9.1 Formation of Coastal Landforms

9.1.1 Weathering

9.1.2 Erosion

9.1.3 Headlands & Bays

9.1.4 Caves, Arches & Stacks

9.1.5 Wave-Cut Platforms & Cliffs

9.1.6 Waves

9.1.7 Longshore Drift

9.1.8 Coastal Deposition

9.1.9 Spits, Bars & Sand Dunes

9.2 Coast Management

9.2.1 Management Strategies for Coastal Erosion

9.2.2 Case Study: The Holderness Coast

9.2.3 Case Study: Lyme Regis

9.2.4 End of Topic Test - Coasts

10 Glaciers

10.1 Overview of Glaciers & How They Work

10.1.1 Distribution of Glaciers

10.1.2 Types of Glaciers

10.1.3 The Last Ice Age

10.1.4 Formation & Movement of Glaciers

10.1.5 Shaping of Landscapes by Glaciers

10.1.6 Glacial Landforms Created by Erosion

10.1.7 Glacial Till & Outwash Plain

10.1.8 Moraines

10.1.9 Drumlins & Erratics

10.1.10 End of Topic Tests - Glaciers

10.1.11 Tourism in Glacial Landscapes

10.1.12 Strategies for Coping with Tourists

10.1.13 Case Study - Lake District: Tourism

10.1.14 Case Study - Lake District: Management

11 Tectonics

11.1 Continental Drift & Plate Tectonics

11.1.1 The Theory of Plate Tectonics

11.1.2 The Structure of the Earth

11.1.3 Tectonic Plates

11.1.4 Plate Margins

11.2 Volcanoes

11.2.1 Volcanoes & Their Products

11.2.2 The Development of Volcanoes

11.2.3 Living Near Volcanoes

11.3 Earthquakes

11.3.1 Overview of Earthquakes

11.3.2 Consequences of Earthquakes

11.3.3 Case Study: Christchurch, New Zealand Earthquake

11.4 Tsunamis

11.4.1 Formation of Tsunamis

11.4.2 Case Study: Japan 2010 Tsunami

11.5 Managing the Risk of Volcanoes & Earthquakes

11.5.1 Coping With Earthquakes & Volcanoes

11.5.2 End of Topic Test - Tectonics

12 Climate Change

12.1 The Causes & Consequences of Climate Change

12.1.1 Evidence for Climate Change

12.1.2 Natural Causes of Climate Change

12.1.3 Human Causes of Climate Change

12.1.4 The Greenhouse Effect

12.1.5 Effects of Climate Change on the Environment

12.1.6 Effects of Climate Change on People

12.1.7 Climate Change Predictions

12.1.8 Uncertainty About Future Climate Change

12.1.9 Mitigating Against Climate Change

12.1.10 Adapting to Climate Change

12.1.11 Case Study: Bangladesh

13 Global Population & Inequality

13.1 Global Populations

13.1.1 World Population

13.1.2 Population Structure

13.1.3 Ageing Populations

13.1.4 Youthful Populations

13.1.5 Population Control

13.1.6 Mexico to USA Migration

13.1.7 End of Topic Test - Development & Population

14 Urbanisation

14.1 Urbanisation

14.1.1 Rural Characterisitcs

14.1.2 Urban Characteristics

14.1.3 Urbanisation Growth

14.1.4 The Land Use Model

14.1.5 Rural-Urban Pull Factors

14.1.6 Rural-Urban Push Factors

14.1.7 The Impacts of Migration

14.1.8 Challenges of Urban Areas in Developed Countries

14.1.9 Challenges of Urban Areas in Developing Countries

14.1.10 Urban Sustainability

14.1.11 Case Study: China's Urbanisation

14.1.12 Major UK Cities

14.1.13 Urbanisation in the UK

14.1.14 End of Topic Test- Urbanisation

14.1.15 End of Topic Test - Urban Issues

15 Ecosystems

15.1 The Major Biomes

15.1.1 Distribution of Major Biomes

15.1.2 What Affects the Distribution of Biomes?

15.1.3 Biome Features: Tropical Forests

15.1.4 Biome Features: Temperate Forests

15.1.5 Biome Features: Tundra

15.1.6 Biome Features: Deserts

15.1.7 Biome Features: Tropical Grasslands

15.1.8 Biome Features: Temperate Grasslands

15.2 Case Study: The Amazon Rainforest

15.2.1 Interdependence of Rainforest Ecosystems

15.2.2 Nutrient Cycling in Tropical Rainforests

15.2.3 Deforestation in the Amazon

15.2.4 Impacts of Deforestation in the Amazon

15.2.5 Protecting the Amazon

15.2.6 Adaptations of Plants to Rainforests

15.2.7 Adaptations of Animals to Rainforests

16 Life in an Emerging Country

16.1 Case Studies

16.1.1 Mumbai: Opportunities

16.1.2 Mumbai: Challenges

17 Analysis of Africa

17.1 Africa

17.1.1 Desert Biomes in Africa

17.1.2 The Semi-Desert Biome

17.1.3 The Savanna Biome

17.1.4 Overview of Tropical Rainforests

17.1.5 Colonisation History

17.1.6 Population Distribution in Africa

17.1.7 Economic Resources in Africa

17.1.8 Urbanisation in Africa

17.1.9 Africa's Location

17.1.10 Physical Geography of Africa

17.1.11 Desertification in Africa

17.1.12 Reducing the Risk of Desertification

17.1.13 Case Study: The Sahara Desert - Opportunities

17.1.14 Case Study: The Sahara Desert - Development

18 Analysis of India

18.1 India - Physical Geography

18.1.1 Geographical Location of India

18.1.2 Physical Geography of India

18.1.3 India's Climate

18.1.4 Natural Disasters in India

18.1.5 Case Study: The Thar Desert

18.1.6 Case Study: The Thar Desert - Challenges

18.2 India - Human Geography

18.2.1 Population Distribution in India

18.2.2 Urabinsation in India

18.2.3 The History of India

18.2.4 Economic Resources in India

19 Analysis of the Middle East

19.1 The Middle East

19.1.1 Physical Geography of the Middle East

19.1.2 Human Geography of the Middle East

19.1.3 Climate Zones in the Middle East

19.1.4 Climate Comparison with the UK

19.1.5 Oil & Natural Gas in the Middle East

19.1.6 Water in the Middle East

19.1.7 Population of the Middle East

19.1.8 Development Case Studies: The UAE

19.1.9 Development Case Studies: Yemen

19.1.10 Supporting Development in Yemen

19.1.11 Connection to the UK

19.1.12 Importance of Oil

19.1.13 Oil & Tourism in the UAE

20 Analysis of Bangladesh

20.1 Bangladesh Physical Geography

20.1.1 Location of Bangladesh

20.1.2 Climate of Bangladesh

20.1.3 Rivers in Bangladesh

20.1.4 Flooding in Bangladesh

20.2 Bangladesh Human Geography

20.2.1 Population Structure in Bangladesh

20.2.2 Urbanisation in Bangladesh

20.2.3 Bangladesh's Economy

20.2.4 Energy & Sustainability in Bangladesh

21 Analysis of Russia

21.1 Russia's Physical Geography

21.1.1 Russia's Climate

21.1.2 Russia's Landscape

21.2 Russia's Human Geography

21.2.1 Population of Russia

21.2.2 Russia's Economy

21.2.3 Energy & Sustainability in Russia

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Flood Risk Factors


Risks and coping strategies in the floodplain economic belt amid drought–flood challenges and climate change.

He Dong,

  • 1 Centre of Ecology, Environment and Sustainable Management, Capital University of Economics and Business, Beijing, China
  • 2 Heilongjiang Water Resources and Hydropower Research Institute, Harbin, China
  • 3 School of Water Conservancy and Electric Power Heilongjiang University, Harbin, China

In this study, a coupla risk combinations and coping strategies have been developed for confronting conflicts between population–economy development and water resource management (PEWM) due to population–industry transformation (PIT) into a floodplain of the economic belt under climate change. A location-entropy-based PVAR (Panel Vector Autoregression) model coupla-risk analysis (LPCR) can be introduced into PEWM to reflect the adverse effects of population–industry transformation on a special function area (e.g., floodplain) of the economic belt, where the coupla risk map has been addressed. Meanwhile, an adaptive scenario analysis-based stochastic–fuzzy method (ASSF) can be joined to deal with multiple uncertainties and their interactions due to subjective and artificial factors. The proposed LPCR and ASSF can be integrated into a risk-based stochastic–fuzzy scenario method framework (RASF) to apply for a practical PEWM case study of the Yongding River floodplain in the context of the coordinative development of the Beijing–Tianjin–Hebei economic belt, China. The results can be presented as follows: 1) the PIT was accelerated into the floodplain due to the fact that it can require more water resources, where each additional unit can promote the regional gross domestic product (GDP) by 0.019 units, increasing the water demand by 0.046 units. It can strengthen various risks such as water shortage, soil loss, and flood control investment in a floodplain of the economic belt; 2) the farmland returning to wetland (CFW) can decrease the water demand and shortage ratio (would be 0% at highest) for irrigation in the long run, but this would reduce the direct economic income of irrigation in the short run; 3) backward irrigative schemes and low water utilization efficiency require a cleaner production mode, which could reduce water shortages by 77.23 × 10^3 m 3 at the highest; and 4) the combined policy (S12) can reduce coupla risks (including the risk of water shortage, soil loss, and flood) compared to other individual policies (S2, S4, S6, and S8). The findings can assist local decision-makers to gain insights into adjusting interegional strategies not only for remitting population–resource stress in core cities but also for improving the resilience against drought and flood in a floodplain area, which is conducive of the special function of the floodplain to support an integrated sustainable development of the economic belt.

1 Introduction

The development of urbanization agglomeration based on new function design for a reginal collaborated goal can accelerate industrial relayout and population movement in an economic belt, which has accelerated populations and industries concentrated in core areas (such as key cities) and also radiation-driving population and economy into the specialized function areas (such as flood storage areas) surrounding central cities ( White et al., 2007 ; Halkos et al., 2016 ). It could cause siphonic effects (such as urban disease, resource crisis, and environmental pressure) for core areas and surrounding special function areas (such as the floodplain area surrounding key cities in an economic belt), which would increase the drought–flood challenge under climatic changes. On the one hand, excessive human activities due to population–industry transformation (PIT) into the floodplain from the core cities need more resource supply (e.g., water or land), which causes great pressure due to limited natural capacity. On the other hand, the urbanization process can disturb the surface circulation characteristics of precipitation and destroy soil storage capacity, which would damage the source function of the floodplain. In addition, climate change can enhance population–economy exposure by the drought–flood risk due to extreme events ( Yu et al., 2016 ; Kumar et al., 2017 ; Li et al., 2019 ; Park et al., 2023 ). All the above issues can potentially increase the risk of disaster, drought, and loss of flood control investment. Therefore, how to identify the anthroponatural influence on the floodplain due to PIT and climate changes is an important issue in the sustainable development of the economic belt.

Previously, various researchers have focused on the effects of excessive human activities and climate changes in some special function location areas. For example, Prokop and Ploskonka (2014) analyzed historical reports, combined topographic maps, and satellite images to study the effect of excessive human activities on land use and soil properties. Zhang et al. (2020) used ecological risk assessment methods to reflect the adverse influence of high population density and urban expansion on ecological properties, land use structure, and environment in Chinese coastal areas. Guo et al. (2023) used Pearson’s correlation and a fixed-effects model to analyze the impact of climate variations and unreasonable human activities on grassland productivity. In general, previous research has focused on the simulation method, risk assessment, and statistical analysis model for reflecting drought and flood stress due to population–economy development, which indicated that population–economy development would cause a great challenge for population–economy development and water resource management (PEWM). Therefore, a number of research works have displayed effective ways (e.g., optimization method, adaptive management mode, and policy analysis) to balance the relationship between population–economy development and water resource management. For instance, Viljoen and Booysen (2006) introduced a strategic approach to flood damage assessment and control, integrated into the disaster management policy framework, with the aim of addressing the risks arising from large-scale urbanization and population growth enabling large numbers of people settling and living in urban areas and their surrounding floodplain areas. Zhang and Song (2014) proposed a site-specific model combined with nine scenarios to optimize the spatial distribution and functional areas so as to maximize the risk of high population density and large economic outputs and improve the benefits while improving the ecologic environment. Zeng et al. (2021) designed a wetland reallocation (WR) plan to remit conflict among human activities, flood control, and ecological protection. Previous research presents various engineering methods (such as withdrawn farmland project, wetland construction, and water diversion) and policy regulations to confront the negative effects of excessive human activities.

However, a number of uncertainties and their interactions in a PEWM issue can enhance the difficulty of generating effective and adaptive policies in a floodplain of the economic belt. For instance, PIT into the floodplain can generate new water use structures, leading to changed water demands. Meanwhile, the process of urbanization can have destructive effects on the floodplain, which would reduce the played function, leading to flood risks and water shortage. Under climate change, random precipitation (such as extreme rainfall), regarded as stochastic factors, can drive floods and droughts, which would result in net system benefit fluctuation due to spatial and temporal variations. Based on these situations, multiple uncertainties and their interactions can enhance the complexity of PEWM in a floodplain area of the economic belt, which requires more robust methods. Therefore, two-stage stochastic programing (TSP) has been joined to build a linkage between changed water need (due to population–industry transformation) and random water flow on a floodplain of the economic belt, which can generate a comprehensive plan based on recourse actions if the pre-regulated targets are violated ( Zeng et al., 2019 ; Nematian, 2023 ). However, TSP cannot handle fuzziness in response to limited data (such as data error and deficiency). Thus, fuzzy programing (FP) can be introduced to increase the expression of ambiguity ( Huang and Loucks, 2000 ; Inuiguchi, 2012 ; Zeng, et al., 2021 ; Nematian, 2023 ). In addition, a scenario analysis is embedded to reflect the potential future outcomes with the aim of supporting the generation of effective and adaptive policies ( Peterson, et al., 2003 ; Swarta, et al., 2004 ; Kumar et al., 2016 ; Kumar et al., 2017 ; Liu et al., 2023 ). Nevertheless, few research works have proposed hybrid methods (e.g., TDSP, FCP, and SA) into a framework to deal with multiple uncertainties for confronting adverse effects of excessive human activities and climate change on a floodplain of the economic belt.

Therefore, the objective of this study is developing a coupla risk combinations and coping strategies for confronting conflicts between PEWM due to population–industry transformation of an economic belt under climate change. A coupla risk-based stochastic–fuzzy scenario method framework (RASF) has been proposed to reflect anthroponatural impacts and optimize the relationship of PEWM under uncertainties, which integrates a location-entropy-based PVRA model coupla-risk analysis (LPCR) and an adaptive scenario analysis-based stochastic–fuzzy method (ASSF) into the RASF framework. Among them, RASF can be introduced to reflect the adverse effects of PIT and climatic changes in a human–water system (HWS), where coupla risks have been shown in the risk maps of the floodplain in the processes of urban agglomeration. Meanwhile, the ASSF is transformed into the RASF to deal with multiple uncertainties. The proposed RASF is applied to a practical case study of the Yongding floodplain, China. The results were obtained to contemporarily reflect the negative effects of PIT in the context of the coordination of Beijing–Tianjin–Hebei urban agglomeration and corresponding damages, function degradation, and its risks. Meanwhile, the results obtained under various policy scenarios (such as farmland returning to wetland (CFW), improvement in water resource utilization efficiency, water diversion, and flood control) can be designed to recover the source function of the floodplain, which is effective in adjusting current population–economy strategies and water management patterns in a risk-averse and sustainable manner.

2 Materials and methods

2.1 case study.

The Beijing–Tianjin–Hebei economic belt (BTHEB) is located in the heart of the Bohai Sea, which is the largest and most vigorous region in northern China. It entered the high-speed development channel from 2015 due to the implementation of the Beijing–Tianjin–Hebei coordinated development strategy ( Wang, 2023 ). The new function design of each cities based on a comprehensive function for a cross-reginal collaborated goal can accelerate industrial relayout and population movement. Central cities can distribute labor-intensive industries to backward areas for optimizing their industrial structure and release resource–environment stress, but this would cause new ecological challenges for some backward areas. In particular, in some special function location areas (e.g., stored floodwater district) of backward areas, excessive human activities due to the mitigation of the population and industry can damage the source function to reduce the resource–environment-carrying capacity, leading to various negative effects. For instance, the Yongding River floodplain in Hebei Province has undertaken the responsibility of protecting the flood control safety of Beijing, Tianjin, and Hebei areas (as shown in Figure 1 ). In the past half century, it has always been considered an effective measure to enhance the flood storage capacity of water conservancy projects and reduce the flood disaster in the Beijing–Tianjin–Hebei (BTH) region. Meanwhile, since it is seated in the semi-arid climate zone and warm temperate zone, the weather condition is suitable for irrigation. Thus, more land resources have been exploited as irrigative land in recent decades, which can support the pillar position of agriculture in Hebei Province.

Figure 1 . Study area.

However, in the context of the coordination of the BTHEB, a number of backward industries have been transferred to Hebei Province due to the new function design of the BTHEB. Under these situations, more populations can be driven by industrial transformation, which would have negative impacts on the Yongding River floodplain ( Men, et al., 2017 ): 1) driven population due to industrial transformation into the floodplain can change water demand scales and structures, which would not only enhance water stress in dry seasons but also accelerate excessive water utilization and exploitation, leading to the reduction in ecological water and water resource conservation capacity; 2) dense population–economic distribution in the function of flood storage and detention areas can increase the exposure risk of drought and flood, leading to potential disaster and losses in flood control investments. Meanwhile, it is quite difficult to move out populations and disperse related industries, which would intensify the destruction of soil storage and storage capacity; 3) excessive water-resource exploitation and irrational irrigative schemes can accelerate soil erosion as the rain is washed away, which would not only result in land function generation but also lead to increased water shortages; and 4) the randomness of rainfall and available water resources due to climate change can increase the difficulty of water resource management. Therefore, identifying the negative effects and corresponding risks due to PIT would be an important step to remit conflict between the human–water interaction in the Yongding River floodplain; meanwhile, various coping strategies (such as withdrawn farmland project, wetland construction, and water diversion) and uncertain programming should be designed to improve decision-making in a risk-averse and robust manner.

2.2 Method development

In this study, a RASF has been developed to reflect the anthroponatural impact and optimize a PEWM issue in the context of the coordinative development of the BTHEB (as shown in Figure 2 ).

Figure 2 . Framework of coupla risk-based stochastic–fuzzy scenario method (RASF) for optimizing a population–economy development and water resource management (PEWM) issue.

In this RASF, the tendency of PIT and mitigation based on the aggregation extent of the population industry can be reflected by the location entropy method according to the coordination of the BTHEB. Location entropy is generally used to measure the spatial distribution of factors in a region and the status and role of a region in the country. Among them, the industrial location entropy value can be modulated as shown in Eq. 1 .

where E denotes the industrial location entropy value; I denotes the industrial output value; I Z denotes the industrial output value in the study area; G D P denotes the gross domestic product; and G D P Z denotes the gross domestic product in the study area.

The population location entropy value can be modulated as shown in Eq. 2 .

where M denotes the population location entropy value; P denotes the population size; P Z denotes the population size in the study area; A R E A denotes the regional area; and A R E A Z denotes the regional area in the study area.

Then, the effects on water resources due to PIT can be responded by the PVRA model. Under these situations, water-need scales and use patterns can be changed by PIT, which would consume more water resources, damage the capacity of soil conservation, and increase potential flood loss, enhancing various risk levels in the long term ( Johannes and Leeuwen, 2017 ). Therefore, a risk assessment combining the geographic information system (RG) can be used for reflecting various risks (including water and soil erosion, flooding risk, and water shortage), where the soil loss risk can be calculated by the empirical model Revised Universal Soil Loss Equation (RUSLE) according to the land use in the Yongding River basin as shown in Eq. 3 .

where A denotes the annual soil loss; R denotes the rainfall erosivity factor; K denotes the soil erosion factor; L denotes the slope length; S denotes the slope factor; C denotes the ground cover management; and P denotes the soil and water conservation measure factor. Meanwhile, flood risk can be measured by precipitation, topography, surface water resources, and land use types as shown in Eq. 4 .

where F is the flood risk value; R is the rainfall erosivity factor; SW is the amount of surface water resources; and θ is the roughness coefficient of the underlying surface ( Liu et al., 2011 ). In addition, the risk of water shortage can be analyzed on water use patterns and land use types.

However, the above individual risk regarded as uncertainties can be influenced by both objective and subjective factors; meanwhile, the correlation among various risks would increase their complexity. In general, the traditional risk calculation system cannot deal with these complexities, which require an effective way. Thus, a coupla function can be introduced to measure the dependence of multidimensional variables (or indicators), as shown in Figure 3 . Based on coupla risk analysis, a comprehensive plan with policy scenarios should be designed to confront the drought–flood challenge due to PIT into a floodplain of the economic belt. However, in the conflicts between PEWM, multiple uncertainties (such as random precipitation due to climate change, changed water utilization due to PIT, dynamic policy due to the coordination of the BTH belt, and fuzzy engineering effects and benefits) can enhance the complexity of the PEWM issue. Thus, an ASSF can be proposed for handling uncertainties (as shown in Supplementary Appendix SA1 ).

Figure 3 . Risk identification based on the coupla function.

2.3 Modeling formulation

In the study region, decision-makers in a floodplain of the economic belt strive to allocate water resources to meet increasing water needs due to PIT in the dry season; meanwhile, they propose to control flood damage and risk in the flooding season. Under the background of coordination strategies in the BTHEB, the expected water demand was increasing because PIT may exceed what the natural system can afford in the dry season, leading to water shortage losses. Meanwhile, excessive and irrational water use patterns (such as irrigative schemes) may damage the water/soil conservation capacity of the floodplain, which would result in indirect environmental penalties. Moreover, flood event occurrence can cause economic loss in the flooding season. To cope with these problems, an optimal PEWM model with various scenarios (such as withdrawn farmland project, wetland construction and water diversion, population–economy policy, and water management pattern) can be designed, which could not only recover the source function of the floodplain but also maximize benefits with the consideration of risks as shown in Eq. 5 .

In Eq. 5, f ∼ , deemed as the total system benefit, can reflect the tradeoff between population–economy development (benefits) and adverse effects (losses) based on PEWM in the no-flooding/flooding season within a floodplain area of the BTHEB (¥ RMB). Among them, corresponding benefits (including E B S H t , E B S I t , E B S A t , E B S E t , B I W E t , C I W R t , B I W R t , and B B S E t ) and losses (including L B S A t and L F F t ) are explained in “ Supplementary Appendix SA2. ” p o s m is the probability of each scenario occurrence; A m n ( P M t n j b ) is the overall performance of various policy scenarios, which means that the expected target will be obtained with different attitudes. The detailed nomenclatures for the subscript and notation are shown in “ Supplementary Appendix SA3. ” Meanwhile, various policy scenarios can be designed to remit the conflict between PEWM. Moreover, the ASSF can be proposed for handling multiple uncertainties. Under these situations, a number of constraints associated with population development scales, available land resources, water availability, irrigative production scales, livestock breeding scales, and the capacities of technique improvement are given in “ Supplementary Appendix SA2. ”

2.4 Data acquirement

The data contain both natural and economic aspects, which include human living, agriculture, industry, service, landscape, and wetland. Among them, since random rainfall can impact available water resources, five water availability levels (i.e., very low, low, medium, high, and very high levels) are considered, of which the corresponding probabilities would be 0.1, 0.2, 0.4, 0.2, and 0.1 simulated by previous precipitation from 2005 to 2018 (SYH, 2006–2016; WRH, 2006–2016). Meanwhile, the economic data (as shown in Table 1 ) can be calculated by the expert evaluation method according to regional statistical yearbooks (SYH, 2006–2016; WRH, 2006–2016) as fuzzy sets.

Table 1 . Fuzzy sets of economic data.

In addition, in order to reduce water shortage in the dry season and flood risk in the flooding season, various policy scenarios can be designed as follows: 1) scenario 0 (S0) is the basic scenario presenting current population–industry development and water resource management; 2) various individual policies (including improvement in water resource utilization efficiency, water diversion, and farmland returning to wetland) can be considered. Among them, scenarios 1–4 (S1–S4) display the policies with the improvement in water-use efficiency by prompting water saving and recycling ratios (0%, 5%, and 15%). Scenarios 5–6 (S5–S6) show that farmland returning to wetland, where the constructed wetland would be 2 and 4 times the area under S0. Scenarios 7–8 (S7–S8) present policies associated with water diversion to remit water shortages; and 3) scenarios 9–12 present mixed policies based on S1–S8.

3.1 Adverse effects and risks of floodplain due to population–industry transformation under S0

3.1.1 adverse effects due to population–industry transformation.

In order to reflect the adverse impact of PIT on specific functional areas (such as the floodplain), the location entropy method can be used for reflecting the aggregation extent of the population and industry in the given location, which would indicate the tendency of PIT and mitigation based on the strategy of the BTHEB. The results (as shown in Figure 4 ) present that the location entropy of agriculture in Beijing and Tianjin decreases continuously, but the agriculture in Hebei is better than that in Beijing and Tianjin due to the function of the BTHEB. Meanwhile, the location entropy of the industry in Tianjin and Hebei provinces would increase, but it would decrease in Beijing, which indicated the industrial transformation from Beijing to Tianjin and Hebei provinces. Moreover, the location entropy of the tertiary industry in Beijing is still much higher than that in Hebei and Tianjin. Based on industrial transformation, the population concentration in Beijing and Tianjin would be higher than that in Hebei Province from 2002 to 2014. However, the entropy of population in Hebei Province was increased from 2015, which indicates that the driven effect of industrial transformation would increase the population concentration in Hebei Province.

Figure 4 . Location entropy of population–industry transformation in the Beijing–Tianjin–Hebei economic belt (BTHEB) from 2002 to 2019.

Under the situations of PIT, the effects of PIT on water resource management in the Yongding River floodplain of Hebei Province were tested by the PVRA model. Table 2 displays the stability of the data by the ADF test, which indicated that all the indicators would be stationary time-series data, meeting the preconditions of model estimation, impulse response, and variance decomposition, where C, T, and P, represent whether there is a constant term, a trend term, and a lag and the lag order, respectively. Meanwhile, according to the test results of AIC, SBIC, and HQIC under different lag orders, when each variable lags for three periods, it is consistent with the result that AIC, SBIC, and HQIC have the smallest amount of information. Therefore, the optimal lag period of the model is determined as three periods.

Table 2 . Test data stability results by the ADF test.

Table 3 shows that industrial transformation into the floodplain can promote the gross regional product (GDP) and average GDP per person significantly at the level of 5% and also increase the water demand at the level of 1%. Each additional unit can promote the regional GDP by 0.019 units and increase the water demand by 0.046 units, which should drive the improvement in water-saving technology. However, service transformation has a positive impact on regional economic growth but increases the water demand than the industry.

Table 3 . Empirical test results of the impact of population–industry transition on water resources.

3.1.2 Risks due to population–industry transformation into a floodplain under S0

Since PIT into a floodplain would increase the water demand for various water sectors (such as daily life, industrial use, and agriculture), it causes great stress on water resources, leading to corresponding risks. Figure 5 presents the risks of water shortage, soil loss, and loss of flood control based on the current population–industry scale in period 1 under S0 when α is 0.6. Based on the basic scenario (under S0), water shortage would occur since the expected water demand for the current population–industry scale cannot be satisfied in dry seasons when α is 0.6 and vice versa . Among various sectors, the irrigation has the highest water shortage, which indirectly influences the water availability in the landscape and wetland protection. For example, the water-deficit area for irrigation would be 9.36 × 10 3  ha when water flow is low. Nevertheless, excessive human activities would increase the risk of flood in flooding seasons.

Figure 5 . Risks of water shortage, soil loss, and flood control based on the current population–industry scale in period 1 under scenario 0 (S0) when α is 0.6.

3.2 Various coping strategy scenarios

3.2.1 water shortages and allocations under individual policy scenarios (s1–s8).

In order to reduce water shortage, technique improvement (water saving and recycling) can be designed under S1–S4, which can present the total water shortage among various water use sectors when α is 0.6 (as shown in Figure 6 ). The results show that improvements in water saving and recycling could prompt water utilization efficiencies, which would reduce water shortages, particularly in the wet season. For example, the water shortages would be 77.23 × 10 3  m 3 and 48.34 × 10 3  m 3 under S1 and S4, respectively, when water flow is low. Although technique improvement can be deemed as an effective method to reduce water shortage, the high cost of generalization would be a big challenge for policymakers on the Yongding River floodplain of the BTHEB.

Figure 6 . Total water shortage under various water levels among various industrial sectors with the consideration of technique improvement (S1–S4) when α is 0.6.

The CFW project can be deemed as an effective method to encourage the improvement in source functions (i.e., flood control, remit shortage, and mitigation of pollution) in a floodplain area, which can remit pressure due to overdeveloped irrigation. Figure 7 displays water shortages and corresponding shortage ratios between irrigation and wetland with CFW (S5–S6) when α is between 0.6 and 0.99. In this region, CFW can decrease the water demand and shortage ratio for irrigation in the long run, but this requires water resources to guard CFW. Meanwhile, CFW can prompt the risk control in the flooding season, but this would reduce the direct economic income of irrigation in the short run. Therefore, how to balance the tradeoff between short-term losses for output reduction in irrigation and long-term benefits for flood damage reduction can be an important issue for policymakers in floodplains.

Figure 7 . Water shortage and corresponding shortage ratios between irrigation and wetland with the consideration of withdrawn farmland to wetland (S5–S6) when α is between 0.6 and 0.99.

Figure 8 displays optimal water allocations with the consideration of water diversion (S7–S8) when α is between 0.6 and 0.9. In the comparison of various water use sectors, water allocation in irrigation can be highest to indicate its key role in the study region. However, the backward irrigative scheme can be deemed as an obstacle, which results in higher water shortage in the dry season. Water diversion (e.g., south–north water transfer project) can solve water deficiency to improve the satisfaction rate of optimal water allocation. For example, the optimal water allocation would be 315.43 × 10 3  m 3 and 378.25 × 10 3  m 3 under S7 and S8, respectively, when water flow is low in period 1, which would be better than the situation under S1. In addition, several α levels can reflect various credibility satisfaction levels by different combinations of fuzzy sets, which indicated that a lower credibility satisfaction level would correspond to higher water availability, leading to higher water allocation and vice versa .

Figure 8 . Optimal water allocations with the consideration of water diversion (S7–S8) when α is between 0.6 and 0.9.

3.2.2 Water shortages and floods under combined scenarios (S0–S12)

In the study region, the concurrence of water deficiency due to PIT into the floodplain in the dry season and flood risk due to functional degeneration of the floodplain would increase the difficulty of decision-making. Thus, water shortage and flood control (WSFC) can be taken into account at the same time for policymakers; meanwhile, mixed policy implementation (including the improvement in water resource utilization efficiency, water diversion, and farmland returning to wetland) can be considered in the the WSFC issue (as shown in Figure 9 ). Figure 9 shows water shortages and flood controls under S0–S12 when α is between 0.6 and 0.9. The results display that mixed policies (such as S10 and S12) have a better effect than the individual policies (S0, S2, S4, S6, and S8) in water shortage. Meanwhile, S6 has a good result in flood control, but it has little ability to reduce water shortages.

Figure 9 . Water shortages and floods under S0–S12 when α is between 0.6 and 0.9.

3.2.3 Coupla risks of water shortage, soil loss, and flood control under various scenarios (S0–S12)

Figure 10 shows coupla risks of water shortage, soil loss, and flood control under S0–S12 when α is 0.6. Among them, S2 and S4 (technique improvement) have the advantage of risk control in water shortage instead of soil loss and flood control. Meanwhile, S8 (water diversion) has the highest risk-control level for water shortages. Moreover, S6 (withdrawn farmland to wetland) is effective in reducing risks of soil loss and flood control, but it requires more water resources to increase water-shortage risks. In comparison, the combined policy (S12) can reduce coupla risks (including risks of water shortage, soil loss, and flood) than other individual policies (S2, S4, S6, and S8).

Figure 10 . Coupla risks of water shortage, soil loss, and flood under S0–S12 when α is 0.6.

3.3 System benefit under S0–S12

Figure 11 displays system benefits under S1–S12 when α is between 0.6 and 0.9. The current population–industry scale due to PIT into the floodplain can increase the loss of water in response to limited water resource-carrying capacity, which would lead to a lower system benefit under S1. The current technical level can only support water-saving and recycling techniques increased by 9% at highest (under S3). Although CFW can bring about a higher benefit in a long run, lower direct economic incomes from wetland construction would generate lower benefits in the short run (S5 and S6). Since the cost of water diversion is relative low for floodplain in the context of the south-to-north water diversion project funded by the state, it can remit the losses of water shortage for current population economy pattern in the mass, which would bring about higher benefits (S7 and S8). A comprehensive combined policy (including improvement of technique, CFW, water diversion) would bring about better results for water shortage reduction, which can lead higher benefits (S9–S12).

Figure 11 . System benefits under S1–S12 when α is between 0.6 and 0.9.

4 Discussion

In this study, an ASSF has been developed for planning a PEWM issue under various risks from artificial and natural uncertainties, which has the following advantages: 1) a location-entropy-based PVRA model can assess adverse effects on water resources in the floodplain due to PIT in the process of urban agglomeration; 2) coupla-risk analysis can combine various types of risks (including water shortage, soil loss, and flood control) as coupla risk maps; 3) an ASSF can deal with water resource planning problems resulting from randomness in water flow that is expressed in a probabilistic way due to data deficiencies; 4) it can reflect the tradeoffs between the predefined economic targets and the associated water shortage penalties/flood losses, as well as the fuzziness of the water availability (i.e., fuzzy way).

The applications of ASSF for balancing the relationship between population–economy development and water resource management in the Yongding River floodplain can support decision-makers in identifying the coupla risk combinations and coping strategies, which are worth implementing in other places as follows: 1) in the process of economic belt formation, PIT can promote the regional productivity of the floodplain, but it would also damage its source function, causing new challenges of climate change for the entire economic belt. Thus, PIT scales into the floodplain should be regulated by policymakers with the consideration of coordinated goal coupling economic and ecological targets; 2) CFW can recover the source function of the floodplain to reduce the coupla risk of water shortage, soil loss, and flood, which can be advocated in some special function areas (such as ecological protection or floodplain area) to support the resource carrying capacity of the economic belt; 3) unpredictable climate shocks, limited data availability, and policy interaction can increase the complexity of the PEWM issue, which would pose new threats for interregional decision-makers in the economic belt. The ASSF has been developed to help them handle multiple uncertainties caused by objective and subjective factors, which can reduce the adverse effects and flood-disaster damage caused by climate change in a floodplain. All the above experiences and inspiration can be generalized to the area where the economic belt is initially formed, particularly in developing countries.

5 Conclusion

Using the PEWM in the Yongding River floodplain, the following discoveries were obtained: 1) in the context of the “strategy of Beijing–Tianjin–Hebei coordinated development” (from 2013), the driven effect of industrial transformation would increase the population concentration in the Yongding River floodplain (Hebei Province) from 2015. Accelerated PIT into the floodplain can require more water resources, where each additional unit can promote the regional GDP by 0.019 units, increasing the water demand by 0.046 units. It can strengthen various risks such as water shortage, soil loss, and flood control, which needs a comprehensive regulation of PIT into the floodplain based on its source function to support a sustainable PEWM in the floodplain of the economic belt; 2) CFW can decrease the water demand and shortage ratio for irrigation in the long run, but this would reduce the direct economic income of irrigation in the short run. It can be deemed as an effective method to restrict excessive land exploitation and recover the source function of the floodplain in an economic belt. The obtained results show that the lowest water shortage ratio in irrigation and CFW would be 0% and when water flow is high; 3) backward irrigative schemes and low water utilization efficiency are not accommodated by the regional water carrying capacity in the study area. Thus, cleaner production modes and water-saving patterns should be introduced into the PEWM issue to remit the water crisis from the water-use side. The results show that improvement in water saving and recycling could reduce water shortages by 77.23 × 10^3 m 3 and 48.34 × 10^3 m 3 under S1 and S4, respectively, when water flow is low; 4) an individual policy has its own advantages to control single risk, but it is not suitable for complex risk combinations in a practical PEWM issue. For instance, S2 and S4 (technique improvement) have advantages of risk control in water shortages instead of soil loss and flood control. Meanwhile, S8 (water diversion) has the highest risk control level for water shortages. Moreover, S6 (withdrawn farmland to wetland) is effective in reducing the risks of soil loss and flood, but it requires more water resources to increase the water shortage risk. The combined policy (S12) can reduce coupla risks (including the risk of water shortage, soil loss, and flood) compared to other individual policies (S2, S4, S6, and S8). Thus, the tradeoff between economic benefits and costs of various policy scenarios should be analyzed by policymakers in the long term.

Although the developed RASF can effectively deal with uncertainties existing in PEWM planning, there are also several limitations that are subject to further improvement. For example, in the practical PEWM in a floodplain area of the economic belt, various types of uncertainties (e.g., nonlinearity and interval features) can generate different uncertainties and risks. Meanwhile, interregional policies with optimistic/pessimistic preferences of policymakers would affect the efficiency of PEWM planning. Therefore, more robust optimization techniques should be introduced into RASF to enhance its capacity in handling uncertainties amid drought–flood challenges and climate change.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

HD: formal analysis, funding acquisition, investigation, and writing–original draft. XA: data curation, methodology, software, and writing–original draft. XZ: funding acquisition, methodology, resources, supervision, and writing–review and editing. TL: writing-review and editing. YT: writing-review and editing. XZ: writing-review and editing. SL: writing-review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Major Research and Development Project (2022YFD1500402) and the Social Science Foundation of Yunnan Province, grant number YB2023014.


The authors thank the editors and the reviewers for their insightful comments and suggestions.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at:

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Keywords: location-entropy-based PVRA model, coupla risk, population–industry transformation, adaptive scenario, uncertainties

Citation: Dong H, An X, Zeng X, Li T, Teng Y, Zhang X and Li S (2024) Risks and coping strategies in the floodplain economic belt amid drought–flood challenges and climate change. Front. Environ. Sci. 12:1395118. doi: 10.3389/fenvs.2024.1395118

Received: 03 March 2024; Accepted: 17 April 2024; Published: 15 July 2024.

Reviewed by:

Copyright © 2024 Dong, An, Zeng, Li, Teng, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xueting Zeng, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

River Tees Example


It also provides an example of how river basins can be managed against flooding.

River Tees Features

River Tees Catchment factors

Physical (natural Factors) The River Tees catchment is located in the north east of England. It has three main rivers, the River Tees, the River Skerne and the River Leven. The River Tees drains the eastern slopes of Cross Fell in the Pennines and flows eastward to the North Sea. The length of the channel from source to sea is approximately 160 kilometres. The River Tees rises on the slopes of Cross Fell at a height of 893metres. The area receives over 2,000 millimetres of rain each year. The rainfall reaches the river quickly because the slopes are steep and very little water can infiltrate due to the impermeable rocks and saturated peat. After heavy rainfall or when the snow melts in the spring the river level can rise quickly and the water also flows downstream quickly. This can cause the ‘Tees roll’ or ‘Tees wave’, when the river level may rise as much as a metre in 15 minutes. There is a long history of flooding along the River Tees, especially in its lower course.

The catchment has areas with distinctly different characteristics. The rivers in the Upper Tees have steep channel gradients and valley sides. In the mid-catchment, the valley widens out and channel slopes become much gentler. The lower catchment is close to sea level and predominantly tidal in nature.

Human Factors

The Tees Barrage forms an artificial barrier between the Tees Estuary and the upstream catchment. This helps maintain water levels for amenity purposes and eliminates tidal effects further upstream. Land use in the west of the area is mainly moorland and pasture. On the lower slopes and middle catchment the land use changes to a greater amount of pasture and woodland.

To the east land use is mainly arable farmland interspersed with large built up areas, including Middlesbrough and Stockton-on-Tees. Approximately 687,000 people live within the catchment area concentrated in the main towns of Darlington, Hartlepool, Middlesbrough, Redcar and Cleveland, and Stockton-on-Tees.   There are also large industrial areas such as the ICI chemical works. Flood damage can run into hundreds of thousands of pounds. There is also a huge demand for water for use in homes, industry and agriculture.

Flood events along the River Tees “ The Tees catchment has a long history of flooding with reported flooding dating back over 400 years. Over the years, a number of engineering schemes have been implemented to reduce the risk of flooding in the catchment. At present over 9,600 properties including some key infrastructure buildings are at risk of flooding (not taking into account defences) during the one per cent flood event. In the future due to climate change this number is expected to increase to 11,230 .” David Dangerfield, Director – Yorkshire and North East Environment Agency Tees Catchment Flood Management Plan

Flood Hydrograph for Baranard Castle in 1995  Flooding in Yarm in 1995

Historic events On the night of 16/17th November 1771 the North East of England suffered some of the worst and most destructive flooding on record. The river Tees burst its banks in a number of places with Yarm being one of the areas worst affected.  At the height of the flood some stretches of Yarm High Street were submerged in 20 feet of water. The floods were caused by a sudden thaw of the ice in upper Teesdale, and a cloud burst over the Pennines. The rain began in the early hours of Saturday morning and continued heavily throughout the day and night without a break. On the main street alone, six houses were completely destroyed and many more left uninhabitable.  The Shambles was washed away and the town’s main church was also damaged.  ‘The pews were upturned and tossed about, the pulpit was overturned and several windows were shattered’. Nine of the townspeople lost their lives in the floods.  In one home alone where three people were staying, two drowned, and one survived by hanging from the top of a window for nineteen hours with floodwater up to his chin. Source The river flooded again in 1995 and 2015. Within the Tees catchment the main consequences of flooding occur in the urban areas of the catchment. In total there are almost 8,500 residential properties and over 1,200 commercial properties at risk of flooding. This means almost three per cent of the catchment population is at risk from the one per cent flood event.This means that flood management is definitely needed in this catchment

Properties River Tees flooding

Above - Properties at risk of flooding on River Tees

Flood Management along the Tees

The Flood defence scheme in Yarm Since the 1995 flood event a new flood defence scheme costing £2.1 million has been built. They have also used improved flood warning systems which liaise with the Meteorological Office, police and other emergency services. New development has discouraged building on low-lying and flood-prone land (land is used for activities not damaged by inundation e.g. playing fields, parks, urban forests/walks etc.) In the Yarm area the Environment Agency have;

    Put in Reinforced concrete walls with metal flood gates for access by people and vehicles     Used Earth Embankments     Put in Gabions to protect walls and embankments from erosion Included Fishing platforms, street lighting and replanting to improve the environment Kept building materials in keeping with existing architecture

Cow Green Reservoir

Flood management techniques river Tees

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River Landscape Case Study: River Tees (Upper Course - Cow Green…

  • The Source is called Cross Fell
  • The Source of the water comes from the Pennine Hills
  • In the River Tees, the water quality is very high which makes it suitable for use as a water supply for homes and industry
  • The high rainfall also gives a reliable water supply
  • As the River Tees fluctuates, the excess water ends up in the Cow Green Reservoir . This helps prevent flooding in the Lower Course
  • The dam is about 0.25 miles long and took 3 years to build
  • Holds 40 billion litres of water
  • Was built to supply the Industries of Teeside
  • Is a waterfall on the River Tees, near Middleton-in-Teesdale, County Durham
  • High Force is 30m high
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  • The River Tees meanders the floodplain and completely surrounds the town of Yarm
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  • River Channelisation - The meanders were cut-off to save time and money for naviagation
  • Yarm Flood Defences - Concrete walls were made to prevent flooding and babions to protect walls from erosion
  • Tees Barrage - Built in 1995, it has acted as a catalyst for £500 million worth of development offices, housing, leisure and education

Efficient risk assessment of landslide dam breach floods in the Yarlung Tsangpo river basin

  • Original Paper
  • Published: 18 July 2024

Cite this article

river tees flooding case study

  • Danyi Shen 1 ,
  • Zhenming Shi 2 ,
  • Ming Peng   ORCID: 2 ,
  • Hongchao Zheng 3 ,
  • Jiangtao Yang 2 &
  • Limin Zhang 4  

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The sudden and unpredictable breach of landslide dams in the Yarlung Tsangpo river basin usually causes megafloods, posing great risks to human lives and infrastructures in the downstream areas. This study proposed an efficient and quantitative risk assessment framework of breach floods caused by landslide dam failures in the mainstream and tributaries of the Yarlung Tsangpo river basin with limited data. The impact of dam breach floods on human risks was evaluated. The flood attenuation along rivers, strategies for mitigating overlapping floods, and sensitivity analysis of human risks were also discussed. The results show that the developed framework successfully assessed flood risks caused by the breach of landslide dams. The flood attenuation ratio increased with river length but decreased with the peak discharge at dam site. A higher peak discharge and a larger inundated area downstream were predicted when the breach floods of two landslide dams, one in mainstream and the other in a tributary, overlapped at the confluence. The overlapping flood could be mitigated by reducing peak discharges of the two landslide dams or increasing time interval between the two peaks. The simulations also outlined the downstream peak discharge resulting from the cascading breach was larger than that of a single dam. However, it was smaller than the combined peak discharges of two separate dams, because the erosion during the breach of the downstream dam incurred energy dissipation. The human risks in the Pasighat village were greater when overlapping flood occurred due to the increased water depth and more hazardous inundated buildings. In the case of multi-peak floods, the warnings for the former peak flood would also warn the peak flood thereafter when individuals were notified multiple peaks. Otherwise, individuals might be misled by the warning of the previous peak flood, resulting in catastrophic flood impacts. A parametric analysis indicated that early evacuation warnings were needed to avoid serious loss of life and flood damages, especially in cases of dam breaches occurring at nighttime or for areas in close proximity to the dam site.

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This work was supported by the National Natural Science Foundation of China (Nos., 42307196, 42061160480, 42071010, and U23A2044), and the China Postdoctoral Science Foundation (2023M733029).

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Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China

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Appendix: Momentum based junction method in the HEC-RAS model

The program solves for the water surfaces at sections B and D by performing a momentum balance across the junction.

where SF A , SF B , SF D is specific force;  \(F_{{\mathit f}_{\mathit B\mathit-\mathit A}}\) ,  \(F_{{\mathit f}_{\mathit D\mathit-\mathit A}}\) is frictional force;  \(W_{X_{B-A}}\) ,  \(W_{X_{D-A}}\)   is weight force in the X direction (the direction of flow based on cross-section A of reach 3); θ 1 , θ 2 is angle between reach 1 and reach 3 flow line, and reach 2 and reach 3 flow line (Fig. 19 ).

figure 19

Junction for confluence of river reaches

where Q A , Q B , Q D is discharge at each section; β is momentum coefficient; A A , A B , A D is flow area in which there is motion; A t A , A t B , A t D is total flow area, including ineffective flow area;  \(\overline{Y_A},\overline{Y_B},\overline{Y_D}\)   is depth from the water surface to centroid of the area; g is gravitational acceleration.

where  \(\overline{{\mathit S}_{{\mathit f}_{\mathit B\mathit-\mathit A}}}\) ,  \(\overline{{\mathit S}_{{\mathit f}_{\mathit D\mathit-\mathit A}}}\)   is slope of the energy grade line between two sections (friction slope);  \({\mathrm L}_{\mathrm B-\mathrm A}\) ,  \({\mathrm L}_{\mathrm D-\mathrm A}\) is reach length between two sections along the X axis.

where  \({\mathrm{S}}_{0_{\mathrm{B}-\mathrm{A}}}\) ,  \({\mathrm{S}}_{0_{\mathrm{D}-\mathrm{A}}}\)  is slope of the channel.

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Shen, D., Shi, Z., Peng, M. et al. Efficient risk assessment of landslide dam breach floods in the Yarlung Tsangpo river basin. Landslides (2024).

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Prediction of adaptability of typical vegetation species in flood storage areas under future climate change: a case in hongze lake fdz, china.

river tees flooding case study

1. Introduction

2. materials and methods, 2.1. study area, 2.2. field investigation and collection of occurrence data of species, 2.3. environmental variables, 2.4. model simulation and model evaluation, 2.5. potential distribution under current climate conditions, model accuracy, 2.6. environmental variable weight and response curve, 2.7. typical vegetation adaptability patterns under current climatic conditions, 2.8. distribution changes under future climate scenarios, 3. discussion, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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pseudacorus L.
demeser L.
verticillata (Linn. f.) Royle
wrightii Morong
Number of
typist (L.) Persoon
perenne L.
australis (Cav.)
ex steu
hydropiper L.
Number of occurrences3521641084454
VegetationLagerstroemia indica L.Amorpha
fruticosa Linn
chinensis Lour
repens L.
Number of occurrences163550257602
indica L.
nucifera Gaertn
angustifolia L.
peltata (S. G. Gmelin)
Number of occurrences250191379444
domestica Thunb
Triarrhena sacchariflora (Maxim.) NakaiPennisetum
alopecuroides (L.) Spreng
crispus L.
Number of occurrences126249509335
Data sourceson-the-spot investigation + (GBIF, , accessed on 13 February 2023), (CVH, , accessed on 13 February 2023), ( , accessed on 15 February 2023)
Environment VariablesVariable Description
Bio1Annual Mean Temperature (°C)
Bio2Mean diurnal range (mean of monthly (max temp–min temp)) daily average temperature range (°C).
Bio3Isothermicity (Bio 2/Bio 7) (×100) Isothermicity
Bio4Temperature seasonality (standard deviation × 100) coefficient of seasonal variation of temperature.
Bio5Max Temperature of Warmest Month Maximum temperature in hottest month (°C)
Bio6Min Temperature of Coldest Month Minimum temperature in coldest month (°C)
Bio7Temperature annual range (Bio 5–Bio 6) Temperature annual range (°C)
Bio8Mean temperature of wet quarter (°C)
Bio9Mean Temperature of Driest Quarter Average temperature in the driest quarter (°C)
Bio10Mean temperature of warm quarter average temperature in the warmest quarter (°C)
Bio11Mean temperature of coldest quarter average temperature (°C)
Bio12Annual Precipitation (mm)
Bio13Precipitation of Wettest Month in the wettest month (mm)
Bio14Precipitation of Driest Month (mm)
Bio15Seasonal variation of precipitation (coefficient of variation) (mm)
Bio16Precipitation in the driest quarter of precision of wet quarter (mm)
Bio17Precipitation in the wettest quarter of precision of drill quarter (mm)
Bio18The warmest quarterly precipitation of Precipitation of Warmest Quarter (mm)
Bio19The coldest quarterly precipitation of Precipitation of Coldest Quarter (mm)
Vegetation NameBio1Bio2Bio3Bio4Bio5Bio6Bio7Bio8Bio9Bio10Bio11Bio12Bio13Bio14Bio15Bio16Bio17Bio18Bio19
Iris pseudacorus L. 47.727.5 29.930.8 30.1 36
Ceratophyllum demersum L. 9.3 35.152.3 44.1 13.3
Hydrilla verticillata (Linn. f.) Royle 23.710.2 17.8 57.1 19.5 26.3
Potamogeton wrightii Morong 23.59.4 20.9 58.728.6
Nandina domestica Thunb 15.634.7 21.1 31.2 32.3 45.6
Lagerstroemia indica L. 13.610.7 24.6 56.2 25.419.131
Amorpha fruticosa Linn 0.9 36.830.6 17.8 9
Tamarix chinensis Lour 27.5 3.7 4.1 36.618.916
Trifolium repens L. 16.410.5 7.4 17.612.1 15.6
Pennisetum alopecuroides (L.) Spreng 6.5 16.966.1 35.46.513
Cynodon dactylon (L.) Persoon 22.671 7 3.835.3
Lolium perenne L. 22.812.8 15.7 14.326.839.9
Phragmitesaustralis (Cav.) Trin. ex steu 7.23.8 7.4 6 14.770.2
Polygonum hydropiper L. 38.614.4 6.9 45.8 12.130.6
Nelumbo nucifera Gaertn 4.318.7 8.3 56.6 19.2 21.7
Triarrhena sacchariflora (Maxim) Nakai4.2 4.2 404.621.8 4.917.2 2
Canna indica L. 17.317.7 30.5 26.4 42.2 22.1
Typha angustifolia L. 8.49.4 20.6 66.9
Nymphoides peltata (S. G. Gmelin) Kuntze 7.353.5 22.6 39.7 55.7
Potamogeton crispus L. 7.55.7 27.9 59.4 39.714.9
Vegetation NameChange Range of Suitable Area
Iris pseudacorus L.−2~2.9−2~3.0−2~2.9−2~3.1
Ceratophyllum demersum L.8.7~10.68.9~11.98.3~10.47.9~11.0
Hydrilla verticillate (Linn. f.) Royle4.7~7.44.0~7.35.0~7.64.7~7.8
Potamogeton wrightii Morong10.2~18.212.9~19.39.2~16.48.8~18.5
Nandina domestica Thunb5.9~10.85.7~8.95.5~10.74.5~9.8
Lagerstroemia indica L.17.6~24.416.2~24.218.1~24.816.7~24.0
Amorpha fruticose Linn53.7~65.955.6~65.953.5~65.751.7~64.5
Tamarix chinensis Lour42.2~46.841.5~46.444.8~48.844.3~49.2
Trifolium repens L.35.5~44.435.3~48.235.4~44.437.7~45.8
Pennisetum alopecuroides (L.) Spreng11.0~18.04.7~18.210.4~17.83.7~16.9
Cynodon dactylon (L.) Pers46.3~48.946.2~49.046.6~49.447.2~49.8
Lolium perenne L.34.3~37.935.0~38.033.3~38.330.9~37.4
Phragmites australis (Cav.) Trin. ex steu0.2~12.60.3~13.80.0~12.60.0~12.6
Polygonum hydropiper L.22.3~28.122.3~28.113.3~27.412.9~28.0
Nelumbo nucifera Gaertn−10.0~−2.5−9.8~−2.5−10.3~−2.5−10.2~−2.8
Triarrhena sacchariflora (Maxim.) Nakai4.2~7.44.2~6.54.2~6.54.0~7.4
Canna indica L.72.8~73.373.3~74.672.6~73.472.7~73.5
Typha angustifolia L.5.8~9.85.9~10.36.2~10.06.5~10.0
Nymphoides peltate (S. G. Gmelin) Kuntze0.4~1.00.5~1.30.3~1.00.3~1.0
Potamogeton crispus L.22.7~26.825.2~28.122.8~26.122.9~25.8
Vegetation SSPsFuture (~2040s)
NameSSP126SSP 245SSP 370SSP 585
Classification of
Suitable Habitat
Trifolium repens L. 35.84 35.84 35.84 35.84
Tamarix chinensis Lour 35.84 35.84 35.84 35.84
Triarrhena sacchariflora (Maxim.) Nakai35.84 35.84 35.84 35.84
Cynodon dactylon (L.) Persoon 35.84 35.84 35.84 35.84
Hydrilla verticillata (Linn. f.) Royle35.84 35.84 35.84 35.84
Ceratophyllum demersum L. 35.84 35.84 35.84 35.84
Pennisetum alopecuroides (L.)
12.42 23.42 14.12 21.72 13.97 21.87 20.77 15.07
Phragmites australis (Cav.) Trin. ex steu 35.84 35.84 35.84 35.84
Canna indica L. 35.84 35.84 35.84 35.84
Nandina domestica Thunb35.84 35.84 35.84 35.84
Typha angustifolia L.35.84 35.84 35.84 35.84
Amorpha fruticosa Linn 1.0434.78 0.6635.18 0.9134.93 18.3317.51
Potamogeton crispus L. 35.84 35.84 35.84 35.84
Lolium perenne L. 35.84 35.84 35.84 35.84
Iris pseudacorus L.35.84 35.84 35.84 35.84
Nelumbo nucifera Gaertn35.84 35.84 35.84 35.84
Potamogeton wrightii Morong35.84 35.84 35.84 35.84
Polygonum hydropiper L. 35.84 35.84 35.84 35.84
Nymphoides peltata (S. G. Gmelin) Kuntze35.84 35.84 35.84 35.84
Lagerstroemia indica L.35.84 35.84 35.84 35.84
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Wang, L.; Cheng, J.; Jiang, Y.; Liu, N.; Wang, K. Prediction of Adaptability of Typical Vegetation Species in Flood Storage Areas under Future Climate Change: A Case in Hongze Lake FDZ, China. Sustainability 2024 , 16 , 6331.

Wang L, Cheng J, Jiang Y, Liu N, Wang K. Prediction of Adaptability of Typical Vegetation Species in Flood Storage Areas under Future Climate Change: A Case in Hongze Lake FDZ, China. Sustainability . 2024; 16(15):6331.

Wang, Liang, Jilin Cheng, Yushan Jiang, Nian Liu, and Kai Wang. 2024. "Prediction of Adaptability of Typical Vegetation Species in Flood Storage Areas under Future Climate Change: A Case in Hongze Lake FDZ, China" Sustainability 16, no. 15: 6331.

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Internet Geography

River Flooding and Management

Floods can bring both advantages and disadvantages to an area. Floods can deposit rich, fertile alluvium in agricultural areas. Also, flood water can replenish irrigation channels. On the other hand, floods can destroy food supplies, homes and transport infrastructures.

What are the factors affecting flood risk?

A flood occurs when water can no longer be contained within a river channel. The flood risk can be increased by physical and human factors.

Physical factors affecting flood risk

  • Precipitation – When there is intense or extended rain, the ground can become overly saturated. This condition prevents water from being absorbed, leading to quicker runoff and a higher chance of flooding.
  • Geology – The type of rock in an area can affect flooding risk. Non-porous rocks, meaning they don’t let water seep through, can cause water to flow rapidly across the surface, increasing the likelihood of floods.
  • Steep slopes – Water can move swiftly downhill in regions with steep terrain, such as mountains. This rapid movement contributes to a greater risk of flooding in these areas.

Human factors affecting flood risk

Some land uses increase the risk of flooding.

  • Farming – Agricultural practices can influence how water moves. For instance, fields without crops, especially during winter when soils are wet, can quickly channel water. Also, ploughing down slopes can form small channels that accelerate water flow directly to rivers.
  • Urbanisation – City development affects water movement as well. Hard, non-absorbent surfaces like concrete and asphalt, along with drainage systems, can speed up water flow to rivers. Plus, the scarcity of greenery means less rainwater is absorbed or evaporated.
  • Deforestation – Cutting down forests impacts water transfer. Trees normally act like an umbrella, slowing down water movement and using or storing rainwater. Without trees, more water flows directly to rivers, raising the potential for flooding.

Effects of flooding

Flooding can have a significant impact on people. These are known as social impacts. Social impacts can include:

  • damage to property
  • injury or loss of life

Flooding can also have an impact on wealth. This is known as an economic impact . Economic impacts of flooding include:

  • cost of repairs
  • reduced tourism

Finally, flooding can have an impact on the surrounding landscape . Environmental impacts of flooding can include:

  • loss of livestock
  • loss of habitat
  • destruction of crops

Managing Flooding – Hard Engineering

Dams Although very expensive, dams can significantly reduce the risk of flooding downstream

Levees These are man-made embankments constructed along the edge of the river. They increase the capacity of the channel to prevent the overflow of water.

The video below shows a levee constructed at Cockermouth, Cumbria following the 2009 floods.

Flood Defence Barriers Similar to levees, only constructed from man-made materials flood defence barriers increase the channel size of a river which prevents the overflow of water.

The video below shows flood defence barriers at Cockermouth, constructed after the 2009 floods.

Managing Flooding – Soft Engineering

Afforestation Planting more trees reduces run-off and increases interception.

Flood Plain Zoning Floodplain zoning policies influence how land on, or close to, floodplains are used.

River restoration involves removing any hard-engineering strategies and restoring the river to its original course. This can involve making the river less straight which can slow the flow of the river.

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