USING SOIL INFORMATION IN NATURAL DISASTER ANALYSIS: VALUE FOR MONEY
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1 USING SOIL INFORMATION IN NATURAL DISASTER ANALYSIS: VALUE FOR MONEY Duration Hrs Victor Jetten Dept. of Earth Systems Analysis, ITC
2 USING SOIL INFORMATION IN NATURAL DISASTER ANALYSIS: VALUE FOR MONEY What is a disaster, concept of risk. a framework for Disaster Management Research Role of soil science in Disaster Management: as a source primary data for hazard analysis, 2 examples from flash flood and land slide modelling as a disappearing resource: what is the risk associated with soil degradation Concluding remarks 2
3 A DEFINITION OF DISASTERS A serious disruption of the functioning of a community or a society, causing widespread human, material, economic or environmental damage which exceed the ability of the affected community to cope using its own resources. (EEA, 2005) 3
4 NATURAL HAZARDS AND DISASTERS serious A serious disruption of the functioning of a community or a society, disruption causing widespread human, causing material, widespread economic or environmental damage which exceed damage the ability of the affected community to cope using its own resources. (EEA, 2005) RISK ability to cope 4
5 RISK IS OVERLAP HAZARD & VULNERABILITY / VALUE Dynamics Vulnerability Triggers Frequency Natural Hazards RISK Society Inventory Elements at risk Awareness Coping strategies Location Value Cost DISASTER 5
6 Disaster management research framework Cause Effect Response Rapid disasters Hazards Landslides, floods, earthquakes Geophysical and climate driven Risk analysis Vulnerability, urbanization, direct and indirect risk Disaster mitigation Damage assessment, planning, adaptation, mitigation, protection, awareness Land degradation Hazards Desertification and erosion Climate and land use change Risk Analysis On-site and off-site effects, rural risk, food security Sustainable Land Management Soil and water conservation, adaptation, awareness Where does soil science come in? 6
7 EXAMPLE 1: STORM RUNOFF CAPE VERDE Semi arid with 4 months of rainfall Cyclone storms on steep terrain causing flashfloods Many conservation measures (mainly to prevent erosion) Predict storm runoff with classical event based runoff modelling Where is runoff generated? Juan Sanchez (PhD), Chris Mannaerts, Jaques Tavares, Isaurinda Baptista 7
8 HOW TO USE THE SOIL INFORMATION AVAILABLE? 1. Course scale soil map (ASG1) 2. More detailed land suitability map based providing texture information (ASG2) 3. Field measurements Ordinary Kriging (OK) 4. Combination: Kriging with External Drift combined with texture units (KED) 8
9 Leads to 4 datasets of soil parameters, all legitimate 9
10 DISCHARGE AND SPATIAL PATTERNS OF RUNOFF Monitoring point center of catchment Outlet at coastal plain 10
11 CONCLUSIONS CAPE VERDE Equifinality problem, all datasets can be calibrated to give reasonable results. Datasets with large map units need larger calibration adjustments. Possible solutions: More (detailed) soil data? Monte Carlo simulation? Ask farmers what happens during a heavy rainstorm: map the effects! 11
12 EXAMPLE 2: LANDSLIDE HAZARD AND RISK Two important factors determine the hazard and risk: Triggers and source areas: Groundwater fluctuations, earthquakes. Predictive modelling is data driven (statistics) or deterministic: groundwater on steep slopes. Byron Quan Luna, Cees van Westen, Haydar Yussif Hussin Flow dynamics and runout distance modelling: Reach, velocity and kinetic energy are determined by: mass & density, moisture content, viscosity & friction, terrain geometry.
13 Maximum velocity (m) DEBRIS FLOWS MODELLING RAMMS model simulation of debris flows near Barcelonnette (Alps) Volume of cannot be explained by source area volumes, but is increased by entrainment. Soil volume available for erosion needs to be known. bridge 12 Ubaye 10 8 river RAMMS program Chainage line (m) DAN 3D program 13
14 SOIL DEPTH AS A MAJOR PARAMETER Estimate with different methods of interpolation from field data From electrical resistivity measurements ITC: Phd Muhammad Shafique 14
15 WHAT SOIL DATA DO WE HAVE AVAILABLE Web Portals, compilation of exiting soil information: JRC European Soil Data centre (ESDAC) USDA National Resources Conservation Service ASRIS Australian Soil Resource Information System ISRIC National databases GlobalSoilMap.net: focus on digital soil mapping of soil properties, compilation of national datasets, but reprocessed in identical fashion (100 m gridcells, KED, pedotransfer functions etc.) 15
16 REMAKRS SOIL AS A DATA SOURCE Soil surface properties becoming more and more available as a valuable data source. Mapping properties instead of soil types Soil unit boundaries used indirectly, e.g. geostats. Can be combined with pedotransfer functions Sub-soil data less well known but very important: Needed in hazard analysis involving groundwater Needed in landslide analysis (source areas and entrainment) Needed in earthquake damage modelling (wave propagation) not shown here 16
17 SOIL DEGRADATION AS A DISASTER Cause Effect Response Rapid disasters Hazards Landslides, floods, earthquakes Geophysical and climate driven Risk analysis Vulnerability, urbanization, direct and indirect risk Disaster mitigation Damage assessment, planning, adaptation, mitigation, protection, awareness Land degradation Hazards Desertification and erosion Climate and land use change Risk Analysis On-site and off-site effects, rural risk, food security Sustainable Land Management Soil and water conservation, adaptation, awareness
18 HAZARD ANALYSIS Analyzing and predicting the (bio-physical) effects of soil degradation is well known: Erosion Drought Compaction, crusting Salinisation Pollution Effects on crop growth or biodiversity Modelling, remote sensing analysis etc. Hazards Risk Analysis SLM 18
19 SUSTAINABLE LAND MANAGEMENT Hazards Risk Analysis We know very well how to do conservation and mitigate land degradation, technically and socially An example is the WOCAT system ( World Overview of Conservation Approaches and Technologies The FP6 project as an example of stakeholder based research SLM 19
20 RISK CONCEPT APPLIED TO SOILS Hazards Risk Analysis In spite of stakeholder based approach, large scale SLM remains problematic When there is a direct economic gain in SLM: e.g. more water = more yield, or when there is subsidy There is little notion of more/better soil = more yield No immediate risk apparent and soil is still considered an infinite resource (at least in politics) SLM RISK = Hazard * (Vulnerability + Value) Main question: how much value does the soil have? 20
21 COST OF LAND DEGRADATION For example Barry, Olsen and Campbell (2003): Data from 7 countries suggest damages are annually in the order of 4-7% of GDP, while the response is in the order of 0.1% GDP or less Incompleteness of data, mostly addressing direct on-site problems Off-site problems not taken into account Strong links with poverty, environmental policies are crucial Data is sufficient to open dialogs with governments Need for a comprehensive approach There will be no useful soil conservation policy unless we put a price tag on soils, and the ecosystem functions of soils 21
22 SOILS IN DISASTER MANAGEMENT: VALUE FOR MONEY Soil data is imperative for natural hazard analysis Trend towards mapping soil properties is very good, sufficient data for spatial analysis is becoming available Focus on the surface but more data needed on subsoil properties (especially a simple parameter as soil depth) To limit soil degradation we have to seriously design a comprehensive methodology for the economic value of soils Considering on-site functions and off-site functions Mapping the extent of this value, like any other resource 22
23 Yanan, Loess Plateau China, 1999 Yanan, Loess Plateau China, 2010 THANK YOU 23
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