Work Package 1

Hydrological modelling and biophysical mapping at the catchment level

WP1 02th A review of previous flood modelling studies within the two study sites (Kratie Province and Ba catchment) will ensure adopted methods build on existing work (e.g. Piman et al. 2013, Brown et al. 2010). GIS data required for the hydrological model including digital elevation models (DEM), hydrological records (precipitation, evaporation and flow gauge data), land cover (Kuenzer et al., 2014) and soil properties will be collated at a catchment scale and where available at a finer sub-catchment scale sourced through the Pacific Risk Information Systems (PaRIS), global spatial data portals, local partners, academic organizations and government agencies. Detailed maps of influential model parameters, including soil characteristics and land cover, will be derived using Landsat 8 satellite imagery and field verification. Land cover change within the target catchments over the past 10 years will also be quantified from current and legacy Landsat data using multivariate alteration detection (MAD) methods (Nielsen et al. 1998) to map recent land conversion (vegetation clearing and afforestation). The derived maps will be spatially correlated with qualitative data on land practices and local adaptive responses. Soil and Water Assessment Tool - SWAT (Arnold and Fohrer, 2005) models will be developed to model the impacts of land use change and climate change on flood heights at main river gauging stations. Where possible, an open source hydrodynamic model such as HEC-RAS will be used to model flood extent under the different land use and climate scenarios (e.g. Brown et al. 2013; Ty et al., 2012). Both models will be validated using stream gauging data and/or flood extent data determined by delineating floods from Landsat and/or MODIS (Sakamoto et al., 2007) satellite images.

References

Arnold, J G, Fohrer, N. (2005) SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrological Processes, 19(3): 563-572.
Brown P, Daigneault, A, Gawith D, Aalbersberg W, Comley J, Fong P, Morgan F (2014) Evaluating ecosystem-based adaptation for disaster risk reduction in Fiji, Landcare Research.
Kuenzer C, Leinenkugel P, Vollmuth M, Dech S (2014) Comparing global land-cover products – implications for geoscience applications: an investigation for the transboundary Mekong Basin, International Journal of Remote Sensing 35(8): 2752-2779.
Nielsen, A A, Conradsen, K, Simpson, J J (1998) Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies. Remote Sensing of Environment 64:1-19.
Piman, T, Lennaerts, T and Southalack, P (2013) Assessment of hydrological changes in the lower Mekong Basin from Basin‐Wide development scenarios. Hydrological Processes 27(15): 2115-2125.
Sakamoto T, Van Nguyen N, Kotera A, Ohno H, Ishitsuka N and Yokozawa M (2007) Detecting Temporal Changes in the Extent of Annual Flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS Times-Series Imagery, Remote Sensing of Environment 109(3): 295-313.
Ty T V, Sunada K, Ichikawa Y, Oishi S (2012) Scenario-based Impact Assessment of Land Use/Cover and Climate Changes on Water Resources and Demand: A Case Study in the Srepok River Basin, Vietnam-Cambodia. Water Resources Management 26(5): 1387-1407.