Simple power-law models to predict flow metrics for water resource and risk management along the Mekong tributaries. [Abstract only] - Lacombe, Guillaume; Douangsavanh, Somphasith; Vogel, R.; McCartney, Matthew P.; Chemin, Yann H.; Rebelo, Lisa-Maria; Sotoukee, Touleelor (2014)
Increasing demographic pressure, economic development and resettlement policies in the Lower Mekong Basin induce greater population dependency on river flow to satisfy growing domestic and agricultural water demands. This dependency is particularly tight in upland areas where alternative water resources (groundwater) are scarce. As a result, communities tend to live closer to rivers, and so are more vulnerable to floods. This situation requires improved knowledge of flow variability for better management of water resources and risks. Unfortunately, stream flow measurements are scarce, especially in remote areas inhabited by the poorest and most vulnerable populations. Several water resource models have been developed to simulate and predict flows in the Lower Mekong Basin. However, most of these models have been designed to predict flow along the Mekong mainstream, precluding accurate assessments in headwater catchments. In most cases, their complexity and lack of transparency restricts potential users to modelling experts, and largely excludes those practitioners working closely with affected populations. The most integrated and informative way to characterize flow, at a specific location on a river, is to compute a flow duration curve which provides the percentage of time (duration) any particular flow is exceeded over a historical period. Using hydro-meteorological records from more than 60 gauged catchments in the Lower Mekong Basin, and a 90-meter digital elevation model, we used multiple linear regressions to develop power-law models predicting flow duration curves. These simple equations allow assessment of low, medium and high flow metrics, at any point on rivers in the Lower Mekong Basin, using easily determined geomorphological and climate characteristics. We believe that this parsimonious, transparent and highly predictive tool (89% <R2< 95%) can be used by a wide range of practitioners working in the fields of livelihood, water infrastructure engineering and agriculture.