A second equation is provided for the flow metrics which are better predicted with an additional explanatory variable related to land cover. Except the Q0.95 model whose predictive power is greatly improved by the inclusion of paddy area as an explanatory variable, R-squared learn more increments for the other models are modest. It should

be noted that the predictive power of all models may reduce if they are applied to catchments with characteristics outside the range of values reported in Table 2. Drainage directions, soil characteristics, longitude and wetland areas were found not to have significant explanatory power for any of the flow metrics. These exclusions do not necessarily mean that the mentioned variables have no effect on the catchments’ hydrological behavior. For instance, the hydrological effects of soils and wetlands are complex and depend on various context-specific situations (Ribolzi et al., 2011 and Acreman this website and Holden,

2013) which may not be reflected by the available metrics that we used. In addition, it should be noted that the surface area of wetlands never exceeds 1.23% of the catchment areas, for the catchments used in the analyses. This likely explains their negligible role in hydrological responses. Annual rainfall is an explanatory variable in all models with associated coefficients exhibiting the lowest variability between models (variation coefficient < 10%). Values are much greater than unity (average = 2.59) indicating that an increase

of x% in annual rainfall would induce an >x% increase in any of the studied flow metrics. The rainfall coefficient associated to the model predicting mean annual flow (β1 = 2.543) corresponds to the rainfall elasticity of streamflow. It is greater than Interleukin-2 receptor the value 1.99 obtained by Hapuarachchi et al. (2008) for the whole Mekong Basin. These elasticity coefficients can help assess the impact of projected changes in rainfall on future changes in the studied streamflow metrics. The drainage area is an explanatory variable for mean annual flow and high-flow variables (Max, 0.10, 0.20, 0.30 and Mean). The coefficients for this variable are slightly lower than 1, depicting a slight tendency for reduction in runoff depth as catchment size increases. This is in agreement with Pilgrim et al. (1982) who observed a tendency of increased seepage in larger catchments. In contrast, low-flow variables (0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95 and Min) are better explained by the catchment perimeter rather than the catchment area. The perimeter provides information related to the shape of the catchment. For a given catchment area, a greater perimeter implies a longer time for water to reach the catchment outlet, thus explaining the positive correlation with low flow variables.