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This study focuses on Laos, a landlocked nation located in SouthEast Asia with subtropical climate and highly seasonal rainfall distribution. Laos is one of the world's least developed countries, and currently witnesses an unprecedented level of development that is highly reliant on its natural resources, including groundwater. There is currently very limited data and no nationwide assessment of shallow (<30 m) groundwater resources to support sustainable management. This study provides a first step towards addressing this issue by (i) identifying the major aquifer units of the country; (ii) integrating localized data and regional maps into an assessment of the groundwater potential; and (iii) producing quantitative maps of key hydrogeological indicators. Eight aquifer units have been described and evaluated: (i) Basement aquifers, (ii) Volcanic aquifers, (iii) Schists, (iv) Paleozoic sedimentary, (v) Karsts, (vi) Limestones, (vii) Mesozoic sedimentary and (viii) Alluvial sediments. The Mesozoic sandstones and the Alluvial aquifers are the most extensive and productive hydrogeological systems in the country. The Volcanic and Karstic aquifers, although poorly known, might also have important potential. This assessment, along with the maps of quantitative aquifer indicators, provide a significant improvement in both spatial resolution and accuracy compared to previously available information. It will likely support improved management plans and the identification of areas with higher potential for groundwater development.

Study region: Increasing demographic pressure and economic development in the Mekong Basin result in greater dependency on river water resources and increased vulnerability to streamflow variations. Study focus: Improved knowledge of flow variability is therefore paramount, especially in remote catchments, rarely gauged, and inhabited by vulnerable populations. We present simple multivariate power-law relationships for estimating streamflow metrics in ungauged areas, from easily obtained catchment characteristics. The relations were derived from weighted least square regression applied to streamflow, climate, soil, geographic, geomorphologic and land-cover characteristics of 65 gauged catchments in the Lower Mekong Basin. Step-wise and best subset regressions were used concurrently to maximize the prediction R-squared computed by leave-one-out cross-validations, thus ensuring parsimonious, yet accurate relationships. New hydrological insights for the region: A combination of 3–6 explanatory variables – chosen among annual rainfall, drainage area, perimeter, elevation, slope, drainage density and latitude – is sufficient to predict a range of flow metrics with a prediction R-squared ranging from 84 to 95%. The inclusion of forest or paddy percentage coverage as an additional explanatory variable led to slight improvements in the predictive power of some of the low-flow models (lowest prediction R-squared = 89%). A physical interpretation of the model structure was possible for most of the resulting relationships. Compared to regional regression models developed in other parts of the world, this new set of equations performs reasonably well

The publications in this series cover a wide range of subjects-from computer modeling to experience with water user associations-and vary in content from directly applicable research to more basic studies, on which applied work ultimately depends. Some research reports are narrowly focused, analytical and detailed empirical studies; others are wide-ranging and synthetic overviews of generic problems.Although most of the reports are published by IWMI staff and their collaborators, we welcome contributions from others. Each report is reviewed internally by IWMI staff, and by external reviewers. The reports are published and distributed both in hard copy and electronically (www.iwmi.org) and where possible all data and analyses will be available as separate downloadable files. Reports may be copied freely and cited with due acknowledgment. About IWMIThe International Water Management Institute (IWMI) is an international, research-for-development organization that works with governments, civil society and the private sector to solve water problems in developing countries and scale up solutions. Through partnership, IWMI combines research on the sustainable use of water and land resources, knowledge services and products with capacity strengthening, dialogue and policy analysis to support implementation of water management solutions for agriculture, ecosystems, climate change and inclusive economic growth. Headquartered in Colombo, Sri Lanka, IWMI is a CGIAR Research Center and leads the CGIAR Research Program on Water, Land and Ecosystems (WLE). www.iwmi.org