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Integrated water resources management requires consistent and accurate data on available water storage in reservoirs as well as water stress level. Vietnam is enduring a significant deficit in collecting necessary information to manage its water resources in that manner. While reservoirs are abundant, the majority of them were constructed a long ago and often lack of regular and adequate measurement on storage volume. Furthermore, the condition of water stress is often missing or remains bias leading to certain risks in reservoir operation, e.g. during water scarcity period. This paper presents how remote sensing data can be used to acquire needed information that is fundamental to understand water resources conditions. The results indicated that Sentinel-1 and Moderate Resolution Imaging Spectroradiometer (MODIS) can be applied to determine water surface area and water stress, through vegetation health index (VHI). This information is deemed necessary to improve water resources monitoring and management and hence, ensure long-term drought resilience and water and food security.

Improving irrigation monitoring and efficiency is a current priority of the Government of Vietnam, focusing primarily on the agricultural sector which consumes most of the available surface and groundwater. This paper presents how remote sensing can be used in an integrated manner to achieve better understanding of key eco-hydrological processes including precipitation, evapotran-spiration, irrigation and crop growth. The results indicated that Normalized Difference Vegetation Index derived from Moderate Resolution Imaging Spectroradiometer (MODIS) can be applied to determine irrigated pixels on a spatial and temporal basis. The validation using measured water level showed a Pearson correlation of 0.7 proving the high accuracy of this method. The inclusion of these technologies is deemed necessary to improve water resources monitoring and management and hence, ensure long-term drought resilience and water and food security. Ca River Basin in the central Vietnam was selected as a case study to test this approach.

Distributed hydrological models are usually calibrated against the measured outflow of a certain drainage area, provided flow data is available. A close match with flow does however not mean that the spatially distributed hydrological processes are properly understood and simulated. In this paper, remotely sensed precipitation, evapotranspiration (ET) and leaf area index (LAI) from open access data sources were used to calibrate the SWAT model for the Day Basin, a tributary of the Red River in Vietnam. The efficacy of the SWAT-CUP parameter sensitivity and optimization model developed by Abbaspour (2015) was tested with spatial remote sensing input parameters. The innovation is that the parameters of the soil-vegetation processes were optimized for every Hydrological Response Unit for which remotely sensed monthly ET and LAI values were available. Such level of detail cannot be achieved from flow measurements, which are the integrated result of many processes over large areas. A total of 15 soil-vegetation process parameters were calibrated. The SUFI2 algorithm in SWAT-CUP appeared to be an adequate practical tool for the calibration process. Simulated monthly ET correlations with remote sensing estimates showed an R2 = 0.71 and NSE = 0.65 while monthly LAI showed correlations of R2 = 0.59 and NSE = 0.57 over a five year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET: a non-significant difference of 302 mm (5.3 %). Because river flow was not optimized during the calibration process, it could be used as an independent validation of the calibrated model simulations. The monthly flow at two flow measurement stations were adequately estimated (R2 = 0.78 and 0.55, NSE = 0.71 and 0.63 for Phu Ly and Ninh Binh, respectively). The estimated total water withdrawal from the Red River was 1.934 billion m3/yr with a peak flow of approximately 200 m3/s during the months of February and July. The availability of a reliable set of parameters will make SWAT a useful tool for optimizing water conservation, agricultural outputs, and ecosystem services such as reduced soil erosion, better water quality standards, carbon sequestration, micro-climate cooling amongst others. Such calibrated distributed eco-hydrological models can be used for appraising scenarios of green growth.