Despite playing a critical role in the division of precipitation between runoff and infiltration, soil moisture (SM) is difficult to estimate at the catchment scale and at frequent time steps, as is required by many hydrological, erosion and flood simulation models. In this work, an integrated methodology is described to estimate SM at the root zone, based on the remotely-sensed evaporative fraction (Λ) and ancillary information on soil and meteorology. A time series of Terra MODIS satellite images was used to estimate SM maps with an eight-day time step at a 250-m spatial resolution for three diverse catchments in Europe. The study of the resulting SM maps shows that their spatial variability follows the pattern of land cover types and the main geomorphological features of the catchment, and their temporal pattern follows the distribution of rain events, with the exception of irrigated land. Field surveys provided in situ measurements to validate the SM maps' accuracy, which proved to be variable according to site and season. In addition, several factors were analyzed in order to explain the variation in the accuracy, and it was shown that the land cover type, the soil texture class, the temporal difference between the datasets' acquisition and the presence of rain events during the measurements played a significant role, rather than the often referred to scale difference between in situ and satellite observations. Therefore, the proposed methodology can be used operationally to estimate SM maps at the catchment scale, with a 250-m spatial resolution and an eight-day time step.

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There is an increasing variety of hydrometeorological information sources available for operational water management. These comprise in-situ measurements, Earth Observation, meteorological models, and hydrological models. The effective use of all these information sources together is challenged by two aspects. First, there is an information and communication technology (ICT) challenge of acquiring, processing, merging, and presenting the various data streams operationally. Secondly, there are methodological gaps on how to integrate multiple hydrometeorological information sources in a useful way. An information system, the MyWater platform, has been developed to cope with the technological challenges. This platform allows setting-up automatic service chains, e.g. for flood early warning or drought monitoring, with customised output results and visualisation. The objectives of the paper presented hereafter are to introduce the range of hydrometeorological information sources available, and to implement the MyWater platform to combine these for hydrological model simulation operationally. The case study of the Umbeluzi catchment in Southern Africa was selected as example. A SWAT hydrological model was developed and forced with precipitation input from two different meteorological models in addition to scarce rain gauges. A successful operational test period showed that ICT for Water has developed such that information availability does not have to be a limiting factor in complex water management services. Platforms such as MyWater facilitate hindcast analysis and multi-variate and multidimensional (space-time) cross-validation of hydrological model predictions. The platform has strong capability in handling a wide array of spatial and time series data. And it has highly customisable visualisation, workspace design, and reporting tools, and is as a whole focussed on intuitive ease of use. The MyWater platform development is part of MyWater Project from EU FP7.

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