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Earth observation satellites yield a wealth of data for scientific, operational and commercial exploitation. However, the redistribution of mass in the system Earth is not yet part of the standard inventory of Earth Observation (EO) data products to date. It is derived from the Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On mission (GRACE-FO). Among many other applications, mass redistribution provides fundamental insights into the global water cycle. Changes in continental water storage impact the regional water budget and can, in extreme cases, result in floods and droughts that often claim a high toll on infrastructure, economy and human lives. The initiative for a European Gravity Service for Improved Emergency Management (EGSIEM) established three different prototype services to promote the unique value of mass redistribution products for Earth Observation in general and for early-warning systems in particular. The first prototype service is a scientific combination service to derive improved mass redistribution products from the combined knowledge of the European GRACE analysis centres. Secondly, the timeliness and reliability of such products is a primary concern for any early-warning system and therefore EGSIEM established a prototype for a near real-time service that provides dedicated gravity field information with a maximum latency of five days . Third, EGSIEM established a prototype of a hydrological / early warning service that derives wetness indices as indicators of hydrological extremes and assessed their potential for timely scheduling of high-resolution optical/radar satellites for follow-up observations in case of evolving hydrological extreme events.

Water availability during summer in Central Asia is controlled by the snow melt in the surrounding mountains. Reliable forecasts of river discharge during this period are essential for the management of water resources. This study tests the predictive power of GRACE gravity-based water storage anomalies in a linear regression framework for two large catchments. The results show substantial improvements of the forecasts in the larger Amudarya catchment compared to forecasts using just climate, snow cover, and discharge data. In this catchment, GRACE water storage anomalies even provide the largest share of explained variance. This leads to the conclusion that GRACE data can improve the forecast of seasonal water availability for large basins in Central Asia. The GRACE-FO mission launched in May 2018 opens up the possibility of operational forecasts utilizing upcoming near-real time products from satellite gravimetry for Central Asia and similar environments.

Satellite altimeters have been launched with the objective to monitor changes in sea level and glacial ice sheet topography. More recently, their potential to monitor inland water bodies such as lakes, rivers and wetlands has been recognised. The objective of this study was to assess the accuracy of measuring surface water elevation changes of large multi-channel inland river systems using data from multiple altimetry satellite sensors and different retracking methods. We initially validated satellite altimetry data with in situ gauge data on Lake Argyle and Lake Eildon (Australia), and then investigated data of the only presently operational altimeter, Jason-2/Ocean Surface Topography Mission (OSTM), data at six locations on large inland rivers where temporary gauges were installed during 2011-2012 floods.Our analysis on Lake Argyle showed that the application of an alternative retracking algorithm significantly improved the agreement of altimeter and gauge data. We also found that the altimeter with the smallest footprint (50-90. m) and the highest along-track resolution (40. Hz, ~170. m), ICESat, provided more accurate lake water surface elevation measurements (mean. = 0.00. m, RMSE. = 0.04. m) than other altimeters, Jason-2 (mean. = -0.04. m, RMSE. = 0.28. m), Envisat (mean. = 0.25. m, RMSE. = 0.42. m), Jason-1 (mean. = -0.04. m, RMSE. = 1.07. m), GFO (mean. = 0.5. m, RMSE. = 0.89. m) and T/P (mean. = 0.77. m, RMSE. = 1.5. m).This study also investigated altimetry satellite data accuracy at six Jason-2/OSTM ground track sites crossing the Cooper/Diamantina Rivers where water level loggers were installed to collect data during the 2011-2012 floods (N.B. ICESat ceased operations in 2009). It is shown that satellite altimetry data is able to simulate moderate to major flood events in these large multi-channel inland river systems (R=0.90-0.98). Altimetry data further revealed a variation of water height in the channels across the river system. The general usefulness of satellite altimetry in hydrological applications in remote data sparse regions is confirmed, more specifically to study large multi-channel anabranching river systems such as the Cooper/Diamantina Rivers of the Lake Eyre Basin in Australia and similar systems worldwide where conventional gauging methods are difficult to use. This study also highlights the potential operational applications for monitoring inland flood wave characteristics and hydrodynamic behaviour of remote and multi-channel floodplain systems particularly using the still-operational Jason-2 platform.

Since April 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has been churning out water storage anomaly data, which has been shown to be a unique descriptor of large-scale hydrological extreme events. Nonetheless, efforts to assess the comprehensive information from GRACE on total water storage variations for near-real time flood or drought monitoring have been limited so far, primarily due to its coarse temporal (weekly to monthly) and spatial (> 150.000 km2) resolution and the latency of standard products of about 2 months,. Pending the status of the aging GRACE satellite mission, the Horizon 2020 funded EGSIEM (European Gravity Service for Improved Emergency Management) project is scheduled to launch a 6 month duration nearreal time test run of GRACE gravity field data from April 2017 onward, which will provide daily gridded data with a latency of 5 days. This fast availability allows the monitoring of total water storage variations related to hydrological extreme events, as they occur, as opposed to a ’confirmation after occurrence’, which is the current situation. This contribution proposes a global GRACE-derived gridded wetness indicator, expressed as a gravity anomaly in dimensionless units of standard deviation. Results of a retrospective evaluation (April 2002-December 2015) of the proposed index against databases of hydrological extremes will be presented. It is shown that signals for large extreme floods related to heavy/monsoonal rainfall are picked up really well in the Southern Hemisphere and lower Northern Hemisphere (Africa, S-America, Australia, S-Asia), while extreme floods in the Northern Hemisphere (Russia) related to snow melt are often not. The latter is possibly related to a lack of mass movement over longer distances, e.g. when melt water is not drained due to river ice blocking.

European Gravity Service for Improved Emergency Management - Status and project highlights

The uncertainty of the GIS based rainfall runoff model LisFlood has been investigated within the Generalized Likelihood Uncertainty Estimation (GLUE) framework. Multipliers for the saturated and unsaturated hydraulic conductivity, the porosity of the upper and lower soil layer, channel and overland flow roughness and the maximum percolation from upper to lower storages have been sampled within a Monte Carlo analysis from a uniform random distribution. With each parameter set the model has been computed with input for the 1995 flood event of the river Meuse situated in France, Belgium and The Netherlands. Eight gauging stations have been used for model evaluation by the Multicomponent Mapping (M x ) method. All parameters demonstrate equifinality and no parameter set could be classified as behavioural for all the evaluation datasets. However, the results of the prediction of uncertainty percentiles on the flow are very satisfactory and encouraging. The model did further show the capability to pred...

Recent advances in meteorological forecast skill now enable significantly improved estimates of precipitation quantity, timing and spatial distribution to be made up to 10 days ahead for model scales of 40 km in forecast mode. Here we outline a prototype methodology to downscale these precipitation estimates using regional Numerical Weather Prediction models to spatial scales appropriate to hydrological forecasting and then use these to drive high‐resolution scale (1 or 5 km grid scale) water balance and rainfall‐runoff models. The aim is to develop a European Flood Forecasting System (EFFS) and determine what flood forecast skill can be achieved for given basins, meteorological events and prediction products. The output from the system is a probabilistic assessment of n‐day ahead discharge exceedence risk (where n < 10) for the whole of Europe at 5 km resolution which may then be updated as the forecast lead time reduces. At each stage the discharge estimates can be used to drive detailed (25–10...

A measurement procedure has been developed and tested to determine horizontal and vertical polarization radiative transfer properties, i,e., single scattering albedo (/spl omega/) and optical depth (/spl tau/), of vegetation under field conditions. The procedure was applied to a wheat crop for a series of biomass densities. The measurements were done using two different radiometers (1.4 and 5 GHz) and for different view angles. The measurements and calculations indicated that the ratios of horizontal and vertical polarization radiative transfer properties (/spl alpha/=/spl Gamma//sub h///spl Gamma//sub /spl nu//, /spl alpha/'=/spl tau//sub h///spl tau//sub /spl nu// and /spl beta/=/spl omega/h//spl omega//sub /spl nu//) are slightly dependent on view angle. However, no significant dependence on biomass density could be discerned.

A surface moisture model for large-scale semiarid land application has been extended with a moisture flow routine for capillary flow. The model has been applied to a field-scale data set of topsoil moisture and latent heat flux of an arable site in central Spain. A comparison of the soil hydraulic properties, determined by model simulations and measurements in the field, demonstrates the capability of the model to determine these properties out of a range of soil classes as defined according to the parameterization by Clapp and Hornberger [1978]. Estimation of area average or 'effective' soil physical parameters according to this approach requires time series of topsoil moisture and actual evapotranspiration on a patch or regional scale. Therefore this approach has potential for use with remotely sensed data.

The large observation footprint of low-frequency satellite microwave emissions complicates the interpretation of near-surface soil moisture retrievals. While the effect of sub-footprint lateral heterogeneity is relatively limited under unsaturated conditions, open water bodies, if not accounted for, cause a strong positive bias in the satellite-derived soil moisture retrieval. This bias is generally assumed static and associated with large, continental lakes and coastal areas. Temporal changes in the extent of smaller water bodies as small as a few percent of the sensor footprint size, however, can cause significant and dynamic biases. We analysed the influence of such small open water bodies near-surface soil moisture retrieval data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for three areas in Oklahoma, USA. Differences between on-ground observations, model estimates and AMSR-E retrievals were compared to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR-E data and another derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). The comparisons demonstrates that seasonally varying biases of up to 30 vol.% soil water content can be attributed to the presence of relatively small areas (

The large observation footprint of low-frequency satellite microwave emissions complicates the interpretation of near-surface soil moisture retrievals. While the effect of sub-footprint lateral heterogeneity is relatively limited under unsaturated conditions, open water bodies (if not accounted for) cause a strong positive bias in the satellite-derived soil moisture retrieval. This bias is generally assumed static and associated with large, continental lakes and coastal areas. Temporal changes in the extent of smaller water bodies as small as a few percent of the sensor footprint size, however, can cause significant and dynamic biases. We analysed the influence of such small open water bodies on near-surface soil moisture products derived from actual (non-synthetic) data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for three areas in Oklahoma, USA. Differences between on-ground observations, model estimates and AMSR-E retrievals were related to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR-E data, a second derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and a third through inversion of the radiative transfer model, used to retrieve soil moisture. The comparison demonstrates the presence of relatively small areas (

In 2002 the Hydrosphere State Mission (Hydros) was selected by NASA as the alternate mission for a flight opportunity under its Earth System Science Pathfinder program. The Hydros mission objective was to collect the first global scale measurements of the Earth's soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz, respectively. Although NASA cancelled the Hydros mission in December 2005 due to insufficient funding and its reversion back to alternate mission status, the development of accurate soil moisture retrieval algorithms and associated error analyses begun under the Hydros project are still relevant to SMOS and to other potential future soil moisture missions.

ERA-40 stands for ECMWF Re Analysis and refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Numerical Weather Prediction (NWP) model for the period September 1957- August 2002 utilizing all state-of-the-art information and satellite data input presently available. From this dataset the top layer volumetric soil water is extracted and evaluated against surface moisture retrievals derived from the SMMR instrument on board the Nimbus-7 satellite for the European window. The evaluation of NWP model output with observed data is relevant to the initialization of land surface conditions in these models, which is important for accurate short term to long range meteorological and hydrological prediction. Because land surface parameters are highly integrated states, errors in land surface forcing, model physics and parameterization tend to accumulate in the land surface stores, such as soil moisture, of these models This has a direct effect on the model's water and energy balance calculations, and will eventually result in inaccurate weather predictions. It is expected that improved accuracy in defining initial conditions for NWPs along with continuous internal bias corrections for baseline data generated by uncoupled Land Surface Models (LSM), will lead to highly improved shortterm to long-range weather forecasting capability. Preliminary analysis presented here reveals the off set between the two data sets, although distinct, is relatively constant, which suggests a potential for improved initialization and bias correction by an optimized accuracy and spatial representation of the soil moisture data fields.

ERA-40 stands for ECMWF Re Analysis and refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Global Circulation model for the period September 1957- August 2002 utilizing all state-of-the-art information and satellite data input presently available. Here, a selection of the ERA-40 atmospheric output data at the surface is used to force the catchment hydrological model LISFLOOD to simulate historic river flows for the whole of Europe on a 5km grid resolution. Once evaluated against observed rainfall and point flow data, the output constitutes an extensive and coherent 40+ year database of pan-European calibrated river flow time series, providing a wealth of information and potential for a range of evaluation purposes. For example, statistical analyses could serve to detect flooding and/or drying frequency trends. Alternatively, the output dataset could be used to assess flood alert levels in a consistent and uniform manner at any point for any river in Europe. The set would also be extremely useful as a basis for scenario studies, investigating the impact of policy and decision making such as de/aforestation and water reservoir management on flow regimes. Further, in the context of the general scientific consensus of an expected increasing trend in extreme events, the database may serve as a resource of information for extrapolated future scenarios.

The ECMWF Re Analysis (ERA-40) refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Numerical Weather Prediction (NWP) model for the period September 1957- August 2002 employing all state-of-the-art information and satellite data input presently available. A selection of the reanalysis atmospheric output data can potentially be used to run a hydrological model to simulate historic river flows for the whole of Europe. Once evaluated against observed time series of rainfall and river flow, the output would constitute an extensive and coherent 40+ year database of pan-European calibrated river flow time series, providing a wealth of information and allowing a range of evaluation possibilities. Here, in order to separate the meteorological model performance from the hydrological model performance, the ERA-40 near-surface rainfall aggregates, which come as a by-product of the ECMWF NWP system, are evaluated against interpolated fields of observed surface precipitation. The ERA-40 rainfall fields consist of forecast data with a 36 hr lead time at midday and midnight and a 6 hr lead time at 6:00 and 18:00 UTC, allowing different combinations of lead and base times to compute daily rainfall aggregates. The evaluation of these aggregated precipitation fields against observed totals is relevant to the spin-up time of the ECMWF NWP system and the forecast reliability with increasing lead time. Interpolated fields of observed daily rainfall totals are provided by the Monitoring Agriculture with Remote Sensing (MARS) database (1990-2001) based at the European Commission Joint Research Centre (JRC).