Hydrology & water management
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Introduction of the H SAF project, the history of the SAFs, the introduction of the HSAF Workshop and its agenda from the main organizers.
An introduction of the HSAF project, the history of the SAFs, the introduction of the HSAF Workshop and its agenda from the main organizers.
Humberto Barbosa presents a study which provides a comprehensive evaluation of extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and soil moisture over different time windows between 1980 and 2020.
The São Francisco River Basin (SFRB) plays a key role for the agricultural and hydropower sectors in Northeast Brazil (NEB). The purpose of this study is to provide a comprehensive evaluation of extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and soil moisture over different time windows between 1980 and 2020. The Standardized Precipitation-Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI) at 3- and 12- month time scales derived from ground data were used as benchmark drought indices. The selfcalibrating Palmer Drought Severity Index (scPDSI) and the Soil Moisture and Ocean Salinity-based Soil Water Deficit Index (SWDIS) were used to assess the agricultural drought. The Water Storage Deficit Index (WSDI) and the Groundwater Drought Index (GGDI) both derived from the Gravity Recovery and Climate Experiment (GRACE) were used to assess the hydrological drought. The SWDISa and WSDI showed the best performance in assessing agricultural and hydrological droughts across the whole SFRB.
Tommaso Abrate presents the efforts of WMO to coordinate with its Expert Network on updating the satellite data and product requirements for Flood Forecasting and seasonal and long term hydrological forecasts.
In order to better capture the complexity of interlinked natural phenomena related to the atmosphere, ocean, hydrosphere and cryosphere, WMO has adopted a holistic Earth System monitoring approach. The operational implementation of this approach is supported by WMO Congress decisions related to the establishment of a global basic observing network GBON, and the adoption of a unified data policy, aimed at improving the sharing and interoperability of data among users, contributing to better numerical weather prediction and more accurate flood and drought forecasts. To achieve these results, it is important to benefit from emerging approaches in order to combine different data sources such as satellites, citizen observations, low-cost devices, Internet of Things, Big Data. This approach also allows ensuring at least partial information overt hose vast areas of the world where conventional state-funded monitoring approaches are insufficient. WMO is developing technical solution (standards, best practices) to overcome the discrepancies in data quality and the multiplication of different data format. In this context satellite. WMO, in coordination with its Expert Network is working on updating the satellite data and product requirements for Flood Forecasting and seasonal and long term hydrological forecasts and outlook.
Christine Träger-Chatterjee presents the prototype Data Cube for Drought and Vegetation Monitoring, and tools to manipulate the data in the cube.
EUMETSAT provides a prototype Data Cube for Drought and Vegetation Monitoring, and tools to manipulate the data in the cube. This prototype consists of long-term data records on a regular latitude / longitude grid and in CF-compliant NetCDF via THREDDS.
The prototype seeks to explore how well EUMETSAT and partners can bring together data from multiple sources and from multiple grids to ease barriers to use of the data for thematic applications.
This presentation reports on the lessons learnt as regards the creation, provision and use of the data cube.
Mariette Vreugdenhil demonstrates the use of the EUMETSAT H SAF soil moisture (H116, SM) and SM2RAIN (H64) products to predict yields for Morocco and Senegal.
We demonstrate the use of the EUMETSAT HSAF soil moisture (H116, SM) and SM2RAIN (H64) products to predict yields for Morocco and Senegal. Root-zone SM was calculated from SM, and NDVI was used as a vegetation indicator. Data on yields was obtained from the Food and Agriculture Organization of the United Nations.
Yield prediction was done for main crops using multiple linear regression and a time for space approach. SM improved yield prediction, especially early in the growing season, improving early warning capabilities. NDVI showed better predictions later in the growing season. SM2RAIN outperformed other benchmark rainfall datasets.
Antonio Parodi presents a critical review of the forecasting performances of each model involved in the CIMA hydrometeorological chain on the example of Medicane Apollo.
During the last week of October 2021 an intense Mediterranean hurricane (medicane), named Apollo, affected many countries on the Mediterranean coasts. The deaths toll peaked up to 7 people, due to flooding from the cyclone in the countries of Tunisia, Algeria, Malta, and Italy.
The Apollo medicane persisted over such areas for about one week (24 October – 1 November 2021) and produced very intense rainfall phenomena and widespread flash flood and flood episodes especially over eastern Sicily on 25-26 October 2021.
CIMA Foundation hydro-meteorological forecasting chain, including the cloud-resolving WRF model assimilating radar data and in situ weather stations (WRF-3DVAR), the fully distributed hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, has been operated in real-time to predict the weather evolution and the corresponding hydrological and hydraulic impacts of the medicane Apollo, in support of the Italian Civil Protection Department early warning activities and in the framework of the H2020 LEXIS and E-SHAPE projects.
This work critically reviews the forecasting performances of each model involved in the CIMA hydrometeorological chain, with special focus on temporal scales ranging from very short-range (up to 6 hours ahead) to short-range forecasts (up to 48 hours ahead).
Ján Kanák presents the operational satellite products for precipitation detection, the procedure for their validation and a case study showing the use of these products in evaluating the long-term accumulated precipitation.
Primary satellite data processed into higher-level products are still used less frequently, especially in the context of processing by NWC SAF software, or directly by SAF products received by the EUMETCast Satellite receiving system. Such products include the hydrology support products of the EUMETSAT H SAF (Hydrological Satellite Application Facility). SHMÚ, as a member of the consortium, has long been involved in the task of validation of products for precipitation detection and hydrological applications of these products. In this article we present the operational satellite products for precipitation detection, the procedure for their validation and a case study presenting the use of these products in evaluating the long-term accumulated precipitation. Accumulated precipitation can be used to monitor periods of droughts with precipitation deficits and surpluses. The ambition of this work is to show future users of satellite data that satellite products of a higher level of processing have the potential for climatological studies. A significant increase in this potential is expected in the near future with the launch of the new generation of MTG (third generation Meteosat) and EPS-SG (second generation European Polar System) satellites.
Jean-Christophe Calvet presents a neural network that has been trained using the modelled surface soil moisture (SSM), soil temperature, rainwater interception by leaves, and satellite-derived LAI observations from Copernicus.
In the context of climate warming, the frequency and the intensity of extreme events such as droughts is increasing, and better modelling the response of vegetation to climate is needed. Monitoring the impact of extreme events on terrestrial surfaces involves a number of variables of the soil-plant system such as surface albedo, the soil water content and the vegetation leaf area index (LAI). These variables can be monitored by either using the unprecedented amount of data from the Earth observation satellite fleet, or using land surface models. Another solution consists in combining all available sources of information by assimilating satellite observations into models. In this work, level 1 ASCAT backscatter values (sigma0) are assimilated in the ISBA land surface model of Meteo-France using the LDAS-Monde tool. First, an observation operator is built using machine learning. A neural network (NN) is trained using the modelled surface soil moisture (SSM), soil temperature, rainwater interception by leaves, and satellite-derived LAI observations from Copernicus. The NN is then used for simulating sigma0, making LDAS-Monde capable of assimilating ASCAT sigma0 observations. It is shown that the assimilation of sigma0 alone is able to markedly improve the simulated LAI and soil moisture.
Boris Gratadoux presents an automatic flood forecast tool that combines two kinds of information used as input of a Machine Learning algorithm.
Thales has developed an automatic flood forecast tool combining two kinds of information used as input of a Machine Learning algorithm:
- - Forecasted flows at the outlet of the watershed of interest, obtained with an ensemble data assimilation using a particle filter;
- - Current soil state information from space-based observations;
The prototype allows forecasting time series of flood occurrence associated with a confidence index for a 6-day flood forecast with a 6-hour time-step. It has been tested and validated on a French watershed with good results.
Yves Tramblay presents the results of a study that evaluates satellite rainfall products for hydrological modelling over 12 basins in Morocco using four different hydrological models.
Morocco, as many African countries, has limited observed precipitation data that is a major obstacle for water management, flood monitoring and climate change adaptation planning using hydrological models. The objective of this study is to evaluate satellite rainfall products for hydrological modelling over 12 basins in Morocco using four hydrological models: IHACRES, MISDc, GR4J and CREST. Six satellite products are used in this analysis: the H03, H05, H64 and H67 HSAF products, in addition to SM2RAINASCAT product and GPM IMERG-E. The results showed that the best results to reproduce river runoff are achieved with the SM2RAINASCAT and H64 products, using the CREST and MISDc hydrological models. However, there are strong interplays between the different precipitation products and hydrological model structures in different basins, highlighting the need to carefully select hydrological models according to the intended application. This first evaluation over 12 Moroccan basins suggests that the use of satellite rainfall products for hydrological modelling could a viable alternative to observed rainfall in basins where precipitation is not monitored.
Daniele Casella presents a new algorithm for the Micro-Wave Sounder (MWS) radiometer on board the EPS Second Generation satellites.
The development of precipitation retrieval techniques can now benefit from the availability of unique cloud and precipitation observations by the two space borne radars currently available: the Dualfrequency Precipitation Radar (DPR) on board the NASA/JAXA GPM Core Observatory, and the NASA CloudSat Cloud Profiling Radar (CPR). These two radars have demonstrated their complementarity in the monitoring of precipitation. While DPR has shown a high accuracy in the estimate of medium and intense precipitation regimes, CPR has proven to be very suitable for the retrieval of light rain and snowfall. Within the H SAF a new algorithm for the Micro-Wave Sounder (MWS) radiometer on board the EPS Second Generation satellites (MetOp-SG) has been developed. Different machine learning approaches were tested in order to select the most suitable for optimizing the performance of the algorithm for the detection (a classification problem) and estimate (a regression problem) of rainfall and snowfall.
The details concerning the coincidence datasets creation, the design of the ML modules and the algorithm input selection procedure will be presented, together with results of the algorithm's performance.
Giulia Panegrossi presents the challenges and recent advancements in satellite-based snowfall quantification and global monitoring.
Snow plays an important role in the Earth energy exchange processes, and is a fundamental element of the water cycle. The use of satellites for snowfall monitoring and quantification and for retrieving snow cover properties and variability is necessary to globally quantify water resources. Recent studies have evidenced how space borne multi-channel microwave (MW) radiometer measurements respond to both snowfall and snow cover properties. Improvement in both monitoring of high latitude precipitation and in our understanding on microphysical and dynamical processes that influence high latitude precipitation patterns, intensity and type must be driven by concerted observations of active radars and passive microwave radiometers. This has been recently demonstrated through the development of machine learning-based algorithms for snowfall detection and retrieval, exploiting global observational datasets built from passive and active microwave space borne sensors. In this presentation the challenges and recent advancements in satellite-based snowfall quantification and global monitoring will be discussed. Moreover, retrieval strategies based on machine learning approaches that are being adopted within the EUMETSAT H SAF in view of the future EPS-SG mission, will be presented.