The ASCAT soil moisture product: a review of its specifications, validation results, and emerging applications

Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is – with the exception of arid environments –comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.The Austrian Science Fund (FWF) through the Vienna Doctoral Programme on Water Resource Systems (http://www.waterresources.at/,DK-plusW1219-N22

Megafloods in Europe can be anticipated from observations in hydrologically similar catchments

Megafloods that far exceed previously observed records often take citizens and experts by surprise, resulting in extremely severe damage and loss of life. Existing methods based on local and regional information rarely go beyond national borders and cannot predict these floods well because of limited data on megafloods, and because flood generation processes of extremes differ from those of smaller, more frequently observed events. Here we analyse river discharge observations from over 8,000 gauging stations across Europe and show that recent megafloods could have been anticipated from those previously observed in other places in Europe. Almost all observed megafloods (95.5%) fall within the envelope values estimated from previous floods in other similar places on the continent, implying that local surprises are not surprising at the continental scale. This holds also for older events, indicating that megafloods have not changed much in time relative to their spatial variability. The underlying concept of the study is that catchments with similar flood generation processes produce similar outliers. It is thus essential to transcend national boundaries and learn from other places across the continent to avoid surprises and save lives

Prozessorientierte Hochwasservorhersage : Berechnungsmethoden und Unsicherheiten

Zsfassung in dt. SpracheDie Verfügbarkeit von genauen Hochwasservorhersagen, bis zu Prognosefristen von mehreren Tagen, wird in zunehmendem Maß auch für kleinere Einzugsgebiete ge-wünscht. Die Änderung des Anforderungsprofils macht die methodische Weiter¬entwicklung von Hochwasservorhersagesystemen zu einem wichtigen, interdiszi¬plinären Betätigungsfeld in der Hydrologie, Meteorologie und Fernerkundung. Ziel dieser Arbeit ist die Entwicklung und Analyse von Methoden zur Beschreibung bzw. Reduktion von Unsicherheiten bei der Messung und Modellierung der abflussrelevan-ten hydrometeorologischen Prozesse. Die Analysen in dieser Arbeit basieren auf Si-mulationen mit einem flächendetaillierten hydrologischen Niederschlags-Abfluss-Modell zur Beschreibung von Schneeakkumulation und -schmelze, des Bodenfeuch-tehaushaltes und des Abflusses am Hang und im Gerinne.Der erste Teil der Arbeit beschäftigt sich mit der Quantifizierung der Größenordnung der Vorhersageunsicherheit durch die Verwendung von Ensembleprognosen, welche maßgebend durch die vorherrschenden meteorologischen und hydrologischen Randbedingungen bestimmt wird. Die Beurteilung der Prognosen erfolgt dabei durch die Analyse der Warncharakteristik, welche durch die Häufigkeit von Fehlwarnungen und zutreffenden Warnungen beschrieben wird. Die Ergebnisse dieser Arbeit zeigen, dass der abgeleitete Unsicherheitsbereich eine wertvolle Zusatzinformation für die Risikoabschätzung im Rahmen der Entscheidungsfindung beim Hochwassermana-gement darstellt.Der zweite Teil der Arbeit beschäftigt sich mit der Reduktion von hydrologischen Un-sicherheiten durch die Einbeziehung von aktuell verfügbaren Abflussmessungen. Die Zielsetzung, den Prognosefehler so klein als möglich zu halten, kann durch die Ver-bindung der Modellergebnisse mit den Messdaten unter Abwägung der jeweils einge-tragenen Unsicherheiten erreicht werden. Diese Vorgangsweise wird als Modellnach-führung bezeichnet, und erfolgt in dieser Arbeit durch die Implementierung eines En-semble-Kalman-Filters in die Modellstruktur. Zusätzlich erfolgt, abhängig von der je-weils vorherrschenden hydrometeorologischen Situation, eine Korrektur der Abfluss-vorhersagen durch ein Fehlermodell auf der Basis der zeitlichen Korrelationen. Durch das Fehlermodell können die Unsicherheiten während der ersten Stunden der Prog-nosefrist deutlich reduziert werden. Die Wirkung des Ensemble-Kalman-Filters ist zwar geringer, erstreckt sich allerdings über die gesamte Vorhersagefrist.Das Hauptaugenmerk im dritten Teil der Arbeit liegt auf der Beurteilung des Potenti-als der satellitenbasierten Messung von Bodenfeuchtemustern als zusätzliche Infor-mationsquelle bei der Identifikation einer realistischen Modellstruktur und geeigneter Parameter. Der Problematik von geringen Eindringtiefen bei der satellitenbasierten Bodenfeuchtemessung, wird durch wird die Erweiterung des bestehenden hydrologi-schen Modells um eine dünne, oberflächennahe Bodenschichte Rechnung getragen. Der Vergleich von modellierter Bodenfeuchte und Bodenfeuchte aus der Fernerkun-dung zeigt eine gute Übereinstimmung beider Methoden hinsichtlich der räumlichen und zeitlichen Bodenfeuchtedynamik. Weiters zeigen die Auswertungen, dass die Maskierung der Satellitenbodenfeuchte für Zeitpunkte mit Schneebedeckung oder gefrorenem Boden zu einer deutlichen Verbesserung der Übereinstimmung beider Methoden während der Wintermonate führt. Generell zeigen die Ergebnisse dieser Arbeit, dass die zusätzliche Verwendung von aktuellen Messdaten, in dieser Arbeit Abflussmessungen und Bodenfeuchtemuster aus der Fernerkundung, einen wertvollen Beitrag zur Reduktion von Unsicherheiten bei der hydrologischen Modellierung leistet. Damit werden operationelle Hochwas-servorhersagen auf eine solide methodische Basis gestellt, und die Anwendbarkeit für außerordentliche hydrometeorologische Situation erhöht.Flood forecasting is becoming increasingly important for small catchments where the forecast uncertainties tend to be larger than in large catchments. In addition, also the increase of the forecast lead time is associated with larger uncertainties. These is-sues make the development of flood forecasting systems an important interdiscipli-nary task in hydrology, meteorology and remote sensing. In this study, the aim is the development and the analysis of methods to describe and reduce uncertainties in measurement and modelling of hydrometeorological processes. The analyses are based on simulations with a distributed hydrological rainfall-runoff-model which de-scribes snow accumulation and melt, the changes in soil moisture and catchment and stream routing functions.The quantification of the forecast uncertainty is in the focus of the first part of the stu-dy. The uncertainty is quantified using a set of equally probalbe forecasts (an en-semble) which are affected by the meteorological and hydrological boundary condi-tions. The assessment of the ensemble forecasts is based on the analysis of the fre-quency of false and correct alarms. The results indicate that the ensemble forecasts are a valuable and important source of information for flood forecasting. Even though the ensemble characteristics do not exactly match the forecast errors, they do pro-vide information about the expected forecast errors.In the second part of the study the hydrological uncertainties are reduced by using online available runoff measurements. To increase forecast accuracy, two real-time updating procedures are used in this study. The first procedure assimilates runoff data to update the catchment soil moisture state based on Ensemble Kalman filter-ing. The second procedure consists of an additive error model that updates runoff directly. This error model exploits the autocorrelation of the forecast error and in-volves an exponential decay of the correction. The error model clearly reduces the forecast uncertainties in the first hours of the forecast lead time. The impact of the Ensemble Kalman filter is smaller, but it affects the entire forecast lead time.Remotly sensed soil moisture data are used in the third part of the study as additional source of information to identify a realistic model structure and parameters. To ac-count for the shallow penetration depth of the remote sensing data the hydrological model is extended by a skin soil layer which represents only the first centimetres of the landsurface. A comparison of simulated soil moisture and soil moisture derived from remote sensing data shows excellent consistency between the spatial patterns of soil moisture. Analyses indicate that the masking of remote sensing data with in-formation about snow covered areas significantly improves the correlation between the simulated and remotely sensed soil moisture data.The results of this study show that observed runoff data and remote sensing data are a valuable source of information to reduce uncertainties in hydrological modelling. They allow for a solid methodical basis of operational flood forecasts and guarantee the applicability of the flood forecasting system in extraordinary hydrometeorological situations.10

Real time flood forecasting in the Upper Danube basin

This paper reports on experience with developing the flood forecasting model for the Upper Danube basin and its operational use since 2006. The model system consists of hydrological and hydrodynamic components, and involves precipitation forecasts. The model parameters were estimated based on the dominant processes concept. Runoff data are assimilated in real time to update modelled soil moisture. An analysis of the model performance indicates 88% of the snow cover in the basin to be modelled correctly on more than 80% of the days. Runoff forecasting errors decrease with catchment area and increase with forecast lead time. The forecast ensemble spread is shown to be a meaningful indicator of the forecast uncertainty. During the 2013 flood, there was a tendency for the precipitation forecasts to underestimate event precipitation and for the runoff model to overestimate runoff generation which resulted in, overall, rather accurate runoff forecasts. It is suggested that the human forecaster plays an essential role in interpreting the model results and, if needed, adjusting them before issuing the forecasts to the general public

Fluctuations of Winter Floods in Small Austrian and Ukrainian Catchments

Studying the changes in extreme river runoff induced by climate change is of utmost importance, as the variability of floods directly affects life and human activities. This study examines the fluctuations and persistence of winter floods in 14 catchments in the Rika River Basin (Ukraine) and ten catchments in the Steyr River Basin (Austria). The catchments represent typical hydrological regimes in the Danube River region. The fluctuations and persistence of floods are analyzed by the hydro-genetic method and a seasonality analysis for the period 1951–2015. The results show a much more pronounced fluctuation pattern in the upper Rika catchments than in the upper Steyr catchments. This pattern indicates an increase in winter flood magnitudes between the mid-1960s and the 1990s, followed by a decrease until recently. The flood seasonality shows a large inter-annual variability in both regions. The most significant winter floods tend to occur in November and December. The winter flood fluctuations are compared with changes in associated climate characteristics, i.e., seven-day maximum precipitation, a melt index, and annual maximum snow depth. The seasonality of these characteristics has a strong inter-annual variability and only partly explains the winter flood fluctuations