Improving the rainfall nowcast for fine temporal and spatial scales suitable for urban hydrology

Accurate Quantitative Precipitation Forecasts (QPF) at high spatial and temporal resolution are crucial for urban flood prediction. Typically, Lagrangian persistence based on radar data is used to nowcast rainfall intensities with up to 3 hours lead time, but nevertheless is not able to deliver reliable QPFs past 20 min lead time (known as well as the predictability limit). Especially, for extreme events causing pluvial floods, accurate QPFs cannot be achieved past 5 min lead time. Furthermore when compared to gauge recordings, the QPFs are not useful at all. There is an essential need to provide better QPFs by improving the rainfall field supplied to the nowcast and by employing non-linear processes for the extrapolation of rainfall into the future. This study is focused on these two main problems, and it investigates different geostatistical and data-driven methods for the improvement of the QPFs at fine scales. The study was conducted within the Hannover radar range where observations between 2000 to 2018 were available. The skill of the nowcast models was assessed on the point (1 km2 and 5 min) and storm scale, based on continuous criteria comparing both radar and gauge observations. A total of 100 gauge measurements inside the study area were as well employed for the assessment. From the period 2000-2012, 93 events of different properties were distinguished and used as a basis for the method development and assessment. Two state-of-the-art nowcast models (HyRaTrac and Lucas-Kanade) were chosen as reference and used as benchmarks for improvement. To improve the rainfall field, a real time merging between radar and gauge data was investigated. Among different merging techniques (mean field bias, quantile bias correction and kriging interpolation), conditional merging (CM) yielded the best rainfall field. When fed to the reference nowcast models, it led to improvements of up to 1 hour of the predictability limit and of the agreement between radar based QPFs and gauge data. To improve the QPF accuracy even further, two different data driven techniques were developed in order to learn non-linear behaviours from past observed rainfall. First, a nearest neighbour approach (k-NN) was developed and employed instead of Lagrangian Persistence on the HyRaTrac nowcast model. The k-NN method accounts for the non-linearity of the storm evolution by consulting k-similar past storms. A deterministic nowcast issued by averaging the behaviours from the 3 most similar storms yielded the best results, extending the predictability limit at the storm scale to 2-3 hours. Second, an ensemble nowcast accounting for the 10 closest neighbours was generated in order to estimate the uncertainty of the QPF. Third, a deep convolution neural network (CNN) was trained on past merged data, in order to learn the non-linearity of the rainfall process. The network based on the last 15 min of observed radar images proved to successfully capture death and decay and partly birth processes, and extended the rainfall predictability limit at the point scale to 3 hours. Lastly, the methods were tested on 17 convective extreme events, extracted from the period 2013-2018, to compare the tested methods for an urban flood nowcast application. The CNN based on merged data outperformed both reference methods as well as the k-NN based nowcast, with the predictability limit reaching 30 – 40 min. The k-NN, although better than the Lagrangian persistence, suffered greatly from the shortcomings of the storm tracking algorithm present under fast moving and extreme storms. To conclude, even though clear improvements were achieved, there is a clear limit to the data-driven methods that cannot be overcome, unless coupled with the convection initialization from Numerical Weather Prediction (NWP) models. Nevertheless, complex relationships learned from past observed data, together with a better rainfall field as input, were proven to be useful in increasing the QPF accuracy and predictability limits for urban hydrology application.Quantitative Niederschlagsvorhersagen (QPF) in hoher räumlicher und zeitlicher Auflösung sind entscheidend für die Prognose urbaner Sturzfluten. Der auf Radardaten basierende Lagrange Ansatz wird typischerweise für Regenintensitätsvorhersagen mit einem Horizont von 3 Stunden verwendet. Zuverlässig ist dieser allerdings nur bis 20 Minuten (bekanntes Prognoselimit). Bei extremen Niederschlagsereignissen, die urbane Sturzfluten verursachen, ist das Limit sogar bereits bei 5 Minuten erreicht. Außerdem kommt es zu deutlichen Abweichungen zwischen der QPF und den Messdaten an Niederschlagsstationen. Eine Verbesserung der QPF ist demnach zwingend erforderlich. Eine solche Verbesserung kann durch die Anpassung des Eingabe-Niederschlagsfeldes und durch die Anwendung nichtlinearer Prozesse für die Extrapolation des Niederschlags erreicht werden. Die vorliegende Studie konzentriert sich auf diese beiden Hauptprobleme und untersucht verschiedene geostatistische und Data-Mining Methoden zur Verbesserung der QPF auf solchen Skalen. Die Studie wurde im Radarbereich von Hannover durchgeführt, wo Beobachtungsdaten von 2000 bis 2018 verfügbar sind. Die Güte der Nowcast-Modelle wurde auf der Punkteskala (1 km2 und 5 min.) anhand kontinuierlicher Kriterien evaluiert und in Relation zu Radar- und Stationsbeobachtungen gesetzt. Hierfür wurden insgesamt 100 Stationsmessungen innerhalb des Untersuchungsgebietes verwendet. Aus dem Zeitraum 2000 bis 2012 wurden 93 Ereignisse mit unterschiedlichen Eigenschaften als Grundlage für die Methodenentwicklung und -beurteilung ausgewertet. Zwei gängige Nowcast-Modelle (HyRaTrac und Lucas-Kanade) wurden als Referenzmodelle ausgewählt und als Maßstab für Verbesserungen eingesetzt. Um das Niederschlagsfeld zu verbessern, wurden Radar- und Stationsdaten in Echtzeit zusammengeführt. Unter den verschiedenen Methoden (Mean Field Bias, Quantile Mapping Bias, Kriging-Interpolation) ergab das Conditional Merging (CM) das optimalste Niederschlagsfeld. Als Input für die beiden Referenzmodelle verwendet, führte das CM zu einer Verlängerung des Prognoselimits auf bis zu eine Stunde. Auch die Übereinstimmung der radargestützten QPF mit den Stationsdaten verbesserte sich. Um das Prognoselimit noch weiter auszudehnen, wurden zwei verschiedene Data-Mining Techniken entwickelt, um die nichtlinearen Verhaltensweisen aus vergangenen Regenfällen zu erlernen: Zunächst wurde ein Nächster-Nachbar-Ansatz (k-NN) entwickelt und anstelle der Lagrange Persistenz im HyRaTrac-Nowcast-Modell eingesetzt. Die k-NN-Methode berücksichtigt die Nichtlinearität der Regensturmentwicklung, indem k-ähnliche vergangene Stürme herangezogen werden. Ein deterministischer Nowcast, der durch Mittelwertbildung der Verhaltensweisen der drei ähnlichsten Stürme erstellt wurde, lieferte die besten Ergebnisse und verlängerte das Prognoselimit auf bis zu zwei-drei Stunden. Ein Ensemble-Nowcast, bei dem die zehn nächsten Nachbarn berücksichtigt wurden, wurde ebenfalls erstellt, um die Unsicherheit des QPF abzuschätzen. Zudem wurde ein künstliches neuronales Netz (CNN) basierend auf vergangenen Daten entwickelt, um die Nichtlinearität des Niederschlagsprozesses zu berücksichtigen. Das neuronale Netz, das mit den beobachteten Radarbildern der letzten 15 Minuten gefüttert wurde, erwies sich als erfolgreich in der Erfassung von Todes-, Zerfalls- und Geburtsprozessen von Stürmen und konnte das Prognoselimit auf bis zu drei Stunden erweitern. Um die Wirksamkeit der entwickelten Methoden für die Vorhersage urbaner Sturzfluten zu untersuchen, wurden sie auf 17 konvektive Extremereignisse aus dem Zeitraum 2013 bis 2018 angewendet. Der k-NN Ansatz war zwar besser als die Lagrange Persistenz, litt aber stark unter den Fehlern des Sturmverfolgungs-Algorithmus bei schnellen und extremen Stürmen. Das CNN übertraf sowohl die Referenzmethoden als auch den k-NN-basierten Nowcast. Das Prognoselimit konnte so von 5 auf 30 bis 40 Minuten erweitert werden. Für eine weitere Verbesserung zeichnete sich letztlich eine klare Grenze ab, die nur mit der Konvektionsinitialisierung aus Numerischen Wettervorhersagemodellen (NWP-Modellen) überwunden werden kann. Im Vergleich mit den ausgewählten Referenzmodellen, können, durch die hier entwickelten Methoden, die Genauigkeit und das Prognoselimit der QPF in der städtischen Hydrologie erheblich verbessert werden

Uncertainty estimation of regionalised depth–duration–frequency curves in Germany

The estimation of rainfall depth–duration–frequency (DDF) curves is necessary for the design of several water systems and protection works. These curves are typically estimated from observed locations, but due to different sources of uncertainties, the risk may be underestimated. Therefore, it becomes crucial to quantify the uncertainty ranges of such curves. For this purpose, the propagation of different uncertainty sources in the regionalisation of the DDF curves for Germany is investigated. Annual extremes are extracted at each location for different durations (from 5 min up to 7 d), and local extreme value analysis is performed according to Koutsoyiannis et al. (1998). Following this analysis, five parameters are obtained for each station, from which four are interpolated using external drift kriging, while one is kept constant over the whole region. Finally, quantiles are derived for each location, duration and given return period. Through a non-parametric bootstrap and geostatistical spatial simulations, the uncertainty is estimated in terms of precision (width of 95 % confidence interval) and accuracy (expected error) for three different components of the regionalisation: (i) local estimation of parameters, (ii) variogram estimation and (iii) spatial estimation of parameters. First, two methods were tested for their suitability in generating multiple equiprobable spatial simulations: sequential Gaussian simulations (SGSs) and simulated annealing (SA) simulations. Between the two, SGS proved to be more accurate and was chosen for the uncertainty estimation from spatial simulations. Next, 100 realisations were run at each component of the regionalisation procedure to investigate their impact on the final regionalisation of parameters and DDF curves, and later combined simulations were performed to propagate the uncertainty from the main components to the final DDF curves. It was found that spatial estimation is the major uncertainty component in the chosen regionalisation procedure, followed by the local estimation of rainfall extremes. In particular, the variogram uncertainty had very little effect on the overall estimation of DDF curves. We conclude that the best way to estimate the total uncertainty consisted of a combination between local resampling and spatial simulations, which resulted in more precise estimation at long observation locations and a decline in precision at unobserved locations according to the distance and density of the observations in the vicinity. Through this combination, the total uncertainty was simulated by 10 000 runs in Germany, and it indicated that, depending on the location and duration level, tolerance ranges from ± 10 %–30 % for low-return periods (lower than 10 years) and from ± 15 %–60 % for high-return periods (higher than 10 years) should be expected, with the very short durations (5 min) being more uncertain than long durations

Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics

The nowcast of rainfall storms at fine temporal and spatial resolutions is quite challenging due to the unpredictable nature of rainfall at such scales. Typically, rainfall storms are recognized by weather radar and extrapolated in the future by the Lagrangian persistence. However, storm evolution is much more dynamic and complex than the Lagrangian persistence, leading to short forecast horizons, especially for convective events. Thus, the aim of this paper is to investigate the improvement that past similar storms can introduce to the object-oriented radar-based nowcast. Here we propose a nearest-neighbour approach that measures first the similarity between the “to-be-nowcasted” storm and past observed storms and later uses the behaviour of the past most similar storms to issue either a single nowcast (by averaging the 4 most similar storm responses) or an ensemble nowcast (by considering the 30 most similar storm responses). Three questions are tackled here. (i) What features should be used to describe storms in order to check for similarity? (ii) How should similarity between past storms be measured? (iii) Is this similarity useful for object-oriented nowcast? For this purpose, individual storms from 110 events in the period 2000–2018 recognized within the Hanover Radar Range (R∼115 km2), Germany, are used as a basis for investigation. A “leave-one-event-out” cross-validation is employed to test the nearest-neighbour approach for the prediction of the area, mean intensity, the x and y velocity components, and the total lifetime of the to-be-nowcasted storm for lead times from + 5 min up to + 3 h. Prior to the application, two importance analysis methods (Pearson correlation and partial information correlation) are employed to identify the most important predictors. The results indicate that most of the storms behave similarly, and the knowledge obtained from such similar past storms helps to capture better the storm dissipation and improves the nowcast compared to the Lagrangian persistence, especially for convective events (storms shorter than 3 h) and longer lead times (from 1 to 3 h). The main advantage of the nearest-neighbour approach is seen when applied in a probabilistic way (with the 30 closest neighbours as ensembles) rather than in a deterministic way (averaging the response from the four closest neighbours). The probabilistic approach seems promising, especially for convective storms, and it can be further improved by either increasing the sample size, employing more suitable methods for the predictor identification, or selecting physical predictors

Potential Sources of Surface and Groundwater Contamination in Tirana- Durres Region

The objective of this paper is to assess and evaluate the conditions of the surface andgroundwater resources in the Tirana-Durres region. The most important aquifer is that of Tiranaand it supplies with drinkable water (1200 -1300 l/sec) Tirana, Vora, Kamza and the other vicinities. The risk of pollution is very high because the aquifer's cover layer is very thin; the running rivers (Lana, Ishmi and Gjola Rivers) are very polluted due to over urbanization and thehigh usage of the sand soil for the construction industry. In addition, the groundwater layer inDurres region has high values of Cl-, K+ and Na+ ions, indicating a possible progression of sea water in the coastal aquifer of Durres. The potential for sea water contamination of the fresh water-bearing zones probably will continue to increase in west Durres, as freshwater zones continues to decline. The current situation in the region is evaluated through data from wells and spring and Water budget to evaluate the ground water characteristics. The distributions of different chemical properties are mapped to identify the most problematic zones by using GIS techniques. The study focuses on problems such as seawater intrusion in the area of Durres and stream water contamination in Tiran region. The effects of these phenomena are studied andsome possible remedies are discussed in this paper