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Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve - FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.
Recent climatic changes have the potential to severely alter river runoff, particularly in snow-dominated river basins. Effects of changing snow covers superimpose with changes in precipitation and anthropogenic modifications of the watershed and river network. In the attempt to identify and disentangle long-term effects of different mechanisms, we employ a set of analytical tools to extract long-term changes in river runoff at high resolution. We combine quantile sampling with moving average trend statistics and empirical mode decomposition and apply these tools to discharge data recorded along rivers with nival, pluvial and mixed flow regimes as well as temperature and precipitation data covering the time frame 1869-2016. With a focus on central Europe, we analyse the long-term impact of snow cover and precipitation changes along with their interaction with reservoir constructions.
Our results show that runoff seasonality of snow-dominated rivers decreases. Runoff increases in winter and spring, while discharge decreases in summer and at the beginning of autumn. We attribute this redistribution of annual flow mainly to reservoir constructions in the Alpine ridge. During the course of the last century, large fractions of the Alpine rivers were dammed to produce hydropower. In recent decades, runoff changes induced by reservoir constructions seem to overlap with changes in snow cover. We suggest that Alpine signals propagate downstream and affect runoff far outside the Alpine area in river segments with mixed flow regimes. Furthermore, our results hint at more (intense) rain-fall in recent decades. Detected increases in high discharge can be traced back to corresponding changes in precipitation.
The sensitivity of key hydrologic variables and hydropower generation to climate change in the Lake Malawi and Shire River basins is assessed. The study adapts the mesoscale Hydrological Model (mHM) which is applied separately in the Upper Lake Malawi and Shire River basins. A particular Lake Malawi model, which focuses on reservoir routing and lake water balance, has been developed and is interlinked between the two basins. Climate change projections from 20 Coordinated Regional Climate Downscaling Experiment (CORDEX) models for Africa based on two scenarios (RCP4.5 and RCP8.5) for the periods 2021-2050 and 2071-2100 are used. An annual temperature increase of 1 degrees C decreases mean lake level and outflow by 0.3 m and 17%, respectively, signifying the importance of intensified evaporation for Lake Malawi's water budget. Meanwhile, a +5% (-5%) deviation in annual rainfall changes mean lake level by +0.7 m (-0.6 m). The combined effects of temperature increase and rainfall decrease result in significantly lower flows in the Shire River. The hydrological river regime may change from perennial to seasonal with the combination of annual temperature increase and precipitation decrease beyond 1.5 degrees C (3.5 degrees C) and -20% (-15%). The study further projects a reduction in annual hydropower production between 1% (RCP8.5) and 2.5% (RCP4.5) during 2021-2050 and between 5% (RCP4.5) and 24% (RCP8.5) during 2071-2100. The results show that it is of great importance that a further development of hydro energy on the Shire River should take into account the effects of climate change, e.g., longer low flow periods and/or higher discharge fluctuations, and thus uncertainty in the amount of electricity produced.
Elevation-dependent compensation effects in snowmelt in the Rhine River Basin upstream gauge Basel
(2021)
In snow-dominated river basins, floods often occur during early summer, when snowmelt-induced runoff superimposes with rainfall-induced runoff. An earlier onset of seasonal snowmelt as a consequence of a warming climate is often expected to shift snowmelt contribution to river runoff and potential flooding to an earlier date. Against this background, we assess the impact of rising temperatures on seasonal snowpacks and quantify changes in timing, magnitude and elevation of snowmelt. We analyse in situ snow measurements, conduct snow simulations and examine changes in river runoff at key gauging stations. With regard to snowmelt, we detect a threefold effect of rising temperatures: snowmelt becomes weaker, occurs earlier and forms at higher elevations. Due to the wide range of elevations in the catchment, snowmelt does not occur simultaneously at all elevations. Results indicate that elevation bands melt together in blocks. We hypothesise that in a warmer world with similar sequences of weather conditions, snowmelt is moved upward to higher elevation. The movement upward the elevation range makes snowmelt in individual elevation bands occur earlier, although the timing of the snowmelt-induced runoff stays the same. Meltwater from higher elevations, at least partly, replaces meltwater from elevations below.
The study examined the potential future changes of drought characteristics in the Greater Lake Malawi Basin in Southeast Africa. This region strongly depends on water resources to generate electricity and food. Future projections (considering both moderate and high emission scenarios) of temperature and precipitation from an ensemble of 16 bias-corrected climate model combinations were blended with a scenario-neutral response surface approach to analyses changes in: (i) the meteorological conditions, (ii) the meteorological water balance, and (iii) selected drought characteristics such as drought intensity, drought months, and drought events, which were derived from the Standardized Precipitation and Evapotranspiration Index. Changes were analyzed for a near-term (2021-2050) and far-term period (2071-2100) with reference to 1976-2005. The effect of bias-correction (i.e., empirical quantile mapping) on the ability of the climate model ensemble to reproduce observed drought characteristics as compared to raw climate projections was also investigated. Results suggest that the bias-correction improves the climate models in terms of reproducing temperature and precipitation statistics but not drought characteristics. Still, despite the differences in the internal structures and uncertainties that exist among the climate models, they all agree on an increase of meteorological droughts in the future in terms of higher drought intensity and longer events. Drought intensity is projected to increase between +25 and +50% during 2021-2050 and between +131 and +388% during 2071-2100. This translates into +3 to +5, and +7 to +8 more drought months per year during both periods, respectively. With longer lasting drought events, the number of drought events decreases. Projected droughts based on the high emission scenario are 1.7 times more severe than droughts based on the moderate scenario. That means that droughts in this region will likely become more severe in the coming decades. Despite the inherent high uncertainties of climate projections, the results provide a basis in planning and (water-)managing activities for climate change adaptation measures in Malawi. This is of particular relevance for water management issues referring hydro power generation and food production, both for rain-fed and irrigated agriculture.
Extreme Regenereignisse von kurzer Dauer im Bereich von Stunden und darunter rücken aufgrund der dadurch bedingten Schäden durch Sturzfluten und auch wegen ihrer möglichen Intensivierungen durch den anthropogenen Klimawandel immer stärker in den Fokus. Die vorliegende Studie untersucht auf Basis von teilweise sehr langen (> 50 Jahre) und zeitlich hochaufgelösten Zeitreihen (≤ 15 Minuten) mögliche Trends in Starkregenintensitäten für Stationen aus schweizerischen und österreichischen Alpenregionen sowie für das Emscher-Lippe-Gebiet in Nordrhein-Westfalen. Es wird deutlich, dass es eine Zunahme der extremen Niederschlagsintensitäten gibt, welche gut durch die Erwärmung des regionalen Klimas erklärt werden kann: Die Analysen langfristiger Trends der Überschreitungssummen und Wiederkehrniveaus zeigen zwar erhebliche Unsicherheiten, lassen jedoch eine Zunahme in einer Größenordnung von 30 % pro Jahrhundert erkennen. Zudem wird in diesem Beitrag, basierend auf einer "mittleren" Klimasimulation für das 21. Jahrhundert, für ausgewählte Stationen der Emscher-Lippe-Region eine Projektion für extreme Niederschlagsintensitäten in sehr hoher zeitlicher Auflösung beschrieben. Dabei wird ein gekoppeltes räumliches und zeitliches "Downscaling" angewendet, dessen entscheidende Neuerung die Berücksichtigung der Abhängigkeit der lokalen Regenintensität von der Lufttemperatur ist. Dieses Verfahren beinhaltet zwei Schritte: Zuerst werden großräumige Klimafelder in täglicher Auflösung durch Regression mit den Temperatur- und Niederschlagswerten der Stationen statistisch verbunden (räumliches Downscaling). Im zweiten Schritt werden dann diese Stationswerte mithilfe eines sogenannten multiplikativen stochastischen Kaskadenmodells (MC) auf eine zeitliche Auflösung von 10 Minuten disaggregiert (zeitliches Downscaling). Die neuartige, temperatursensitive Variante berücksichtigt zusätzlich die Lufttemperatur als erklärende Variable für die Niederschlagsintensitäten. Dadurch wird der mit einer Erwärmung zu erwartende höhere atmosphärische Feuchtegehalt, welcher sich aus der Clausius-Clapeyron-Beziehung (CC) ergibt, mit in das zeitliche Downscaling einbezogen.
Für die statistische Auswertung der extremen kurzfristigen Niederschläge wurden die oberen Quantile (99,9 %), Überschreitungssummen (ÜS, P > 5 mm) und 3-jährliche Wiederkehrniveaus (WN) einer Dauerstufe von ≤ 15-Minuten betrachtet. Diese Auswahl erlaubt die gleichzeitige Analyse sowohl von Extremwertstatistiken als auch von deren langfristigen Trends; leichte Abweichungen von dieser Wahl beeinflussen die Hauptergebnisse nur unwesentlich. Nur durch die Hinzunahme der Temperatur wird die beobachtete Temperaturabhängigkeit der extremen Quantile (CC-Scaling) gut wiedergegeben. Bei Vergleich von Beobachtungsdaten und Gegenwartssimulationen der Modellkaskade zeigt das temperatursensitive Verfahren konsistente Ergebnisse. Im Vergleich zu den Entwicklungen der letzten Jahrzehnte werden für die Zukunft ähnliche oder sogar noch stärkere Anstiege der extremen Niederschlagsintensitäten projiziert. Dies ist insofern bemerkenswert, als diese anscheinend hauptsächlich durch die örtliche Temperatur bestimmt werden, denn die projizierten Trends der Niederschlags-Tageswerte sind für diese Region vernachlässigbar.
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
(2023)
Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
(2023)
Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.
Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.