Displays

Accurate rainfall measurements are very important in hydrology, meteorology, agriculture and other fields. Traditionally rain gauges combined with radar have been used to measure rain rates. However, these instruments are not always available. Also combining point measurements at the ground with measured reflectivities of volumes in the air to an accurate rain rate map at ground level poses challenges. Commercial microwave link networks can help in these areas as these can provide measurements at a high temporal resolution and tend to be available wherever people live, with highest network densities where most people are. They also measure precipitation along a path near ground level and offer a way to close the gap between rain gauge measurements and radar.

In this study we highlight the work SMHI has performed on deriving rain rates from commercial microwave links since 2015. This started with a pilot study in Gothenburg. The signal strengths of 364 microwave links were sampled every ten seconds and were used to create rainfall maps at a one-minute temporal resolution and 500m spatial resolution. These rain maps were then applied in a hydrological experiment and compared to rain gauge and radar measurements. The results were very promising, not only due to the high temporal and spatial resolution, but also with the accuracy of the actual measurements. The correlation was found to be equal to those of the rain gauges, while links were found to overestimate rainfall volumes on average. A demo site was created showing the one-minute rain rate maps and can be found at: https://www.smhi.se/en/services/professional-services/microweather/. Since then the methodology has been further improved and also applied within Stockholm in a new hydrological experiment. Currently new regions are being considered, as well as novel ways to merge data sources to create high quality precipitation maps. This contribution summarizes the progress to date.

How to cite: van de Beek, R. (C. Z. )., Andersson, J., Olsson, J., and Hansryd, J.: Five years of commercial microwave link network derived rainfall research in Sweden, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7969, https://doi.org/10.5194/egusphere-egu2020-7969, 2020.

Rainfall retrieval with commercial microwave links (CMLs) relies on the relation between radiowave attenuation and rainfall intensity. The CMLs used to operate predominantly at 15-40 GHz frequency region where the relation between rainfall and attenuation was close-to-linear and only slightly dependent on drop size distribution (DSD) (Berne and Uijlenhoet, 2007). New generation of CMLs operated within cellular backhaul utilizes increasingly the E-band frequencies, specifically frequency region 71 - 86 GHz. The attenuation-rainfall relation at this region is, however, substantially more dependent on DSD.

One year of DSD data retrieved from Parsivel OTT disdrometer is used to simulate theoretical attenuation and quantify the effect of DSD on CML rainfall estimates. The results show that E-band CMLs are highly sensitive to DSD. The relative error related to DSD variability reaches up to 40%, which is about two to three times higher value compared to errors by CMLs operated at 15-40 GHz. These errors can be, however, reduced to approx. 20% when distinguishing between stratiform and convective rainfalls and introducing two different parameter sets for attenuation-rainfall relation, accordingly.  The improvement of CML rainfall estimates when adapting parameters of attenuation-rainfall relation is demonstrated on real attenuation data acquired from 4.8 km long E-band CML operated within cellular backhaul in Prague (CZ).

Variable drop size distribution represents a significant source of uncertainty in rainfall estimates retrieved from E-band CMLs. This uncertainty can be substantially reduced by adapting parameters of attenuation-rainfall model to rainfall type (DSD).

References:

Berne, A., Uijlenhoet, R., 2007. Path-averaged rainfall estimation using microwave links: Uncertainty due to spatial rainfall variability. Geophys. Res. Lett. 34, L07403. https://doi.org/10.1029/2007GL029409

How to cite: Fencl, M. and Bares, V.: Effect of drop size distribution on microwave link rainfall retrieval at E-band, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20217, https://doi.org/10.5194/egusphere-egu2020-20217, 2020.

To calculate the effect of rainfall in detaching particles and initiating soil erosion, it is important to represent relationship between recorded drop size distributions (DSD) and fall velocity across various scales of measurement. Commonly used relationships between kinetic energy (KE) and rainfall rate (R) exhibit strong dependence on the temporal resolution at which analysis is carried out. Here we aim at developing a scale invariant relationship relying on the framework of Universal Multifractals (UM), which has been widely used to analyze and characterize geophysical fields that exhibit extreme variability over measurement scales.

Rainfall data is collected using three optical disdrometers working on different underlying technologies (one Campbell Scientific PWS100 and two OTT Parsivel2 instruments) and operated by Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech in the Paris area (France). They provide access to the size and velocity of drops falling through sampling areas of few tens of cm². Such data enables estimation of rainfall microphysics, R and KE at various resolutions. The temporal variation of this geophysical data over wide range of scales is then characterized in the UM framework. A power law relation has been developed for describing the dependence of KE on R. The developed equation using scale invariant features of UM are valid not only at a single scale, but also across scales. The amount of uncertainty is further characterized by comparing actual data with simulated rainfall data from Sense-City climate chamber.

Keywords: rainfall intensity; rainfall kinetic energy; disdrometer; multi fractal; scale invariant

How to cite: Jose, J., Gires, A., Schertzer, D., Roustan, Y., Ruas, A., and Tchiguirinskaia, I.: Variability in Rainfall and Kinetic Energy across scales of measurement: evaluation using disdrometers in Paris region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-994, https://doi.org/10.5194/egusphere-egu2020-994, 2020.

Rainfall kinetic energy (KE) is an important indicator for the potential soil loss due to rainfall in erosion risk assessment. Kinetic energy-intensity (I) relationships have been developed as a means to calculate the KE of rainfall, when only the rainfall intensity is known. The direct measurement of KE has been enabled due to the use of disdrometers, which measure the size and velocity of raindrops. Previous measurements have shown that rainfall measurements for the same site differed among disdrometer types. Therefore, the best fitting KE-I relationship is likely dependent on the type of disdrometer. In this study, the influence of the disdrometer-specific drop size and velocity measurements on the formulation of new KE-I relationships as well as the fit of existing equations from literature was investigated. Disdrometer rainfall data was collected in 1-minute intervals from six laser-based disdrometers. Two disdrometers of each of the following three types were compared: the PWS100 Present Weather Sensor from Campbell Scientific, the Laser Precipitation Monitor from Thies Clima and the first generation Parsivel from OTT Hydromet. The disdrometers were set up individually at sites in Austria, Czech Republic and New Zealand. Rainfall was measured between 2014 and 2019 with varying amounts of collected data for each site. The results revealed the inherent differences in drop size and velocity distribution estimation between different types of devices. The same pattern of rainfall drop size and velocity distribution could be seen for disdrometers of the same type despite spatial separation. This indicates that actual spatial differences in rainfall characteristics may be difficult to discern when comparing data from different types of disdrometers. New exponential KE-I relationships based on disdrometer data were formulated for each site and device. To confirm the use of the new KE-I equations, one of the equations was validated using rain gauge data from the same site. The best fit of literature KE-I equation varied among sites and devices. The relationship employed in the Revised Universal Soil Loss Equation (RUSLE) always underestimated KE with a percent bias ranging from -2 to -30 %. This study highlights the differences in disdrometer rainfall kinetic energy measurements and how these influence the formulation and evaluation of KE-I relationships, which are important in rainfall erosivity studies.

How to cite: Lolk Johannsen, L., Zambon, N., Strauss, P., Dostal, T., Neumann, M., Zumr, D., A. Cochrane, T., and Klik, A.: Influence of disdrometer type on rainfall kinetic energy measurement, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5317, https://doi.org/10.5194/egusphere-egu2020-5317, 2020.

Observing and modelling rainfall at high spatial and temporal resolution is known to be key for hydrologic applications in urban areas, but little is known about the relevance of high density observations in natural headwater catchments. In this contribution, we present the case of the Vallon de Nant experimental catchment (Switzerland) where high resolution rainfall observations have been carried out with low cost (drop-counting) sensors to develop a new sub-kilometer scale stochastic rainfall model and to investigate the relevance of high resolution rainfall observations to understand the rainfall-runoff response of a small alpine headwater catchment (13.4 km²).

We will give an overview over the experimental set-up (in place for two consecutive summers), the reliability of the used sensors (Driptych Pluvimate) and the potential of such a network to inform high resolution stochastic rainfall field models and hydrologic models. A special focus will be on the developed methodological framework to assess the importance of high resolution observations for hydrological process research. Given the relatively low cost of the deployed rainfall sensors (around 600 USD each), the presented methods are readily transferable to similar hydrologic settings, in natural as well as urban areas.

How to cite: Benoit, L., Michelon, A., Schaefli, B., and Mariéthoz, G.: Measuring and modelling high resolution rainfall fields for hydrologic process understanding, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9755, https://doi.org/10.5194/egusphere-egu2020-9755, 2020.

Breakdown coefficients of multifractal cascades have been shown, in various contexts, to be ergodic in their (marginal) probability distribution functions, however the necessary connection between the cascading process (or a tracer thereof, such as rainfall) and the breakdown coefficients of the measure generated by the cascade, was missing. This work presents a method of parameterization of certain types of multiplicative cascades, using the breakdown coefficients of the measures they generate. The method is based on asymptotic properties of the probability distributions of the breakdown coefficients in “dressed” cascades, as compared with the respective distributions of the cascading weights. An application to rainfall intensity time series is presented.

How to cite: Aguilar Flores, C. and Carsteanu, A. A.: Parameterization of multifractal cascade models based on their breakdown coefficients , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-593, https://doi.org/10.5194/egusphere-egu2020-593, 2020.

Optimized management of storm water management in the Paris area is needed to both avoid urban flooding and maximize water depollution. Such management requires improving the ability to measure and model hydro-meteorological events at the highest possible resolution. Hence, the interest of meteorological radars, given their unique ability to measure rainfall in both space and time.

In this study, we focus on the data collected by a dual polarimetric X-band radar data operated by Ecole des Ponts ParisTech in the framework of the Fresnel Platform is used. The space resolution is of 250 m and the time one is of 3 min and 25 seconds. Seven rainfall events that occurred in 2018 are studied. They cover a wide range of meteorological situations, including hail. More precisely several products are compared; some relying on a simple Marshall Palmer power law relation between the measured reflectivity and the rain rate; and others using the dual polarization capabilities for heavy rainfall through a power law relation between the measured specific differential phase shift and the rain rate. Constant and varying parameters for these laws are tested. In addition, these radar products are compared with various products obtained with a C-band radar operated by Meteo-France and 8 rain gauges. Temporal evolutions of rain rates are compared and classical metrics (Nash Sutcliff, correlation…) are computed. In addition, outputs of hydro-dynamic models’ simulations using this rainfall data are compared.

It appears that the results strongly depend on rainfall event, and even given peaks, with no clear tendency between the radar products. In addition, a strong dependency on the radar data processing, and especially the coefficients of the radar relation, is found. This suggests that further work should be done to improve their determination for this area and depending on the weather conditions. In addition, this study highlights the need to develop morphological comparison techniques that would be valid not only at a single scale but across scales.

Authors greatly acknowledge support of the chair Hydrology for Chair of Hydrology for Resilient Cities (endowed by Veolia) of the Ecole des Ponts ParisTech.

How to cite: Salih, W., Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Comparison of various X-band radar products over the Paris area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13807, https://doi.org/10.5194/egusphere-egu2020-13807, 2020.

At present, climate change is modifying rainfall regimes at a global level with effects on local activities as well as changes in the variability of perceptible rainfall at a very short time scale. This phenomenon places in a scenario of high vulnerability to all activities that depend on rainfall for its development. In this sense, the detailed knowledge of the patterns and microphysical characteristics of the precipitations that occurred in the middle zone of the Santa River catchment (Western Andes of Peru) is of high importance mainly for dryland agricultural activity.

On the one hand, there is the presence of intense precipitation that causes erosion and, on the other hand, precipitation of less intensity beneficial for the improvement of the soil structure. In this regard one of the main parameters that define the characteristics of precipitation and are directly related to the origin of its formation (convective and stratiform) and intensity is the distribution of the size of raindrops (RSD). Through RSD the type of precipitation occurred in the catchment can be defined and classified.

In such context, the main objective of the study is to characterize the distribution of the size of raindrops and associate them with a type of precipitation. For this we use the Micro Radar of Precipitation (MRR-2) installed in the city of Huaraz (between the Cordillera Blanca and Cordillera Negra), with data from March 2017 to December 2019 (34 months). A frequency analysis with the distribution data of RSD is carried out as well as an analysis of main components to relate it to a type of rain. The results reveal the different types of rainfall that occurred in the area during the analysis period, and also identify the periods and frequencies of these rains due to the current weather patterns.

How to cite: Robles Sánchez, D., Figueroa Tauquino, R., Fernández Rosales, C., Villanueva Ramírez, R., Huggel, C., Seidel, J., Muñoz, R., and Motschmann, A.: Rain drop size distribution (RSD) associated with precipitation types in the middle zone of the Santa River catchment, Peru, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13561, https://doi.org/10.5194/egusphere-egu2020-13561, 2020.

It is commonly assumed that a rain drop falls vertically at a speed equal to its so called “terminal fall velocity” which has been determined both empirically and theoretically by equating the net gravity force with the drag force due to the fact the drop is moving in the atmosphere. This velocity depends on the size of the drop, usually characterized by its equivolumic diameter.

In this investigation we study the temporal evolution of the velocity of a rain drop falling through turbulent wind field. The equation governing a rain drop motion relates the acceleration to the forces of gravity and buoyancy along with the drag force. The latter depends non-linearly on the instantaneous relative velocity between the drop and the local wind. The whole complexity of the resulting behaviour arises from this feature. In this work, the drag force is expressed in a standard way with the help of a drag coefficient, which is itself determined according to a Reynolds number. It should be mentioned that in this initial work, the strong assumption that the drops remain spherical in their fall is made. It is well known that its not true for drops greater than typically 1-2 mm which tend to become oblate, and potential effects on the results will be discussed.

An explicit numerical scheme is implemented to solve this equation for 3+1D turbulent wind field to study the temporal evolution of the velocities as well as the trajectories of rain drops over few hundreds of meters. The variations in both space and time of the wind field are simulated with the help of a Universal Multifractals which are a framework that has been widely used to characterize and simulate geophysical fields extremely variable over a wide range of scales such as wind.

Temporal multifractal analysis are then carried out on the simulated drop velocity, which enables to characterize the behaviour of drops according to their size, and notably a scale below which turbulent eddies have a limited impact on their motion. Finally the consequences of these findings on rainfall remote sensing with radars are briefly discussed.

How to cite: Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Temporal evolution of rain drops’ velocities in a turbulent wind field, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11648, https://doi.org/10.5194/egusphere-egu2020-11648, 2020.

In Taiwan, the hydraulic structures of groundsill, check dam and embankment are frequently used in wild creek in order to prevent longitudinal and lateral scour. The benefit of these structures could not be numerically evaluated before construction without movable bed computational software. In recent years, the downstream scour-and-fill of hydraulic structures in wild creek could be carried out by software of River Flow 2D. This study used this software to evaluate the various setups of hydraulic structures in Jianshi, Hsinchu. Before carrying out software, the unmanned aerial vehicle (UAV) was operated to capture aerial photos of watershed. Then, the digital surface model (DSM) and orthomosaic photos were produced by Pix4Dmapper. Because most of wild creeks have no vegetation on their own creek bed, the DTM could be replaced by DSM. Associated with the various setups of hydraulic structures, Global mapper, QGIS and designed rainfall data, the software of River Flow 2D could give the downstream scour-and-fill of various setups of hydraulic structures. And, the convenient setup could be selected after evaluating the various setups of hydraulic structures.

How to cite: Tu, C.-H. and Huang, H.-P.: Evaluation of various setups of hydraulic structures in wild creek by River Flow2D, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4953, https://doi.org/10.5194/egusphere-egu2020-4953, 2020.

In recent years, the arched groundsill has frequently used to prevent downstream scour and make ecologic habitat in Taiwan. However, the relationship between the depth of downstream scour and curvatures of arched groundsill is still unclear among the specialists and engineers. In order to explore this relationship, this study carried out flume test and calibrated computational software. The result shows that the maximum impact increases with increasing curvatures of both of upward and downward arched groundsills. And, the downstream flow tubes tend to concentration with increasing curvatures of upward arched groundsill while the downstream flow tubes tend to spread uniformly with increasing curvatures of downward one. These phenomena would affect the scale of downstream scour and make the new river geomorphology. Result could be as a reference for choosing convenient curvature when specialists and engineers design arched groundsill.

How to cite: Yang, F.-H. and Huang, H.-P.: Comparison of the downstream scour to various curvatures of arched groundsill by flume test and computational software, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4588, https://doi.org/10.5194/egusphere-egu2020-4588, 2020.

In recent years, two-dimensional sediment transportation on movable bed models have been widely used in hydraulic engineering. Because of different assumptions, each model has its own feasibility on specified issues and areas. The SRH-2D model is an implicit method of the finite-volume method without CFL stability conditions, and requires more calculations than the explicit one at each time step. On the other hand, RiverFlow2D is an explicit method of finite-volume method with CFL conditions and saves much more time. In order to compare the results from these two software, a case study of Natorsa Creek, Kaohsiung, Taiwan, is carried out on the sensitivity analysis and different structure setups associated with rainfall data, water level record and DTM. The principle results are as following: This study uses average absolute error (MAE) and mean square root error (RSML) to investigate the sensitivities of SRH-2D and RiverFlow2D models and finds out the operation time increased with shorter time interval. Although SRH-2D supports three formulas while RiverFlow2D supports ten, it takes more factors into account like secondary flow, sediment size distribution, and the bed armoring effect. The simulation of scour-and-fill condition by these two models has shown similar result. However, there still exists small discrepancy between software simulation and field investigation.

How to cite: Su, P.-N.: Simulation of sediment transportation on Natorsa Creek by RiverFlow2D and SRH-2D , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9180, https://doi.org/10.5194/egusphere-egu2020-9180, 2020.

In Taiwan, arched groundsill is frequently used as soil-and-water conservation structures for stabilizing creek bed, guiding flow direction, decreasing the slope of creek bed and reducing the scour effect. Even though much more arched grounsill was built in wild creek recently, its mechanical mechanism is still unclear.

In order to explore the characteristics of arched groundsill, this study intends to find out the scale of stress, moment and displacement distribution on the various curvature arched groundsills by means of the structural analysis software, ABAQUS. Simultaneously, the three-dimensional computational fluid dynamics software, ANSYS-FLUENT, is applied to show the flow condition of different setups. Preliminary result shows that the maximum stress and displacement of arched groundsill increase with curvature. The maximum moment decreases slightly firstly and increases sharply later with curvature.

How to cite: Hou, Y.-J. and Huang, H.-P.: Stress analyses of various curvature arched groundsills, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12824, https://doi.org/10.5194/egusphere-egu2020-12824, 2020.

We link areal reduction factors (ARFs, the ratio of annual maxima catchment precipitation and point precipitation) to the dominating precipitation mechanisms in Austria (84,000km²), using a new efficient method of estimating ARFs based on block kriging. A better understanding of the precipitation mechanisms help assess the plausibility of the ARFs estimated, but ARFs likewise contribute to a better understanding of the precipitation mechanisms as they are a fingerprint of the spatial statistical behavior of extreme precipitation. Our main focus is on two sub-regions in the West and East of Austria, dominated by stratiform and convective precipitation, respectively. ARFs are estimated using rain gauge data with hourly resolution across five durations. ARFs decay faster with increasing area in regions of pronounced convective activity than in regions dominated by stratiform processes. Low ARF values are linked to increased lightening activity (as a proxy for convective activity), but low ARFs can likewise occur in areas of reduced lightning activity as, in summer, convective precipitation can occur everywhere in the country. ARFs tend to decrease with increasing return period, possibly because the contribution of convective precipitation is higher. Our analysis is a key component towards a better understanding of the hydrometeorology in the region, as the process links of the ARFs relate to the space-time scaling of floods.

How to cite: Breinl, K., Müller-Thomy, H., and Blöschl, G.: Space-time characteristics of areal reduction factors and precipitation mechanisms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5219, https://doi.org/10.5194/egusphere-egu2020-5219, 2020.

In the European Alps, climate change has determined changes in extreme precipitation and river flood events, which impact the population living downstream with increasing frequency. The objectives of our work are:

To these aims, we will compile and analyze historical time series of precipitation and discharge in order to identify events in terms of intensity, duration, and spatial extent. We will use the ETCCDI indices as a measure of the precipitation distribution and hydrograph separation techniques for flow events, following the methodology of Tarasova et al. (2018). We will then characterize each event in terms of generation mechanisms. Furthermore, we will analyze the frequency and magnitude of the different event types in different locations and time of the year and determine whether clusters exist by applying automatic techniques (e.g. K-means clustering algorithm). Finally, we will correlate statistics of precipitation and flood event types with climate indices related to large scale atmospheric circulation, such as Atmospheric Blocking, NAO, etc. (Ciccarelli et al. 2008). Results will be then used for the projection of future storm and flood scenarios.

We will first apply the methodology in Piedmont by comparing the station-based time series with the NWIOI dataset (ARPA Piemonte) and reanalysis datasets by ECMWF (ERA5, ERA5-Land). We will use a rainfall-runoff model at the daily and sub-daily timescale, through calibration at the regional scale, useful for the simulation of soil saturation and snowpack. We expect to find a statistical correlation between the different datasets, but with changing statistical features over space and time within the single datasets. We aim to provide a detailed picture of the different types of events according to the spatial location and season. The results will be useful, from a scientific perspective, to better understand storm and flood regimes and their change in the Alpine Region, and, from a practical perspective, to better mitigate the risk associated with the occurrence of extreme events.      

Ciccarelli, N., Von Hardenberg, J., Provenzale, A., Ronchi, C., Vargiu, A., & Pelosini, R. (2008). Climate variability in north-western Italy during the second half of the 20th century. Global and Planetary Change, 63(2-3), 185-195. https://doi.org/10.1016/j.gloplacha.2008.03.006

Tarasova, L., Basso, S., Zink, M., & Merz, R. (2018). Exploring controls on rainfall-runoff events: 1. Time series-based event separation and temporal dynamics of event runoff response in Germany. Water Resources Research, 54, 7711–7732. https://doi.org/10.1029/2018WR022587

How to cite: Pesce, M., Tarasova, L., Merz, R., von Hardenberg, J., and Viglione, A.: Characterization of extreme meteo-hydrological events in the Alpine Region: historical picture and future scenarios, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-498, https://doi.org/10.5194/egusphere-egu2020-498, 2020.

The impact of climate change on environmental extremes such as high flows or rainfall, is routinely investigated by fitting non-stationary extreme value distributions to long-term observational records. These investigations often use regression models in which one or more distribution parameters is allowed to change as a function of time or some other preocess-related covariate. The changes in quantiles implied by different regression model are quantified in this study using different quantile change metrics. We expose the mathematical structure of these change metrics for various commonly used non-stationary models, showing how for most commonly used models the resulting changes in the estimated quantiles are a non-intuitive function of the distribution parameters, leading to results which are difficult to interpret and therefore of little practical use in engineering design. Further, it is posited that the most commonly used non-stationary models do not preserve fundamental scaling properties of environmental extremes. 

A new (parsimonious) model is proposed which results in changes in the quantile function that are easy to interpret, and for which the scaling properties are maintained, so that when the location parameter is allowed to change so is the scale. The proposed parameterization is applicable within a range of commonly used distributions (e.g. GEV, GLO, Kappa, ...) and is better suited for investigating changes in environmental extremes as it provides more interpretable description of changes in design events under a non-stationary model. The empirical behaviour of the quantile change metrics under different modelling frameworks when applied to river flow data in the UK is investigated to showcase the usefulness of the proposed model. 

How to cite: Prosdocimi, I. and Kjeldsen, T.: The parametrisation of statistical models of change in extremes and its impact on the description of change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2594, https://doi.org/10.5194/egusphere-egu2020-2594, 2020.

Flood frequency curves are the basis to design ordinary engineering structures and devise strategies aimed at mitigating an increasing flood risk. Moreover, they are a crucial tool of risk assessment for insurance and reinsurance purposes. This work is concerned with the presence of abrupt increases of the flood frequency curve (i.e., sudden increments of streamflow magnitudes for a certain return period, named step changes), and investigate their occurrence by means of the physically-based extreme value distribution (PHEV!) of streamflow. This is an analytic probability distribution of extremes, which emerges from a lumped mechanistic-stochastic description of runoff generation and rainfall, soil moisture and discharge dynamics.

In the study, long synthetic time series of streamflow for river catchments exhibiting step changes have been generated and randomly resampled to construct sub-series of decreasing length. These shorter series are then used to test the performance of the PHEV!, of standard purely statistical distributions of the extremes, and of empirical observation-based estimates of the flood frequency curve in detecting the existence of a step change in the long time series from scarce data. Findings show that the PHEV! robustly detects the occurrence of step changes also when only short time series (e.g., 10 years) are used for parameter estimation. Conversely, the alternative methods tested mostly fails in this objective. These results indicate that the PHEV! might be a reliable tool for detecting the propensity of rivers to generate extreme floods in regions lacking long series of discharge observations.

How to cite: Basso, S., Domin, A., Merz, R., Botter, G., and Miniussi, A.: Detecting hazardous rivers: the physically-based extreme value distribution (PHEV!) , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21888, https://doi.org/10.5194/egusphere-egu2020-21888, 2020.

Hydrological time series are characterized by variability over a wide range of temporal scales. While lower frequency variability has been widely studied in hydrology, it is seldom explicitly accounted for in extreme value models. This limitation arises as a consequence of the limited data available for inference on extremes, and especially in the case of hydrological processes exhibiting relevant variability over yearly or longer time scales. Motivated by the statistical analysis of extreme rainfall, here we present a Bayesian hierarchical model developed for estimating the probability distribution of extreme values of intermittent random sequences. This method relaxes the asymptotic assumptions ordinarily employed in extreme value theory, and models the entire underlying parent distribution of the events. The hierarchical structure of the model explicitly separates the ‘fast’ time scale of event occurrence from a lower-frequency variability component, which is modeled through latent-level variables. In the case of rainfall, this latent level represents the inter-annual variability in the distributions of both of event magnitudes and in the frequency of their occurrence. Inference is conducted numerically by means of a Bayesian approach, thus allowing for the inclusion of relevant prior information, and leading to a fully probabilistic description for the quantities of practical interest, such as high return times quantiles. Here we test the proposed model by means of a simulation study, and include an application to rainfall data obtained from long instrumental records. Our results show that this approach I) leads to improved inference in the case of relatively short datasets, and II) can benefit from prior information on the physical processes involved in order to reduce estimation uncertainty. Moreover, we show that the presence of low frequency variability leads to statistical models characterized by heavier tails, thus underlining the importance of low frequency variability in determining the extreme-value statistical properties even in the case of stationary models. While the focus of our application in on rainfall extremes, the structure of the model is quite general and applications to other environmental variables are discussed.

How to cite: Zorzetto, E., Canale, A., and Marani, M.: Hierarchical Bayesian modelling of hydrological extremes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10904, https://doi.org/10.5194/egusphere-egu2020-10904, 2020.

Using weather generators to produce scenarios with changed statistics commonly involves the output of numerical climate models and/or leveraging the correlations in an observed data set. This contribution proposes vine copulas as a method to improve the latter in a parsimonious way. The vine copula construction flexibly models multivariate dependence structures as it breaks these down into pair-wise relationships that can be modelled individually with the wide variety of bivariate copula families. Setting up the vine tree carefully allows a user-supplied change in one specific variable, e.g. air temperature, to spread to the other simulated variables according to the fitted dependence structure.
In order not to increase dramatically the number of free parameters, the copula is only employed for time-invariant, inter-variate dependence, leaving all temporal and inter-site dependencies to Phase Randomization. Phase Randomization is a spectral method which generates "surrogate time series" that share their autocorrelation function with a source time series. It can be modified to handle cross-correlations in multivariate time series as well.
Precipitation occurrence and amounts are simulated in a joint fashion, using a latent variable constructed with information from other meteorological variable at the same locations.  The methodology will be illustrated with an example involving daily air temperature, precipitation, sunshine duration and relative humidity from measurement stations in southern Germany. 

How to cite: Schlabing, D. and Bárdossy, A.: A weather generator based on vine copulas and phase randomization for producing scenarios, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7715, https://doi.org/10.5194/egusphere-egu2020-7715, 2020.

Understanding extreme precipitation under changing climatic conditions is crucial to manage weather- and flood-related hazards. Global and regional climate models are able to provide coarse scale information on future conditions under different emission scenarios, but large uncertainties affect the projected precipitation amounts, extremes in particular, so that frequency analyses cannot be quantitatively trusted. This study uses, for the first time, the Simplified Metastatistical Extreme Value (SMEV) approach to directly exploit synoptic scale information, better represented by climate models, for obtaining projections of future extreme precipitation frequency.

We use historical rainfall data from >400 stations in Israel and Jordan to (a) provide a climatology of extreme daily precipitation (e.g., the 100-year return period amounts) in the steep climatic gradients of the region and (b) improve understanding of the SMEV description under changing climate. We demonstrate that, using SMEV, it is possible to (c) present the sensitivity of extreme quantiles to occurrence and intensity of Mediterranean lows and other synoptic systems, and (d) project future extreme quantiles starting from synoptic scale information generated by earlier climate-model-based studies. Under our working hypotheses, we project a general decrease of extreme precipitation quantiles for the RCP8.5 scenario; an increase is detected in the coastal region and the Negev arid lands. We discuss the apparent contrast of these results with previous findings.

How to cite: Marra, F., Armon, M., Zoccatelli, D., Gazal, O., Garfinkel, C., Adam, O., Dayan, U., Rostkier-Edelstein, D., Enzel, Y., and Morin, E.: Future extreme precipitation frequency in the eastern Mediterranean: a new approach exploiting climate model projections, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-592, https://doi.org/10.5194/egusphere-egu2020-592, 2020.

Extreme events have a strong impact on economic and ecological systems, causing dramatic effects on agriculture, health and other socio-economic activities. Predicting these impacts is of great importance, that is why climate studies over the last decades have focused on weather and climate change extremes both in the future and in the past.

Statistical analysis of observational data is still considered as the basic one in climatology. It allows one to study regional manifestations of the global processes in the climate system of different temporal scales of variability. The results of such a retrospective analysis are usually used for validation of global or regional climate models, for statistical forecasting of expected changes as well as for implementation of methods for dynamical and empirical-statistical downscaling of global climate model output to the regional scales. This issue becomes particularly relevant when studying extreme meteorological events in a changing climate.

The goal of the present study is to develop an algorithm of empirical diagnosis and forecasting of extremes in a changing climate. The algorithm suggested here is based on (a) technique of nonlinear time series decomposition into empirical modes from noise to trend - EMD method (Huang et al., 1998), (b) modeling of extreme values distribution by GEV, (c) reproduction of correlation structure of climatic series with long "memory" using fractional-integrated autoregressive models - moving average (FARIMA), (d) generation of ensemble of "artificial" surrogate time series using stochastic iterative amplitude adjusted Fourier transform algorithm (Venema V. et al., 2006).

Such an approach allows one not only to make a thorough statistical diagnosis of regional meteorological extremes in a non-stationary climate but also to make an empirical forecasting of the weather and climate anomalies into the near future. The algorithm was implemented and tested using daily data on air temperature and precipitation at meteorological stations of different climate regions: the upper Volga region, the territory of the Northern Caucasus and the Azov-Black sea coast region.

This research was supported by the Russian Foundation for Basic Research (projects No. 18-05-01073 and 19-29-05243).

How to cite: Evstigneev, V. and Natalya, L.: Empirical diagnosis and forecasting of extremes in a changing climate: a case study of Russia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21251, https://doi.org/10.5194/egusphere-egu2020-21251, 2020.

Many processes in hydrology and Earth system science relate to moisture recycling, the contribution of terrestrial evaporation to precipitation. For example, the effects of land-cover changes on regional rainfall regimes depend on this process. To study moisture recycling, a range of moisture tracking models are in use that are forced with output from atmospheric models, but differ in various ways. They can be Eulerian (grid-based) or Lagrangian (trajectory-based), have two or three spatial dimensions, and rely on a range of other assumptions. Which model is most suitable depends on the purpose of the study, but also on the quality and resolution of the data with which it is forced. Recently, the high-resolution ERA5 reanalysis dataset has become the state-of-the-art, paving the way for a new generation of moisture tracking models. However, it is unclear how the new data can best be used to obtain accurate estimates of atmospheric moisture flows. Here we develop a set of moisture tracking models forced with ERA5 data and systematically test their performance regarding continental evaporation recycling ratio, distances of moisture flows, and <q>footprints</q> of evaporation from seven point sources across the globe. We report simulation times to assess possible trade-offs between accuracy and speed. Three-dimensional Lagrangian models were most accurate and ran faster than Eulerian versions for tracking water from single grid cells. The rate of vertical mixing of moisture in the atmosphere was the greatest source of uncertainty in moisture tracking. We conclude that the recently improved resolution of atmospheric reanalysis data allows for more accurate moisture tracking results in a Lagrangian setting, but that considerable uncertainty regarding turbulent mixing remains. We present an efficient Lagrangian method to track atmospheric moisture flows from any location globally using ERA5 reanalysis data and make the code for this model publicly available.

How to cite: Tuinenburg, O. and Staal, A.: Tracking the global flows of atmospheric moisture , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7940, https://doi.org/10.5194/egusphere-egu2020-7940, 2020.

The summer precipitation in South China (SC) has experienced a pronounced interdecadal variation during 1983–2013

with trend transition in the late 1990s. This study quantitatively investigates the precipitation variation and its connection to

water vapor transport by combining the Lagrangian trajectory-based Dynamic Recycling Model and the clustering method

of self-organizing map. The external moisture outside of SC explains most (84%) of the mean and the interdecadal variation

of the summer rainfall, mainly through the southwest transport pathways. A long-distance southwest pathway related to

cross-equatorial flow and eastward flow over the Northern Indian Ocean explains 31.5% of mean precipitation and 50.4% of

the upward precipitation trend before 1997. The other branch of the southwest pathways has relatively shorter length over

North Indian Ocean, South China Sea, and Southeast Asia, explaining 35.7% of the mean and 51.2% of the downward trend

after 1997. Also, for the downward trend, the westerly-driven moisture transport over Eurasia acts as the second contributor

(32.2%) to the precipitation decrease. However, the western-Pacific pathway explains the smallest portion (≤ 3%) of

the trends, suggesting weak influence from the subtropical high. The large-scale circulation anomaly in the form of zonal

and meridional wave trains control the interdecadal variability of the SC precipitation. It is found that the circumglobal teleconnection

and Pacific–Japan teleconnection significantly correlate to the two wave trains, whose match relation strongly

modulates the trend transition in the 1990s for the SC summer precipitation.

How to cite: Hua, L.: A quantitative study of moisture transport variation on the interdecadal variation of the summer precipitation in South China from 1979 to 2015, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1803, https://doi.org/10.5194/egusphere-egu2020-1803, 2020.

The expected intensification of the global water cycle in a warming climate comes along with an increase in the frequency and intensity of extreme events, such as droughts and floods. From a drought perspective, local limitations of terrestrial evaporation can cause a reduction of water vapor in the atmosphere and thus further induce local and remote precipitation deficits. Despite the existing myriad of tools and models to assess the origin of precipitation, trends and uncertainties in such source–sink relationships remain largely unexplored. The main reason is the scarcity of observations to explore these relationships and validate moisture-tracking models, which are commonly subject to assumptions that limit their reliability and applicability. Lagrangian models, for example, typically establish source–sink relationships based on moisture changes along air parcel trajectories, yet tend to be heavily affected by numerical noise. Moreover, they do not assess the plausibility of a given moisture change by considering the increasing saturation point of air with increasing temperatures, which hampers reliable assessments of trends under global warming. 

Here, we present a holistic framework for the process-based evaluation of atmospheric trajectories to infer source–sink relationships of moisture. Building upon previous process-based evaluations of trajectories, we extend the analysis to a coupled heat and moisture diagnosis that includes physics-based limits for the detection of evaporation and precipitation from humidity changes along each trajectory. The framework comprises three steps: (i) the coupled moisture and heat diagnosis of fluxes from Lagrangian trajectories using multi-objective criteria, (ii) the attribution of sources following a mass- and energy-conserving algorithm, and (iii) the bias correction of diagnosed fluxes and the corresponding source–sink relationship. Applying this framework to simulations from the Lagrangian particle dispersion model FLEXPART, driven with ERA-Interim reanalysis data, allows us to quantify errors and uncertainties associated with the resulting source–sink relationships. A comparison to alternative methodologies illustrates the benefit of our coupled heat and moisture tracking approach. Moreover, the multivariate character of this framework paves the way for a cohesive assessment of the spatial dependencies that cause water cycle changes in a warming climate.

How to cite: Keune, J., Schumacher, D. L., and Miralles, D. G.: A coupled heat and moisture tracking framework to assess water cycle changes in a warming climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4128, https://doi.org/10.5194/egusphere-egu2020-4128, 2020.

The recent 2018 summer drought in Europe has been particularly extreme in terms of intensity and impact. However, how did this drought develop in time and space in such an extreme way, and what role did the change in land-atmosphere feedbacks play in the propagation and intensification of the drought in Europe.

To answer those questions, we used remote sensing products of soil moisture and NDVI to see where the 2018 drought started and how it developed over time and space. Then we used the atmospheric water vapour flow tracking method (WAM-2layers) to investigate whether the drought intensification and displacement was related to the lack of water vapour transport from the regions that first experienced the drought. To this end, we identified the anomalies in the atmospheric water vapour imports and exports within Europe during  the spring, summer, and autumn seasons 2018.

Our soil moisture and NDVI analysis shows that the 2018 drought started in June in the Scandinavian countries and the British Isles and with time started to intensify and to move toward the west of Europe and after that to the southeast of Europe. The lack of land water vapour transportation from upwind regions (Scandinavian countries and British Isles) was partly responsible for the lack of re-precipitated water vapour in the downwind regions (West, South, Southeast, and East of Europe). From this study, we can conclude that extreme drought events propagate and intensify with time from upwind regions to downwind regions.

How to cite: Al Hasan, F., J. van der Ent1, R., and C. Steele‐Dunne, S.: The effect of land –atmospheric feedbacks on the intensification and propagation of the 2018 drought in Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20717, https://doi.org/10.5194/egusphere-egu2020-20717, 2020.

Numerous studies have demonstrated that forests considerably alter temperatures at the land surface. These alterations vary in space and time due to a complex interplay of several modified energy fluxes at the land surface. The effect of forests on the amount and pattern of precipitation has gained less attention, despite its high socio-economic relevance. Previous work has demonstrated that the high precipitation amounts in tropical rain forests are self-sustained by the abundance of those forests itself. Yet, the impact of forests on precipitation in extra-tropical regions has gained only little attention. This study attempts to identify a relationship between the amount of precipitation and the abundance of forests over the European continent. Such a relationship can originate from two kinds of interactions: (1) The amount of precipitation can drive the abundance of forests, as water is a crucial resource for forests ecosystems. (2) The energy and water redistribution at the land surface associated with forests can alter processes in the atmospheric air column, which in term could affect the amount of precipitation at the location of the forest. Here, we aim to isolate the second kind of effects, as those are more relevant for human decision making.

Establishing a causal relationship between the abundance of forests and the amount of precipitation is complex due this two-way interaction. Hence, three different data sources are employed to advance our understanding of how forests influence precipitation patterns. Firstly, a geographically weighted regression is applied to the spatially-continuous, observation-based precipitation data set MSWEP2.2 (Beck et al., 2017). Besides the forest fraction, a number of topographical variables are considered as predictor variables to account for potential confounding factors (i.e, to assure that interactions of the first kind are not misinterpreted as interactions of the second kind). Secondly, closely-located, paired sites that resemble in topography, but differ in forest fraction are identified in the GHCN (Menne et al., 2012) and the GSDR (Lewis et al., 2019) rain gauge data sets. This allows to evaluate the results based on MSWEP2.2. Thirdly, the same geographically weighted regression is applied to convection-resolving regional climate simulations. By artificially defining the forest fraction distribution in model simulations, interactions of the first kind can be disabled, further fostering the understanding about the causality on the relations identified using the observations. Further sensitivity experiments could be conducted, to improve the process understanding on interactions of the second kind. Overall, our results indicate, that the abundance of a forest increases the amount of precipitation in the order of 100 mm/yr in many locations of Europe. This increased amount of precipitation is more pronounced during the winter months, while the summer signal is more close to zero.

How to cite: Meier, R., Davin, E., Schwaab, J., and Seneviratne, S.: The Effect of Forests on the Amount of Incoming Precipitation over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21573, https://doi.org/10.5194/egusphere-egu2020-21573, 2020.

Gaps persist in our comprehension of forest-water interactions and how forest cover potentially alters and sustains precipitation at continental scales. We analyze high-resolution, remote sensing data on forest cover, annual average wind speed and total annual precipitation amounts in order to better understand how forest cover impacts windspeed, and how the forest impact on windspeed can influence the transport and potential re-deposition of atmospheric moisture as rainfall. In this first look at these interactions over the South American continent, uur analysis indicates forests slow windspeed, providing more opportunity for the accumulation and aggregation of both incoming atmospheric moisture and local evapotranspiration, thereby contributing to its increased potential re-deposition as rainfall. Our findings indicate rainfall is greater where forest cover has the effect of slowing windspeed. Moreover, in slowing windspeed, greater forest cover intensifies the hydrologic cycle, providing more opportunities for atmospheric moisture and evapotranspiration to condense and precipitate, as well as re-evaporate and re-transpire back to the atmosphere, thereby potentially increasing the terrestrial rainfall recycling and thus water use and availability across continental surfaces. We are hopeful improved understanding of how forest cover, windspeed and rainfall interact can help motivate future study and promote the development of a more rigorous approach to preserving the hydrologic cycle through the pursuit of Nature-based Solutions to forest landscape restoration.

How to cite: Ellison, D. and Ellis, E.: Forest Cover, Windspeed, and Precipitation: A South American Case Study of the Impact of Forest Ecosystems on Wind and Rainfall Patterns, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22235, https://doi.org/10.5194/egusphere-egu2020-22235, 2020.

The soil-moisture feedback describes how precipitation amount, timing and intensity react to spatial anomalies in surface moisture. For heterogeneous moisture distributions with moist/dry patches on the scale of 10− 50km, numerical studies supported by observations indicate a negative soil-moisture feedback, where it rains more over dry patches (Imamovic, 2018; Rieck et al., 2014). The circulation established by the heterogeneous soil-moisture patches not only modifies the spatial rain distribution but allows for more water to be extracted from the atmosphere, thereby increasing the domain mean precipitation.

We here suggest that the negative soil-moisture feedback can be exploited when irrigating agricultural land: if farmers cooperate by following a spatially heterogeneous irrigation pattern, they can increase both their collective time-mean precipitation and thus the total water available for growing crops. However, the spatially non-local nature of the feedback allows individual farmers to exploit this strategy, thereby saving their own resources; a typical ‘tragedy of commons’ situation.
We formulate this setup in terms of an optimisation problem and study its parameter phase space, both analytically and numerically, in order to understand optimal rules and the consequences of the players’ choice to cooperate vs. compete. Different constraints in terms of water availability (reservoir) and average soil moisture as defined by the evaporation timescale are explored.

Reducing the details of the land-atmosphere interaction into simple feedback parameters helps to elucidate the complex interactions between the precipitation, soil moisture and the human intervention by irrigation. Taking into account the negative soil-moisture feedback in irrigation models opens up new strategies to optimise water management and thereby increase crop yield.

How to cite: Meyer, B., Parker, D. J., and Haerter, J. O.: A dynamical systems approach to optimizing irrigation strategy under the influence of land-atmosphere feedbacks., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20644, https://doi.org/10.5194/egusphere-egu2020-20644, 2020.