Untitled

Pfister, Laurent; Humbert, Joël; Hoffmann, Lucien

doi:10.1023/a:1005567808533

Cited by 47 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study area is situated in the temperate oceanic climate zone (Cfb according to the Köppen-Geiger classification; Köppen and Geiger, 1930). Precipitation is mainly associated with synoptic flow regimes with a westerly wind component and amounts to around 850 mm (Pfister et al, 2005(Pfister et al, , 2000, ranging from 760 mm in the center to about 980 mm in the northwestern region (Faber, 1971) due to orography and luvlee effects. Especially during intensive rain events within a synoptic northwesterly flow regime, the main part of the Attert region lies in the rain shadow of the Ardennes, whereas the outermost northwestern part is excluded from this lee effect and is still within the region of local rain enhancement by upward orographic lift (Schmithüsen, 1940).…”

Section: Study Area and Hydroclimatementioning

confidence: 99%

Quantitative precipitation estimation with weather radar using a data- and information-based approach

Neuper

Ehret

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In this study we propose and demonstrate a data-driven approach in an “information-theoretic” framework to quantitatively estimate precipitation. In this context, predictive relations are expressed by empirical discrete probability distributions directly derived from data instead of fitting and applying deterministic functions, as is standard operational practice. Applying a probabilistic relation has the benefit of providing joint statements about rain rate and the related estimation uncertainty. The information-theoretic framework furthermore allows for the integration of any kind of data considered useful and explicitly considers the uncertain nature of quantitative precipitation estimation (QPE). With this framework we investigate the information gains and losses associated with various data and practices typically applied in QPE. To this end, we conduct six experiments using 4 years of data from six laser optical disdrometers, two micro rain radars (MRRs), regular rain gauges, weather radar reflectivity and other operationally available meteorological data from existing stations. Each experiment addresses a typical question related to QPE. First, we measure the information about ground rainfall contained in various operationally available predictors. Here weather radar proves to be the single most important source of information, which can be further improved when distinguishing radar reflectivity–ground rainfall relationships (Z–R relations) by season and prevailing synoptic circulation pattern. Second, we investigate the effect of data sample size on QPE uncertainty using different data-based predictive models. This shows that the combination of reflectivity and month of the year as a two-predictor model is the best trade-off between robustness of the model and information gain. Third, we investigate the information content in spatial position by learning and applying site-specific Z–R relations. The related information gains are only moderate; specifically, they are lower than when distinguishing Z–R relations according to time of the year or synoptic circulation pattern. Fourth, we measure the information loss when fitting and using a deterministic Z–R relation, as is standard practice in operational radar-based QPE applying, e.g., the standard Marshall–Palmer relation, instead of using the empirical relation derived directly from the data. It shows that while the deterministic function captures the overall shape of the empirical relation quite well, it introduces an additional 60 % uncertainty when estimating rain rate. Fifth, we investigate how much information is gained along the radar observation path, starting with reflectivity measured by radar at height, continuing with the reflectivity measured by a MRR along a vertical profile in the atmosphere and ending with the reflectivity observed by a disdrometer directly at the ground. The results reveal that considerable additional information is gained by using observations from lower elevations due to the avoidance of information losses caused by ongoing microphysical precipitation processes from cloud height to ground. This emphasizes both the importance of vertical corrections for accurate QPE and of the required MRR observations. In the sixth experiment we evaluate the information content of radar data only, rain gauge data only and a combination of both as a function of the distance between the target and predictor rain gauge. The results show that station-only QPE outperforms radar-only QPE up to a distance of 7 to 8 km from the nearest station and that radar–gauge QPE performs best, even compared with radar-based models applying season or circulation pattern.

show abstract

Section: Study Area and Hydroclimatementioning

confidence: 99%

Quantitative precipitation estimation with weather radar using a data- and information-based approach

Neuper

Ehret

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Aerts et al, 2006;Booij, 2005;Bultot et al, 1988Bultot et al, , 1990De Wit et al, 2001Gellens, 1991;Gellens and Roulin, 1998;Giorgi and Coppola, 2007;Kwadijk and Rotmans, 1995;Leander et al, 2008;Middelkoop et al, 2004;Pfister et al, 2000;Tu, 2006;Van den Hurk et al, 2007;Van Deursen and Middelkoop, 2002;Vanneuville and Holvoet, 2009;Van Pelt et al, 2009;Ward et al, 2007Ward et al, , 2008Ward et al, , 2011. Although there are differences in the results, most studies suggest that the frequency of floods will increase in the future due to climate change.…”

Section: Past Studies: Climate Hydrology and Riskmentioning

confidence: 99%

How are flood risk estimates affected by the choice of return-periods?

Ward

Moel

Aerts

2011

Nat. Hazards Earth Syst. Sci.

141

View full text Add to dashboard Cite

Abstract. Flood management is more and more adopting a risk based approach, whereby flood risk is the product of the probability and consequences of flooding. One of the most common approaches in flood risk assessment is to estimate the damage that would occur for floods of several exceedance probabilities (or return periods), to plot these on an exceedance probability-loss curve (risk curve) and to estimate risk as the area under the curve. However, there is little insight into how the selection of the return-periods (which ones and how many) used to calculate risk actually affects the final risk calculation. To gain such insights, we developed and validated an inundation model capable of rapidly simulating inundation extent and depth, and dynamically coupled this to an existing damage model. The method was applied to a section of the River Meuse in the southeast of the Netherlands. Firstly, we estimated risk based on a risk curve using yearly return periods from 2 to 10 000 yr (C 34 million p.a.). We found that the overall risk is greatly affected by the number of return periods used to construct the risk curve, with over-estimations of annual risk between 33 % and 100 % when only three return periods are used. In addition, binary assumptions on dike failure can have a large effect (a factor two difference) on risk estimates. Also, the minimum and maximum return period considered in the curve affects the risk estimate considerably. The results suggest that more research is needed to develop relatively simple inundation models that can be used to produce large numbers of inundation maps, complementary to more complex 2-D-3-D hydrodynamic models. It also suggests that research into flood risk could benefit by paying more attention to the damage caused by relatively high probability floods.

show abstract

“…Although the strong interdependence of these physical, chemical, and biological processes is well-known and their influence on A successful modeling study where an attempt was made to model the behavior of an entire catchment using parameters of a single representative hillslope was undertaken by Loritz et al (2017) in the Attert experimental catchment, Luxembourg (Pfister, Humbert, & Hoffmann, 2000). This catchment comprises two different subcatchments that are heavily instrumented and where elaborate field data are collected by the Catchments As Organized Systems research program (Zehe et al, 2014), but the catchment is not in a controlled environment.…”

Section: Introductionmentioning

confidence: 99%

Effects of differential hillslope‐scale water retention characteristics on rainfall–runoff response at the Landscape Evolution Observatory

Heuvel

Troch

Booij

et al. 2018

Hydrological Processes

View full text Add to dashboard Cite

Hillslopes turn precipitation into runoff and thus exert important controls on various Earth system processes. It remains difficult to collect reliable data necessary for understanding and modeling these Earth system processes in real catchments. To overcome this problem, controlled experiments are being conducted at the Landscape Evolution Observatory at Biosphere 2, The University of Arizona. Previous experiments have revealed differences in hydrological response between 2 landscapes within Landscape Evolution Observatory, even though both landscapes were designed to be identical. In an attempt to discover where the observed differences stem from, we use a fully 3‐dimensional hydrological model (CATchment HYdrology) to show the effect of soil water retention characteristics and saturated hydraulic conductivity on the hydrological response of these 2 hillslopes. We also show that soil water retention characteristics can be derived at hillslope scale from experimental observations of soil moisture and matric potential. It is found that differences in soil packing between the 2 landscapes may be responsible for the observed differences in hydrological response. This modeling study also suggests that soil water retention characteristics and saturated hydraulic conductivity have a profound effect on rainfall–runoff processes at hillslope scale and that parametrization of a single hillslope may be a promising step in modeling rainfall–runoff response in real catchments.

show abstract

Untitled

Cited by 47 publications

References 13 publications

Quantitative precipitation estimation with weather radar using a data- and information-based approach

Quantitative precipitation estimation with weather radar using a data- and information-based approach

How are flood risk estimates affected by the choice of return-periods?

Effects of differential hillslope‐scale water retention characteristics on rainfall–runoff response at the Landscape Evolution Observatory

Contact Info

Product

Resources

About