scholarly journals Runoff reaction from extreme rainfall events on natural hillslopes: a data set from 132 large-scale sprinkling experiments in south-western Germany

2020 ◽  
Vol 12 (1) ◽  
pp. 245-255
Author(s):  
Fabian Ries ◽  
Lara Kirn ◽  
Markus Weiler
Keyword(s):  
Large Scale ◽  
Subsurface Flow ◽  
Economic Damage ◽  
Runoff Generation ◽  
Data Set ◽  
Large Variability ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
Western Germany ◽  
Precipitation Events

Abstract. Pluvial or flash floods generated by heavy precipitation events cause large economic damage and loss of life worldwide. As discharge observations from such extreme occurrences are rare, especially on the scale of small catchments or even hillslopes, data from artificial sprinkling experiments offer valuable information on runoff generation processes, overland and subsurface flow rates, and response times. We conducted 132 large-scale sprinkling experiments on natural hillslopes at 23 sites with different soil types and geology on pastures and arable land within the federal state of Baden-Württemberg in south-western Germany. The experiments were realized between 2016 and 2017. Simulated rainfall events of varying durations were based on (a) the site-specific 100-year return periods of rainfall with different durations and (b) the maximum rainfall intensity observed locally. The 100 m2 experimental area was divided into three individual plots, and overland and subsurface flow, soil moisture, and water level dynamics in the temporarily saturated soil zone were measured at 1 min resolution. Furthermore, soil characteristics were described in detail for each site. The data were carefully processed and corrected for measurement errors and combined into a consistent and easy-to-use database. The experiments revealed large variability in possible runoff responses to similar rainfall characteristics. In general, agricultural fields produced more overland flow than grassland. The latter generated hardly any runoff during the first simulated 100-year event on initially dry soils. The data set provides valuable information on runoff generation variability from natural hillslopes and may be used for the development and evaluation of hydrological models, especially those considering physical processes governing runoff generation during extreme precipitation events. The data set presented in this paper is freely available from the FreiDok plus data repository at https://doi.org/10.6094/UNIFR/151460 (Ries et al., 2019).

scholarly journals Runoff reaction from extreme rainfall events on natural hillslopes: A data set from 132 large scale sprinkling experiments in south-west Germany

2019 ◽  
Author(s):  
Fabian Ries ◽  
Lara Kirn ◽  
Markus Weiler
Keyword(s):  
Large Scale ◽  
Subsurface Flow ◽  
West Germany ◽  
Runoff Generation ◽  
Large Variability ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
South West ◽  
Precipitation Events

Abstract. Pluvial or flash floods generated by heavy precipitation events cause high economic damages and loss of life worldwide. As discharge observations from such extreme occurrences are rare especially on the scale of small catchments or even hillslopes, data from artificial sprinkling experiments offer valuable information on runoff generation processes, overland and subsurface flow rates and response times. We conducted 132 large-scale sprinkling experiments on natural hillslopes at 23 sites with different soil types and geology on pastures and arable lands within the federal state of Baden-Württemberg in south-west Germany. The experiments were realized between 2016 and 2017. Simulated rainfall events of varying durations were based on a) the site-specific 100-year return periods of rainfall with different durations and b) the maximum rainfall intensity observed locally. The 100 m2 experimental area was divided into three individual plots and overland and subsurface flow, soil moisture and water level dynamics in the temporarily saturated soil zone were measured at 1-minute resolution. Furthermore, soil characteristics were described in detail for each site. The data was carefully processed and corrected for measurement errors and combined to a consistent and easy to use database. The experiments revealed a large variability of possible runoff responses to similar rainfall characteristics. In general, agricultural fields produced more overland flow than grassland. The latter generated hardly any runoff during the first simulated 100-year event on initially dry soils. The dataset provides valuable information on runoff generation variability from natural hillslopes and may be used for the development and evaluation of hydrological models, especially those considering physical processes governing runoff generation during extreme precipitation events. The dataset presented in this paper is freely available from the FreiDok plus data repository (https://doi.org/10.6094/UNIFR/149650, Ries et al., 2019).

A novel method to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations

2021 ◽  
Author(s):  
Jérôme Kopp ◽  
Pauline Rivoire ◽  
S. Mubashshir Ali ◽  
Yannick Barton ◽  
Olivia Martius
Keyword(s):  
Time Scales ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Time Window ◽  
Heat Waves ◽  
Reanalysis Data ◽  
Data Set ◽  
Temporal Clustering ◽  
Extreme Precipitation Events ◽  
Precipitation Events

<p>Temporal clustering of extreme precipitation events on subseasonal time scales is a type of compound event, which can cause large precipitation accumulations and lead to floods. We present a novel count-based procedure to identify subseasonal clustering of extreme precipitation events. Furthermore, we introduce two metrics to characterise the frequency of subseasonal clustering episodes and their relevance for large precipitation accumulations. The advantage of this approach is that it does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this methodology to the ERA5 reanalysis data set, we identify regions where subseasonal clustering of annual high precipitation percentiles occurs frequently and contributes substantially to large precipitation accumulations. Those regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southeast of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the subseasonal time window (here 2 – 4 weeks). The procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (e.g. for drought years). <span>For a complementary study on the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers, please refer to Barton et al.</span></p>

scholarly journals Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

2021 ◽  
Vol 169 (3-4) ◽  
Author(s):  
Mark D. Risser ◽  
Daniel R. Feldman ◽  
Michael F. Wehner ◽  
David W. Pierce ◽  
Jeffrey R. Arnold
Keyword(s):  
Observational Data ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Time Of Day ◽  
Economic Damage ◽  
Data Set ◽  
Extreme Precipitation Events ◽  
Daily Precipitation Data ◽  
Precipitation Events ◽  
Made In

AbstractExtreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which downscaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the overall performance of LOCA-derived changes in extreme daily precipitation. We show that the low (negative) bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.

scholarly journals Identifying and Correcting Biases in Localized Downscaling Estimates of Daily Precipitation Return Values

2021 ◽  
Author(s):  
Mark Risser ◽  
Daniel Feldman ◽  
Michael Wehner ◽  
David Pierce ◽  
Jeff Arnold
Keyword(s):  
Observational Data ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Time Of Day ◽  
Economic Damage ◽  
Data Set ◽  
Extreme Precipitation Events ◽  
Daily Precipitation Data ◽  
Precipitation Events ◽  
Made In

Abstract Extreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which down scaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the over all performance of LOCA-derived changes in extreme daily precipitation. We show that the low bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.

scholarly journals Large-Scale Flow Patterns Associated with Extreme Precipitation and Atmospheric Rivers over Norway

2019 ◽  
Vol 147 (4) ◽  
pp. 1415-1428 ◽  
Author(s):  
Imme Benedict ◽  
Karianne Ødemark ◽  
Thomas Nipen ◽  
Richard Moore
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Moisture Flux ◽  
Cold Season ◽  
Fuzzy Cluster ◽  
The South ◽  
Atmospheric Rivers ◽  
The North ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract A climatology of extreme cold season precipitation events in Norway from 1979 to 2014 is presented, based on the 99th percentile of the 24-h accumulated precipitation. Three regions, termed north, west, and south are identified, each exhibiting a unique seasonal distribution. There is a proclivity for events to occur during the positive phase of the NAO. The result is statistically significant at the 95th percentile for the north and west regions. An overarching hypothesis of this work is that anomalous moisture flux, or so-called atmospheric rivers (ARs), are integral to extreme precipitation events during the Norwegian cold season. An objective analysis of the integrated vapor transport illustrates that more than 85% of the events are associated with ARs. An empirical orthogonal function and fuzzy cluster technique is used to identify the large-scale weather patterns conducive to the moisture flux and extreme precipitation. Five days before the event and for each of the three regions, two patterns are found. The first represents an intense, southward-shifted jet with a southwest–northeast orientation. The second identifies a weak, northward-shifted, zonal jet. As the event approaches, regional differences become more apparent. The distinctive flow pattern conducive to orographically enhanced precipitation emerges in the two clusters for each region. For the north and west regions, this entails primarily zonal flow impinging upon the south–north-orientated topography, the difference being the latitude of the strong flow. In contrast, the south region exhibits a significant southerly component to the flow.

scholarly journals Large-Scale Predictors for Extreme Hourly Precipitation Events in Convection-Permitting Climate Simulations

2018 ◽  
Vol 31 (6) ◽  
pp. 2115-2131 ◽  
Author(s):  
Steven C. Chan ◽  
Elizabeth J. Kendon ◽  
Nigel Roberts ◽  
Stephen Blenkinsop ◽  
Hayley J. Fowler
Keyword(s):  
Extreme Precipitation ◽  
Land Surface ◽  
Large Scale ◽  
Future Climate ◽  
Cumulus Parameterization ◽  
Hourly Precipitation ◽  
Vertical Stability ◽  
Extreme Precipitation Events ◽  
Climate Simulations ◽  
Precipitation Events

Midlatitude extreme precipitation events are caused by well-understood meteorological drivers, such as vertical instability and low pressure systems. In principle, dynamical weather and climate models behave in the same way, although perhaps with the sensitivities to the drivers varying between models. Unlike parameterized convection models (PCMs), convection-permitting models (CPMs) are able to realistically capture subdaily extreme precipitation. CPMs are computationally expensive; being able to diagnose the occurrence of subdaily extreme precipitation from large-scale drivers, with sufficient skill, would allow effective targeting of CPM downscaling simulations. Here the regression relationships are quantified between the occurrence of extreme hourly precipitation events and vertical stability and circulation predictors in southern United Kingdom 1.5-km CPM and 12-km PCM present- and future-climate simulations. Overall, the large-scale predictors demonstrate skill in predicting the occurrence of extreme hourly events in both the 1.5- and 12-km simulations. For the present-climate simulations, extreme occurrences in the 12-km model are less sensitive to vertical stability than in the 1.5-km model, consistent with understanding the limitations of cumulus parameterization. In the future-climate simulations, the regression relationship is more similar between the two models, which may be understood from changes to the large-scale circulation patterns and land surface climate. Overall, regression analysis offers a promising avenue for targeting CPM simulations. The authors also outline which events would be missed by adopting such a targeted approach.

scholarly journals A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns

2017 ◽  
Vol 30 (4) ◽  
pp. 1307-1326 ◽  
Author(s):  
Siyu Zhao ◽  
Yi Deng ◽  
Robert X. Black
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Warm Season ◽  
Base Function ◽  
Regional Patterns ◽  
Statistical Characterization ◽  
Climate System Model ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract Regional patterns of extreme precipitation events occurring over the continental United States are identified via hierarchical cluster analysis of observed daily precipitation for the period 1950–2005. Six canonical extreme precipitation patterns (EPPs) are isolated for the boreal warm season and five for the cool season. The large-scale meteorological pattern (LMP) inducing each EPP is identified and used to create a “base function” for evaluating a climate model’s potential for accurately representing the different patterns of precipitation extremes. A parallel analysis of the Community Climate System Model, version 4 (CCSM4), reveals that the CCSM4 successfully captures the main U.S. EPPs for both the warm and cool seasons, albeit with varying degrees of accuracy. The model’s skill in simulating each EPP tends to be positively correlated with its capability in representing the associated LMP. Model bias in the occurrence frequency of a governing LMP is directly related to the frequency bias in the corresponding EPP. In addition, however, discrepancies are found between the CCSM4’s representation of LMPs and EPPs over regions such as the western United States and Midwest, where topographic precipitation influences and organized convection are prominent, respectively. In these cases, the model representation of finer-scale physical processes appears to be at least equally important compared to the LMPs in driving the occurrence of extreme precipitation.

scholarly journals Climatology of Daily Precipitation and Extreme Precipitation Events in the Northeast United States

2015 ◽  
Vol 16 (6) ◽  
pp. 2537-2557 ◽  
Author(s):  
Laurie Agel ◽  
Mathew Barlow ◽  
Jian-Hua Qian ◽  
Frank Colby ◽  
Ellen Douglas ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Seasonal Cycle ◽  
Large Scale ◽  
Daily Precipitation ◽  
Warm Season ◽  
Extreme Precipitation Events ◽  
Tail Distribution ◽  
Hourly Data ◽  
Early Fall ◽  
Precipitation Events

Abstract This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979–2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2–5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1–3 stations concurrently. The majority of extreme days (75%–100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days.

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