Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe

Hundecha, Yeshewatesfa; Pinya, Maria Antonia Sunyer; Lawrence, Deborah; Madsen, Henrik; Willems, Patrick; Bürger, Gerd; Kriaučiūnienė, Jūratė; Λουκάς, Αθανάσιος; Martínková, Marta; Osuch, Marzena; Vasiliades, Lampros; Christierson, Birgitte von; Vormoor, Klaus; Yücel, İsmail

doi:10.1016/j.jhydrol.2016.08.033

Cited by 41 publications

(24 citation statements)

References 42 publications

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“…There have been many comparisons between dynamical and statistical downscaling focusing on hydrological applications (Fowler and Wilby 2007). These include assessments of downscaled future projections (Mearns et al 1999;Hundecha et al 2016;Onyutha et al 2016) or current weather conditions Haylock et al 2006;Huth et al 2015;Casanueva et al 2016;Vaittinada Ayar et al 2016;Roux et al 2018). These studies show that statistical and dynamical methods have comparable skill at simulating the present climate and should be regarded as complementary tools Haylock et al 2006;Osma et al 2015).…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

et al. 2019

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There have been numerous statistical and dynamical downscaling model comparisons. However, differences in model skill can be distorted by inconsistencies in experimental set-up, inputs and output format. This paper harmonizes such factors when evaluating daily precipitation downscaled over the Iberian Peninsula by the Statistical DownScaling Model (SDSM) and two configurations of the dynamical Weather Research and Forecasting Model (WRF) (one with data assimilation (D) and one without (N)). The ERA-Interim reanalysis at 0.75 • resolution provides common inputs for spinning-up and driving the WRF model and calibrating SDSM. WRF runs and SDSM output were evaluated against ECA&D stations, TRMM, GPCP and EOBS gridded precipitation for 2010-2014 using the same suite of diagnostics. Differences between WRF and SDSM are comparable to observational uncertainty, but the relative skill of the downscaling techniques varies with diagnostic. The SDSM ensemble mean, WRF-D and ERAI have similar correlation scores ( r = 0.45-0.7), but there were large variations amongst SDSM ensemble members ( r = 0.3-0.6). The best Linear Error in Probability Space ( LEPS = 0.001-0.007) and simulations of precipitation amount were achieved by individual members of the SDSM ensemble. However, the Brier Skill Score shows these members do not improve the prediction by ERA-Interim, whereas precipitation occurrence is reproduced best by WRF-D. Similar skill was achieved by SDSM when applied to station or gridded precipitation data. Given the greater computational demands of WRF compared with SDSM, clear statements of expected value-added are needed when applying the former to climate impacts and adaptation research.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Electronic Supplementary Materialsmentioning

confidence: 99%

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

et al. 2019

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“…An active field of research is to evaluate the performance of statistical downscaling methods with regard to distributional or temporal statistics of a meteorological variable or intervariable dependencies (e.g., Wilcke, Mendlik, & Gobiet, ; Ivanov & Kotlarski, ). At the same time, different studies evaluate statistical downscaling methods from an impact assessment point of view and assess the choice of statistical downscaling methods with regard to their influence on, for example, change in extreme flows (e.g., Dobler, Hagemann, Wilby, & Stötter, ; Graham, Hagemann, Jaun, & Beniston, ; Hundecha et al, ). A key open question is the ability of different downscaling methods—and, more generally, climate model projections—to preserve the intervariable consistency of the meteorological conditions that lead to flood events.…”

Section: Introductionmentioning

confidence: 99%

Delineation of flood generating processes and their hydrological response

Keller

Rößler

Martius

et al. 2017

Hydrological Processes

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Knowledge about flood generating processes can be beneficial for numerous applications.Especially in the context of climate change impact assessment, daily patterns of meteorological and catchment state conditions leading to flood events (i.e., storylines) may be of value. Here, we propose an approach to identify storylines of flood generation using daily weather and snow cover observations. The approach is tested for and applied to a typical pre-Alpine catchment in the period between 1961 and 2014. Five precipitation parameters were determined that describe temporal and spatial characteristics of the flood associated precipitation events. The catchment's snow coverage was derived using statistical relationships between a satellite-derived snow cover climatology and station snow measurements. Moreover, (pre-) event snow melt sums were estimated using a temperature-index model. Based on the precipitation and catchment state parameters, 5 storylines were identified with a cluster analysis: These are (a) long duration, low intensity precipitation events with high precipitation depths, (b) long duration precipitation events with high precipitation depths and episodes of high intensities, (c) shorter duration events with high or (d) low precipitation intensity, respectively, and (e) rain-on-snow events. The event groups have distinct hydrological characteristics that can largely be explained by the storylines' respective properties. The long duration, high intensity storyline leads to the most adverse hydrological response, namely, a combination of high peak magnitudes, high volumes, and long durations of threshold exceedance. The results show that flood generating processes in mesoscale catchments can be distinguished on the basis of daily meteorological and catchment state parameters and that these process types can explain the hydrological flood properties in a qualitative way. Hydrological simulations of daily resolution can thus be analysed with respect to flood generating processes.

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“…There are significant uncertainties in developing future realizations of flood risk with contributions from multiple aspects of the multi-scale, multi-model process [6,[32][33][34][35][36][37]. For example, while climate models generally do well at representing decadal variability, and to some extent, monthly variability, and representation of annual extremes is challenging.…”

Section: Frequency Analysismentioning

confidence: 99%

Integrated Modeling Approach for the Development of Climate-Informed, Actionable Information

Judi¹,

Rakowski²,

Waichler³

et al. 2018

Preprint

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Flooding is a prevalent natural disaster with both short and long-term social, economic, and infrastructure impacts. Changes in intensity and frequency of precipitation (including rain, snow, and rain on snow) events create challenges for the planning and management of resilient infrastructure and communities. While there is general acknowledgement that new infrastructure design should account for future climate change, no clear methods or actionable information is available to community planners and designers to ensure resilient design considering an uncertain climate future. This research used climate projections to drive high-resolution hydrology and flood models to evaluate social, economic, and infrastructure resilience for the Snohomish Watershed, WA, U.S.A. The proposed model chain has been calibrated and validated. Based on the established model chain, the peaks of precipitation and streamflows were found to shift from spring and summer to earlier winter season. The nonstationarity of peak discharges was discovered with more frequent and severe flood risks projected. The peak discharges were also projected to decrease for a certain period in the near future, which might be due to the reduced rain-on-snow events. This research was expected to provide a clear method for the incorporation of climate science in flood resilience analysis and to also provide actionable information relative to the frequency and intensity of future precipitation events.

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Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe

Cited by 41 publications

References 42 publications

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

Delineation of flood generating processes and their hydrological response

Integrated Modeling Approach for the Development of Climate-Informed, Actionable Information

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