New perspectives on rainfall from a discrete view

Michele, Carlo De; Ignaccolo, Massimiliano

doi:10.1002/hyp.9782

Cited by 8 publications

(5 citation statements)

References 87 publications

(79 reference statements)

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“…We used a moving window of 41 days (W = 20), which is comparable to the time scale of monthly forecasts performed by DWR during winter and spring (Rosenberg et al, 2011). The only exception was precipitation-phase partitioning based on dew-point temperature, for which we assumed W = 1 to take into account intermittency of precipitation (De Michele & Ignaccolo, 2013). For the same reason, precipitation phase was the only variable for which local RMSEs were calculated even if some days within the evaluation window had no evaluation measurement (that is, no precipitation).…”

Section: Step 2 Andmentioning

confidence: 99%

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Avanzi

Maurer

Glaser

et al. 2020

Journal of Hydrology

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Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. This work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget.

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Section: Step 2 Andmentioning

confidence: 99%

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Avanzi

Maurer

Glaser

et al. 2020

Journal of Hydrology

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“…In this context, several stochastic models have been proposed. The modeler must initially decide whether rainfall will be viewed as a continuous or discrete process; in this context a more thorough review of rain models can be found, for example, in De Michele and Ignaccolo []. Once this choice has been made, various approaches can be used.…”

Section: Introductionmentioning

confidence: 99%

Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution

et al. 2015

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Rainfall is a physical phenomenon resulting from the combination of numerous physical processes involving a wide range of scales, from microphysical processes to the general circulation of the atmosphere. Moreover, unlike other geophysical variables such as water vapor concentration, rainfall is characterized by a relaxation behavior that leads to an alternation of wet and dry periods. It follows that rainfall is a complex process which is highly variable both in time and space. Precipitation is thus characterized by the following features: rain/no-rain intermittency, multiple scaling regimes, and extreme events. All these properties are difficult to model simultaneously, especially when a large time and/or space scale domain is required. The aim of this paper is to develop a simulator capable of generating high-resolution rain-rate time series (15 s), the main statistical properties of which are close to an observed rain-rate time series. We also attempt to develop a model having consistent properties even when the fine-resolutionsimulated time series are aggregated to a coarser resolution. In order to break the simulation problem down into subcomponents, the authors have focused their attention on several key properties of rainfall. The simulator is based on a sequential approach in which, first, the simulation of rain/no-rain durations permits the retrieval of fractal properties of the rain support. Then, the generation of rain rates through the use of a multifractal, Fractionally Integrated Flux (FIF), model enables the restitution of the rainfall's multifractal properties. This second step includes a denormalization process that was added in order to generate realistic rain-rate distributions.

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“…The fixed‐duration approach contrasts with events‐based modelling of rainfall where independent rainstorms are extracted from timeseries data (Restrepo‐Posada & Eagleson, 1982 Serinaldi & Grimaldi, 2007 De Michele & Ignaccolo, 2013 Serinaldi & Kilsby, 2013 Jun et al, 2018). Here rainstorm duration is not constrained and is instead determined using statistical or empirical methods.…”

Section: Introductionmentioning

confidence: 99%

The creation and climatology of a large independent rainfall event database for Great Britain

Herrera

Blenkinsop

Guerreiro

et al. 2023

Intl Journal of Climatology

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Extreme rainfall studies generally focus on hourly or daily rainfall accumulations. Additionally, studies focus on annual maxima (AM) or ‘peak intensities’. Although this approach is useful, the use of fixed‐duration accumulations simplifies inherently continuous rainfall processes which, at a rain gauge, are experienced as rainstorms of varying duration. A gap also exists in the study of sub‐hourly rainfall extremes which are an important source of flash floods. Here, we present the first large‐scale study of rainstorms in the United Kingdom. We leverage a recently created sub‐hourly resolution rain gauge dataset for Great Britain to identify over 70,000 rainstorms that generated AM rainfall intensities across a range of sub‐hourly to daily durations at 1279 rain gauges up to 2018. Our findings are consistent with previous studies of UK rainfall extremes and their climatology, where strong east–west differences are found in the magnitude of autumn and winter long‐duration (over 12‐h) rainstorms. We observe broad similarities in the behaviour of short (under 4‐h) rainstorms across GB, which are dominated by seasonal variations in convective activity rather than by geographical location. Our study reveals substantial overlaps between AM of different durations, with single rainstorms containing AM across a wide range of durations. We also demonstrate the substantial correlation between rainstorm duration and volume. These results challenge common independence assumptions used in the statistical modelling of rainfall extremes using extreme value theory. Our results represent an opportunity to refine rainfall design methods towards improved, robust representations of rainstorms.

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New perspectives on rainfall from a discrete view

Cited by 8 publications

References 87 publications

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution

The creation and climatology of a large independent rainfall event database for Great Britain

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