Stochastic Scenarios for 21st Century Rainfall Seasonality, Daily Frequency, and Intensity in South Florida

Cioffi, Francesco; Conticello, Federico Rosario; Lall, Upmanu

doi:10.1061/(asce)wr.1943-5452.0001250

Cited by 4 publications

(4 citation statements)

References 54 publications

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“…The wildfire regime aligns with seasonal precipitation in this dry-to-moist subtropical environment; the wet season (June to October) can bring >75 cm of precipitation per month, while the dry season (November to May) averages 25-50 cm Three landscape elements directly influence the Greater Everglades fire regime: the plain-like landform (flat topography), seasonal subtropical moist climate, and fire-adapted shrubs and grasses. The wildfire regime aligns with seasonal precipitation in this dryto-moist subtropical environment; the wet season (June to October) can bring >75 cm of precipitation per month, while the dry season (November to May) averages 25-50 cm per month (Figure 2B) [33] (gauge data same as the figure above). Thus, wildfire season typically peaks toward the end of the dry season in April and May, when even the waterdominated sloughs (i.e., swampy backwaters of the open coast) can carry fire because the above-water portions of grasses are sufficiently dry.…”

Section: Introductionmentioning

confidence: 72%

Mapping South Florida Daily Fire Risk for Decision Support Using Fuel Type, Water Levels, and Burn History

Jones

Vukomanovic

2023

Fire

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Mapping fire risk in South Florida depends on spatially varying water levels, fuel characteristics, and topography. When surface water levels recede below the lowest topographic features (cypress strands, marshes, etc.), the ecosystem loses its natural, wetted fire breaks, and landscape-level fire risk increases. We developed a geospatial method to generate daily, categorical fire risk maps; the maps visualize low-to-high risk areas using a newly developed 100 m DEM, modeled water levels, fuel types, and fire management units. We assigned fire risk by creating a water level distribution for each unique combination of fuel type and fire management unit; fire risk was then assigned for each pixel based on risk percentiles commonly used by fire management agencies. Assigning risk based on unique fuel types and management units helped avoid over- or under-assigning fire risk that may occur when applying landscape-level “average” risk relationships. Daily maps also incorporated (1) energy release component data to better estimate fuel moisture and (2) historical burn footprints to reduce risk in recently burned areas. Our data-driven approach generated at management-relevant spatial scales may enable more informed prescribed burn planning and may increase the efficiency of staff and resource allocation across the landscape on high-wildfire-risk days.

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Section: Introductionmentioning

confidence: 72%

Mapping South Florida Daily Fire Risk for Decision Support Using Fuel Type, Water Levels, and Burn History

Jones

Vukomanovic

2023

Fire

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“…To sum up, all the rainfall patterns produced by NHMM are well consistent with their corresponding large‐scale circulation regime. It is clear that our NHMM is an efficient statistical downscaling algorithm with excellent capacity in climate diagnosis (Cioffi et al., 2016, 2020; Fu et al., 2013; Khalil et al., 2010; Pineda & Willems, 2016; Robertson et al., 2004). It is worthy of note that NHMM, although purely statistical, has the excellent capacity of “learning” from station‐based rainfall observation and reanalysis data of atmospheric circulation, and digesting them to produce physically consistent behaviors and relations.…”

Section: Resultsmentioning

confidence: 99%

Increase of Future Summer Rainfall in the Middle and Lower Reach of the Yangtze River Basin Projected With a Nonhomogeneous Hidden Markov Model

Guo

Jiang

et al. 2022

Geophysical Research Letters

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Among the statistical downscaling tools available for regional climate simulation, the nonhomogeneous hidden Markov model (NHMM) is a powerful and efficient algorithm. It consists of establishing statistical relations between predictand and predictors through a hidden layer of Markov process and a covariate nonhomogeneous term as external forcing. Here, it is used to simulate summer daily precipitation in the middle and lower reach of the Yangtze River basin (MLYRB), including future projection. Results show that NHMM well identifies large‐scale circulation features that are physically consistent with regional rainfall patterns. MLYRB exhibits a general wetness for 1.5°C and 2°C global warming targets, with smaller (larger) increase for the west (east). Such changes correspond to increases in the occurrence frequency of synoptic weather patterns with stronger and more westward Western Pacific Subtropical High and stronger westerly jet. This contributes to moistening MLYRB with increasing occurrence frequency for those wetter rainfall patterns.

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“…In addition, a multinomial logistic function is used to model the dependence of the hidden state transition matrix on such exogenous variables. Despite the reliable results of precipitation variabilities obtained by NHMM downscaling in several applications, the NHMM yields inaccurate simulations of mid-latitude precipitation in spring and autumn as well as extreme precipitation (Cioffi et al, 2020). Given the spatio-temporal variability of predictors, the NHMM is limited in its ability to capture extreme precipitation behaviours.…”

mentioning

confidence: 99%

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

Jiang,

Cioffi,

Conticello

et al. 2023

Hydrological Processes

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Global circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact‐related models at local scales. The non‐homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central‐eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario.

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Stochastic Scenarios for 21st Century Rainfall Seasonality, Daily Frequency, and Intensity in South Florida

Cited by 4 publications

References 54 publications

Mapping South Florida Daily Fire Risk for Decision Support Using Fuel Type, Water Levels, and Burn History

Mapping South Florida Daily Fire Risk for Decision Support Using Fuel Type, Water Levels, and Burn History

Increase of Future Summer Rainfall in the Middle and Lower Reach of the Yangtze River Basin Projected With a Nonhomogeneous Hidden Markov Model

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

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