1. Introduction
Mesoscale convective systems (MCSs) play an important hydrological role in the central United States because they are responsible for 30%–70% of the warm season rainfall in the region and they have been associated with severe floods (Fritsch et al. 1986; Houze et al. 1990; Haberlie and Ashley 2019). These organized convections are often initiated near the foothills of the Rocky Mountains and grow while propagating eastward to the Great Plains (Laing and Fritsch 1997; Houze 2004; Ashley et al. 2003). Most regions in the Great Plains experience 4–5 MCSs each month in the warm season (Feng et al. 2019). Compared with other non-MCS rainfall produced by isolated convections and stratiform clouds, MCS rainfall is ∼7 times more intense (Hu et al. 2020a). Therefore, MCS storms are one of the key meteorological phenomena that can produce extreme precipitation, particularly in the central Great Plains (Kunkel et al. 2012; Stevenson and Schumacher 2014). Consequently, higher surface runoff is a common terrestrial response to the intense MCS rainfall and is intimately linked with flooding (Hu et al. 2020b). Case studies of flooding associated with MCSs have been well documented (Maddox et al. 1978; Bosart and Sanders 1981; Houze et al. 1990; Schumacher and Johnson 2005, 2008), and flood occurrence in the central United States that peaks in the warm season has been attributed to MCSs (Villarini 2016; Dougherty and Rasmussen 2019). However, the climatology-based quantification of floods in association with MCSs has not been available until a recent work. By combining a newly developed MCS tracking database with a storm event database, Hu et al. (2021a) found that MCSs account for most of the floods during the warm season in the central and eastern United States, particularly for the slow-rising floods and hybrid floods (flood episodes consisting of both flash and slow-rise flood events).
Notably, some MCSs can develop closely in time and space due to favorable environments (e.g., abundant moisture, persistent ascending mechanisms by frontal processes, mesoscale vortices, cold pools, and moderate wind shear). One extreme case of clustered MCSs occurred in May 2015, when more than 20 MCSs swept across the Texas–Oklahoma area (Fig. 1a), producing total rainfall that can “cover the entire state of Texas 8 inches deep” (Martinez and Brunfeld 2015). Consequently, this MCS cluster caused ∼45 million U.S. dollars of flood damage in Houston alone (AP 2015) and substantial losses in life and properties in surrounding areas including Oklahoma and Louisiana due to flash and extensive floods and even tornados associated with the clustered MCSs. This case has been linked with the persistent and strengthened El Niño sea surface temperature pattern that deepened the stationary trough west of Texas and enhanced the Great Plains low-level jet and further attributed to the warming climate (Wang et al. 2015). Sharing similar favorable large-scale atmospheric dynamic and thermodynamic conditions, multiple MCSs may develop and interact with each other through modulation of their common large-scale environment to form an MCS cluster. Such clustering of MCS events can collectively affect areas well beyond that of each individual MCS, leading to significant hydrologic impacts at larger scales than the more localized effect of an individual MCS. Therefore, MCS clusters may contribute importantly to floods for two reasons. First, local water budget is sensitive to precipitation input, and rainfall intensity can have a major influence on surface runoff production (Vischel et al. 2009; Boone et al. 2009; Best et al. 2015). Second, the occurrence of clustered MCSs exposes substantial areas under the influence of intense rainfall. Because rainfall area is positively correlated with MCS-related flooding occurrence (Hu et al. 2021a), the intense rainfall of MCSs with or without significant overlapping area can possibly initiate floods over a more extended area covered by clustered MCSs. However, studies of clustered MCSs were limited to cases or one season (Trier et al. 2006, 2014), while the occurrence and seasonality of clustered MCSs and their associated flood risk are largely unknown.
(a) Hovmöller diagram for MCS rainfall in May 2015 averaged over 28°–40°N and 110°–84°W, with MCS clusters indicated by blue shading and missing rainfall data indicated by gray shading. (b)–(d) Rainfall (mm) accumulated over the lifetime of MCSs A, B, and C. (e)–(g) Surface runoff generated by MCSs A, B, and C. (h)–(j) Flood episodes associated with MCSs A, B, and C, with rainfall coverage area indicated by gray shading. Flood episodes are categorized as slow-rising flood (blue circle), flash flood (orange star), and hybrid flood (green triangle). Each row in (b)–(j) shares the color bar on the right.
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
In addition to the wide coverage area characterized by intense rainfall, another interesting aspect associated with clustered MCSs is the overlapping of rainfall areas among MCS events that can repeatedly affect the same area within a short time period. This is particularly the case for clustered MCSs associated with long-lived mesoscale convective vortex generated by MCSs, which can induce organized secondary convections within or in the proximity of the mesoscale convective vortex (Fritsch et al. 1994; Bartels and Maddox 1991; Trier et al. 2000; Trier and Davis 2002). The resultant successive time–space coherent episodes of heavy rainfall occurring over approximately the same location have been described as heavy rainfall “corridors” (Tuttle and Davis 2006; Trier et al. 2014) and may contribute more readily to flooding. This is likely the case because earlier MCSs within the cluster can effectively moisten the soils to precondition for saturation–excess flow and contribute to flooding when subsequent MCSs from the same cluster pass over the same area. Such preconditioning of land surface has been known to play a central role in flood frequency and magnitude (Berghuijs et al. 2016; Ye et al. 2017). With a wet initial soil condition, flood discharges can be very sensitive to even short-lived rainfall events, while in a dry condition substantial amounts of rainfall can infiltrate into the soils and contribute to slower subsurface processes instead of a rapid flood response (Zhu et al. 2018).
To better understand flood risk associated with the causative meteorological events and other flood generation processes over an extended area well beyond traditional catchment scale has been one of the emerging perspectives to advance flood research (Merz et al. 2014; Kundzewicz et al. 2014). Floods and weather systems are closely connected because weather systems cannot only substantially determine precipitation characteristics such as intensity and area coverage, but also affect soil moisture states through precipitation wetting and evaporative drying by modulating cloud and precipitation processes and surface energy balances, respectively. Both precipitation characteristics and soil conditions are critical drivers of flooding and thus different types of weather systems are intimately related with the regional flood characteristics (Villarini 2016; Saharia et al. 2017). This effort to associate flood risk with causative weather events has been possible by linking flood statistics with continental-to-global-scale precipitation datasets and/or terrestrial features (Dougherty and Rasmussen 2019; Saharia et al. 2017). For MCS-related flooding, Hu et al. (2021a) examined the flood likelihood by linking the warm-season flood occurrence documented by the National Centers for Environment Information (NCEI) Storm Events Database with a long-term MCS tracking dataset (Feng 2019). While the work in Hu et al. (2021a) set the stage for our current study by demonstrating the strong connection between floods and MCS rainfall, our emphasis for this study is the flood risk associated with clustered MCSs. Our focus on clustered MCS events is largely motivated by their much broader area of impact beyond individual MCSs, which can expose more regions to flooding solely due to their strong rainfall intensity. We are also interested in the role of land surface in affecting flooding likelihood, particularly for regions experiencing repeated wetting due to successive MCS passages. In addition to their high impact, a better understanding of clustered MCSs may also help reduce uncertainties in flood forecasting because the favorable atmospheric environments that sustain clustered MCSs may be more predictable than those of individual MCSs (Trier et al. 2014), and the flood probability of precipitation conditioned on wet soils may be higher (Sharma et al. 2018; Zhu et al. 2018; Wright et al. 2020), which is likely the case for MCS clusters.
Therefore, in this study, we aim to extend the MCS–flood linkage identified by Hu et al. (2021a) by quantifying the flood frequency associated with MCS clusters. Our goal is to explore the relationship between flood occurrence and rainfall characteristics associated with MCS clusters, as well as the role of initial soil wetness on modulating flood responses. In this study, flood frequency quantification is mainly examined in the context of MCS-related floods rather than all floods, as we emphasize the differences between clustered and nonclustered MCSs, while the flood frequency differences between MCS and non-MCS events are discussed in Hu et al. (2021a). Our analysis is designed to address three questions: 1) What is the flood likelihood associated with MCS clusters? 2) What are the differences of MCS characteristics and terrestrial conditions for clustered and nonclustered MCSs, and how are flood occurrences modulated by these characteristics? 3) For each MCS cluster, what are the dominant factors for flood occurrence and durations?
To develop a joint view of MCS-related rainfall and land surface conditions on flood responses, we link flood occurrence reported by the NCEI Storm Events Database with a high-resolution observation-based MCS dataset (Hu et al. 2021a) and land surface conditions and processes provided by a land surface simulation. Section 2 describes the three datasets and the algorithm we used to identify MCS clusters, as well as how we attribute floods to the rainfall and soil conditions associated with MCS clusters. Section 3 presents the extreme case in May 2015 to illustrate the interactions between rainfall and terrestrial responses, while section 4 explores the climatology relationships between flood occurrence and MCS clusters. We test the sensitivity of the parameters determining MCS cluster in section 5 and conclude in section 6.
2. Materials and methods
a. MCS dataset
Similar to Hu et al. (2021a), we use the high-resolution (4 km, hourly) MCS dataset (Feng 2019) developed by Feng et al. (2019), which provides the track of each MCS occurring between January 2004 and December 2017 over the United States. This MCS database is obtained by applying the Flexible Object Tracker (FLEXTRKR) algorithm (Feng et al. 2018) to track MCSs defined by large cold cloud shields containing a radar-defined precipitation feature (PF, a contiguous area with rain rate > 1 mm h−1) with a major axis length > 100 km and a convective feature containing radar reflectivity > 45 dBZ at any vertical level that persists for longer than 6 h (Feng et al. 2018, 2019). The cold cloud, precipitation, and radar information are obtained from the NASA Global MergedIR satellite dataset (Janowiak et al. 2001), Stage IV multisensory precipitation dataset (Lin 2011) and 3D radar reflectivity mosaic from the GridRad NEXRAD radar dataset (Bowman and Homeyer 2017), respectively [see Feng et al. (2019) for more technical details]. By tracking each MCS, this MCS dataset provides important quantifications of MCS PF characteristics (e.g., lifetime, propagation, and convective and stratiform rainfall separation) that are used in this study to explore the linkages with flood frequency.
We note that the above algorithm and thresholds for PF size, radar reflectivity and durations used by FLEXTRKR are rooted in the current definition of MCS events with the goal to detect the largest form of deep convective storms. The MCS dataset in our study generally yields consistent climatology of MCS in the United States from other studies (Geerts 1998; Pinto et al. 2015; Prein 2017; Haberlie and Ashley 2019), with an emphasis on the long-lived MCSs by requiring a minimum of 6-h lifetime. It is possible that a detected MCS is embedded in a synoptic system (e.g., extratropical cyclone) or demonstrates frontal features (Kunkel et al. 2012; Ralph et al. 2011; Trier et al. 2014). Therefore, it is important to note that the MCS dataset does not exclude events that might be driven by synoptic systems other than mesoscale processes, but rather it aims to capture all the rainy events that match the MCS characteristics.
It is also important to note that the rain rate threshold value we use to define PFs (1 mm h−1) seems moderate, but using a low-to-moderate threshold value is critical to capture the complete life cycle of each MCS. Defining PF areas using a higher threshold value would certainly lead to smaller PF areas at each time step, but it does not reduce the detected number of MCS events substantially (see an example in Fig. S1 in the online supplemental material for 2015). In other words, most of the MCSs are characterized by active precipitation areas with rain rate exceeding 5 mm h−1, which lasts for at least 6 h. Compared to using 1 mm h−1 to define PFs, using a higher threshold value could arbitrarily truncate the lifetime of an MCS by excluding time steps after its initiation or before its decay, causing an underestimation of the MCS duration and coverage area (Fig. S1). For this study, it is critical to capture the complete life cycle of each MCS to correctly identify the initial soil conditions at the time of MCS initiation. Truncation of the MCS lifetime due to the use of a higher rain rate threshold can result in initial soil moisture state that has been preconditioned by earlier stages of the same MCS with lighter rain rate instead of the pre-event soil conditions.
b. NCEI Storm Events Database
The flood dataset we link with MCSs are the floods documented by the NCEI Storm Events Database. The Storm Events Database records the time and locations for significant meteorological events and related phenomena of great societal impacts since 1950, compiled from National Weather Service (NWS) and other sources including media and law enforcement (NWS 2018). Due to its emphasis on societal impacts, this dataset tends to weigh more heavily on events affecting urban areas or areas with dense population, while small events or large events with less socioeconomic impacts might be underreported (Ashley and Ashley 2008; Barthold et al. 2015). However, comparisons with other streamflow based hydrologic flood datasets support the sufficient coverage of floods both spatially and temporally by this Storm Events Database (Dougherty and Rasmussen 2019; Gourley et al. 2013). In addition, this database distinguishes “flash floods” from other floods because of their rapid surge of water and the shortage of time to mitigate that can cause significant fatality and property damages (NWS 2018). Other floods with high flow, overflow, or inundation that cause damage are grouped as slow-rising floods (Dougherty and Rasmussen 2019).
Our analysis of flood frequency and durations is based on flood episodes from the Storm Events Database, which can encompass multiple flood events associated with the same synoptic meteorological system (NWS 2018). Therefore, in addition to flash and slow-rising flood episodes that embody only flash or slow-rising flood events, flood episodes involving a combination of flash and slow-rising flood events are categorized as hybrid flood episodes (following Dougherty and Rasmussen 2019). From April to August in 2007–17, a total of 7106 flash flood episodes, 2842 slow-rising flood episodes, and 1238 hybrid flood episodes are collected east of 110°W in the United States, which corresponds to the spatial coverage of the MCS dataset. Note that we exclude the data before 2007 because flood occurrence is substantially lower than that after 2007, presumably due to coverage biases (Hu et al. 2021a). These flood episodes are attributed to MCSs and MCS clusters (explained in section 2e).
c. Land surface data
To account for the land surface processes that can be critical for flood generation, we use a set of high-resolution model output from the Noah-MP land surface model (Niu et al. 2011). The simulation is configured to cover a similar spatial area as the North American Land Data Assimilation System (NLDAS; 1/8° grids) but with a finer 4-km grid spacing to better account for the land surface heterogeneity (1/8°-grid forcing data bilinearly interpolated to the finer land surface model grid). The default soil layer configurations (0.1, 0.3, 0.6, 1.0 m in thickness from top to bottom) is used. The model is initialized at 0000 UTC 1 March of each year using the soil states from the Noah simulation in the NLDAS archive for the same year and run for 6 months until 31 August, driven by the NLDAS atmospheric forcing including precipitation, radiation, near-surface air temperature, wind and humidity, and surface pressure. The model reaches an equilibrium within the first month (see an example in Fig. S2), so data from April to August in 2007–17 are used in our analysis.
This dataset has been validated by comparing observational-based evapotranspiration estimation and other NLDAS model outputs (Hu et al. 2020b). For evapotranspiration, our simulation reproduces the large east–west gradients in the central United States but overestimates the evapotranspiration, especially over the northern Great Plains relative to the MODIS and FLUXNET estimated evapotranspiration. However, our land surface data produce very similar patterns as seen in other land surface simulations archived by NLDAS-2 produced by other land surface models, including evapotranspiration, surface runoff, and soil moisture (see more details in Hu et al. 2020b). In this study, we use the soil moisture and surface runoff from our Noah-MP simulations at 4-km grid spacing to analyze the terrestrial conditions and responses to individual MCSs and MCS clusters. Note that like most of the current land surface models, the Noah-MP model dataset does not explicitly represent the terrain slopes and related lateral water movement, which likely affect soil moisture and runoff. However, datasets accounting for terrain effects with similar spatial and temporal coverages are not currently available.
d. Identify MCS clusters
If the MCS PFs have circular shapes, D12 represents the distance between the edges of the MCS PFs. However, MCSs in the United States are frequently organized to some form of quasi-linear modes (Parker and Johnson 2000; Cui et al. 2021) and noncircular MCSs that occur close to each other in space and time may result in a negative distance [Eq. (1)].
To ensure the MCSs in a cluster are close in time and space, we use the thresholds for distance (dx) and time (dt) to be 0 and 4 h. Two circular PFs with D12 < 0 would suggest an overlapping of PF areas while noncircular PFs, which may not overlap, are at least in the vicinity of each other and may contribute to the same flooding at a basin outlet if they produce rainfall in the same drainage area. And a 4-h time window allows some time lag between MCSs to capture events that have similar pathways but a few hours apart. Using dx = 0 and dt = 4 h, a total of 291 MCS clusters are identified during April–August 2007–17, with a total of 1262 MCSs considered in clustered form out of a total of 3447 MCSs (data before 2007 are not used due to some issues with the flood data; see section 2b). These 291 MCS clusters detected with dx = 0 and dt = 4 h are used for the majority of analysis of this study. Despite using this simple method to identify MCS clusters based on major axis distances, we confirm that pixel-level overlapping between PFs occurs among the MCSs within every cluster using this set of parameters. However, we note that the parameters dx and dt are somewhat arbitrarily defined to constrain the spatial and temporal distances between the PFs of the MCS pairs that define the MCS clusters. As expected, the number of MCS clusters are subject to the different values of dx and dt. To address this, we test the sensitivity of MCS clusters and their related floods to these parameters in section 5.
In addition, merging and splitting of storms are taken into account in the MCS tracking (Feng et al. 2019). While it is possible that two MCSs merging into a larger MCS (or a large MCS splitting into smaller MCSs) may contribute to the same MCS cluster, each of them must meet the criteria of MCS features defined by the FLEXTRKR algorithm before merging (or after splitting). For example, we visually checked the MCS life cycle characteristics for 2015 and found that out of 149 clustered MCSs, 36 split from their “parent” MCSs while 12 merged into preexisting MCSs. Each of these child and parent systems maintain their sizes and lifetimes as proper MCSs. They often occur within clusters consisting of more than 5 MCSs and thus using a different approach to treat splitting/merging is unlikely to affect the cluster occurrence. Therefore, merging or splitting MCSs are considered as part of an MCS cluster as they can collectively have significant hydrologic impacts regardless of their origin or evolution.
e. Attribute floods to MCSs and MCS clusters
The flood episodes of different types in April–August (2007–17) are then attributed to MCS events and MCS clusters according to their time and location of occurrence (Hu et al. 2021a). Flood episodes are attributed to an MCS if floods occur within its total PF coverage area or less than 20 km apart during the lifetime of the MCS. We note that the 20-km threshold is tested by Hu et al. (2021a) and selected due to its optimal effect to distinguish floods associated with MCS and non-MCS storms but also allow for the downstream effects of rainstorms on flooding. The same is applied to MCS clusters by attributing flood episodes to an MCS cluster if they occur within or less than 20 km from the total PF coverage area over the lifetime of the MCS cluster.
Due to their potential importance to flooding, we pay particular attention to the overlapping PF areas among MCS clusters, defined as PF areas with at least 2 MCS passages in the same cluster. As mentioned earlier, all the MCS clusters have overlapping PF areas by the method described in section 2d. Such overlapping of PF areas among MCSs in the same cluster indicates repeated wetting of the same region within a short time period before substantial adjustments of soil moisture such as evapotranspiration and subsurface drainage so the terrestrial conditions are more favorable for flooding. On one hand, we use the overlapping areas as one of the PF characteristics to examine its relationship with the overall flood frequency associated with each cluster. On the other hand, we quantify the likelihood of flood occurrence as a function of the overlapping areas by further attributing the cluster-related floods to their overlapping areas if the floods occur within the 20-km range of the PF overlap areas, following the same general methodology. Although storms can co-contribute to basin saturation and potentially flooding due to lateral movement of water even without spatial overlapping of storms, our ability to analyze such effect is limited because such lateral runoff responses are generally not represented in most current land surface models (Fan et al. 2019) or land surface datasets (section 2c). More advanced hydrologic models that account for lateral routing would be necessary for such purposes (Getirana et al. 2014; Gochis et al. 2018), which are beyond the scope of this study. Currently, we focus on the land surface responses to storms with overlapping PF coverage areas, which happen in all MCS clusters based on our definition.
f. Rainfall and terrestrial characteristics of MCSs and MCS clusters
To explore the factors controlling or modulating the flood occurrence and potentially durations associated with each MCS or MCS cluster, we combine the PF characteristics from the MCS dataset (section 2a) and the terrestrial responses from the land surface dataset (section 2c). In addition to the instantaneous PF characteristics, event-total PF characteristics are particularly important for flood frequency (Hu et al. 2021a) and therefore are of our major focus. Accordingly, the soil moisture conditions and runoff generation over the event-total PF coverage areas are extracted and used for our analysis (section 4). The soil moisture conditions are quantified in two ways: soil moisture anomaly is calculated by comparing the soil moisture with the climatological value of the same day in the seasonal cycle, and the percentile of soil moisture with respect to the historical samples on the same day of the seasonal cycle over the same coverage area. To contrast the differences of the overlapping areas with the rest (section 4c), the PF characteristics and terrestrial variables are also extracted over the overlapping areas. For most of our analysis, we use the initial soil moisture states immediately before the initiation of an MCS or a MCS cluster to study their relationship with flood characteristics. As the initial soil moisture anomalies capture the soil states before they are modulated by the MCS or MCS clusters, they reflect the net effect of water gaining (e.g., snowmelt, non-MCS precipitation, earlier-season MCS events) and losing processes (e.g., evapotranspiration, runoffs) unrelated to the MCS or MCS clusters. To illustrate the role of successive wetting by clustered MCSs, we also compare the soil moisture conditions after the passage of MCSs over the overlapping areas in section 4c.
3. MCS clusters in May 2015
We use the extreme case of May 2015 as an example to demonstrate the detection of MCS clusters and the impact of repeated wetting due to overlapping PF. Using the method described in section 2d, 6 MCS clusters are identified in May 2015 (shaded in blue in Fig. 1a). The first MCS cluster occurring during 4–6 May mainly produced rainfall over northern Texas, while the following 5 MCS clusters were shifted a bit eastward and passed over eastern Texas and Oklahoma (MCS tracks for each cluster are shown in Fig. 2). The second MCS cluster during 9–13 May is particularly interesting, not only because this MCS cluster consisted of 9 MCSs, but 3 of them followed very similar paths (referred to as MCSs A, B, C) over the Texas–Oklahoma region within 50 h (Figs. 1b–d).
(a)–(f) MCS tracks within each MCS cluster identified in May 2015 and (g)–(l) flood episodes associated with each MCS cluster. Each MCS is tracked with a unique ID in the MCS dataset and color shaded in (a)–(f). Gray shadings in (g)–(l) indicate the total PF coverage area of each MCS cluster.
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
MCS A (9–10 May) strongly affected Oklahoma, but areas with accumulated rainfall exceeding 50 mm also extended to Arkansas and northeast Texas (Fig. 1b). Analysis of the land surface data indicates that ∼13% of rainfall from this MCS is estimated to contribute to runoff (Fig. 1e), while the rest enters the soils and can later contribute to evapotranspiration on a longer time scale (Hu et al. 2021b). MCS A induced two flash floods (episodes 94239 and 94240) and two hybrid floods that lasted until 11 May (Table 1). It also contributed to the two slow-rising floods on the Red River in southeast Oklahoma and northeast Texas that were induced by the first MCS cluster on 4–6 May (Figs. 1h, 2a and Table 1). The next MCS B (10 May) was weaker in terms of accumulated rainfall but a slightly higher fraction (14.3%) of its rainfall became runoff (Figs. 1c,f). It contributed to multiple preexisting slow-rising and hybrid floods, but also induced a slow-rising flood in Arkansas and a hybrid flood in South Dakota (Fig. 1i) due to the preexisting anomalously wet soil conditions at these locations (Fig. S3). On the next day, MCS C (11 May) struck the same area by bringing substantial amounts of rainfall over eastern Texas and Arkansas, with 16.5% of its rainfall turning into runoff (Figs. 1d,g). Consequently, it enhanced the slow-rising and hybrid floods in Oklahoma, Texas, and Arkansas, and produced flash floods in these states and slow-rising floods in Missouri and Illinois (Fig. 1j and Table 1). Notice that the Red River flooding observed in Texas and Oklahoma and other rivers in Arkansas remained above the flood stage at the end of May (episodes 95612, 95615, 97778; all ended abruptly on 1 June, probably due to the flood entry segregated by month), associated with the excessive rainfall over similar area by the subsequent MCSs and MCS clusters.
For the entire cluster that includes 9 MCSs (9–13 May), the overlapping PF areas is 1.678 × 106 km2 while the total PF coverage area is 3.759 × 106 km2, resulting in an overlapping fraction of 44.6% (Fig. 2b). Twenty-three of the 26 flood episodes (4 slow-rising, 13 flash, and 6 hybrid floods) are found in the proximity of the overlapping areas (Figs. 2b,h). Consistent with the increase of runoff generation ratio associated with MCSs A, B, and C, the initial soil moisture conditions over the overlapping areas also become wetter following the passage of the sequential of MCSs, changing from 48.8% to 91.7%. Due to uncertainties in the land surface data, the runoff generation ratios and soil moisture percentiles should only be used as indications of terrestrial dynamics rather than exact quantifications of the surface water budgets. The continuous moistening of the soils illustrates that MCSs sequentially passing over similar areas may contribute to/induce slow-rising floods by producing a higher fraction of saturation–excess surface runoff that can continuously converge toward the basin outlet and extend the flood durations. Meanwhile, Fig. 2 also indicates a wide span of flood locations that can possibly occur over each cluster’s PF coverage area, including flash floods that were induced by MCSs in clustered form that produced rainfall intensity exceeding the infiltration rate of the land surface.
4. Establishing the links between floods and MCS clusters
a. Seasonal mean of flood frequency associated with MCS clusters
Hu et al. (2021a) separately related flood frequency with MCS and non-MCS storms and found that MCSs account for most of the warm-season floods in the United States east of 110°W, especially for slow-rising and hybrid floods. But flash floods in July and August are also frequently associated with non-MCS convections (Fig. 3a) that occur often in the Rocky Mountains and Appalachian Mountains. As noted, we focus on quantifying flood frequency associated with MCS clusters in the context of MCS-related floods rather than all floods in the following analysis.
(a) Flood episode frequency in each month and flood attribution to MCSs and clustered MCSs aggregated over the area to the east of 110°W. (b) Monthly frequency of MCSs and clustered MCSs. (c)–(h) Spatial distributions of MCS-related flood frequency of each flood type and fraction of each flood type associated with clustered MCSs. Panels (c), (e), and (g) share the same color bar at the bottom left, and (d), (f), and (h) share the same color bar at the bottom right.
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
Using 11 years of data in 2007–17, we find that MCS-related flooding is more likely to be associated with MCS clusters in early warm season (April–June), regardless of flood types (cf. black hatched and red stippled areas in Fig. 3a). For slow-rising floods that occur more frequently in April–June than later months over the northern Great Plains (north of 35°N), more than 75% of MCS-related flooding is associated with MCS clusters in these months (83.3%, 85.0%, and 75.2% respectively for April, May, and June; Figs. 3c, Figs. S4a–c, S5a–c). In July and August, the fraction is reduced to 44.1% and 48.5%, resulting in less than 10 flood episodes per month. Similar fractions and decreasing trend with months during the warm season are found for hybrid floods observed in 37°–43°N (Fig. 3h and Figs. S5k–o).
For flash floods, most MCS-related floods occur between 105° and 90°W (Fig. 3e), while flash floods near the Rocky Mountains and Appalachian Mountains attributable to non-MCS thunderstorms are not included in Fig. 3e. In April and May when flash floods are often observed in the southern Great Plains, greater fractions of MCS-related floods are associated with MCS clusters: 79.3% of in April and 58.9% in May (Fig. 3a and Figs. S5f–j). But for June–August when flash floods occur more frequently, the fraction is reduced to 56.5%, 31.0%, and 43.3%, respectively. While the flash floods shift northward in these months, we find that clustered MCSs tend to favor flash floods in the southern states (Figs. S4f–j, S5f–j).
The larger fractions of flood episodes associated with MCS clusters in early months are consistent with the higher occurrence of MCS clusters in these months (Fig. 3b). Using the dx = 0 and dt = 4 h criteria, the fraction of clustered MCSs is 57.2% in April, ∼40% in May and June, and 22.7% and 30.7% in July and August. As a result, 23–29 MCSs in clustered form occur in each month from April–June, although fewer MCSs are observed in April and May. This can be partially attributed to the fact that MCSs in the earlier warm season are subject to stronger synoptic forcing (Song et al. 2019), which are more likely to support the development of multiple spatially and/or temporally connected MCSs. MCS PF areas are also larger in the early months, resulting in smaller PF distances between MCSs. We also find that the frequency of clustered MCSs shows a northward migration from April to August (Fig. 6), similar to the northward migration of all MCSs through the warm season. However, the fraction of MCSs associated with MCS clusters shows higher values south of ∼37°N throughout the warm season (Figs. 4k–o). This explains the greater fraction of MCS-related flash floods in the southern states (Figs. S5f–j), while no such preference is noticeable for slow-rising or hybrid floods that mostly occur in the northern Great Plains (Figs. S5a–e,k–o). This southern preference for clustered MCSs might be related to its proximity to the Gulf of Mexico that can provide abundant moisture supply for the redevelopment of MCSs in a short period of time. More specific investigations of the synoptic environments in the future would be necessary in order to fully explain the southern preference of MCS clusters. Notice that the fractions of floods associated with clustered MCSs to total MCS-related floods (Figs. 3d,f,h) always exceed the fractions of clustered MCSs to total MCSs (Figs. 4k–o), regardless of flood types. This indicates that clustered MCSs are more effective in producing flooding. This is also supported by Fig. 5c showing significantly higher frequency of slow-rising and hybrid floods induced by a clustered MCS compared with MCSs that are not clustered. Clustered MCSs also tend to produce floods with significantly longer durations. The factors that can contribute to the more effective and long-lasting floods associated with clustered MCSs is examined in section 4b.
(a)–(e) MCS frequency, (f)–(j) clustered MCS frequency, and (k)–(o) the fractions of clustered MCS to all MCS in each month.
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
(a) MCS cluster frequency in each month color coded by the dominant flood type associated with each MCS cluster. (b) Averaged flood occurrence of each type associated with flood-producing MCS clusters in each month. (c) Flood occurrence (left y axis) and durations (right y axis) associated with nonclustered and clustered MCSs, with boxplots showing durations shaded by orange.
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
On average, about 4–7 MCS clusters (each consisting of 4.3 MCSs on average) occur in each month during the warm season, with a peak frequency in June (Fig. 5a). Grouping the MCS clusters by the dominant flood type associated with each MCS cluster, about 2.5 MCS clusters cause slow-rising flood in April–June and 2–4 MCS clusters cause flash floods in each month, with a peak in June. Furthermore, in each month, less than one MCS cluster is not associated with any type of flooding so there is a high likelihood of flooding when an MCS cluster occurs. Notably, only 8 MCS clusters (out of 291 in total) are not associated with flooding. For the MCS clusters that lead to flooding, each MCS cluster in April can lead to 24 flood events, with ∼12 slow-rising floods, 9 flash floods, and 3 hybrid floods (Fig. 5b). The flood number decreases to ∼18 in May and June with comparable number of slow-rising and flash floods, and further reduces to ∼10 floods in July and August with mostly flash floods because of the greater MCS rainfall intensity in these months.
b. Differences between clustered and nonclustered MCSs and relationship with flood frequency
To understand the factors contributing to the greater flood occurrence associated with clustered MCSs relative to nonclustered MCSs (Fig. 5c), we now examine their differences in both MCS rainfall characteristics and land surface conditions. For the MCS PF characteristics, MCSs that belong to clusters have longer durations and larger instantaneous rainfall areas compared to nonclustered MCSs, while propagation speeds and instantaneous rain rates are not significantly different (Figs. 6a–d; means and significance level indicated by Table S2). As a result, each clustered MCS has significantly larger event-total rainfall coverage area and greater total rainfall volume than individual MCSs that are not clustered (Figs. 6e,f). The greater rainfall areas and volumes per MCS in clustered MCSs are likely contributed by favorable synoptic conditions that support sequential development of MCSs over consecutive days [see case studies by Trier et al. (2006, 2014)]. In addition to the fact that clustered MCSs are generally stronger in terms of PF coverage area and rainfall volume, their preferential association with flooding is also supported by the terrestrial conditions. We find that clustered MCSs generally occur over wetter initial soil moisture conditions (Figs. 6g,h) in the late summer months and becomes especially important for generating floods as soils are generally drier in those months. Such wetter initial soil moisture conditions can be a result of repeated wetting that we see in the May 2015 case (section 3), but it is also possible that MCS clusters are more likely to occur over a wetter surface as a result of land–atmosphere interactions (Trier et al. 2008). However, the causes of these MCS clusters are beyond the scope of this study but could be a very interesting topic for future research. Due to the differences in rainfall characteristics and land surface conditions, each MCS in clustered form on average can produce substantially greater runoff than a nonclustered MCS (Fig. 6i).
Boxplots of different characteristics for MCSs that are not clustered and clustered grouped by early warm-season (April–June) and later warm season (July–August). Asterisks indicate where differences are significant (>99% confidence level using the Kolmogorov–Smirnov test; also see Table S1).
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
Because not all MCSs are associated with flooding, it is also important for us to understand the differences in flood-producing MCSs and the rest that do not produce floods. Due to the favorable conditions for flooding associated with clustered MCSs, we find that ∼64% clustered MCSs are associated with floods (807 out of 1262 clustered MCSs) while ∼50% of nonclustered MCSs are associated with floods (1102 out of 2185 nonclustered MCSs). Note that this fraction is different from the flood-producing MCS clusters (Fig. 5a) because each MCS belonging to an MCS cluster is treated individually rather than treating each MCS cluster as an entity. Comparing MCSs that do not produce floods, perhaps the most apparent difference is the PF areas in both clustered and nonclustered MCSs (Figs. 7c,e). Significantly faster propagations are also observed in clustered MCSs for flood-producing ones but not seen in nonclustered MCSs (Fig. 7b). Accordingly, flood-producing MCSs are characterized by significantly greater PF coverage areas and event-total rainfall volumes than nonflood producing ones (Figs. 7e,f). Also significantly different is the initial soil wetness that is supported by the wetter initial soil moisture for flood-producing MCSs, which exists in both clustered and nonclustered MCSs (Figs. 7g,h). As a result, flood-producing MCSs are characterized by substantially greater runoffs (Fig. 7i).
Boxplots of different characteristics for MCSs that are flood-producing (light blue and brown boxes) and not flood-producing (gray boxes) grouped by nonclustered and clustered MCSs. Asterisks indicate where differences are significant (>99% confidence level using the Kolmogorov–Smirnov test; also see Table S2).
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
The critical roles of MCS PF characteristics and wetter initial soils for floods are further supported by the relationship between these factors and flood occurrence (Fig. 8). In Fig. 8, we show the variations of MCSs’ rainfall and terrestrial characteristics with slow-rising and flash flood occurrence separately in each panel, while the results of hybrid floods are not included as hybrid floods have limited occurrence and they display similar relationships associated with slow-rising and flash floods. Flood occurrence is shown in both percentile and actual number in the x axis of each panel. This analysis further confirmed the predominant role of rainfall area on flood frequency identified by Hu et al. (2021a), with a larger MCS PF area favoring higher flood occurrence. We see a similar increasing trend of flood frequency with PF areas for each average MCS that is nonclustered and clustered (Figs. 8a), while consistent with Fig. 8e, the PF areas are generally larger for an average MCS that is clustered. Similar increasing relationships are found for the total PF rain volume (Fig. 8b), which exceeds the rate of increase in the event-total PF coverage area thus resulting in a greater rainfall per area with increasing flood occurrence (Fig. 8c). For the land surface conditions, we also find a clear increasing trend of flood occurrence for MCSs occurring over wetter initial soils (Fig. 8d). As a consequence of the joint role of greater rainfall per area and wetter soils, MCSs that produce greater runoff and also greater runoff generation ratios tend to result in more floods (Figs. 8e,f). Such evident relationship between greater runoff and flooding likelihood is consistent with precipitation excess as the dominant flood generating mechanism over the central United States (Berghuijs et al. 2016). The important roles of PF characteristics (dominated by PF coverage areas) and initial soil wetness are respectively tested by further subgrouping MCSs with similar initial soil conditions and those with similar PF coverage areas, and the relationships we see in Fig. 8 still hold (Figs. S6, S7), indicating that they can each play a distinctive role on flood frequency besides their joint effect.
Boxplots of event-total characteristics for MCSs that are nonclustered and clustered, binned by the occurrence of each flood type they are associated with. The different percentiles of flooding occurrence are listed at the bottom of each column, color coded by flood types. See median values and correlations in Table S3.
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
c. Relationship between cluster-total characteristics and floods and the role of overlapping PFs
With each MCS in clustered form characterized with stronger PF features (PF area and rainfall volumes), integrating over an entire MCS cluster that contains at least 3 MCSs can be of more significant impact. Indeed, an MCS cluster is very likely to produce severe floods as described earlier in Fig. 6a. Therefore, we integrate the individual MCS PF characteristics within each MCS cluster treated as a unit (cluster-total PF characteristics) and investigate their relationship with floods. By integrating the PF features over the entire clusters, we find a similar and evident increase of flood occurrence with the cluster-total PF coverage areas and rain volumes (Figs. 9a,b). Interestingly, MCS clusters associated with more floods are also characterized with greater overlapping PF ratios (calculated as the ratio of PF areas with at least 2 MCS PF passages to cluster-total PF coverage areas) among the MCSs in each cluster (Fig. 9c), with overlapping PF illustrated by the examples we see in section 3. Such increase of overlapping areas thus results in a greater rainfall per area over the overlapping areas and thus increases rainfall per area over the whole cluster-total PF coverage areas (Fig. 9d).
Boxplots of cluster-total precipitation feature characteristics for MCS clusters, binned by the occurrences of each flood type they are associated with. The different percentiles of flooding occurrence are listed at the bottom of each column, color coded by flood types. See median values and correlations in Table S4.
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
Analysis of initial soil moisture conditions over the cluster-total PF coverage area using percentiles also indicates an increasing trend of floods with initial soil wetness (Fig. 10a) as we see in individual MCSs (Fig. 8d), reiterating the important role of soil wetness to floods. Note that further testing by subgrouping MCS clusters with similar cluster-total PF coverage areas confirms such relationship (not shown). Subsequently, we see an increasing trend of flood occurrence with MCS clusters occurring over wetter initial soils that generate greater amounts of surface runoff per area and greater runoff ratios (Figs. 10b,c).
Boxplots of cluster-total terrestrial characteristics for MCS clusters, binned by the (a)–(d) occurrences and (e)–(h) durations (orange shaded) of each flood type they are associated with. The different percentiles of flooding occurrence or duration (h) are listed at the bottom of each column, color coded by flood types. See median values and correlations in Table S4.
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
We also find a more important role of terrestrial responses in flood durations in contrast to PF characteristics. In general, we find that floods associated with MCS clusters tend to last longer if more surface runoff generation is present (Figs. 10f,g), while such a relationship is not found for MCS PF areas or rain volumes (not shown). Note that flood durations refer to the mean durations of flood episodes associated with each MCS cluster, and MCS clusters binned by flood durations in Figs. 9f,g only account for flood durations but do not jointly account for flood occurrence. MCS clusters causing one or two floods but with long durations, presumably due to favorable local terrestrial conditions, can fall into the 90%–100% duration bin, but these clusters are not necessarily the strongest ones leading to more flood occurrence. This is the case for the relatively low surface runoff ratios associated with the moderate MCS clusters, leading to 1–2 slow-rising floods with >491.5 h of durations (Figs. 9f,g). We also note that initial soil wetness can be important to other aspects of flood severity (e.g., inundation area) that are not available from the Storm Events Database.
To better understand the effect of the overlapping PF areas on flooding indicated by Fig. 9c and the example in May 2015 (section 3) with successive MCS passages over the same area, we quantify the flood frequency that occurs in the proximity of the overlapping areas and analyze their rainfall and terrestrial features over those areas. We find that 46.6% of floods associated with MCS clusters occurs within or close to the overlapping PF areas (43.4% for slow-rising, 45.8% for flash, and 57.7% for hybrid floods). These fractions well exceed the fraction of overlapping areas to total PF coverage areas (20.9% on average; see variations in Fig. 9c), which indicates a higher likelihood of floods occurring over overlapping areas. Such higher flood occurrence is also supported by Fig. 10d and is closely related with the repeated wetting of clustered MCSs. Compared with the entire cluster-total PF coverage areas, overlapping PF areas that receive rainfall from more than one MCS are characterized by a much greater rainfall per area (Fig. 11a). With a similar initial soil wetness, the overlapping areas end up producing greater runoff per area with a much wetter soil condition after the passage of the entire cluster than the cluster-total PF coverage areas (Figs. 11a,b). And such wetting occurs progressively throughout each cluster (Fig. 11c), providing a continuously favorable condition for flooding for the subsequent MCSs in the same cluster, while a similar wetting trend is not observed for the nonoverlapping areas (not shown).
Boxplots showing (a) the differences between the PF coverage area-averaged and overlapping area-averaged rainfall per area and surface runoff per area and (b) soil moisture conditions before and after the entire MCS clusters. (c) The inner-cluster changes of soil moisture states over the overlapping PF areas grouped by the order of occurrence (e.g., first 1/3 indicates MCSs that occur in the earliest third of the cluster lifetime).
Citation: Journal of Hydrometeorology 23, 11; 10.1175/JHM-D-22-0038.1
5. Sensitivity to MCS clusters definition
As noted in section 2d, the detection of MCS clusters is subject to the threshold parameters (dx and dt) that we used to identify MCS clusters. In this section, we test the sensitivity of our results to the dx and dt parameters we use to identify MCS clusters. To achieve this, we perform two sets of analyses by varying the dx and dt values, respectively, and comparing how the MCS characteristics and flood occurrences change with these parameters.
As expected, relaxing the dx or dt thresholds lead to more MCSs in clusters while restraining dx and dt to smaller values would identify fewer MCSs in clusters (black lines in Figs. S8a,f). The number of MCS clusters also increases by relaxing dt/dx, but at a slower rate (red lines in Figs. S8a,f). This indicates more MCSs in each cluster with increasing dt/dx. However, we find that the monthly distribution of clustered MCSs is altered by the dt/dx changes (Figs. S8b,g). We see an evident increase of clustered MCSs being identified in June with increasing dt/dx, which shifts the peak month of cluster MCSs from April with the smallest dt/dx thresholds to June with the largest dt/dx (Figs. S8b,g). This shift of peak month to June, which is slightly more evident with the dx changes (Fig. S8g), is related to the generally smaller MCS PF areas but higher MCS frequency in June (Figs. 3a, 8c). As a result, we see a more noticeable shift of the MCS characteristics of clustered MCSs toward the characteristics of June MCSs with increasing dx, which tend to have slower propagation and smaller PF rainfall volume (Figs. S8c,d,h,i). Consistent with the monthly changes of clustered MCS with dt/dx, more flood episodes in June are found to be associated with MCS clusters with greater dt/dx values, while flood frequency associated with MCS clusters peaks in April with smaller dt/dx (Figs. S8e,j). However, the differences between nonclustered and clustered MCSs in Fig. 6 and the relationship between flood occurrence and cluster-total characteristics shown in Fig. 9 still hold by changing the dt/dx thresholds (see examples in Figs. S9–14). Therefore, we conclude that our results showing the different features of clustered MCSs, particularly the greater PF rainfall per volume and greater runoff generation, and their relationship with flood occurrence are not sensitive to the dt and dx parameters we use to define MCS clusters.
6. Discussion and conclusions
In this study, we extend the MCS–flood linkage established by Hu et al. (2021a) by focusing on the relationship between flood frequency and MCS clusters, which can have a much larger impact than each individual MCS and tend to produce more extreme floods. Importantly, we incorporate terrestrial conditions in our analysis in addition to MCS PF characteristics, which are critical for flood generation but not discussed in Hu et al. (2021a). By linking floods reported by the NCEI Storm Event Database with MCS clusters in the warm season that occurred in the central-eastern United States, we quantify the flood occurrence associated with MCS clusters and examine how the flood occurrence and duration changes with the different MCS and terrestrial characteristics. In particular, we examine the role of overlapping PF areas within each MCS cluster that can experience sequential rainfall. We summarize the key findings as below:
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MCS clusters occur more frequently in the earlier warm season (April–June) and over the southern states (Figs. 3b and 4). Accordingly, floods in early warm season are more likely to be associated with MCS clusters, while the southern preference for floods is less evident (Fig. 3).
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Each flood-producing MCS in clustered form lasts significantly longer and can lead to more floods than MCSs that are not clustered (Fig. 7). Clustered MCSs are more effective in producing floods due to their significantly greater rainfall volumes as well as wetter initial soil conditions that cause greater surface runoff than nonclustered MCSs (Fig. 8).
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Integrating over the cluster, flood occurrence also increases with cluster-total PF coverage areas and initial soil wetness, resulting in greater runoff ratios (Figs. 9 and 10).
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Floods are more likely to occur around the overlapping PF areas in each MCS cluster, due to the successive rainfall from multiple MCSs over the overlapping areas and thus a continuous moistening of the soils, creating a more flood-prone condition (Fig. 11).
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Terrestrial runoff responses are not only important for flood occurrence associated with each MCS cluster, but also to flood durations because MCS clusters producing greater runoffs can lead to floods with longer durations (Fig. 10).
In this study we have only examined the relationship between MCS clusters and floods in terms of the flood occurrence and durations. This is because other metrics that may better quantify flood severity (e.g., inundation area and depth) are not available from the NCEI Storm Events Database. However, we use a high-resolution land surface dataset to account for the land surface conditions and responses to the different MCS events. We confirm an important role of initial soil moisture for flood occurrence related to individual MCSs as well as MCS clusters. By incorporating the land surface conditions and examining the different aspects of PF characteristics of MCSs, we are able to decompose the different factors contributing to flood occurrence and durations. Building upon the close relationship between flooding and MCSs demonstrated by Hu et al. (2021a), we further examined the links between floods and MCSs that are clustered and not clustered. Because clustered MCSs tend to occur in more favorable meteorological conditions and can produce substantially more precipitation than individual MCS events, the link we establish between flooding and MCS clusters can potentially help improve the prediction of flood occurrence by improving prediction of clustered MCSs and reduce uncertainty in flooding occurrence in response to a stronger precipitation forcing. This linkage can also allow us to better evaluate variability of flood likelihood in response to changes in different aspects (e.g., favorable for clustered/nonclustered MCSs, changes in terrestrial conditions) with climate change. While recognizing the importance of initial soil conditions on floods, whether it can play a role on initiating or maintaining the MCS clusters is an open question as noted earlier.
While it is true that relaxing the spatial or temporal thresholds to define clustered MCSs tends to shift the peak distribution of clustered MCSs from early warm-season (April–June) toward June, we confirm that the above conclusions are not sensitive to the threshold parameters we use to define MCS clusters. The effectiveness of clustered MCSs in producing more floods with longer durations highlights the hydrological and socioeconomic impacts of clustered MCS events. The implication can be twofold. First, we need a better understanding of the favorable synoptic conditions of MCS clusters (e.g., the May 2015 case), their relationships with different climate modes of variability (e.g., ENSO), and their potential changes in the future to understand the predictability of clustered MCSs and to better anticipate variability and changes in flood risks associated with them. Case studies indicate an important role of large-scale external factors (e.g., tropospheric anticyclone position, Great Plains low-level jet) and internal feedbacks (Trier et al. 2014). With a consistent strengthening of the Great Plains low-level jet projected by climate models in the future (Tang et al. 2017; Torres-Alavez et al. 2021), its implication for possible changes of MCS clusters and their associated flooding risks can be important questions to explore. Second, MCS clusters producing significant amounts of rainfall within consecutive days might have a distinctive effect on water storage in the terrestrial system compared to nonclustered MCSs and non-MCS storms. On one hand, a larger contribution to surface runoff suggests a limited role of rainfall from clustered MCSs in providing soil moisture and contributing to ecosystem productivity, which would benefit from intermittent and moderate rainfall throughout the growing season. On the other hand, a portion of the rainfall that saturates the soil might infiltrate to the deeper soil layers due to the higher hydraulic conductivity and stronger vertical water pressure gradient during flood events. Soil moisture at the deeper layers might contribute to groundwater storage or moisten the upper layers through hydraulic redistribution. The long-term impacts of clustered MCS events in the water cycle beyond the flooding impacts at short time scales can be important for seasonal predictions of hydrometeorological events and agricultural outcomes.
Acknowledgments.
This research is supported by the U.S. Department of Energy Office of Science Biological and Environmental Research as part of the Regional and Global Model Analysis and Multi-sector Dynamics program areas. PNNL is operated for the Department of Energy by Battelle Memorial Institute under Contract DE-AC05-76RL01830.
Data availability statement.
The 4-km hourly MCS database covering 2004–17 is obtained from the U.S. Department of Energy Atmospheric Radiation Measurement program (https://doi.org/10.5439/1571643). The NCEI Storm Events Database is obtained from the NOAA’s National Centers for Environmental information (ftp://ftp.ncdc.noaa.gov/pub/data/swdi/stormevents/csvfiles/).
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