Peak Rainfall Intensity Research Articles

Sensitivity of selected summertime rainfall characteristics to pre-event atmospheric and near-surface conditions

Predicting the erosive potential of rain events and the overall risk of soil erosion (and other hydrologic phenomena triggered by or related to extreme rainfall) requires understanding of the background meteorology that affects rainfall characteristics. The sensitivity of total rainfall kinetic energy, 15-min peak intensities, and event total depth were evaluated with respect to pre-event atmospheric conditions in the northern part of the Pannonian Plain. Five static stability parameters: the Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), Total Totals index (TT), K-index, and the composite CAPE-shear parameter were used as proxies describing the atmospheric static stability and the effect of wind shear on the development of convective precipitation. In addition to these stability parameters, near-surface air temperature and dew point temperature at 2-m above the ground were used as additional covariates in bivariate quantile regression. The primary objective of this paper was to determine the sensitivity of the rainfall characteristics to changes in the analyzed covariates and their ability to explain changes in the distribution of the considered rainfall characteristics. The analyses revealed a strong responsiveness of rainfall kinetic energy and 15-min peak rainfall intensities to dew point temperature with incremental changes following the super–CC (7–14%K−1) scaling regime when near-surface dew point temperature exceeds ~288 K. The findings have important implications for forecasting the erosive potential of rains and estimations of soil erosion in the future.

Impacts of permeable interlocking concrete pavement on the runoff hydrograph: Volume reduction, peak flow mitigation, and extension of lag times

AbstractPermeable pavements are implemented to provide at‐source treatment of urban stormwater runoff while supporting vehicular and pedestrian use. Studies on these systems have mainly focused on those treating only direct rainfall and installed atop well‐drained soils which typically provide substantial hydrologic mitigation through exfiltration that may not be representative of more hydrologically taxing conditions. A single lane parking area retrofitted with permeable interlocking concrete pavement in Vermilion, OH, USA was monitored over a 15‐month period to quantify its hydrologic performance under such conditions. The 470 m2 permeable pavement was underlain by silt loam soils and a shallow bedrock layer and treated run‐on from the adjacent 324 m2 asphalt drive lane. Observed data were compared to a calibrated SWMM model developed to simulate the pre‐retrofit conditions of the site (i.e., a completely impervious parking lot). Cumulative runoff volumes were reduced by 43% across all events in the monitoring period compared to a fully impervious parking lot. While median peak flows were reduced by 75%, substantial mitigation was limited to smaller, lower intensity events with longer antecedent dry periods (i.e., non‐flood producing events). The permeable pavement significantly delayed the occurrence of peak flows from the site following peak rainfall intensity by a median 29 min. Results from this study demonstrate that permeable pavements which receive run‐on from adjacent imperious cover and are installed atop poorly drained soils can significantly reduce runoff volumes and peak flow rates and delay the occurrence of peak discharge. The modelling approach implemented can provide a better estimation of diffuse inflows to green infrastructure stormwater controls and aid in refining design features which enhance the hydrologic performance in systems underlain by poorly drained soils.

Debris flow magnitude, frequency, and precipitation threshold in the eastern North Cascades, Washington, USA

We examine the magnitude, frequency, and precipitation threshold of the extreme flood hazard on 37 low-order streams in the lower Stehekin River Valley on the arid eastern slope of the North Cascades. Key morphometric variables identify the magnitude of the hazard by differentiating debris flood from debris flow systems. Thirty-two debris flow systems are fed by basins 10°. Five debris flood systems have larger drainage areas and debris fans with slopes 7–10°. The debris flood systems have Melton ruggedness ratios from 0.42–0.64 compared to 0.78–3.80 for debris flow basins. We record stratigraphy at seven sites where soil surfaces buried by successive debris flows limit the age of events spanning 6000 years. Eighteen radiocarbon ages from the soils are the basis for estimates of a 200 to1500-year range in recurrence interval for larger debris flows and a 450 ± 50-year average. Smaller events occur approximately every 100 years. Fifteen debris flows occurred in nine drainage systems in the last 15 years, including multiple flows on three streams. Summer storms in 2010 and 2013 with peak rainfall intensities of 7–9 mm/h sustained for 8–11 h triggered all but one flow; the fall 2015 event on Canyon Creek occurred after 170 mm of rain in 78 h. A direct link between fires and debris flows is unclear because several recent debris flows occurred in basins that did not burn or burned at low intensity, and basins that burned at high intensity did not carry debris flows. All but one of the recent flows and fires occurred on the valley’s southwest-facing wall. We conclude that fires and debris flows are linked by aspect at the landscape scale, where the sunny valley wall has flashy runoff due to sparse vegetation from frequent fires.

Constructing rainfall thresholds for debris flow initiation based on critical discharge and S-hydrograph

Debris flows caused by channel bed erosion present major hazards affecting life, livelihoods, and the built environment in mountainous regions. An efficient way to decrease hazard impact is through reliable hazard forecasts and appropriate early-warning strategies. Rainfall thresholds are fundamental in achieving reliable hazard forecasts. However, a lack of rainfall records often impedes the empirical establishment of such thresholds. This paper constructs rainfall intensity-duration thresholds based on process-based critical runoff discharge for the initiation of debris flows and a mathematical approximation among peak discharge, rainfall intensity and duration. Simulations of conditions that triggered debris flow and non-debris flow events allowed determination of the lower and upper limits of critical discharge for debris flow initiation. In turn, these critical discharge limits are compared with four estimates derived from process-based approaches to test which approach best delimit the critical conditions. Hydrological simulations derive S-hydrographs for recorded rainfall events. Further analysis of the S-hydrographs results in a mathematical approximation of peak discharge as a function of rainfall intensity and duration and the establishment of the minimum rainfall required to produce a particular peak discharge. The minimum rainfall threshold to trigger an event can be calculated by setting the process-based critical discharge as the peak dis charge. In turn, this enables the establishment of a conventional rainfall I-D threshold for debris flow initiation. This process-based approach enables the construction of valley-specific I-D thresholds in data-poor areas and provides a promising pathway to improve the reliability of debris flow hazard forecasts and early warnings.

A multistate first-order Markov model for modeling time distribution of extreme rainfall events

The time distribution of extreme rainfall events is a significant property that governs the design of urban stormwater management structures. Accuracy in characterizing this behavior can significantly influence the design of hydraulic structures. Current methods used for this purpose either tend to be generic and hence sacrifice on accuracy or need a lot of model parameters and input data. In this study, a computationally efficient multistate first-order Markov model is proposed for use in characterizing the inherently stochastic nature of the dimensionless time distribution of extreme rainfall. The model was applied to bivariate extremes at 10 stations in India and 205 stations in the United States (US). A comprehensive performance evaluation was carried out with one-hundred stochastically generated extremes for each historically observed extreme rainfall event. The comparisons included: 1-h (15-min); 2-h (30-min); and, 3-h (45-min) peak rainfall intensities for India and (US) stations, respectively; number of first, second, third, and fourth-quartile storms; the dependence of peak rainfall intensity on total depth and duration; and, return levels and return periods of peak discharge when these extremes were applied on a hypothetical urban catchment. Results show that the model efficiently characterizes the time distribution of extremes with: Nash–Sutcliffe-Efficiency > 0.85 for peak rainfall intensity and peak discharge; < 20% error in reproducing different quartile storms; and, < 0.15 error in correlation analysis at all study locations. Hence the model can be used to effectively reproduce the time distribution of extreme rainfall events, thus increasing the confidence of design of urban stormwater management structures.

An improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards

This paper presents an improved method of using threshold of peak rainfall intensity for robust flood/flash flood evaluation and warnings in the state of São Paulo, Brazil. The improvements involve the use of two tolerance levels and the delineating of an intermediate threshold by incorporating an exponential curve that relates rainfall intensity and Antecedent Precipitation Index (API). The application of the tolerance levels presents an average increase of 14% in the Probability of Detection (POD) of flood and flash flood occurrences above the upper threshold. Moreover, a considerable exclusion (63%) of non-occurrences of floods and flash floods in between the two thresholds significantly reduce the number of false alarms. The intermediate threshold using the exponential curves also exhibits improvements for almost all time steps of both hydrological hazards, with the best results found for floods correlating 8-h peak intensity and 8 days API, with POD and Positive Predictive Value (PPV) values equal to 81% and 82%, respectively. This study provides strong indications that the new proposed rainfall threshold-based approach can help reduce the uncertainties in predicting the occurrences of floods and flash floods.

Storm runoff generation in headwater catchments on the Chinese Loess Plateau after long-term vegetation rehabilitation

Storm runoff is important for maintaining surface water resources, while this function is significantly affected by land use and land cover changes, e.g., afforestation and reforestation. The Chinese Loess Plateau (CLP) has undergone large-scale vegetation rehabilitation, especially afforestation over the past 20 years. We hypothesize that afforestation has profoundly changed the amounts and mechanisms of storm runoff generation in headwater catchments on the CLP. To test this hypothesis, rainfall, soil moisture, and streamflow were monitored in a grass catchment and an adjacent forest catchment for two consecutive years. The objective of this study was to elucidate the varied mechanisms of storm runoff generation in these two contrasting revegetated catchments. Results showed that (1) average runoff coefficient in the grassland catchment (0.042) was approximately ten times higher than that in the forestland catchment (0.004), confirming the impact of catchment afforestation on the suppression of storm runoff generation. (2) Peak rainfall intensity was the first-order control of the runoff coefficient in the grassland catchment, but not in the forestland catchment. (3) Threshold values for antecedent soil moisture (~18%) and the sum of antecedent soil moisture index and event precipitation (~210 mm) were identified in the grassland catchment, above which storm runoff significantly increased. (4) Two extraordinary high runoff coefficient events were observed in the grassland catchment, one due to high peak rainfall intensity and strong surface runoff and the other due to high rainfall amount and high antecedent soil moisture. We conclude that long-term afforestation has changed the mechanisms and patterns of storm runoff generation, and different conditions of rainfall intensity, rainfall amount and antecedent soil moisture determined the hydrological connectivity between the upper hillslope and downhill gully in the catchment. This study deepens our understanding of the mechanisms and thresholds of storm runoff generation in headwater catchments on the CLP.

The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17 %–44 % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

Thresholds for post‐wildfire debris flows: Insights from the Pinal Fire, Arizona, USA

AbstractWildfire significantly alters the hydrologic properties of a burned area, leading to increases in overland flow, erosion, and the potential for runoff‐generated debris flows. The initiation of debris flows in recently burned areas is well characterized by rainfall intensity‐duration (ID) thresholds. However, there is currently a paucity of data quantifying the rainfall intensities required to trigger post‐wildfire debris flows, which limits our understanding of how and why rainfall ID thresholds vary in different climatic and geologic settings. In this study, we monitored debris‐flow activity following the Pinal Fire in central Arizona, which differs from both a climatic and hydrogeomorphic perspective from other regions in the western United States where ID thresholds for post‐wildfire debris flows are well established, namely the Transverse Ranges of southern California. Since the peak rainfall intensity within a rainstorm may exceed the rainfall intensity required to trigger a debris flow, the development of robust rainfall ID thresholds requires knowledge of the timing of debris flows within rainstorms. Existing post‐wildfire debris‐flow studies in Arizona only constrain the peak rainfall intensity within debris‐flow‐producing storms, which may far exceed the intensity that actually triggered the observed debris flow. In this study, we used pressure transducers within five burned drainage basins to constrain the timing of debris flows within rainstorms. Rainfall ID thresholds derived here from triggering rainfall intensities are, on average, 22 mm h−1 lower than ID thresholds derived under the assumption that the triggering intensity is equal to the maximum rainfall intensity recorded during a rainstorm. We then use a hydrologic model to demonstrate that the magnitude of the 15‐min rainfall ID threshold at the Pinal Fire site is associated with the rainfall intensity required to exceed a recently proposed dimensionless discharge threshold for debris‐flow initiation. Model results further suggest that previously observed differences in regional ID thresholds between Arizona and the San Gabriel Mountains of southern California may be attributed, in large part, to differences in the hydraulic properties of burned soils. © 2019 John Wiley &amp; Sons, Ltd.

Modelling Effects of Rainfall Patterns on Runoff Generation and Soil Erosion Processes on Slopes

Rainfall patterns and landform characteristics are controlling factors in runoff and soil erosion processes. At a hillslope scale, there is still a lack of understanding of how rainfall temporal patterns affect these processes, especially on slopes with a wide range of gradients and length scales. Using a physically-based distributed hydrological model (InHM), these processes under different rainfall temporal patterns were simulated to illustrate this issue. Five rainfall patterns (constant, increasing, decreasing, rising-falling and falling-rising) were applied to slopes, whose gradients range from 5° to 40° and projective slope lengths range from 25 m to 200 m. The rising-falling rainfall generally had the largest total runoff and soil erosion amount; while the constant rainfall had the lowest ones when the projective slope length was less than 100 m. The critical slope of total runoff was 15°, which was independent of rainfall pattern and slope length. However, the critical slope of soil erosion amount decreased from 35° to 25° with increasing projective slope length. The increasing rainfall had the highest peak discharge and erosion rate just at the end of the peak rainfall intensity. The peak value discharges and erosion rates of decreasing and rising-falling rainfalls were several minutes later than the peak rainfall intensity.

Anomalies of dwellers' collective geotagged behaviors in response to rainstorms: a case study of eight cities in China using smartphone location data

Abstract. Understanding city residents' collective geotagged behaviors (CGTBs) in response to hazards and emergency events is important in disaster mitigation and emergency response. It is a challenge, if not impossible, to directly observe CGTBs during a real-time matter. This study used the number of location requests (NLR) data generated by smartphone users for a variety of purposes such as map navigation, car hailing, and food delivery to infer the dynamics of CGTBs in response to rainstorms in eight Chinese cities. We examined rainstorms, flooding, and NLR anomalies, as well as the associations among them, in eight selected cities across mainland China. The time series NLR clearly reflects cities' general diurnal rhythm, and the total NLR is moderately correlated with the total city population. Anomalies of the NLR were identified at both the city and grid scale using the Seasonal Hybrid Extreme Studentized Deviate (S-H-ESD) method. Analysis results demonstrated that the NLR anomalies at the city and grid levels are well associated with rainstorms, indicating that city residents request more location-based services (e.g., map navigation, car hailing, food delivery, etc.) when there is a rainstorm. However, the sensitivity of the city residents' collective geotagged behaviors in response to rainstorms varies in different cities as shown by different peak rainfall intensity thresholds. Significant high peak rainfall intensity tends to trigger city flooding, which leads to increased location-based requests as shown by positive anomalies in the time series NLR.

The effects of rainfall regimes and rainfall characteristics on peak discharge in a small debris flow-prone catchment

Peak discharge plays an important role in triggering channelized debris flows. The rainfall regimes and rainfall characteristics have been demonstrated to have important influences on peak discharge. In order to explore the relationship between rainfall regimes and peak discharge, a measuring system was placed at the outlet of a small, debris flow-prone catchment. The facility consisted of an approximately rectangular stilling basin, ending with a sharp-crested weir. Six runoff events were recorded which provided a unique opportunity for characterizing the hydrological response of the debris flow-prone catchment. Then, a rainfall-runoff model was tested against the flow discharge measurements to have a deep understanding of hydrological response. Based on the calibrated rainfall-runoff model, twelve different artificially set rainfall patterns were regarded as the input parameters to investigate the effect of rainfall regimes on peak discharge. The results show that the rainfall patterns have a significant effect on peak discharge. The rainfall regimes which have higher peak rainfall intensity and peak rainfall point occur at the later part of rainfall process are easy to generate larger peak discharge in the condition of the same cumulative rainfall and duration. Then, in order to explore the relationship between rainfall characteristics and peak discharge under different cumulative precipitation and different duration, 167 measured rainfall events were also collected. On the basis of rainfall depth, rainfall duration, and maximum hourly intensity, all the rainfall events were classified into four categories by using K-mean clustering. Rainfall regime 1 was composed of rainfall events with a moderate mean P (precipitation), a moderate D (duration), and a moderate I60 (maximum hourly intensity). Rainfall regime 2 was the group of rainfall events with a high mean P, long D. Rainfall regime 3, however, had a low P and a long D. The characteristic of Rainfall regime 4 was high I60 and short duration with large P. The results show that the rainfall regime 2 and 4 are easier to generate peak discharge as the rainfall intensity plays an important role in generating peak discharge. The results in this study have implications for improving peak discharge prediction accuracy in debris flow gully.

Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations

Urban pluvial flooding is one of the most costly natural hazards worldwide. Risks of flooding are expected to increase in the future due to global warming and urbanization. The complexity of the involved processes and the lack of long-term field observations means that many crucial aspects related to urban flood risks still remain poorly understood. In this paper, the possibility to gain new insight into urban pluvial flooding using citizen flood observations is explored. Using a ten-year dataset of radar rainfall maps and 70,000 citizen flood reports for the city of Rotterdam, we derive critical thresholds beyond which urban pluvial flooding is likely to occur. Three binary decision trees are trained for predicting flood occurrences based on peak rainfall intensities across different temporal scales. Results show that the decision trees correctly predict 37%–52% of all flood occurrences and 95%–97% of all non-flood occurrences, which is a fair performance given the uncertainties associated with citizen data. More importantly, all models agree on which rainfall features are the most important for predicting flooding, reaching optimal performance whenever short- and long-duration rainfall peak intensities are combined together to make a prediction. Additional feature selection using principal component analysis shows that further improvement is possible when critical rainfall thresholds are calculated using a linear combination of peak rainfall intensities across multiple temporal scales. The encouraging results suggest that citizen observatories, although prone to larger errors and uncertainties, constitute a valuable alternative source of information for gaining insight into urban pluvial flooding.

Analysis of Soil Erosion Induced by Heavy Rainfall: A Case Study from the NE Abruzzo Hills Area in Central Italy

Soil erosion induced by heavy rainfall deeply affects landscape changes and human activities. It depends on rainfall distribution (e.g., intensity, duration, cumulative per event) and is controlled by the interactions between lithology, orography, hydrography, land use, and vegetation. The Abruzzo piedmont coastal hilly area has been affected by several heavy rainfall events in the last decades. In this work, we investigated three ~1-day heavy rainfall (&gt;35 mm/h and 100–220 mm/day) events in 2007, 2011, and 2012 that occurred in the clayey hilly coastal NE Abruzzo area, analyzing cumulative rainfall, intensity, and duration while mapping triggered geomorphological effects (soil erosion and accumulation) and evaluating average erosion. The analysis provides contributions to a soil erosion assessment of clayey landscapes that characterizes the Adriatic hilly area, with an estimation of rainfall-triggering thresholds for heavy soil erosion and a comparison of erosion in single events with rates known in the Mediterranean area. The triggering threshold for heavy soil erosion shows an expected value of ~100–110 mm. The estimated average soil erosion is from moderate to high (0.08–3.08 cm in ~1-day heavy rainfall events) and shows a good correlation with cumulative rainfall and a poor correlation with peak rainfall intensity. This work outlines the strong impact of soil erosion on the landscape changes in the Abruzzo and Adriatic hilly areas.

Rainfall events with shallow landslides in the Entella catchment, Liguria, northern Italy

Abstract. In recent decades, the Entella River basin, in the Liguria Apennines, northern Italy, was hit by numerous intense rainfall events that triggered shallow landslides and earth flows, causing casualties and extensive damage. We analyzed landslide information obtained from different sources and rainfall data recorded in the period 2002–2016 by rain gauges scattered throughout the catchment, to identify the event rainfall duration, D (in h), and rainfall intensity, I (in mm h−1), that presumably caused the landslide events. Rainfall-induced landslides affected the whole catchment area, but were most frequent and abundant in the central part, where the three most severe events hit on 23–24 November 2002, 21–22 October 2013 and 10–11 November 2014. Examining the timing and location of the slope failures, we found that the rainfall-induced landslides occurred primarily at the same time or within 6 h from the maximum peak rainfall intensity, and at or near the geographical location where the rainfall intensity was largest. Failures involved mainly forested and natural surfaces, and secondarily cultivated and terraced slopes, with different levels of maintenance. Man-made structures frequently characterize the landslide source areas. Adopting a frequentist approach, we define the event rainfall intensity–event duration (ID) threshold for the possible initiation of shallow landslides and hyper-concentrated flows in the Entella River basin. The threshold is lower than most of the curves proposed in the literature for similar mountain catchments, local areas and single regions in Italy. The result suggests a high susceptibility to rainfall-induced shallow landslides of the Entella catchment due to its high-relief topography, geological and geomorphological settings, meteorological and rainfall conditions, and human interference. Analysis of the antecedent rainfall conditions for different periods, from 3 to 15 days, revealed that the antecedent rainfall did not play a significant role in the initiation of landslides in the Entella catchment. We expect that our findings will be useful in regional to local landslides early warning systems, and for land planning aimed at reducing landslide risk in the study area.

Comparing CLIGEN-Generated Storm Patterns with 1-Minute and Hourly Precipitation Data from China

AbstractClimate Generator (CLIGEN) is a stochastic weather generator that has been widely used to generate daily precipitation and storm patterns for hydrological and erosion prediction models. Rainfall data with measurement intervals ≤ 30 min are required to compute two parameters for generating storm patterns, namely, the cumulative distribution of the time to peak rainfall intensity (TimePk) and the mean daily maximum 30-min rainfall intensity (MX.5P). High-resolution rainfall data, however, are not widely available around the world. One-minute precipitation data for 18 stations in eastern and central China were aggregated into hourly intervals to evaluate methods to optimally prepare TimePk and MX.5P for CLIGEN. Four sets of the two parameters were used to run CLIGEN for comparison: C0, using the original 1-min data; C1, replacing TimePk with those computed with hourly data; C2, replacing MX.5P with those computed with hourly data with an adjustment factor; and C3, replacing both parameters with those computed with hourly data, and the MX.5P was adjusted as for C2. Results showed that 1) MX.5P computed with hourly data was systematically lower than that computed with the 1-min data, and the bias could be corrected by multiplying by an adjustment factor of 1.40; 2) the difference in generated storm patterns between C0 and C1 was insignificant; and 3) results from C2 and C3 agreed well with those generated from C0. Hourly precipitation data can be used to prepare CLIGEN input parameter values for generating storm patterns for sites where only hourly data are available.

How intermittency affects the rate at which rainfall extremes respond to changes in temperature

Abstract. A detailed analysis of how intermittency (i.e., the alternation of dry and rainy periods) modulates the rate at which sub-daily rainfall extremes depend on temperature is presented. Results show that hourly extremes tend to be predominantly controlled by peak intensity, increasing at a rate of approximately 7 % per degree in agreement with the Clausius–Clapeyron equation. However, a rapid increase in intermittency upward of 20–25 °C is shown to produce local deviations from this theoretical scaling, resulting in lower scaling rates. On the other hand, rapidly decreasing intermittency with temperature between 10 and 20° can result in higher net scaling rates than expected, potentially exceeding Clausius–Clapeyron. In general, the importance of intermittency in controlling the scaling rates of precipitation with temperature grows as we progress from hourly to daily aggregation timescales and beyond. Thermodynamic effects still play an important role in controlling the maximum water-holding capacity of the atmosphere and therefore peak rainfall intensity, but the observational evidence shows that, beyond a few hours, storm totals become increasingly dominated by dynamical factors. The conclusion is that Clausius–Clapeyron scaling alone cannot be used to reliably predict the net effective changes in rainfall extremes with temperature beyond a few hours. A more general scaling model that takes into account simultaneous changes in intermittency and peak intensity with temperature is proposed to help better disentangle these two phenomena (e.g., peak intensity and intermittency). The new model is applied to a large number of high-resolution rain gauge time series in the United States, and results show that it greatly improves the representation of rainfall extremes with temperature, producing a much more consistent and reliable picture of extremes across scales than using Clausius–Clapeyron only.

LANDSLIDES AND PRECIPITATION CHARACTERISTICS DURING THE TYPHOON LIONROCK IN IWATE PREFECTURE, JAPAN

In August 2016, the typhoon Lionrock made landfall on Japan's northeastern coast and causedfloods and landslides. Lionrock b rought heavy precipitation to Japan, which Shimotokusari station (33201)recorded 24-hour rainfall amount over 200 mm and the peak rainfall intensity was approximately 65 mm/hr.The total cost of damage within the Iwate pre fecture is over 700 million dolla rs, moreover, 20 lives were lostand 4 people missing. Therefore, the objective of this paper is to present the results of the post-disasterinvestigation, including, the back-analysis of landslides and precipitation due to the typhoon Lionrock. Therainfall and landslides relat ionship is significantly important for rainfall intensity -duration threshold. For thistyphoon, the empirical rain fall intensity-duration threshold has been derived as I = 20.24D-0.33. The slopefailure could be broadly categorized into the debris flo w, surfic ial erosion, and soil slide, mo reover, occurredon slopes ranging from 19 to 58 degrees.

Rainfall characteristics and thresholds for periglacial debris flows in the Parlung Zangbo Basin, southeast Tibetan Plateau

The Parlung Zangbo Basin in the southeastern Tibet Plateau is affected by the summer monsoon from the Indian Ocean, which produces large rainfall gradients in the basin. Rainfall data during 2012–2015 from five new meteorological stations are used to analyse the rainfall characteristics. The daily rainfall, rainfall duration, mean rainfall intensity, and peak rainfall intensity are consistent, but sometimes contrasting. For example, these values decrease with increasing altitude, and the gradient is large downstream and small upstream, respectively. Moreover, the rainfall intensity peaks between 01:00 and 06:00 and increases during the afternoon. Based on the analysis of 14 debris flow cases in the basin, differences in the rainfall threshold differ depending on the location as sediment varieties. The sediment in the middle portions of the basin is wet and well structured; thus, long-duration, high-intensity rainfall is required to generate debris flows. Ravels in the upstream area are arid and not well structured, and short-duration rainfall is required to trigger debris flows. Between the above two locations, either long-duration, low-intensity rainfall or short-duration, high-intensity rainfall could provoke debris flows. Clearly, differences in rainfall characteristics and rainfall thresholds that are associated with the location must be considered in debris flow monitoring and warnings.

Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery

Abstract. Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River valley at the Soufrière Hills volcano (SHV), Montserrat, collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity (PRI) as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak 1 h rainfall intensity throughout the 2-year dataset and is higher under given rainfall conditions in year 1 than year 2. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones) are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic-regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery.