Single Precipitation Events Research Articles

Inventory of shallow landslides triggered by extreme precipitation in July 2023 in Beijing, China

The extreme meteorological events caused by climate change have increasingly caused serious clustered landslides. A systematic and timely inventory of triggered landslides is a prerequisite for quantifying and evaluating the impact of extreme meteorological events. In addition, a landslide inventory can provide basic data for any subsequent analysis of the event or be used innovatively in landslide risk analysis. The Rainfall-induced Landslides in Beijing (RLBJ) inventory presented here contains data on 15,383 rainfall-induced shallow landslides triggered by a single extreme precipitation event in July 2023 in an area of ~3,250 km2 in western mountainous areas of Beijing, China. High-resolution satellite images before and after this rainstorm event were used to visually analyze the landslides. All landslides were reported as vectorized points and polygon features and classified according to their motion forms. This inventory is now freely available for the benefit of international geohazard researchers.

Towards understanding the influence of seasons on low-groundwater periods based on explainable machine learning

Abstract. Seasons are known to have a major influence on groundwater recharge and therefore groundwater levels; however, underlying relationships are complex and partly unknown. The goal of this study is to investigate the influence of the seasons on groundwater levels (GWLs), especially during low-water periods. For this purpose, we train artificial neural networks on data from 24 locations spread throughout Germany. We exclusively focus on precipitation and temperature as input data and apply layer-wise relevance propagation to understand the relationships learned by the models to simulate GWLs. We find that the learned relationships are plausible and thus consistent with our understanding of the major physical processes. Our results show that for the investigated locations, the models learn that summer is the key season for periods of low GWLs in fall, with a connection to the preceding winter usually only being subordinate. Specifically, dry summers exhibit a strong influence on low-water periods and generate a water deficit that (preceding) wet winters cannot compensate for. Temperature is thus an important proxy for evapotranspiration in summer and is generally identified as more important than precipitation, albeit only on average. Single precipitation events show by far the largest influences on GWLs, and summer precipitation seems to mainly control the severeness of low-GWL periods in fall, while higher summer temperatures do not systematically cause more severe low-water periods.

Time Series Analysis of Nonlinear Head Dynamics Using Synthetic Data Generated with a Variably Saturated Model.

The performance of time series models is assessed using synthetic head series simulated with a numerical model that solves Richards' equation for variably saturated flow. Heads were simulated in a homogeneous unconfined aquifer between two parallel canals; measured daily precipitation and potential evaporation are specified at the land surface and root water uptake is simulated. The head response to a precipitation event is nonlinear and depends on the saturation degree and rainfall before and after the precipitation event while evaporation reduction occurs during summers. Synthetic series were generated for 27 years and three different soil types; the unsaturated zone thickness varies between 0 and >5 m. The synthetic head series were simulated with a linear and nonlinear time series model. Performance of a linear time series model with four parameters, using a scaled Gamma response, gave R2 values ranging from 0.67 to 0.96. The nonlinear time series model with five parameters simulates recharge using a root zone reservoir after which the head response to recharge is simulated with a scaled Gamma response function. The nonlinear time series model was able to simulate all synthetic head series very well with R2 values above 0.9 for almost all models. The head response of the nonlinear model to a single precipitation event compares well to the response of the variably saturated groundwater model. The provided scripts may be used to simulate synthetic head series for other climates or for systems with additional complexity to assess the performance of other data-driven models.

Analysis of the driving factors of precipitation change during the development of the Jing-Jin-Ji urban agglomeration

Urbanization has an important impact on local precipitation changes. Previous studies of urban precipitation have largely considered single precipitation events, with few long-term statistical studies based on multiple samples. Therefore, based on WRF model simulations, this study extracted the latent heat flux, sensible heat flux, net radiation, friction velocity, albedo, and precipitation of 156 urban units in Beijing-Tianjin-Hebei (Jing-Jin-Ji) urban agglomeration for the summers of 2001–2019, and identified the main factors driving long-term urban precipitation changes based on multiple samples. There were three key results. 1) The factors that had the greatest impact on urban precipitation changes in Jing-Jin-Ji were the sensible heat flux, latent heat flux, and friction velocity. The latent heat flux was the leading factor driving precipitation changes during the urbanization of Jing-Jin-Ji. 2) In the different stages of urbanization, there were different factors affecting urban precipitation changes. In highly urbanized cities, the sensible heat flux had the greatest impact on precipitation changes, followed by the friction velocity. For medium-sized cities, the friction velocity was the most important variable. For small cities, the latent heat flux was the most important factor. 3) The friction velocity was the leading factor in precipitation change at the 0.95 quantile in Jing-Jin-Ji, and was therefore the most important factor driving changes in urban extreme precipitation. The results provide a theoretical basis for the formulation of climate policies, and disaster prevention and mitigation in Jing-Jin-Ji. They will also assist in promoting the construction of an ecological civilization and building safe and livable cities.

Response of fatal landslides to precipitation over the Chinese Loess Plateau under global warming

Rain-induced loess landslides are especially prevalent in the Chinese Loess Plateau (CLP). Some became fatal landslide disasters, leading to numerous casualties and significant socioeconomic losses. Extreme precipitation is the main cause of landslide occurrence. Therefore, in this study we discuss the correlation between seven extreme precipitation indices, single continuous precipitation events and fatal landslides in the CLP using Pearson correlation analysis. We also predict future precipitation under climate changes using five optimal CMIP6 models. During the period 2004–2016, fatal landslides in the CLP increased at a rate of 0.6 per year, with frequent landslide events occurring especially in the central and southwestern parts of the CLP. We find that SDII (simple daily intensity precipitation index) and R×5day (max 5-day precipitation amount) show spatial distribution that are consistent with fatal landslides. Extreme precipitation events were frequent after year 2000; and several extreme precipitation indices show an increasing trend with a higher magnitude since 2000 than before 2000. In particular, in 2013 when the number of fatal landslides was as high as 17, SDII, R95pTOT (extremely wet days), R25mm (very heavy precipitation days), and R×5day all showed abrupt increases. Single continuous precipitation events have profound effects on fatal landslides. We show that single continuous precipitation events with cumulative precipitation of 185–235 mm and duration of 6 days or longer have the highest correlation with fatal landslides. As the increasing occurrence of extreme rainfall events by the global warming, the CLP may face more fatal landslides in the future, especially in the high emission scenario of greenhouse gases (GHGs).

Responses of soil water dynamics to precipitation events in an alpine meadow ecosystem of the Qinghai Lake Basin based on high‐precision lysimeter measurements

AbstractIn high‐altitude cold areas, how precipitation and freeze–thaw processes affect soil water dynamics is not well understood due to a lack of high real‐time resolution measurements. This study measured soil water balance components with a high‐precision lysimeter in an alpine meadow ecosystem in the Qinghai Lake watershed from June 30, 2020, to June 30, 2021. The results showed that the total precipitation (TP), evapotranspiration (ET), soil water storage change (ΔS) and the vertical water flow (WF) of this ecosystem were 705.18, 633.21, 72.58 and −0.60 mm, respectively, during the study period. Continuous wet days with precipitation events larger than 10.00 mm led to percolation, while continuous dry days with strong ET caused an upward WF. Pearson correlation analysis suggests that a larger instantaneous precipitation intensity may be more conducive to an increase in shallow soil moisture (R2 > 0.72, p < 0.05). Furthermore, the TP thresholds in the complete thawing period inducing the soil water response in the mattic epipedon layer (0–20 cm) and organic layer (20–40 cm) were 3.24 mm and 16.61 mm, respectively. The difference in the average response time of soil water to a single precipitation event between the mattic epipedon layer and organic layer was 5.52 ± 3.65 h. These results deepen the understanding of the temporal pattern of water flux in alpine meadow ecosystems on the Tibetan Plateau.

Modeling multi-year phosphorus dynamics in a bioretention cell: Phosphorus partitioning, accumulation, and export

Phosphorus (P) export from urban areas via stormwater runoff contributes to eutrophication of downstream aquatic ecosystems. Bioretention cells are a Low Impact Development (LID) technology promoted as a green solution to attenuate urban peak flow discharge, as well as the export of excess nutrients and other contaminants. Despite their rapidly growing implementation worldwide, a predictive understanding of the efficiency of bioretention cells in reducing urban P loadings remains limited. Here, we present a reaction-transport model to simulate the fate and transport of P in a bioretention cell facility in the greater Toronto metropolitan area. The model incorporates a representation of the biogeochemical reaction network that controls P cycling within the cell. We used the model as a diagnostic tool to determine the relative importance of processes immobilizing P in the bioretention cell. The model predictions were compared to multi-year observational data on 1) the outflow loads of total P (TP) and soluble reactive P (SRP) during the 2012–2017 period, 2) TP depth profiles collected at 4 time points during the 2012–2019 period, and 3) sequential chemical P extractions performed on core samples from the filter media layer obtained in 2019. Results indicate that exfiltration to underlying native soil was principally responsible for decreasing the surface water discharge from the bioretention cell (63 % runoff reduction). From 2012 to 2017, the cumulative outflow export loads of TP and SRP only accounted for 1 % and 2 % of the corresponding inflow loads, respectively, hence demonstrating the extremely high P reduction efficiency of this bioretention cell. Accumulation in the filter media layer was the predominant mechanism responsible for the reduction in P outflow loading (57 % retention of TP inflow load) followed by plant uptake (21 % TP retention). Of the P retained within the filter media layer, 48 % occurred in stable, 41 % in potentially mobilizable, and 11 % in easily mobilizable forms. There were no signs that the P retention capacity of the bioretention cell was approaching saturation after 7 years of operation. The reactive transport modeling approach developed here can in principle be transferred and adapted to fit other bioretention cell designs and hydrological regimes to estimate P surface loading reductions at a range of temporal scales, from a single precipitation event to long-term (i.e., multi-year) operation.

The influence of sampling on landslide susceptibility mapping using artificial neural networks

Landslides can have serious environmental, economic and social consequences. Using artificial neural networks (ANN) to map these landslides is becoming more frequent every year, being one of the most reliable methods for this. Among the prime influences on the generated maps, sample areas are significantly interesting, since they directly influence the result. In this research, we investigated how the performance of these models is influenced by the use of partial sampling (with landslides caused by a single precipitation event – Single Model) or total (with landslides caused by multiple precipitation events – Full Model). This is one of the main topics that our study approaches. This study aims to evaluate the criteria for landslide sampling and ANN modeling by analyzing the influence of distance on the sampling processes, the use of multiple landslide events, and the relationship between terrain attributes and susceptibility models. To this end, were used five sampling areas (1638 points samples of landslides) in the Serra Geral, southern Brazil, distance buffers in the non-occurrence sampling process (2–40 km), and 16 terrain attributes. The training of the multilayer network was carried out by backpropagation algorithm, and the accuracy was calculated using the Area Under the Receiver Operating Characteristic Curve. The results showed that the greater the distances of the non-occurring samples, the greater the accuracy of the model, with a 40 km buffer resulting in the best models. They also showed that the use of multiple events (Full Model) produced better results than each event used separately (Single Model), obtaining accuracies of 0.954 and 0.931, respectively. This is mainly because there is greater differentiation between occurrence and non-occurrence samples when using multiple events, thus facilitating the distinction between more and less susceptible areas.

Understanding the hydrological regime based on the runoff events in a mountainous catchment with seasonally frozen soil in the Qinghai‐Tibet plateau

AbstractSeasonal thawing of frozen soil will significantly affect the hydrological regime, water storage capacity and ecosystem services in the future, which contributes to the uncertainty of regional water resources. However, based on the scale of runoff events, the mechanism of seasonal frozen soil thawing on the hydrological regime are still unclear. In this study, we systematically analysed the runoff characteristics and interactive effects of environmental factors and pre‐event catchment state on runoff processes based on the runoff event scale, using data sets of air temperature, precipitation, runoff, soil moisture, and soil temperature observed in a mountainous catchment with seasonally frozen soil distribution on the Qinghai‐Tibet Plateau from 2010 to 2019. It was found that the event runoff coefficient did not increase significantly with the increase of precipitation. Event runoff coefficients exhibited strong seasonality. The catchment exhibited a gradual increase in the event runoff coefficient with increasing pre‐event baseflow and antecedent soil moisture, especially during stable period (August to November). Event runoff generation through infiltration excess as the result of intensive rainfall seemed to be only possible in the stable period, while heavy precipitation events were common during this period. Normalized event peak discharge and event runoff coefficient showed positive trends with changes in pre‐event catchment state indicators, which were commonly observed phenomena in runoff events. For this catchment, the increased storage capacity pre‐event saturation may be the main mechanism of event runoff generation, as it was unlikely that a single precipitation event would lead to catchment saturation. Soil temperature affects runoff processes through both direct effects on runoff processes and indirect effects on pre‐event catchment state. Temperature indicators were the most important predictors of runoff changes in the Qinghai‐Tibet Plateau.

Comparative Evaluation of Five Hydrological Models in a Large-Scale and Tropical River Basin

Hydrological modeling is an important tool for water resources management, providing a feasible solution to represent the main hydrological processes and predict future streamflow regimes. The literature presents a set of hydrological models commonly used to represent the rainfall-runoff process in watersheds with different meteorological and geomorphological characteristics. The response of such models could differ significantly for a single precipitation event, given the uncertainties associated with the input data, parameters, and model structure. In this way, a correct hydrological representation of a watershed should include the evaluation of different hydrological models. This study explores the use and performance of five hydrological models to represent daily streamflow regimes at six hydropower plants located in the Tocantins river basin (Brazil). The adopted models include the GR4J, HYMOD, HBV, SMAP, and MGB-IPH. The evaluation of each model was elaborated considering the calibration (2014–2019) and validation period (2005–2010) using observed data of precipitation and climatological variables. Deterministic metrics and statistical tests were used to measure the performance of each model. For the calibration stage, results show that all models achieved a satisfactory performance with NSE values greater than 0.6. For the validation stage, only the MGB-IPH model present a good performance with NSE values greater than 0.7. A bias correction procedure were applied to correct the simulated data of conceptual models. However, the statistical tests exposed that only the MGB-IPH model could preserve the main statistical properties of the observed data. Thus, this study discusses and presents some limitations of the lumped model to represent daily streamflows in large-scale river basins (>50,000 km2).

Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System

AbstractConvective precipitation systems in the summer often cause sudden heavy precipitation and largely affect various human activities, but the rapid evolution limits our predicting capability. Phased‐array weather radars (PAWRs) with a high spatiotemporal resolution are useful for observing such precipitation system. A recently developed numerical weather prediction (NWP) system assimilates PAWR observations with a 500‐m mesh NWP model. It initiates 30‐min extended forecasts every 30 s, much more frequently than the operational NWP and nowcasting systems. This study investigates the benefits of the 30‐s‐updating NWP system in a single but representative convective precipitation event in which a convective cloud developed within 10 min, and its evolution was not well predicted by operational precipitation nowcasting. The rapidly updating NWP system successfully predicts the evolution of the convective cloud. Assimilating the PAWR observations every 30 s continuously modifies the moisture and dynamical fields and improves the forecast accuracy consistently.

Soil Moisture Variations in Response to Precipitation Across Different Vegetation Types on the Northeastern Qinghai-Tibet Plateau.

An understanding of soil moisture conditions is crucial for hydrological modeling and hydrological processes. However, few studies have compared the differences between the dynamics of soil moisture content and soil moisture response to precipitation infiltration under different types of vegetation on the Qinghai-Tibet Plateau (QTP). In this study, a soil moisture sensor was used for continuous volumetric soil moisture measurements during 2015 and 2016, with the aim of exploring variations in soil moisture and its response to precipitation infiltration across two vegetation types (alpine meadow and alpine shrub). Our results showed that temporal variations in soil moisture at the surface (0–20 cm) and middle soil layers (40–60 cm) were consistent with precipitation patterns for both vegetation types. However, there was a clear lag in the soil moisture response to precipitation for the deep soil layers (80–100 cm). Soil moisture content was found to be significantly positively related to precipitation and negatively related to air temperature. Aboveground biomass was significantly negatively associated with the surface soil moisture content (0–20 cm) during the growing season. Statistically significant differences were observed between the soil water content of the surface, middle, and deep soil layers for the two vegetation types (p < 0.05). Soil moisture (19.81%) in the surface soil layer was significantly lower than that in the deep soil layer (24.75%) for alpine shrubs, and the opposite trend was observed for alpine meadows. The maximum infiltration depth of alpine shrubs was greater than that of alpine meadows under extremely high-precipitation events, which indicates that alpine shrubs might be less susceptible to surface runoff under extreme precipitation events. Furthermore, low precipitation amounts did not affect precipitation infiltration for either vegetation type, whereas the infiltration depth increased with precipitation for both vegetation types. Our results suggest that a series of small precipitation events may not have the same effect on soil moisture as a single large precipitation event that produces the equivalent total rainfall.

Moisture Origin and Isotope Composition of Single Precipitation Events in a Western Mediterranean Catchment (Apuan Alps, Italy)

Moisture Origin and Isotope Composition of Single Precipitation Events in a Western Mediterranean Catchment (Apuan Alps, Italy)

Heavy precipitation forecasts over Switzerland – An evaluation of bias-corrected ECMWF predictions

Heavy precipitation episodes are particularly hazardous in mid-sized alpine catchments where runoff tends to increase rapidly after strong rainfall, leaving limited time for warning. A high-quality, unbiased forecast of heavy precipitation with long enough lead times (2–5 days) and an adequate spatial resolution is thus crucial for decision makers. However, at present, weather forecast models are sometimes still too coarsely resolved for such catchments or limited to short lead times. Here we present a computationally cheap post-processing approach for operational applications to bias-correct deterministic heavy precipitation forecasts at medium-range lead times. We assessed forecast performance of uncorrected ECMWF Integrated Forecasting System (IFS) high-resolution (HRES) and ensemble median run (ENS) forecasts with lead times of 2 and 5 days between 2010 and 2019 and evaluated the improvement after interpolation and bias correction. The study is based on observations from 787 meteorological stations, with a focus on areal precipitation in three medium-sized Swiss catchments (Emme, Simme, and Vispa). We found a significantly higher performance of the HRES forecast compared to ENS, especially for a lead time of 5 days. The post-processing approach that we present in this study removes large biases, and lowers false alarm rates. Nonetheless, improvements may be limited for single heavy precipitation events where the model considerably underestimates daily precipitation. Also, a lower false alarm rate can be at the cost of lower hit rates. Quantile mapping is clearly limited for short term prediction in the cases where the error does not substantially exceed natural variability. Despite these limitations, we conclude that bias-corrected forecasts can help improving forecast credibility, which is a key element for decision makers to take actions.

Distinction between Natural and Anthropogenic Contaminants of Atmospheric Precipitates from Northeastern Kansas Based on Their Elemental Contents and Strontium Isotopic Signatures

This study was designed to identify and possibly evaluate the changing occurrence of major pollutants in different atmospheric precipitates (rain, snow and hail) that were collected in northeastern Kansas next to the Konza Prairie Preservation site by analyzing their elemental and Sr isotope compositions. Potential pollutants like the local soils and their clay material, as well as the fly ash of a nearby coal-burning power plant were also analyzed. Positively correlated with K in the analyzed precipitates, the Na contents of the precipitates suggest a supply of fertilizers and/or natural plant organics. Combining the four identified pollutants of the precipitates that is to say the soils, the fly ash, the fertilizers and the plants allows evaluation of their changing contribution during a single precipitation event. The duration of the rain events monitors also the changing contribution of the identified pollutants. Variations of the 87Sr/86Sr ratio from precipitates during lasting events are confirmed by changing distribution patterns of the REEs. In fact, soil minerals contribute mostly at the beginning of the precipitation events and are replaced progressively by the industrial fly ash that becomes dominant towards the end of the precipitation events, depending on the duration and wind directions. At last not least, the different contaminants are recognizable by changing elemental contributions, REE distribution patterns and 87Sr/86Sr ratios. Their variable occurrence can be followed in the wet solutes, but a strict quantification cannot be provided due to elemental and isotopic interconnections between the natural and the anthropogenic contributors.

Patterns of rainfall-threshold for debris-flow occurrence in the Wenchuan seismic region, Southwest China

In recent years, debris flows induced by rainstorms have caused severe casualties and property losses in the Wenchuan earthquake region, Southwest China. The different rainfall patterns point to a composite rainfall threshold for the occurrence of debris flows. The rainfall intensity and antecedent rainfall of debris flow events were analyzed after the Wenchuan earthquake. The results show that the rainfall pattern in time can be classified into three types: a short-term rainfall pattern, a medium persistent rainfall pattern, and a long-term intermittent rainfall pattern. The main differences between the three patterns of a single precipitation event are rainfall intensity, duration, and cumulative rainfall, which can result in different infiltration, saturation, and triggering mechanisms of debris flows. The short-term rainfall pattern can be associated with the highest rainfall intensity and the least cumulative rainfall. The medium persistent rainfall pattern is related to intermediate rainfall intensity and the medium cumulative rainfall, and a long-term intermittent rainfall pattern can be related to the lowest rainfall intensity and the longest rainfall duration. At last, this paper sets up the patterns of rainfall-threshold for debris-flow occurrence. According to the critical initiation, conditions of the debris flow in the five watersheds can be predicted by the intensity-duration curve (ID curve) under different rainfall patterns.

Characteristics of Stable Isotopes and Moisture Sources of Two Typical Precipitation Events in Lanzhou City

To better understand the isotope variations on a short time scale, this study focused on a long-term rainfall event with light precipitation (June 26-27) and a short-term one with heavy precipitation (July 28) in Lanzhou City in the summer of 2019. Combined with HYSPLIT model, samples collected during a continuous precipitation event every 10 min and 30 min were analyzed to explore the characteristics and mechanism of stable hydrogen and oxygen isotopes in precipitation. The results indicate that the effect of sub-cloud secondary evaporation makes the slope of the sequential meteoric water line (SMWL) smaller at the beginning of the rainfall event. Most of the continuous sampling points are distributed above the global meteoric water line (GMWL) and local meteoric water line (LMWL). Moreover, the deuterium excess is larger than the local average annual deuterium (8.13), indicating that the samples have experienced moisture recycling to a certain extent. During two consecutive days (June 26-27) of rainfall, the variations in oxygen isotope δ18O did not follow the effect of precipitation amount; the precipitation δ18O of the first day was "L" shaped, and it fluctuated the next day. On July 28, δ18O steadily decreased, and the range of δ18O exceeded 9‰. On June 26, the moisture transport path was short at the height of 500 m and on June 27 local evaporation was the main pathway. On July 28, with a relatively stable air mass, the moisture source of the entire precipitation event did not change significantly, neither did the isotope value. Therefore, for a single precipitation event on a short time scale, the difference in moisture sources is one of the reasons for isotope variations.

Characteristics and Distribution of efficient ice nucleating particles in rainwater and soil

Ice nucleating particles (INPs) might influence precipitation and are related to the hydrologic cycle by initiating ice formation. The contribution of efficient INPs (and almost all biological particles) is significant in catalyzing the freezing of ice at relatively warm temperatures (> −10 °C). However, to what extent biological matter functions as INPs in clouds is poorly understood and the large gap in investigating the properties of ice nucleating ability in rainwater and variation in INPs during a single precipitation event needs to be addressed. In this study, we tested the ice nucleating activity in rainwater and soil to probe the characteristics and distribution of INPs and tried to trace the possible sources of INPs. We suggested that the biological INPs dubbed efficient INPs are commonly present in rainwater and soil at > −10 °C, and it is surprising that unknown efficient INPs, probably derived from microbes, were shown to dominate at warmer temperatures by detection of the potential known ice nucleating active genera through high-throughput sequencing analysis. Furthermore, certain heat-resistant efficient INPs were found in rainwater and a few nanoscale efficient biological INPs were retained partially in soil. Moreover, we also found that nonlocally sourced inefficient INPs dominated in precipitation induction and efficient INPs became gradually more involved as precipitation proceeded by comparing the diverse periods of one entire heavy rainfall event.

Meteorological conditions leading to the 2015 Salgar flash flood: lessons for vulnerable regions in tropical complex terrain

Abstract. Flash floods are a recurrent hazard for many developing Latin American regions due to their complex mountainous terrain and the rainfall characteristics in the tropics. These regions often lack the timely and high-quality information needed to assess, in real time, the threats to the vulnerable communities due to extreme hydrometeorological events. The systematic assessment of past extreme events allows us to improve our prediction capabilities of flash floods. In May 2015, a flash flood in the La Liboriana basin, municipality of Salgar, Colombia, caused more than 100 casualties and significant infrastructure damage. Despite the data scarcity, the climatological aspects, meteorological conditions, and first-order hydrometeorological mechanisms associated with the La Liboriana flash flood, including orographic intensification and the spatial distribution of the rainfall intensity relative to the basin's geomorphological features, are studied using precipitation information obtained using a weather radar quantitative precipitation estimation (QPE) technique, as well as from satellite products, in situ rain gauges from neighboring basins, quantitative precipitation forecasts (QPFs) from an operational Weather Research and Forecasting Model (WRF) application, and data from reanalysis products. The La Liboriana flash flood took place during a period with negative precipitation anomalies over most of the country as a result of an El Niño event. However, during May 2015, moist easterly flow towards the upper part of La Liboriana caused significant orographic rainfall enhancement. The overall evidence shows an important role of successive precipitation events in a relatively short period and of orography in the spatial distribution of rainfall and its intensification as convective cores approached the steepest topography. There were three consecutive events generating significant rainfall within the La Liboriana basin, and no single precipitation event was exceptionally large enough to generate the flash flood, but rather the combined role of precedent rainfall and the extreme hourly precipitation triggered the event. The results point to key lessons for improving local risk reduction strategies in vulnerable regions with complex terrain.

Increased flood risk in Indian sub-continent under the warming climate

Millions of people in Indian sub-continent are affected by floods, which have increased during the past decades and likely to increase further in response to warming climate. However, changes in the frequency of floods and associated risks under the warming climate in Indian sub-continent remain largely unrecognized. Using the observations and simulations from Noah-MP land surface hydrological model, we examine the observed and projected changes in extreme precipitation and floods events. A majority of river basins in the Indian-subcontinent experienced a rise in extreme precipitation events during 1966–2005; however, these increasing trends are mostly insignificant at 5% level. Using downscaled and bias-corrected future and historic climate projections from Coupled Model Intercomparison Project (CMIP5) and the Noah-MP model, we show that the frequency of single and multi-day extreme precipitation and flood events are projected to increase substantially in the future over the Indian sub-continental river basins. A significant increase in multi-day (1–5 day) frequency of extreme precipitation and flood events, exceeding return levels based on the historic period (1966–2005), is projected under RCP 2.6 and RCP 8.5 emission scenarios. Our results show that the projected increase in flood frequency can be reduced to half under the low (RCP 2.6) emission scenario in comparison to the high emission scenario (RCP 8.5). More than half of the precipitation extremes result in flood events of 20, 50, and 100 year return periods in the majority of river basins in the Indian sub-continent. Moreover, the multi-day flood events are projected to increase with a faster rate in the future than the single day events, which can have strong implications for agriculture and infrastructure.