Stochastic Rainfall Generator Research Articles

Development of a stochastic rainfall generator to yield unprecedented rainfall events

Unprecedented rainfall events are defined as rainfall events that have extremely high magnitude with low probability of occurrence. Although such events have occurred in the recent past, there might be no historical record of unprecedented rainfall. While significant efforts have been made to understand the major drivers of unprecedented events, the use of statistical modelling to estimate the likelihood of unprecedented events has gained little to no attention. The current study proposes a ‘serial’ type stochastic rainfall generator (SRG) to simulate daily rainfall with an additional focus on unprecedented events. The study introduces resampling of model parameters to yield the non-stationarity in simulated rainfall distribution. The proposed approach is applied to simulate daily rainfall over twenty-six grid points in Southeast Texas, which experienced Hurricane Harvey in 2017. Towards this, the study obtains rainfall accumulation data during Harvey from eight weather stations during. We found that the proposed approach can successfully generate unprecedented events if two power law tail tuning parameters are adjusted with respect to future warming levels. Rainfall magnitudes correspond to 50, 100, 250 and 500-year return periods simulated by the proposed model are found to be substantially higher than historical (i.e., observed) return period. Furthermore, the study found that the estimated return periods of Hurricane Harvey like rainfall is almost the same as the return period estimated by a former study of Emmanuel (2017). The potential benefit of the proposed approach in hydrological risk assessment has also been discussed.

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

AbstractExisting stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6‐hr and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico—the key flood‐generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large‐scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine‐scale precipitation arising from a specific large‐scale atmospheric environment. Model parameters were fitted monthly from December–May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1‐ to 500‐year events in the future period (2022–2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high‐resolution precipitation over continental‐scale river basins, demonstrating the value of conditioning such stochastic models on large‐scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins.

Possibility of using the STORAGE rainfall generator model in the flood analyses in urban areas

In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

Process‐Based Quantification of the Role of Wildfire in Shaping Flood Frequency

AbstractModerate to high severity wildfire can abruptly alter watershed properties and enhance extreme hydrologic responses such as debris flows and floods. The compounding effects of wildfire on flood hazard, represented here via flood frequency analysis (FFA; e.g., 100‐year flood) are of growing importance. Standard statistical FFA approaches are ill‐suited to examining this issue because wildfire‐affected flood peak observations are limited in number and violate the assumption of independent and identically distributed events. Here, we developed a process‐based FFA framework that integrates a stochastic rainfall generator, wildfire simulation, inverse modeling, and a physics‐based hydrological model to directly simulate the impacts of wildfire on FFA. We applied this framework in the upper Arroyo Seco watershed in Southern California, which experienced Moderate to high burn during the 2009 Station Fire. An FFA analysis, performed with simulated peak flows from the first year since fire demonstrates the 100‐year flood can be three times larger than simulations that only consider peak flows in non‐fire‐affected years. On the other hand, coupling process‐based FFA with stochastically simulated wildfire events and watershed's time‐varying hydrologic recovery yields “fire continuum FFA”, a concept introduced here for the first time. Fire continuum FFA accounts for multiple wildfires within very long synthetic time series. Variability in upper tail flood peaks is substantially higher in fire continuum results as compared with pre‐wildfire FFA. This result highlights the importance of wildfire inter‐arrival time and post‐wildfire recovery processes, both of which are expected to change as a result of climatic change and evolving fire management strategies.

Combined basin-scale and decentralized flood risk assessment: a methodological approach for preliminary flood risk assessment

In this study, we focused on a combined basin-scale and decentralized (localized) approach (municipalities) to riverine flood risk mapping and assessment in the Gidra River Basin (Slovakia). The methodology applies direct spatial and probabilistic relation among the physical processes of riverine flooding with a given vulnerability. Flood hazard mapping at basin scale was based on ungauged conditions. First, we calculated the design peak discharges using the rainfall generation model and rainfall-runoff model, in particular, the Stochastic Rainfall Generator (STORAGE) and the Continuous Simulation Model for Small and Ungauged Basins (COSMO4SUB). After that, we used the obtained flood scenarios for Hydrologic Engineering Center´s River Analysis System (HEC-RAS) hydraulic modelling. The modelled flood extent, flow depth, and velocity were considered as hazard indicators. The results were expressed in terms of flood hazard (FHI), vulnerability (FVI), and risk index (FRI): the highest values were obtained for municipalities located in the northern-central part of the investigated area. The proposed methodology can constitute a conceptual basis for updating the Preliminary Flood Risk Assessment (PFRA) in Slovakia.

Generating Continuous Rainfall Time Series with High Temporal Resolution by Using a Stochastic Rainfall Generator with a Copula and Modified Huff Rainfall Curves

In this study, a stochastic rainfall generator was developed to create continuous rainfall time series with a high temporal resolution of 10 min. The rainfall-generation process involved Monte Carlo simulation for stochastically generating rainfall parameters such as rainfall quantity, duration, inter-event time, and type. A bivariate copula was used to preserve the correlation between rainfall quantity and rainfall duration in the generated rainfall series. A modified Huff curve method was used to overcome the drawbacks of rainfall type classification by using the conventional Huff curve method. The number of discarded rainfall events was lower in the modified Huff curve method than in the conventional Huff curve method. Moreover, the modified method includes a new rainfall type that better represents rainfall events with a relatively uniform temporal pattern. The developed rainfall generator was used to reproduce rainfall series for the Yilan River Basin in Taiwan. The statistical indices of the generated rainfall series were close to those of the observed rainfall series. The results obtained for rainfall type classification indicated the necessity and suitability of the proposed new rainfall type. Overall, the developed stochastic rainfall generator can suitably reproduce continuous rainfall time series with a resolution of 10 min.

Stochastic daily rainfall generation on tropical islands with complex topography

Abstract. Stochastic rainfall generators are probabilistic models of rainfall space–time behavior. During parameterization and calibration, they allow the identification and quantification of the main modes of rainfall variability. Hence, stochastic rainfall models can be regarded as probabilistic conceptual models of rainfall dynamics. As with most conceptual models in earth sciences, the performance of stochastic rainfall models strongly relies on their adequacy in representing the rain process at hand. On tropical islands with high elevation topography, orographic rain enhancement challenges most existing stochastic models because it creates localized precipitations with strong spatial gradients, which break down the stationarity of rain statistics. To allow for stochastic rainfall modeling on tropical islands, despite non-stationarity of rain statistics, we propose a new stochastic daily multi-site rainfall generator specifically for areas with significant orographic effects. Our model relies on a preliminary classification of daily rain patterns into rain types based on rainfall space and intensity statistics, and sheds new light on rainfall variability at the island scale. Within each rain type, the distribution of rainfall through the island is modeled by combining a non-parametric resampling of past analogs of a latent field describing the spatial distribution of rainfall, and a parametric gamma transform function describing rain intensity. When applied to the stochastic simulation of rainfall on the islands of O`ahu (Hawai`i, United States of America) and Tahiti (French Polynesia) in the tropical Pacific, the proposed model demonstrates good skills in jointly simulating site-specific and island-scale rain statistics. Hence, it provides a new tool for stochastic impact studies in tropical islands, in particular for watershed water resource management.

The Benefit of Continuous Hydrological Modelling for Drought Hazard Assessment in Small and Coastal Ungauged Basins: A Case Study in Southern Italy

Rainfall-runoff modelling in small and ungauged basins represents one of the most common practices in hydrology. However, it remains a challenging task for researchers and practitioners, in particular in a climate change context and in areas subject to drought risk. When discharge observations are not available, empirical or event-based approaches are commonly used. However, these schemes can be affected by several relevant assumptions. In the last years, continuous models have been developed in order to address the major drawbacks of event-based approaches. With this goal in mind, in this work we applied a synthetic rainfall generation model (STORAGE; stochastic rainfall generator), constituting the implementation of a modified version of Neymann-Scott rectangular pulse (NSRP) model, and a continuous rainfall-runoff framework (COSMO4SUB; continuous simulation modelling for small and ungauged basins) specifically designed for ungauged basins within a climate change context. The modeling approach allows one to investigate the drought hazard using specific indicators for rainfall and runoff in a small watershed located in southern Italy. Results show that the investigated area seems to tend to a mild/moderate drought in a future time period of approximately 30 years, with a decrease in seasonal water volumes availability in the range of 15–30%. Finally, our results confirm that the continuous modelling is suitable for rapid and effective design simulations supporting drought hazard assessment.

Long-term rainfall projection based on CMIP6 scenarios for Kurau River Basin of rice-growing irrigation scheme, Malaysia

Rainfall is a vital component in the rice water demand model for estimating irrigation requirements. Information on how the future patterns are likely to evolve is essential for rice-growing management. This study presents possible changes in the future monthly rainfall patterns by perturbing model parameters of a stochastic rainfall using the change factor method for the Kerian rice irrigation scheme in Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were employed to project rainfall from 2021 to 2080. The results show that the stochastic rainfall generator performed well at preserving the statistical properties between simulated and observed rainfall. Most scenarios predict the increasing trend of the mean monthly rainfall with only a few months decreasing in April and May occurring in off (dry) season. The future patterns 2051–2080 show a significant increasing trend during main (wet) season compared to the near future period (2021–2050). The projected future rainfall under SSP1-2.6 and SSP2-4.5 are higher than SSP5-8.5 from January to July, and December but lower from August to November. The projected annual rainfall will significantly increase toward 2080 during the main-season but uniform during the off-season except under SSP5-8.5, which is significantly decreasing. The output results are essential for rice farmers and water managers to manage and secure future rice irrigation water under the impact of future climate change. The projected changes in rainfall on the river basin demand further study before concluding the impact consequences for the rice sector.Article highlightsThe rainfall generator performs well in simulating future rainfall based on an ensemble of five different GCMs considering three different scenarios emission (low, medium, and high) caused by greenhouse gas and radiative forcing.The future rainfall projection predicted that off (dry) season would become wet, and main (wet) season would become wetter due increase in monthly and annual rainfall.The outcomes of this paper are beneficial for rice farmers and water managers of the study area to manage their rice cultivation and water release from the reservoir schedules to avoid losses due to flood and drought.

Comparative Evaluation of the Rainfall Erosivity in the Rieti Province, Central Italy, Using Empirical Formulas and a Stochastic Rainfall Generator

Soil erosion caused by intense rainfall events is one of the major problems affecting agricultural and forest ecosystems. The Universal Soil Loss Equation (USLE) is probably the most adopted approach for rainfall erosivity estimation, but in order to be properly employed it needs high resolution rainfall data which are often unavailable. In this case, empirical formulas, employing aggregated rainfall data, are commonly used. In this work, we select 12 empirical formulas for the estimation of the USLE rainfall erosivity in order to assess their reliability. Moreover, we used a Stochastic Rainfall Generator (SRG) to simulate a long and high-resolution rainfall time series with the aim of assessing its application to rainfall erosivity estimations. From the analysis, performed in the Rieti province of Central Italy, we identified three equations which seem to provide better results. Moreover, the use of the selected SRG seems promising and it could help in solving the problem of hydrological data scarcity and consequently guarantee major accuracy in soil erosion estimation.

Frequency analysis of storm-scale soil erosion and characterization of extreme erosive events by linking the DWEPP model and a stochastic rainfall generator

Soil erosion affects agricultural landscapes worldwide, threatening food security and ecosystem viability. In arable environments, soil loss is primarily caused by short, intense rainstorms, typically characterized by high spatiotemporal variability. The complexity of erosive events challenges modeling efforts and explicit inclusion of extreme events in long-term risk assessment is missing. This study is intended to bridge this gap by quantifying the discrete and cumulative impacts of rainstorms on plot-scale soil erosion and providing storm-scale erosion risk analyses for a cropland region in northern Israel. Central to our analyses is the coupling of (1) a stochastic rainfall generator able to reproduce extremes down to 5-minute temporal resolutions; (2) a processes-based event-scale cropland erosion model (Dynamic WEPP, DWEPP); and, (3) a state-of-the-art frequency analysis method that explicitly accounts for rainstorms occurrence and properties. To our knowledge, this is the first study in which DWEPP runoff and soil loss are calibrated at the plot-scale on cropland (NSE is 0.82 and 0.79 for event runoff and sediment, respectively). We generated 300-year stochastic simulations of event runoff and sediment yield based on synthetic precipitation time series. Based on this data, the mean annual soil erosion in the study site is 0.1 kg m−2 [1.1 t ha−1]. Results of the risk analysis indicate that individual extreme rainstorms (>50 return period), characterized by high rainfall intensities (30-minute maximal intensity > ~60 mm h−1) and high rainfall depth (>~200 mm), can trigger soil losses even one order of magnitude higher than the annual mean. The erosion efficiency of these rainstorms is mainly controlled by the short-duration (≤30 min) maximal intensities. The results demonstrate the importance of incorporating the impact of extreme events into soil conservation and management tools. We expect our methodology to be valuable for investigating future changes in soil erosion with changing climate.

STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series

The MS Excel file with VBA (Visual Basic for Application) macros named STORAGE (STOchastic RAinfall GEnerator) is introduced herein. STORAGE is a temporal stochastic simulator aiming at generating long and high-resolution rainfall time series, and it is based on the implementation of a Neymann–Scott Rectangular Pulse (NSRP) model. STORAGE is characterized by two innovative aspects. First, its calibration (i.e., the parametric estimation, on the basis of available sample data, in order to better reproduce some rainfall features of interest) is carried out by using data series (annual maxima rainfall, annual and monthly cumulative rainfall, annual number of wet days) which are usually longer than observed high-resolution series (that are mainly adopted in literature for the calibration of other stochastic simulators but are usually very short or absent for many rain gauges). Second, the seasonality is modelled using series of goniometric functions. This approach makes STORAGE strongly parsimonious with respect to the use of monthly or seasonal sets for parameters. Applications for the rain gauge network in the Calabria region (southern Italy) are presented and discussed herein. The results show a good reproduction of the rainfall features which are mainly considered for usual hydrological purposes.

Review: Can temperature be used to inform changes to flood extremes with global warming?

As climate change alters flood risk, there is a need to project changes in flooding for water resource management, infrastructure design and planning. The use of observed temperature relationships for informing changes in hydrologic extremes takes many forms, from simple proportional change approaches to conditioning stochastic rainfall generation on observed temperatures. Although generally focused on understanding changes to precipitation, there is an implied transfer of information gained from precipitation-temperature sensitivities to flooding as extreme precipitation is often responsible for flooding. While reviews of precipitation-temperature sensitivities and the non-stationarity of flooding exist, little attention has been given to the intersection of these two topics. Models which use temperature as a covariate to assess the non-stationarity of extreme precipitation outperform both stationary models and those using a temporal trend as a covariate. But care must be taken when projecting changes in flooding on the basis on precipitation-temperature sensitivities, as antecedent conditions modify the runoff response. Although good agreement is found between peak flow-temperature sensitivities and historical trends across Australia, there remains little evaluation of flood projections using temperature sensitivities globally. Significant work needs to be done before the use of temperature as a covariate for flood projection can be adopted with confidence. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

A Transient Stochastic Rainfall Generator for Climate Changes Analysis at Hydrological Scales in Central Italy

In this work, a comprehensive methodology for trend investigation in rainfall time series, in a climate-change context, is proposed. The crucial role played by a Stochastic Rainfall Generator (SRG) is highlighted. Indeed, SRG application is particularly suitable to obtain rainfall series that are representative of future rainfall series at hydrological scales. Moreover, the methodology investigates the climate change effects on several timescales, considering the well-known Mann–Kendall test and analyzing the variation of probability distributions of extremes and hazard. The hypothesis is that the effects of climate changes could be more evident only for specific time resolutions, and only for some considered aspects. Applications regarded the rainfall time series of the Viterbo rain gauge in Central Italy.

A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data

Physically distributed hydrological models are effective in hydrological simulations of large river basins, but the complex characteristics of hydrological features limit their application. An easy-to-use and high-efficiency hydrological model is needed for efficient water resource management in practice. Machine learning (ML) based models have the potential to provide fast mapping pathways between meteorological predictors and hydrological responses without detailed descriptions of the corresponding physical processes. However, the extensive data requirements, ignoring of spatial variability and poor performance for extreme flows limit the application of ML models. This study attempts to develop an ML-based hydrological model by combining physically based distributed hydrological model with an artificial neural networks (ANN), computer vision (CV) and a categorization approach (CA). To solve the insufficient training problem, we use a physically distributed hydrological model (GBHM) together with a stochastic rainfall generator to generate a large amount of synthetic data (GBHM-ANN). To improve the extreme flow simulation, we add the categorization approach into GBHM-ANN (GBHM-ANN-CA). To capture the spatial variability of the predictors, we also use a local binary pattern-based computer vision method to form GBHM-ANN-CA-CV model. The effectiveness of the three modeling approaches are demonstrated by synthetic case studies. We finally evaluate GBHM-ANN-CA-CV using the real data from the upper Chao Phraya Basin in Thailand. The results show that the prediction accuracy of our new data-driven model is greatly improved in data-limited watersheds. Specifically, the CV extracted spatial information can improve the robustness of the data-driven hydrological model, and the CA can greatly improve high flow simulations. The combined model yields a satisfactory accuracy for long-term daily streamflow simulations. This study demonstrates the potential of ML-based hydrological models in water resource management, especially in changing environments.

Spatial-temporal rain field generation for the Guangdong-Hong Kong-Macau Greater Bay Area considering climate change

A stochastic rainfall generator is required to provide rainfall inputs for the analysis and mitigation of such hydrological or geologic hazards as floods and rain-induced landslides. This paper presents a new spatial-temporal rainstorm generator for generating simultaneous rainfall processes at numerous locations considering the spatial correlation among these locations and interpolating the point processes into an areal rain field. The generator is able to include the effect of climate change by adjusting the parameters of the marginal distributions of variables constituting rainfall events. A case study on the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), one of the regions that are most prone to storm-related disasters in the world, is presented. The performance of the proposed generator is excellent in reproducing the historical statistical characteristics of regional rainfall. The model is adapted to climate change through extrapolation of the variation trend of the model parameters in the observation period to explore possible future scenarios of regional rainfall in GBA. The simulation results indicate a significant increase in rainfall extremes, especially for short-duration rainfall, at the end of 21st century in GBA.

A new framework for multi-site stochastic rainfall generator based on empirical orthogonal function analysis and Hilbert-Huang transform

Weather generators (WGs) are tools that create synthetic weather data, which are statistically similar to the observed data. Considering the limitations of most rainfall generators to preserve satisfactorily the fundamental statistical properties (e.g., spatial and temporal coherence) simultaneously, we propose a new framework for multisite rainfall generation. The framework consists of three main components: (1) a spatiotemporal rainfall field, described as spatial modes and their corresponding temporal evolution based on empirical orthogonal function analysis (EOFA). (2) The time series of these spatial modes, decomposed into intrinsic mode functions (IMFs) with characteristic frequencies (periods) using Hilbert-Huang transform (HHT). (3) Stochastic simulation (SS), achieved by assigning random phases for the specific IMFs. The current model, EHS (EOFA + HHT + SS), is compared with two other typical multi-site rainfall generators, MulGETS (parametric model) and KNN (non-parametric model) for a network of 12 stations in Xiang River basin, China. These three models are assessed based on their ability to simulate sequences with statistical attributes that are similar to those observed. We compare the basic statistics (mean, standard deviation, skewness), extreme value characteristics (95th percentile and maximum), spatial dependence (spatial correlation and spatial continuity ratio), and temporal dependence statistic (autocorrelation, wet/dry spells, and low-frequency variability). The results show that EHS rainfall generator has a similar capacity as KNN model in reproducing the spatial structure of the original rainfall field, and has a greater ability than MulGETS and KNN model to preserve the historical temporal statistics, especially the autocorrelation at various time scales and low-frequency variability. Overall, EHS is a useful model for generating realistic multi-site rainfall field and can be expected to generate plausible scenarios for impact studies.

A daily spatially explicit stochastic rainfall generator for a semi-arid climate

Many semi-arid regions of the world experience rainfall patterns characterized by high spatial variability. Accurate spatial representation of different types of rainfall will facilitate the application of distributed hydrological models in these areas. This study presents a daily, spatially distributed, stochastic rainfall generator based on a first-order Markov chain model, calibrated using 50 years of rainfall observations at 88 gages from 1967 through 2016 in the 148-km2 Walnut Gulch Experimental Watershed. Three types of rainfall, including convective, frontal, and tropical depression storms, were simulated separately in the generator using biweekly parameterization. Convective storms were simulated based on an elliptical shape rain cell conceptual model, whereas frontal and tropical depression storms were simulated as uniform rainfall fields over the whole watershed with introduced random variability. The rainfall generator was evaluated by comparing the mean statistics of 30 sets of 50-year simulated data versus the 50-year rain gage observed data. Most individual storm statistics and aggregated seasonal rainfall statistics were similar to the measured rainfall observations. The long-term mean values of both summer and winter rainfall amount were statistically satisfactory. This model can serve as a guide for application in areas with convective, frontal, and tropical depression storms.

A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales

Abstract. A novel approach to stochastic rainfall generation that can reproduce various statistical characteristics of observed rainfall at hourly to yearly timescales is presented. The model uses a seasonal autoregressive integrated moving average (SARIMA) model to generate monthly rainfall. Then, it downscales the generated monthly rainfall to the hourly aggregation level using the Modified Bartlett–Lewis Rectangular Pulse (MBLRP) model, a type of Poisson cluster rainfall model. Here, the MBLRP model is carefully calibrated such that it can reproduce the sub-daily statistical properties of observed rainfall. This was achieved by first generating a set of fine-scale rainfall statistics reflecting the complex correlation structure between rainfall mean, variance, auto-covariance, and proportion of dry periods, and then coupling it to the generated monthly rainfall, which were used as the basis of the MBLRP parameterization. The approach was tested on 34 gauges located in the Midwest to the east coast of the continental United States with a variety of rainfall characteristics. The results of the test suggest that our hybrid model accurately reproduces the first- to the third-order statistics as well as the intermittency properties from the hourly to the annual timescales, and the statistical behaviour of monthly maxima and extreme values of the observed rainfall were reproduced well.

Modeling impacts of climate change on return period of landslide triggering

Quantifying the potential influence of climate change on future landslide hazard requires methodologies that allow to properly take into account nonstationarities in the hydro-meteorological causes.In this paper we provide a methodology for estimating return period of landslide triggering under climate change.The methodology capitalizes on the combined use of a stochastic rainfall generator and a hydrological and slope stability model. The stochastic rainfall generator takes into account the statistical dependency between rainfall event duration and intensity through copulas. The hydrological model is based on an analytical solution of a simplified version of the Richards vertical infiltration equation and slope stability is assessed by the infinite slope model. The combined model enables to estimate landslide probability through Monte Carlo simulations. Climate change is then introduced by perturbing the parameters of the rainfall stochastic generator based on factors of change derived from the comparison of future scenarios and the baseline climate as simulated by Regional climate models (RCMs). The Monte Carlo simulations are conducted sequentially on a future moving time window, to derive a yearly series of future landslide triggering probability. This series is then used to compute landslide return period by formulas suitable under nonstationary conditions.An application to the landslide prone region of the Peloritani Mountains, Southern Italy, is carried out to demonstrate the proposed approach. For the application, climate change projections of three RCMs of the MED-CORDEX initiative are used, and a preliminary assessment of the impacts of intermediate- and high-emission Representative concentration pathways (RCPs) 4.5 and 8.5 is carried out.