Rainfall Measurements Research Articles

Comparison of Multi-Satellite Precipitation Data from the Global Precipitation Measurement Mission and Tropical Rainfall Measurement Mission Datasets: Seasonal and Diurnal Cycles

A comparison was made between seasonal and diurnal variations in the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) version 06B Final run and TRMM Multi-Satellite Precipitation Analysis (TMPA) 3B42 version 7 products from April 2014 to March 2019. As for earlier IMERG versions, systematic differences between IMERG version 06B precipitation and TMPA precipitation data were larger over the oceans than over land. Systematic annual mean differences between the IMERG and TMPA data over the oceans were smaller for IMERG version 06B than for earlier IMERG versions, possibly because of updated calibration processes. The mean differences between the IMERG version 06B and TMPA data for tropical oceans were relatively smtropical Pacific for all four seasons were not. The diurnal amplitudes of the IMERG were smaller than those of the TMPA over most continents, and the differences increased with mean diurnal amplitudes. The diurnal amplitudes of the IMERG were larger than those of the TMPA data over the oceans. The differences between the phases of the precipitation diurnal harmonics in the IMERG and TMPA datasets varied widely in all four seasons, but the mean phases were almost the same over both the oceans and the land. The sources of the differences in diurnal precipitation amplitudes in the Bay of Bengal and along the west coast of Central America, which showed large diurnal ranges and rather different diurnal amplitudes, were assessed. Differences in seasonal means caused differences in diurnal amplitudes in the Bay of Bengal, but for Central America, differences in diurnal amplitudes were associated with seasonal mean diurnal amplitudes.

The effect of rainfall changes on economic production

Macro-economic assessments of climate impacts lack an analysis of the distribution of daily rainfall, which can resolve both complex societal impact channels and anthropogenically forced changes1-6. Here, using a global panel of subnational economic output for 1,554 regions worldwide over the past 40 years, we show that economic growth rates are reduced by increases in the number of wet days and in extreme daily rainfall, in addition to responding nonlinearly to the total annual and to the standardized monthly deviations of rainfall.Furthermore, high-income nations and the services and manufacturing sectorsaremost strongly hindered by both measures of daily rainfall, complementing previous work that emphasized the beneficial effects of additional total annual rainfall in low-income, agriculturally dependent economies4,7. By assessing the distribution of rainfall at multiple timescales and the effects on different sectors, we uncover channels through which climatic conditions can affect the economy. These results suggest that anthropogenic intensification of daily rainfall extremes8-10 will have negative global economic consequences that require further assessment by those who wish to evaluate the costs of anthropogenic climate change.

A Social-Ecological Approach to Studying Variation in Urban Trees and Ecosystem Services in the National Municipal District of Santo Domingo, Dominican Republic

Maintaining a diverse urban forest that provides ecosystem services can promote urban sustainability and resilience to environmental change. Around the world, cities have taken to inventorying their urban trees and quantifying their ecosystem services but more so in industrialized counties than in Latin America. Here we describe the results of an i-Tree inventory that established 206 survey plots in the National Municipal District of Santo Domingo (NMDSD). We used social-ecological theory to evaluate potential factors that may influence urban forest structure, composition, and ecosystem services diversity across three wards with distinct social and urban characteristics. Rarefaction curves showed a diverse urban forest dominated by non-native trees that have ornamental and medicinal uses. Wards differed in species composition with palms being particularly dominant in Wards 1 and 2 where the proportion of low-income houses is smaller. Ward 1 supports high-income residential areas and Ward 3 is the area with higher population and housing densities and lower income residents. On average, we found no significant differences among wards in tree species richness, average dbh, leaf area, and percent tree cover per plot. Trees in Ward 2 were taller, on average, than those in Ward 1 but were comparable to those in Ward 3. Likewise, tree density per plot was highest in Ward 2, followed by Ward 1 and Ward 3. Despite these significant differences in stem densities, average values in four ecosystem services involving measures of carbon, rainfall, and contaminants (C-sequestration, C-storage, avoided runoff, and removal of air pollutants) were non-significant across wards. We found disproportionately more street trees in Ward 1 relative to Wards 2 and 3 and more trees in public spaces in Wards 1 and 2 relative to Ward 3. Evidence for the luxury effect on tree distribution in the NMDSD was subtle and manifested mostly through differences in species composition and tree distribution across public and private domains as well as the amount of planting space. Overall results point to inequalities in the potential of reforestation among NMDS wards and an overabundance of non-native species, which should guide urban forest management with ecosystem services and conservation goals.

Global Assessment of the Capability of Satellite Precipitation Products to Retrieve Landslide-Triggering Extreme Rainfall Events

Abstract Rainfall-induced landsliding is a global and systemic hazard that is likely to increase with the projections of increased frequency of extreme precipitation with current climate change. However, our ability to understand and mitigate landslide risk is strongly limited by the availability of relevant rainfall measurements in many landslide prone areas. In the last decade, global satellite multisensor precipitation products (SMPP) have been proposed as a solution, but very few studies have assessed their ability to adequately characterize rainfall events triggering landsliding. Here, we address this issue by testing the rainfall pattern retrieved by two SMPPs (IMERG and GSMaP) and one hybrid product [Multi-Source Weighted-Ensemble Precipitation (MSWEP)] against a large, global database of 20 comprehensive landslide inventories associated with well-identified storm events. We found that, after converting total rainfall amounts to an anomaly relative to the 10-yr return rainfall R*, the three products do retrieve the largest anomaly (of the last 20 years) during the major landslide event for many cases. However, the degree of spatial collocation of R* and landsliding varies from case to case and across products, and we often retrieved R* > 1 in years without reported landsliding. In addition, the few (four) landslide events caused by short and localized storms are most often undetected. We also show that, in at least five cases, the SMPP’s spatial pattern of rainfall anomaly matches landsliding less well than does ground-based radar rainfall pattern or lightning maps, underlining the limited accuracy of the SMPPs. We conclude on some potential avenues to improve SMPPs’ retrieval and their relation to landsliding. Significance Statement Rainfall-induced landsliding is a global hazard that is expected to increase as a result of anthropogenic climate change. Our ability to understand and mitigate this hazard is strongly limited by the lack of rainfall measurements in mountainous areas. Here, we perform the first global assessment of the potential of three high-resolution precipitation datasets, derived from satellite observations, to capture the rainfall characteristics of 20 storms that led to widespread landsliding. We find that, accounting for past extreme rainfall statistics (i.e., the rainfall returning every 10 years), most storms causing landslides are retrieved by the datasets. However, the shortest storms (i.e., ∼3 h) are often undetected, and the detailed spatial pattern of extreme rainfall often appears to be distorted. Our work opens new ways to study global landslide hazard but also warns against overinterpreting rainfall derived from satellites.

Diurnal Cycle of Rainfall and Convective Properties over West and Central Africa

The need to investigate diurnal weather cycles in West Africa originates from the fact that complex interactions often result between mesoscale and synoptic weather processes. This study investigates diurnal cycles of rainfall and convective properties using six (6) hour interval data from the ERA-Interim and derived products from the Tropical Rainfall Measurement Mission (TRMM). Results showed that the land-ocean warming contrast is more strongly sensitive to the seasonal cycle, being very weak during March-May (MAM) but clearly spelled out during June-September (JJAS). Dipoles of wind convergence/divergence and wet/dry precipitation, between CASS and Nigeria Savannah zones, were identified in the morning and evening hours of MAM, whereas distinct night and day anomalies, same location in CASS, were found to be consistent during the JJAS season. The locations of flash count and system sizes agree with the climatology of convective properties, that morning and day-time hours are dominated by stratiform precipitation and small system sizes. Most results clearly showed that the eastern locations of Sudano and Sahel are consistently dry because rainfall and precipitation features are predominantly few. Very unique results about the dipole of wind and precipitation between two zones and the unusual dry zones of Sudan and Sahel have been found. Results presented had shown the importance of diurnal variation in understanding precipitation, flash count, system sizes patterns at diurnal scales, and understanding land-ocean contrast, precipitation and wind field anomaly at diurnal scales.

Tipping-Bucket Self-Powered Rain Gauge Based on Triboelectric Nanogenerators for Rainfall Measurement

Tipping-Bucket Self-Powered Rain Gauge Based on Triboelectric Nanogenerators for Rainfall Measurement

Trend Analysis of Rainfall in the Phu Quoc Island

Assess the trend of rainfall change in Phu Quoc island is an interesting topic which establishes a scientific basis for rainfall forecast trends as well as regional development planning. Rainfall measurement data from 1985 to 2018 was used to analyse with different methods included Sen’s slope, linear regression, Mann-Kendall (MK) test, CV coefficient (Coefficient of Variation), PCI (Precipitation Concentration Index); examine drought level over time by using SPI - standardized rainfall index. Results show that, according to the linear regression analysis, regional annual rainfall is not much fluctuated, the rainy season and annual rainfall tend to decrease, while rainfall in the dry season tends to increase. According to Sen’s slope chart, July rain gains highest increasement, while that of August tends to decrease most. In general, the precipitation concentration index revealed the presence of a high and very high concentration of rainfall.

Long-term changes in the Arabian Peninsula rainfall and their relationship with the ENSO signals in the tropical Indo-Pacific

We investigate long-term changes in winter rainfall patterns across the Arabian Peninsula (AP) through an analysis of the Climate Research Unit (CRU) gridded rainfall dataset, and long-term rainfall measurements collected at 39 stations distributed across the AP over the period 1951–2010. We reveal a long-term increase in winter rainfall of about 25–30% over the eastern AP and a long-term decrease of about 10–20% in the southern and northeastern AP. A partial correlation analysis suggests that canonical El Niños are associated with significant negative winter rainfall anomalies in the southern and southwest AP during the 1951–1980 period. However, the extent of the El Niño-induced rainfall deficit decreased in subsequent decades. In fact, a significant above-average rainfall occurs in recent decades over Ethiopia, southwest Yemen and central AP during canonical El Niños. Furthermore, positive phases of the Indian Ocean basin mode (IOBM), which lags the canonical ENSO signal by 3–4 months, are linked with significant below-average winter rainfall over the central and northern AP, but only until the 1970 s. We investigated the teleconnections between the variability of AP winter rainfall and various atmospheric parameters from the European Centre for Medium Range Weather Forecasting (ECMWF) twentieth century (ERA-20C) reanalysis. Notably, sub-tropical westerly jet (STJ) shifted southward and intensified over the AP during recent decades. This shift of the STJ favoured an increase in the frequent passage of transients, which contributed to increased winter rainfall over AP. These events anomalously strengthen the upper level westerlies during El Niño Modokis, adding to the recently-strengthened STJ over the AP, thereby further intensifying the transient activity. This large-scale background change likely weakened the impact of canonical El Niño and the IOBM events.

The Implementation of a Filter Kalman Method Forecasting Rainfall Obtained Through Model ARIMA in Kota Jambi

In the last three years the climatic conditions in Jambi City have experienced erratic weather conditions. One way to predict rainfall is using the Kalman Filter approach. However, in this case, the Kalman Filter method is implemented on the forecasting results from ARIMA (Autoregressive Integrated Moving Average) because there has been rainfall measurement data from 2008 to 2017 at the BMKG Muaro Jambi Climatology Station which is also a function of time and the existing pattern will be described with using Time Series Analysis. Time series data is data that has a time series of more than one year on one object or data collected from time to time on one object. ARIMA model will be used to predict the next data. Kalman filter is a model part of state space that can be applied in forecasting models. The Kalman filter consists of a prediction stage and a correction stage. This method uses a recursive technique to integrate the latest observational data into the model to correct previous predictions and make further predictions. This study aims to determine the implementation of the kalman filter method in predicting rainfall obtained through the ARIMA model in Jambi City. The results of the 2018 Jambi City rainfall prediction research show that the best ARIMA model formed is the ARIMA model (1,0,1). In the Kalman Filter model, a MAPE value of 24.92% is obtained, which indicates that the Kalman Filter has a fairly good predictive ability.

Satellite-based rainfall estimates evaluation using a parsimonious hydrological model in the complex climate and topography of the Nile River Catchments

Satellite-based rainfall products with high spatial and temporal resolutions can provide an excellent data source for regions where rain-gauge networks are missing or unevenly and sparsely distributed. The objective of this study was to examine the suitability of six high-resolution satellite-based rainfall products, Climate Hazards Group InfraRed Precipitation with Stations Version 8 (CHIRPSv8), African Rainfall Estimation Algorithm Version 2 (RFEv2), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis version 7 (TMPA3B42v7), Integrated Multi-SatelliteE Retrievals for Global Precipitation Measurement version 6 (IMERGv6), Multi-Source Weighted Ensemble Precipitation version 2.8 (MSWEPv2.8) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), for the complex topography and climate of the Nile headwater catchments. The reliability of these products was assessed using rainfall estimates as an input to force a parsimonious and high-resolution hydrological model. A spatially distributed and parsimonious hydrological model was developed in Wflow-PCRaster/Python modelling framework to simulate streamflow using rain-gauge and satellite-based rainfall inputs. The model was calibrated and validated at three locations for each rainfall product and ground rainfall measurement. The accuracy of satellite-based rainfall products as input to the hydrological model for daily streamflow forecasting was dependent upon the product type. CHIRPSv8, IMERGv6 and RFEv2 demonstrated a reliable capability to simulate the streamflow at different spatial scales. In contrast, the TMPA3B42v7 and PERSIANN products failed to capture the ground streamflow measurements. All products performed inconsistently when simulating large floods and low flows. While TMPA3B42v7 and MSWEPv2.8 underestimated the streamflow with a large magnitude of variation in peak flow, the remaining products underestimated it throughout the study period. CHIRPSv8 and IMERGv6 product outperformed all other products consistently across all periods and locations followed by RFEv2. PERSIANN failed to capture the observed streamflow with lower performance indices. A better agreement of simulated streamflow with ground streamflow measurements was attained from CHIRPSv8 and IMERGv6 compared to the gauge-driven rainfall inputs. Performance of streamflow simulations improved when catchment area was increased from 481 to 46,000 km2. Overall, this study demonstrated the potential of CHIRPSv8 and IMERGv6 rainfall products as an alternate source of rainfall data for hydrological applications in the Nile basin catchments and the potential for its use in other regions which lack direct rainfall measurement capacity.

Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

Abstract Precipitation plays a critical role in hydrometeorological studies. A predictive analysis of gridded rainfall datasets may provide a cost-effective alternative to conventional rain gauge observations. Here, our objective is to evaluate the performance of satellite and reanalysis precipitation products in the hydrological modeling of a mesoscale watershed. The research also examines the accuracy of hydrological simulations in a sizeable flood-prone watershed in the absence of observed data associated with the myriad water retaining structures present in the catchment. We use three precipitation products, namely Tropical Rainfall Measurement Missions (TRMM) 3B42 Version 7, Climate Forecast System Reanalysis (CFSR), and daily precipitation data recorded at multiple rain gauges in the upper Huai River Basin to simulate streamflow. The Soil & Water Assessment Tool (SWAT) is utilized for runoff modeling, while SWAT-CUP is used to perform sensitivity analysis and to calibrate and validate the simulation results. Nash–Sutcliffe efficiency, percent bias, and Kling-Gupta efficiency (KGE) are employed to evaluate modeling efficiency for three precipitation datasets on different temporal scales. The results indicate that TRMM and CFSR datasets provide satisfactory results on both daily and monthly scales. Specifically, the SWAT model performs better at monthly simulations than daily simulations for all precipitation datasets used.

Study on the different type of hydrological input towards the accuracy of catchment discharge hydrograph

For hydrological applications, several rainfall measurement techniques are available, each with its own spatial and temporal resolution and errors. When these rainfall datasets are used as input for hydrological models, errors and uncertainties are propagated throughout the hydrological system. This research paper is using rainfall data from two different measurement tools namely rain gauge and radar. Using these two types of measurement tools, it is expected dissimilar output due to the different working principle. Arau catchment in Perlis were selected as the study area. The rainfall data used is in the unit of intensity with time interval 10 minutes for both instruments. Storm Water Management Model 5 (SWMM5) were used as model to simulate the discharge of catchment area. Sensitivity analysis is carried out in this study to determine the major parameters that influence to shape of hydrograph and peak flow. Then, calibration process using five (5) storm events have been performed using available information such as conduits lengths, shape of conduits, impervious and pervious surface using trial and error process. Good correlation between observed and simulated hydrograph on 18 September 2006 has been found to be the best Correlation coefficient (r) and Root mean square error (RMSE) equal to 0.92 and 0.14 respectively. The same parameter used for that storm event was chosen to be applied in validation event. Validation results also in the acceptable range with r is found more than 50% correlation. Next, using the rainfall data captured by radar that converted using equation Z=40R1.6 developed by researcher [1] for the similar date of storm event and similar catchment conditions, all the hydrograph shape shows significant drop. As a conclusion, different type of measurement tools for rainfall gives significant different to the catchment discharge and can be seen that rain gauge are better to use to simulate rainfall compared to the radar.

Comparing erosion levels on slope classes below 40% and above 40% on pine stands in Hasanuddin University education forest

This study aimed to compare erosion rates on land with slope classes of <40% with those of slope classes> 40%, under pine stands and to determine the relationship between rainfall and erosion. The study was conducted during September 2016 until March 2017 and recorded 36 rain events. Measurement of rainfall using an observatory type rainfall. The amount of erosion is known through direct measurements in the field on the plot measuring 22 x 4 m and followed by the analysis of sediment samples in the laboratory. The measurement results in the field obtained the highest rainfall of 95.6 mm and the lowest of 2.7 mm with a total of 770.50 mm. Erosion is obtained from times result of the sediment concentration with the volume of samples accommodated in the container per thousand. The calculation result is known that the average of erosion on slope class> 40% is in the amount of 278.94 kg / ha and on slope class <40% is 94.93 kg / ha. The relationship of rainfall to erosion is Obtained through a simple regression analysis Y = -3.723 + 0.448x (R2 0.94), Y = -3.502 + 0.645x (R2 0.93), Y = -4.482 + 0.540x (R20.95), Y = -1.644 + 0.269x (R20.91), Y = 0.006 + 0.008x (R2 0.84), and Y = -1.497 + 0.239x (R2 0.90).

Ardières‐Morcille in the Beaujolais, France: A research catchment dedicated to study of the transport and impacts of diffuse agricultural pollution in rivers

AbstractThe 8‐km2 Morcille catchment, which is a sub‐catchment of the 150‐km2 Ardières catchment in the Beaujolais region of France, is one of the first sites in Europe where research has been conducted on surface water contamination by pesticides. A consolidated hydrological and chemical dataset has been set up with data collected since 2002 on the Morcille River and since 2011 on the Ardières River. Additional data on the ecotoxicological and ecological impacts of pesticides on aquatic microbial communities and macroinvertebrates has also been recorded in both rivers since 2005. The ‘Site Atelier Ardières‐Morcille Dataset’ described here combines rainfall and stream water height measurements at gauged stations with concentrations of two trace elements and nine pesticides (mainly herbicides, fungicides and some of their metabolites) in both the Ardières and Morcille rivers. All contaminant concentration data showed spatial and temporal variability in water quality associated with pesticide use and rainfall patterns. This long‐term monitoring framework made it possible to estimate the persistence of two herbicides (diuron and norflurazon) after legislation banning them. It took 4 years for diuron and more than 10 years for norflurazon concentrations to fall below 0.1 μg/L. Concurrent biological data showed a gradient of impacts consistent with chemical anthropogenic pressure, and rapid recovery of phototrophic microbial communities after the diuron ban. Finally, monitoring data on pesticide transport in a grassed strip set on a slope of the Morcille catchment confirmed that vegetative filter strips can effectively reduce diuron fluxes in surface runoff (&gt;80% abatement) and infiltration water (&gt;70% abatement). The full dataset offers a valuable resource for the validation of hydrological models and the development of global approaches to better understand the pressure–transport–exposure–impact chain and aquatic community resilience at the small catchment scale.

The USA National Phenology Network's Buffelgrass Green‐up Forecast map products

Abstract Buffelgrass (Cenchrus ciliaris syn. Pennisetum ciliare) was introduced to Sonoran Desert in the early 20th century and has become widespread at low elevations. This perennial bunchgrass accumulates abundant biomass that can carry fires through ecosystems not adapted to fire, resulting in devastating impacts for native cacti and other plant and animal life. Buffelgrass is most effectively managed through the application of herbicide when the grass is at least 50% green. Because the grass rapidly greens up following summer monsoon rainfall, it is possible to forecast green‐up using daily rainfall measurements. In 2019, the USA National Phenology Network (USA‐NPN) released daily Buffelgrass Green‐up Forecast maps for the state of Arizona based on the PRISM 4 km daily total precipitation product. The daily digital Buffelgrass Green‐up Forecast maps are a freely available data product and meet the FAIR principles of findability, accessibility, interoperability and reusability. They are permanently archived and publicly accessible as raster and image layers from the USA‐NPN website. These map layers support planning the timing of management activities to maximize buffelgrass treatment efficacy and researchers seeking to incorporate daily estimates of buffelgrass greenness in their analyses.

Exploring Temporal Rainfall Variability and Trends Over a Tropical Region Using Tropical Rainfall Measurement Mission (TRMM) and Observatory Data

Kerala is the gateway of the Indian southwest monsoon. The Tropical Rainfall Measurement Mission (TRMM) rainfall data is an efficient approach to rainfall measurement. This study explores the temporal variability in rainfall and trends over Kerala from 1998-2019 using TRMM data and observatory data procured from India Meteorological Department (IMD). Direct comparison with observatory data at various time scales proved the reliability of the TRMM data (monthly, seasonal and annual). The temporal rainfall converted by averaging the data on an annual, monthly and seasonal time scale, and the results have confirmed that the rainfall estimated based on satellite data is dependable. The station wise comparison of rainfall in monsoon season provides satisfactory results. However, estimation of rainfall in mountainous areas is challenging task using the TRMM. In the basins of humid tropical regions, TRMM data can be a valuable source of rainfall data for water resource management and monitoring with some vigilance. In Kerala, the study found an insignificant increase in the southwest monsoon and winter season rainfall during last two decades. The rainfall over Kerala showed uncertainty in the distribution of monthly, seasonal and yearly time scales. This study provides a preview of recent weather patterns that would enable us to make better decisions and improve public policy against climate change.

Rainfall retrieval using commercial microwave links: Effect of sampling strategy on retrieval accuracy

This study presents the first evaluation of using commercial microwave link (CML) data for rainfall measurements in Australia, with the test site being the greater Melbourne Metropolitan area. More than 100 CMLs with microwave frequency ranging between 10 and 40 GHz have been used for the rainfall retrieval. The 15-minute received signal levels (RSLs) for each CML based on two sampling strategies (average and minimum/maximum) collected for 2 years provided a unique dataset to compare performances of rainfall retrievals. The open source algorithm RAINLINK was used for deriving rainfall from the 15-minute RSL data. From two years of data, a subset of 30 rainy days distributed across this period were used for calibrating the RAINLINK parameters, with the remaining data used for validation. For this study, only path-averaged rainfall intensities were validated based on a gauge-adjusted radar product serving as the reference. The result of the wet-dry classification showed that the minimum and maximum RSL data performed better, with lower probability of false detection and higher Matthews correlation coefficient than average RSL data. For the rainfall retrieval, both datasets showed similar correlation with the gauge adjusted radar product. However, based on other statistics (RMSE, bias and CV) minimum and maximum RSL data outperformed average for the rainfall retrieval. Overall, this study highlights the robust accuracy of commercial microwave links for rainfall retrieval while using only minimum and maximum RSL data.

Comparison of the return period for landslide‐triggering rainfall events in Japan based on standardization of the rainfall period

AbstractThe intensity of rainfall events with potential to cause landslides has varying temporal characteristics. In this study, the time at which the 72‐h accumulated rainfall reached its maximum was used to standardize the period of rainfall measurement. The proposed standardization of the rainfall period was used in conjunction with the return level of rainfall intensity, obtained from intensity–duration–frequency curves, to investigate rainfall intensity anomalies associated with 10 hazardous rainfall events that triggered numerous landslides at the regional scale in Japan. These landslides included shallow landslides in volcanic and non‐volcanic areas, as well as deep‐seated landslides. The rainfall events that triggered the shallow landslides were divided into two types: downpours that repeatedly reached close to the 100‐year return level within approximately 3–4 h, and accumulated rainfall that reached close to 200–400 mm over longer time intervals but within 72 h. Lithological differences seemed unrelated to the differences between the two types of shallow‐landslide‐triggering rainfall; however, precipitation &gt;1000 mm was necessary to trigger deep‐seated landslides. Although the characteristics of the hyetographs differed markedly among the landslide‐triggering rainfall events, all the landslides could have been triggered when the mean rainfall intensity reached the 100‐year rainfall level during the standardized period. Thus, the landslide trigger can be evaluated indirectly based on the increase in the return level of the mean rainfall intensity, which could provide a means for estimating the time of landslide occurrence.

Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

Something fishy going on? Evaluating the Poisson hypothesis for rainfall estimation using intervalometers: results from an experiment in Tanzania

Abstract. A new type of rainfall sensor (the intervalometer), which counts the arrival of raindrops at a piezo electric element, is implemented during the Tanzanian monsoon season alongside tipping bucket rain gauges and an impact disdrometer. The aim is to test the validity of the Poisson hypothesis underlying the estimation of rainfall rates using an experimentally determined raindrop size distribution parameterisation based on Marshall and Palmer (1948)'s exponential one. These parameterisations are defined independently of the scale of observation and therefore implicitly assume that rainfall is a homogeneous Poisson process. The results show that 28.3 % of the total intervalometer observed rainfall patches can reasonably be considered Poisson distributed and that the main reasons for Poisson deviations of the remaining 71.7 % are non-compliance with the stationarity criterion (45.9 %), the presence of correlations between drop counts (7.0 %), particularly at higher arrival rates (ρa&gt;500 m-2s-1), and failing a χ2 goodness-of-fit test for a Poisson distribution (17.7 %). Our results show that whilst the Poisson hypothesis is likely not strictly true for rainfall that contributes most to the total rainfall amount, it is quite useful in practice and may hold under certain rainfall conditions. The parameterisation that uses an experimentally determined power law relation between N0 and rainfall rate results in the best estimates of rainfall amount compared to co-located tipping bucket measurements. Despite the non-compliance with the Poisson hypothesis, estimates of total rainfall amount over the entire observational period derived from disdrometer drop counts are within 4 % of co-located tipping bucket measurements. Intervalometer estimates of total rainfall amount overestimate the co-located tipping bucket measurement by 12 %. The intervalometer principle shows potential for use as a rainfall measurement instrument.