Daily Rainfall Intensity Research Articles

Rainfall events and daily mortality across 645 global locations: two stage time series analysis

ObjectiveTo examine the associations between characteristics of daily rainfall (intensity, duration, and frequency) and all cause, cardiovascular, and respiratory mortality.DesignTwo stage time series analysis.Setting645 locations across 34 countries or regions.PopulationDaily...

Modelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugal

Rainfall-triggered landslides pose a significant threat to both infrastructure and human lives, making it crucial to comprehend the factors that contribute to their occurrence. Specifically, understanding the relationship between these factors and the amount of rain that is necessary for triggering such events is essential for effective prediction and mitigation strategies. To address this issue, our study proposes a statistical modelling approach using machine learning, specifically the Random Forest algorithm, to investigate the connection between landslide predisposing factors and the daily rainfall intensity threshold necessary for the initiation of shallow landslides in Portugal. By leveraging a comprehensive dataset comprising historical landslide events, associated critical rainfall, and ten distinct landslide predisposing factors, we developed several models and used cross-validation technique to evaluate their performance. Our findings demonstrate that the Random Forest model effectively captures a relationship among landslide predisposing factors, critical daily rainfall intensity, and landslide occurrences. The models exhibit a satisfactory accuracy in assessing the spatial variation of critical daily rainfall intensity based on the predisposing factors, with a mean absolute percentage error (MAPE) of around 17%. Furthermore, the models provide valuable insights into the relative importance of various predisposing factors in landslide triggering, highlighting the significance of each factor. It was found that it takes higher rainfall intensity to trigger shallow landslides in the north region of Portugal when considering critical rainfall events of 3 and 13 days. Slope aspect, slope angle, and clay content in the soil are among the main predisposing factors used for defining the spatial variation of the daily rainfall intensity threshold.

Analysis of land use change on flood discharge of Bengawan Solo Upstream sub-basin

Abstract This study investigated land use changes of Bengawan Solo Upstream sub-watershed impact toward flood discharge between year 2015 and year 2019. The daily rainfall intensity was calculated statistically by using analysis frequency, and effective rainfall was acquired by compensation of potential storage and land use. Runoff discharge was calculated using the Nakayasu and the Soil Conservation Service method, while base flow discharge calculated using equation suggested by Hydrograph Synder Synthetic Gama-1. Land use change is represented by curve number (CN) value for year 2015 and year 2019 at values of 78.11 and 78.07, respectively. Decline of CN value was caused by surge of vegetation land area. Agricultural land CN value was not much different from settlement CN value. Overall, flood discharge that occurred plummeted between year 2015 and year 2019, although it was not significant. Decrease in flood discharge corresponds to decrease in CN value. Decline of CN value as much as 0.05 % yielded decrease of flood discharge as much as 0.16 % until 0.22 % related to return period.

A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness.

Influence of vegetation index to the rainfall intensity in Pasuruan Area, East Java Province, Indonesia

An increase in population increases the rate of urbanization. This results in changes in land cover from vegetation to artificial material. As a result, much of the land surface reflects the sun's energy. Consequently, this increases the surface temperature of the land. An increase in land surface temperature (LST) will increase the intensity of rainfall. Therefore, the present study aimed to investigate the relationship between the increase in LST and rainfall intensity. Changes in land cover can be detected by normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) parameters. Landsat satellite imagery was used to detect NDVI, NDBI, and LST. Image processing was done for imageries scanned in 1995, 2015, 2017, and 2021. Two areas in the East Java Province of Indonesia, namely Malang City and Pasuruan Area, were selected. The daily rainfall intensity data were collected from related rainfall stations in the same year. The Mononobe method was applied to analyze hourly and minute rainfall intensity. IDF curves were drawn from the analyzed results. The relationship between both parameters was analyzed by comparing the LST and hourly rainfall intensity from the IDF curve. The studied results showed that the maximum temperature increase from 1995 to 2021 for the Malang City and Pasuruan Area was 2.60 C and 7.60 C, respectively. For rain, the maximum rainfall intensity increased by 58 mm for Malang City and 18 mm for the Pasuruan Area. LST and rainfall intensity change trends of the two areas had a positive coefficient of regression. The findings can be used to predict the rainfall intensity and floods based on the LST data.

Application of the ITA approach to analyze spatio-temporal trends in monthly maximum rainfall categories in the Vu Gia-Thu Bon, Vietnam

This study aims to investigate the trend behavior of monthly maximum in daily rainfall categories in the Vu Gia-Thu Bon river basin located in central Vietnam. Daily maximum rainfall series from 12 rainfall stations for the period 1979–2018 were utilized to characterize six categories of the intensity of daily maximum rainfall: light (0–4 mm/day, category A), mild-moderate (4–16 mm/day, category B), moderate-heavy (16–32 mm/day, category C1), heavy (32–64 mm/day, category C2), heavy-torrential (64–128 mm/day, category D1), and torrential (≥ 128 mm/day, category D2). The new approach of the Innovative Trends Analysis was then applied to the six classified categories. The results revealed that category B had a dominant increasing trend (32% of rain events) for all the stations in January (5.85%) and February (3.44%). In March and April, category A was dominant with 45% and 20%, respectively. In July, category C1 was dominant with 25%, while in August and September, category C2 prevailed over all stations with 45% (all stations) and 20%, respectively. The categories D1 and D2 were observed at all stations in December and November, with 26% and 31% of events, respectively. These results indicate an increasing trend in the categories B, C1, C2, and D1.

Rainfall Prediction using Machine Learning Algorithms

Rainfall prediction is a beneficiary one, but it is a challenging task. Machine learning techniques can use computational methods and predict rainfall by retrieving and integrating the hidden knowledge from the linear and non-linear patterns of past weather data. This paper investigates the effectiveness of two machine learning algorithms - Logistic Regression (LR) and Random Forest (RF) - for rainfall prediction using historical datasets. This study would assist researchers in analysing the most recent work on rainfall prediction with an emphasis on machine learning techniques and providing a reference for possible guidance and comparisons. Anaconda framework is used, and the coding language used is Python, which is portable and dynamic. NumPy, matplotlib, seaborn, and pandas are the libraries used for the implementation. The main objective of this paper is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. This project focuses on harnessing the power of basic machine learning algorithms to unravel insights from a historical rainfall dataset. Insights are interpreted through feature importance analysis and visualization of decision tree structures. In essence, this project showcases the efficiency of basic machine learning algorithms in uncovering hidden information within a rainfall dataset.

Evaluating the performances of gridded satellite/reanalysis products in representing the rainfall climatology of Ethiopia

This study evaluated performances of the Climate Hazard Group Infrared Precipitation with stations version 2.0 (CHIRPS v2.0) and Multi-Source Weighted-Ensemble Precipitation version 2.8 (MSWEP v2.8) products against observed data. Rainfall climatology was simulated for different agro-ecological zones (AEZs) of Ethiopia during 1991–2020 at different temporal scales. Performance evaluations were made using continuous and statistical performance measures as well as Probability Density Function (PDF). CHIRPS v2.0 for estimating monthly, seasonal, and annual rainfall totals, and MSWEP v2.8 for daily rainfall have shown better performance over all AEZs. The two products display comparable performance for detecting daily rainfall occurrences over alpine AEZ, but MSWEP v2.8 is superior in the rest four AEZs. CHIRPS v2.0 outperforms MSWEP v2.8 for detecting most of the daily rainfall intensity classes over all AEZs. The findings will play a noteworthy role to improve the quality of hydro-climate studies in Ethiopia.

Characterization of Extreme Rainfall and River Discharge over the Senegal River Basin from 1982 to 2021

Extreme hydroclimate events usually have harmful impacts of human activities and ecosystems. This study aims to assess trends and significant changes in rainfall and river flow over the Senegal River Basin (SRB) and its upper basin during the 1982–2021 period. Eight hydroclimate indices, namely maximum river discharge (QMAX), standardized flow index, mean daily rainfall intensity index (SDII), maximum 5-day consecutive rainfall (RX5DAY), annual rainfall exceeding the 95th percentile (R95P), annual rainfall exceeding the 99th percentile (R99P), annual flows exceeding the 95th percentile (Q95P), and annual flows exceeding the 99th percentile (Q95P), were considered. The modified Mann–Kendall test (MMK) and Innovative Trend Analysis (ITA) were used to analyze trends, while standard normal homogeneity and Pettit’s tests were used to detect potential breakpoints in these trends. The results indicate an irregular precipitation pattern, with high values of extreme precipitation indices (R95p, R99p, SDII, and RX5DAY) reaching 25 mm, 50 mm, 20 mm/day, and 70 mm, respectively, in the southern part, whereas the northern part recorded low values varying around 5 mm, 10 mm, 5 mm/day, and 10 mm, respectively, for R95P, R99P, SDII, and RX5DAY. The interannual analysis revealed a significant increase (p-value < 5%) in the occurrences of heavy precipitation between 1982 and 2021, as manifested by a positive slope; a notable breakpoint emerged around the years 2006 and 2007, indicating a transition to a significantly wetter period starting from 2008. Concerning extreme flows, a significant increase was observed between 1982 and 2021 with Sen’s slopes for extreme flows (29.33 for Q95P, 37.49 for Q99P, and 38.55 for QMAX). This study provides a better understanding of and insights into past hydroclimate extremes and can serve as a foundation for future research in the field.

Trends and impacts of climate change on crop production in Burkina Faso

Abstract Understanding past climate trends and their impacts in the Sahel region is fundamental for climate change (CC) adaptation and mitigation. This study analyses climate trends from 1961 to 2020 in three climatic zones in Burkina Faso and the impacts of CC on five major crops production. Long time series of daily rainfall and temperature data from National Meteorology Agency for the period 1961 to 2020 has been compiled. Crop production data (1984–2020) were retrieved from the agriculture department. Climate temporal variations in each climatic zone were analyzed using extreme climate indices and principal component analysis. Linear regression was used to assess climate impacts on crop production. The results showed a high rainfall variability and changes in temperature extremes in the three zones. The climate window, 1991–2020, was hotter than 1961–1990, while the last decade (2011–2020) was the wettest. Most climate indices (67%) showed significant correlations with crop yields. Dry spells, cool days, cold nights, average daily wet days and rainfall intensity showed positive and negative effects on maize, cowpea, millet and sorghum yields. This study highlights the importance of climate-smart policy promoting drought-resistant and short-duration varieties in addressing the adverse effects of CC on crop production.

Rainfall Prediction using Machine Learning and Neural Network

Abstract: Predicting the amount of daily rainfall improves agricultural productivity and secures food and water supply to keep citizens healthy. To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets. An erratic rainfall distribution in the country affects the agriculture on which the economy of the country depends on. Wise use of rainfall water should be planned and practiced in the country to minimize the problem of the drought and flood occurred in the country. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The Pearson correlation technique was used to select relevant environmental variables which were used as an input for the machine learning model. Two commonly used models predict seasonal rainfall such as Linear and Non- Linear models. The first models are ARIMA Model. While using Artificial Neural Network (ANN) predicting rainfall can be done using Back Propagation NN, Cascade NN or Layer Recurrent Network. Artificial NN is same as Biological Neural Networks.

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Changes in the West Africa Monsoon Precipitation Extremes during ENSO developing Phases

ABSTRACT A 37-year record of rainfall gridded data covering West Africa and a Global Sea Surface Temperature (SST) dataset are used to investigate the remote influence of SST anomalies in the Equatorial Pacific on the interannual variability of West Africa’s extreme rainfall indices over the period 1981–2018. The top five (5) years with the strongest and weakest peak of Niño3.4 SST monthly anomalies are selected, and May-to-September (MJJAS) composite anomalies of the total and extreme rainfall indices are performed. Results reveal that Equatorial Pacific SST’s impacts on daily rainfall intensity are generally more robust than that on their frequency. The significant changes in the mean zonal atmospheric circulation associated with the SST lead to significant dynamic and thermodynamic changes that affect the West African monsoon system locally. During El Niño (La Niña) years, (i) a weakening (strengthening) of the Tropical Easterly Jet (TEJ), (ii) a strengthening (weakening), and southward (northward) shift position of the African Easterly Jet (AEJ), and (iii) a decrease (increase) of the monsoon flow are noted. These changes in the atmospheric circulation prevent (encourage) a supply of moisture, resulting in a reduction (increase) in extreme precipitation observed across West Africa. Equatorial eastern Pacific warming (cooling) is also shown to lead to stable (unstable) atmospheric conditions over West Africa that block (generate) the development of convective systems.

Cloud Macro- and Microphysical Properties in Extreme Rainfall Induced by Landfalling Typhoons over China

Extreme rainfall induced by landfalling typhoon (ERLTC) can cause destructive natural disasters throughout China. Cloud properties in ERLTC are not yet well understood and parameterized, which limits the forecast accuracy of ERLTC to some extent. The 99th percentile intensity of daily rainfall associated with LTC is objectively defined as ERLTC and using the CloudSat tropical cyclone (CSTC) dataset from 2006 to 2018, cloud macro- and microphysical characteristics are statistically investigated. Results show that the proportion of single-layer (double-layered) clouds increases (decreases) significantly on the occurrence day of ERLTC. In the TC inner core region, the proportion of deep convective cloud at 2–10 km is the highest, reaching 50%. In the TC envelop region, deep convective cloud at the height of 3–8 km and cirrus at the height of 12–14 km account for the highest proportions. For the TC outer region, cirrus around 13 km has the highest proportion. During the ERLTC period, the ice-water content is mainly distributed in 5–18 km, and is mostly distributed in the TC inner core, followed by the envelop region. A large number of smaller ice particles are gathering in the upper troposphere at 13–18 km, while a small number of larger ones is gathering in the middle levels around 8–10 km. These results are useful for evaluating the ERLTC simulations and are expected to provide new forecasting factors for ERLTC in cloud macro- and microphysical perspectives.

Influence of changes in extreme daily rainfall distribution on the stability of residual soil slopes

ABSTRACT Many landslides triggered by intense rainfall have occurred in mountainous areas in Thailand, causing major economic losses and infrastructure damage. Extreme daily rainfall is a significant trigger for hillslope instability. Increases in extreme daily rainfall intensity due to climate change may be one of the key factors responsible for the increased landslides. Thus, in this context, changes in the intensity of extreme daily rainfall in Chiang Mai Province in North Thailand and their effects on hillslope stability are analyzed. Extreme rainfall is modeled using a generalized extreme value distribution and estimated for various return periods. A numerical analysis of seepage and an infinite slope stability model are combined to understand the hillslope response under extreme rainfall conditions. The analysis period is divided into two periods of 34 years: 1952 to 1985 and 1986 to 2019. According to the analysis results, the distribution of extreme daily rainfall changes in terms of location. The average annual daily maximum rainfall increased by approximately 11.13%. The maximum decrease in the safety factor is approximately 4.5%; therefore, these changes in extreme daily rainfall should be considered in future landslide prevention policies.

Spatio-Temporal Rainfall Distribution and Markov Chain Analogue Year Stochastic Daily Rainfall Model in Ethiopia

This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model. Particularly, in this study, we used a Markov Chain Analogue Year (MCAY) model that is, Markov Chain with Analogue year (AY) component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull, Log normal, mixed exponential and Gamma distributions. The MCAY model best describes the occurrence process of daily rainfall, this is due to the AY component included in the MC to model the frequency of daily rainfall. Then, by combining the occurrence process model and amount process model, we developed Markov Chain Analogue Year Weibull model (MCAYWBM), Markov Chain Analogue Year Log normal model (MCAYLNM), Markov Chain Analogue Year mixed exponential model (MCAYMEM) and Markov Chain Analogue Year gamma model (MCAYGM). The performance of the models is assessed by taking daily rainfall data from 21 weather stations (ranging from 1 January 1984–31 December 2018). The data is obtained from Ethiopia National Meteorology Agency (ENMA). The result shows that MCAYWBM, MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM. The mean absolute percentage error (MAPE) in the four models: MCAYGM, MCAYWBM, MAYMEM and MCAYLNM are 2.16%, 2.27%, 2.25% and 11.41% respectively. Hence, MCAYGM, MCAYWBM, MAYMEM models have shown an excellent performance compared to MCAYLNM. In general, the light tailed distributions: Weibull, gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially, when these distributions are combined with MCAYM.

Temporal and Spatial Evaluations of Extreme Rainfall Relationship with Daily Surface Air Temperature in Peninsular Malaysia

Extreme rainfall is expected to increase as the climate warms, with the rate of increase at 7% per degree of warming following the Clausius-Clapeyron (CC) relationship. However, studies showed that the rate of increase might not necessarily follow the relationship. This study investigates temporal and spatial variations of daily rainfall relationship with surface air temperature in Peninsular Malaysia using data from the Global Historical Climatology Network (GHCN) using a fixed temperature interval binning method and quantile regression method. Investigation reveals that a negative scaling of daily extreme rainfall was observed for all surface air temperature and percentiles investigated, in contrast with the expectations from the CC relationship. The largest seasonal variation at 99th percentile was observed in December-January (DJF), while the lowest scaling rate was in March-May (MAM). The scaling rate tends to be higher in Subang and Melaka, and lower in Bayan Lepas, Kota Bharu and Kuantan. The scaling rate also tends to be stronger at 99th percentile compared to the lower rainfall events at 95th and 75th percentile and in warmer seasons compared to colder seasons. Further, the results show that the intensity of daily extreme rainfall in Peninsular Malaysia is decreasing with increasing surface air temperature.

Daily rainfall intensity and temporal trends in eastern Paraná state – Brazil

The present study aims to analyze the intensity of daily extreme rainfall and trends for precipitation in Castro, Curitiba and Paranaguá stations, located in the state of Paraná, Brazil. Historical series of rainfall registered by the Brazilian National Institute of Meteorology were used considering the period from 1976 to 2015. The Mann-Kendall and Pettitt statistical tests were applied to identify trends and ruptures in historical rainfall series. Other indicators of trends in rainfall and temperature were calculated using the RClimDex software, developed by the Canadian Meteorological System. The classification of cumulative daily rainfall in 24 h with the application of the quantile technique was defined as a standard for percentiles. The months of January, February and December presented greater occurrence frequency of rainfall equal to or greater than 95% and 99% of the quantiles for all locations. The comparative analysis showed that Paranaguá station has the largest accumulated values of rainfall in 24 h during the rainiest months, between September and April, while Castro and Curitiba stations recorded the highest daily totals between May and August. Increasing daily precipitation totals were identified in all quantile's classes with statistical significance only in Curitiba and Paranaguá station, which is attributed to greater urban densities and its direct impacts over daily rainfall totals. Significant trends for increased rainfall were observed, especially in Curitiba, a condition that is associated with the expressive and also significant increase in climate indicators of air temperature.

Evaluation of GPM-IMERG rainfall estimates at multiple temporal and spatial scales over Greece

Accurate precipitation data in space and time are among the most important variables for analyzing, modelling and forecasting many hydrometeorological applications and hazards. For countries like Greece, which have a complex terrain and great spatial variability of precipitation, satellite precipitation products (SPPs) are an alternative source of precipitation information that could complement the sparse rain gauge network. The objective of this study is to evaluate the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B Final-run product over Greece for a 5-year period (January 2015 to December 2019) using as reference measurements from 263 rain gauges. The evaluation of IMERG is performed at multiple temporal (annual, monthly, daily) and spatial (country, region, grid) scales by employing several statistical and graphical methods. The rainfall detection capability of IMERG is examined with categorical verification metrics and the accuracy of the estimated rainfall amounts with continuous verification metrics. In addition, the performance of IMERG under different rainfall intensities, elevations and seasons is examined.On the monthly and annual scale evaluation the intercomparison between the calibrated (IMERGcal) and uncalibrated (IMERGuncal) IMERG estimates revealed the superiority of IMERGcal, but on the same time it highlighted the weakness of the GPCC gauge-based calibration product over Greece. The monthly IMERGcal estimates could reproduce the annual cycle of precipitation with a slight overestimation of monthly precipitation (RBIAS = 5.1%). On a daily scale IMERGcal performed well on detecting rainfall events (POD = 0.77), but it also produced many false alarm events (FAR = 0.40) which are primarily found over the islands of Aegean and Ionian Sea and along the coastline of mainland Greece. The assessment of daily rainfall intensities by IMERGcal showed a moderate correlation across Greece (0.49), with overestimation of light and moderate precipitation events (0.4–20 mm/day) and underestimation of heavy rainfall events (>20 mm/day). On a regional scale, IMERGcal tends to underestimate the daily rainfall over the rainfall-prone western part of Greece because in addition to the smoothing of high rainfall values by the GPCC calibration, satellite sensors fail to detect the magnitude of orographic rainfall. The complete opposite situation was found over the Aegean and Ionian Sea and many areas of the coastline, with great overestimation of rainfall. The results of our study highlight the potentials and limitations of IMERG rainfall estimates over Greece and they can be very helpful for future users of IMERG in Greece.

Road crashes and field rainfall data: mathematical modeling for the Brazilian mountainous highway BR-376/PR

Recent studies analyze the influence of rainfall on traffic crashes, indicating that precipitation intensity is an important factor, for modeling crashes occurrence. This research presents a relationship between daily-basis traffic crashes and precipitation, from 2014 to 2018, in a rural mountainous Brazilian Highway (BR-376/PR), where field rain gauges were used to obtain precipitation data. Data modeling considered a Negative Binomial regression for precipitation influence in crash frequency. Separate regression models were estimated to account for the rainfall effect in different seasons, and for different vehicle types. All models analyzed presented a positive relationship between daily rainfall intensity and daily crashes number. This can indicate that generally rainfall presence is a hazardous factor. Different critical seasons for rainfall influence were also highlighted, alerting for the possible necessity of distinct road safety policies concerning seasonality. Finally, for the vehicle type analysis, typically, rainfall seemed to have a greater effect in lighter vehicles. Moreover, results are useful for traffic control, in order to increase safety conditions.