Rainfall Intensity Data Research Articles

A novel modelling framework for real-time prediction of path travel times using both traffic and weather data

The problems of path travel time predictions have been studied over decades. Traditional approaches to this challenge have relied on different traffic data. However, in metropolitan cities with frequent rainfall, there is a need to consider the weather effects on real-time prediction of path travel times. Hence, this research integrates traffic data with weather data, including rainfall intensity data and weather forecast data to consider the temporal relationships between weather data and path travel times. The proposed modelling framework is evaluated with data from a major expressway and an urban arterial road in Hong Kong. Results unequivocally demonstrate that incorporating weather data significantly boosts prediction accuracy. This study also examines the impact of different frequencies on weather data, as well as weather forecast correctness, on prediction accuracy. Finally, the applicability of the proposed modelling framework has been verified without and with the input of ground truth on path travel times.

Utilizing statistical distribution tests to develop rainfall intensity–duration–frequency curves for enhanced hydrological analysis in Kirkuk city, Iraq

ABSTRACT Rainfall intensity is considered one of the basic factors in designing hydrological models based on rainstorm data. The objective of this research is to employ novel intensity–duration–frequency curves and develop empirical equations for rainfall intensity in the city of Kirkuk. The reduction formula adopted by the Indian Meteorological Department was used to divide the maximum daily rainfall for short periods of 0.5, 1, 2, 4, 6, 12, and 24 h. Three key methods of frequency analysis (lognormal distribution, log Pearson type III distribution, and Gumbel extreme value distribution) were utilized to formulate a statistical relationship based on rainfall intensity data from between the years 1981 and 2023 for a gauging station upstream of the Kirkuk city basin to provide the best data set for all periods of 100, 50, 25, 10, 5, and 2 years. Research has shown that the logarithmic distribution is the best fit for modelling the relationship between the annual maximum rainfall at Kirkuk station and duration of the rainfall. The goodness-of-fit results indicate that the lognormal distribution statistically outperforms other distribution models. Hence, the generated rainfall intensity, duration, and frequency curves that were developed led to estimating the intensity of precipitation to build forecasting and hydrological behaviour of the Kirkuk city basin.

Identifying and classifying broader scale of disaster impacts to better inform disaster management policies and practice

AbstractDisasters are often assessed by factors such as their magnitude, severity and impact on affected communities. Typically, disaster management policies are largely informed by learnings from large-scale disasters. Many studies, however, have shown that cumulative smaller scale disaster impacts have similar outcomes comparable to larger scale disasters. This indirectly contributes to a lack of consistency in and paucity of data related to both smaller-scale disasters and their associated cumulative impacts. This paper argues that greater attention needs to be paid to collecting disaster impact data on a range of disaster-scale events along with their cumulative impacts to better inform disaster management policies and practice. The paper aims to test the hypothesis that a better understanding of a wide range of disaster scale types and their impacts on communities, including small-scale disasters, enables the development of more just, consistent and equitable disaster management policies and practice. Analysing economic costs, evacuation and rainfall data from past flood events that affected Aotearoa-New Zealand over the last three decades, the paper found discrepancies and lack of coherence in disaster data collection and reporting, along with a predominant reliance on rainfall intensity data for disaster risk reduction and planning. The findings also confirmed the importance of identifying economic damages for a range of disaster scales, including those from small-scale disasters. This highlights the need for developing a spectrum to better classify and identify disaster impacts based on various spatial and temporal contexts. The paper concludes by affirming that such database would provide much needed evidence for developing a methodology that enables the identification of the level of disaster impacts; thereby guiding the implementation of more just, consistent and equitable disaster management policies and practice.

Syphons in tipping bucket rain gauges: How do they affect rainfall intensity estimates derived from inter-tip times?

Tipping-bucket rain gauges (TBRGs) are widely used globally to record rainfall amounts over nominated accumulation durations such as 15-minute rainfall, hourly rainfall, and daily rainfall. To reduce the under-estimation of rainfall amounts that results from high rates of inflow to the tipping buckets in intense rainfall, small syphons are commonly installed between the rain-collecting funnel and the bucket mechanism. The syphon is designed to regulate the inflow, reducing the systematic error inherent in the basic TBRG mechanism.The inter-tip times (ITTs) of a TBRG are often used as a source of rainfall intensity data, since the ITTs provide the highest temporal resolution possible from such gauges. However, the influence of the syphon is generally neglected in this approach to intensity estimation. Experimental results are presented here from a high-quality TBRG fed with known rates of water inflow, simulating rain of various constant intensities. Tests were replicated with the syphon installed, and with it removed.Results confirm that the syphon significantly perturbs the sequence of ITTs, causing apparent fluctuations in intensity that do not in fact exist. At least two factors contribute to this. The first is the lack of correspondence between syphon and bucket volumes, such that more than one syphon operation may be needed to fill one bucket. The second factor is the continued inflow to the syphon during its emptying cycle. This lengthens the time required for the syphon to cycle, as well as worsening any mismatch between the amount of water discharged through the syphon to the tipping-bucket mechanism and the bucket capacity. In fact, there can be no fixed relationship between the syphon discharge volume and the capacity of a TBRG bucket. Therefore, one syphon emptying cycle can deliver more than the nominal 0.2 mm needed to trigger a tip event, such that the varying excess (dependent on the rainfall intensity) drains to the second bucket, which in turn tips earlier than would be expected. Consequently, even at constant intensity, the ITTs display increased variability, with widely-varying apparent intensities among successive ITTs. The variance among ITTs is shown always to be greater in data from a syphon-equipped TBRG than from a “straight-through” (no-syphon) gauge, such that the reliability of intensity data obtained from the ITTs of a syphon-equipped TBRG are reduced by the presence of the syphon.

Analysis of Land Cover Changes Impact on Design Flood Estimation Case study: Upper Cimanuk Watershed in Garut City

The unexpected flooding events that occurred in the years 2021 and 2022 in the city of Garut became unforeseen incidents. According to the National Disaster Management Agency (BNPB), the flood disasters were caused by several factors, one of which was changes in land cover. Based on this background, this research is conducted to examine the causes of changes in flood discharge that have occurred in the city of Garut due to land cover changes from the years 2000, 2010, and 2020. The evaluation of the factors causing the flood magnitude is performed by conducting a hydrological analysis in the Cimanuk Watershed. An analysis of land cover changes using the ArcGIS application to create land cover samples and HEC-HMS application was conducted to determine the percentage increase in discharge and to understand the percentage of land cover changes that occurred in the Cimanuk Watershed. The data used included satellite image maps, digital elevation models (DEM), rainfall intensity, and cross-sectional data. The accuracy of the simulation was assessed using the Nash-Sutcliffe Efficiency (NSE). The simulation results show an increase in the inflow discharge into Garut city ranging from 23.7% to 32.2% for each return period. The NSE value obtained for the model was 0.83, indicating that the HEC-HMS hydrological model performed well.

Limit Equilibrium Method Based Slope Stability Analysis of One of the Most Highly Landslide Susceptible Region of Guwahati

Abstract: Guwahati, situated in a hilly region, faces the constant threat of landslides due to its susceptibility to high rainfall. The city has a history of several instances of landslides, causing substantial loss of life and property. The Assam State Disaster Management Authority (ASDMA) has identified at least 366 locations within the city as highly seismic-prone areas. In response to this, a comprehensive analysis of slope vulnerability has been initiated. One of the targeted sites, recently affected by landslides, is undergoing thorough examination using the Limit Equilibrium Method (LEM). To achieve this, a detailed survey of the slope profile has been conducted, including the collection of geological data and soil samples. The geotechnical parameters are being meticulously analysed based on the obtained soil test reports, considering factors such as drainage conditions and historical rainfall intensity data. This crucial information serves as input for a LEM-based software, which is commonly available for slope stability analysis. The software utilizes sophisticated algorithms to assess the vulnerability of the slope under varying rainfall intensities, offering valuable insights into potential landslide risks. By combining field surveys, geotechnical testing, and advanced software analysis, this comprehensive approach aims to provide a holistic understanding of slope stability, aiding in the formulation of effective strategies for landslide mitigation in the identified vulnerable regions of Guwahati. This initiative aligns with the broader goal of enhancing disaster preparedness and resilience in the face of natural hazards in the region.

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.

The Impact of Extreme Rainstorms on Escarpment Morphology in Arid Areas: Insights From the Central Negev Desert

AbstractThe impact of climate on topography, which is a theme in landscape evolution studies, has been demonstrated, mostly, at mountain range scales and across climate zones. However, in drylands, spatiotemporal discontinuities of rainfall and the crucial role of extreme rainstorms raise questions and challenges in identifying climate properties that govern surface processes. Here, we combine methods to examine hyperarid escarpment sensitivity to storm‐scale forcing. Using a high‐resolution DEM and field measurements, we analyzed the topography of a 40‐km‐long escarpment in the Negev desert (Israel). We also used rainfall intensity data from a convection‐permitting numerical weather model for storm‐scale statistical analysis. We conducted hydrological simulations of synthetic rainstorms, revealing the frequency of sediment mobilization along the sub‐cliff slopes. Results show that cliff gradients along the hyperarid escarpment increase systematically from the wetter (90 mm yr−1) southwestern to the drier (45 mm yr−1) northeastern sides. Also, sub‐cliff slopes at the southwestern study site are longer and associated with milder gradients and coarser sediments. Storm‐scale statistical analysis reveals a trend of increasing extreme (>10 years return‐period) intensities toward the northeast site, opposite to the trend in mean annual rainfall. Hydrological simulations based on these statistics indicate a higher frequency of sediment mobilization in the northeast, which can explain the pronounced topographic differences between the sites. The variations in landscape and rainstorm properties across a relatively short distance highlight the sensitivity of arid landforms to extreme events.

Recording Rainfall Intensity: Has an Optimum Method Been Found?

Many design principles for rain gauges that have the capacity to record rainfall intensity have been proposed or developed. These are here grouped into 15 categories, and the abilities and limitations of each are discussed. No standard or optimum method has emerged, despite more than 80 years of effort in the last two centuries, together with prior work from the 17th C onwards. Indeed, new methods continue to be explored for both point-based and area-wide collections of intensity data. Examples include the use of signal attenuation by rain along the tower-to-tower links of cellular phone networks, monitoring the speed of vehicle windscreen wipers, and exploiting the sound or vision from security and traffic-monitoring cameras. Many of these approaches have the potential to provide vastly more observation sites than conventional meteorological stations equipped with rain gauges. Some of these contemporary approaches seek to harness the potential of crowdsourced or citizen-science data. It is hoped that the present overview of methods will provide a guide for those wishing to collect or analyses rainfall intensity data for application in areas such as soil erosion processes, ecohydrology, agrochemical washoff, or urban flash flooding. Because rainfall intensity is one of the key aspects of the hydrologic cycle likely to respond as climate change and variability proceed, the choice of appropriate data collection methods has additional contemporary importance for the monitoring of regional and global precipitation changes.

Erosivity Factor of the Revised Universal Soil Loss Equation (RUSLE) - A Systematized Review

The Revised Universal Soil Loss Equation (RUSLE) is a globally accepted erosion model which has gained good acceptability. Among the five influences of the RUSLE method of soil erosion estimation, the erosivity factor (R) represents rainfall event’s ability to produce erosion. It is mainly affected by rainfall intensity and kinetic energy of the rain. The erosion index represented by EI30 is the most common R-factor estimation method. Due to the non-availability of rainfall intensity data in many watersheds, researchers have developed methods for erosivity estimation using rainfall depth. The Modified Fournier Index method has gained popularity. Recently, different models using machine learning techniques and ANN are also being set up to establish the R-factor for soil loss estimation. These models can estimate the R-factor quickly and more accurately. They can even predict the R-factor for the future to predict soil loss and plan conservation measures accordingly. An attempt has been made here to review different methodologies proposed by scientists across the globe for arriving at the R-factor for soil loss estimation using RUSLE model.

Application of Artificial Neural Networks using the Matlab Application for Predicting the Rate of Development of Rainfall Intensity (Case Study in Rangkasbitung District)

With the development of increasingly advanced information technology at this time, the MatLab application program created by applying an Artificial Neural Network can be used to predict the rate of development of rainfall intensity. Information technology can now be used to assist in processing, obtaining, compiling, storing, and managing data to produce information for researchers in compiling research so that research becomes very easy. Food crop production is influenced by rainfall during the development and growth of food crops; this causes rainfall prediction to be essential in agricultural planning. Prediction is the most crucial tool in determining everything effectively and efficiently. Therefore, several methodologies are used in compiling this research, namely collecting rainfall intensity data from each year, checking each problem, and then matching it with the required data. Samples were taken to determine the criteria for rainfall intensity based on BPS (Central Statistics Agency) data, namely data on rainfall intensity in Rangkasbitung sub-district from 2010 to 2020. The data is presented in tabular form. The following is a table of rainfall intensity data. 1) The backpropagation algorithm can be used in the prediction process; whether it is good or not is influenced by a learning material and the number of neurons in the hidden layer. 2) If the data is getting more significant on the unit in the hidden layer, the prediction results will be closer to the targeted value. 3) Using an artificial neural network in the MatLab software is proven to predict rainfall intensity development rate.

Block thresholding wavelet estimation of copula density based on NSD assumption

In this article, we consider the wavelet estimation of copula density for negatively superadditive dependent random variables. Using wavelet methods, we propose and develop a new estimation procedure for this problem. In particular, a BlockShrink estimator is constructed and we prove that it enjoys powerful mean integrated squared error properties over Besov balls. The main result is prepared to display the performance of the wavelet-based estimator and a simulation study to compare the behavior of the proposed wavelet estimator with the kernel copula density estimator are also given. Finally, we consider a real life application in hydrology for rainfall intensity data.

Hypothetical Ground Radar-Like Rain Rate Generation of Geostationary Weather Satellite Using Data-to-Data Translation

This study proposes a deep-learning-based data-to-data (D2D) translation framework to simulate a radar-like retrieval of rainfall rates using the advantages of spatial coverage and temporal resolution of geostationary (GEO) satellite observation. The D2D method comprises normalization and denormalization in pre- and post-processing and an adversarial learning structure for an inter-domain conversion between physical values of data such as albedo and brightness temperature (BT) unlike the image-to-image translation using digital number values in image data. The GEO-KOMPSAT-2A (GK2A) and radar hybrid surface rainfall (HSR) datasets over the Korean Peninsula from September 2019 to September 2021 were used as the source and target domains for training and testing the D2D model. The constructed D2D model for ground radar-like rainfall generation was validated using the ground radar-observed rainfall data and compared to the GK2A rainfall rate (RR), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) rainfall products. The D2D model exhibited excellent performance for various rain types in the study area compared to the GK2A RR, PERSIANN-CCS, and IMERG data. Consequently, the D2D model can provide valuable and accurate radar-like rainfall intensity and distribution data with a high temporal resolution and complementary rainfall information over lands and oceans without radar observation.

Advancing debris flow hazard and risk assessments using debris flow modeling and radar derived rainfall intensity data

Debris flow hazard and risk assessments are critical tools in mitigating and planning for these events. Existing debris flow hazard assessments can provide a rapid view of the likelihood of debris flows in recently burned watersheds and along stream segments within the watershed. Furthermore, debris flow volumes can be predicted for these watersheds and along the stream segments. Advances in modeling and remote-sensing data can add further value to the rapid assessments. Here, modeled debris flow volumes and a more detailed understanding of rainfall conditions highlight a need to reconcile debris flow probabilities and volumes using local conditions. Modeled debris flow volumes are consistently lower than even the lowest predicted volumes from empirical models used in the debris flow hazard assessments. Watershed probability and volume relations also over predict based on our probabilities derived from rainfall intensity from Multi-Radar/Multi-Sensor System 1-hr data. Probability and volume measures need to be further considered as the conservative measures of the rapid assessments have implication for risk analyses required for planning and management decisions, and ultimately for design and cost of mitigation to manage risk.

Study of Climate Change in the Mandalika International Circuit Area Using Neural Network Backpropagation

Climate change is a global phenomenon that also causes small-scale effects. This study aims to determine future climate changes in the Mandalika International Circuit area using the artificial neural network of the MATLAB GUI-based backpropagation method. The simulation stage used daily rainfall intensity data collected in the Mandalika International Circuit area from 2012-2021 (365 data). A preliminary analysis concluded that the Mandalika International Circuit area is dominated by a very wet climate according to the Schmidt-Ferguson classification, which occurred in 2012, 2013, 2017, and 2021. This study used two architectural models with two and three hidden layers. The TRAINRP training function and the LOGSIG activation function were utilized at each hidden layer. Between the two architectures, the better architecture was selected, namely the 100-50-10-1 (three hidden layers) that resulted in an accuracy rate of 99.90% and an MSE of 0.0412376 achieved in the 258th iteration. These results indicate that the area has a very wet climate with the highest rain intensity in March and the lowest in January. The results of this study show that the backpropagation method can be used to help formulate an alternative policy on the measures for handling and mitigating extreme climate change in upcoming periods, especially during international events at the Mandalika International Circuit area.

Dewatering requirements assessment for the Central Kalimantan NCP open pit gold mine

One of the most critical aspects of open pit mining is the dewatering and mine drainage systems. The NCP Open Pit Gold Mine is located in Central Kalimantan. This study area has a range of rainfall intensities and durations from moderate to heavy. Good dewatering is required to manage runoff water and reduce runoff from entering the pit and mine front loading. The study used daily rainfall intensity data from 1994 to 2018. Using the Mononobe Method, the hydrological data for this area were evaluated by determining the value of the rainfall intensity plan. According to the evaluation of rainfall data from 1994 to 2018, the research area saw a rainfall intensity of 86.23 mm/day over a two-year return period. The majority of water extracted from mines is from precipitation and runoff rather than groundwater. An open channel was made around the open pit, flowing water naturally into the sump to reduce water entering the mining area. The water was pumped into the settling pond with 520 m3/hour and 780 m3/hour capacity pumps.

The 50- and 100-year Exceedance Probabilities as New and Convenient Statistics for a Frequency Analysis of Extreme Events: An Example of Extreme Precipitation in Israel

The misinterpreted statistics of extreme events’ probability in drainage design codes can lead to an insufficient capacity of drainage systems. This is one of the main factors of frequent floods, especially in coastal cities. Heavy rainfalls, the so-called “1-in-50-year” or “1-in-100-year” events followed by floods, may occur there every 2–3 years. We hope to contribute to the correct understanding of extreme events’ statistics. To achieve this goal, we first recall the technique of calculating the exceedance probability of the extreme event over a period of years. Then, to illustrate the theoretical results (among which is the well-known 63.2% minimal exceedance probability over a period of N years for an event with a 1/N annual exceedance probability), we use the rainfall intensity data archived at the Israel Meteorological Service for 1938–2021. We estimate their exceedance probabilities over periods of 50 and 100 years and offer the latter as convenient measures for comparative analysis of the intensity of extreme events. These illustrations highlight the importance of considering return periods that are significantly longer than the 50- or 100-year periods currently used in drainage design, to reduce the risk of flooding and damage caused by heavy rainfalls.

Development of environmental contours from rainfall intensity and duration data for slopes

Development of environmental contours from rainfall intensity and duration data for slopes

Spatio-temporal analysis in determination of flood extent based on rainfall intensity and water level data in Kuala Krai, Kelantan

Kuala Krai is one of the districts in Kelantan that affected by flooding almost every year. The recent worse case is during Kelantan big yellow flood event in 2014. The main objective of this paper is to descriptively interpret the relationship between rainfall event and flooding in Kuala Krai, Kelantan using pass data of rainfall intensity and water level. Those data have been verified using ground data collection of flood level six month right after Kelantan big yellow flood event in 2014. Other related primary data also have been collected among the 2014 flood victims in Kuala Krai using survey questionnaire. The observed flood level data and other related primary data have been delineated and spatially analysed using ArcGIS to visualize the flood extent maps of each study area in Kuala Krai based on extreme flooding data on 2014. It can be concluded that urban area gave high percentage of flood extent compared to rural area of Kuala Krai.

A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data

AbstractWildfire is a global phenomenon that has dramatic effects on erosion and flood potential. On steep slopes, burned areas are more likely to experience significant overland flow during heavy rainfall leading to post fire debris flows (PFDFs). Previous work establishes methods for PFDF hazard assessment, often relying on regional‐scale parameterizations with in‐situ rainfall measurements to categorize hazard as a function of meteorological and surface properties. We present a globally scalable approach to extend the benefit these models provide to new areas. Our new model relies on publicly available satellite‐based inputs with a global extent to provide first order hazard assessments of recently burned areas. Our results show it is possible to identify the conditions relevant for PFDF‐initiation processes across a variety of physiographic settings. Improvements to satellite‐borne rainfall intensity data and increased availability of PFDF occurrence data worldwide are expected to enhance model skill and applicability further.