Rainfall Characteristics Research Articles

Improving the understanding of rainfall-runoff processes: Temporal dynamic of event runoff response in Loess Plateau, China.

Enhancing the understanding of the rainfall-runoff temporal dynamics in semi-arid and semi-humid regions is crucial for flood disaster mitigation. Loess Plateau is a unique environment within semi-arid and semi-humid regions, characterized by its deep loess soil, prevalent short-duration intense rainfall, and changes in underlying surface conditions. In this research, 25 catchments from the Loess Plateau were chosen to examine the temporal variations in event runoff responses across different time scales. The study analyzed event rainfall characteristics, catchment wetness conditions, and runoff responses to identify the factors influencing event-to-event variability, seasonal patterns, and long-term shifts in runoff behavior. The results show that the event runoff response exhibits distinct patterns across different ecological regions of Loess Plateau. In the Loess Hilly and Gully region, the event runoff response is primarily influenced by rainfall intensity characteristics and occurs due to infiltration excess. In the River Valley Plain and Earth-rocky Mountainous regions characterized by a humid climate, event runoff response is influenced by pre-event soil moisture and shows a strong correlation with rainfall volume. In the Loess Hilly and Gully region, catchments experience a noticeable transition between saturated and unsaturated states over long-term changes, driven by the combined impacts of land use and climate changes. The study provides insights into the mechanisms behind runoff generation at the catchment scale and offers valuable information for flood forecasting in semi-arid and semi-humid regions.

Exploring management and environment effects on edge-of-field phosphorus losses with linear mixed models.

Evaluating how weather, farm management, and soil conditions impact phosphorus (P) loss from agricultural sites is essential for improving our waterways in agricultural watersheds. In this study, rainfall characteristics, manure application timing, tillage, surface condition, and soil test phosphorus (STP) were analyzed to determine their effects on total phosphorus (TP) and dissolved phosphorus (DP) loss using 125 site-years of runoff data collected by the University of Wisconsin Discovery Farms and Discovery Farms Minnesota. Three linear mixed models (LMMs) were then used to evaluate the influence of those factors on TP and DP losses: (1) a model that included all runoff events, (2) manured sites only, and (3) precipitation events only. Results show that the timing of manure application relative to the timing of a runoff event only had a marginal association with P loads and concentrations, although the majority of the runoff events were collected after 10 days of manure application. Tillage was as influential factor, with greater DP loads and concentrations associated with no-till, especially during frozen conditions. Fields in this study had high STP values, but the model results only showed positive associations between DP load and DP flow-weighted mean concentration (FWMC) loss at the 0- to 15-cm depth. The precipitation event LMM (which included precipitation characteristics) was the model that resulted in the largest R2 value. While the predictive capacity of the LMMs was low, they did illuminate the relative importance of management and environmental variables on P loss, and can be used to guide future research on P loss in this region.

Analysis on Characteristics of Short-Time Heavy Rainfall in Yantai City, Shandong Province

Analysis on Characteristics of Short-Time Heavy Rainfall in Yantai City, Shandong Province

Characteristics of extreme rainfall from warm clouds associated with a mesoscale convective system: sensitivity of different microphysical and cumulus parameterization schemes

Characteristics of extreme rainfall from warm clouds associated with a mesoscale convective system: sensitivity of different microphysical and cumulus parameterization schemes

ASSESSING THE IMPACT OF CLIMATE CHANGE INDUCED-URBAN FLOODING ON THE ENVIRONMENT

This study focuses on the effects of climate change on urban flooding in Lokoja Nigeria, which lies at the confluence of the two major rivers in Nigeria; Niger and Benue and therefore is vulnerable to flooding as a result of a changing precipitation pattern. The study focuses on three main objectives: assessing the rainfall characteristics, assessing the current and possible flood mitigation measures and assessing the social economic and environmental effects of flooding in Lokoja. Through quantitative means, the study relied on secondary climate data obtained from the Nigerian Meteorological Agency for the period between 1988 and 2022 and primary data obtained from an online survey of residents of the most flood-prone neighbourhoods. Trend analysis was conducted to analyse rainfall data, multiple regression was used to determine environmental and socio-economic impact of flooding and statistical method was used to evaluate adaptive measures. The study establishes that climate change has led to an increased and more frequent occurrence of rainfall which consequently has led to increased flooding in Lokoja. The study also shows that poor infrastructure particularly drainage systems make the area more prone to floods. Therefore, residents’ experiences reveal the importance of developing flood resistance cities, reliable Warnings, and Community-Based Adaptation. Thus, the study highlights the need to improve the capacity of Lokoja to adapt to climate-induced urban flooding and presents findings that can be useful for other flood affected areas of the world.

Surface runoff estimation at the Densu River Basin using geographic information system (GIS) and remote sensing (RS)

Accurate estimation of runoff depth and volume is essential for effective watershed management. Runoff, resulting from rainfall, is influenced by numerous factors, including soil type, vegetation, land use patterns, and rainfall characteristics. The Densu River Basin, located in the Greater Accra region of Ghana, has experienced flooding incidents, partly attributed to changes in land use and land cover. To address these challenges and facilitate proper flood management, drainage network design, hydropower generation, and other applications, this study aims to estimate surface runoff depth in the Densu River Basin, Ghana. The Natural Resources Conservation Services Curve Number (NRCS-CN) method, combined with Geographic Information System (GIS) and Remote Sensing (RS), is employed for runoff depth estimation. The research involves supervised classification of Landsat images from 2001, 2011, and 2022 to determine land use patterns, calculate grass cover percentages, identify hydrologic soil categories, extract rainfall intensity data, compute maximum soil storage, and estimate runoff depths for 10 year, 25 year, and 50 year return periods. The study reveals a significant increase in direct surface runoff depth, from 138.29 mm to 144.70 mm, for soil type D (Clay loam), the dominant soil type in the basin, during the 10 year return period, attributed to changes in land use and climate within the basin. The findings from this study hold valuable insights for mitigating environmental hazards in the area and improving water resource management.

Shifting in Rainy Season Features in the Mekong Delta under the Background of Global Climate Change

Background: Shifting in the rainy season features (RSFs) have caused impishly cultivation activities around the world, especially under the impacts of enhancing climate change. In the Mekong Delta, rainfall is a crucial factor that determines crop sowing and harvesting dates. Methods: This study investigated the RSFs, including the rainy season onset date (RSOD), cessation date (RSCD) and length (LRS), in the Mekong Delta from 1995 to 2022. The study aimed to understand the spatial and temporal variability of the rainfall features and their implications across the Mekong Delta. The study utilized rainfall data at gauge stations across the Mekong Delta, covering a period of 30 years. The study employed descriptive statistical methods to analyze the RSFs, including RSOD, RSCD and LRS. Result: The results revealed the spatial and temporal variability of RSFs across the study area. The analysis of RSOD, RSCDand LRS revealed consistent patterns. However, the SD and CV values suggested moderate variability in these features, indicating the need for further investigation into the factors influencing these variations. The findings highlight the importance of considering RSFs for effective rain-fed resource management and planning in the Mekong Delta.

ANALISIS CURAH HUJAN EKSTREM DI KEPULAUAN MALUKU DAN HUBUNGANNYA DENGAN FENOMENA ENSO

Global climate anomalies can cause extreme climates, especially in Indonesia, one of which includes extreme rainfall events. Rainfall in Indonesia itself is coherent and correlated with ENSO variations in the Pacific Ocean. Apart from the influence of ENSO, there are other influences such as regional, local factors and regional topography which also greatly determine the characteristics of rainfall, especially in the Maluku Islands which have different rainfall patterns from other areas in Indonesia. The complex phenomena that influence rainfall in the Maluku region attract the attention of researchers to analyze extreme rainfall patterns and their relationship to ENSO relationships. This research uses daily rainfall data at BMKG and Metomanz stations from 1992 to 2022 for 30 years, and uses a simple linear regression method obtained from the results of ERA 5 reanalysis data with BMKG data, at stations that do not have complete data for 30 years. This research was conducted using the Mann-Kendall and Sen's Slope methods. The Mann-Kendall method is used to determine the degree of significance and change in positive or negative direction. Meanwhile, the Sen's Slope Method is used to determine the magnitude of the linear slope of the resulting trend. Extreme climate patterns in the Maluku Islands are influenced by the ENSO phenomenon and geographical conditions of the region. There are significant trend changes in CDD, CWD, and PRCPTOT. The Rnmm index is divided into three index limits (R20mm, R15mm, and R10mm). Increasing trends of CDD, CWD, PRCPTOT, occurred sequentially at Mathilda Batlayeri, Kuffar, Bandaneira stations. The Rnmm index is divided into three index limits R20mm, R15mm, and R10mm and there is a sequential increasing trend at Bandaneira, Namlea and Mathilda Batlayeri stations

Indian Summer Monsoon Rainfall Characteristics Derived From Multiple Gridded Precipitation Datasets: A Comparative Assessment

ABSTRACTPrecipitation, a crucial component of the Earth system processes, regulates the spatiotemporal cyclicity of water, energy, and carbon fluxes. Accurate precipitation datasets leverage the understanding of precipitation dynamics and are vital for hydro‐climatological studies. South Asian monsoon is a complex, multi‐scale interacting, synoptic, and ocean–land–atmosphere coupled system, contributing to significant spatial and temporal variability in summer monsoonal rainfall across India. This study evaluates four types of gridded (observational, satellite, reanalysis, and hybrid) precipitation products in their ability to replicate Indian Summer Monsoonal Rainfall (ISMR) characteristics using the India Meteorological Department (IMD) 0.25° gridded data as the baseline. A comparative assessment is performed in this study that uses several continuous and interval‐based performance measures to evaluate the overall rainfall magnitude detectability and time‐matched capturing of rainfall events. A new metric, rank score, is developed by aggregating multiple measures to find the best product. The analyses based on several performance measures indicate that MSWEP is the best dataset (rank one) that closely approximates the occurrence and magnitude of IMD‐based rainfall events, while APHRODITE, CHIRPS, and IMDAA are ranked as the next best set of products. PGF is ranked the lowest among all products evaluated and is not recommended for applications. Nonetheless, APHRODITE suffers from strong negative biases, while the reanalysis (IMDAA, ERA5‐Land, PGF) datasets show significant positive biases. Among the products evaluated, APHRODITE, ERA5‐Land, and IMDAA have shown a limited ability to detect excess, normal, and deficit monsoon years, respectively. In general, the performance of satellite‐based data products is superior to that of reanalysis datasets in accurately characterising the monsoon years. ERA5‐Land is noted to be the best‐performing dataset among the reanalysis products. The comprehensive comparative assessment carried out in this study benefits the selection and use of appropriate gridded precipitation products for hydroclimatic modelling, climate variability, and change studies.

Assessment of overflow points in the sewer network using canoué software: case study of the city of Annaba, Northeastern Algeria

Despite their complexity and inherent randomness, which make their analysis challenging, a thorough understanding of local rainfall characteristics is crucial for the proper sizing of urban stormwater drainage systems. In the Mediterranean city of Annaba, frequent flooding events highlight the critical need for efficient rainwater management. The lack of effective drainage infrastructure has led to significant social and economic consequences, particularly during intense rainfall episodes. This study investigates rainfall occurrences recorded at the Pont-Bouchet station, covering a 22-year period (1977-1999) with 1,866 rainfall events. Detailed analysis of the data includes the construction of Intensity-Duration-Frequency (IDF) curves and the application of double-triangle project storms across various periods (1977-1981; 1982-1986; 1987-1992; 1993-1999). The hydrological simulation of this rainfall data for various periods provides valuable insights into areas of the drainage network susceptible to overflow. The findings aim to improve stormwater management strategies and mitigate the adverse impacts of flooding in urban areas.

IDENTIFIKASI KONDISI BANJIR DAN KARAKTERISTIK SALURAN DRAINASE DI KAWASAN KECAMATAN DENPASAR BARAT

West Denpasar, as part of Denpasar City, often faces flood problems caused by weather factors, buildings, and inadequate drainage infrastructure. This article aims to identify flood conditions and assess the characteristics of drainage channels in the areas of Jalan Gunung Tangkuban Perahu, Jalan Gunung Salak, and Jalan Pura Demak. This research was carried out through field observation, measurement, and data analysis regarding rainfall and drainage channel characteristics. The results of qualitative data show that 80% of the local community has experienced flooding, with the majority complaining of clogged drainage conditions. Meanwhile, quantitative data shows that the drainage channel on Jalan Pura Demak has an average width of only 25 cm, which worsens flood conditions with inundation depths reaching 70 cm for more than 6 hours after rain. The conclusion of this study emphasizes the need to increase the maintenance and improvement of drainage infrastructure, as well as community involvement in maintaining the cleanliness of channels to reduce the risk of repeated floods.

Studying rainfall characteristics affecting low-impact development runoff control measures via numerical simulation

ABSTRACT To gain a comprehensive understanding of the intricate interplay between rainfall characteristics and the effectiveness of runoff control, this study focuses on a representative urban district equipped with low-impact development (LID) features within the Xi'an New Area, China. By utilizing a diverse range of rainfall profiles and employing the GPU-Accelerated Surface Water Flow and Transport Model (GAST), a sophisticated two-dimensional hydrodynamic model was developed to simulate surface runoff dynamics. Our findings highlighted the substantial influence of rainfall intensity, duration, and peak coefficients on the efficiency of runoff control. Specifically, we observed that a rainfall event with a duration of 2 h and a peak coefficient of 0.2 resulted in a runoff control rate varying from 86.17 to 45.44%, depending on the specific rainfall conditions. Furthermore, when rainfall intensity was varied from 19.20 to 87.22 mm, while maintaining a peak coefficient of 0.5, the runoff control rate fluctuated between 82.70 and 44.97%. This investigation reveals a quantifiable framework that elucidates the intricate relationship between rainfall attributes and runoff control effectiveness, providing crucial insights into the development of informed urban flood management strategies.

Corrigendum to “A Systematic Review on landslide triggering thresholds based on rainfall characteristics”

Corrigendum to “A Systematic Review on landslide triggering thresholds based on rainfall characteristics”

Locally Defined Seasonal Rainfall Characteristics within the Horn of Africa Drylands from Rain Gauge Observations

Abstract Seasonal rainfall is critical to lives and livelihoods within the Horn of Africa drylands (HAD), but it is highly variable in space and time. The main HAD rainfall seasons are typically defined as March–May (MAM) and October–December (OND). However, these 3-month periods are only generalized definitions of seasonality across the HAD, and local experience of rainfall may depart from these substantially. Here, we use daily rain gauge data with a duration of at least 10 years from 69 stations across the drylands of Kenya, Somalia, and Ethiopia to locally delineate key rainfall seasons. By calculating local seasonal rainfall timings, totals, and extremes, we obtain more accurate estimates of the spatial variability in rainfall delivery across the HAD, as well as climatological patterns. Results show high spatial variability in season onset, cessation, and length across the region, indicating that a homogenous classification of rainfall seasons across the HAD (e.g., MAM and OND) is inadequate for representing local rainfall characteristics. Our results show that the “long rains” season is not significantly longer than the “short rains” season over the period of study. This could be related to the previously documented decline of the “long rains” seasonal totals over recent decades. Several rainfall metrics also vary spatially between seasons, and the rainfall on the most extreme days can accumulate to double the local mean seasonal total. The locally defined rainfall seasons better capture the bulk of the rainfall during the season, giving improved characterization of rainfall metrics, consistent with the aim of a better understanding of rainfall impacts on local communities.

Development of a Flood Impact Assessment Framework Integrating Crowdsourced Data and Geospatial Information for Data Sparse Urban Regions

High spatial heterogeneity of urban floods poses challenges in its modeling and assessment, and a flood database is a basic requirement but it is lacking for many cities. The proposed Twitter-based framework addresses the issues via developing a finer resolution flood database and a product. The framework has multiple components including data quality control, validation of flood database via newspapers and flood impact assessment. Three flood events differing in rainfall characteristics are selected to showcase the utility of the proposed framework for the city of Hyderabad, India. Analysis of tweets highlighted the resourcefulness of video tweets and wide coverage of the study area in terms of flood reporting. Tweets exhibited an association with tweet time and rainfall aspects. Further, tweets based flood locations are found to be in agreement with newspaper based flooding instances. A novel flood impact score (FIS) is developed for each flood location using analytical hierarchy process based weights for five variables (Twitter based attributes, rainfall, elevation), and the use of FIS is demonstrated in identifying flood impact areas. These kinds of databases and products, with a scope to improve further, serve as a potential tool to cater flood preparedness and management thereby making cities flood resilient.

Applications of Innovative Polygon Trend Analysis and Trend Polygon Star Concept Methods for the Variability of Precipitation at Synoptic Stations in Benin (West Africa)

Climate variability poses new risks and uncertainties. In the sub-Saharan region, the impacts are already being felt and represent an additional level of obstacles for most vulnerable people, as well as a threat to sustainable development. This study analyzes the variability of precipitation in Benin using new approaches. The precipitation data used is the monthly average recorded at synoptic stations from 1970 to 2019 by the Metéo-Bénin agency. Two innovative graphical trend methods, innovative polygon trend analysis (IPTA) and trend polygon star concept (TPSC), are applied to the data. Both methods allow for the assessment of periodic characteristics of the monthly average rainfall and visually interpreting the transition trends between two consecutive months. The results show that the average monthly precipitation does not follow a regular pattern. There is also a general upward trend in precipitation for most months at the stations used. Most TPSC arrows were found in regions I and III. According to the TPSC graphs, the longest transition arrows between two consecutive months were observed in quadrant III. They were noted between the months of June and July in Cotonou, October and November in Bohicon and Save, and between September and October for the remaining stations. The results of this study are of great importance for policies regarding ongoing climate change in the agricultural, health, economic, security, and environmental sectors.

Prediction of Monthly Rainfall with Using Monte Carlo Simulation in the Medan City Area

The climate in Indonesia, which is a tropical region, is always uncertain and makes it difficult to predict weather conditions. Weather conditions can be influenced by rainfall, air temperature, wind speed, air humidity and light radiation intensity. Rainfall is relatively high and varies throughout the year, the average monthly rainfall is around 150-300 mm in the rainy season and 50-100 mm in the dry season. There are several characteristics of rainfall, namely convective rain, frontal rain and orographic rain. For this reason, a method is needed that can solve problems in predicting monthly rainfall properties using the Monte Carlo simulation method. From this study, the results of the prediction of rainfall properties were obtained with 36 data from 2021 to 2024 which had a MAPE test result of 12.28%. The test results came from the average calculation carried out on the Monte Carlo method prediction with 5 variables.

Comparative effects of intermittent and continuous simulated rainstorms on rill erosion based on photogrammetry

AbstractDespite artificial rainfall simulation proves invaluable for the study of soil erosion processes and model construction, it still fails to fully replicate the characteristics of natural rainfall. Currently, most artificial rainfall experiments have carried out a large number of continuous high‐intensity rainfall due to the focus on the characteristics of short duration and high intensity of natural rainstorm but have ignored the erosion effects caused by intermittent rainstorm with low intensity and long duration. In this study, two sets of artificial rainfall simulation experiments of intermittent low‐intensity rainstorm (RR1) and continuous high‐intensity rainstorm (RR2) were conducted to evaluate the effects of rainfall characteristics on erosion morphology, runoff generation and soil loss. The evolution morphology monitored by a digital close‐range photogrammetry technology demonstrated the difference between the two rainstorm regimes. The soil surface was damaged more seriously under rainfall of RR2, and the rill morphological indicators of RR1 were all less than that of RR2. As rainfall proceeded, morphological indicators except for rill width‐depth ratio gradually increased. As a result, the runoff rate and sediment yield between two regimes were distinct. The segmented and total soil loss, average runoff rate and sediment concentration of RR1 were all less than that of RR2, with the total soil loss of the two rainstorm regimes being 275 and 683 kg, respectively. Water infiltration, rainfall intensity, duration and frequency may be the main factors leading to the difference in soil loss and erosion morphology between two rainstorm regimes. The inconsistency of these factors can easily cause the deviation of understanding of soil erosion mechanism. Therefore, the comparison of erosion effects under different rainstorm regimes has important implications for the improvement of natural rainstorm simulation and the comprehensive understanding of erosion mechanism.

Rainfall depth dominates overland flow and sediment yields of the coarse-textured soil: A case study in Southern China

Soil degradation, particularly due to water erosion, is a significant issue in many regions, including the mountainous areas of Southern China. This study investigated the response of overland flow and sediment yield to different rainfall patterns on a coarse-textured soil slope in Southern China. Three distinct rainfall regimes were identified through K-means clustering analysis, based on event rainfall depth, duration, and maximum 30-min intensity (I30). The results showed that rainfall regime II, characterized by moderate rainfall depth, duration, and I30, generated the highest accumulated surface runoff (97.3 mm) and soil erosion (1584.9 t km−2). Further analysis revealed that overland flow and sediment yield were positively correlated with various rainfall characteristics, including rainfall depth, mean intensity, duration, I30, rainfall energy, and rainfall erosivity. However, rainfall depth was identified as the dominant factor, exhibiting the stronger correlations with both the runoff depth (R2 = 0.77, RMSE = 0.396) and the erosion (R2 = 0.58, RMSE = 0.658) than did the mean rainfall intensity, I30, and duration. Note that RMSE values were expressed in logarithmic units in this study. Piecewise regression models were developed to predict overland flow and sediment yield based on rainfall depth, and these models were validated through in-situ rainfall simulation experiments. The results showed that these models produced acceptable soil and water loss predictions, with a mean bias of −0.093 mm and RMSE of 0.197 mm for the runoff depth, and a mean bias of −0.156 t km−2 and RMSE of 0.456 t km−2 for the sediment yield. Thus, rainfall depth is the dominant driver of soil and water loss in the coarse-textured soils of Southern China, and the developed regression models can be used to estimate overland flow and sediment yield in similar environments.

Partitioning of Heavy Rainfall in the Taihang Mountains and Its Response to Atmospheric Circulation Factors

The spatial and temporal distribution of heavy rainfall across the Taihang Mountains exhibits significant variation. Due to the region’s unstable geological conditions, frequent heavy rainfall events can lead to secondary disasters such as landslides, debris flows, and floods, thus intensifying both the frequency and severity of extreme events. Understanding the spatiotemporal evolution of heavy rainfall and its response to atmospheric circulation patterns is crucial for effective disaster prevention and mitigation. This study utilized daily precipitation data from 13 meteorological stations in the Taihang Mountains spanning from 1973 to 2022, employing Rotated Empirical Orthogonal Function (REOF), the Mann–Kendall Trend Test, and Continuous Wavelet Transform (CWT) to examine the spatiotemporal characteristics of heavy rainfall and its relationship with large-scale atmospheric circulation patterns. The results reveal that: (1) Heavy rainfall in the Taihang Mountains can be categorized into six distinct regions, each demonstrating significant spatial heterogeneity. Region I, situated in the transition zone between the plains and mountains, experiences increased rainfall due to orographic lifting, while Region IV, located in the southeast, receives the highest rainfall, driven primarily by monsoon lifting. Conversely, Regions III and VI receive comparatively less precipitation, with Region VI, located in the northern hilly area, experiencing the lowest rainfall. (2) Over the past 50 years, all regions have experienced an upward trend in heavy rainfall, with Region II showing a notable increase at a rate of 14.4 mm per decade, a trend closely linked to the intensification of the hydrological cycle driven by global warming. (3) The CWT results reveal significant 2–3-year periodic fluctuations in rainfall across all regions, aligning with the quasi-biennial oscillation (QBO) characteristic of the East Asian summer monsoon, offering valuable insights for future climate predictions. (4) Correlation and wavelet coherence analyses indicate that rainfall in Regions II, III, and IV is positively correlated with the Southern Oscillation Index (SOI) and the Pacific Warm Pool (PWP), while showing a negative correlation with the Pacific Decadal Oscillation (PDO). Rainfall in Region I is negatively correlated with the Indian Ocean Dipole (IOD). These climatic factors exhibit a lag effect on rainfall patterns. Incorporating these climatic factors into future rainfall prediction models is expected to enhance forecast accuracy. This study integrates REOF analysis with large-scale circulation patterns to uncover the complex spatiotemporal relationships between heavy rainfall and climatic drivers, offering new insights into improving heavy rainfall event forecasting in the Taihang Mountains. The complex topography of the Taihang Mountains, combined with unstable geological conditions, leads to uneven spatial distribution of heavy rainfall, which can easily trigger secondary disasters such as landslides, debris flows, and floods. This, in turn, further increases the frequency and severity of extreme events.