Intensity Of Heavy Rainfall Events Research Articles

Rainfall impacts on nonpoint nitrogen and phosphorus dynamics in an agricultural river in subtropical montane reservoir region of southeast China

The increased frequency and intensity of heavy rainfall events due to climate change could potentially influence the movement of nutrients from land-based regions into recipient rivers. However, little information is available on how the rainfall affect nutrient dynamics in subtropical montane rivers with complex land use. This study conducted high-frequency monitoring to study the effects of rainfall on nutrients dynamics in an agricultural river draining to Lake Qiandaohu, a montane reservoir of southeast China. The results showed that riverine total nitrogen (TN) and total phosphorus (TP) concentrations increased continuously with increasing rainfall intensity, while TN:TP decreased. The heavy rainfall and rainstorm drove more than 30% of the annual N and P loading in only 5.20% of the total rainfall period, indicating that increased storm runoff is likely to exacerbate eutrophication in montane reservoirs. NO3−-N is the primary nitrogen form lost, while particulate phosphorus (PP) dominated phosphorus loss. The main source of N is cropland, and the main source of P is residential area. Spatially, forested watersheds have better drainage quality, while it is still a potential source of nonpoint pollution during rainfall events. TN and TP concentrations were significantly higher at sites dominated by cropland and residential area, indicating their substantial contributions to deteriorating river water quality. Temporally, TN and TP concentrations reached high values in May-August when rainfall was most intense, while they were lower in autumn and winter than that in spring and summer under the same rainfall intensities. The results emphasize the influence of rainfall-runoff and land use on dynamics of riverine N and P loads, providing guidance for nutrient load reduction planning for Lake Qiandaohu.

MIKE 모델을 이용한 미계측 소유역의 강우유출 및 침수분석

Recently, due to the impact of climate change, many regions have experienced an increase in both the frequency and intensity of heavy rainfall events compared to the past. These heavy rainfall events increase the likelihood of flooding in downstream basins. To prevent flooding, it is necessary to calculate the appropriate design flood volume. When estimating the design flood volume, it is necessary to calculate the runoff in consideration of spatial distribution in ungauged basin. In this study, the runoff of the ungauged basin was calculated using the MIKE SHE model, and the flooding impact of the downstream area was analyzed using the MIKE 21 Flow Model. The study confirmed the runoff, downstream travel time, and flooding occurrence times based on rainfall events in the ungauged basin. It has been confirmed that when estimating the design flood volume, consideration must be given to intense rainfall occurring in a short period. This study can be utilized as one of the methods for calculating design flood volume and can be deemed to establish a disaster management system.

Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China.

Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier. In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China. Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02-1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days. These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.

Characteristics of dynamical and thermo-dynamical variables during heavy rainfall events over the Indian region

There has been an increase in the frequency and intensity of heavy rainfall events (HREs) over the Indian region in recent years, and it is a huge challenge for the forecaster to accurately predict HREs with adequate lead time. In this study, four HREs, i.e., Kerala (KRA), Mumbai (MUM), Chennai (CHE), and Uttarakhand (UTK), occurred over different geographical regions of India considered. Total of five days (2 days before and after the event day-D0) of the HREs are extensively investigated. The ECMWF Fifth Generation Reanalysis (ERA5) and India Meteorological Department (IMD) data are used to examine rainfall and associated variables in these HREs. ERA5 is able to capture rainfall reasonably well in most of the HREs; however, there is a distinct underestimation found in UTK compared to IMD. Overall, the rainfall skills of ERA are mediocre for higher rainfall thresholds (>100 mm/hr). In general, higher convective available potential energy (CAPE) and lower convective inhibition (CIN) is distinctly noted for HREs. The strong monsoon flow facilitating intense moisture transport is the key factor for KRA and MUM events. Besides, unique convective signatures such as sudden and intense vertical velocity, flaring of relative vorticity, convergence, and moisture accumulation are seen for UTK, and thereafter, these variables rapidly weaken, indicating a short duration cloud burst type HRE. This is in clear contrast to other HRE events where convective conditioning and weakening of key variables occur in a gradual manner before and after D0, respectively. The ice hydrometeors are the major source of heavy rainfall in HREs except for UTK, where snow is the major contributor of rainfall in terms of rainwater and cloud liquid water. Further, it is revealed that an elevated moist static energy both at the lower and upper troposphere is found for UTK and MUM events compared to CHE and KRA. The intense lower troposphere moisture, low-level convergence, upper-level divergence, and intense relative vorticity, along with vertical motions, are key precursors for these HREs. Finally, a consolidated schematic flow of mechanisms responsible for HRE is proposed. The findings of this study are highly useful for improving early warning and disaster preparedness.

Role of Electrical Effects in Intensifying Rainfall Rates in the Tropics

AbstractIn the backdrop of a significant improvement in weather prediction with Numerical Weather Prediction models, quantitative prediction of the intensity of heavy rainfall events and associated disasters has remained a challenge. Encouraged by the recent emergence of compelling observational evidence for a significant electrical influence on cloud/rain microphysical processes (Mudiar, Pawar, Gopalakrishnana et al., 2021, https://doi.org/10.1029/2021gl093577), here we propose a hypothesis that the modification of the raindrop size distribution (RDSD) towards larger drop sizes facilitated by cloud electric fields could be one factor responsible for realistic rainfall intensity in weather/climate models. The robustness of the proposed hypothesis is confirmed through a series of simulations of strongly electrified rain events with the Weather Research and Forecasting model incorporating the electrically modified RDSD parameters in the model physics. Our results indicate a possible roadmap for improving hazard prediction associated with extreme rainfall events in weather prediction models.

Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.

Estimating Rainfall Erosivity Factor Using Future Climate Projection in the Myjava Region (Slovakia)

Abstract Rainfall erosivity factor (R) of the USLE model is one of the most popular indicators of areas potentially susceptible to soil erosion. Its value is influenced by the number and intensity of extreme rainfall events. Since the regional climate models expect that the intensity of heavy rainfall events will increase in the future, the currently used R-factor values are expected to change as well. This study investigates possible changes in the values of R-factor due to climate change in the Myjava region in Slovakia that is severely affected by soil erosion. Two rain gauge stations with high-resolution 1-minute data were used to build a multiple linear regression model (r 2 = 0.98) between monthly EI 30 values and other monthly rainfall characteristics derived from low-resolution daily data. The model was used to estimate at-site R-values in 13 additional rain gauge stations homogeneously dispersed over the whole region for four periods (1981–2010, 2011–2040, 2041–2070, 2071–2100). The at-site estimates were used to create R-factor maps using a geostatistical approach. The results showed that the mean R-factor values in the region might change from 429 to as much as 520 MJ.mm.ha−1.h−1.yr−1 in the second half of the 21st century representing a 20.5% increase.

Impact‐Based Forecasting for Pluvial Floods

AbstractPluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof‐of‐concept for an impact‐based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network‐based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio‐temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact‐based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact‐based forecast could be used to disseminate impact‐based early warnings.

Short-range forecast of heavy rainfall over the Kama River basin in 2019 with atmospheric models ICON, GFS and WRF

The summer of 2019 was one of the wettest in the observation history in the western part of the Ural region. Excessive rainfall formed several rain flood events on the rivers of the Kama reservoir basin. In this study, we assess the accuracy of short-range (with a 15-27 h lead time) forecasts of heavy rainfall events observed in 2019 with global numerical weather prediction (NWP) models, ICON and GFS, and a mesoscale model, WRF, launched in a convection-permitting mode. Two metrics, Critical Success Index (CSI) and Extreme Dependency Score (EDI), are used to assess the forecast skills of the global NWP models. Throughout the study period, both global NWP models underestimate the frequency and intensity of heavy rainfall events, mainly due to the omission of local rainstorms. However, heavy precipitation related to cyclones or frontal waves is predicted quite successfully. In addition, we analyse in detail a heavy rainfall event on July 14-16, 2019 which caused a high flood in the western part of the Perm region. The most reliable simulation results have been obtained with the WRF model.

Urban policy adaptation toward managing increasing pluvial flooding events under climate change

Climate change is increasing the frequency and intensity of heavy rainfall events. In urban areas, these cloudbursts cause localized flooding and disrupt urban life. However, there is no universal method for urban rainfall adaptation. Here, we demonstrate that while six developed cities; New York City, Vancouver, Sydney, Auckland, Copenhagen and Amsterdam, each present a different management approach toward cloudburst events, these six cities also use a mixture of five common policy alternatives to manage stormwater runoff. Thus, while individual cities might pursue different management approaches toward pluvial flooding, the repetition of the policy alternatives across these six cities indicates the foundation of a global consensus toward the policy adaptation techniques available. As climate change continues to impact our urban environments, it is important to identify and understand policy tools for effective adaptation, ensuring knowledge can be transferred across all cities facing an unpredictable future.

Climate Perspectives in the Intra–Americas Seas

The Intra–Americas Seas region is known for its relevance to air–sea interaction processes, the contrast between large water masses and a relatively small continental area, and the occurrence of extreme events. The differing weather systems and the influence of variability at different spatio–temporal scales is a characteristic feature of the region. The impact of hydro–meteorological extreme events has played a huge importance for regional livelihood, having a mostly negative impact on socioeconomics. The frequency and intensity of heavy rainfall events and droughts are often discussed in terms of their impact on economic activities and access to water. Furthermore, future climate projections suggest that warming scenarios are likely to increase the frequency and intensity of extreme events, which poses a major threat to vulnerable communities. In a region where the economy is largely dependent on agriculture and the population is exposed to the impact of extremes, understanding the climate system is key to informed policymaking and management plans. A wealth of knowledge has been published on regional weather and climate, with a majority of studies focusing on specific components of the system. This study aims to provide an integral overview of regional weather and climate suitable for a wider community. Following the presentation of the general features of the region, a large scale is introduced outlining the main structures that affect regional climate. The most relevant climate features are briefly described, focusing on sea surface temperature, low–level circulation, and rainfall patterns. The impact of climate variability at the intra–seasonal, inter–annual, decadal, and multi–decadal scales is discussed. Climate change is considered in the regional context, based on current knowledge for natural and anthropogenic climate change. The present challenges in regional weather and climate studies have also been included in the concluding sections of this review. The overarching aim of this work is to leverage information that may be transferred efficiently to support decision–making processes and provide a solid foundation on regional weather and climate for professionals from different backgrounds.

Extreme rainfall and drought events in Tamil Nadu, India

In the present study, we examined the exposure of the Tamil Nadu region, India, to droughts and extreme rainfall events using the Standardised Precipitation Evapotranspiration Index (SPEI) and a classification scheme based on daily rainfall. We used high-resolution temperature and rainfall observations from the India Meteorological Department for the period 1951-2016. The robustness of the results was tested using the Mann-Kendall trend (M-K) test and the Kolmogorov-Smirnov (K-S) test. During the study period, there were statistically significant increasing trends in drought area (90% significance level), maximum drought intensity (99% significance level) and maximum drought severity (99% significance level) over the Tamil Nadu region. There has also been an increase in the frequency and intensity of heavy rainfall events in recent years. The spatio-temporal dimensions of this study suggest an increasing exposure of this semi-arid, rain shadow region to severe droughts and extreme rainfall events in recent decades. The results provide sufficient grounds to substantiate the necessity of immediate interventions at the policy level.

Corrigendum to “Evaluating Linkages between Atmospheric Blocking Patterns and Heavy Rainfall Events across the North-Central Mississippi River Valley for Different ENSO Phases”

Over the last six to seven decades, there has been a substantial increase in atmospheric research to better understand the dynamics and evolution of atmospheric blocking events. It is well known that atmospheric blocking serves as a catalyst for increasing the frequency of atmospheric flow regime stagnation and forecast unpredictability. This study built upon the results of previous work by expanding upon the findings of various climatologies and case studies. This work analyzes specific trends observed in association with atmospheric blocking predominantly across the central and eastern Pacific Ocean. Such trends include the relationship between the size, duration, and onset position of atmospheric blocking events and the frequency, duration, and intensity of heavy rainfall events across the central United States. A strong focus is placed on examining the duration and spatial extent of atmospheric blocking which has been found to influence the intensity of heavy rainfall events. The goal is to further bridge the gap between the location and duration of blocking highs and the intensity, duration, and frequency of heavy rainfall events which occur downstream of such blocking events.

Evaluating Linkages between Atmospheric Blocking Patterns and Heavy Rainfall Events across the North-Central Mississippi River Valley for Different ENSO Phases

Over the last six to seven decades, there has been a substantial increase in atmospheric research to better understand the dynamics and evolution of atmospheric blocking events. It is well known that atmospheric blocking serves as a catalyst for increasing the frequency of atmospheric flow regime stagnation and forecast unpredictability. This study built upon the results of previous work by expanding upon the findings of various climatologies and case studies. This work analyzes specific trends observed in association with atmospheric blocking predominantly across the central and eastern Pacific Ocean. Such trends include the relationship between the size, duration, and onset position of atmospheric blocking events and the frequency, duration, and intensity of heavy rainfall events across the central United States. A strong focus is placed on examining the duration and spatial extent of atmospheric blocking which has been found to influence the intensity of heavy rainfall events. The goal is to further bridge the gap between the location and duration of blocking highs and the intensity, duration, and frequency of heavy rainfall events which occur downstream of such blocking events.

A Comprehensive Review of Blue-Green Infrastructure Concepts

Climate change is expected to result in more intense and longer-lasting droughts and increase in the frequency and intensity of heavy rainfall events. The combination of drought followed by intense rainfall increases the risk of severe flooding, with impacts on a range of natural and anthropogenic systems, including infrastructure (road washouts, damage to houses), and impacts on agriculture (soil erosion, and loss of crops and livestock). Blue-Green Infrastructure (BGI) is an interconnected network of natural and designed landscape components, including water bodies and green and open spaces, which provide multiple functions such as: (i) water storage for irrigation and industry use, (ii) flood control, (iii) wetland areas for wildlife habitat or water purification, among many others. This paper provides a review of information on the impact of BGI on environments, particularly on water resources, and vegetation. Efforts have been made to review the BGI development situation in different countries. Based on the research reviewed, the authors suggest the following as priorities for future research into the environmental impacts of BGI: determining the feasibility of BGI, and determining the impacts of applying the BGI for major regions where there have been considerable nature-based recreation and tourism resources.

The long-term variation of extreme heavy precipitation and its link to urbanization effects in Shanghai during 1916–2014

Using the hourly precipitation records of meteorological stations in Shanghai, covering a period of almost a century (1916–2014), the long-term variation of extreme heavy precipitation in Shanghai on multiple spatial and temporal scales is analyzed, and the effects of urbanization on hourly rainstorms studied. Results show that: (1) Over the last century, extreme hourly precipitation events enhanced significantly. During the recent urbanization period from 1981 to 2014, the frequency of heavy precipitation increased significantly, with a distinct localized and abrupt characteristic. (2) The spatial distribution of long-term trends for the occurrence frequency and total precipitation intensity of hourly heavy precipitation in Shanghai shows a distinct urban rain-island feature; namely, heavy precipitation was increasingly focused in urban and suburban areas. Attribution analysis shows that urbanization in Shanghai contributed greatly to the increase in both frequency and intensity of heavy rainfall events in the city, thus leading to an increasing total precipitation amount of heavy rainfall events. In addition, the diurnal variation of rainfall intensity also shows distinctive urban-rural differences, especially during late afternoon and early nighttime in the city area. (3) Regional warming, with subsequent enhancement of water vapor content, convergence of moisture flux and atmospheric instability, provided favorable physical backgrounds for the formation of extreme precipitation. This accounts for the consistent increase in hourly heavy precipitation over the whole Shanghai area during recent times.

Using dynamical downscaling to examine mechanisms contributing to the intensification of Central U.S. heavy rainfall events

AbstractThe frequency and intensity of heavy rainfall events have increased in the Central U.S. over the last several decades, and model projections from dynamical downscaling suggest a continuation with climate change. In this study, we examine how climate change might affect mechanisms related to the development of heavy rainfall events that occur on the scale of mesoscale convective systems over the Central U.S. To accomplish these goals, we incorporate dynamical downscaled simulations of two Coupled Model Intercomparison Project phase 5 models in the Weather Research and Forecasting model that accurately simulate heavy rainfall events. For each model, a set of heavy rainfall events that match the frequency, timing, and intensity of observed events are objectively identified in historical and future simulations. We then examine multimodel composites of select atmospheric fields during these events in simulations of historical and future scenarios, enabling an identification of possible physical mechanisms that could contribute to the intensification of heavy rainfall events with climate change. Simulations show that additional moisture is transported into convective updrafts during heavy rain events in future simulations, driving stronger evaporative cooling from the entrainment of drier midtropospheric air. This results in the formation of a stronger low‐level cold pool, which enhances moisture convergence above the cold pool and increases rainfall rates during future heavy precipitation events. In addition, a warmer profile in future simulations might allow for heavier rainfall rates as a deeper atmospheric column can support additional collision‐coalescence of liquid hydrometeors.

Effects of rainfall patterns on water quality in a stratified reservoir subject to eutrophication: Implications for management

The seasonal variation of hydrological conditions caused by shifting rainfall patterns observed in recent years has significant effects on water quality. High-volume inflows following heavy rainfall events that significantly disturb stratification lead to increased dissolved oxygen (DO) at the bottom of the reservoir, inhibiting the release of nutrients from sediments and causing a rapid reduction of algal biomass in the reservoir. However, the duration and extent of these effects depend not only on the frequency and intensity of heavy rainfall events but also on the period of thermal stratification in the reservoir. The effects of heavy rainfall events on water quality during three typical stratification periods of the reservoir were systematically investigated using extensive field data. The continuous heavy rainfall that occurred in September 2011 (stratification began to diminish) completely mixed the reservoir and produced a high concentration of DO along with a low phytoplankton concentration throughout the reservoir until stratification occurred the following year. Conversely, several days were required for anoxic conditions (in the hypolimnion) and cyanobacterial blooms to reappear after the storm runoff that occurred during the stable period of stratification (August 2012). In addition, the heavy rainfall that occurred in May 2013 accelerated the formation of an anoxic zone at the bottom of the reservoir and promoted cyanobacterial blooms due to the high nutrient input and the increased water temperature after the storm runoff ended. Water-lifting aerators (WLAs) were employed in the Shibianyu Reservoir to inhibit algal growth and to control the release of nutrients. Based on our field observations and theoretical analyses, optimized management strategies are recommended to improve water quality in the reservoir under different rainfall patterns at a reduced cost.

Application of the Pseudo Global Warming Dynamic Downscaling Method to the Tokai Heavy Rain in 2000

The Tokai region in central Japan often receives heavy rainfall because of typhoons. Furthermore, because of global warming, the intensity of heavy rainfall events is expected to increase in the future. Therefore, assessment of possible differences in such events between the present and future is important. In this study, a record heavy rainfall event in the Tokai region on 11 September 2000, the so-called Tokai Heavy Rain (THR), was numerically simulated by weather research and forecasting model with triple nesting grid system of 50-, 10-, and 2-km horizontal resolution. Simulated results present characteristics of rainfall and atmospheric conditions similar to the actual event. Thus, the simulation is considered valid for reproducing rainfall processes of the THR. In addition, variations of heavy rainfall events in future climate scenarios are investigated using numerical simulations based on pseudo global warming (PGW) conditions, constructed using third-phase results of Coupled Model Intercomparison Project multi-model global warming experiments. Under certain future climate scenarios, the Tokai region may experience heavy rainfall events in which maximum hourly rainfall and extent of heavy rainfall areas increases. Such variations are mainly attributed to increased specific humidity in the lower troposphere. In some PGW runs, there was no significant rainfall around the Tokai region. There was increased specific humidity in these runs, and the horizontal distribution of lower atmospheric air temperature was favorable for the formation of a mesoscale convergence zone, as seen in PGW runs with heavy rainfall. However, vertical profiles of equivalent potential temperature and saturated equivalent potential temperature showed unsaturated and stable atmospheric stratifications that are unfavorable for convective activity. Even in cases with increased atmospheric temperature and specific humidity caused by global warming, differences in their spatial distributions and vertical profiles could lead to contrasting effects of global warming on a specific extreme weather event.

Linear trend of occurrence and intensity of heavy rainfall events on Northeast Brazil

AbstractA comprehensive dataset of daily rainfall covering the period from 1972 to 2002 was used to compute the climatology and trend of occurrence and intensity of heavy, weak and normal events of rainfall on Northeast Brazil (NEB). For selection of rainfall events, we used the technique of quantiles and the trend was identified using the nonparametric Mann–Kendall test. The heavy events are modulated by La Niña and El Niño occurrences and in general they presented negative trend concerning to number of episodes and positive trend to daily rainfall.