Daily Rainfall Research Articles

Machine Learning Techniques to Predict Rainfall of Vidarbh Region

Accurate daily rainfall forecasting is vital for proper agricultural planning and managing resources efficiently. This study evaluates the performance of various methods, with an emphasis on temperature and precipitation, such as linear regression, random forest, SVM regression, and XGBoost. Linear regression displays minimal RMSE and a flawless R-squared, whereas random forest performs effectively with low RMSE and encouraging R2 results. However, the presence of a negative R-squared indicates potential overfitting. The MAE, MSE, and RMSE statistics for SVM are competitive. The study draws attention to the unexplored Vidarbha region, which includes 11 districts, using Nagpur district as a representative instance. Additionally, future plans involve the utilization of deep learning models like ARIMA and LSTM to enhance rainfall prediction accuracy across Vidarbha. This investigation yields valuable insights into climate prediction, offering support for well-informed decision-making.

Publisher Correction: The Effectiveness of a Probabilistic Principal Component Analysis Model and Expectation Maximisation Algorithm in Treating Missing Daily Rainfall Data

Publisher Correction: The Effectiveness of a Probabilistic Principal Component Analysis Model and Expectation Maximisation Algorithm in Treating Missing Daily Rainfall Data

Markov Chain analysis of rainfall of Coimbatore

Rainfall is considered one of the most important weather parameters which helps in deciding the time of sowing, pest and disease management and harvesting. Markov chain analysis deals with predicting future values based on past values. In the present study, Markov Chain analysis was used to predict the future probability of monthly rainfall and examine the pattern and distribution of rainfall using daily rainfall data from the year 1982 to 2016 (34 years) in the Coimbatore district. This study mainly analysed the probability of rainfall in the Coimbatore district of Tamil Nadu based on Markov chain process. Based on the National Center for Hydrology and Meteorology, the intensity of rainfall per day was categorized and a day is considered as no rain if rainfall was less than 0.1 mm, low rain if rainfall was between 0.1 mm to 10 mm, moderate rain if rainfall was between 10 mm to 20 mm and heavy rain if rainfall was above 20 mm. By calculating the transition probability matrices and steady-state probability matrices for each month based on the conditional probability of rain on a particular day given that rain on the previous day which is to predict the state of rainfall on the next day. This study reported that the availability of water for crop production is higher during the winter, pre-monsoon, the onset of the southwest monsoon, and at the end of the northeast monsoon. There may be a scarcity of water from August to November for agricultural activities. Based on this study, farmers can plan for a better cropping system in advance to get a better yield.

Assessment of rainfall erosivity for Bundelkhand region of central India using long-term rainfall data

Prevention of soil erosion requires long term assessment of rainfall erosivity and other related variables of the region. In the present study, four variables related to rainfall erosivity i.e. modified Fournier index (MFI), rainfall erosivity (R) factor, erosivity density (ED) and precipitation concentration index (PCI) were calculated. Long-term (1901-2021) daily rainfall data of Bundelkhand region (Central India) were used in the analysis. The above variables were assessed for spatial and temporal variability on annual and seasonal scale. The R-factor values range from 3010.61 MJ.mm ha-1 h-1 to 5346.53 MJ.mm ha-1 h-1, showing the region belongs to moderate to severe erosivity class. The mean annual R-factor, MFI, ED and PCI values for the Bundelkhand region were calculated as 4072.86 MJ.mm ha-1 h-1, 270.55 mm, 19.13 MJ ha-1 h-1 and 28.88, respectively. This study provides the insights of soil erosion problems of Bundelkhand region and would help in adopting the preventive measures and watershed development activities.

Analysis of long-term trends of rainfall and extreme rainfall events over Andaman & Nicobar and Lakshadweep Islands of India

The two major archipelagos of India, the Andaman & Nicobar Islands and the Lakshadweep situated in the climate-hazardous areas of the Bay of Bengal and Arabian Sea respectively are largely affected by weather systems developing over the sea and heavy rainfall activities. The recent two daily gridded rainfall data sets published by IMD; Rajeevan et al. (2010) at 1° × 1° spatial resolution and Pai et al. (2014) at 0.25° × 0.25° spatial resolution extending for a period of more than 100 years have been extensively used by researchers to study the rainfall characteristics at various spatiotemporal scales over the Indian mainland. However, these data sets do not include the grids over these two island meteorological subdivisions of India mainly because of the absence of daily rainfall observation for this long period. In this study, an attempt has been made to develop daily gridded rainfall data over these island subdivisions for the recent 70 years (1951 to 2020) in two spatial resolutions, viz., 1° × 1° and 0.25° × 0.25° using all the available islands station data during the period and carry out statistical analyses of various rainfall characteristics over these islands. The 0.25° × 0.25° data set was observed to be more comparable with the official rainfall time series of IMD for both these two Island subdivisions, and hence this data set has been used to carry out the trend analysis of Daily events of rainfall DER (> = 5 mm) for these two island subdivisions for the whole data period of 1951-2020 and the climate regime shift period of 1971-2020. DER was classified into two categories DMR (5-100 mm), daily moderate rainfall events and DHR (100 mm and above) daily heavy rainfall events. Signs and magnitude of the long-term trends in the frequency of DER (with DMR & DHR) showed significant changes during the recent period 1971-2020.

Exploring the Spatio-Temporal Trends of Geomorphological Incidents Induced by Precipitation on Chinese Highways

The spatiotemporal distribution of geomorphological incidents was examined in the present study, including the characteristics of obstruction distances and durations, by utilizing nationwide incident mitigation data and precipitation observation records from the period spanning 2019 to 2022. By comparing rainfall features over different temporal scales across various regions, the aim of the present study was to enhance the current comprehension of the patterns through which regional precipitation initiates incidents on highways by comparing rainfall characteristics over distinct temporal scales across diverse geographical areas. The findings indicate that: (1) The spatial distribution of highway incidents in China is significantly correlated with regional natural environments, predominantly concentrated in the southern parts of the country’s second and third topographical terraces. The temporal distribution closely aligns with annual and monthly precipitation patterns, with the majority of occurrences taking place from June to September. Further, notable disparities in the distribution of highway-related incidents were observed among counties across most provinces; (2) National highways experience a notably higher frequency of incidents than expressways and provincial roads, with most obstruction lengths concentrated within 1 km and durations predominantly under 3 days; (3) The probability of daily rainfall inducing highway incidents is distinctly higher than that of short-duration rainfall, with eastern and southern China experiencing significantly greater inducing precipitation volumes than other regions. The majority of areas are susceptible to incidents within a 3-day window following heavy rainfall or within 24 h after intense short-duration rainfall. Moreover, it is observed that incidents are more closely associated with extreme precipitation occurring within a single day; (4) There is a certain lag between the timing of incidents and the occurrence of extreme short-duration heavy rainfall, with the highest frequency of incidents coinciding with continuous rainfall periods of 3 to 6 days.

Evaluating Rainwater Harvesting Systems for Water Scarcity Mitigation in Small Greek Islands under Climate Change

Rainfall variability, exacerbated by climate change, poses significant challenges to water resource management, particularly in regions prone to intense droughts and floods. The Greek islands, characterized by poor water potential, face interannual water supply issues dating back to their earliest habitation. Rainwater harvesting (RWH) systems emerge as a promising solution to address water scarcity in these regions. This study simulates RWH systems for two small Greek islands, Fourni and Nisyros, representing similar rainfall regimes. Multiple scenarios are explored, and system reliability is assessed in light of simulated daily rainfall time series incorporating climate change projections. Utilizing eight low/medium (RCP 4.5) and eight high (RCP 8.5) emission scenarios over a future 35-year period, the study evaluates system reliability based on model parameters (collection area: 40 to 140 m2, rainwater tank volume: 5 to 30 m3, number of household members: 2, 3), with 30% coverage of total daily water demand (180 L/d). Negligible evapotranspiration effects are assumed due to closed-type tanks. Results indicate that the RWH system demonstrates high efficiency in general. The investigation for the future period revealed that the system’s performance varies, with instances where daily demand targets are not met, even with a 30 m3 tank. This research underscores the potential of RWH systems as a cost-effective “green” solution, particularly in regions with deficient rainfall regimes. It highlights the importance of localized water management strategies, reducing reliance on mainland water transportation, and assisting desalination unit operations. In conclusion, this study contributes to the assessment of RWH systems, demonstrating their viability as a sustainable water management solution in regions facing water scarcity, contingent on local rainfall conditions and system design parameters.

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.

Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts

Study regionThis study focuses on the Chao Phraya (CPY) Basin, Thailand. Study focusThis study aims to improve the skill of the CPY flood forecasting system, developed by Hydro-Informatics Institute (HII) and DHI A/S since 2012. It introduces two pre-processing workflows that are applied to the raw numerical weather prediction (NWP) provided by Weather Research and Forecasting model (WRF): quantile mapping bias correction (QM) and random forest regression (RF). Rainfall forecasts, updated with two pre-processing methods, WRF-QM and WRF-RF, were evaluated against daily rainfall measurements from HII’s stations in each subcatchment. Six hydrological re-forecasting experiments were conducted using a hydrological model to compare runoff forecasts with and without preprocessing method as well as with in-situ rainfall, climatology and persistence benchmark. We assessed rainfall and runoff predictions during training (2016–2019) and testing periods (2020–2021). New Hydrological Insights for the RegionUtilizing pre-processing methods in rainfall prediction enhances the accuracy for raw rainfall and runoff predictions. The WRF-QM and WRF-RF methods improved rainfall prediction by 12% and 18% in RMSE’s terms during testing period, respectively. Overall performance results indicate runoff forecasting with WRF-QM and WRF-RF pre-processing reduces RMSE by 34% and 40%, respectively, compared to Raw WRF. Wilcoxon signed-test confirmed significant improvement with pre-processing methods. Our study demonstrates the potential of pre-processed NWP to enhance the skill of hydrologic forecasting systems. Pre-processing methods boost flood forecasting reliability, addressing challenges caused by more frequent and severe hydrologic extremes.

Performance assessment of rainfall forecasting models for urban Guwahati City using machine learning techniques and singular spectrum analysis

Abstract Rainfall forecasting is pivotal for issuing flood warnings and flood management. Machine learning (ML) models are popular as they can effectively manage extensive data and non-stationarity of the data series with improved performance and cost-effective solutions. However, more studies are required to understand the dynamic characteristics of rainfall. This study proposes a hybrid model and demonstrates its efficiency in improving the daily rainfall forecast. Singular spectrum analysis (SSA) was used as a data pre-processing technique (successfully removing and identifying the nature of noise) and coupled with ML models (artificial neural network (ANN) and support vector machine (SVM)) improving daily scale forecast. Since the current response of the hydrological system depends on previous responses, rainfall at the next time step was derived with the previous 2-, 3-, 5- and 7-day rainfall. Study shows that the first eigen vector derived through SSA is the trend component which has a maximum contribution of 18.75%, suggesting it can explain 18.75% of the given rainfall series. The 16.42% (eigen vector 2-9) contributes to periodicity, with period of 1 year, 6 months, and 4 months within the data. Conclusively, the hybrid SSA-ML model outperformed the single model for daily rainfall forecasts.

Association of exposure to extreme rainfall events with cause-specific mortality in North Carolina, US

Extreme rainfall events could influence human health. However, the associations between extreme rainfall events and mortality remain rarely explored. Here, we conducted a time-series study using county-level mortality data in North Carolina during 2015–2018 to estimate the associations between extreme rainfall events and cause-specific mortality. We defined an extreme rainfall event as a day when a county’s daily total precipitation exceeded the 95th percentile of daily rainfall measurements from all of North Carolina’s counties during the study period. We employed a two-stage analysis where we first estimated the associations for each county and then used the estimates to obtain the state-wide associations by meta-analysis. Exposure to an extreme rainfall event was significantly associated with an increase in total, non-accidental, cardiovascular disease, respiratory disease, and external mortality by 2.24% (95% CI: 0.67%, 3.83%), 2.38% (95% CI: 0.76%, 4.03%), 3.60% (95% CI: 0.69%, 6.60%), 6.58% (95% CI: 1.59%, 11.82%), and 6.92% (95% CI: 1.28%, 12.86%), respectively. We did not find significant differences in the mortality risks within age, sex, or race groups or by seasonality. Our findings suggest that extreme rainfall events may trigger the risk of mortality, especially from non-accidental diseases such as respiratory mortality.

Less concentrated precipitation and more extreme events over the Three River Headwaters region of the Tibetan Plateau in a warming climate

Widely known as the “China Water Tower”, changes in precipitation concentration and insufficient rainfall days over the Three River Headwaters region (TRH) may threaten food and water security in TRH and downstream areas. However, current research on rainfall occurrences in TRH lacks a comprehensive understanding of the spatiotemporal variations in precipitation concentration, the seasonal distribution of rainy days across different rainfall categories, and their correlations with global warming. In this study, we utilize the CN05.1 gridded daily rainfall dataset to address these gaps. Our analysis reveals a decreasing trend in both the precipitation concentration index (PCI) and precipitation concentration degree (PCD), accompanied by an earlier onset of the precipitation concentration period (PCP) in TRH over the past six decades. The distribution of monthly rainfall becomes more uniform throughout the year, with precipitation spreading across a greater number of days annually. Notably, the reduction in both the number of rainy days and the proportion of rainfall during the summer, along with the decrease in the frequency of light and moderate rainfall days and the increase in the frequency of moderately heavy and heavier rainfall days, will lead to a more extreme distribution of summer precipitation in TRH. The increase in the number of rainy days across all intensity categories and the proportion of precipitation during winter, has favorable implications for the sustainable development and water security in TRH. Additionally, the relative increase in the occurrence of heavy and extremely heavy rainfall days in spring demonstrates the most pronounced change, with a notable rise of 12.74% to 28.24% per decade. This provides a certain degree of alleviation for spring drought in TRH. Furthermore, we find that for every 1-degree Celsius increase in global temperature, both PCI and PCD decrease by 9.82% and 8.5%, and PCP advances by 6.41 days. With the 50th percentile of precipitation serving as a dividing line, stronger precipitation events beyond this threshold exhibit a higher positive response to global warming. Conversely, weaker precipitation events falling below the 50th percentile show a higher negative response to global warming. Light rainy days decrease by 16.2% per degree Celsius of warming, while very heavy and extremely heavy rainy days increase by 26.4% and 27.5%, respectively. These findings have important implications for mitigating droughts and floods, as well as for developing a comprehensive framework for the management of water resources and agricultural irrigation in TRH.

Enhancing adoption studies of LID-BMP’S for Storm Water Management Drainage (SWMD) national stadium in Jayapura, Indonesia

The essential of this paper is implementing Low Impact Development (LID) type stormwater Best Management Practices (BMP’s) to reduce stormwater run-off in urban areas. The case study taken is the development area of Jayapura National Stadium. The drainage system at the main stadium is divided into subsurface and surface drainage. Meanwhile, the development area will enhance the study of adopting several LID-BMP’s practices to manage and optimize the design to control stormwater run-off in the stadium before it is discharged into the urban drainage system. The methodology in this study is divided into data collection and planning. The data collected include daily maximum rainfall data, stadium master plan, soil investigation, macro drainage system, drainage master plan, and topography around the stadium. Meanwhile, planning includes calculating rainfall design, soil structure analysis, hydraulic planning, pipe dimensions, and planning for water storage. The rainfall design used is a 5-year return period with Log Person III, the best design from the suitability test analysis, which is 134.39 mm/day. The study examined drainage design effectiveness to control stormwater run-off on the drainage design at the main stadium and surrounding stadiums using the LID-BMP’s framework and compared them. The LID-BMP’s adoption study was to optimize the drainage design to obtain maximum run-off control that can reduce up to 7-10% of stormwater run-off due to design and soil structure. The results provided recommendations to the authorities to modify the current design to achieve the benefits.

Impacts of improved horizontal resolutions in the simulations of mean and extreme precipitation using CMIP6 HighResMIP models over West Africa.

We conducted an analysis of 16 historical simulations from the High-Resolution Model Intercomparison Project (HighResMIP) as part of the Coupled Model Intercomparison Project (CMIP) phase 6 (CMIP6). These simulations encompass both high- and low-resolution models and aim to investigate the impact of improved horizontal resolution on mean and extreme precipitation in West Africa between 1985 and 2014. Six Expert Team on Climate Change Detection and Indices (ETCCDI) were used to charactererize extreme indices. Bias adjustment was used to detect and adjust the biases in the models. Our observations indicate that the southeastern and southwestern regions of West Africa experience the most significant precipitation, which aligns with the simulations from HighResMIP. The enhanced horizontal resolution notably influences the simulation of orographically induced rainfall in elevated areas and intensifies precipitation in various aspects. When examining the highest 1-day precipitation, our observations reveal that most of the Guinea Coast region had 1-day rainfall exceeding 100mm. However, this was overestimated and in some simulations underestimated by HighResMIP simulations. Furthermore, an increase in horizontal resolution appears to enhance the ability of high-resolution models to replicate the observed patterns of heavy precipitation (R10mm) and very heavy rainfall (R20mm) days. Spatial and temporal analysis suggests that uncertainty exists in the simulation of extreme precipitation in both high- and low-resolution simulations over West Africa. Also, bias adjustment shows a significant bias in the simulations. To address this issue, we employed a bias adjustment approach.

Water use optimization of Benel Reservoir in Jembrana Regency, Bali Province

Benel Reservoir has a storage capacity of 1.71 million cubic meters (MCM) and has been supplying water for irrigation purposes. In line with population growth, the Benel Reservoir is projected to be capable of meeting the domestic water demand in the Jembrana Regency. As a result, this research aimed to determine the optimal water use in the Benel Reservoir for irrigation and domestic uses. The irrigation water requirements are necessary for an area of 1,047.3 hectares, while the domestic water demands are 64 l/s. F.J Mock Method of rainfall-runoff model is used to calculate water availability using 10 years of daily rainfall data and has been calibrated using observed discharged data from the Benel water level gauge station. The optimization model is formulated using linear programming. During wet years, the optimization result for water utilization with inflow discharge yields a cropping intensity value of 300%. The cropping intensity value of 287.50% in the scenario of normal inflow discharge. Furthermore, the cropping intensity value is 275% in the case of dry-year inflow discharge. Irrigation and domestic water demand met the minimum value limit of 0.70 and 0.85 for the k-factor. The reliability in supplying irrigation and domestic water reached 100%.

Ten daily rainfall forecasting using SSA algorithms and Seasonal ARIMA model to determine the beginning of the rainy season

Rainfall is one of the most important hydrological parameters. Based on the prediction of rainfall, several plans related to rainfall can be carried out, including predicting the start of the planting season based on information on the beginning of the rainy season and preparing cropping patterns. The purpose of this study is to predict the ten daily rainfall for 12 months from January to December of the following year and predict the start of the rainy season based on BMKG criteria. Modeling and forecasting is carried out on the ten daily regional rainfall in the catchment of Citarum-Majalaya water gauge using the Singular Spectrum Analysis (SSA) algorithm and the Seasonal ARIMA model. Forecasting model performance is measured using error values, namely mean absolute percentage error (MAPE). The results of these performance tests illustrate that the SSA algorithm and the Seasonal ARIMA model have fairly good accuracy for predicting rainfall for the next 6 months, where each has a MAPE value of 36.8 percent and 40.0 percent. Prediction of the beginning of the rainy season at the study location based on SSA forecasting results is most likely to occur on the 3rd of October, in accordance with BMKG predictions. whereas based on the results of the SARIMA forecasting model, the beginning of the rainy season is likely to occur on the 1st of November, and another possibility that the beginning of the rainy season will occur on the 2nd of October.

Attributing Extreme Precipitation Characteristics in South China Pearl River Delta Region to Anthropogenic Influences Based on Pseudo Global Warming

AbstractIn the context of human‐induced warming climate, the atmosphere is expected to hold a greater amount of water vapor, leading to heavier precipitation on a global scale. However, the extent to which changes in extreme rainfall can be attributed to human influences varies at regional scales. Here we conduct attribution analyses on 40 extreme precipitation events in different seasons during 1998–2018 over the Pearl River Delta (PRD), by using the Weather Research and Forecasting (WRF) model and applying the pseudo global warming (PGW) method. The model was integrated with the factual and counterfactual conditions separately, with the latter derived from differences between the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and historical‐natural runs. By comparing parallel experiments, extreme daily rainfall (>95th percentile) in PRD enhanced by 8%–9.5% for 0.9–1.1 K near‐surface warming (nearly Clausius‐Clapeyron, or CC scaling) in the May‐to‐September (MJJAS) and 12.4% at most for 0.6–0.8 K warming (∼2 × CC rate) in non‐MJJAS seasons; the probability of those extremes increased by 10%–30% during MJJAS (20%–40% in other seasons). While moisture‐related thermodynamic effects play a similar role in modulating rainfall, the wind circulation‐related dynamic effects act differently in different seasons. Changes in MJJAS extremes are related to stronger low‐level southerly winds, while non‐MJJAS rainfall is exacerbated by strengthened low‐level wind convergence and updrafts. Moisture budget analysis suggests that thermodynamic effects associated with the increased moisture amount account for the mean rainfall increase, whereas dynamic effects related to wind circulation changes are responsible for extreme precipitation, regardless of seasons.

Predicting Damage Characteristics of Heavy Rain Disasters Using Artificial Neural Network

Heavy rain disaster is a representative natural disaster in Korea, and causes considerable damage every year as a result of typhoons and heavy rain. Various preventive measures and reduction facilities have been introduced to reduce heavy rain damage, but damage continues to occur. Accurate damage prediction is necessary for preemptive response to heavy rain disasters. A prediction study of the damage characteristics of such disasters using machine learning was conducted. Artificial neural network (ANN) was applied as a machine learning technique, and damage data of the aforementioned disasters from 1999 to 2019 were used. Damage prediction using an ANN was analyzed in terms of total rainfall, maximum daily rainfall, and total damage amount. The prediction of the damage characteristics was most accurate for the total damage amount. Total and daily maximum rainfall are less correlated with the characteristics of various damage amounts, which may explain the low accuracy of the analysis.

Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

Precipitable water vapor (PWV) is a crucial factor in regulating the Earth’s climate. Moreover, it demonstrates a robust correlation with precipitation. Situated in a region known for the generation and development of tropical cyclones, Guangxi in China is highly susceptible to floods triggered via intense rainfall. The atmospheric water vapor in this area displays prominent spatiotemporal features, thus posing challenges for precipitation forecasting. The water vapor products within the MERRA-2 and ERA5 reanalysis datasets present an opportunity to overcome constraints associated with low spatiotemporal resolution. In this study, the PWV data derived from GNSS and meteorological measurements in Guangxi from 2016 to 2018 were used to evaluate the accuracy of MERRA-2 and ERA5 water vapor products and their relationship with water vapor variations during extreme rainfall. Using GNSS PWV as a reference, the average bias of MERRA-2 PWV and ERA5 PWV for heavy rainfall was −0.22 mm and 1.84 mm, respectively, with average RMSE values of 3.72 mm and 3.31 mm. For severe rainfall, the average bias of MERRA-2 PWV and ERA5 PWV was −0.14 mm and 2.92 mm, respectively, with average RMSE values of 4.28 mm and 4.01 mm. During heavy rainfall days from Days 178 to 184 in 2017, the average bias of MERRA-2 PWV and ERA5 PWV was 0.92 mm and 2.42 mm, respectively, with average RMSE values of 4.04 mm and 3.40 mm. The accuracy was highest at the Guiping and Hechi stations and lowest at the Hezhou and Rongshui stations. Furthermore, when comparing MERRA-2/ERA5 PWV with GNSS PWV and actual precipitation, the trends in the variations of MERRA-2/ERA5 PWV were generally consistent with GNSS PWV and aligned with the increasing or decreasing trends of actual precipitation. In addition, ERA5 PWV exhibited high accuracy. Before the onset of heavy rainfall, PWV has a sharp surge. During heavy rainfall, PWV reaches its peak value. Subsequently, after the cessation of heavy rainfall, PWV tends to stabilize. Therefore, the reanalysis data of PWV can effectively reveal significant changes in water vapor and actual precipitation during periods of heavy rainfall in the Guangxi region.

Profile of Rainfall Patterns and Extreme Air Temperatures 1981-2020 in the Mosunal Region of Indonesia (Case Study: South Sumatra)

Increasing concentrations of greenhouse gases have led to changes in extreme climate events. This study aims to describe the picture of climate change in the South Sumatra Region during the last 40 years, namely from 1981-2020. Rainfall and extreme temperature data were obtained from BMKG data for South Sumatra Province at the Palembang City Climatology Station (SK Palembang City) and the Sultan Mahmud Badaruddin II Meteorological Station (SM SMB II). Maximum temperature data, minimum temperature and rainfall were obtained based on parameter profiles obtained from BMKG data for South Sumatra Province based on the ETCCDMI provisions. Maximum temperature data, minimum temperature and rainfall are processed with RClimDex software so that temperature and extreme rain index data are obtained. These indices are extreme climate indices consisting of TN90p, TX90p, TNn, TNx,TXx, TXx, TMAXmean, TMINmean, RX1day, RX5day, SDII, CDD and CWD. The results of this study indicate that the trend of air temperature will increase significantly in South Sumatra Province, especially the minimum air temperature, then the precipitation pattern will also increase, especially the accumulation of rainfall for 5 consecutive days. There is an increase in the number of dry days and a decrease in the number of wet days, as well as the higher accumulation of daily rainfall but the wet days are decreasing day by day. This is also increasingly having an impact on climate change in South Sumatra Province which is getting hotter day by day and the smoke disaster due to land and peat fires in South Sumatra Province is increasingly worrying.