Rainfall Values Research Articles

Synoptic weather patterns conducive to compound extreme rainfall–wave events in the NW Mediterranean

Abstract. The NW Mediterranean coast is highly susceptible to the impacts of extreme rainstorms and coastal storms, which often lead to flash floods, coastal erosion, and flooding across a highly urbanised territory. Often, these storms occur simultaneously, resulting in compound events that intensify local impacts when they happen in the same location or spread impacts across the territory when they occur in different areas. These multivariate and spatially compound events present significant challenges for risk management, potentially overwhelming emergency services. In this study, we analysed the prevailing atmospheric conditions during various types of extreme episodes, aiming to create the first classification of synoptic weather patterns (SWPs) conducive to compound events involving heavy rainfall and storm waves in the Spanish NW Mediterranean. To achieve this, we developed a methodological framework that combines an objective synoptic classification method based on principal component analysis and k-means clustering with a Bayesian network. This methodology was applied to a dataset comprising 562 storm events recorded over 30 years, including 112 compound events. First, we used the framework to determine the optimal combination of domain size, classification variables, and number of clusters based on the synoptic skill to replicate local-scale values of daily rainfall and significant wave height. Subsequently, we identified SWPs associated with extreme compound events, which are often characterised by upper-level lows and trough structures in conjunction with Mediterranean cyclones, resulting in severe to extreme coastal storms combined with convective systems. The obtained classification demonstrated strong skill, with scores exceeding 0.4 when considering factors like seasonality or the North Atlantic Oscillation. These findings contribute to a broader understanding of compound terrestrial–maritime extreme events in the study area and have the potential to aid in the development of effective risk management strategies.

Unveiling the climate–conflict nexus: Examining the influence of weather extremes on conflict emergence in the Philippine Bangsamoro

Extreme weather events have been closely associated with the escalation of violent conflicts. This article introduces empirical evidence that underscores the vulnerability of conflict emergence to extended deficit rainfall values within Maguindanao, a province situated in the Bangsamoro region of the southern Philippines. Using a combination of satellite data, hydrological analysis and an examination of conflict databases, our study unveils the pivotal role played by protracted droughts and intensified precipitation in fostering human insecurity, consequently elevating the likelihood of violent conflicts. Our research spotlights the climate–conflict nexus within this relatively less-explored geographical area. Our findings underscore a robust association between instances of violent conflicts, protracted deficits or episodes of intense rainfall, and the subsequent surge in food prices. Notably, disruptions in food prices align temporally with heightened participation in violent conflicts by individuals. In essence, the emergence of violent conflicts exacerbates fragility, thereby engendering a vicious cycle that intertwines climate change, conflict dynamics and the pursuit of peace.

Possibility of using the STORAGE rainfall generator model in the flood analyses in urban areas

In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

Assessment of NASA POWER reanalysis products as data resources alternative for weather monitoring in West Sumbawa, Indonesia

The climatic reanalysis datasets represent a crucial form of data that can help to address the shortage of data observations. This study evaluates the accuracy of NASA POWER reanalysis data by comparing it with AMMAN Environmental Department in-situ observations from four weather stations in West Sumbawa, collected from 2013 to 2022. The reanalysis data includes surface daily average temperatures, average wind speed, wind direction, relative humidity, and rainfall. The statistical analysis used in this research includes Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to measure the differences between the observed and predicted values, both of which expressed in the same unit as the variable being measured. The comparison of 10 years of historical data revealed that the temperature parameter had the lowest RMSE and MAE values of 0.003 and 0.18, respectively, while the highest values were 0.46 and 5.11. For humidity, the lowest RMSE and MAE values were 0.001 and 3.6, respectively, and the highest values were 1.3 and 14.2. The RMSE and MAE values for rainfall were 0.02 and 0.9 (lowest) and 2.2 and 5.7 (highest). Regarding wind speed, the lowest RMSE and MAE values were 0.001 and 0.27, while the highest values were 0.07 and 1. Finally, the lowest RMSE and MAE values for wind direction were 0.1 and 6.6, and the highest values were 15.7 and 67.6. The comparison between the predicted and observed values showed a relatively high level of similarity for temperature, rainfall and wind speed parameters. However, parameters such as humidity and wind direction resulted varying degrees of deviation between the weather station data and the NASA POWER reanalysis data. These results highlight both the accuracy and discrepancy in the reanalysis data, emphasizing the importance of considering the limitations of such datasets when addressing the shortage of observational data.

Equações universais para as distribuições de chuva dos métodos SCS e Huff

ABSTRACT The temporal distribution of design rainfall hyetographs can be obtained through various procedures. One such method involves using temporal distribution curves to disaggregate a rainfall value of longer duration into chronological values within shorter time intervals. This is demonstrated by SCS distribution curves for durations of 6 and 24 hours and the well-known Huff curves. Another approach involves deriving temporal distribution using design hyetograph calculation methods, such as the Chicago and alternating block methods, based on Intensity-Duration-Frequency (IDF) equations. This paper aims to combine these approaches and develop universal equations capable of accurately fitting smooth rainfall time distribution curves. The advantage is the replacement of tables, like SCS and Huff, with analytical equations, enhancing usability in spreadsheets. Furthermore, these universal equations can determine the temporal rainfall distribution using an IDF equation or fitting a specified design hyetograph like Euler type II. Four numerical examples are provided.

Rainfall Intensity-Duration-Frequency Relationships: A Robust Foundation for Urban Decision-Making and Flood Management: A Case Study

Ahmedabad, a city in India, experiences varying climatic patterns and is prone to extreme rainfall and relies on established intensity-duration-frequency (IDF) relationships for critical infrastructure planning and flood management. While the city has existing IDF relationships, these curves were established some time ago and still lack well-established IDF Curves. To fill this research gap this study is dedicated to the development of IDF curves for Ahmedabad City, utilizing 45 years of rainfall data (1978 to 2022) sourced from the Indian Meteorological Department (IMD) making our study a pioneering effort in this direction. Employing frequency analysis methodologies, the relationships between rainfall intensity, storm duration, and return periods were established. IDF curves were generated for durations of 5, 10, 15, 30 minutes, and 1, 2, 6, 12 and 24 hours for Ahmedabad City. Short-term rainfall intensities were derived from daily rainfall values using the IMD formula. The empirical approach, Gumbel distribution, and Log Pearson Type-III distribution were applied. The Gumbel method tends to produce slightly higher rainfall intensities for shorter return periods, while the LPT III method exhibits the opposite pattern for longer return periods, such as 25, 50, and 100 years. In general, both approaches yield closely matching results across most return periods. The IDF curves produced in this study offer a versatile set of rainfall scenarios for diverse applications, including the design and modification of hydraulic structures, stormwater management infrastructure, and flood mitigation systems in Ahmedabad City. The outcomes of this research contribute not only to filling a crucial research gap but also empower decision-makers and urban planners with updated and reliable data crucial for enhancing the city’s resilience against extreme rainfall events.

Generating Spatial Distribution and Forecasting the Rainfall by Suitable ML Models-A Case Study of Aiyar River Basin, Tiruchirappalli District

Rainfall plays a prominent role in managing of water resources. The accurate prediction of rainfall is the greatest challenge in the field of hydrologic studies. The prediction of rainfall is necessary to overcome natural disasters like flood and drought. The inaccurate prediction of rainfall causes either dry or overflow in water storage structures. In this study different types of Machine Learning (ML) and deep learning techniques are adopted to predict rainfall pattern of Aiyar river basin, in Tiruchirappalli district. The comparative study of these ML models is done to identify the best ML model for the study area. The comparison was done for different scenarios and time intervals. The rainfall data from years 1987 to 2023 is used for predicting the daily rainfall in the basin. The rainfall data from years 1987 to 2007 is used for testing and the remaining years data is used for training the data set. The Theisen polygon method is used to average and weighted the rainfall data in the basin. The ML models and deep learning techniques used in this study are Linear model, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models. The rainfall was predicted for different time scenario by using different ML algorithms like Autocorrelation method. The accuracy of the predicted model results was tested with RMSE, MASE and R square values. The result shows coefficient between 0.5 to 0.9 within the limit from the daily rainfall values. From the overall model comparison, it is observed that the SVM model accuracy is high compared to the other models involved in this study. It is concluded that two different methods ML and deep learning methods have been applied with same data in which SVM ML techniques gives better results in this study area. In future the predicted rainfall data of this study can be used for accurate flood forecasting and modelling of Aiyar basin.

Dynamic probabilistic analytical modeling for estimating rainfall–runoff transformation rates in drylands

Abstract Precipitation-runoff changes used in the development of precipitation-runoff models depict general runoff mechanisms in physical precipitation-runoff processes. For this purpose, it must be able to adequately express the characteristics of the physical system. Similar to other water resources management models, analytical-probabilistic models may be developed with different levels of complexity according to different types of rainfall–runoff developments. In this research, a log-normal probabilistic model was used to estimate the return period of rainfall and Soil and Water Assessment Tool (SWAT) software was used to convert rainfall values into discharge and runoff height in agricultural lands. The slope and the level of plant shade cover were considered the two basic factors in the estimation of the subsurface flow used in agriculture. Slopes of less than 0.015 and land cover level of more than 70% of extreme points were obtained to ensure subsurface water sources and prevent erosion.

Analisis Pengaruh Ketersediaan Air Permukaan Terhadap Debit Mata Air

Springs are one of the water sources that people can use to meet their clean water needs. The Balinese people in particular, apart from using spring water to fulfill their daily needs, also use it as a means of religious ceremonies so that its sustainability is strictly maintained. One of the springs that can be found in Bali is the Tirta Salaka spring. Tirta Salaka spring is located in Sading Village, Mengwi District, Badung Regency, Bali Province. The purpose of this study was to analyze the effect of surface water availability on Tirta Salaka springs. The method used in the research is a combination of quantitative and analytical methods. Based on calculations, the discharge value of the Tirta Salaka spring is smaller than the general water balance value from November to March. The discharge value of the Tirta Salaka spring from November 2022 to March 2023 is included in class VI with values of 1.111.931 liters/month, 1.196.208 liters/month, 1.413.677 liters/month, 1.036.800 liters/month, and 914.717 liters/month. This shows that the amount of discharge from the Tirta Salaka spring is influenced by rainfall. The higher the rainfall value, the higher the discharge of the Tirta Salaka spring

Existence and influence of mixed states in a model of vegetation patterns

Abstract. The Rietkerk vegetation model is a system of partial differential equations, which has been used to understand the formation and dynamics of spatial patterns in vegetation ecosystems, including desertification and biodiversity loss. Here, we provide an in-depth bifurcation analysis of the vegetation patterns produced by Rietkerk's model, based on a linear stability analysis of the homogeneous equilibrium of the system. Specifically, using a continuation method based on the Newton–Raphson algorithm, we obtain all the main heterogeneous equilibria for a given size of the domain. We confirm that inhomogeneous vegetated states can exist and be stable, even for a value of rainfall for which no vegetation exists in the non-spatialized system. In addition, we evidence the existence of a new type of equilibrium, which we call “mixed state”, in which the equilibria are always unstable and take the form of a mix of two equilibria from the main branches. Although these equilibria are unstable, they influence the dynamics of the transitions between distinct stable states by slowing down the evolution of the system when it passes close to it. Our approach proves to be a helpful way to assess the existence of tipping points in spatially extended systems and disentangle the fate of the system in the Busse balloon. Overall, our findings represent a significant step forward in understanding the behaviour of the Rietkerk model and the broader dynamics of vegetation patterns.

ENSO sentinels in the Americas' humid tropics: We need combined hydrometric and isotopic monitoring for improved El Niño and La Niña impact prediction

AbstractThis Scientific Briefing presents results from a nearly 10‐year hydrometric and isotope monitoring network across north‐central Costa Rica, a region known as a headwater‐dependent system. This monitoring system has recorded different El Niño and La Niña events and the direct/indirect effects of several hurricane and tropical storm passages. Our results show that El Niño‐Southern Oscillation (ENSO) exerts a significant but predictable impact on rainfall amount anomalies, groundwater level and spring discharge, as evidenced by second‐order water isotope parameters (e.g., line conditioned‐excess or line‐conditioned (LC)‐excess). Sea surface temperature anomaly (El Niño Region 3) is correlated with a reduction in mean annual and cold front rainfall across the headwaters of north‐central Costa Rica. During El Niño conditions, rainfall is substantially reduced (up to 69.2%) during the critical cold fronts period, limiting groundwater recharge and promoting an early onset of minimum baseflow conditions (up to 5 months). In contrast, La Niña is associated with increased rainfall and groundwater recharge (up to 94.7% during active cold front periods). During La Niña, the long‐term mean spring discharge (39 Ls−1) is exceeded 63–80% of the time, whereas, during El Niño, the exceedance time ranges between 26% and 44%. The regional hydroclimatic variability is also imprinted on the hydrogen and oxygen isotopic compositions of meteoric waters. Drier conditions favoured lower LC‐excess in rainfall (−17.3‰) and spring water (−6.5‰), whereas wetter conditions resulted in greater values (rainfall = +17.5‰; spring water = +10.7‰). The lower and higher LC‐excess values in rainfall corresponded to the very strong 2014–2016 El Niño and 2018 La Niña, respectively. During the recent triple‐dip 2021–23 La Niña, LC‐excess exhibited a significant and consistently increasing trend. These findings highlight the importance of combining hydrometric, synoptic and isotopic monitoring as ENSO sentinels to advance our current understanding of ENSO impacts on hydrological systems across the humid Tropics. Such information is critical to constraining the 21st century projections of future water stress across this fragile region.

Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia

Missing values in rainfall records might result in erroneous predictions and inefficient management practices with significant economic, environmental, and social consequences. This is particularly important for rainfall datasets in Peninsular Malaysia (PM) due to the high level of missingness that can affect the inherent pattern in the highly variable time series. In this work, 21 target rainfall stations in the Johor River Basin (JRB) with daily data between 1970 and 2015 were used to examine 19 different multiple imputation methods that were carried out using the Multivariate Imputation by Chained Equations (MICE) package in R. For each station, artificial missing data were added at rates of up to 5%, 10%, 20%, and 30% for different types of missingness, namely, Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR), leaving the original missing data intact. The imputation quality was evaluated based on several statistical performance metrics, namely mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash-Sutcliffe efficiency (NSE), modified degree of agreement (MD), coefficient of determination (R2), Kling-Gupta efficiency (KGE), and volumetric efficiency (VE), which were later ranked and aggregated by using the compromise programming index (CPI) to select the best method. The results showed that linear regression predicted values (norm.predict) consistently ranked the highest under all types and levels of missingness. For example, under MAR, MNAR, and MCAR, this method showed the lowest MAE values, ranging between 0.78 and 2.25, 0.93–2.57, and 0.87–2.43, respectively. It also consistently shows higher NSE and R2 values of 0.71–0.92, 0.6–0.92, and 0.66–0.91, and 0.77–0.92, 0.71–0.93, and 0.75–0.92 under MAR, MCAR, and MNAR, respectively. The methods of mean, rf, and cart also appear to be efficient. The incorporation of the compromise programming index (CPI) as a decision-support tool has enabled an objective assessment of the output from the multiple performance metrics for the ranking and selection of the top-performing method. During validation, the Probability Density Function (PDF) demonstrated that even with up to 30% missingness, the shape of the distribution was retained after imputation compared to the actual data. The methodology proposed in this study can help in choosing suitable imputation methods for other tropical rainfall datasets, leading to improved accuracy in rainfall estimation and prediction.

Integration of data acquisition system for rainfall measurement on IoT-based tipping bucket rain gauge

Rainfall is an important parameter in determining weather conditions. Rainfall data can be used for various purposes, such as for consideration in planning a civil building. If rainfall cannot be measured properly, it will result in an inaccurate design. In actual conditions, rain gauges are usually placed in locations far from power sources. However, to obtain data in digital form, a power source is required. In obtaining digital data, electronic components and monitoring systems are needed which require a power source and an internet connection to send measurement data to the website. The rainfall gauge used in this study is of the tipping bucket type. In the rain gauge used, there is a magnetic switch that functions to calculate the tip that occurs when it rains so that the rainfall value is obtained. The rainfall gauge used in this study has an accuracy of 97.85%. This tool is equipped with an IoT-based monitoring system, namely using a website. Based on the results of the tests that have been carried out, and the range of rainfall per 5 minutes that can be measured is 0 mm-74,5 mm, the range of rainfall per 1 hour that can be measured is 0 mm-894 mm, and the range of rainfall per 1 day that can be measured is 0 mm-21456 mm. In receiving data from the rainfall gauge used in this study, the Arduino Mega microcontroller and the GSM 808 module were used to send data reading the rainfall gauge to the website. On the website display, you can see the rainfall that is happening, and you can see a graph of rainfall. There is a report menu that can be used to download rainfall data per 5 minutes, per 1 hour, and per 1 day with the date selection feature of the rainfall data you want to download. Downloaded rainfall data is in the form of an Excel file.

Deriving Gridded Hourly Rainfall on O‘ahu by Combining Gauge and Radar Rainfall

Abstract High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resource management and emergency responses, particularly for small watersheds, such as those in Hawai‘i in the United States. Unfortunately, fine temporal (subdaily) and spatial (<1 km) resolutions of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of the rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth.” There are potential advantages to combining the two, which have not been fully explored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m × 250 m gridded dataset for the tropical island of O‘ahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, kona low, and a mix of upper-level trough and kona low), and different rainfall structures (e.g., stratiform and convective). KED-merged rainfall estimates outperformed both the radar-only and gauge-only datasets by 1) reducing the error from radar rainfall and 2) improving the underestimation issues from gauge rainfall, especially during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures. Significance Statement The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O‘ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai’i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting.

Influence of Some Selected Climatic Variables on Yam Production in Bosso Local Government of Niger State

This study focused on the influence of Some Selected Climatic Variables on Yam Production in Bosso Local Government of Niger State. Secondary data was obtained from the Nigerian Meteorological Agency, Minna (NIMET). The data acquired include rainfall and humidity data. Also, data for crop was obtained from the Agricultural development program Minna. The result of the study therefore shows that positive relationship exists between yam production and rainfall/relative humidity in the area under study. As for the rainfall, it was observed that in 2020, where the rainfall value was maximum, the accompanying yam production was also very high, also in 2010 when the rainfall amount was recorded least, the yam production for that year was not much. Any variation in result could be associated with the source of data and the study areas. Summarily, it can be stated that there is an established positive relationship between climatic data (rainfall and relative humidity) and the production of yam for the period under study. The study recommended that there is a need to develop and promote yam varieties that can withstand drought conditions given the impact of rainfall and humidity on yam production. Farmers should also be encouraged to adopt improved irrigation systems to mitigate the impact of low rainfall on yam production. This could include the use of drip irrigation systems or rainwater harvesting. Finally, farmers should be advised to plant yam at optimal times, taking into consideration the climatic conditions of their region.

Integrated geospatial approach for adaptive rainwater harvesting site selection under the impact of climate change

The impact of global climate change on water resources is a pressing concern, particularly in arid and semi-arid regions, where water shortages are becoming increasingly severe. Rainwater harvesting (RWH) offers a promising solution to address these challenges. However, the process of selecting suitable RWH sites is complex. This paper introduces a comprehensive methodology that leverages various technologies and data sources to identify suitable RWH locations in the northern region of Iraq, considering both historical and future scenarios. The study employs remote sensing and geographic information systems to collect and process geospatial data, which are essential for the site selection process. AHP is utilized as a decision-making tool to assess and rank potential RWH locations based on multiple criteria, helping to prioritize the most suitable sites. The WLC approach is used to combine and weigh various factors, enabling a systematic evaluation of site suitability. To account for the uncertainty associated with future climate conditions, a stochastic weather generator is employed to simulate historical and future precipitation data for period (1980–2022) and (2031–2100). This ensures that the assessment considers changing climate patterns. Historical precipitation values ranged from 270 to 490 mm, while future projections indicate a decrease, with values varying from 255 to 390 mm. This suggests a potential reduction in available water resources due to climate change. The runoff for historical rainfall values ranged from 190 mm (poor) to 490 mm (very good). In the future projections, runoff values vary from 180 mm (very poor) to 390 mm (good). This analysis highlights the potential impact of reduced precipitation on water availability. There is a strong correlation between rainfall and runoff, with values of 95% for historical data and 98.83% for future projections. This indicates that changes in precipitation directly affect water runoff. The study incorporates several criteria in the model, including soil texture, historical and future rainfall data, land use/cover, slope, and drainage density. These criteria were selected based on the nature of the study region and dataset availability. The suitability zones are classified into four categories for both historical potential and future projections of RWH zones: very high suitability, covering approximately 8.2%. High suitability, encompassing around 22.6%. Moderate suitability, constituting about 37.4%. Low suitability, accounting for 31.8% of the study region. For the potential zones of RWH in the future projection, the distribution is as follows: very high suitability, approximately 6.1%. High suitability, around 18.3%. Moderate suitability, roughly 31.2%. Low suitability, making up about 44.4% of the study region. The research's findings have significant implications for sustainable water resource management in the northern region of Iraq. As climate change exacerbates water scarcity, identifying suitable RWH locations becomes crucial for ensuring water availability. This methodology, incorporating advanced technology and data sources, provides a valuable tool for addressing these challenges and enhancing the future of water management to face of climate change. However, more investigations and studies need to be conducted in near future in the study region.

ANALISIS BANJIR SUNGAI MENGGUNAKAN MODEL HEC-RAS DI WILAYAH DAS TABANIO KABUPATEN TANAH LAUT PROVINSI KALIMANTAN SELATAN

This study aims to determine the amount of water runoff and water depth in the Tabanio Watershed, Tanah Laut Regency. The study was conducted using the HEC-RAS software to generate a flood simulation model. Data analysis included Digital Elevation Model for National Scale (DEMNAS) data, flood discharge data, rainfall data, river bathymetry, and tidal data. DEMNAS data was obtained from the website of the National Geospatial Information Agency (BIG), rainfall data was obtained from the Center for Hydrometeorology and Remote Sensing (CHRS) website. Rainfall data analysis involves selecting the maximum daily rainfall value to determine the flood discharge. The hourly flood discharge is calculated using the Synthetic Unit Hydrograph method with the Nakayasu approach. River bathymetry data is processed as additional data for DEM, with interval recording done every 0.2 seconds. Tide data was obtained through prediction based on the location constant closest to the research area and analyzed using the Pasut.exe software for a period of 6 days. Based on the results of flood analysis research using the HEC-RAS model in the Tabanio Watershed, Tanah Laut Regency, South Kalimantan Province, it can be concluded that the model results show the water depth and area of water runoff at maximum discharge. as follows: less than 0.5 meters with a very low category covering an area of 4,168.86 hectares, 0.51 to 1.5 meters with a low category covering an area of 6,417.79 hectares, 1.51 to 2.5 meters with a medium category covering an area of 1,987, 25 hectares, 2.51 to 3.5 meters with a high category of 741.47 hectares, and above 3.51 meters with a very high category of 113.69 hectares. So that the total water runoff reaches 13,429.05 hectares.

A Structured Graph Neural Network for Improving the Numerical Weather Prediction of Rainfall

AbstractThe current challenges of numerical weather prediction (NWP) of rainfall mainly stem from the complex and multiscale nature of rainfall. In recent years, as observation capability improved worldwide, there has been an increased feasibility to use data‐driven models to enhance forecasting performance with rainfall observation. Compared to traditional statistical and machine learning models, deep‐learning models show considerable promise in capturing the spatial‐temporal features of weather processes from multiple predictors, but the convolution‐based feature extractor is suboptimal due to the linear nature of convolution kernels. In this study, a multilevel forecasting model is proposed to forecast each rainfall level, in which each submodel adopts a graph neural network for feature extraction. Spatial and temporal propagation functions based on grid structure are designed to explicitly represent feature fusion and propagation of multiple predictors across multiple scales. On model training, a weight setting strategy that balances the impact of samples with different rainfall values on the total training loss is proposed, and a soft classification label is designed to convert observed rainfall into the probability of rainfall above each threshold. The proposed model was trained and validated on NWP data provided by European Center for Medium‐Range Weather Forecast, and results show significant improvement over the NWP in terms of threat score (TS) and Heidke Skill Score (HSS) scores. Analysis of the forecast results for two typical rainfall processes also illustrates that the proposed method can predict rainfall with more reasonable location and intensity.

Analysis of extreme annual rainfall in North-Eastern India using machine learning techniques

Abstract The machine learning techniques of Multiple Linear Regression (MLR), Generalized Additive Models (GAMs), and the Random Forest (RF) Method have been used to analyze the extreme annual rainfall in the six states of Assam, Meghalaya, Tripura, Mizoram, Manipur, and Nagaland in North-Eastern (NE) India. Latitude, longitude, altitude, and temperature were the covariates that were used in this study. Ordinary Kriging was used to interpolate the predicted outcomes of each dataset. Statistical metrics like Mean Absolute Errors (MAE), Root Mean Square Error (RMSE), Coefficients of Determination (COD-R2), and Nash–Sutcliffe Efficiency (NSE) were also assessed. When compared to satellite rainfall data, all techniques performed significantly better for ground rainfall data. For prediction, GAM's predicted rainfall values triumph over MLR or RF. RF ranks a close second, while the linearity of MLR prohibits it from making precise predictions for a physical phenomenon like rainfall. The MAE and RMSE of GAM forecasts are significantly lower than those of MLR and RF in most circumstances. Additionally, the COD and NSE of GAM predictions are significantly better than both MLR and RF in most cases, showing that GAM, out of MLR, GAM, and RF, is the best model for predicting rain in our research area.

Natural parasitism of the coffee leaf miner: climate factors, insecticide, and landscape affecting parasitoid diversity and their ecosystem services in coffee agroecosystems.

Climate factors, pesticides, and landscape in coffee agroecosystems directly affect the populations of the coffee leaf miner and its parasitoids. This study aimed to investigate the effects of climate factors, insecticide use, and landscape on natural parasitism, parasitoid diversity, and infestation of L. coffeella in coffee plantations in the Planalto region, Bahia, Brazil. Mined leaves were collected monthly in six coffee plantations with varying edge density, vegetation cover, landscape diversity in scales of 500 to 3000 m of radius, insecticide use, and climate factors. Closterocerus coffeellae, and Proacrias coffeae (Eulophidae) predominated in the pest's natural parasitism. Our record is the first for the occurrence of Stiropius reticulatus, Neochrysocharis sp. 1, Neochrysocharis sp. 2, and Zagrammosoma sp. in Bahia. Higher temperature and larger forest cover increased the coffee leaf miner infestation. Higher rainfall values, insecticide use, and landscape diversity decreased the pest infestations. Natural parasitism and species diversity are favoured by increase in temperature, forest cover, and edge density, while increase in rainfall, insecticide use, and landscape diversity lead them to decrease.The natural parasitism and diversity of parasitoid species of the coffee leaf miner have been enhancing in the areas with greater forest cover and edge density associated with low use of insecticides. The areas composed of different lands with annual croplands surrounding the coffee plantations showed less natural parasitism and parasitoid species diversity. The ecosystem services provided by C. coffeellae and P. coffeae in coffee crops areas require conservation and these species are potential bioproducts for applied biological control programmes.