Rainfall Maps Research Articles

Mapping of annual and frequency rainfall in the Cheliff catchment area (Northwestern Algeria)

In recent years, there has been a common use of automatic precipitation mapping based on various interpolation methods. Based on multiple linear regression, the study primarily focuses on the entire Cheliff basin (43 750 km²), to search a suitable model for mapping mean annual precipitation based on 89 rainfall stations of the observed series (1968/69–2001/2002). Then conducts on the upper part of Cheliff basin downstream the Boughzoul dam (4777 km²) using 11 rainfall stations of annual precipitation series relative to the period 1982/83–2020/2021. The same approach was used to map median annual precipitations and quantiles of rain associated with the return periods of 5 and 10 years, representing both wet and dry periods. Indeed, this work is based on the backward elimination method between the observed annual precipitation and four predictors: smoothed altitude (Zs), longitude (X), latitude (Y) and distance from the sea (D). The model’s results are satisfactory with a global correlation coefficient for both regional (R = 0,89) and local scale (R > 0,81). The final maps obtained are produced after interpolating by kriging the residuals. The median annual precipitation map was compared to that elaborated by the National Hydraulic Resources Agency (ANRH). The comparison results of 2296 nodes of the grid covering the Upper Cheliff basin showed negative deviations on average - 10% and reaching - 34% in northern area reflecting a dry trend in precipitation. The final grids of rainfall can be integrated into Geographical Information Systems related to many sectors including water management and climate change.

The evolution of spatial and temporal distribution of rainfall erosivity in Henan Province, central China

With the advancement of urbanization, there has been a significant reduction in cultivated land, accompanied by soil erosion. Concurrently, the regularity of rainfall in recent years has been erratic, adversely impacting the grain economy and agricultural development in certain regions. Henan Province, spanning the basins of the Yangtze River, the Yellow River, the Huaihe River, and the Haihe River, possesses complex hydrological conditions and serves as a pivotal agricultural zone in China. Therefore, this paper utilizes daily rainfall data collected over 54 years (1969–2022) from 112 rain measuring stations in Henan Province to calculate the rainfall erosivity using the Zhang model and the erosivity model from the first national water survey. Meanwhile, spatial analysis was performed using the Kriging interpolation method in the ArcGIS Geostatistical Wizard, resulting in detailed spatial distribution maps of rainfall and rainfall erosivity. The study also employed Wavelet and Mann–Kendall tests to analyze the abrupt changes, trends and periodicity of rainfall and rainfall erosivity within the target region. The findings indicate that the average rainfall (1969–2022) in Henan province was 718.26 mm, while the average rainfall erosivity (R) was 3213.46 MJ mm/(hm2 h). R values are positively correlated with rainfall intensity and volume, displaying an annual upward trend. Spatially, R values increase gradually from northwest to southeast, closely aligning with topographical variations. Additionally, the analysis revealed a predominant periodic cycle of 54 years in the precipitation patterns. These results offer valuable insights for environmental and agricultural management in other regions of central China.

Estimation of energy available in rain droplets from meteorological rainfall data

Abstract Even though the amount of rainfall is well documented in the reports of the India Meteorological Department (IMD), the rainfall energy available in different parts of India is unknown to the best of our knowledge. This paper reports a theoretical framework to convert the meteorological rainfall data to rainfall energy. The amount of rainfall over the south-west Monsoon period of 2022 is first converted to an average rainfall rate. Using the information on rain droplet size distribution for a rainfall of a given rainfall rate, the total energy contained in the rainfall is derived. In this, both the kinetic and surface energies of rain droplets of different sizes are considered. Based on the developed theoretical framework, rainfall map of India is converted to rainfall energy map showing states/union territories with maximum and minimum rainfall energy. Furthermore, for selected states in India, the districts showing maximum and minimum rainfall energy are also highlighted. The framework developed in the present study would help attempts to develop efficient rain energy harvesting systems.

Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

Hydrological models that are used to analyse flood risk induced by tropical cyclones often input ERA5 reanalysis data. However, ERA5 precipitation has large systematic biases, especially over heavy precipitation events like Tropical Cyclones, compromising its usefulness in such scenarios. Few studies to date have performed bias correction of ERA5 precipitation and none of them for extreme rainfall induced by tropical cyclones. Additionally, most existing works on bias adjustment focus on adjusting pixel-wise metrics of bias, such as the Mean Squared Error (MSE). However, it is equally important to ensure that the rainfall peaks are correctly located within the rainfall maps, especially if these maps are then used as input to hydrological models. In this paper, we describe a novel machine learning model that addresses both gaps, RA-Ucmpd, based on the popular U-Net model. The key novelty of RA-Ucmpd is its loss function, the compound loss, which optimizes both a pixel-wise bias metric (the MSE) and a spatial verification metric (a modified version of the Fractions Skill Score). Our results show how RA-Ucmpd improves ERA5 in almost all metrics by 3-28%—more than the other models we used for comparison which actually worsen the total rainfall bias of ERA5—at the cost of a slightly increased (3%) error on the magnitude of the peak. We analyse the behaviour of RA-Ucmpd by visualizing accumulated maps of four particularly wet tropical cyclones and by dividing our data according to the Saffir-Simpson scale and to whether they made landfall, and we perform an error analysis to understand under what conditions our model performs best.

Statistical modeling and mapping of rainfall in the endorheic basins of Northern Algeria: a comparison of spatial interpolation methods

Statistical modeling and mapping of rainfall in the endorheic basins of Northern Algeria: a comparison of spatial interpolation methods

Analisis Ketersediaan dan Kebutuhan Air Sub DAS Rupit Menggunakan Remote Sensing dan GIS

The water balance of a river basin is crucial for estimating water availability to meet current and future domestic and economic water demand and supply. Parameters such as rainfall, temperature, evapotranspiration, deficit, and surplus soil water content determine the water availability of a river basin (DAS), influenced by factors like land use, soil type, seasonal variations, and other environmental factors. This research aims to estimate the water balance of the Rupit sub-watershed using the FJ Mock model. The Sentinel 1 image of the area was processed to produce a land use map. GIS modeling generated maps of rainfall, land use, soil, slope, morphometry catchment, excess moisture, evapotranspiration, and surface run-off. The analysis reveals that the highest rainfall and evapotranspiration occur in January, while other months average around 56 - 66 mm per month. Excess water during the wet months (November-March) leads to flooding, damaging infrastructure such as residential buildings, roads, and bridges. Conversely, April-October experiences water shortages. The annual weighted watershed deficit is 46.6 mm, with almost no surplus detected. Rainfall run-off simulations demonstrate significant and coherent watershed responses to rainfall patterns. Due to low rainfall and soil moisture levels from preceding months, March exhibits the highest discharge at 15.35 m3/s, with a run-off coefficient of 0.863.

Sosialisasi Penguatan Wawasan Kesiapsiagaan Bencana dalam Merespons Desa Tanggap Bencana di Desa Doyong Kabupaten Sragen

Desa Doyong is one of the villages located in Miri District, Sragen Regency, Central Java. The natural disaster of flooding in Doyong Village occurred in 2023, with a prolonged water recession process. Besides floods, droughts and strong winds also occurred due to extreme weather. The community's unpreparedness in facing disasters resulted in social and economic impacts. Therefore, the purpose of this community service is to enhance the knowledge, abilities, and independence of the community in facing and addressing disaster issues, as well as to build community preparedness for potential future disasters. The research employs a descriptive qualitative approach, collecting primary data through in-depth interviews, observations, and focus group discussions (FGDs). Secondary data were obtained from official BPBD documents, documentation, and relevant literature. Activities included rainfall mapping, community and school disaster preparedness education, the Village Clean Week (GUBERDES) program, reforestation, and collaboration with BPBD Sragen Regency. Results indicate that effective resource mobilization and involvement of various stakeholders, can improve community disaster preparedness.

Delineation of Groundwater Potential Zones Using Remote Sensing and Gis Techniques of Phewa Watershed (Nepal)

Groundwater is the primary source of water in the study region, both for domestic and agricultural needs. The main objective of this research is to identify the groundwater potential zones via remote sensing (RS) and Geographic information system (GIS), which is essential in evaluating, preserving, and monitoring various groundwater-related development programs. Man-made pressures include over-exploitation of groundwater and climate changes have led to a strain on groundwater resources. As the use of consumable water increases for human consumption, agriculture, industrial growth, municipal, and pharmaceutical needs to evaluate the groundwater potential and aquifer productivity also increase. Groundwater inspections have been historically done by field survey method, which is inefficient or not practical in terms of time and resources. Arc GIS software is utilized to manipulate datasets. The LULC map of the study area will be developed using Landsat 8 satellite data. A soil map and rainfall map will be created using the data sources from USGS, a Parent soil map will creates using the data from NARC and slope map, lineament density and drainage density from ASTER from earth data. Seven thematic maps will be applied, each with appropriate weight and rank assigned based on its characteristics and connection with groundwater. All the thematic layers combine into a GIS domain, and weight values are put to the attribute table for each polygon. The groundwater potential zone map will be classified into different zones, i.e., very poor, poor, moderate, good, and very good. These results will help hydrogeologists, decision-makers, planners, and local authorities formulate better groundwater resource planning in the Phewa watershed area.

DSADNet: A Dual-Source Attention Dynamic Neural Network for Precipitation Nowcasting

Accurate precipitation nowcasting is of great significance for flood prevention, agricultural production, and public safety. In recent years, spatiotemporal sequence models based on deep learning have been widely used for precipitation nowcasting and have achieved better prediction results than traditional methods. These models commonly use radar echo extrapolation and utilize the Z-R relationship between radar and rainfall to predict rainfall. However, radar echo data can be affected by various noises, and the Z-R correlation linking radar and rainfall encompasses several variables influenced by factors like terrain, climate, and seasonal variations. To solve this problem, we propose a dual-source attention dynamic neural network (DSADNet) for precipitation nowcasting, which is a network model that utilizes a fusion module to extract valid information from radar maps and rainfall maps, together with dynamic convolution and the attention mechanism, to directly predict future rainfall through encoding and decoding structure. We conducted experiments on a real dataset in Jiangsu, China, and the experimental results show that our model had better performance than the other examined models.

Precipitation Modeling Based on Spatio-Temporal Variation in Lake Urmia Basin Using Machine Learning Methods

The amount of rainfall in different regions is influenced by various factors, including time, place, climate, and geography. In the Lake Urmia basin, Mediterranean air masses significantly impact precipitation. This study aimed to model precipitation in the Lake Urmia basin using monthly rainfall data from 16 meteorological stations and five machine learning methods (RF, M5, SVR, GPR, and KNN). Eight input scenarios were considered, including the monthly index, longitude, latitude, altitude, distance from stations to Lake Urmia, and distance from the Mediterranean Sea. The results revealed that the random forest model consistently outperformed the other models, with a correlation rate of 0.968 and the lowest errors (RMSE = 5.66 mm and MAE = 4.03 mm). This indicates its high accuracy in modeling precipitation in this basin. This study’s significant contribution is its ability to accurately model monthly precipitation using spatial variables and monthly indexes without measuring precipitation. Based on the findings, the random forest model can model monthly rainfall and create rainfall maps by interpolating the GIS environment for areas without rainfall measurements.

Determination of the Groundwater Potential Zones in Babylon Using Remote Sensing & GIS Techniques

Freshwater found underground in the tiny spaces (pores) of soil and rocks is known as groundwater. Groundwater is a valuable resource that considerably contributes to the yearly supplies. To preserve water quality and oversee groundwater systems, evaluation of the potential zone of groundwater recharge is crucial. Using remote sensing and Geographic Information System (GIS) tools, groundwater potential zones are identified. This study proposes to use satellite imagery and the software program (ArcGIS) to find a groundwater potential zone in Babylon City, Iraq. A groundwater map has been created using various data, namely slope, drainage density, lineament density, land use/land cover, geology type, soil type, and rainfall map. All these data are collected using a weighted overlay tool. Five categories were created based on the groundwater results: very poor, poor, moderate, good, and exceptional. The 'good' class covers about 32% of the study area. The current study shows that excellent groundwater class exists in Al-Hindiyah Dam, Al-Muhaweel, Al-Midhatiyah, Al-Kifil, and Al-Mashrua’a. The excellent groundwater class in Al-Shommali covers approximately 198.0054 km2. Certain areas like Al-Musaib Center, Al-Hilla Center, Al-Hashimiyah, and Abi Garaq don't have much groundwater. The moderate and excellent classes cover around 19% of the study area. The main conclusion is that Remote Sensing and GIS have gained recognition as a practical approach to mapping groundwater potential zones due to their ability to integrate diverse spatial data.

Analysis of potential soil degradation on agricultural land based on geographic information systems in Slogohimo District, Wonogiri Regency

Slogohimo District is one of the districts located in Wonogiri Regency with an area of 6.415 ha. Population growth in Slogohimo District has increased by 7,068 people from 2017 to 2022. The total population in 2022 is 53,403 people with the majority livelihood being farmers. Population increase will have an impact on increasing the demand for biomass. Land management that does not pay attention to conservation principles will cause changes in the soil’s basic properties and impact the biomass produced. About 84,91% of the land in Slogohimo District is used as agricultural land in paddy fields, moor, and plantations. The increase in population and the need for biomass, triggers more intensive processing of agricultural land which has the potential to cause soil degradation potential degradation. Based on these problems, it is important to carry out an assessment of the potential for degradation to agricultural land in Slogohimo District. The method used is overlaying thematic maps consisting of soil type maps, rainfall maps, slope maps, and land use maps through spatial data analysis based on Geographic Information Systems (GIS). The potential for soil degradation is obtained from the scoring results which refer to the standard criteria in the Technical Guidelines for Preparation of Soil Degradation Degradation Status Maps for Biomass Production. The results showed that potential degradation to the soil on agricultural land in Slogohimo District was classified into three classes by area or proportion of land, namely low class covering 1.558,08 ha, medium class covering 2.800,70 ha, and high class covering 919,93 ha.

Landslide potential mapping in Pituruh District of Purworejo by using geographic information system

Located in Purworejo Regency, Pituruh is an area that prone to a landslide. Disaster mapping was a strategy that has been done so far by the government institutions and academia. According to its topography, Purworejo has been dominated by slope with more than 30 degree. The creation of landslide vulnerability map was carried out through several stages, namely, calculating rainfall at 6 stations located in Pituruh district, then producing average rainfall data to be used as a reference to create a rainfall map, then overlay process. Referring to the 4 parameters used, slope became an important parameter to determine the level of landslide vulnerability in Pituruh. The development of Geographic Information Systems can provide various types of spatial data related to the earth’s surface, and has the ability to perform accurate spatial analysis. Mitigation aims to reduce risks that have the potential to become disasters or reduce the effects of disasters. This study represents the level of vulnerability in Pituruh district by classifying the existing level. The results of this study have 5 classifications with different areas, namely highly vulnerable that covers 240.27 ha, moderately vulnerable for about 2237.81 ha, moderate with 3322.68 ha, vulnerable by 3040.14 ha, and not vulnerable area of 24.72 ha.

Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.

Hydroclimatic projection: statistical learning and downscaling model for rainfall and runoff forecasting

Abstract The study is carried out to investigate the surface runoff depth with changing precipitation due to climate change in the study area where sandy loam and loamy soil are dominant. In this study, future rainfall is projected by a statistical downscaling model (SDSM) using a set of predictors derived from a Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) [the Norwegian Earth System Model (NorESM)] with updated scenarios SSP 4.5 and SSP 8.5. Daily rainfall values for the observed period (1981 to 2014) are validated using statistical learning and evaluated with matrices, namely, root mean square error (RMSE), coefficient of correlation, and Nash–Sutcliffe efficiency (NSE), which are found to be valid for further predictions. Rainfall projections show a decrease in rainfall trend of 50% from 2030 to 2040 for scenario SSP 4.5 and an increase of 7% from 2040 to 2050. Predicted rainfall for scenario SSP 8.5 shows a similar trend of decreasing rainfall of 24% for the period 2030–2040 and an increase of 19% in the period 2040–2050. Furthermore, these rainfall values are spatially modelled in a geographic information system (GIS) and rainfall maps are obtained. The obtained rainfall map, land-use map, and soil map are overlaid to compute curve numbers and runoff depths. A similar trend of decrease in runoff is observed for the period 2030–2050. The overall trend of climate change shows a water-stressed scenario.

A Machine Learning Approach to Model Over-Ocean Tropical Cyclone Precipitation

Abstract Extreme rainfall found in tropical cyclones (TCs) is a risk for human life and property in many low- to midlatitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computationally expensive, and existing models are largely unable to model key rainfall asymmetries such as rainbands and extratropical transition. Here, a machine learning–based framework is developed to model overwater TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest (QRF) models are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain-rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r2 value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain-rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rainbands, wavenumber asymmetries, and possibly extratropical transition.

Data Management and Application System of Geological Gisaster Monitoring Data

The geological disaster monitoring data management and application system adopts B/S technology structure, based on data pre-processing technology and comprehensive analysis technology such as statistic calculation of the monitoring data, displacement vector map and comprehensive analysis map of displacement and rainfall, etc., this paper expounds the management and application of geological disaster monitoring data, and solves the problems of the lack of data, the poor quality of data and the lack of analytical models in geological disaster monitoring work. The ability of comprehensive processing and scientific analysis of geological disaster monitoring data has been further improved.

Determination of annual rainfall in north-east India using deterministic, geospatial, and machine learning techniques

Abstract Analysis of extreme annual rainfall in the six north-east Indian states of Assam, Meghalaya, Nagaland, Manipur, Mizoram, and Tripura using the deterministic interpolation technique of inverse distance weighting (IDW), the geospatial interpolation technique of Ordinary Kriging (OK) and the machine learning prediction technique of generalised additive model (GAM). GAM is used only for prediction and hence the results are then subsequently interpolated by OK to create the rainfall maps. The datasets considered for this study are a training dataset of 171 points which consisted of satellite rainfall and a testing dataset with ground rain gauge data of 33 points which was used for validation of the former. A combined dataset of training + testing was also interpolated and mapped to compare for visual accuracy of each technique. It was seen that OK was a superior and a much more realistic interpolation technique than IDW, since it took the altitude of each site into consideration along with latitude and longitude, unlike IDW, which only interpolated over the x–y plane and didn't rely on altitude. When the predictions of the training dataset through GAM were mapped using OK, it showed almost parallel contours, which is undesirable for natural phenomenon like rain.

Remote Sensing Techniques for Identifying Flood Prone Areas Using the Vegetation Index, Water Index, and Simple Additive Weighting Methods for Mekarsari Areas, Cianjur Regency, West Java

Flood is a natural disaster with an intensity that often occurs in cities on a scale with excess water discharge in some areas. This research was conducted to determine the risk of flooding in Mekarsari Village and its surroundings, Cianjur Regency, West Java. (According to InaRisk, 2022) Risk and danger of flooding Moderate – High. This study uses data for the entire year 2022. With several parameters used in this study including land cover, rainfall, soil type, slope, NDVI, NDWI and SAVI. Simple Additive Weighting or weighting for each parameter with different results, according to the classification of each of these parameters. The results of this weighting will become a Flood Hazard Risk Map which was previously the result of overlay and intersection of land cover map, rainfall map, soil type map, slope map, Normalized Difference Vegetation Index map, Normalized Difference Water Index map and Soil Adjusted Vegetation Index map. On the Flood Disaster Risk Map there are five categories of vulnerability levels Not Hazardous, Less Hazardous, Moderate, Hazardous and Very Hazardous. The cause of the risk of flooding in the study area is that most of the land use is diverted to rice fields, plantations, dry agricultural land and settlements so that the lack of vacant land causes low absorption of rainwater and also steep slopes and high elevations which also cause rainwater to be stored in the lowlands. settlements) with moderate to high rainfall intensity every month. There is also a lack of education for the community to mitigate the risk of flood disasters so that waste management and waterways in settlements are inadequate and well managed.

High-Resolution Rainfall Maps from Commercial Microwave Links for a Data-Scarce Region in West Africa

Abstract We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-min temporal and 0.55-km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a 5-min and aggregated daily scales. We observed high spatiotemporal variability on the 5-min scale that cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation, only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement, with a Pearson correlation coefficient > 0.95 and an underestimation of the CML rainfall maps of ∼10%. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and approximately one-half of all CMLs were removed. We further compared ERA5 and GPM IMERG data with the rain gauge and found that they had much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data-scarce but densely populated African cities. Significance Statement In this study, we investigate the possibility of deriving accurate high-resolution rainfall maps from commercial microwave link (CML) data in West Africa. The main challenges are the lack of reference data in this area and the adoption of existing processing tools without reference data. We show CML rainfall maps for Ouagadougou, Burkina Faso, with a resolution of 5 min and 0.55 km, which is unprecedented in this region. The comparison with the only available rain gauge, which provides data only at a daily resolution, yields a Pearson correlation of >0.95. An analysis of synthetically thinned-out networks shows that this accuracy is valid for the whole domain. Comparing reanalysis and satellite data with the rain gauge and CML data showed a poor performance of these gridded reference datasets. Also, a high coincidence of temporal dynamics between CML rainfall maps and satellite products was observed. Overall, these findings support the potential of CMLs for future hydrometeorological applications in West Africa.