Rainfall Model Research Articles

Cascaded-ANFIS to simulate nonlinear rainfall–runoff relationship

Hydrologic models require atmospheric, dynamic and static models to simulate river flow from catchments. Thus the accuracy of hydrologic modelling highly depends on the data quality. Therefore, simulation is always challenging in data-scarcity environments. In addition, physical flow measurements are infeasible in the Spatiotemporal domain, and soft computing techniques are helpful in river flow simulation in data-scarcity environments. In this research paper, an efficient and accurate Cascaded-ANFIS-based model for rainfall–runoff was proposed and evaluated using five case studies in three countries: Japan, Vietnam, and Sri Lanka. The investigation focused on predicting streamflow by the influence of past data, with each river’s dataset examined to determine the best configuration of past rainfalls affecting streamflow volume. The proposed algorithm was compared against six state-of-the-art regression algorithms. The results showed that it outperformed the other algorithms in every case study except the Kalu River dataset, with zero bias calculated. The developed R-R model can be considered a generic model for streamflow prediction in data-scarcity environments, with excellent acceptability of simulated river flows against measured river flows observed across different geographic and climatic regions.

Assessing North Atlantic Tropical Cyclone Rainfall Hazard Using Engineered-Synthetic Storms and a Physics-Based Tropical Cyclone Rainfall Model

Abstract In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.

Hydrochemical Delineation of Spring Recharge in an Urbanized Karst Basin, Central Kentucky

ABSTRACT Because of well-integrated surface and subsurface drainage in karst terrains, springs can exhibit relatively rapid hydraulic, chemical, and thermal responses to storms. In urbanized karst basins, impervious cover, stream channelization, and utility infrastructure can alter infiltration, provide alternate pathways for subsurface flow, and affect ambient water quality. We combined continuous logging of electrical conductivity (EC) and water temperature with analyses of stable isotopes (deuterium and oxygen-18) to differentiate focused and diffuse recharge in a karst basin in Lexington, Kentucky, during 2018. Logging occurred at the McConnell Springs Blue Hole and a sinkhole that drains to it; isotopes, specific conductance, and temperature were manually monitored at those sites and along two losing stream reaches. Water temperature at McConnell Springs and stable isotope abundances showed seasonal variability. The Blue Hole responded within hours to stormwater infiltration at the sinkhole (∼2.1 km upgradient), with recharge events marked by colder stormwater in winter and warmer stormwater in spring to early autumn. Stable isotopes indicated that sinkhole infiltration was minimally affected by evaporation during periods of ponding (up to 9 days). Spring discharge appeared to represent a mixture of focused and diffuse, partly evaporated recharge, consistent with a simple hydrologic model of rainfall, runoff, and infiltration in the basin. EC spikes at the spring during January–March were consistent with pulses of road salt or brine in runoff or snowmelt. Despite limited monitoring data, results suggest that restoration of the sinkhole and its inlet stream were effective in stormwater management during an un usually wet year.

A study on using nano calcite as the accelerated agent in microbially induced calcium carbonate precipitation for soil-sand bio-cementation

Bio-cementation through bacterial mineralization can be considered an “eco-friendly” solution for soft or loose sandy soils, particularly in ground slopes and high rainfall zones, to achieve sustainable development goals. Using urelytical bacteria with the ability to decompose urea for calcium carbonate precipitation can be selected as the primary agent for the bio-cementation process. This study clarifies the effects of adding nano calcite as nucleation sites for enhancing the bio-cementation with Vietnam-isolated bacteria. Both microstructure of the precipitation and MICP capacity were studied. Microscopic, SEM/EDS, and X-ray diffraction data were used to characterize and identify the mineral compositions. Also, a simulated rainfall model and wind flow were set up to evaluate the erosion resistance of sand samples.

A study on the applicability of microbially induced calcium carbonate precipitation on soil-sand stabilization through the bio-cementation process

Soft or loose sandy soils, particularly in ground slopes and high rainfall zones, are potentially dangerous and have caused many problems such as damage, landfall, or even erosion. Bio-cementation through bacterial mineralization can be considered a “green” and “eco-friendly” solution toward sustainable development. Urelytical bacteria that decompose urea for calcium carbonate precipitation were selected as the primary agent for the bio-cementation process. This study clarifies the effects of particle sizes on bio-cementation. Moreover, the Vietnam isolated species and the commercial one were investigated to compare the biomineralization capability. A simulated rainfall model was set up to evaluate the sand samples by the water prevention and erosion and wash-out tests. Also, the microstructure observations were conducted through optical microscopes, scanning electron microscopes, and phase composition analysis.

Physical and artificial intelligence-based hybrid models for rainfall–runoff–sediment process modelling

ABSTRACT This study evaluates the performance of the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), Soil and Water Assessment Tool (SWAT), feedforward neural network (FFNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and multilinear regression (MLR) for modelling the rainfall–runoff–sediment process in Katar catchment, Ethiopia. Afterward, neural network ensemble (NE), weighted average ensemble (WE) and simple average ensemble (SE) techniques were developed to improve the performance of single models. The performance of the models was evaluated using Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE). The results show that the ANFIS model outperformed the other single models for rainfall–runoff–sediment modelling. Moreover, the integration of artificial intelligence and physically-based models resulted in improved performance, with the NE technique demonstrating better accuracy by improving individual models by 5.8–27.6% for rainfall–runoff and 3.59–37.9% for suspended sediment load modelling in the validation phase.

The Best of Hilsa Rearing in Marine-Cages, Else with the Global Rainfall Model Namely, “Hhhfarm” in a Very Long Runs to Coastal Zones as Well, Facilitating Hilsa and Every Other Marine Species

All we may know dillution is a new life. Every marine species has to migrate to fresh-water or estuary. Hilsa migrates to upstream fresh-waters while to breed naturally. By the way Hilsa species are being caught and hence no breeding is performed and hence we loss a huge amount of Hilsa Geneplasms, and here we stand Ecotechnology of Hilsa seed production with matured or semi matured Hilsa Ova and Sperm Cells under known water quality conditions, or else in Freshwaters. To get Hilsa seeds as per our needs. Hence Hilsa upstream migration to rivers owing to perform natural breeding may not be mandatory if there is adequate rainfall in costal environments.We describe HHHFARM a coastal or global rainfall model helping Hilsa fisheries as well. alternative rainfall model “HHHFARM” facilitating hilsa spp to breed well with ecotchnology Hilsa Seeds mere obtained in freshwaters here with adopting Ecotechnology or else naturally in Nadia Murshidabad and Culturally adjoining areas with known water qualitites and can be grown in marine-cages with the mentioned in a range Total Dissolved Solids TDS of principal water quality parameter whose value rangeing from 150 to 250 ppm (Table 1) with maximum tollerable TDS is 610 ppm. Beyond 610 ppm of TDS Hilsa species may get environmental stresses. Tenualosa ilisha migration may not be mandatory if there is HHHFARM be implemented, in a long centuries run. We propose this HHHFARM in Fisheries, a Global Coastal Rainfall model may establishing preferably in coastal zones, as well apart from terrestrials. This model specially in Fisheries says that OSP osmotic pressure can be minimized with rainfall. Every rainfall is attracted with ‘Hydrogen to Hydrogen ion based Hydrophilic Farming Apical Rainfall Model’ or i.e. HHHFARM of Plant canopies in Coastal-zones causing optimum osmotic pressure to Hilsa species as well and hence after many many years from now may be natural breeding may even possible without migrations to fresh waters.

Trends in Extreme Precipitation Indices over Bhutan

With the changing climate, the frequency and intensity of extreme precipitation events are increasing. Climate change is projected to increase both mean and extreme precipitation. Socio-economic damages can be immense and require a difficult recovery, especially for developing countries such as Bhutan. Furthermore, changing precipitation patterns affect land productivity and water availability. The Experts Team on Climate Change Detection Indices (ETCCDI) is used to find the changes associated with extreme precipitation in Bhutan. The study of extreme precipitation is important for Bhutan, whose economy is dependent on agriculture and hydropower. Even for a small country, there were varying patterns of precipitation in different districts. Deothang district received less frequent and more intense rainfall, while Haa, Bumthang, Trashiyangtse, and Chukha received weak but persistent rainfall. Mann–Kendall trends revealed a drier climate for two districts, Dagana and Trashiyangtse, and a wetter climate for the Mongar district. Modeling of the extreme rainfall with extreme value theory (EVT) revealed that the generalized extreme value (GEV) distribution by their T = 50-year return value, indicating an increasing value of annual maximums for all stations. This study is the first of its kind for Bhutan, and the findings can be used for decision support and the planning of appropriate adaptation strategies for hydro-meteorological disasters, hydropower, and agriculture sectors in Bhutan.

Lake and Stream Buffer Zone Widths' Effects on Nutrient Export to Lake Rawapening, Central Java, Indonesia: A Simple Simulation Study

Lake ecosystems in Indonesia face serious environmental problems. One of those problems is eutrophication caused by excessive plant nutrients, particularly nitrogen (N) and phosphorus (P). Water quality degradation and biodiversity loss are the effects of eutrophication. The government of Indonesia (GoI) has issued a regulation on determining lake and stream buffer zones, but it has not been fully implemented in the field. Additionally, the data related to the effects of each buffer zone width is not available. This study aims to begin to fill this gap. It simulates the effect of lake and stream buffer zone widths on nutrient export to Rawapening Lake. The Nutrient Retention sub-model, which is part of InVEST (Integrated Valuation of Environmental Services and Tradeoffs) software, has been used for this research to analyse information from several data sources, including a Digital Elevation Model (DEM) and measurements of soil depth, annual rainfall, land cover/use, watershed/sub-watershed boundaries, and biophysical conditions. Several studies of eutrophication in Rawapening Lake have measured the magnitude of eutrophication but have not discussed the effects of buffer zone widths. Therefore, this study accommodates the updated data on how much effect of buffer zone widths on the reduction of nutrient export. Five scenarios of buffer zone width are considered: 30 m., 90 m., and 150 m, where the lake buffer zone widths and the stream buffer zone width are 30 m. The results indicated that the maximum nutrient export reduction of lake buffer zones was only 2.63% (for N) and 3.56% (for P). On the other hand, the 30 m stream buffer zone width reduced the nutrient export to Rawapening Lake by up to 43.05% for N and by 44.90% for P. A 30 m combined lake and stream buffer zone width slightly increases the nutrient export reduction effectiveness, i.e., 0.41% and 0.56% for N and P, respectively.

Leveraging Machine Learning based Ensemble Time Series Prediction Model for Rainfall Using SVM, KNN and Advanced ARIMA+ E-GARCH

Today's precipitation is growing increasingly variable, making forecasting increasingly difficult. The Indian Meteorological Department (IMD) currently employs Composite and Stochastic approaches to forecast spring storm precipitation in Asia. As a corollary, planners are unlikely to predict the macroeconomic effects of disasters (due to excessive precipitation) or famine (less precipitation). The amount of precipitation that drops dependent on a variety of factors, including the temperature of the atmosphere, humidity, velocity, mobility, and weather conditions. This paper would then employ the Hybrid time-series predictive ARIMA+ E-GARCH (Exponential Generalized Auto-Regressive Conditional Heteroskedasticity) to predict precise runoff by taking into account different climatic considerations such as maritime tension, water content, relative dampness, min-max heat, heavy ice, geostrophic tallness, breeze patterns, soil dampness, and barometric force. In perspective of RMSE, MAE, and MSE, the proposed hybrid ARIMA+E-GARCH paradigm outperformed single simulations and latest hybrid techniques.

A Study on the Accessibility of the Emergency Medical Services for Urban Kindergartens and Nursing Homes Based on Urban Pluvial Flooding Scenarios

Vulnerable groups such as children and the elderly are the focus of emergency medical rescue during urban pluvial floods. Taking the Erqi District of Zhengzhou City as an example, the SCS-CN model and Chicago rainfall model are used to simulate pluvial flooding based on the comprehensive consideration of urban rainfall, runoff, topography, and drainage. Additionally, the accessibility of emergency medical aid for kindergartens and nursing homes is evaluated in the Erqi District of Zhengzhou under different pluvial flooding scenarios using GIS network analysis technology. The results showed that the number of kindergartens and nursing homes without timely access to emergency medical rescue increased with the increase in precipitation return periods. Under the 500-year and 1000-year pluvial flooding scenarios, kindergartens and nursing homes that can obtain emergency medical rescue had delayed response times. Furthermore, with the increase in the precipitation return periods, both the number and delay time of kindergartens and nursing homes significantly increased. The accessibility and delay time of emergency medical rescue in kindergartens and nursing homes were determined by the intensity of pluvial flooding (including inundation area and depth), road traffic conditions, and the number and location of medical institutions, nursing homes, and kindergartens. The research results can provide a scientific basis for improving the refinement level of urban flood disaster management and emergency response services.

The Reliability of W-flow Run-off-Rainfall Model in Predicting Rainfall to the Discharge

This research intends to predict the discharge (run-off) from rainfall for which the model is built using W-flow. The research location is in the Gajah Mungkur reservoir (Wonogiri) in Indonesia. The estimation of reservoir inflow has an important role, mainly in the scheme of reservoir operation and management. However, the heterogeneity of complex spatial and temporal patterns of rainfall and also the physiographic context of a watershed cause the development of a model of real-time run-off and rainfall that can accurately predict the reservoir inflow to become a challenge in the development of water resources. In relation to the analysis and prediction of rainfall, the constraint and problem that is still often faced is the minimal availability of observed rainfall data spatially as well as temporally; the time series of rainfall data is not long and complete enough; and the number of rainfall stations is less evenly distributed. The methodology consists of carrying out the literature study, collecting as much rainfall data as possible to build a W flow model, then carrying out the model calibration and analyzing the prediction of real-time reservoir inflow for operation. The result shows that the dependable discharge of the Wonogiri watershed shows that there are two peak discharges, which happened on February II (the second half of February) and December II (the second half of December). However, the discharge is decreasing in July and reaching its lowest level in October II (the second half of October). Doi: 10.28991/CEJ-2023-09-07-015 Full Text: PDF

Random forest-based nowcast model for rainfall

Random forest-based nowcast model for rainfall

Evaluation of statistical models for rainfall at rars: Jagitial

Evaluation of statistical models for rainfall at rars: Jagitial

Model Hibrid Harmonik, ARMA dan Outlier Curah Hujan di Surabaya, Malang dan Banyuwangi

The main factors affecting the climate in Indonesia are monsoons, El Nino Southern Oscillation (ENSO) and sunspot cycles. Another influence is local characteristics, namely the geography and topography of each region. The harmonic model is an applied statistical model of Fourier series analysis. Because the harmonic model is a deterministic model with residuals that often still have autocorrelation, it is necessary to add a stochastic Autoregressive Integrated Moving Average (ARIMA) model. With the existence of 3 main factors that influence the climate in Indonesia with different seasonal periods, plus local influences, the bulk modeling with Seasonal ARIMA, Double S ARIMA is not enough so that a combination of harmonics, ARMA and Additive Outliers (AO) appears. The purpose of this study is to find the periodicity of rainfall and model rainfall time series data with hybrid harmonics, ARMA outliers. Periodogram analysis of monthly rainfall data for three cities in East Java shows a 1-year seasonal return period influenced by monsoons, an 11-year return period influenced by sunspots that cannot be detected. The monthly rainfall model for the city of Surabaya is the 2nd order Harmonic Hybrid Model, ARMA([1,9,11],0,0) and the outlier type , for the city of Banyuwangi is the 1st order Harmonic Hybrid Model, ARMA (1,0,0) and 2 outliers of type , , Malang city are first order Harmonic Hybrid models, ARMA([6,7],0,0) and outliers of type . First order Harmonic Hybrid models, ARMA and Outlier are better than ARIMA and Outlier models.
 Key words: Monsoon, ENSO, sunspot, harmonics, ARMA and outliers.

Stochastic disaggregation of seasonal precipitation forecasts of the West African Regional Climate Outlook Forum

AbstractSeasonal rainfall forecasts from the West African Regional Climate Outlook Forum (RCOF) are essential for adapting to climate variability. However, their temporal aggregated nature is a strong limitation, especially when used with impact models requiring daily resolution, such as hydrological or crop models. To address this issue, this study proposes a temporal disaggregation method for these forecasts using in situ data from two districts (Kandi and Parakou) in northern Benin, spanning from 1971 to 2020. A resampling technique was used to construct a daily historical record that aligns with seasonal rainfall forecasts. Three stochastic disaggregation models for rainfall (SRGs) were developed, including two parametric models (SRG1 and SRG2) and one semiparametric (SRG3). Their parameters were estimated from the resampled record to generate daily synthetic data replicating the forecasts. Evaluation of the SRGs revealed that SRG2, which combined a first‐order Markov chain with a mixed exponential distribution, performs well in simulating various characteristics of the rainy season, including dry spells, wet spells and daily precipitations. Furthermore, SRG2 maintained the trends of the initial forecasts and outperformed SRG1 and SRG3, as confirmed by the chi‐square test. Indeed, a good agreement was observed between the probabilities of the initial prediction and those calculated from the temporal disaggregation with the SRG2 method. Also, for the forecasts expressed by probabilities 15–35–50 and 20–50–30, the cumulative distribution function curves (CDF) of the SRGs exhibited appropriate shifts compared to climatology. These forecasts were specific to the Kandi area in 2008 and 2003, respectively, during the West African RCOFs. Although this study focused specifically on the Kandi and Parakou districts, the temporal disaggregation methodology used can be applied to other locations within West Africa or other RCOFs worldwide. This study offers valuable guidance for generating sector‐specific seasonal forecasts for the West African region.

A climate stress testing method for changes in spatially variable rainfall

With emerging evidence that anthropogenic climate change is affecting the spatial pattern of rainfall, it is important to understand how such changes affect a range of climate-sensitive systems. A method for generating multi-site rainfall sequences is presented that enables the exploration of changes in spatial rainfall patterns separately from various other potential changes (e.g. to the averages, extremes, seasonality), thereby enabling the separation of key drivers of change as part of climate stress tests. The proposed method is based on the ‘inverse approach’ to stochastic weather generation, and is evaluated through a case study with rainfall at 12 sites in the Barossa Valley, South Australia, using a multi-site latent variable stochastic rainfall model. The new method accurately captures (1) observed marginal attributes at each site (i.e. annual totals, seasonality, extreme rainfall, number of dry days, and intermittency), (2) spatial correlation between sites, and (3) observed attributes for areal average rainfall. We then apply the new method to stress test the response of streamflow in the region to changes in spatial rainfall patterns, quantifying how reductions in spatial correlation (while holding all other attributes constant) will lead to reduced mean annual flow and extreme flows, and increased number of low-flow days. The results show that not only can a broad range of rainfall properties (including both marginal and spatial attributes) be simulated using the inverse approach, but also that changes to the spatial properties of rainfall can have large implications on water resources systems, with the potential to amplify or dampen other changes to rainfall attributes.

Assessing the controlling factors on watershed soil erosion during intense rainstorm events using radar rainfall and process-based modeling

The evaluation of erosion risk in dry areas is challenging because erosion is often an outcome of individual rainstorms and is highly dependent on rainfall spatiotemporal patterns and on local land-use and topography. This study integrates a hybrid erosion model with rainfall data from high-resolution weather radar to simulate soil erosion during 22 high-intensity flash-flood generating rainstorms in a Mediterranean watershed (69 km2). We examine erosion over individual hillslopes and their spatial average over the watershed, representing intra-watershed and watershed-scale erosion, respectively. Our objectives are to: (a) determine how intra-watershed erosion corresponds to various physiographic factors (rainfall, land-use, topography); (b) determine which of these factors contributes to intra-watershed erosion the most; (c) quantify the effect of temporal variations in rainfall intensities on storm-scale erosion in relation to land-use type. We use for the first time a hybrid erosion model (K2-RHEM-DWEPP) based on the watershed-scale KINEROS2 model, that integrates the hillslope-scale Dynamic WEPP (DWEPP) and RHEM models, which were individually developed to represent erosion processes in croplands and rangelands, respectively. Watershed-scale storm erosion is best correlated with spatially-averaged 10-minutes maximum intensities (R2 = 0.58), and the correlation decreases for longer durations (R2 ≤ 0.54). When the spatially-averaged 10-minutes maximum intensity is multiplied by the area that contributes sediment, a better correlation with watershed-scale erosion is observed (R2 = 0.75). Hillslope erosion rates are higher when both rainfall intensities and topographic slopes are high, while land-use has a second-order effect. Higher storms maximal intensities result in higher hillslope erosion rates, especially over croplands.Our conclusions are useful to target locations for conservation practices and to better understand the effects of climate change on soil erosion.

Hydrochemical Delineation of Spring Recharge in an Urbanized Karst Basin, Central Kentucky

ABSTRACT Because of well-integrated surface and subsurface drainage in karst terrains, springs can exhibit relatively rapid hydraulic, chemical, and thermal responses to storms. In urbanized karst basins, impervious cover, stream channelization, and utility infrastructure can alter infiltration, provide alternate pathways for subsurface flow, and affect ambient water quality. We combined continuous logging of electrical conductivity (EC) and water temperature with analyses of stable isotopes (deuterium and oxygen-18) to differentiate focused and diffuse recharge in a karst basin in Lexington, Kentucky, during 2018. Logging occurred at the McConnell Springs Blue Hole and a sinkhole that drains to it; isotopes, specific conductance, and temperature were manually monitored at those sites and along two losing stream reaches. Water temperature at McConnell Springs and stable isotope abundances showed seasonal variability. The Blue Hole responded within hours to stormwater infiltration at the sinkhole (∼2.1 km upgradient), with recharge events marked by colder stormwater in winter and warmer stormwater in spring to early autumn. Stable isotopes indicated that sinkhole infiltration was minimally affected by evaporation during periods of ponding (up to 9 days). Spring discharge appeared to represent a mixture of focused and diffuse, partly evaporated recharge, consistent with a simple hydrologic model of rainfall, runoff, and infiltration in the basin. EC spikes at the spring during January–March were consistent with pulses of road salt or brine in runoff or snowmelt. Despite limited monitoring data, results suggest that restoration of the sinkhole and its inlet stream were effective in stormwater management during an unusually wet year.

Accuracy dominant frequencies of Lomb - Scargle periodogram in modelingmonthly rainfall

In the field of hydrology, many researchers have developed time series models. Usually, the modelling uses time series data, such as daily rainfall, monthly rainfall, etc. In order to produce a good model, the frequencies used in the model must represent the data being modelled. Otherwise, the inaccurate frequencies used to cause the resulting model to be inaccurate. Several methods or techniques have been used to estimate the frequencies contained in time series data. These methods are the Fast Fourier Transform and Lomb-Scargle periodogram methods. This study used the Lomb-Scargle periodogram method to predict monthly rainfall time series data. In this research, the Lomb periodogram results in rainfall frequencies. The frequencies are created by using monthly rainfall time series data. Using the frequencies, the dominant frequencies can be selected. Periodic modelling of monthly rainfall time series can be adequately simulated using the dominant frequencies. The correlation coefficient between the monthly rainfall data with the monthly rainfall model can be used to measure the accuracy of the monthly rainfall model.