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Journal articles on the topic 'Rain and rainfall Mathematical models'
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1
Guideli, Leandro Canezin, André Lucas dos Reis Cuenca, Milena Arruda Silva, and Larissa de Brum Passini. "Road crashes and field rainfall data: mathematical modeling for the Brazilian mountainous highway BR-376/PR." TRANSPORTES 29, no. 4 (December 2, 2021): 2498. http://dx.doi.org/10.14295/transportes.v29i4.2498.
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Recent studies analyze the influence of rainfall on traffic crashes, indicating that precipitation intensity is an important factor, for modeling crashes occurrence. This research presents a relationship between daily-basis traffic crashes and precipitation, from 2014 to 2018, in a rural mountainous Brazilian Highway (BR-376/PR), where field rain gauges were used to obtain precipitation data. Data modeling considered a Negative Binomial regression for precipitation influence in crash frequency. Separate regression models were estimated to account for the rainfall effect in different seasons, and for different vehicle types. All models analyzed presented a positive relationship between daily rainfall intensity and daily crashes number. This can indicate that generally rainfall presence is a hazardous factor. Different critical seasons for rainfall influence were also highlighted, alerting for the possible necessity of distinct road safety policies concerning seasonality. Finally, for the vehicle type analysis, typically, rainfall seemed to have a greater effect in lighter vehicles. Moreover, results are useful for traffic control, in order to increase safety conditions.
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Rauch, W., N. Thurner, and P. Harremoës. "Required accuracy of rainfall data for integrated urban drainage modeling." Water Science and Technology 37, no. 11 (June 1, 1998): 81–89. http://dx.doi.org/10.2166/wst.1998.0441.
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It is standard practice in integrated urban water management to apply mathematical models of the total drainage system for calculating the frequency of occurrence of critical states in the receiving water body. The model input for such computations are long term time-series of rainfall data. However, it is inevitable that those rainfall data measurements deviate from reality. This is a result of inaccuracy of the measurement devices, errors in data transmission, local meteorological effects, etc. In this work we investigate the effect of such uncertainty in the rainfall data on the return period of the occurrence of oxygen depletion in the river due to the drainage of storm water. The errors in the rain data measurements are simulated by means of both stochastic and deterministic models. A comparison of the results obtained from the erroneous data series against the reference data reveals the small effect of random deviation in rain measurements. Only a constant and significant offset of the measured data (greater 20%) has an equally significant effect on the modeling result.
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Yakovleva, Valentina, Aleksey Zelinskiy, Roman Parovik, Grigorii Yakovlev, and Aleksey Kobzev. "Model for Reconstruction of γ-Background during Liquid Atmospheric Precipitation." Mathematics 9, no. 14 (July 11, 2021): 1636. http://dx.doi.org/10.3390/math9141636.
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With regard to reconstructing the gamma background dose rate, existing models are either empirical with limited applicability or have many unknown input parameters, which complicates their application in practice. Due to this, there is a need to search for a new approach and build a convenient, easily applicable and universal model. The paper proposes a mathematical model for reconstructing the temporal evolution of the ambient equivalent γ-radiation dose rate during rain episodes, depending on the density of radon flux from the soil surface, as well as the duration and intensity of rain. The efficiency of the model is confirmed by the high coefficient of determination (R2 = 0.81–0.99) between the measured and reconstructed ambient equivalent dose rate during periods of rain, the simulation of which was performed using Wolfram Mathematica. An algorithm was developed for restoring the dynamics of the ambient equivalent γ-radiation dose rate during rainfall. Based on the results obtained, assumptions were made where the washout of radionuclides originates. The influence of the radionuclides ratio on the increase in the total γ-radiation dose rate was investigated.
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Cowpertwait, Paul, Valerie Isham, and Christian Onof. "Point process models of rainfall: developments for fine-scale structure." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463, no. 2086 (July 18, 2007): 2569–87. http://dx.doi.org/10.1098/rspa.2007.1889.
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A conceptual stochastic model of rainfall is proposed in which storm origins occur in a Poisson process, where each storm has a random lifetime during which rain cell origins occur in a secondary Poisson process. In addition, each cell has a random lifetime during which instantaneous random depths (or ‘pulses’) of rain occur in a further Poisson process. A key motivation behind the model formulation is to account for the variability in rainfall data over small (e.g. 5 min) and larger time intervals. Time-series properties are derived to enable the model to be fitted to aggregated rain gauge data. These properties include moments up to third order, the probability that an interval is dry, and the autocovariance function. To allow for distinct storm types (e.g. convective and stratiform), several processes may be superposed. Using the derived properties, a model consisting of two storm types is fitted to 60 years of 5 min rainfall data taken from a site near Wellington, New Zealand, using sample estimates taken at 5 min, 1 hour, 6 hours and daily levels of aggregation. The model is found to fit moments of the depth distribution up to third order very well at these time scales. Using the fitted model, 5 min series are simulated, and annual maxima are extracted and compared with equivalent values taken from the historical record. A good fit in the extremes is found at both 1 and 24 hour levels of aggregation, although at the 5 min level there is some underestimation of the historical values. Proportions of time intervals with depths below various low thresholds are extracted from the simulated and historical series and compared. A tendency for underestimation of the historical values is evident at some time scales, with a close fit being obtained as the threshold is increased.
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Ramesh, Nadarajah I., Gayatri Rode, and Christian Onof. "A Cox Process with State-Dependent Exponential Pulses to Model Rainfall." Water Resources Management 36, no. 1 (November 29, 2021): 297–313. http://dx.doi.org/10.1007/s11269-021-03028-6.
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AbstractA point process model based on a class of Cox processes is developed to analyse precipitation data at a point location. The model is constructed using state-dependent exponential pulses that are governed by an unobserved underlying Markov chain. The mathematical formulation of the model where both the arrival rate of the rain cells and the initial pulse depth are determined by the Markov chain is presented. Second-order properties of the rainfall depth process are derived and utilised in model assessment. A method of moment estimation is employed in model fitting. The proposed model is used to analyse 69 years of sub-hourly rainfall data from Germany and 15 years of English rainfall data. The results of the analysis using variants of the proposed model with fixed pulse lifetime and variable pulse duration are presented. The performance of the proposed model, in reproducing second-moment characteristics of the rainfall, is compared with that of two stochastic models where one has exponential pulses and the other has rectangular pulses. The proposed model is found to capture most of the empirical rainfall properties well and outperform the two alternative models considered in our analysis.
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Parsons, Scott A., and Robert A. Congdon. "Plant litter decomposition and nutrient cycling in north Queensland tropical rain-forest communities of differing successional status." Journal of Tropical Ecology 24, no. 3 (May 2008): 317–27. http://dx.doi.org/10.1017/s0266467408004963.
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Abstract:Soil processes are essential in enabling forest regeneration in disturbed landscapes. Little is known about whether litterfall from dominating pioneer species in secondary rain forest is functionally equivalent to that of mixed rain-forest litter in terms of contribution to soil processes. This study used the litterbag technique to quantify the decomposition and nutrient dynamics of leaf litter characteristic of three wet tropical forest communities in the Paluma Range National Park, Queensland, Australia over 511 d. These were: undisturbed primary rain forest (mixed rain-forest species), selectively logged secondary rain forest (pioneer Alphitonia petriei) and tall open eucalypt forest (Eucalyptus grandis). Mass loss, total N, total P, K, Ca and Mg dynamics of the decaying leaves were determined, and different mathematical models were used to explain the mass loss data. Rainfall and temperature data were also collected from each site. The leaves of A. petriei and E. grandis both decomposed significantly slower in situ than the mixed rain-forest species (39%, 38% and 29% ash-free dry mass remaining respectively). Nitrogen and phosphorus were immobilized, with 182% N and 134% P remaining in E. grandis, 127% N and 132% P remaining in A. petriei and 168% N and 121% P remaining in the mixed rain-forest species. The initial lignin:P ratio and initial lignin:N ratio exerted significant controls on decomposition rates. The exceptionally slow decomposition of the pioneer species is likely to limit soil processes at disturbed tropical rain-forest sites in Australia.
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Ke, Shitang, Wenlin Yu, and Yaojun Ge. "Wind Load Characteristics and Action Mechanism on Internal and External Surfaces of Super-Large Cooling Towers under Wind-Rain Combined Effects." Mathematical Problems in Engineering 2018 (July 8, 2018): 1–22. http://dx.doi.org/10.1155/2018/2921709.
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By focusing on wind-rain two-way coupling algorithm, simulation iterations of wind field and raindrops in the world highest cooling tower (210m) in northwest China were carried out using continuous phase and discrete phase models based on CFD numerical simulation. Firstly, influence laws of 9 wind velocity-rainfall intensity combinations on wind-induced rainfall, raindrop additional force, and equivalent pressure coefficient on internal and external surface of the tower body were discussed. On this basis, speed flow line, turbulence energy strength, raindrop running speed, and track on the tower body in the wind-rain coupling field were disclosed. Finally, qualitative and quantitative contrastive analyses on wind pressure, rain pressure, and equivalent pressure coefficient on internal and external surfaces of the tower body were conducted under different working conditions. Thus, the most unfavorable wind-rain combination was identified. Calculation formulas of equivalent internal and external pressure coefficients of super-large cooling towers were fitted from nonlinear least square method. Research results demonstrate that the 3D effect of equivalent internal and external pressure coefficients with considerations to wind-rain two-way coupling is more prominent. Particularly, there is strong transition on the windward region of the external surface and leeside region at bottom of internal surface. The quantity of caught raindrops on the structural surface is negatively related to wind velocity but is positively related to rainfall intensity. Rain load and rainfall coefficients on the external surface are significantly higher than those on the internal surface. Equivalent internal pressure coefficient has a sharp reduction on the leeside region under different working conditions. Besides, equivalent internal pressure coefficient of different meridians decreases with the increase of height. The maximum and minimum are -0.574 and -0.282, respectively. The proposed equivalent internal and external pressure coefficients of super-large cooling tower can predict wind load under extreme climate conditions accurately.
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Sansom, John, and Peter Thomson. "Fitting hidden semi-Markov models to breakpoint rainfall data." Journal of Applied Probability 38, A (2001): 142–57. http://dx.doi.org/10.1239/jap/1085496598.
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The paper proposes a hidden semi-Markov model for breakpoint rainfall data that consist of both the times at which rain-rate changes and the steady rates between such changes. The model builds on and extends the seminal work of Ferguson (1980) on variable duration models for speech. For the rainfall data the observations are modelled as mixtures of log-normal distributions within unobserved states where the states evolve in time according to a semi-Markov process. For the latter, parametric forms need to be specified for the state transition probabilities and dwell-time distributions.Recursions for constructing the likelihood are developed and the EM algorithm used to fit the parameters of the model. The choice of dwell-time distribution is discussed with a mixture of distributions over disjoint domains providing a flexible alternative. The methods are also extended to deal with censored data. An application of the model to a large-scale bivariate dataset of breakpoint rainfall measurements at Wellington, New Zealand, is discussed.
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Klamerus-Iwan, Anna, and Maciej Sporysz. "Laboratory determination of potential interception of young deciduous trees during low-intense precipitation." Folia Forestalia Polonica 56, no. 1 (March 1, 2014): 3–8. http://dx.doi.org/10.2478/ffp-2014-0001.
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Abstract The research issue focuses on potential interception, which is the maximum amount of water that can be stored on plant surface. Tests under controlled conditions remain the best way to enhance knowledge on interception determinants in forest communities. Such tests can provide data for identification of mathematical models based on ecological criteria. The study presented in this paper concerned tree interception under simulated rain in a range from 2 to 11 mm/h. To perform the experiment a set of sprinklers was designed and built. The study included two deciduous species: beech (Fagus sylvatica L.) and oak (Quercus robur L.). Descriptive characteristic and nonlinear estimation were suggested for the obtained data. Interdependence of potential interception, the intensity of rain and the size of raindrops were described using exponential equation. The intensity and drop size of simulated rainfall significantly influence the obtained values of potential interception. Data analysis shows a decrease of interception value with an increase of intensity of simulated rainfall for both analysed species. Every run of the experiment that differed in the intensity and size of raindrops reached an individual level of potential interception and time needed to realize it. The formation of ability of plants to intercept water depends both on the dynamics and the time of spraying.
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Wang, Zhenlong, Yingying Xu, Guoqiang Dong, Haishen Lv, Yue Fan, and Yining Wang. "Methods for calculating phreatic evaporation on bare grounds on rainy and dry days." Hydrology Research 51, no. 6 (July 7, 2020): 1221–37. http://dx.doi.org/10.2166/nh.2020.017.
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Abstract In order to depict the impact of rainfall on phreatic evaporation, this study analyzes phreatic evaporation and the phreatic evaporation coefficient between surface evaporation and soil depth in Shajiang black soil and Fluyo-aquic soil. We have improved the existing commonly used mathematical framework, established two rainless day phreatic evaporation calculation models, and then calculated the calculation model of the phreatic evaporation reduction on rainy days. Finally, rainy day evaporation calculation models on two soils were proposed. The results show that the evaporation coefficient is affected by both depth and the evaporation ability of the surface water. The evaporation reduction of Shajiang black soil increased with depth and the increasing trend gradually slowed down until it approached zero. The evaporation reduction of the Fluyo-aquic soil phreatic decreased first and then increased with depth, reaching a minimum at 0.4 m. The reduction of phreatic evaporation in both soils decreased with the increase in rainfall level and decreased with the increase in rainfall duration showing ‘inverted S-type’. In summary, the phreatic evaporation composite calculation models on rainy days and rainless days have good fitting and prediction results, which can improve the accuracy of phreatic evaporation calculations.
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Christofilakis, Vasilis, Giorgos Tatsis, Spyridon K. Chronopoulos, Alexandros Sakkas, Anastasios G. Skrivanos, Kostas P. Peppas, Hector E. Nistazakis, Giorgos Baldoumas, and Panos Kostarakis. "Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature." Symmetry 12, no. 9 (September 1, 2020): 1440. http://dx.doi.org/10.3390/sym12091440.
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Many works have been conducted relevant to rainfall measurements, while the first relevant ones were based on the power loss estimation function from wireless links located back to the early 1940s. It is notable, though, that this innovative idea conduced to many theoretical models correlating the signal attenuation to the rainfall intensity. This type of parameter strongly contributes to the mechanism of frequency attenuation above 10 GHz. Consequently, in the last twenty years, there has been a significant boost to this research topic. Researchers all around the world have worked thoroughly on the issue of estimating rain with the use of earth-to-earth microwave signal attenuation. Nevertheless, the issue remains intriguing and challenging. This paper presents a literature survey, of the last decade, on this challenging issue focusing on measurements from backhaul cellular microwave links and experimental setups. Research challenges and future trends are also presented.
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Head, P. C., D. H. Crawshaw, P. Dempsey, and C. J. Hutchings. "Bathing in the Rain - The Use of Mathematical Models for Storm Water Management to Achieve Bathing Water Quality (The Fylde Coast-NW England)." Water Science and Technology 25, no. 12 (June 1, 1992): 59–68. http://dx.doi.org/10.2166/wst.1992.0337.
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One of the major problems in trying to design wastewater treatment schemes to protect bathing water for coastal communities with combined sewerage systems, is to ensure that discharges of storm water do not prejudice compliance with the requirements of the EC Bathing Water Directive. In order to develop an appropriate storm water management strategy for the Fylde coast it was necessary to integrate a number of mathematical models simulating the hydraulic behaviour of the sewerage system and the dispersion of discharges in the receiving waters. From the sewerage system modelling it was apparent that frequent discharges of storm water to the bathing waters could only be avoided by the provision of considerable additional storage in the system. By means of a suitably calibrated simplified sewer model it was possible to investigate the volumes of storm water generated by a 15 year record of local rainfall when different amounts of extra storage and different pumping regimes were employed. The results from these investigations were used to determine the probable concentrations of faecal bacteria in the coastal waters for each of the 15 bathing seasons and determine the percentage of time for which faecal coliform concentrations exceeded the Bathing Water directive standards for the model grid cells representing the identified bathing waters. As a result of the extensive integrated modelling programme for the Fylde coast it has been possible to design a base flow and storm water management system which should maximize the flow passed forward for treatment whilst also ensuring that there is just sufficient storage to ensure protection of the towns from flooding and the compliance of the beaches with the Bathing Water Directive standards.
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Ávila-Dávila, Laura, Manuel Soler-Méndez, Carlos Francisco Bautista-Capetillo, Julián González-Trinidad, Hugo Enrique Júnez-Ferreira, Cruz Octavio Robles Rovelo, and José Miguel Molina-Martínez. "A Compact Weighing Lysimeter to Estimate the Water Infiltration Rate in Agricultural Soils." Agronomy 11, no. 1 (January 18, 2021): 180. http://dx.doi.org/10.3390/agronomy11010180.
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Infiltration estimation is made by tests such as concentric cylinders, which are prone to errors, such as the lateral movement under the ring. Several possibilities have been developed over the last decades to compensate these errors, which are based on physical, electronic, and mathematical principles. In this research, two approaches are proposed to measure the water infiltration rate in a silty loam soil by means of the mass values of a lysimeter weighing under rainfall conditions and different moisture contents. Based on the fact that with the lysimeter it is possible to determine acting soil flows very precisely, then with the help of mass conservation and assuming a downward vertical movement, 12 rain events were analyzed. In addition, it was possible to monitor the behavior of soil moisture and to establish the content at field capacity from the values of the weighing lysimeter, from which both approach are based. The infiltration rate of these events showed a variable rate at the beginning of the rainfall until reaching a maximum, to descend to a stable or basic rate. This basic infiltration rate was 1.49 ± 0.36 mm/h, and this is because soils with fine textures have reported low infiltration capacity. Four empirical or semi-empirical models of infiltration were calibrated with the values obtained with our approaches, showing a better fit with the Horton’s model.
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Cardoso, Dione Pereira, Fábio Ribeiro Pires, and Robson Bonomo. "Avaliação de modelos matemáticos para estimativa da erosividade da chuva na região de São Mateus-ES." Revista Verde de Agroecologia e Desenvolvimento Sustentável 11, no. 3 (August 14, 2016): 98. http://dx.doi.org/10.18378/rvads.v11i3.4132.
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<p>Objetivou-se estimar a erosividade da chuva, mediante seis modelos matemáticos, de regressão linear avaliando entre estes, qual é mais indicado para as condições climáticas da região de São Mateus-ES. Os dados pluviométricos foram obtidos junto à Agência Nacional das Águas-ANA, sendo de 1947 a 2014 para Itauninhas, de 1971 a 2014 para Barra Nova, de 1981 a 2014 para São João da Cachoeira Grande e de 1993 a 2014 para Boca da Vala. Para estimar a erosividade da chuva, a partir da precipitação anual e do coeficiente de chuva, foram utilizadas diferentes equações utilizadas em outros estados com aplicação ao estado do Espírito Santo ou ajustadas para o próprio estado. Para os modelos matemáticos (II) e (I), os valores médios foram de 6.541,2 MJ ha<sup>-1</sup> mm h<sup>-1</sup> ano<sup>-1</sup> a 936,357 MJ ha<sup>-1</sup> mm h<sup>-1</sup> ano<sup>-1</sup> (Itauninhas), de 6.995,855 MJ ha<sup>-1</sup> mm h<sup>-1</sup> ano<sup>-1</sup> a 1.420,296 MJ ha<sup>-1</sup> mm h<sup>-1</sup> ano<sup>-1</sup> (Barra Nova), de 6.297,272 MJ ha<sup>-1</sup> mm h<sup>-1</sup> ano<sup>-1</sup> a 1.014,815 MJ ha<sup>-1</sup> mm h<sup>-1</sup> ano<sup>-1</sup> (São João da Cachoeira Grande) e de 5.427,659 MJ ha<sup>-1</sup> mm h<sup>-1</sup> ano<sup>-1</sup> a 1.626,489 MJ ha<sup>-1</sup> mm h<sup>-1</sup> ano<sup>-1</sup> (Boca da Vala). Para os municípios de Barra Nova e Boca da Vala a erosividade da chuva foi estimada pela equação EI<sub>30</sub> = 6,4492*pi – 391,63 com distribuição leptocúrtica. Para as outras duas localidades, a distribuição foi platicúrtica. A estação climatológica com o maior valor de erosividade média da chuva foi Barra Nova, enquanto Boca da Vala apresentou a menor erosividade, considerando apenas a estimativa da erosividade da chuva pelo modelo matemático II. Os maiores e menores valores de erosividade da chuva foram obtidos com os modelos matemáticos I e II. Para estimar a erosividade da chuva, nas condições climáticos da região de São Mateus-ES, o modelo matemático mais adequado é o II.</p><p align="center"><strong><em>Evaluation of mathematical models to estimate rainfall erosivity in the region of São Mateus-ES</em></strong></p><p><strong>Abstract</strong><strong>: </strong>This study aimed to estimate the rainfall erosivity by six mathematical models, linear regression, and evaluate these, which is more suitable for the climatic conditions of São Mateus-ES region. The rainfall data were obtained from the National Water Agency-ANA, and 1947-2014 for Itauninhas, 1971-2014 to Barra Nova, 1981-2014 for São João da Cachoeira Grande and 1993-2014 for Boca da Vala. To estimate the rainfall erosivity, from the annual precipitation and rainfall coefficient were used different equations used in other states with application to the state of the Holy Spirit or adjusted to the state itself. For mathematical models (II) and (I), the average values were 6541.2 MJ ha<sup>-1</sup> mm h<sup>-1</sup> year<sup>-1</sup> to 936.357 MJ ha<sup>-1</sup> mm h<sup>-1</sup> year<sup>-1</sup> (Itauninhas) of 6995.855 MJ mm ha<sup>-1</sup> h<sup>-1</sup> year<sup>-1</sup> to 1420.296 MJ ha<sup>-1</sup> mm h<sup>-1</sup> year<sup>-1</sup> (Barra nova), to 6297.272 MJ ha<sup>-1</sup> mm h<sup>-1</sup> year<sup>-1</sup> and 1014.815 MJ mm ha<sup>-1</sup> h<sup>-1</sup> year<sup>-1</sup> (São João da Cachoeira Grande) and 5427.659 MJ ha<sup>-1</sup> mm h<sup>-1</sup> year<sup>-1</sup> to 1626.489 MJ ha<sup>-1</sup> mm h<sup>-1</sup> year<sup>-1</sup> (Boca da Vala). For the municipalities of Barra Nova and Boca da Vala the rainfall erosivity was estimated by EI<sub>30</sub> = 6.4492*pi - 391.63 with leptokurtic distribution. For the other two locations, the distribution was platykurtic. The climatological station with the highest amount of average rainfall erosivity was Barra Nova, while Boca da Vala had the lowest erosivity, considering only an estimated rainfall erosivity by the mathematical model II. The highest and lowest values erosivity of the rain were obtained with the mathematical models I and II. To estimate the rainfall erosivity in the climatic conditions of São Mateus-ES region, the most suitable mathematical model is II.</p>
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van Dijk, J., and E. R. Morgan. "The influence of water and humidity on the hatching of Nematodirus battus eggs." Journal of Helminthology 86, no. 3 (July 26, 2011): 287–92. http://dx.doi.org/10.1017/s0022149x1100040x.
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AbstractThis paper examines the influence of water on the ecology of the eggs of Nematodirus battus, with a view to estimating the importance of including rainfall in mathematical models of parasite abundance. The literature suggests that, under pasture conditions, the availability of moisture is unlikely to be limiting for egg development, while eggs and infective larvae are highly resistant to desiccation. In the presented experiment, eggs that had been kept in salt sludges at 95% and 70% RH and were subsequently put at 15°C produced only a mildly accelerated, but not a mass, hatch, in the first few days after return to water. Eggs kept at higher osmotic pressures died. Mass hatching of infective larvae, described at pasture when spells of rain follow periods of drought, is unlikely to occur as the result of a sudden water influx into eggs. Since water is not necessary for migration of infective larvae from the soil on to grass, such peaks in larval abundance are more likely to arise from the effects of temperature on hatching of eggs.
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Mendes, T. A., S. F. Sousa Júnior, and S. A. S. Pereira. "Implementation of the Green-Ampt Infiltration Model: Comparative between different numerical solutions." Trends in Computational and Applied Mathematics 22, no. 4 (October 26, 2021): 645–58. http://dx.doi.org/10.5540/tcam.2021.022.04.00645.
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The phenomena of infiltration and the percolation of water in the soil are of fundamental importance for the evaluation of runoff, groundwater recharge, evapotranspiration, soil erosion and transport of chemical substances in surface and groundwater. Within this context, the quantitative determination of the infiltration values is extremely important for the different areas of knowledge, in order to evaluate, mainly the surface runoff. Several types of changes in vegetation cover and topography result in significant changes in the infiltration process, making it necessary to use mathematical models to assess the consequences of these changes. Thus, this article aims to implement the Green-Ampt model using two numerical methods - Newton-Raphson method and W-Lambert function - to determine soil permeability parameters - K and matric potential multiplied by the difference between initial and of saturation - comparing them to the real data obtained in simulations using an automatic rainfall simulator from the Federal University of Goiás - UFG. The Green-Ampt model adjusted well to the data measured from the rain simulator, with a determination coefficient of 0.978 for the Newton-Raphson method and 0.984 for the W-Lambert function.
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Salaeh, Nureehan, Pakorn Ditthakit, Sirimon Pinthong, Mohd Abul Hasan, Saiful Islam, Babak Mohammadi, and Nguyen Thi Thuy Linh. "Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand." Symmetry 14, no. 8 (August 3, 2022): 1599. http://dx.doi.org/10.3390/sym14081599.
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Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers’ plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use rainfall as one of their components. This paper aimed to investigate the applicability of six machine learning (ML) techniques (i.e., M5 model tree: (M5), random forest: (RF), support vector regression with polynomial (SVR-poly) and RBF kernels (SVR- RBF), multilayer perceptron (MLP), and long-short-term memory (LSTM) in predicting for multiple-month ahead of monthly rainfall. The experiment was set up for two weather gauged stations located in the Thale Sap Songkhla basin. The model development was carried out by (1) selecting input variables, (2) tuning hyperparameters, (3) investigating the influence of climate variables on monthly rainfall prediction, and (4) predicting monthly rainfall with multi-step-ahead prediction. Four statistical indicators including correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and overall index (OI) were used to assess the model’s effectiveness. The results revealed that large-scale climate variables, particularly sea surface temperature, were significant influence variables for rainfall prediction in the tropical climate region. For projections of the Thale Sap Songkhla basin as a whole, the LSTM model provided the highest performance for both gauged stations. The developed predictive rainfall model for two rain gauged stations provided an acceptable performance: r (0.74), MAE (86.31 mm), RMSE (129.11 mm), and OI (0.70) for 1 month ahead, r (0.72), MAE (91.39 mm), RMSE (133.66 mm), and OI (0.68) for 2 months ahead, and r (0.70), MAE (94.17 mm), RMSE (137.22 mm), and OI (0.66) for 3 months ahead.
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Delrieu, Guy, Anil Kumar Khanal, Frédéric Cazenave, and Brice Boudevillain. "Sensitivity analysis of attenuation in convective rainfall at X-band frequency using the mountain reference technique." Atmospheric Measurement Techniques 15, no. 11 (June 3, 2022): 3297–314. http://dx.doi.org/10.5194/amt-15-3297-2022.
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Abstract. The RadAlp experiment aims at improving quantitative precipitation estimation (QPE) in the Alps thanks to X-band polarimetric radars and in situ measurements deployed in the region of Grenoble, France. In this article, we revisit the physics of propagation and attenuation of microwaves in rain. We first derive four attenuation–reflectivity (AZ) algorithms constrained, or not, by path-integrated attenuations (PIAs) estimated from the decrease in the return of selected mountain targets when it rains compared to their dry weather levels (the so-called mountain reference technique – MRT). We also consider one simple polarimetric algorithm based on the profile of the total differential phase shift between the radar and the mountain targets. The central idea of the work is to implement these five algorithms all together in the framework of a generalized sensitivity analysis in order to establish useful parameterizations for attenuation correction. The parameter structure and the inherent mathematical ambiguity of the system of equations makes it necessary to organize the optimization procedure in a nested way. The core of the procedure consists of (i) exploring with classical sampling techniques the space of the parameters allowed to be variable from one target to the other and from one time step to the next, (ii) computing a cost function (CF) quantifying the proximity of the simulated profiles and (iii) selecting parameters sets for which a given CF threshold is exceeded. This core is activated for a series of values of parameters supposed to be fixed, e.g., the radar calibration error for a given event. The sensitivity analysis is performed for a set of three convective events using the 0∘ elevation plan position indicator (PPI) measurements of the Météo-France weather radar located on top of the Moucherotte mountain (altitude of 1901 m a.s.l. – above sea level). It allows the estimation of critical parameters for radar QPE using radar data alone. In addition to the radar calibration error, this includes the time series of radome attenuation and estimations of the coefficients of the power law models relating the specific attenuation and the reflectivity (A–Z relationship) on the one hand and the specific attenuation and the specific differential phase shift (A–Kdp relationship) on the other hand. It is noteworthy that the A–Z and A–Kdp relationships obtained are consistent with those derived from concomitant drop size distribution measurements at ground level, in particular with a slightly non-linear A–Kdp relationship (A=0.28 Kdp1.1). X-Band radome attenuations as high as 15 dB were estimated, leading to the recommendation of avoiding the use of radomes for remote sensing of precipitation at such a frequency.
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Silva, Reginaldo Barboza da, Piori Iori, and Francisca Alcivania de Melo Silva. "PROPOSIÇÃO E VALIDAÇÕES DE EQUAÇÕES PARA ESTIMATIVA DA EROSIVIDADE DE DOIS MUNICÍPIOS DO ESTADO DE SÃO PAULO." IRRIGA 14, no. 4 (June 18, 2018): 533–47. http://dx.doi.org/10.15809/irriga.2009v14n4p533-547.
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PROPOSIÇÃO E VALIDAÇÕES DE EQUAÇÕES PARA ESTIMATIVA DA EROSIVIDADE DE DOIS MUNICÍPIOS DO ESTADO DE SÃO PAULO Reginaldo Barboza da Silva1; Piero Iori2; Francisca Alcivania de Melo Silva11 Universidade Estadual Paulista, Unidade de Registro, Registro, rbsilva@registro.unesp.br2 Departamento de Ciência do Solo, Universidade Federal de Lavras, Lavras, MG 1 RESUMO O uso e extrapolação de equações para localidades cujas características de solo e clima, ainda que parciais, distingam da localidade para a qual tenham sido geradas, ainda permeiam em estudos para estimativa da erosividade (EI30). Este trabalho teve como objetivo propor e validar equações matemáticas para estimativa da erosividade de dois municípios (Sete Barras e Juquiá) do Estado de São Paulo. O ajuste das equações para estimar os valores de erosividade (EI30) em função de valores de coeficiente de chuva (Rc) foi a partir de dados pluviográficos e pluviométricos, respectivamente, utilizando-se de distintas séries históricas. Testes de comparação múltipla e intervalos de confiança foram empregados para comparar médias absolutas de EI30, precipitações (Pp) e Rc. A correlação entre o EI30 e Rc foi verificada pelo coeficiente de correlação de Pearson. O teste da hipótese de igualdade entre as variâncias populacionais foi utilizado para comparar as equações. Dados pluviométricos de uma série histórica diferente das que geraram a equações foram utilizados para validar e avaliar o desempenho das equações obtidas neste estudo e compará-las com outra equação já consolidada pela literatura. Os resultados mostraram que para as condições em que foi realizado o estudo, as equações lineares simples, mostraram ser as mais apropriadas para estimar a erosividade nestes dois municípios. De acordo com o teste da hipótese de igualdade entre as variâncias populacionais. As equações ajustadas para cada município diferiram estatisticamente, de maneira que, a erosividade de cada município deve ser predita por seus modelos respectivos. UNITERMOS: EUPS, erosão, modelagem, Vale do Ribeira. SILVA, R.B.; IORI, P.; SILVA, F. A. de M. PROPOSITION AND COMPARE OF EQUATIONS TO ESTIMATE THE RAINFALL EROSIVITY IN TWO CITIES OF SÃO PAULO STATE 2 ABSTRACT The equations and extrapolation use to localities whose characteristics of soil and climate, even if partial, distinguish the town to which they were generated, still permeate in studies to estimate the rainfall erosivity (EI30). This work has objective to propose and validate mathematical equations to estimate the rainfall erosivity of two cities of Sao Paulo State’s. The adjusted to estimate obtaining and validate data of equations of erosivity (EI30)according to values of coefficient of rain (Rc) were obtained from pluviographic and pluviometric rainfall data, respectively, using of distinct historical rainfall series. Mutiple comparisions test and confidence intervals were performed to compare absolute average of EI30, pluviometric data (Pp), and Rc. The correlation between EI30 and Rc was verified by of Pearson correlation coefficient. Test of the hypothesis of equality between population variance was used to compare the equations. Pluviometrics data of historical series rainfall data different than those that the models were generated were used to validate and to assess the performance of the equations, proposed of this study and compare them with another equation already consolidated in literature. The results show that for the conditions under which the study was conducted, the simple linear equations, shown to be the most appropriate to estimate the rainfall erosivity these two cities. According to the test of the hypothesis of equality variances between populations, the equations adjusted for each city differ statistically so that the rainfall erosivity of each city must be estimated by their respective equation. KEYWORDS: erosion, modeling, USLE, Vale do Ribeira.
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Pasculli, Antonio, Jacopo Cinosi, Laura Turconi, and Nicola Sciarra. "Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow." Water 13, no. 6 (March 10, 2021): 750. http://dx.doi.org/10.3390/w13060750.
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The current climate change could lead to an intensification of extreme weather events, such as sudden floods and fast flowing debris flows. Accordingly, the availability of an early-warning device system, based on hydrological data and on both accurate and very fast running mathematical-numerical models, would be not only desirable, but also necessary in areas of particular hazard. To this purpose, the 2D Riemann–Godunov shallow-water approach, solved in parallel on a Graphical-Processing-Unit (GPU) (able to drastically reduce calculation time) and implemented with the RiverFlow2D code (version 2017), was selected as a possible tool to be applied within the Alpine contexts. Moreover, it was also necessary to identify a prototype of an actual rainfall monitoring network and an actual debris-flow event, beside the acquisition of an accurate numerical description of the topography. The Marderello’s basin (Alps, Turin, Italy), described by a 5 × 5 m Digital Terrain Model (DTM), equipped with five rain-gauges and one hydrometer and the muddy debris flow event that was monitored on 22 July 2016, were identified as a typical test case, well representative of mountain contexts and the phenomena under study. Several parametric analyses, also including selected infiltration modelling, were carried out in order to individuate the best numerical values fitting the measured data. Different rheological options, such as Coulomb-Turbulent-Yield and others, were tested. Moreover, some useful general suggestions, regarding the improvement of the adopted mathematical modelling, were acquired. The rapidity of the computational time due to the application of the GPU and the comparison between experimental data and numerical results, regarding both the arrival time and the height of the debris wave, clearly show that the selected approaches and methodology can be considered suitable and accurate tools to be included in an early-warning system, based at least on simple acoustic and/or light alarms that can allow rapid evacuation, for fast flowing debris flows.
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Prein, A. F., R. M. Rasmussen, D. Wang, and S. E. Giangrande. "Sensitivity of organized convective storms to model grid spacing in current and future climates." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2195 (March 2021): 20190546. http://dx.doi.org/10.1098/rsta.2019.0546.
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Mesoscale convective systems (MCSs) are complexes of thunderstorms that become organized and cover hundreds of kilometres over several hours. MCSs are prolific rain producers in the tropics and mid-latitudes and are the major cause of warm-season flooding. Traditionally, climate models have difficulties in simulating MCSs partly due to the misrepresentation of complex process interactions that operate across a large range of scales. Significant improvements in simulating MCSs have been found in kilometre-scale models that explicitly simulate deep convection. However, these models operate in the grey zone of turbulent motion and have known deficiencies in simulating small-scale processes (e.g. entrainment, vertical mass transport). Here, we perform mid-latitude idealized ensemble MCS simulations under current and future climate conditions in three atmospheric regimes: hydrostatic (12 km horizontal grid spacing; Δ x ), non-hydrostatic (Δ x = 4, 2 and 1 km) and large eddy scale (Δ x = 500 m and 250 m). Our results show a dramatic improvement in simulating MCS precipitation, movement, cold pools, and cloud properties when transitioning from 12 km to 4 km Δ x . Decreasing Δ x beyond 4 km results in modest improvements except for up- and downdraft sizes, average vertical mass fluxes, and cloud top height and temperature, which continue to change. Most important for climate modelling is that Δ x = 4 km simulations reliably capture most MCS climate change signals compared to those of the Δ x = 250 m runs. Significantly different climate change signals are found in Δ x = 12 km runs that overestimate extreme precipitation changes by up to 100%. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.
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De Alcântara, Lucas Ravellys Pyrrho, Artur Paiva Coutinho, Severino Martins Dos Santos Neto, Tássia Dos Anjos Tenório de Melo, Larissa Fernandes Costa, Larissa Virgínia da Silva Ribas, Antonio Celso Dantas Antonino, and Edevaldo Miguel Alves. "Modelos probabilísticos para eventos de precipitações extremas na Cidade de Palmares-PE (Probabilistic modeling for extreme rainfall events in the city of Palmares - PE)." Revista Brasileira de Geografia Física 12, no. 4 (October 15, 2019): 1355. http://dx.doi.org/10.26848/rbgf.v12.4.p1355-1369.
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A estimava da probabilidade de excedência de eventos de precipitações pluviométricas máximas pode ser realizada a partir da associação entre as séries hidrológicas e modelos probabilísticos. O presente trabalho tem por objetivo avaliar a aderência da distribuição empírica de Precipitações Diárias Máximas Anuais (PDMA), as distribuições teóricas de probabilidade de Gumbel, Log-Normal de dois parâmetros, Generalizada de Valores Extremos, Fréchet, Weibull para 2 e 3 parâmetros, Gama, Pearson e Log-Pearson para 3 parâmetros. Foi utilizada uma série histórica de precipitação máxima diária anual oriunda da cidade de Palmares-PE, a partir de dados obtidos da Agência Nacional de Águas (ANA). Para avaliar a qualidade de aderência das distribuições, foram utilizados os testes de aderência de Anderson Darling (AD), Kolmogorov-Smirnov (KS) e o teste Qui-Quadrado de Pearson (χ2). Para quantificação da qualidade dos ajustes estatísticos utilizou-se do coeficiente de determinação (R2). As distribuições de Fréchet e Weibull II não apresentaram aderência a distribuição empírica de frequência. A distribuição de Gumbel foi a que apresentou maior aderência à distribuição empírica de acordo com o teste Qui-Quadrado de Pearson (χ2), enquanto que a GVE e a Pearson III aos testes AD e KS, respectivamente. A B S T R A C TTo analyze and estimate the likelihood of new extreme precipitation events, hydrological data records and probabilistic mathematical modeling can be used associated with different recurrence frequencies. The objective of this study was to adjust the PDMA of the city of Palmares-PE, based on data obtained from the National Water Agency (ANA), the Gumbel probability distributions, Log-Normal of two Parameters, Generalized Extreme Values, Fréchet, Weibull for 2 and 3 parameters, Range, Pearson and Log-Pearson for 3 parameters. In order to evaluate the statistical distributions, the Anderson Darling (AD), Kolmogorov-Smirnov (KS) and Pearson Chi-Square (χ2) tests were used, and the quantification of the quality of the statistical adjustments was done using coefficient of determination (R2). Among the probabilistic distributions analyzed, the only ones that do not fit are the distributions of FRÉCHET and Weibull II. The Gumbel distribution was the best fit for Pearson Chi-square test (χ2), and GVE and Pearson III, respectively, for the AD and KS tests.Keywords: hydrology statistics, return time, intense rain, extreme events, random variables.
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Turco, M., and M. Milelli. "The forecaster's added value in QPF." Advances in Geosciences 25 (March 9, 2010): 29–36. http://dx.doi.org/10.5194/adgeo-25-29-2010.
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Abstract. To the authors' knowledge there are relatively few studies that try to answer this question: "Are humans able to add value to computer-generated forecasts and warnings?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast. Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human Quantitative Precipitation Forecast) in terms of an areal average and maximum value for each of the 13 warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS (Global Telecommunication System) network of rain gauges available that makes possible a high resolution verification. In this work we compare the performances of the latest three years of QPF derived from the meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the skill scores of two competitive forecasts. It is important to underline that the conclusions refer to the analysis of the Piemonte operational alert system, so they cannot be directly taken as universally true. But we think that some of the main lessons that can be derived from this study could be useful for the meteorological community. In details, the main conclusions are the following: – despite the overall improvement in global scale and the fact that the resolution of the limited area models has increased considerably over recent years, the QPF produced by the meteorological models involved in this study has not improved enough to allow its direct use: the subjective HQPF continues to offer the best performance for the period +24 h/+48 h (i.e. the warning period in the Piemonte system); – in the forecast process, the step where humans have the largest added value with respect to mathematical models, is the communication. In fact the human characterization and communication of the forecast uncertainty to end users cannot be replaced by any computer code; – eventually, although there is no novelty in this study, we would like to show that the correct application of appropriated statistical techniques permits a better definition and quantification of the errors and, mostly important, allows a correct (unbiased) communication between forecasters and decision makers.
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Kholodovsky, Vitaly, and Xin-Zhong Liang. "A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns." Advances in Statistical Climatology, Meteorology and Oceanography 7, no. 1 (April 21, 2021): 35–52. http://dx.doi.org/10.5194/ascmo-7-35-2021.
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Abstract. Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale well-connected spatial patterns to smaller-scale more isolated rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic-scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.
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Hu, Caihong, Chengshuai Liu, Yichen Yao, Qiang Wu, Bingyan Ma, and Shengqi Jian. "Evaluation of the Impact of Rainfall Inputs on Urban Rainfall Models: A Systematic Review." Water 12, no. 9 (September 5, 2020): 2484. http://dx.doi.org/10.3390/w12092484.
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Over the past several decades, urban flooding and other water-related disasters have become increasingly prominent and serious. Although the urban rain flood model’s benefits for urban flood simulation have been extensively documented, the impact of rainfall input to model simulation accuracy remains unclear. This systematic review aims to provide structured research on how rain inputs impact urban rain flood model’s simulation accuracy. The selected 48 peer-reviewed journal articles published between 2015 and 2019 on the Web of Science™ database were analyzed by key factors, including rainfall input type, calibration times and verification times. The results from meta-analysis reveal that when a traditional rain measurement was used as the rainfall input, model simulation accuracy was higher, i.e., the Nash–Sutcliffe efficiency coefficient (NSE) of traditional technology for rain measurement was higher than the 0.18 for the new technology rain measurement with respect to flow simulation. In addition, the single-field sub-flood calibration model was better than the multi-field sub-flood calibration model. NSE was higher than 0.14. The precision was better for the verification period; NSE of the calibration value showed a 0.07 higher verification value on average in flow simulation. These findings have certain significance for the development of future urban rain flood models and propose the development direction of the future urban rain flood model. Finally, in view of the rainfall input problem of the urban storm flood model, we propose the future development direction of the urban storm flood model.
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Pendergrass, Angeline G., and Dennis L. Hartmann. "Two Modes of Change of the Distribution of Rain*." Journal of Climate 27, no. 22 (November 4, 2014): 8357–71. http://dx.doi.org/10.1175/jcli-d-14-00182.1.
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Abstract The frequency and intensity of rainfall determine its character and may change with climate. A methodology for characterizing the frequency and amount of rainfall as functions of the rain rate is developed. Two modes of response are defined, one in which the distribution of rainfall increases in equal fraction at all rain rates and one in which the rainfall shifts to higher or lower rain rates without a change in mean rainfall. This description of change is applied to the tropical distribution of daily rainfall over ENSO phases in models and observations. The description fits observations and most models well, although some models also have an extreme mode in which the frequency increases at extremely high rain rates. The multimodel mean from phase 5 of the Coupled Model Intercomparison Project (CMIP5) agrees with observations in showing a very large shift of 14%–15% K−1, indicating large increases in the heaviest rain rates associated with El Niño. Models with an extreme mode response to global warming do not agree as well with observations of the rainfall response to El Niño.
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Benoit, Lionel, Mathieu Vrac, and Gregoire Mariethoz. "Dealing with non-stationarity in sub-daily stochastic rainfall models." Hydrology and Earth System Sciences 22, no. 11 (November 19, 2018): 5919–33. http://dx.doi.org/10.5194/hess-22-5919-2018.
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Abstract. Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeller. It is therefore desirable to establish quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space–time–intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities. Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution.
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Rupp, D. E., P. Licznar, W. Adamowski, and M. Leśniewski. "Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data." Hydrology and Earth System Sciences 16, no. 3 (March 6, 2012): 671–84. http://dx.doi.org/10.5194/hess-16-671-2012.
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Abstract. Capturing the spatial distribution of high-intensity rainfall over short-time intervals is critical for accurately assessing the efficacy of urban stormwater drainage systems. In a stochastic simulation framework, one method of generating realistic rainfall fields is by multiplicative random cascade (MRC) models. Estimation of MRC model parameters has typically relied on radar imagery or, less frequently, rainfall fields interpolated from dense rain gauge networks. However, such data are not always available. Furthermore, the literature is lacking estimation procedures for spatially incomplete datasets. Therefore, we proposed a simple method of calibrating an MRC model when only data from a moderately dense network of rain gauges is available, rather than from the full rainfall field. The number of gauges needs only be sufficient to adequately estimate the variance in the ratio of the rain rate at the rain gauges to the areal average rain rate across the entire spatial domain. In our example for Warsaw, Poland, we used 25 gauges over an area of approximately 1600 km2. MRC models calibrated using the proposed method were used to downscale 15-min rainfall rates from a 20 by 20 km area to the scale of the rain gauge capture area. Frequency distributions of observed and simulated 15-min rainfall at the gauge scale were very similar. Moreover, the spatial covariance structure of rainfall rates, as characterized by the semivariogram, was reproduced after allowing the probability density function of the random cascade generator to vary with spatial scale.
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Rupp, D. E., P. Licznar, W. Adamowski, and M. Leśniewski. "Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data." Hydrology and Earth System Sciences Discussions 8, no. 4 (July 25, 2011): 7261–91. http://dx.doi.org/10.5194/hessd-8-7261-2011.
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Abstract. Capturing the spatial distribution of high-intensity rainfall over short-time intervals is critical for accurately assessing the efficacy of urban stormwater drainage systems. In a stochastic simulation framework, one method of generating realistic rainfall fields is by multiplicative random cascade (MRC) models. Estimation of MRC model parameters has typically relied on radar imagery or, less frequently, rainfall fields interpolated from dense rain gauge networks. However, such data are not always available. Furthermore, the literature is lacking estimation procedures for spatially incomplete datasets. Therefore, we proposed a simple method of calibrating an MRC model when only data from a moderately dense network of rain gauges are available, rather than from the full rainfall field. The number of gauges need only be sufficient to adequately estimate the variance in the ratio of the rain rate at the rain gauges to the areal average rain rate across the entire spatial domain. In our example for Warsaw, Poland, we used 25 gauges over an area of approximately 1600 km2. MRC models calibrated using the proposed method were used to downscale 15-min rainfall rates from a 20 by 20 km area to the scale of the rain gauge capture area. Frequency distributions of observed and simulated 15-min rainfall at the gauge scale were very similar. Moreover, the spatial covariance structure of rainfall rates, as characterized by the semivariogram, was reproduced after allowing the probability density of the random cascade generator to vary with spatial scale.
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Sapan, Elenora Gita Alamanda, Joko Sujono, and Karlina Karlina. "Extreme Rainfall Characteristics Analysis Using Climate Models in the Mount Merapi Area." MEDIA KOMUNIKASI TEKNIK SIPIL 28, no. 1 (July 29, 2022): 99–108. http://dx.doi.org/10.14710/mkts.v28i1.36332.
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Extreme rainfall is one of the trigger factors for debris floods in stratovolcanos. It caused by volcanic materials will be easily eroded in large quantity with surface water flow as the result of extreme rainfall. Extreme rainfall is avnatural phenomenon which is often related with climate change. In the future, changes in extreme rainfall characteristics may occur. Therefore, it’s necessary to conduct extreme rainfall analysis for historical and future periods. In this study, the characteristics of rainfall analyzed were the variability of extreme rain as shown by trend analysis of extreme rain indices namely RTOT. Hourly rainfall data at eight rain stations used as input. Future rainfall data was projected using the global climate model CanESM2 (RCP4.5 and RCP8.5 and downscaling process using Statistical Downscaling Model (SDSM). Comparison of the projection rainfall with historical rainfall shows a different trend at each station. Increasing trend occurred at four stations including Plosokerep, Pucanganom, Sopalan, and Talun stations, with the highest increasing trend occurring at Sopalan stations. In addition, there was also a decreasing trend that occurred at Ngandong station for both scenarios and at Sorasan station in the RCP8.5 scenario. The Jrakah and Randugunting stations show a steady trend.
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Yussuff, A. I. O. "Analysis of Selected Earth-Space Rain Attenuation Models for a Tropical Station." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 2 (August 1, 2016): 383. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp383-391.
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The restrained use of millimeter bands is due to severe rain attenuation. Attenuation is caused when rain cells intersects radio wave’s propagation path; resulting in deep fades. The effect of rainfall is more severe in tropical regions characterized by heavy rainfall intensity and large raindrops; hence, rain attenuation analyses are essential to study rain fade characteristics for use in earth-space link budget analysis, for outage prediction resulting from rain attenuation. Tropical regions are particularly challenged with signal outage, necessitating the formulation and development of suitable prediction model(s) for the region. Therefore, extensive knowledge of the propagation phenomena mitigating system availability and signal quality in these bands are required. Daily rainfall data were collected from the Nigerian Meteorological Services for Lagos for spanning January to December 2010. Results showed that although, the ITU-R model out-performed the other prediction models under consideration, none of prediction models matched the measurement data.
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Y. Mualem and S. Assouline. "Mathematical Model for Rain Drop Distribution and Rainfall Kinetic Energy." Transactions of the ASAE 29, no. 2 (1986): 0494–500. http://dx.doi.org/10.13031/2013.30179.
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Ko, Mu-Chieh, Frank D. Marks, Ghassan J. Alaka, and Sundararaman G. Gopalakrishnan. "Evaluation of Hurricane Harvey (2017) Rainfall in Deterministic and Probabilistic HWRF Forecasts." Atmosphere 11, no. 6 (June 22, 2020): 666. http://dx.doi.org/10.3390/atmos11060666.
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Rainfall forecast performance was evaluated for the first time for the Hurricane Weather Research and Forecasting (HWRF) model. This study focused on HWRF performance in predicting rainfall from Hurricane Harvey in 2017. In particular, two configurations of the 2017 version of HWRF were investigated: a deterministic version of the Basin-scale HWRF (HB17) and an ensemble version of the operational HWRF (H17E). This study found that HB17 generated reasonable rainfall patterns and rain-rate distributions for Hurricane Harvey, in part due to accurate track forecasts. However, the estimated rain rates near the storm center (within 50 km) were slightly overestimated. In the rainband region (150 to 300 km), HB17 reproduced heavy rain rates and underestimated light rain rates. The accumulated rainfall pattern successfully captured Harvey’s intense outer rainband with adequate spatial displacement. In addition, the performance of H17E on probabilistic rainfall has shown that the ensemble forecasts can potentially increase the accuracy of the predicted locations for extreme rainfall. Moreover, the study also indicated the importance of high-resolution dynamical models for rainfall predictions. Although statistical models can generate the overall rainfall patterns along a track, extreme rainfall events produced from outer rainbands can only be forecasted by numerical models, such as HWRF. Accordingly, the HWRF models have the capability of simulating reasonable quantitative precipitation forecasts and providing essential rainfall guidance in order to further reduce loss of life and cost to the economy.
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Singh, Karuna Nidhan. "Development of Single Rain Strom Erosivity Models for Chitrakoot Region." International Journal of Students' Research in Technology & Management 4, no. 1 (March 9, 2016): 17–20. http://dx.doi.org/10.18510/ijsrtm.2016.415.
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In this paper, we review the erosivity studies conducted in Chitrakoot to verify the quality and representativeness of the results generated and to provide a greater understanding of the rainfall erosivity in Chitrakoot. We searched the Google Scholar databases and in recent journals and dissertations to obtain the following information: latitude, longitude, city, states, length of records (15-years from 1999 to 2013), precipitation (daily based), equations calculated and respective determination coefficient .The daily rainfall erosivity in Chitrakoot ranged from 39.846 to 61.841 MJ mm/ha/h. Rainfall erosivity indices, based on intensity and the amount of rainfall, were computed for all precipitations. The lowest values were found in June and the highest values were found in the August in the Chitrakoot region. These equations can be useful to map rainfall erosivity for the entire area.
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Khudayorov, Zafar, Rakhmonberdi Khalilov, Irina Gorlova, Sherzodkhuja Mirzakhodjaev, and Azhargul Mambetsheripova. "Mathematical model of water drop trajectory in artificial rainfall." E3S Web of Conferences 365 (2023): 04011. http://dx.doi.org/10.1051/e3sconf/202336504011.
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In the article, a mathematical model of the movement of a water drop flying out of a nozzle with a deflector nozzle during artificial rain was built. The resulting equations were solved by an approximate method using a specially developed program at EHM. The parameters affecting the irrigation process were analyzed based on the obtained results.
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Yang, Junho, Mikyoung Jun, Courtney Schumacher, and R. Saravanan. "Predictive Statistical Representations of Observed and Simulated Rainfall Using Generalized Linear Models." Journal of Climate 32, no. 11 (May 17, 2019): 3409–27. http://dx.doi.org/10.1175/jcli-d-18-0527.1.
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Abstract This study explores the feasibility of predicting subdaily variations and the climatological spatial patterns of rain in the tropical Pacific from atmospheric profiles using a set of generalized linear models: logistic regression for rain occurrence and gamma regression for rain amount. The prediction is separated into different rain types from TRMM satellite radar observations (stratiform, deep convective, and shallow convective) and CAM5 simulations (large-scale and convective). Environmental variables from MERRA-2 and CAM5 are used as predictors for TRMM and CAM5 rainfall, respectively. The statistical models are trained using environmental fields at 0000 UTC and rainfall from 0000 to 0600 UTC during 2003. The results are used to predict 2004 rain occurrence and rate for MERRA-2/TRMM and CAM5 separately. The first EOF profile of humidity and the second EOF profile of temperature contribute most to the prediction for both statistical models in each case. The logistic regression generally performs well for all rain types, but does better in the east Pacific compared to the west Pacific. The gamma regression produces reasonable geographical rain amount distributions but rain rate probability distributions are not predicted as well, suggesting the need for a different, higher-order model to predict rain rates. The results of this study suggest that statistical models applied to TRMM radar observations and MERRA-2 environmental parameters can predict the spatial patterns and amplitudes of tropical rainfall in the time-averaged sense. Comparing the observationally trained models to models that are trained using CAM5 simulations points to possible deficiencies in the convection parameterization used in CAM5.
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Manokij, Fuenglada, Peerapon Vateekul, and Kanoksri Sarinnapakorn. "Cascading Models of CNN and GRU with Autoencoder Loss for Precipitation Forecast in Thailand." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, no. 3 (November 13, 2021): 333–46. http://dx.doi.org/10.37936/ecti-cit.2021153.240957.
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It is a crucial task to accurately forecast precipitation, especially rainfall in Thailand, since it relates to flood prevention and agricultural planning. In our prior work, we have presented a model based on deep learning approach; however, its performance is still limited due to two main issues. First, there is an imbalance issue, where most rainfall is zero or no rain because Thailand has short rainy season. Second, predicted rainfall is still underestimated since moderate and heavy rainfall cases barely occurs. In this paper, we propose an enhanced deep learning model to forecast rainfall in Thailand. Our model is a cascading of CNN and GRU along with exogenous variables, i.e., temperature, pressure, and humidity. There are two stages in our model. First, CNN is specialized for classifying rain and non-rain events. In this stage, an imbalanced issue is alleviated by applying “focal loss”. Second, GRU is responsible for forecasting rainfall. Its predicted range is lifted using “autoencoder loss”. The experiment was conducted on hourly rainfall dataset between 2012 and 2018 obtained from a public government sector in Thailand. The results show that our enhanced model outperforms ARIMA and CNN-GRU in terms on RMSE of most regions in Thailand.
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Cecinati, Francesca, Antonio Moreno-Ródenas, Miguel Rico-Ramirez, Marie-claire ten Veldhuis, and Jeroen Langeveld. "Considering Rain Gauge Uncertainty Using Kriging for Uncertain Data." Atmosphere 9, no. 11 (November 14, 2018): 446. http://dx.doi.org/10.3390/atmos9110446.
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In urban hydrological models, rainfall is the main input and one of the main sources of uncertainty. To reach sufficient spatial coverage and resolution, the integration of several rainfall data sources, including rain gauges and weather radars, is often necessary. The uncertainty associated with rain gauge measurements is dependent on rainfall intensity and on the characteristics of the devices. Common spatial interpolation methods do not account for rain gauge uncertainty variability. Kriging for Uncertain Data (KUD) allows the handling of the uncertainty of each rain gauge independently, modelling space- and time-variant errors. The applications of KUD to rain gauge interpolation and radar-gauge rainfall merging are studied and compared. First, the methodology is studied with synthetic experiments, to evaluate its performance varying rain gauge density, accuracy and rainfall field characteristics. Subsequently, the method is applied to a case study in the Dommel catchment, the Netherlands, where high-quality automatic gauges are complemented by lower-quality tipping-bucket gauges and radar composites. The case study and the synthetic experiments show that considering measurement uncertainty in rain gauge interpolation usually improves rainfall estimations, given a sufficient rain gauge density. Considering measurement uncertainty in radar-gauge merging consistently improved the estimates in the tested cases, thanks to the additional spatial information of radar rainfall data but should still be used cautiously for convective events and low-density rain gauge networks.
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Shi, Li Juan, Ting Jing, Xiao Hong Chen, and Dong Xiu Ou. "The Effects of Rainfalls on Expressway Travel Time." Applied Mechanics and Materials 361-363 (August 2013): 2255–61. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2255.
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This paper presents an investigation of the effects of rainfall with different levels of precipitation intensity on urban expressway travel time under free-flow speed conditions. The traffic data and corresponding weather data from the expressway section of Longyang in Shanghai for more than one year were used. Statistical analysis was applied to investigate the effects of rainfall on travel time quantitatively. There are two major contributions of this paper. Firstly, four levels of rainfall have varying degrees of significant impacts on travel time in terms of average travel time. Slight rain has no influence on variability of travel time, while heavier rain increases the variability. Secondly, three travel time stochastic models: Normal, Lognormal and Weibull, were proposed. The Lognormal model is the best-fit model under good weather, slight and moderate rainfall conditions, while both Weibull model and Lognormal model are the preferable models under heavy rain and rainstorm conditions. The recommended Lognormal model can be further used for evaluating the performance of the Longyang expressway section in terms of travel time reliability under good weather, slight rain, moderate rain, heavy rain and rainstorm conditions respectively.
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Davidsen, Steffen, Roland Löwe, Nanna H. Ravn, Lina N. Jensen, and Karsten Arnbjerg-Nielsen. "Initial conditions of urban permeable surfaces in rainfall-runoff models using Horton's infiltration." Water Science and Technology 77, no. 3 (November 16, 2017): 662–69. http://dx.doi.org/10.2166/wst.2017.580.
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Abstract Infiltration is a key process controlling runoff, but varies depending on antecedent conditions. This study provides estimates on initial conditions for urban permeable surfaces via continuous simulation of the infiltration capacity using historical rain data. An analysis of historical rainfall records show that accumulated rainfall prior to large rain events does not depend on the return period of the event. Using an infiltration-runoff model we found that for a typical large rain storm, antecedent conditions in general lead to reduced infiltration capacity both for sandy and clayey soils and that there is substantial runoff for return periods above 1–10 years.
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Pendergrass, Angeline G., and Dennis L. Hartmann. "Changes in the Distribution of Rain Frequency and Intensity in Response to Global Warming*." Journal of Climate 27, no. 22 (November 4, 2014): 8372–83. http://dx.doi.org/10.1175/jcli-d-14-00183.1.
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Abstract Changes in the frequency and intensity of rainfall are an important potential impact of climate change. Two modes of change, a shift and an increase, are applied to simulations of global warming with models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The response to CO2 doubling in the multimodel mean of CMIP5 daily rainfall is characterized by an increase of 1% K−1 at all rain rates and a shift to higher rain rates of 3.3% K−1. In addition to these increase and shift modes of change, some models also show a substantial increase in rainfall at the highest rain rates called the extreme mode of response to warming. In some models, this extreme mode can be shown to be associated with increases in grid-scale condensation or gridpoint storms.
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Lanza, L. G. "A conditional simulation model of intermittent rain fields." Hydrology and Earth System Sciences 4, no. 1 (March 31, 2000): 173–83. http://dx.doi.org/10.5194/hess-4-173-2000.
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Abstract. The synthetic generation of random fields with specified probability distribution, correlation structure and probability of no-rain areas is used as the basis for the formulation of a stochastic space-time rainfall model conditional on rain gauge observations. A new procedure for conditioning while preserving intermittence is developed to provide constraints to Monte Carlo realisations of possible rainfall scenarios. The method addresses the properties of the convolution operator involved in generating random field realisations and is actually independent of the numerical algorithm used for unconditional simulation. It requires only the solution of a linear system of algebraic equations the order of which is given by the number of the conditioning nodes. Applications of the methodology are expected in rainfall field reconstruction from sparse rain gauge data and in rainfall downscaling from the large scale information that may be provided by remote sensing devices or numerical weather prediction models. Keywords: Space-time rainfall; Conditioning; Stochastic models
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Gallus, William A., and Moti Segal. "Does Increased Predicted Warm-Season Rainfall Indicate Enhanced Likelihood of Rain Occurrence?" Weather and Forecasting 19, no. 6 (December 1, 2004): 1127–35. http://dx.doi.org/10.1175/820.1.
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Abstract The likelihood of simulated rainfall above a specified threshold being observed is evaluated as a function of the amounts predicted by two mesoscale models. Evaluations are performed for 20 warm-season mesoscale convective system events over the upper Midwest of the United States. Simulations were performed using 10-km versions of the National Centers for Environmental Prediction Eta Model and the Weather Research and Forecasting (WRF) model, with two different convective parameterizations tested in both models. It was found that, despite large differences in the biases of these different models and configurations, a robust relationship was present whereby a substantial increase in the likelihood of observed rainfall exceeding a specified threshold occurred in areas where the model runs forecast higher rainfall amounts. Rainfall was found to be less likely to occur in those areas where the models indicated no rainfall than it was elsewhere in the domain; it was more likely to occur in those regions where rainfall was predicted, especially where the predicted rainfall amounts were largest. The probability of rainfall relative-operating-characteristic and relative-operating-level curves showed that probabilistic forecasts determined from quantitative precipitation forecast values had skill comparable to the skill obtained using more traditional methods in which probabilities are based on the fraction of ensemble members experiencing rainfall. When the entire sample of cases was broken into training and test sets, the probability forecasts of the test sets evidenced good reliability. The relationship noted should provide some additional guidelines to operational forecasters. The results imply that the tested models are better able to indicate the regions where atmospheric processes are most favorable for convective rainfall (where the models generate enhanced amounts) than they are able to predict accurately the rainfall amounts that will be observed.
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Rakhmalia, Riza Indriani, Agus M. Soleh, and Bagus Sartono. "PENDUGAAN CURAH HUJAN DENGAN TEKNIK STATISTICAL DOWNSCALING MENGGUNAKAN CLUSTERWISE REGRESSION SEBARAN TWEEDIE." Indonesian Journal of Statistics and Its Applications 4, no. 3 (November 30, 2020): 473–83. http://dx.doi.org/10.29244/ijsa.v4i3.667.
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Rainfall prediction is one of the most challenging problems of the last century. Statistical Downscaling Technique is one of the rainfall estimation techniques that are often used. The goal of this paper is to develop the modeling of cluster-wise regression with rainfall data set that has Tweedie distribution. The data used in this paper were the precipitation from Climate Forecast System Reanalysis (CFSR) version 2 as the predictor variables and rainfall from BMKG as the response variable. Data were collected from January 2010 to December 2019 on the Bogor, Citeko, Jatiwangi, and Bandung rain posts. The best result of this study is a Cluster-wise Regression model with 4 clusters and using Tweedie distribution in each rain post. The best model was evaluated by the Root Mean Square Error Prediction. RMSEP value on Bogor rain post is 17.11 (three clusters), Citeko rain post 14.85 (two clusters), Jatiwangi rain post 15.26 (three clusters), and Bandung rain post 14.33 (two clusters). This model was able to make models and clusters well on daily rainfall application.
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Jones, Robbie, Vern Manville, Jeff Peakall, Melanie J. Froude, and Henry M. Odbert. "Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery." Natural Hazards and Earth System Sciences 17, no. 12 (December 13, 2017): 2301–12. http://dx.doi.org/10.5194/nhess-17-2301-2017.
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Abstract. Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River valley at the Soufrière Hills volcano (SHV), Montserrat, collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity (PRI) as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak 1 h rainfall intensity throughout the 2-year dataset and is higher under given rainfall conditions in year 1 than year 2. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones) are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic-regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery.
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Yang, Ruxin, Genxu Wang, Junfang Cui, Li Guo, Fei Wang, and Xiangyu Tang. "Improving the Estimation of Throughfall Amounts in Primeval Forests along an Elevation Gradient on Mountain Gongga, Southwest China." Atmosphere 13, no. 4 (April 18, 2022): 639. http://dx.doi.org/10.3390/atmos13040639.
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Differences in rainfall partition into throughfall among different primeval forests distributed along an altitude gradient are inadequately investigated and understood. Through continuous and automatic monitoring of natural rainfall and throughfall along an elevation gradient on Mountain Gongga, we examined the response of throughfall to various rainfall patterns in the broadleaved forest (BF), broadleaved-coniferous mixed forest (MF), and coniferous forest (CF) across individual rain events from May to October in 2019. A series of linear models that estimate throughfall amount were obtained and compared. Results showed that throughfall was jointly controlled by rainfall characteristics (including amount, duration, average, and peak intensity) and leaf area index (LAI). Rainfall amount was the primary control for throughfall amount. The models with all rainfall parameters and LAI as variables did not markedly outperform (R2 enhancement by 0–0.02) the simple linear models with rainfall amount as the only variable; therefore, the latter are recommended due to simplicity and easiness of use. Although the correlation of throughfall with LAI was less prominent compared to rainfall parameters, LAI showed a significant positive linear correlation (p < 0.05) with the estimated rainfall amount threshold (the rainfall required to saturate the canopy) by the single-variable linear models at the monthly scale. Over the study period, penetration proportions of rainfall in BF, MF, and CF were 83%, 75%, and 80%, respectively. The rainfall amount threshold in CF (0.70 mm) was less than those in BF (0.80 mm) and MF (0.92 mm). Rain events of higher intensity exhibited a higher mean penetration proportion than lower intensity rain events. The use of single-variable linear models developed here, despite some overestimations of throughfall amount, could lead to an overall satisfactory estimation of rainfall redistribution in mountainous areas.
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Jackson, Lawrence S., Declan L. Finney, Elizabeth J. Kendon, John H. Marsham, Douglas J. Parker, Rachel A. Stratton, Lorenzo Tomassini, and Simon Tucker. "The Effect of Explicit Convection on Couplings between Rainfall, Humidity, and Ascent over Africa under Climate Change." Journal of Climate 33, no. 19 (October 1, 2020): 8315–37. http://dx.doi.org/10.1175/jcli-d-19-0322.1.
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AbstractThe Hadley circulation and tropical rain belt are dominant features of African climate. Moist convection provides ascent within the rain belt, but must be parameterized in climate models, limiting predictions. Here, we use a pan-African convection-permitting model (CPM), alongside a parameterized convection model (PCM), to analyze how explicit convection affects the rain belt under climate change. Regarding changes in mean climate, both models project an increase in total column water (TCW), a widespread increase in rainfall, and slowdown of subtropical descent. Regional climate changes are similar for annual mean rainfall but regional changes of ascent typically strengthen less or weaken more in the CPM. Over a land-only meridional transect of the rain belt, the CPM mean rainfall increases less than in the PCM (5% vs 14%) but mean vertical velocity at 500 hPa weakens more (17% vs 10%). These changes mask more fundamental changes in underlying distributions. The decrease in 3-hourly rain frequency and shift from lighter to heavier rainfall are more pronounced in the CPM and accompanied by a shift from weak to strong updrafts with the enhancement of heavy rainfall largely due to these dynamic changes. The CPM has stronger coupling between intense rainfall and higher TCW. This yields a greater increase in rainfall contribution from events with greater TCW, with more rainfall for a given large-scale ascent, and so favors slowing of that ascent. These findings highlight connections between the convective-scale and larger-scale flows and emphasize that limitations of parameterized convection have major implications for planning adaptation to climate change.
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Setiono, Rintis Hadiani, Edo Erlangga, and Solichin. "Rainfall Simulation at Bah Bolon Watershed with Backpropagation Artificial Neural Network Based on Rainfall Data Using Scilab." Applied Mechanics and Materials 845 (July 2016): 10–17. http://dx.doi.org/10.4028/www.scientific.net/amm.845.10.
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Abstract. Rainfall simulation is a method of obtaining precipitation data on rainfall station based on precipitation data of other stations in same watershed at the same time, using a linear mathematical model constructed by Artificial Neural Network (ANN) method. The application of the simulation result is useful to provide information for the decision-makers. ANN backpropagation method is the one used in modeling the rainfall in a watershed because it can solve a complex mathematic problem. The objective of research was to find out the hydrologic model and its application to predict precipitation data at Bah Bolon watershed in the future. The input variable of research was data of rain at Bah Jambi rainfall station. The parameters used in this research were Mean Squared Error (MSE)= 0.028, epoch= 1000 iteration, hidden layer number= 2, neuron hidden layer number= 3, momentum= 0.7, learning rate: 0.9, training period= 4 years. The result of model verification shows the very strong correlation between simulated rain data and actual rain data, with score of 0.9664, and the reliability of hydrologic system model at Bah Bolon watershed is 64.48%.
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Franch, Gabriele, Daniele Nerini, Marta Pendesini, Luca Coviello, Giuseppe Jurman, and Cesare Furlanello. "Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events." Atmosphere 11, no. 3 (March 7, 2020): 267. http://dx.doi.org/10.3390/atmos11030267.
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One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms. While deep learning nowcasting models have recently shown to provide better overall skill than traditional echo extrapolation models, they suffer from conditional bias, sometimes reporting lower skill on extreme rain rates compared to Lagrangian persistence, due to excessive prediction smoothing. This work presents a novel method to improve deep learning prediction skills in particular for extreme rainfall regimes. The solution is based on model stacking, where a convolutional neural network is trained to combine an ensemble of deep learning models with orographic features, doubling the prediction skills with respect to the ensemble members and their average on extreme rain rates, and outperforming them on all rain regimes. The proposed architecture was applied on the recently released TAASRAD19 radar dataset: the initial ensemble was built by training four models with the same TrajGRU architecture over different rainfall thresholds on the first six years of the dataset, while the following three years of data were used for the stacked model. The stacked model can reach the same skill of Lagrangian persistence on extreme rain rates while retaining superior performance on lower rain regimes.
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Bell, V. A., and R. J. Moore. "The sensitivity of catchment runoff models to rainfall data at different spatial scales." Hydrology and Earth System Sciences 4, no. 4 (December 31, 2000): 653–67. http://dx.doi.org/10.5194/hess-4-653-2000.
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Abstract. The sensitivity of catchment runoff models to rainfall is investigated at a variety of spatial scales using data from a dense raingauge network and weather radar. These data form part of the HYREX (HYdrological Radar EXperiment) dataset. They encompass records from 49 raingauges over the 135 km2 Brue catchment in south-west England together with 2 and 5 km grid-square radar data. Separate rainfall time-series for the radar and raingauge data are constructed on 2, 5 and 10 km grids, and as catchment average values, at a 15 minute time-step. The sensitivity of the catchment runoff models to these grid scales of input data is evaluated on selected convective and stratiform rainfall events. Each rainfall time-series is used to produce an ensemble of modelled hydrographs in order to investigate this sensitivity. The distributed model is shown to be sensitive to the locations of the raingauges within the catchment and hence to the spatial variability of rainfall over the catchment. Runoff sensitivity is strongest during convective rainfall when a broader spread of modelled hydrographs results, with twice the variability of that arising from stratiform rain. Sensitivity to rainfall data and model resolution is explored and, surprisingly, best performance is obtained using a lower resolution of rainfall data and model. Results from the distributed catchment model, the Simple Grid Model, are compared with those obtained from a lumped model, the PDM. Performance from the distributed model is found to be only marginally better during stratiform rain (R2 of 0.922 compared to 0.911) but significantly better during convective rain (R2 of 0.953 compared to 0.909). The improved performance from the distributed model can, in part, be accredited to the excellence of the dense raingauge network which would not be the norm for operational flood warning systems. In the final part of the paper, the effect of rainfall resolution on the performance of the 2 km distributed model is explored. The need to recalibrate the model for use with rainfall data of a given resolution, particularly for periods of convective rain, is highlighted. Again, best performance is obtained using lower resolution rainfall data. This is interpreted as evidence for the need to improve the distributed model structure to make better use of the higher resolution information on rainfall and topographic controls on runoff. Degrading the resolution of rainfall data, model or both to achieve the smoothing apparently needed is not seen as wholly appropriate. Keywords: rainfall, runoff, sensitivity, scale, model, flood