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Journal articles on the topic 'Daily rainfall'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

Hershenhorn, J., and D. A. Woolhiser. "Disaggregation of daily rainfall." Journal of Hydrology 95, no. 3-4 (November 1987): 299–322. http://dx.doi.org/10.1016/0022-1694(87)90008-4.

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2

Haddad, Khaled, and Ataur Rahman. "Derivation of short-duration design rainfalls using daily rainfall statistics." Natural Hazards 74, no. 3 (May 31, 2014): 1391–401. http://dx.doi.org/10.1007/s11069-014-1248-7.

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3

Connolly, R. D., J. Schirmer, and P. K. Dunn. "A daily rainfall disaggregation model." Agricultural and Forest Meteorology 92, no. 2 (July 1998): 105–17. http://dx.doi.org/10.1016/s0168-1923(98)00088-4.

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4

Yoo, Chulsang, Jae-Soo Lee, and Yong-Nam Yoon. "Climatological Thresholds of Daily Rainfall." Journal of Hydrologic Engineering 6, no. 5 (October 2001): 443–49. http://dx.doi.org/10.1061/(asce)1084-0699(2001)6:5(443).

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5

Jakubowski, W. "A daily rainfall occurrence process." Stochastic Hydrology and Hydraulics 2, no. 1 (March 1988): 1–16. http://dx.doi.org/10.1007/bf01544190.

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6

Palumbo, A. "Lunar daily variations in rainfall." Journal of Atmospheric and Terrestrial Physics 48, no. 2 (February 1986): 145–48. http://dx.doi.org/10.1016/0021-9169(86)90078-4.

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7

Sumner, G., C. Ramis, and J. A. Guijarro. "Daily rainfall domains in Mallorca." Theoretical and Applied Climatology 51, no. 4 (1995): 199–221. http://dx.doi.org/10.1007/bf00867280.

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8

Ledingham, Jamie, David Archer, Elizabeth Lewis, Hayley Fowler, and Chris Kilsby. "Contrasting seasonality of storm rainfall and flood runoff in the UK and some implications for rainfall-runoff methods of flood estimation." Hydrology Research 50, no. 5 (August 14, 2019): 1309–23. http://dx.doi.org/10.2166/nh.2019.040.

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Abstract Using data from 520 gauging stations in Britain and gridded rainfall datasets, the seasonality of storm rainfall and flood runoff is compared and mapped. Annual maximum (AMAX) daily rainfall occurs predominantly in summer, but AMAX floods occur most frequently in winter. Seasonal occurrences of annual daily rainfall and flood maxima differ by more than 50% in dry lowland catchments. The differences diminish with increasing catchment wetness, increase with rainfalls shorter than daily duration and are shown to depend primarily on catchment wetness, as illustrated by variations in mean annual rainfall. Over the whole dataset, only 34% of AMAX daily flood events are matched to daily rainfall annual maxima (and only 20% for 6-hour rainfall maxima). The discontinuity between rainfall maxima and flooding is explained by the consideration of coincident soil moisture storage. The results have serious implications for rainfall-runoff methods of flood risk estimation in the UK where estimation is based on a depth–duration–frequency model of rainfall highly biased to summer. It is concluded that inadequate treatment of the seasonality of rainfall and soil moisture seriously reduces the reliability of event-based flood estimation in Britain.
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9

JOLLIFFE, IAN T., and PETER B. HOPE. "REPRESENTATION OF DAILY RAINFALL DISTRIBUTIONS USING NORMALIZED RAINFALL CURVES." International Journal of Climatology 16, no. 10 (October 1996): 1157–63. http://dx.doi.org/10.1002/(sici)1097-0088(199610)16:10<1157::aid-joc71>3.0.co;2-r.

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10

MALEKINEZHAD, HOSSEIN, and ARASH ZARE-GARIZI. "Regional frequency analysis of daily rainfall extremes using L-moments approach." Atmósfera 27, no. 4 (January 13, 2015): 411–27. http://dx.doi.org/10.20937/atm.2014.27.04.07.

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Daily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall methodwere applied to maximum rainfall records of 47 stations across the study area. Due to complex geographicand hydro-climatological characteristics of the region, an important research issue focused on breaking downthe large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneousregions, based on the cluster analysis of site characteristics and tests for the regional homogeneity.The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneousregions. The difference may be a result of the distinctive climatic and geographic conditions. The estimatedregional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that theestimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when returnperiods are less than 100 years. But, for higher return periods, rainfall estimates should be treated withcaution. More station years, either from longer records or more stations in the regions, would be required forrainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site averagemaximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestanprovince. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfallsat ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequencyanalysis technique could be applied successfully in deriving design rainfall estimates for northeasternIran. The approach utilized in this study and the findings are of great scientific and practical merit, particularlyfor the purpose of planning for weather-related emergencies and design of hydraulic engineering structures
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11

Sirangelo, B., E. Ferrari, and D. L. De Luca. "Occurrence analysis of daily rainfalls through non-homogeneous Poissonian processes." Natural Hazards and Earth System Sciences 11, no. 6 (June 14, 2011): 1657–68. http://dx.doi.org/10.5194/nhess-11-1657-2011.

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Abstract. A stochastic model based on a non-homogeneous Poisson process, characterised by a time-dependent intensity of rainfall occurrence, is employed to explain seasonal effects of daily rainfalls exceeding prefixed threshold values. The data modelling has been performed with a partition of observed daily rainfall data into a calibration period for parameter estimation and a validation period for checking on occurrence process changes. The model has been applied to a set of rain gauges located in different geographical areas of Southern Italy. The results show a good fit for time-varying intensity of rainfall occurrence process by 2-harmonic Fourier law and no statistically significant evidence of changes in the validation period for different threshold values.
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12

Yue, Sheng, and Michio Hashino. "Cumulant estimation of daily runoff from daily rainfall sequences." Doboku Gakkai Ronbunshu, no. 533 (1996): 41–50. http://dx.doi.org/10.2208/jscej.1996.533_41.

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13

Oh, Tae-Suk, and Young-Il Moon. "Characteristic Change Analysis of Rainfall Events using Daily Rainfall Data." Journal of Korea Water Resources Association 42, no. 11 (November 30, 2009): 933–51. http://dx.doi.org/10.3741/jkwra.2009.42.11.933.

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14

Yu, B., and CJ Rosewell. "Rainfall erosivity estimation using daily rainfall amounts for South Australia." Soil Research 34, no. 5 (1996): 721. http://dx.doi.org/10.1071/sr9960721.

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The rainfall erosivity model relating storm erosivity to daily rainfall amounts was tested for 4 sites in South Australia where seasonal rainfall erosivity is generally out of phase with seasonal rainfall because of the predominant winter rainfall. The model worked reasonably well, with the coefficient of efficiency varying from 0.54 to 0.77, and the average discrepancy between actual and estimated monthly distribution was no more than 3%. The model performance in the winter rainfall area is similar to that in the uniform and summer rainfall areas. A set of regional parameter values estimated using a combined dataset is recommended for other sites in the agricultural and viticultural areas of South Australia where the mean annual rainfall ranges from 300 to 500 mm. The R-factor and its seasonal distribution were estimated for 99 sites in South Australia using long-term daily rainfall data. The R-factor varies mostly between 250 and 500 MJ . mm/(ha . h . year). Rainfall erosivity peaks in winter in the southern part of the western agricultural area and the south-east corner of the State, while it peaks in summer in the inland area east of the South Flinders and Mount Lofty Ranges.
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15

Laz, Orpita U., Ataur Rahman, Abdullah Yilmaz, and Khaled Haddad. "Trends in sub-hourly, sub-daily and daily extreme rainfall events in eastern Australia." Journal of Water and Climate Change 5, no. 4 (July 16, 2014): 667–75. http://dx.doi.org/10.2166/wcc.2014.035.

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Intensity and frequency of extreme rainfall are expected to change in future due to anthropogenic climate change; however, this change may not be uniform across spatial and temporal scale. This paper examines the trends of sub-hourly, sub-daily and daily extreme rainfall events from 38 rainfall stations located in southeast Australia. Two non-parametric tests (Mann–Kendall and Spearman Rho) were applied to detect trends at 10, 5 and 1% significance levels. The sub-hourly (6, 12, 18 and 30 min) and sub-daily (1, 2 and 6 h) annual maximum rainfall events generally showed an upward (positive) trend. However, the longer duration rainfall events (12–72 h) generally showed a downward (negative) trend. It was found that stations showing positive trends were characterized by higher elevations compared with the stations showing negative trends. This finding has important implications for urban stormwater management in the near future as most urban stormwater systems operate on a smaller catchment scale where sub-hourly and sub-daily rainfall events are used in their design.
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16

Smith, James A. "Statistical modeling of daily rainfall occurrences." Water Resources Research 23, no. 5 (May 1987): 885–93. http://dx.doi.org/10.1029/wr023i005p00885.

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17

Nadarajah, Saralees, and Dongseok Choi. "Maximum daily rainfall in South Korea." Journal of Earth System Science 116, no. 4 (August 2007): 311–20. http://dx.doi.org/10.1007/s12040-007-0028-0.

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18

Permai, S. D., M. Ohyver, and M. K. B. M. Aziz. "Daily rainfall modeling using Neural Network." Journal of Physics: Conference Series 1988, no. 1 (July 1, 2021): 012040. http://dx.doi.org/10.1088/1742-6596/1988/1/012040.

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19

Teng, Tse-Yu, Tzu-Ming Liu, Yu-Shiang Tung, and Ke-Sheng Cheng. "Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir." Water 13, no. 11 (May 28, 2021): 1516. http://dx.doi.org/10.3390/w13111516.

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With improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data application level. However, there are differences between GCM downscaling daily data and rainfall station data. If GCM data are directly used for hydrology and water resources assessment, the differences in total amount and rainfall intensity will be revealed and may affect the estimates of the total amount of water resources and water supply capacity. This research proposes a two-stage bias correction method for GCM data and establishes a mechanism for converting grid data to station data. Five GCMs were selected from 33 GCMs, which were ranked by rainfall simulation performance from a baseline period in Taiwan. The watershed of the Zengwen Reservoir in southern Taiwan was selected as the study area for comparison of the three different bias correction methods. The results reveal that the method with the wet-day threshold optimized by objective function with observation rainfall wet days had the best result. Error was greatly reduced in the hydrology model simulation with two-stage bias correction. The results show that the two-stage bias correction method proposed in this study can be used as an advanced method of data pre-processing in climate change impact assessment, which could improve the quality and broaden the extent of GCM daily data. Additionally, GCM ranking can be used by researchers in climate change assessment to understand the suitability of each GCM in Taiwan.
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20

Lee, Jeonghoon, and Sangdan Kim. "Temporal Disaggregation of Daily Rainfall data using Stochastic Point Rainfall Model." Journal of the Korean Society of Hazard Mitigation 18, no. 2 (February 28, 2018): 493–503. http://dx.doi.org/10.9798/kosham.2018.18.2.493.

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21

Lewis, Elizabeth, Hayley Fowler, Lisa Alexander, Robert Dunn, Fergus McClean, Renaud Barbero, Selma Guerreiro, Xiao-Feng Li, and Stephen Blenkinsop. "GSDR: A Global Sub-Daily Rainfall Dataset." Journal of Climate 32, no. 15 (July 3, 2019): 4715–29. http://dx.doi.org/10.1175/jcli-d-18-0143.1.

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Abstract Extreme short-duration rainfall can cause devastating flooding that puts lives, infrastructure, and natural ecosystems at risk. It is therefore essential to understand how this type of extreme rainfall will change in a warmer world. A significant barrier to answering this question is the lack of sub-daily rainfall data available at the global scale. To this end, a global sub-daily rainfall dataset based on gauged observations has been collated. The dataset is highly variable in its spatial coverage, record length, completeness and, in its raw form, quality. This presents significant difficulties for many types of analyses. The dataset currently comprises 23 687 gauges with an average record length of 13 years. Apart from a few exceptions, the earliest records begin in the 1950s. The Global Sub-Daily Rainfall Dataset (GSDR) has wide applications, including improving our understanding of the nature and drivers of sub-daily rainfall extremes, improving and validating of high-resolution climate models, and developing a high-resolution gridded sub-daily rainfall dataset of indices.
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22

Diez-Sierra, Javier, and Manuel del Jesus. "Subdaily Rainfall Estimation through Daily Rainfall Downscaling Using Random Forests in Spain." Water 11, no. 1 (January 11, 2019): 125. http://dx.doi.org/10.3390/w11010125.

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Subdaily rainfall data, though essential for applications in many fields, is not as readily available as daily rainfall data. In this work, regression approaches that use atmospheric data and daily rainfall statistics as predictors are evaluated to downscale daily-to-subdaily rainfall statistics on more than 700 hourly rain gauges in Spain. We propose a new approach based on machine learning techniques that improves the downscaling skill of previous methodologies. Results are grouped by climate types (following the Köppen–Geiger classification) to investigate possible missing explanatory variables in the analysis. The methodology is then used to improve the ability of Poisson cluster models to simulate hourly rainfall series that mimic the statistical behavior of the observed ones. This approach can be applied for the study of extreme events and for daily-to-subdaily precipitation disaggregation in any location of Spain where daily rainfall data are available.
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23

Tait, Andrew, and Richard Turner. "Generating Multiyear Gridded Daily Rainfall over New Zealand." Journal of Applied Meteorology 44, no. 9 (September 1, 2005): 1315–23. http://dx.doi.org/10.1175/jam2279.1.

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Abstract Daily rainfall totals are a key input for hydrological models that are designed to simulate water and pollutant flow through both soil and waterways. Within New Zealand there are large areas and many river catchments where no long-term rainfall observations exist. A method for estimating daily rainfall over the whole of New Zealand on a 5-km grid is described and tested over a period from January 1985 to April 2002. Improvement over a spatial interpolation method was gained by scaling high-elevation rainfall estimates using simulated mesoscale model rainfall surfaces that are generated for short periods in 1994 and 1996. This method is judged to produce reasonable and useful estimates of daily rainfall.
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24

Mpelasoka, Freddie S., and Francis H. S. Chiew. "Influence of Rainfall Scenario Construction Methods on Runoff Projections." Journal of Hydrometeorology 10, no. 5 (October 1, 2009): 1168–83. http://dx.doi.org/10.1175/2009jhm1045.1.

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Abstract The future rainfall series used to drive hydrological models in most climate change impact studies is informed by global climate models (GCMs). This paper compares future runoff projections in ∼11 000 0.25° grid cells across Australia from a daily rainfall–runoff model driven with future daily rainfall series obtained using three simple scaling methods, informed by 14 GCMs. In the constant scaling and daily scaling methods, the historical daily rainfall series is scaled by the relative difference between GCM simulations for the future and historical climates. The constant scaling method scales all the daily rainfall by the same factor, and the daily scaling method takes into account changes in the daily rainfall distribution by scaling the different daily rainfall amounts differently. In the daily translation method, the GCM future daily rainfall series is translated to a 0.25° gridcell rainfall series using the relationship established between the historical GCM-scale rainfall and 0.25° gridcell rainfall data. The daily scaling and daily translation methods generally give higher extreme and annual runoff than the constant scaling method because they take into account the increase in extreme daily rainfall (which generates significant runoff) simulated by the large majority of the GCMs. However, the difference between the mean annual runoff simulated with future daily rainfall series obtained using the constant versus daily scaling methods is generally less than 5%, which is relatively smaller than the range of runoff results from the different GCMs of 30%–40%.
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25

Silva Neto, Virgilio Lourenço da, Marcelo Ribeiro Viola, Demetrius David da Silva, Carlos Rogério de Mello, Silvio Bueno Pereira, and Marcos Giongo. "Daily rainfall disaggregation for Tocantins State, Brazil." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 12, no. 4 (June 28, 2017): 605. http://dx.doi.org/10.4136/ambi-agua.2077.

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In order to design effective Brazilian hydraulic structures, it is necessary to obtain data relating to short-duration intense rainfall from historical series of daily rainfall. This recurring need can be fulfilled by rainfall disaggregation methodology. The objective of this study was to determine the intense rainfall disaggregation constants for the State of Tocantins and to compare these constants with those obtained for other regions of Brazil. For the modeling of the frequency of intense rainfall of different durations of less than 24 hours, the Gumbel probability distribution (GPD) was employed using rainfall series from 10 locations in Tocantins state. The results showed that the GPD was adequate by the Kolmogorov-Smirnov and Chi-square tests. The disaggregation constants presented low variability values for different return periods (from 10 to 100 years); the values for Tocantins state are: h12h/h24h=0.93, h6h/h24h=0.86, h4h/h24h=0.82, h3h/h24h=0.78, h2h/h24h=0.72, h1h/h24h=0.61, h50min/h1h=0.92, h40min/h1h=0.83, h30min/h1h=0.68, h20min/h30min=0.76 e h10min/h30min=0.46. The comparison of the results with those from studies developed for other Brazilian regions showed variations of up to -62.30%, allowing us to conclude that the use of local constants is important in the process of rainfall disaggregation.
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26

Vaze, J., D. A. Post, F. H. S. Chiew, J. M. Perraud, J. Teng, and N. R. Viney. "Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs." Journal of Hydrometeorology 12, no. 5 (October 1, 2011): 1100–1112. http://dx.doi.org/10.1175/2011jhm1340.1.

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Abstract Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.
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27

Kyoung, Min-Soo, Bellie Sivakumar, Hung-Soo Kim, and Byung-Sik Kim. "Chaotic Disaggregation of Daily Rainfall Time Series." Journal of Korea Water Resources Association 41, no. 9 (September 2, 2008): 959–67. http://dx.doi.org/10.3741/jkwra.2008.41.9.959.

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28

Sharma, Ashish, and Upmanu Lall. "A nonparametric approach for daily rainfall simulation." Mathematics and Computers in Simulation 48, no. 4-6 (June 1999): 361–71. http://dx.doi.org/10.1016/s0378-4754(99)00016-6.

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29

Olsson, Jonas, and Janusz Niemczynowicz. "Multifractal analysis of daily spatial rainfall distributions." Journal of Hydrology 187, no. 1-2 (December 1996): 29–43. http://dx.doi.org/10.1016/s0022-1694(96)03085-5.

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30

Douglas A. Haith and David E. Merrill. "Evaluation of a Daily Rainfall Erosivity Model." Transactions of the ASAE 30, no. 1 (1987): 0090–93. http://dx.doi.org/10.13031/2013.30407.

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31

Dugdale, G., S. Hardy, and J. R. Milford. "V: Daily catchment rainfall estimated from meteosat." Hydrological Processes 5, no. 3 (July 1991): 261–70. http://dx.doi.org/10.1002/hyp.3360050306.

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32

Romero, R., C. Ramis, J. A. Guijarro, and G. Sumner. "Daily rainfall affinity areas in Mediterranean Spain." International Journal of Climatology 19, no. 5 (April 1999): 557–78. http://dx.doi.org/10.1002/(sici)1097-0088(199904)19:5<557::aid-joc377>3.0.co;2-d.

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33

Wasimi, Saleh A. "A Hybrid Model for Forecasting Daily Rainfall." Water Resources Research 26, no. 11 (November 1990): 2741–46. http://dx.doi.org/10.1029/wr026i011p02741.

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34

Wasimi, Saleh A. ""A hybrid model for forecasting daily rainfall." Water Resources Research 26, no. 11 (1990): 2741–46. http://dx.doi.org/10.1029/90wr01379.

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35

Dhanya, C. T., and D. Nagesh Kumar. "Nonlinear ensemble prediction of chaotic daily rainfall." Advances in Water Resources 33, no. 3 (March 2010): 327–47. http://dx.doi.org/10.1016/j.advwatres.2010.01.001.

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36

Pui, Alexander, Ashish Sharma, Rajeshwar Mehrotra, Bellie Sivakumar, and Erwin Jeremiah. "A comparison of alternatives for daily to sub-daily rainfall disaggregation." Journal of Hydrology 470-471 (November 2012): 138–57. http://dx.doi.org/10.1016/j.jhydrol.2012.08.041.

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37

Galoie, Majid, Fouad Kilanehei, Artemis Motamedi, and Mohammad Nazari-Sharabian. "Converting Daily Rainfall Data to Sub-daily—Introducing the MIMD Method." Water Resources Management 35, no. 11 (August 16, 2021): 3861–71. http://dx.doi.org/10.1007/s11269-021-02930-3.

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38

Van Nguyen, Van-Thanh, and Ganesh Raj Pandey. "Estimation of Short-Duration Rainfall Distribution Using Data Measured at Longer Time Scales." Water Science and Technology 29, no. 1-2 (January 1, 1994): 39–45. http://dx.doi.org/10.2166/wst.1994.0649.

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An investigation on how to estimate the distribution of short-duration (hours or shorter) rainfalls based on available daily rainfall measurements was undertaken. On the basis of the theory of multifractal multiplicative cascades, a scale-independent mathematical model was proposed to represent the probability distribution of rainfalls at various time scales. Using rainfall records from a network of seven recording gauges in the Montreal region in Quebec (Canada), it was found that the proposed model could provide adequate estimates of the distribution of hourly rainfalls at locations where these short-duration rainfall data are not available. Further, it has been observed that one single regional model can be developed to describe the scaling nature of rainfall distributions within the whole study area.
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39

Giarno, G., Muhammad Pramono Hadi, Slamet Suprayogi, and Sigit Heru Murti. "Distribution of Accuracy of TRMM Daily Rainfall in Makassar Strait." Forum Geografi 32, no. 1 (May 9, 2018): 38–52. http://dx.doi.org/10.23917/forgeo.v32i1.5774.

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This research aims to evaluate rainfall estimates of satellite products in regions that have high variations of rainfall pattern. The surrounding area of Makassar Strait have chosen because of its distinctive rainfall pattern between the eastern and western parts of the Makassar Strait. For this purpose, spatial distribution of Pearson’s coefficient correlation and Root Mean Square Error (RMSE) is used to evaluate accuracy of rainfall in the eastern part of Kalimantan Island and the western part of Sulawesi Island. Moreover, we also used the contingency table to complete the parameter accuracy of the TRMM rainfall estimates. The results show that the performance of TRMM rainfall estimates varies depending on space and time. Overall, the coefficient correlation between TRMM and rain observed from no correlation was -0.06 and 0.78 from strong correlation. The best correlation is on the eastern coast of South West Sulawesi located in line with the Java Sea. While, no variation in the correlation was related to flatland such as Kalimantan Island. On the other hand, in the mountain region, the correlation of TRMM rainfall estimates and observed rainfall tend to decrease. The RMSE distribution in this region depends on the accumulation of daily rainfall. RMSE tends to be high where there are higher fluctuations of fluctuating rainfall in a location. From contingency indicators, we found that the TRMM rainfall estimates were overestimate. Generally, the absence of rainfall during the dry season contributes to improving TRMM rainfall estimates by raising accuracy (ACC) in the contingency table.
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40

Moate, Peter J., Jennie E. Pryce, Leah C. Marett, Josie B. Garner, Matthew H. Deighton, Brigid E. Ribaux, Murray C. Hannah, William J. Wales, and S. Richard O. Williams. "Measurement of Enteric Methane Emissions by the SF6 Technique Is Not Affected by Ambient Weather Conditions." Animals 11, no. 2 (February 18, 2021): 528. http://dx.doi.org/10.3390/ani11020528.

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Despite the fact that the sulphur hexafluoride (SF6) tracer technique was developed over 25 years ago to measure methane production from grazing and non-housed animals, no studies have specifically investigated whether ambient wind speed, temperature, relative humidity and rainfall influence the accuracy of the method. The aim of this research was to investigate how these weather factors influence the measurement of enteric methane production by the SF6 technique. Six different cohorts of dairy cows (40 per cohort) were kept outdoors and fed a common diet during spring in 3 consecutive years. Methane production from individual cows was measured daily over the last 5 days of each 32-day period. An automated weather station measured air temperature, wind speed, relative humidity and rainfall every 10 min. Regression analyses were used to relate the average daily wind speed, average daily temperature, average daily relative humidity and total daily rainfall measurements to dry matter intake, average daily methane production and methane yield of each cohort of cows. It was concluded that the modified SF6 technique can be used outdoors during a range of wind speeds, ambient temperatures, relative humidities and rainfall conditions without causing a significant effect on the measurement of methane production or methane yield of dairy cows.
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41

Vemula, Swathi, K. Srinivasa Raju, and S. Sai Veena. "Modelling impact of future climate and land use land cover on flood vulnerability for policy support – Hyderabad, India." Water Policy 22, no. 5 (July 27, 2020): 733–47. http://dx.doi.org/10.2166/wp.2020.106.

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Abstract The study analyses the impact of climate change and land use land cover (LULC) on runoff of Hyderabad city, India for the years 1995, 2005, 2016 and 2031. Flood vulnerability was evaluated for extreme historic and future rainfall events. Maximum daily rainfalls of 132, 181 and 165 mm that occurred in the decades of 1990–2000, 2001–2010 and 2011–2016 were considered for historic rainfall–runoff modelling. Complementarily in climate change, maximum daily rainfall of 266 mm predicted during 2020–2040 by Geophysical Fluid Dynamics Laboratory-Coupled Model 3 (GFDL-CM3) Representative Concentration Pathway (RCP) 2.6, was analysed for rainfall-runoff scenario in 2031. LULC was assessed from historic maps and the master plan of the city. Peak runoff was modelled in Storm Water Management Model (SWMM) for corresponding daily rainfall and LULC. The floodplain of the river Musi was modelled in Hydrological Engineering Center-River Analysis System (HEC-RAS). Results showed that changing rainfall and LULC increased peak runoff by three times, and flood depth in the river increased by 22% from 1995 to 2031. In 2016 and 2031, 48 and 51% of the city was highly vulnerable. Five detention basins were proposed to combat increasing runoff, due to which highly vulnerable areas reduced by 8% in 2016 and 9% in 2031.
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42

Jakob, D., D. J. Karoly, and A. Seed. "Non-stationarity in daily and sub-daily intense rainfall – Part 2: Regional assessment for sites in south-east Australia." Natural Hazards and Earth System Sciences 11, no. 8 (August 19, 2011): 2273–84. http://dx.doi.org/10.5194/nhess-11-2273-2011.

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Abstract. Using data for a common period (1976–2005) for a set of 31 sites located in south-east Australia, variations in frequency and magnitude of intense rainfall events across durations from 6 min to 72 h were assessed. This study was driven by a need to clarify how variations in climate might affect intense rainfall and the potential for flooding. Sub-daily durations are of particular interest for urban applications. Worldwide, few such observation-based studies exist, which is mainly due to limitations in data. Analysis of seasonality in frequency and magnitude of events revealed considerable variation across the set of sites, implying different dominating rainfall-producing mechanisms and/or interactions with local topography. Both these factors are relevant when assessing the potential effects of climate variations on intense rainfall events. The set of sites was therefore split into groups ("north cluster" and "south cluster") according to the characteristics of intense rainfall events. There is a strong polarisation in the nature of changes found for the north cluster and south cluster. While sites in the north cluster typically exhibit decrease in frequency of events, particularly in autumn and at durations of 1 h and longer; sites in the south cluster experience an increase in frequency of events, particularly for summer and sub-hourly durations. Non-stationarity found in historical records has the potential to significantly affect design rainfall estimates. An assessment of quantile estimates derived using a standard regionalisation technique and periods representative of record lengths available for practical applications show that such estimates may not be representative of long-term conditions, so alternative approaches need to be considered, particularly where short records are concerned. Additional rainfall information, in particular radar data, could be used for an in-depth spatial analysis of intense rainfall events.
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Madsen, H., I. B. Gregersen, D. Rosbjerg, and K. Arnbjerg-Nielsen. "Regional frequency analysis of short duration rainfall extremes using gridded daily rainfall data as co-variate." Water Science and Technology 75, no. 8 (February 16, 2017): 1971–81. http://dx.doi.org/10.2166/wst.2017.089.

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A regional partial duration series (PDS) model is applied for estimation of intensity duration frequency relationships of extreme rainfalls in Denmark. The model uses generalised least squares regression to relate the PDS parameters to gridded rainfall statistics from a dense network of rain gauges with daily measurements. The Poisson rate is positively correlated to the mean annual precipitation for all durations considered (1 min to 48 hours). The mean intensity can be assumed constant over Denmark for durations up to 1 hour. For durations larger than 1 hour, the mean intensity is significantly correlated to the mean extreme daily precipitation. A Generalised Pareto distribution with a regional constant shape parameter is adopted. Compared to previous regional studies in Denmark, a general increase in extreme rainfall intensity for durations up to 1 hour is found, whereas for larger durations both increases and decreases are seen. A subsample analysis is conducted to evaluate the impacts of non-stationarities in the rainfall data. The regional model includes the non-stationarities as an additional source of uncertainty, together with sampling uncertainty and uncertainty caused by spatial variability.
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Fatkhuroyan, Fatkhuroyan, Trinah Wati, Alfan Sukmana, and Roni Kurniawan. "Validation of Satellite Daily Rainfall Estimates Over Indonesia." Forum Geografi 32, no. 2 (November 26, 2018): 170–80. http://dx.doi.org/10.23917/forgeo.v32i2.6288.

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Rainfall is the most important factor in the Earth’s water and energy cycles. The aim of this research is to evaluate the accuracy of Global Satellite Mapping of Rainfall (GSMaP) data by referencing daily rain-gauged rainfall measurements across the Indonesian Maritime Continent. We compare the daily rainfall data from GSMaP Moving Kalman Filter (MVK) to readings from 152 rain-gauge observation stations across Indonesia from March 2014 to December 2017. The results show that the correlation coefficient (CC) provides better validation in the rainy season while root mean square error (RMSE) is more accurate in the dry season. The highest proportion correct (PC) value is obtained for Bali-NTT, while the highest probability of detection (POD) and false alarm ratio (FAR) values are obtained for Kalimantan. GSMaP-MVK data is over-estimated compared to observations in Indonesia, with the mean accuracy for daily rainfall estimation being 85.47% in 2014, 85.74% in 2015, 82.73 in 2016, and 82.59% in 2017.
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45

Diodato, Nazzareno, and Gianni Bellocchi. "Using Historical Precipitation Patterns to Forecast Daily Extremes of Rainfall for the Coming Decades in Naples (Italy)." Geosciences 8, no. 8 (August 6, 2018): 293. http://dx.doi.org/10.3390/geosciences8080293.

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The coasts of the Italian peninsula have been recently affected by frequent damaging hydrological events driven by intense rainfall and deluges. The internal climatic mechanisms driving rainfall variability that generate these hydrological events in the Mediterranean are not fully understood. We investigated the simulation skill of a soft-computing approach to forecast extreme rainfalls in Naples (Italy). An annual series of daily maximum rainfall spanning the period between 1866 and 2016 was used for the design of ensemble projections in order to understand and quantify the uncertainty associated with interannual to interdecadal predictability. A predictable structure was first provided, and then elaborated by exponential smoothing for the purposes of training, validation, and forecast. For the time horizon between 2017 and 2066, the projections indicate a weak increase of daily maximum rainfalls, followed by almost the same pace as it was in the previous three decades, presenting remarkable wavelike variations with durations of more than one year. The forecasted pattern is coupled with variations attributed to internal climate modes, such as the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO).
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46

Burić, D., J. Luković, B. Bajat, M. Kilibarda, and N. Živković. "Recent trends in daily rainfall extremes over Montenegro (1951–2010)." Natural Hazards and Earth System Sciences 15, no. 9 (September 16, 2015): 2069–77. http://dx.doi.org/10.5194/nhess-15-2069-2015.

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Abstract. More intense rainfall may cause a range of negative impacts upon society and the environment. In this study we analysed trends in extreme ETCCDI (Expert Team on Climate Change Detection and Indices) rainfall indices in Montenegro for the period between 1951 and 2010. Montenegro has been poorly studied in terms of rainfall extremes, yet it contains the wettest Mediterranean region known as Krivošije. Several indices of precipitation extremes were assessed including the number of dry days and rainfall totals in order to identify trends and possible changes. A spatial pattern relationship between extreme rainfall indices and the North Atlantic Oscillation has also been examined. The results generally suggest that the number of days with precipitation decreased while rainfall intensity increased, particularly in south-western parts of the country. A slight tendency towards intense rainfall events is suggested. The examined rainfall indices and North Atlantic Oscillation over Montenegro seemed to be directly linked to changes in one of the major large-scale circulation modes such as the NAO pattern that is particularly evident during the winter season.
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47

Cheng, Chad Shouquan, Guilong Li, Qian Li, and Heather Auld. "A Synoptic Weather Typing Approach to Simulate Daily Rainfall and Extremes in Ontario, Canada: Potential for Climate Change Projections." Journal of Applied Meteorology and Climatology 49, no. 5 (May 1, 2010): 845–66. http://dx.doi.org/10.1175/2010jamc2016.1.

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Abstract An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a two-step process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score is greater than 0.97 for rainfall events with daily rainfall ≥10 or ≥25 mm, for both model development and validation. For evaluation of daily rainfall quantity simulation models, four correctness classifications of excellent, good, fair, and poor were defined, based on the difference between daily rainfall observations and model simulations. Across four selected river basins, the percentage of excellent and good simulations for model development ranged from 62% to 84% (of 20 individuals, 16 cases ≥ 70%, 7 cases ≥ 80%); the corresponding percentage for model validation ranged from 50% to 76% (of 20 individuals, 15 cases ≥ 60%, 6 cases ≥ 70%).
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د. فهدة فلاح بن حشر, د. فهدة فلاح بن حشر. "Estimation and analysis of the effective rainfall In Tabuk area – Saudi Arabia." journal of King Abdulaziz University Arts And Humanities 28, no. 14 (May 14, 2020): 142–86. http://dx.doi.org/10.4197/art.28-14.4.

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this study examined the effective rainfall in Tabuk area by applying the Lang rainfall factor, De Martonne Index and the (UNEP) arid index. The study methodology was based on a statistical tests using the Normality test (Shapiro-Wilk), the Homogeneity test (Leven test), the ANOVA (LSD test) of the monthly mean of rainfall, the daily maximum rainfall and the total number of rainfall days. The multiple comparison (Tehmane’s Test) had been applied between the studied stations. The results of the Shapiro-Wilk test shows that the distribution of monthly rainfall averages follows normal distribution in all stations except stations Tabuk and Al Bad’ and that the maximum daily rainfall distribution at Duba, Shuw?q, Al Kurr stations is also normal distribution. The Leven test results showed that the significance level was greater than 0.05 and the Leven Test was greater than 0.05 for the average monthly precipitation, for the daily maximum rainfall and for the number of rain days and it indicates the homogeneity of the rainfall variances in the studied stations.The ANOVA analysis of the averages and maximum daily rainfall, shows that the significance level is greater than 0.05 and the LSD test is greater than 0.05 indicating that the differences are not significant. While the LSD was greater than 0.05 for the number of rain days, indicating that the differences between the number of rain days and their distribution at the studied stations are statistically significant differences, and therefore there is no homogeneity in their differences. The results of the Tamhane’s Test of multiple comparison confirmed that the mean level of monthly mean rainfall and daily maximum rainfall is greater than 0.05 indicates that the differences between the monthly rain averages of and the daily maximum rainfall amounts are not significant for 22.2% of total comparisons.The effective rainfall estimate of Lang’s rainfall index shows that the threshold of effective mean rainfall is between 1.3 and 27.4 mm and that the threshold of maximum daily rainfall is 154.1 mm at the station (Al Bad’). Also, the effective rainfall estimate by the DeMartonne index shows that the threshold of average effective rainfall is between 1.2 and 15.2 mm and that the threshold of maximum daily rainfall is 80.4 mm at the station (Al Bad’). Therefore, the effective rainfall estimate by the UNEP index shows that the threshold of average effective rainfall is between 1.8 and 30.3 mm and that the threshold of maximum daily rainfall is 130.5 mm at the station (Al Bad’). Finally, the effective rainfall estimate by the difference between the rainfall and Pan Class“A” Evaporation shows that the threshold of average effective rainfall is between 1.2 and 25.8 mm and that the threshold of maximum daily rainfall is 137.1 mm at the station (Al Bad’).- Key Words:Effective rainfall average, Effective maximum daily rainfall , Number of rainy days, Statistical tests, Lang rainfall factor, DeMartonne index, UNEP arid index, Tabuk area, Saudi Arabia.
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Vantas, Konstantinos, Epaminondas Sidiropoulos, and Chris Evangelides. "Estimating Rainfall Erosivity from Daily Precipitation Using Generalized Additive Models." Environmental Sciences Proceedings 2, no. 1 (August 13, 2020): 21. http://dx.doi.org/10.3390/environsciproc2020002021.

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One of the most important natural processes responsible for soil loss is rainfall-induced erosion. The calculation of rainfall erosivity, as defined in the Universal Soil Loss Equation, requires the availability of rainfall data, either continuous breakpoint, or pluviograph, with sampling intervals on the order of minutes. Due to the limited temporal coverage and spatial scarcity of such data, worldwide, alternative equations have been developed that utilize coarser rainfall records, in an effort to estimate erosivity equivalently to that calculated using pluviograph data. This paper presents the application of generalized additive models (GAMs) to estimate erosivity utilizing daily rainfall records. As a case study, pluviograph data with a time step of 30 min from the Water District of Thrace in Greece were used. By applying GAMs, it became possible to model the nonlinear relation between daily rainfall, seasonal periodicity, and rainfall erosivity more effectively, in terms of accuracy, than the application of two well-known nonlinear empirical equations, both on a daily and an annual basis.
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50

Burić, D., J. Luković, B. Bajat, M. Kilibarda, and V. Ducić. "Recent trends in daily rainfall extremes over Montenegro (1951–2010)." Natural Hazards and Earth System Sciences Discussions 3, no. 4 (April 10, 2015): 2347–77. http://dx.doi.org/10.5194/nhessd-3-2347-2015.

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Abstract. More intense rainfall may cause a range of negative impacts upon society and the environment. In this study we analyzed trends in extreme ETCCDI (Expert Team on Climate Change Detection and Indices) rainfall indices in Montenegro for the period 1951–2010. Montenegro has been poorly studied in terms of rainfall extremes, yet it contains the wettest Mediterranean region known as Krivošije. Several indices of precipitation extremes were assessed including the number of dry days and rainfall totals, and their trends to identify possible changes. The results generally suggest that the number of days with precipitation decreased while rainfall intensity increased particularly in south-western parts of the country. A slight tendency towards intense rainfall events is suggested. Calculated trends for each index are spatially presented and examined using a plotGoogleMaps software package. This study also examined spatial pattern of relationship between extreme rainfall indices and North Atlantic Oscillation. Results suggested negative, mainly statistically significant correlations at annual, winter and autumn scale.
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