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Zohrab A. Samani y George H. Hargreaves. "Estimating Rainfall Probabilities from Average Values". Applied Engineering in Agriculture 2, n.º 2 (1986): 141–43. http://dx.doi.org/10.13031/2013.26729.
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Sarkar, Raju y Kelzang Dorji. "Determination of the Probabilities of Landslide Events—A Case Study of Bhutan". Hydrology 6, n.º 2 (16 de junio de 2019): 52. http://dx.doi.org/10.3390/hydrology6020052.
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Landslides have been and are prominent and devastating natural disasters in Bhutan due to its orography and intense monsoonal rainfall. The damage caused by landslides is huge, causing significant loss of lives, damage to infrastructure and loss of agricultural land. Several methods have been developed to understand the relationship between rainfall and landslide incidences. The most common method to understand the relationship is by defining thresholds using empirical methods which are expressed in either intensity-duration or event rainfall-duration terms. However, such thresholds determine the results in a binary form which may not be useful for landslide cases. Apart from defining thresholds, it is significant to validate the results. The article attempts to address both these issues by adopting a probabilistic approach and validating the results. The region of interest is the Chukha region located along the Phuentsholing-Thimphu Highway, which is a significant trade route between neighbouring countries and the national capital Thimphu. In the present study, probabilities are determined by Bayes’ theorem considering rainfall and landslide data from 2004 to 2014. Singular (rainfall intensity, rainfall duration and event rainfall) along with a combination (rainfall intensity and rainfall duration) of precipitation parameters were considered to determine the probabilities for landslide events. A sensitivity analysis was performed to verify the determined probabilities. The results depict that a combination of rainfall parameters is a better indicator to forecast landslides as compared to single rainfall parameter. Finally, the probabilities are validated using landslide records for 2015 using a threat score. The validation signifies that the probabilities can be used as the first line of action for an operational landslide warning system.
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V.D, SONDGE, SONTAKKE J.S y SHELGE B.S. "Aberrations in monsoons in assured rainfall area of Parabhani - Meteorologic characterization". Madras Agricultural Journal 87, september (2000): 384–88. http://dx.doi.org/10.29321/maj.10.a00478.
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Rainfall records of 52 years (1944-95) of Parbhani station located in the assured rainfall zone of Maharashtra state were critically examined for establishing the long term averages of monthly rainfall and its temporal variability by deploying appropriate statistical techniques. The deviations in normal time(s) of onset and withdrawal of monsoon, depths of monthly rainfall and their distribution were defined as aberrations. The results revealed that the average monsoon rainfall (monthly total) of 849.96 mm was distributed in the proportion of 18.13, 26.94, 25.28, 21.61 and 7.93 per cent during June to October, respectively. The variabilities in normal rainfall during crucial months of August (67.69 %) and October (119.48 %) were relatively higher than remaining monsoon months. The probabilities of normal onset (25th MW) and withdrawal (39th MW) were 44.23 and 50 per cent, respectively. The corresponding probabilities of aberrations were 55.77 and 50 per cent. The per cent probabilities of aberrations in seasonal (June to October) amount of rainfall was 56.54 per cent, with higher proportion of below normal (39.23) than its above normal (27.31) rainfall during June to October. The per cent probabilities of recupation of preceeding deficiency in succeeding months decreased with the advancement of time of occurrence of deficiency.
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KRIPALANI, RH y SV SINGH. "Rainfall probabilities and amounts associated with monsoon depressions over India". MAUSAM 37, n.º 1 (1 de enero de 1986): 111–16. http://dx.doi.org/10.54302/mausam.v37i1.2189.
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Composite charts of distribution of probabilities of 24-hr rainfall amounts >=2.5 mm and >= 65 mm and the average 24-hr rainfall amounts over India are determined for various geographical locations of the depressions by using, daily rainfall data of 220 stations for a 16-year period. The composite charts of rainfall probabilities are used to issue forecasts of probability of rain at 12 stations during five independent years. These forecasts when evaluated by a proper quadratic score showed skill score of 10%.
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Liao, Yifan, Bingzhang Lin, Xiaoyang Chen y Hui Ding. "A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas". Water 12, n.º 4 (20 de abril de 2020): 1177. http://dx.doi.org/10.3390/w12041177.
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Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.
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Bellerby, T. J. "Satellite Rainfall Uncertainty Estimation Using an Artificial Neural Network". Journal of Hydrometeorology 8, n.º 6 (1 de diciembre de 2007): 1397–412. http://dx.doi.org/10.1175/2007jhm846.1.
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Abstract This paper describes a neural network–based approach to estimate the conditional distribution function (cdf) of rainfall with respect to multidimensional satellite-derived input data. The methodology [Conditional Histogram of Precipitation (CHIP)] employs a histogram-based approximation of the cdf. In addition to the conditional expected rainfall rate, it provides conditional probabilities for that rate falling within each of a fixed set of intervals or bins. A test algorithm based on the CHIP approach was calibrated against Goddard profiling algorithm (GPROF) rainfall data for June–August 2002 and then used to produce a 30-min, 0.5° rainfall product from global (60°N–60°S) composite geostationary thermal infrared imagery for June–August 2003. Estimated rainfall rates and conditional probabilities were validated against 2003 GPROF data. The CHIP methodology provides the means to extend existing probabilistic and ensemble satellite rainfall error models, conditioned on a single, scalar, satellite rainfall predictor or upon scalar rainfall-rate outputs, to make full use of multidimensional input data.
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Winters, Karl E. "Floods in Central Texas, September 7–14, 2010". Texas Water Journal 3, n.º 1 (11 de julio de 2012): 14–25. http://dx.doi.org/10.21423/twj.v3i1.3292.
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Severe flooding occurred near the Austin metropolitan area in central Texas September 7–14, 2010, because of heavy rainfall associated with Tropical Storm Hermine. The U.S. Geological Survey, in cooperation with the Upper Brushy Creek Water Control and Improvement District, determined rainfall amounts and annual exceedance probabilities for rainfall resulting in flooding in Bell, Williamson, and Travis counties in central Texas during September 2010. We documented peak streamflows and the annual exceedance probabilities for peak streamflows recorded at several streamflow-gaging stations in the study area. The 24-hour rainfall total exceeded 12 inches at some locations, with one report of 14.57 inches at Lake Georgetown. Rainfall probabilities were estimated using previously published depth-duration frequency maps for Texas. At 4 sites in Williamson County, the 24-hour rainfall had an annual exceedance probability of 0.002. Streamflow measurement data and flood-peak data from U.S. Geological Survey surface-water monitoring stations (streamflow and reservoir gaging stations) are presented, along with a comparison of September 2010 flood peaks to previous known maximums in the periods of record. Annual exceedance probabilities for peak streamflow were computed for 20 streamflow-gaging stations based on an analysis of streamflow-gaging station records. The annual exceedance probability was 0.03 for the September 2010 peak streamflow at the Geological Survey’s streamflow-gaging stations 08104700 North Fork San Gabriel River near Georgetown, Texas, and 08154700 Bull Creek at Loop 360 near Austin, Texas. The annual exceedance probability was 0.02 for the peak streamflow for Geological Survey´s streamflow-gaging station 08104500 Little River near Little River, Texas. The lack of similarity in the annual exceedance probabilities computed for precipitation and streamflow might be attributed to the small areal extent of the heaviest rainfall over these and the other gaged watersheds. Citation: Winters KE. 2012. Floods in Central Texas, September 7–14, 2010. Texas Water Journal. 3(1):14-25. Available from: https://doi.org/10.21423/twj.v3i1.3292.
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GORE, P. G. y V. THAPLIYAL. "Occurrence of dry and wet weeks over Maharashtra". MAUSAM 51, n.º 1 (17 de diciembre de 2021): 25–38. http://dx.doi.org/10.54302/mausam.v51i1.1754.
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Based on the daily rainfall data of the past 90 years (1901-90), the initial and conditional probabilities of a wet week and the probabilities of 2 and 3 consecutive wet weeks have been computed for all the districts of Maharashtra during the southwest monsoon season by using Markov Chain model. A temporal and spatial distribution of probabilities of wet weeks have been studied in detail. Most of the districts show the highest probability of wet weeks during July. A few number of the districts show the second highest probability during August. The western and northeastern parts of the state show 10-16 wet weeks with high probability. The high rainfall districts along the west coast show high wet week probabilities during most of the period of the season. A few number of the districts from moderate rainfall zone, show high probability of a wet week during, July and August. A persistency in rainfall is noticed in only extreme western parts of the state. The east-west variation along 19° N shows 'L' shaped pattern for the high probability wet weeks. While, the north -south variation of the wet weeks with high probability shows a sinusoidal curve from north to south.
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DeGaetano, Arthur T. y Harrison Tran. "Recent Changes in Average Recurrence Interval Precipitation Extremes in the Mid-Atlantic United States". Journal of Applied Meteorology and Climatology 61, n.º 2 (febrero de 2022): 143–57. http://dx.doi.org/10.1175/jamc-d-21-0129.1.
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Abstract Increases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial-duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e., the 2-yr storm) to 1% (i.e., the 100-yr storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the mid-Atlantic region. At approximately one-quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations, the rainfall extremes based on the 1950–99 partial-duration series show a significant (p > 0.90) change in the 100-yr ARI relative to the 1950–2019 period. Collectively, the results indicate that, given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-yr intervals. Significance Statement Engineering design standards for drainage systems, dams, and other infrastructure rely on analyses of precipitation extremes. Often such structures are designed on the basis of the probability of exceeding a specified rainfall rate in a given year. The frequency of extreme rainfall events has increased in the mid-Atlantic region of the United States in recent decades, leading us to evaluate how these changes have affected these exceedance probabilities. From 2000 through 2019, there has been a consistent increase of generally 2.5%–5.0% in design rainfall amounts. The increase is similar across a range of rainfall durations from 1 h to 20 days and also annual exceedance probabilities ranging from 50% to 1% (i.e., from the “2-yr storm” to the “100-yr storm”). The work highlights the need to routinely update the climatological extreme-value analyses used in engineering design, with the results suggesting that a 20-yr cycle might be an appropriate update frequency.
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Adhikari, R. N., M. S. RAMA MOHAN RAO y P. BHASKAR RAO. "Analysis of rainfall data for water management In dry land zone of Karnataka". MAUSAM 44, n.º 2 (1 de enero de 2022): 147–52. http://dx.doi.org/10.54302/mausam.v44i2.3812.
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Bellary region is characterized as one of the semi-arid zones of Karnataka, having only 508 mm of annual rainfall distributed over 35 rainy days. The ill-distribution of rainfall creates at least 5 drought years in every decade. The average rainfa1l distribution shows that there is a total failure in Kharif season. However, some assured rainfall received during September and October a better prospect which assumes for rabi season .This problem can be overcome to. certain extent by scientific management of crops and water. This calls for detailed analysis of any Important water resources Issues. Keeping this mind, an attempt made in this paper to analysis short and long period rainfall data. The probabilities analysis of. rainfall for shorter periods for identification of suitable periods for sowing, return period analysis for designing of soil and water conservation structures and determining the size of storage structures, the identification of number of Various rainfall events for designing water harvesting system for crop and water management are carried out and presented in this paper.
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Kisaka, M. Oscar, M. Mucheru-Muna, F. K. Ngetich, J. N. Mugwe, D. Mugendi y F. Mairura. "Rainfall Variability, Drought Characterization, and Efficacy of Rainfall Data Reconstruction: Case of Eastern Kenya". Advances in Meteorology 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/380404.
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This study examined the extent of seasonal rainfall variability, drought occurrence, and the efficacy of interpolation techniques in eastern Kenya. Analyses of rainfall variability utilized rainfall anomaly index, coefficients of variance, and probability analyses. Spline, Kriging, and inverse distance weighting interpolation techniques were assessed using daily rainfall data and digital elevation model using ArcGIS. Validation of these interpolation methods was evaluated by comparing the modelled/generated rainfall values and the observed daily rainfall data using root mean square errors and mean absolute errors statistics. Results showed 90% chance of below cropping threshold rainfall (500 mm) exceeding 258.1 mm during short rains in Embu for one year return period. Rainfall variability was found to be high in seasonal amounts (CV = 0.56, 0.47, and 0.59) and in number of rainy days (CV = 0.88, 0.49, and 0.53) in Machang’a, Kiritiri, and Kindaruma, respectively. Monthly rainfall variability was found to be equally high during April and November (CV = 0.48, 0.49, and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang’a and Kindaruma. Dry-spell probabilities within growing months were high, (91%, 93%, 81%, and 60%) in Kiambere, Kindaruma, Machang’a, and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.
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Sato, Tadamichi y Yasuhiro Shuin. "Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan". Forests 14, n.º 1 (12 de enero de 2023): 147. http://dx.doi.org/10.3390/f14010147.
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Extreme rainfall causes floods and landslides, and so damages humans and socioeconomics; for instance, floods and landslides have been triggered by repeated torrential precipitation and have caused severe damage in the Kyushu region, Japan. Therefore, evaluating extreme rainfall in Kyushu is necessary to provide basic information for measures of rainfall-induced disasters. In this study, we estimated the probability of daily rainfall in Kyushu. The annual maximum values for daily rainfall at 23 long-record stations were normalized using return values at each station, corresponding to 2 and 10 years, and were combined by the station-year method. Additionally, the return period (RP) was calculated by fitting them to the generalized extreme value distribution. Based on the relationship between the normalized values of annual maximum daily rainfall and the RP, we obtained a regression equation to accurately estimate the RP up to 300 years by using data at given stations, considering outliers. In addition, we verified this equation using data from short-record stations where extreme rainfall events triggering floods and landslides were observed, and thereby elucidated that our method was consistent with previous techniques. Thus, this study develops strategies of measures for floods and landslides.
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Yoo, Chulsang, Jinwook Lee y Yonghun Ro. "Markov Chain Decomposition of Monthly Rainfall into Daily Rainfall: Evaluation of Climate Change Impact". Advances in Meteorology 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7957490.
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This study evaluates the effect of climate change on daily rainfall, especially on the mean number of wet days and the mean rainfall intensity. Assuming that the mechanism of daily rainfall occurrences follows the first-order Markov chain model, the possible changes in the transition probabilities are estimated by considering the climate change scenarios. Also, the change of the stationary probabilities of wet and dry day occurrences and finally the change in the number of wet days are derived for the comparison of current (1x CO2) and 2x CO2conditions. As a result of this study, the increase or decrease in the mean number of wet days was found to be not enough to explain all of the change in monthly rainfall amounts, so rainfall intensity should also be modified. The application to the Seoul weather station in Korea shows that about 30% of the total change in monthly rainfall amount can be explained by the change in the number of wet days and the remaining 70% by the change in the rainfall intensity. That is, as an effect of climate change, the increase in the rainfall intensity could be more significant than the increase in the wet days and, thus, the risk of flood will be much highly increased.
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W. C. Mills, A. W. Thomas, A. L. Dillard y W. M. Snyder. "Computing Watershed Storage Probabilities from Rainfall and Runoff Data". Transactions of the ASAE 35, n.º 3 (1992): 891–97. http://dx.doi.org/10.13031/2013.28675.
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Mills, W. C., A. W. Thomas y G. W. Langdale. "Rainfall retention probabilities computed for different cropping-tillage systems". Agricultural Water Management 15, n.º 1 (noviembre de 1988): 61–71. http://dx.doi.org/10.1016/0378-3774(88)90143-6.
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Bhelawe, Sanjay, M. Manikandan, Rajesh Khavse, J. Chaudhary y S. Patel. "Analysis of Rainfall Probabilities for Strategic Crop Planning in Raipur District of Chhattisgarh State". Current World Environment 10, n.º 1 (30 de abril de 2015): 253–57. http://dx.doi.org/10.12944/cwe.10.1.30.
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Rainfall data of recent forty three years (1971-2013) of Labhandi station, Indira Gandhi Krishi Vishwavidhyalaya Raipur, Chhattisgarh was analysed with the method of incomplete gamma probability. The data revealed that the average rainfall of labhandi station is 1202 mm spread over 61 rainy days. Out of this 1055, 68, 53 and 27 mm received from south west monsoon (June-September), north east (October-December), summer (March-May) and winter season (January -February) respectively. Probability for receiving more than 100 mm of rainfall can be expected only at 25% probability level and that too in four weeks which is leading to the interpretation that rainfed rice production is a challenging task in this region. it has been found that at 75 per cent assured probability level rainfall of more than 200 mms can be expected only in July and August months and this rainfall is hardly sufficient for meeting the water requirement in upland situations. However at 50 per cent probability which is equivalent to average condition, cultivation of rice is possible under well water management conditions. On seasonal basis rainfall at assured probability level of 75% is not sufficient as the quantity is 795 mm rainfall in south-western monsoon season.
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Park, Hyuck-Jin, Kang-Min Kim, In-Tak Hwang y Jung-Hyun Lee. "Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach". Sustainability 14, n.º 5 (24 de febrero de 2022): 2628. http://dx.doi.org/10.3390/su14052628.
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The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods only evaluate the statistical relationships between factors that have triggered landslides in the past rather than the slope failure process. Therefore, a physically based approach with probabilistic analysis was adopted here to estimate the spatial distribution of landslide probability. Meanwhile, few studies have addressed temporal probability because historical records of landslides are not available for most areas of the world. Therefore, an indirect approach based on rainfall frequency and using extreme value analysis and the Gumbel distribution is proposed and used in this study. In addition, to incorporate the nonstationary characteristics of rainfall data, an expanding window approach was used to evaluate changes in the mean annual maximum rainfall and the location and scale parameters of the Gumbel distribution. Using this approach, the temporal probabilities of future landslides were estimated and integrated with spatial probabilities to assess and map landslide hazards.
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Cabrales, Sergio, Jesus Solano, Carlos Valencia y Rafael Bautista. "Pricing rainfall derivatives in the equatorial Pacific". Agricultural Finance Review 80, n.º 4 (20 de junio de 2020): 589–608. http://dx.doi.org/10.1108/afr-09-2019-0105.
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PurposeIn the equatorial Pacific, rainfall is affected by global climate phenomena, such as El Niño Southern Oscillation (ENSO). However, current publicly available methodologies for valuing weather derivatives do not account for the influence of ENSO. The purpose of this paper is to develop a complete framework suitable for valuing rainfall derivatives in the equatorial Pacific.Design/methodology/approachIn this paper, we implement a Markov chain for the occurrence of rain and a gamma model for the conditional quantities using vector generalized linear models (VGLM). The ENSO forecast probabilities reported by the International Research Institute for Climate and Society (IRI) are included as independent variables using different alternatives. We then employ the Esscher transform to price rainfall derivatives.FindingsThe methodology is applied and calibrated using the historical rainfall data collected at the El Dorado airport weather station in Bogotá. All the estimated coefficients turn out to be significant. The results prove more accurate than those of Markovian gamma models based on purely statistical descriptions of the daily rainfall probabilities.Originality/valueThis procedure introduces the novelty of incorporating variables related to the climatic phenomena, which are the forecast probabilities regularly published for the occurrence of El Niño and La Niña.
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Otsu, Y., Y. Takahashi y T. Kozu. "Simultaneous occurrence probabilities of rainfall among nine locations in Japan". Electronics Letters 22, n.º 18 (1986): 937. http://dx.doi.org/10.1049/el:19860638.
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Jones, M. J. y A. Wahbi. "Site-factor Influence on Barley Response to Fertilizer in On-farm Trials in Northern Syria: Descriptive and Predictive Models". Experimental Agriculture 28, n.º 1 (enero de 1992): 63–87. http://dx.doi.org/10.1017/s0014479700023024.
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SummaryThe effects of site and rate of application of fertilizer on the grain and straw production of barley were investigated in 75 trials on representative farmers' fields. Grain and straw production were strongly but curvilinearly correlated with growth-period rainfall over the range 136–568 mm, almost irrespective of soil type, previous crop or fertilizer rate, and responded positively to applied nitrogen and/or phosphorus in 74 of the trials. Responses to nitrogen increased and those to phosphorus decreased with increasing rainfall. Yields tended to be lower but responses to nitrogen were higher where barley followed barley and were influenced by the availability of phosphate and mineral nitrogen in the soil at planting time.These results are summarized in regression equations, which express yield quadratically in terms of fertilizer rates, seasonal rainfall and their interactions over the full 75-trial data set and within representative sub-sets. The wide differences in crop response to fertilizer highlight the problem of recommending fertilizer rates for a low and variable rainfall environment. Simple second-order multiple regressions for different geographical areas, rainfall zones, crop rotations etc., based on mean rainfall values, account for around 40% of the variance in yield response to nitrogen and phosphorus fertilizer; only a small proportion of the remaining variance appears attributable to rainfall variability. Alternatively, yield may be expressed as a function of fertilizer rate and rainfall using rainfall probability values derived from long-term records. This can be used to estimate the probabilities of predetermined yield responses for specified sites and fertilizer rates and to produce maps of those probabilities.
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ARGHA GHOSH1, MANOJ KUMAR NANDA, DEBOLINA SARKAR1, SUKAMAL SARKAR, KOUSHIK BRAHMACHARI y KRISHNENDU RAY. "Assessing the agroclimatic potentiality in Indian Sundarbans for crop planning by analyzing rainfall time series data". Journal of Agrometeorology 23, n.º 1 (24 de octubre de 2021): 113–21. http://dx.doi.org/10.54386/jam.v23i1.96.
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In order to assess the climatological risk in terms of the dry week probabilities and length of the growing period of Indian Sundarbans region for successful crop planning, the present study was conducted using long term rainfall data from 1984 to 2018 received in Gosaba CD (Community Development) block of Indian Sundarbans. The probability of a rainfall events with 25 %, 50%, 75 % probabilities were estimated. Markov Chain model was used to estimate the initial, conditional probabilities of dry and wet weeks along with the probability of two consecutive wet and dry weeks considering 10 mm and 20 mm rainfall thresholds. Length of growing period (LGP) was calculated using Moisture adequacy index computed by the soil water balance method of Thornthwaite and Mather. Weekly rainfall varied from 1.11 mm in 51st Standard Meteorological Week (SMW) with standard deviation of ± 3.41 to 88.49 mm in 29th SMW with standard deviation of ± 58.19.50 % chance of getting more than 20 mm and 10 mm weekly rainfall was observed from 20th (23.37 mm) to 41st SMW (30.64 mm) and 17th (14.63 mm) to 42nd SMW (16.87 mm) respectively. The risk of dry spells was very higher from 42nd to 17th SMW. Average LGP of the study area was 237.4 days with standard deviation of ± 29.88.Probability of a week being stress free growing period and moderately drought period was more than 50 % from 18th to 48th SMW and 49th to 5th SMW respectively. Grass pea, potato with straw mulch and green gram can be included in the rice based cropping system during winter and summer seasons for sustainable intensification of the cropping systems in Indian Sundarbans region.
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Potter, Nicholas J., Francis H. S. Chiew, Stephen P. Charles, Guobin Fu, Hongxing Zheng y Lu Zhang. "Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections". Hydrology and Earth System Sciences 24, n.º 6 (8 de junio de 2020): 2963–79. http://dx.doi.org/10.5194/hess-24-2963-2020.
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Abstract. Dynamical downscaling of future projections of global climate model outputs can provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand in regional water resource planning processes. By explicitly modelling climate processes within and across global climate model grid cells for a region, dynamical downscaling can provide higher-resolution hydroclimate projections and independent (from historical time series), physically plausible future rainfall time series for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias-correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile–quantile mapping to rainfall dynamically downscaled by the NSW and ACT Regional Climate Modelling (NARCliM) Project in the state of Victoria, Australia, and examine the effect of this on (i) biases both before and after bias correction in different rainfall metrics, (ii) change signals in metrics in comparison to the bias and (iii) the effect of bias correction on wet–wet and dry–dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile–quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. Representing quantile–quantile mapping in this way demonstrates that any quantile mapping bias-correction method is unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias-correct dynamical downscaling rainfall outputs.
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Sigauke, Caston y Thakhani Ravele. "Estimating Concurrent Probabilities of Compound Extremes: An Analysis of Temperature and Rainfall Events in the Limpopo Lowveld Region of South Africa". Atmosphere 15, n.º 5 (30 de abril de 2024): 557. http://dx.doi.org/10.3390/atmos15050557.
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In recent years, there has been increasing interest in the joint modelling of compound extreme events such as high temperatures and low rainfall. The increase in the frequency of occurrence of these events in many regions has necessitated the development of models for estimating the concurrent probabilities of such compound extreme events. The current study discusses an application of copula models in predicting the concurrent probabilities of compound low rainfall and high-temperature events using data from the Lowveld region of the Limpopo province in South Africa. The second stage discussed two indicators for monitoring compound high temperature and low rainfall events. Empirical results from the study show that elevations ranging from 100–350 m, 350–700 m and 700–1200 m exhibit varying probabilities of experiencing drought, with mild droughts having approximately 64%, 66%, and 65% chances, moderate droughts around 36%, 39%, and 38%, and severe droughts at approximately 16%, 19%, and 18%, respectively. Furthermore, the logistic regression models incorporating the southern oscillation index as a covariate yielded comparable results of copula-based models. The methodology discussed in this paper is robust and can be applied to similar datasets in any regional setting globally. These findings could be useful to disaster management decision makers, helping them formulate effective mitigation strategies and emergency response plans.
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Wu, C. Y. y S. C. Chen. "Integrating spatial, temporal, and size probabilities for the annual landslide hazard maps in the Shihmen watershed, Taiwan". Natural Hazards and Earth System Sciences 13, n.º 9 (25 de septiembre de 2013): 2353–67. http://dx.doi.org/10.5194/nhess-13-2353-2013.
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Abstract. Landslide spatial, temporal, and size probabilities were used to perform a landslide hazard assessment in this study. Eleven intrinsic geomorphological, and two extrinsic rainfall factors were evaluated as landslide susceptibility related factors as they related to the success rate curves, landslide ratio plots, frequency distributions of landslide and non-landslide groups, as well as probability–probability plots. Data on landslides caused by Typhoon Aere in the Shihmen watershed were selected to train the susceptibility model. The landslide area probability, based on the power law relationship between the landslide area and a noncumulative number, was analyzed using the Pearson type 5 probability density function. The exceedance probabilities of rainfall with various recurrence intervals, including 2, 5, 10, 20, 50, 100 and 200 yr, were used to determine the temporal probabilities of the events. The study was conducted in the Shihmen watershed, which has an area of 760 km2 and is one of the main water sources for northern Taiwan. The validation result of Typhoon Krosa demonstrated that this landslide hazard model could be used to predict the landslide probabilities. The results suggested that integration of spatial, area, and exceedance probabilities to estimate the annual probability of each slope unit is feasible. The advantage of this annual landslide probability model lies in its ability to estimate the annual landslide risk, instead of a scenario-based risk.
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Clausen, Bente y Charles P. Pearson. "How Extreme was the Drought?" Hydrology Research 28, n.º 4-5 (1 de agosto de 1997): 297–306. http://dx.doi.org/10.2166/nh.1998.23.
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In 1993/94 a severe drought on the North Island of New Zealand caused water shortage in the Auckland metropolis. How extreme was the drought? This paper presents comparisons of frequencies of the 1993/94 drought in four North Island regions using different indicators of drought and including both streamflow and rainfall data. With regard to streamflow deficit volume and duration the exceedance probabilities ranged from 1 to 25%; however, the 7-day annual minimum flows in 1993/94 were less extreme with non-exceedance probabilities from 10 to 34%. With regard to rainfall the drought in the Auckland region was most extreme (on average 2% exceedance probability) when annual rainfall totals were analysed compared to seasonal totals (on average 6% exceedance probability for half-annual totals and 13-38% for three-month totals). Thus, the conclusion was that the 1993/94 drought was extreme because of its duration rather than its magnitude.
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Thangjai, Warisa, Sa-Aat Niwitpong y Suparat Niwitpong. "Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions". PeerJ 10 (7 de diciembre de 2022): e14498. http://dx.doi.org/10.7717/peerj.14498.
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Weighted percentiles in many areas can be used to investigate the overall trend in a particular context. In this article, the confidence intervals for the common percentile are constructed to estimate rainfall in Thailand. The confidence interval for the common percentile help to indicate intensity of rainfall. Herein, four new approaches for estimating confidence intervals for the common percentile of several delta-lognormal distributions are presented: the fiducial generalized confidence interval, the adjusted method of variance estimates recovery, and two Bayesian approaches using fiducial quantity and approximate fiducial distribution. The Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths via the R statistical program. The proposed confidence intervals are compared in terms of their coverage probabilities and average lengths, and the results of a comparative study based on these metrics indicate that one of the Bayesian confidence intervals is better than the others. The efficacies of the approaches are also illustrated by applying them to daily rainfall datasets from various regions in Thailand.
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Abraham, Minu Treesa, Neelima Satyam, Biswajeet Pradhan y Abdullah M. Alamri. "Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas". Water 12, n.º 3 (13 de marzo de 2020): 804. http://dx.doi.org/10.3390/w12030804.
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Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Système Hydrologique Européen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region.
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Guzmán, Adela García y William C. Torrez. "Daily Rainfall Probabilities: Conditional upon Prior Occurrence and Amount of Rain". Journal of Climate and Applied Meteorology 24, n.º 10 (octubre de 1985): 1009–14. http://dx.doi.org/10.1175/1520-0450(1985)024<1009:drpcup>2.0.co;2.
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Wu, Chun-Yi y Po-Kai Chou. "Prediction of total landslide volume in watershed scale under rainfall events using a probability model". Open Geosciences 13, n.º 1 (1 de enero de 2021): 944–62. http://dx.doi.org/10.1515/geo-2020-0284.
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Abstract This study established a probability model based on the landslide spatial and size probabilities to predict the possible volume and locations of landslides in watershed scale under rainfall events. First, we assessed the landslide spatial probability using a random forest landslide susceptibility model including intrinsic causative factors and extrinsic rainfall factors. Second, we calculated the landslide volume probability using the Pearson type V distribution. Lastly, these probabilities were joined to predict possible landslide volume and locations in the study area, the Taipei Water Source Domain, under rainfall events. The possible total landslide volume in the watershed changed from 1.7 million cubic meter under the event with 2-year recurrence interval to 18.2 million cubic meter under the event with 20-year recurrence interval. Approximately 62% of the total landslide volume triggered by the rainfall events was concentrated in 20% of the slope units. As the recurrence interval of the events increased, the slope units with large landslide volume tended to concentrate in the midstream of Nanshi River subwatershed. The results indicated the probability model posited can be used not only to predict total landslide volume in watershed scale, but also to determine the possible locations of the slope units with large landslide volume.
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Wang, Jun, Zhongmin Liang, Dong Wang, Tian Liu y Jing Yang. "Impact of Climate Change on Hydrologic Extremes in the Upper Basin of the Yellow River Basin of China". Advances in Meteorology 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1404290.
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To reveal the revolution law of hydrologic extremes in the next 50 years and analyze the impact of climate change on hydrologic extremes, the following main works were carried on: firstly, the long duration (15 d, 30 d, and 60 d) rainfall extremes according to observed time-series and forecast time-series by dynamical climate model product (BCC-CSM-1.1) were deduced, respectively, on the basis that the quantitative estimation of the impact of climate change on rainfall extremes was conducted; secondly, the SWAT model was used to deduce design flood with the input of design rainfall for the next 50 years. On this basis, quantitative estimation of the impact of climate change on long duration flood volume extremes was conducted. It indicates that (1) the value of long duration rainfall extremes for given probabilities (1%, 2%, 5%, and 10%) of the Tangnaihai basin will rise with slight increasing rate from 1% to 6% in the next 50 years and (2) long duration flood volume extremes of given probabilities of the Tangnaihai basin will rise with slight increasing rate from 1% to 6% in the next 50 years. The conclusions may provide technical supports for basin level planning of flood control and hydropower production.
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Deo, Anil, Savin S. Chand, Hamish Ramsay, Neil J. Holbrook, Simon McGree, Andrew Magee, Samuel Bell et al. "Tropical cyclone contribution to extreme rainfall over southwest Pacific Island nations". Climate Dynamics 56, n.º 11-12 (18 de marzo de 2021): 3967–93. http://dx.doi.org/10.1007/s00382-021-05680-5.
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AbstractSouthwest Pacific nations are among some of the worst impacted and most vulnerable globally in terms of tropical cyclone (TC)-induced flooding and accompanying risks. This study objectively quantifies the fractional contribution of TCs to extreme rainfall (hereafter, TC contributions) in the context of climate variability and change. We show that TC contributions to extreme rainfall are substantially enhanced during active phases of the Madden–Julian Oscillation and by El Niño conditions (particularly over the eastern southwest Pacific region); this enhancement is primarily attributed to increased TC activity during these event periods. There are also indications of increasing intensities of TC-induced extreme rainfall events over the past few decades. A key part of this work involves development of sophisticated Bayesian regression models for individual island nations in order to better understand the synergistic relationships between TC-induced extreme rainfall and combinations of various climatic drivers that modulate the relationship. Such models are found to be very useful for not only assessing probabilities of TC- and non-TC induced extreme rainfall events but also evaluating probabilities of extreme rainfall for cases with different underlying climatic conditions. For example, TC-induced extreme rainfall probability over Samoa can vary from ~ 95 to ~ 75% during a La Niña period, if it coincides with an active or inactive phase of the MJO, and can be reduced to ~ 30% during a combination of El Niño period and inactive phase of the MJO. Several other such cases have been assessed for different island nations, providing information that have potentially important implications for planning and preparing for TC risks in vulnerable Pacific Island nations.
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Ebers, Niklas, Kai Schröter y Hannes Müller-Thomy. "Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data". Natural Hazards and Earth System Sciences 24, n.º 6 (21 de junio de 2024): 2025–43. http://dx.doi.org/10.5194/nhess-24-2025-2024.
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Abstract. Rainfall time series with high temporal resolution play a crucial role in various hydrological fields, such as urban hydrology, flood risk management and soil erosion. Understanding the future changes in rainfall extreme values is essential for these applications. Since climate models typically offer daily resolution only, statistical downscaling in time seems a relevant and computationally effective solution. The micro-canonical cascade model conserves the daily rainfall amounts exactly, and having all model parameters expressed as physical interpretable probabilities avoids assumptions about future rainfall changes. Taking into account that short-duration rainfall extreme values are linked with high temperatures, the micro-canonical cascade model is further developed in this study. As the introduction of the temperature dependency increases the number of cascade model parameters, several modifications for parameter reduction are tested for 45 locations across Germany. To ensure spatial coherence with the climate model data, a composite product of radar and rain gauges with the same resolution was used for the estimation of the cascade model parameters. For the climate change analysis the core ensemble of the German Weather Service, which comprises six combinations of global and regional climate models, is applied for both RCP4.5 and RCP8.5 scenarios. For parameter reduction two approaches were analysed: (i) the reduction via position-dependent probabilities and (ii) parameter reduction via scale independency. A combination of both approaches led to a reduction in the number of model parameters (48 parameters instead of 144 in the reference model) with only a minor effect on the disaggregation results. The introduction of the temperature dependency improves the disaggregation results, particularly regarding rainfall extreme values and is therefore important to consider for future studies. For the disaggregated rainfall time series of climate scenarios, an intensification of the rainfall extreme values is observed. Analyses of rainfall extreme values for different return periods for a rainfall duration of 5 min and 1 h indicate an increase of 5 %–10 % in the near-term future (2021–2050) and 15 %–25 % in the long-term future (2071–2100) compared to the control period (1971–2000).
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Yussouf, Nusrat y David J. Stensrud. "Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System during the 2005/06 Cool Season". Monthly Weather Review 136, n.º 6 (1 de junio de 2008): 2157–72. http://dx.doi.org/10.1175/2007mwr2314.1.
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Abstract A simple binning technique developed to produce reliable probabilistic quantitative precipitation forecasts (PQPFs) from a multimodel short-range ensemble forecasting system is evaluated during the cool season of 2005/06. The technique uses forecasts and observations of 3-h accumulated precipitation amounts from the past 12 days to adjust the present day’s 3-h quantitative precipitation forecasts from each ensemble member for each 3-h forecast period. Results indicate that the PQPFs obtained from this simple binning technique are significantly more reliable than the raw (original) ensemble forecast probabilities. Brier skill scores and areas under the relative operating characteristic curve also reveal that this technique yields skillful probabilistic forecasts of rainfall amounts during the cool season. This holds true for accumulation periods of up to 48 h. The results obtained from this wintertime experiment parallel those obtained during the summer of 2004. In an attempt to reduce the effects of a small sample size on two-dimensional probability maps, the simple binning technique is modified by implementing 5- and 9-point smoothing schemes on the adjusted precipitation forecasts. Results indicate that the smoothed ensemble probabilities remain an improvement over the raw (original) ensemble forecast probabilities, although the smoothed probabilities are not as reliable as the unsmoothed adjusted probabilities. The skill of the PQPFs also is increased as the ensemble is expanded from 16 to 22 members during the period of study. These results reveal that simple postprocessing techniques have the potential to provide greatly improved probabilistic guidance of rainfall events for all seasons of the year.
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MOHAN SINGH, JAYANT KUMAR y S. S. BHARDWAJ. "Rainfall probability during dormant and growing seasons of apple in Himachal Pradesh". Journal of Agrometeorology 11, n.º 1 (1 de junio de 2009): 47–50. http://dx.doi.org/10.54386/jam.v11i1.1222.
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Variation of seasonal rainfall and probabilities of occurrence of assured weekly rainfall provide useful information for efficient agricultural management. In the present study, seven stations of Himachal Pradesh have been selected for the analysis of the rainfall data. In general, the station Dalhousie received the higher rainfall during both the growing and dormant seasons along with annual rainfall. Probability percentage of receiving 10 mm, 20 mm, 50 mm and 75 mm rainfall have been computed for standard weeks during growing and dormant seasons at four stations Katrain, Bajaura, Mashobra and Nauni (Solan). A critical examination for spatial and temporal probability distribution revealed that high rainfall belt is located in the north western part of the state. For better apple production, monsoon rainfall plays significant role. It not only moderates the temperature during summer but also recharges the muchneeded moisture, which almost depletes by mid June. Well distributed winter rains and early snowfall before January are beneficial in providing chilling, optimal flowering and good fruit set.
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Taiwo Amoo, Oseni y Bloodless Dzwairo. "Trend analysis and artificial neural networks forecasting for rainfall prediction". Environmental Economics 7, n.º 4 (21 de diciembre de 2016): 149–60. http://dx.doi.org/10.21511/ee.07(4-1).2016.07.
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The growing severe damage and sustained nature of the recent drought in some parts of the globe have resulted in the need to conduct studies relating to rainfall forecasting and effective integrated water resources management. This research examines and analyzes the use and ability of artificial neural networks (ANNs) in forecasting future trends of rainfall indices for Mkomazi Basin, South Africa. The approach used the theory of back propagation neural networks, after which a model was developed to predict the future rainfall occurrence using an environmental fed variable for closing up. Once this was accomplished, the ANNs’ accuracy was compared against a traditional forecasting method called multiple linear regression. The probability of an accurate forecast was calculated using conditional probabilities for the two models. Given the accuracy of the forecast, the benefits of the ANNs as a vital tool for decision makers in mitigating drought related concerns was enunciated. Keywords: artificial neural networks, drought, rainfall case forecast, multiple linear regression. JEL Classification: C53, C45
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Afolabi, ASANI, LUKMAN Salihu, SANI Salaudreen, OJO Stephen y OKE Adesola. "Poisson probability distribution analysis of Makurdi and Abeokuta rainfalls". Fundamental and Applied Agriculture 7, n.º 4 (2022): 285. http://dx.doi.org/10.5455/faa.129095.
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Early information for sustainable utilization of water resources through poisson probability distribution of rainfall is an important regulatory measure for flood control and water security management. As a follow-up to our previous studies on distributions, this paper reports statistical goodness-of-fit evaluations of selected rainfall data. It is the utilization of the maximum likelihood method (MLM) for the poisson probability distribution (PPD)of selected rainfall data. The numerically estimated constant of the density of PPD was estimated by the MLM, and Microsoft Excel Solver (MES). These estimated constants were used to compute probabilities of poisson distributions. The computed probabilities using the constants obtained were evaluated statistically (analysis of variance, (ANOVA), relative error, model of' selection criterion (MSC), Coefficient of Determination (CD) and Correlation coefficient (R). The study established that the poisson probability distribution’s parameter (p) was the average of the logarithm to base 10 of rainfall using the MLM and MES estimators. The constants were found to be 0.665 and 0.535 for Makurdi, 0.695 and 0.478 for Abeokuta using MLM and MES, respectively. The relative errors were found to be 0.479 and 0.743, and 1.141 and 1.509 for Makurdi and Abeokuta using MLM and MES, respectively. The correlation coefficient for Makurdi and Abeokuta using MLM and MES were found to be 0.876 and 0.800, and 0.269 and 0.341, respectively. It was concluded that the MLM constant was better than MES based on the values of MSC, CD, relative error and R. MLM predicted Weibull probability of rainfall intensity better than MES. Utilization of PPD in the estimation of rainfall intensity will help in the prediction of rainfall for agriculture in attaining sustainable goal 2 (zero hunger), regulatory measures for flood control and water security management. There is a need to evaluate MLM and other probability distributions to further assist in attaining sustainable development goals.
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Pabreja, Kavita. "Artificial Neural Network for Markov Chaining of Rainfall Over India". International Journal of Business Analytics 7, n.º 3 (julio de 2020): 71–84. http://dx.doi.org/10.4018/ijban.2020070105.
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Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.
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Morrison, Scott A., Douglas T. Bolger y T. Scott Sillett. "Annual Survivorship of the Sedentary Rufous-Crowned Sparrow (Aimophila Ruficeps): No Detectable Effects of Edge or Rainfall in Southern California". Auk 121, n.º 3 (1 de julio de 2004): 904–16. http://dx.doi.org/10.1093/auk/121.3.904.
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Abstract The Rufous-crowned Sparrow (Aimophila ruficeps) is a nonmigratory passerine that displays an area-sensitive distribution pattern of abundance in fragmented coastal sagescrub habitat of southern California. To determine if habitat fragmentation negatively affected adult survival, we used Cormack-Jolly-Seber models to compare annual survival probabilities of adult sparrows breeding in habitat adjacent to urban-developed edges to those of birds breeding in the interior of large habitat expanses in San Diego County, 1997–2000. During that period, an El Niño event brought heavy rainfall to the study area, and a La Niña event brought drought. Annual survival probabilities were relatively high for a small passerine (females: 0.69 ± 0.05 SE; males: 0.74 ± 0.04 SE) but, given our data, did not differ between habitat types or with rainfall. Annual resighting probabilities for the birds were strongly associated with variation in rainfall, being high in the wet year and low in the dry year. Mate- and site-fidelity were apparently high, and surveys during the nonbreeding season documented that the sparrows stayed paired and on territories year-round. We hypothesize that the high apparent survivorship of this species is related to its nonmigratory habit and its tendency to curtail reproductive effort during periods of food scarcity. Although our survivorship analysis suggests that the urban-wildland interface does not adversely affect survival of territorial Rufous-crowned Sparrows, our power to detect an effect of habitat edge on survival was low. Thus, we urge caution in concluding that edge effects do not have an ecologically important influence on survival rates in this species.
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Fibrianty, F. y S. Sarjiman. "ANALISIS PELUANG DERET HARI KERING PADA TANAMAN PADI TADAH HUJAN DI KABUPATEN KULONPROGO DALAM PERSPEKTIF TOPOSEKUENS (PROBABILITY ANALYSIS OF DRY SPELL ON RAINFED PADDY AT KULON PROGO DISTRICT IN TOPOSEQUENCE PERSPECTIVE)". Agromet 20, n.º 2 (6 de diciembre de 2006): 48. http://dx.doi.org/10.29244/j.agromet.20.2.48-55.
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<p>Climate anomaly has significant impact on rainfall and its pattern, whereas they were important factor in determining onset of wet and dry season. Changes in rainfall pattern need to antisipate with tailoring planting date for minimize harvest risk. This research purpose to analyse dry spell probability and its relation to drought risk on rainfed paddy at Kulon Progo District in Toposequence Perspective.Kulon Progo District was divided into three sequence, that were Fluvial as catchment area (i.e. Samigaluh); Freatik as conservation area (i.e. Kenteng, Kalibawang and Kokap); and Fluksial as user area (i.e. Gembongan- Sentolo, Sapon-Lendah and Wates). The aimed of this research was to analyze the drought periods and its relation with drought risks on rainfed rice plant at Kulon Progo district in toposequence perspective. The probabilities of 15 day dry spell at Fluvial area were 0,27 – 0,44 on May-September. On Freatik area, the probabilities of 15 day dry spell were 0,21 - 0,43 on April-Oktober at Kenteng, while at Kalibawang and Kokap the probabilities were > 0,2 on May-September. The probabilities at Gembongan-Sapon were> 0,2 on May-October, while at Wates the probabilities on May-September were 0,21-0,32. The distribution of probability show that the probability of 15 day dry spell were > 0,2 at May-October and the driest periods were August-September but with various dry level, that Kenteng and Gembongan-Sapon were the driest area. Based on that analysis, the planting periods should be started on November and the last planting should not over than January, especially at Kenteng, Gembongan and Sapon.</p>
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Kumah, Kingsley K., Joost C. B. Hoedjes, Noam David, Ben H. P. Maathuis, H. Oliver Gao y Bob Z. Su. "Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation". Atmosphere 11, n.º 9 (19 de agosto de 2020): 884. http://dx.doi.org/10.3390/atmos11090884.
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Accurate rainfall detection and estimation are essential for many research and operational applications. Traditional rainfall detection and estimation techniques have achieved considerable success but with limitations. Thus, in this study, the relationships between the gauge (point measurement) and the microwave links (MWL) rainfall (line measurement), and the MWL to the satellite observations (area-wide measurement) are investigated for (area-wide) rainfall detection and rain rate retrieval. More precisely, we investigate if the combination of MWL with Meteosat Second Generation (MSG) satellite signals could improve rainfall detection and rainfall rate estimates. The investigated procedure includes an initial evaluation of the MWL rainfall estimates using gauge measurements, followed by a joint analysis of the rainfall estimates with the satellite signals by means of a conceptual model in which clouds with high cloud top optical thickness and large particle sizes have high rainfall probabilities and intensities. The analysis produced empirical thresholds that were used to test the capability of the MSG satellite data to detect rainfall on the MWL. The results from Kenya, during the “long rains” of 2013, 2014, and 2018 show convincing performance and reveal the potential of MWL and MSG data for area-wide rainfall detection.
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Olsson, J. "Evaluation of a scaling cascade model for temporal rain- fall disaggregation". Hydrology and Earth System Sciences 2, n.º 1 (31 de marzo de 1998): 19–30. http://dx.doi.org/10.5194/hess-2-19-1998.
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Abstract. The possibility of modelling the temporal structure of rainfall in southern Sweden by a simple cascade model is tested. The cascade model is based on exact conservation of rainfall volume and has a branching number of 2. The weights associated with one branching are 1 and 0 with probability P(1/0), 0 and 1 with P(0/1), and Wx/x, and 1 - Wx/x, 0 < Wx/x, < 1, with P(x/x), where Wx/x is associated with a theoretical probability distribution. Furthermore, the probabilities p are assumed to depend on two characteristics of the rainy time period (wet box) to be branched: rainfall volume and position in the rainfall sequence. In the first step, analyses of 2 years of 8-min data indicates that the model is applicable between approximately 1 hour and 1 week with approximately uniformly distributed Wx/x values. The probabilities P show a clear dependence on the box characteristics and a slight seasonal nonstationarity. In the second step, the model is used to disaggregate the time series from 17- to 1-hour resolution. The model-generated data reproduce well the ratio between rainy and nonrainy periods and the distribution of individual volumes. Event volumes, event durations, and dry period lengths are fairly well reproduced, but somewhat underestimated, as was the autocorrelation. From analyses of power spectrum and statistical moments the model preserves the scaling behaviour of the data. The results demonstrate the potential of scaling-based approaches in hydrological applications involving rainfall disaggregation.
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Vigaud, N., M. K. Tippett y A. W. Robertson. "Probabilistic Skill of Subseasonal Precipitation Forecasts for the East Africa–West Asia Sector during September–May". Weather and Forecasting 33, n.º 6 (31 de octubre de 2018): 1513–32. http://dx.doi.org/10.1175/waf-d-18-0074.1.
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Abstract The skill of submonthly forecasts of rainfall over the East Africa–West Asia sector is examined for starts during the extended boreal winter season (September–April) using three ensemble prediction systems (EPSs) from the Subseasonal-to-Seasonal (S2S) project. Forecasts of tercile category probabilities over the common period 1999–2010 are constructed using extended logistic regression (ELR), and a multimodel forecast is formed by averaging individual model probabilities. The calibration of each model separately produces reliable probabilistic weekly forecasts, but these lack sharpness beyond a week lead time. Multimodel ensembling generally improves skill by removing negative skill scores present in individual models. In addition, the multimodel ensemble week-3–4 forecasts have a higher ranked probability skill score and reliability compared to week-3 or week-4 forecasts for starts in February–April, while skill gain is less pronounced for other seasons. During the 1999–2010 period, skill over continental subregions is the highest for starts in February–April and for starts during El Niño conditions and MJO phase 7, which coincides with enhanced forecast probabilities of above-normal rainfall. Overall, these results indicate notable opportunities for the application of skillful subseasonal predictions over the East Africa–West Asia sector during the extended boreal winter season.
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Stone, Roger C., Graeme L. Hammer y Torben Marcussen. "Prediction of global rainfall probabilities using phases of the Southern Oscillation Index". Nature 384, n.º 6606 (noviembre de 1996): 252–55. http://dx.doi.org/10.1038/384252a0.
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C.B, GAIKWAD, PATEL J.D, GARE B.N, JADAH J.D y SHEWALA M.R. "THERMAL REQUIREMENT OF CROPS AND CROPPING SYSTEM SOWN ACCORDING TO RAINFALL PROBABILITIES". Madras Agricultural Journal 83, Augest (1996): 491–93. http://dx.doi.org/10.29321/maj.10.a01038.
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Field experiments were conducted for five successive years on solc sunflower and sunflower + red gram intercropping system sown according to rainfall probabilities to understand behaviour of crops and cropping system in utilising thermal energy. The accumulated heat units and number of days for reaching various growth stages decreased with successive delay in sowing. The variation in accumulated heat units and number of days required for reaching various growth stages did not indicate any definite trend. The crop sown earlier received maximum thermal energy than later sown crop during all the year of experimentation. The heat units required to attain the harvesting stage marginally differ within the year under all sowing dates as evidenced from coefficient of variation.
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RIAJAYA, PRIMA DIARINI, MOCH SHOLEH y F. T. KADARWATI. "WAKTU TANAM KAPAS DI JAWA TENGAH". Jurnal Penelitian Tanaman Industri 11, n.º 2 (25 de junio de 2020): 52. http://dx.doi.org/10.21082/jlittri.v11n2.2005.52-59.
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<p>ABSTRAK<br />Curah hujan merupakan salah satu unsur iklim yang sangat<br />berpengaruh terhadap produksi kapas. Variasi hujan di lahan tadah hujan<br />Jawa Tengah sangat tinggi sehingga diperlukan penetapan waktu tanam.<br />Waktu tanam ditetapkan berdasarkan analisis hujan lebih dari 20 tahun<br />dari 31 stasiun hujan yang tersebar di Kabupaten Grobogan, Wonogiri,<br />Blora, Pemalang, Tegal, dan Brebes. Data dianalisis menggunakan metode<br />peluang Markov Order Pertama dan perhitungan peluang selang kering<br />berturut-turut. Peluang hujan yang dianalisis berupa peluang hujan<br />mingguan lebih dari 10, 20, 30, 40, dan 50 mm. Besar peluang hujan<br />mingguan lebih dari 60% untuk mendapatkan hujan lebih dari 20 mm dan<br />30 mm dipakai dalam penentuan minggu tanam, selanjutnya disesuaikan<br />dengan peluang kering berturut-turut. Minggu tanam paling lambat (MPL)<br />di Kabupaten Grobogan dan Wonogiri berkisar minggu I Desember sampai<br />minggu I Januari. MPL di Kabupaten Blora, Pemalang, Tegal, dan Brebes<br />adalah minggu I-IV Januari. Sebagian besar lahan yang digunakan untuk<br />kapas bertekstur liat dengan kandungan liat di atas 60%. Ketersediaan air<br />dari hujan cukup untuk memenuhi kebutuhan air kapas dan didukung oleh<br />kemampuan tanah menyimpan air yang tinggi.<br />Kata kunci : Kapas, Gossypium hirsutum, waktu tanam, periode kering,<br />masa tanam, Jawa Tengah</p><p><br />ABSTRACT<br />Cotton planting times in Central Java<br />Climatic elements particularly rainfall strongly influences successful<br />prediction of rainfed cotton yield. Rainfall variability varies amongst the<br />seasons. Longterm rainfall data were required for rainfall analysis to get<br />reliable probabilities. The rainfall analysis was done using Markov Chain<br />First Order Probability and dryspell probability methods. Initial and<br />conditional probabilities of rainfall for selected amounts (10, 20, 30, 40<br />and 50 mm/week) were analysed. Rainfall probabilities over 60% to have<br />20-30 mm rainfall per week were used to identify cotton planting times.<br />The rainfall data were collected from 31 rainfall stations in Central Java<br />(Grobogan, Wonogiri, Blora, Pemalang, Tegal, and Brebes). The planting<br />times varied from the first week of December to the first week of January<br />for Grobogan and Wonogiri. The planting times in Blora, Pemalang,<br />Tegal, and Brebes ranged from early to late January. The majority of land<br />used for cotton has high clay content with high water holding capacity<br />which is sufficient to meet the cotton water requirement.<br />Key words : Cotton, Gossypium hirsutum, planting time, dryspell,<br />seasonal pattern, Central Java</p>
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Lu, Hang Xian, Guang Wen Ma, Xiu Duo Wang, Feng Ping y Fa Wen Li. "Effects of Abundant-and-Low-Flow Property of Annual Rainfall on Multi-Source Water Intake System Allocation". Applied Mechanics and Materials 353-356 (agosto de 2013): 2450–55. http://dx.doi.org/10.4028/www.scientific.net/amm.353-356.2450.
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Through analysis of the annual rainfall data from 1918 to 2005 in Tianjin, wet-dry year was determined; the abundant and low state of annual rainfall series and its durative in Meijiang lake region was studied. The results show that the occurrence of the dry year is most probable; the durative probabilities in descending order are: low, abundant and normal state; there is the highest probability of transition from low to low state, which indicates a more probable occurrence of large amount of water diversion.
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Rao, Srinivas G. y A. Ramachandra Rao. "Run Analysis of Rainfall Data Affected by Urbanization". Hydrology Research 17, n.º 1 (1 de febrero de 1986): 47–64. http://dx.doi.org/10.2166/nh.1986.0004.
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Urbanization generally increases the amount of rainfall in urban areas and/or downwind areas of urban centers. This paper investigates whether urbanization significantly changes the run properties of rainfall. Three run properties, the run-length, run-sum and run-intensities of positive (surplus) and negative (deficit) runs, of the annual and monthly rainfall are evaluated by using the theory of runs. The annual rainfall is assumed to be normally or gamma distributed, and the monthly rainfall is assumed to follow a two-state Markov chain with stationary, transition probabilities. The methology is applied to annual and monthly rainfall data at La Porte and three surrounding stations in Indiana. The results indicate that these assumptions are adequate to characterize the annual and monthly rainfall. The study demonstrates that although urbanization has inadvertently increased the rainfall amounts at La Porte in relation to its surrounding stations (the so-called La Porte anamoly) the run properties at La Porte are not significantly different from those at the surrounding stations. Further it is found that the run-intensity is a better indicator than run-length or run-sum for evaluating the effects of urbanization on rainfall.
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JHA, T. N. y R. D. RAM. "Study of rainfall departure over catchments of Bihar plains". MAUSAM 61, n.º 2 (27 de noviembre de 2021): 187–96. http://dx.doi.org/10.54302/mausam.v61i2.800.
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Station wise daily rainfall data of sixty years is used to study rainfall departure and variability in Kosi, Kamala/Bagmati/Adhwara and Gandak/Burhi Gandak catchments during monsoon season. Station and catchment wise rainfall time series have been made to compute rainfall departure and Coefficient of Variation (CV). Southern Oscillation Index (SOI), Multivariate ENSO Index (MEI) and ENSO strength based on percentile analysis are used to ascertain their impact on rainfall distribution in the category as excess, normal, deficient and scanty. Results indicate that the variability is greater over Kosi as compared to the other catchments. Probability of normal rainfall is found 0.75 and there is no possibility of scanty rain over the catchments during El Nino and La Nina year. Similarly probabilities of normal, deficient, excess rainfall are found as 0.67, 0.18 and 0.15 respectively during mixed year. SOI has emerged as principal parameter which modifies the departure during El Nino and La Nina year. MEI along with ENSO strength are more prominent during mixed year particularly to ascertain deficient and excess rain in weak and strong- moderate La Nina respectively .
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Fu, Guangtao y Zoran Kapelan. "Flood analysis of urban drainage systems: Probabilistic dependence structure of rainfall characteristics and fuzzy model parameters". Journal of Hydroinformatics 15, n.º 3 (16 de noviembre de 2012): 687–99. http://dx.doi.org/10.2166/hydro.2012.160.
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Flood analysis of urban drainage systems plays a crucial role for flood risk management in urban areas. Rainfall characteristics, including the dependence between rainfall variables, have a significant influence on flood frequency. This paper considers the use of copulas to represent the probabilistic dependence structure between rainfall depth and duration in the synthetic rainfall generation process, and the Gumbel copula is fitted for the rainfall data in a case study of sewer networks. The probabilistic representation of rainfall uncertainty is combined with fuzzy representation of model parameters in a unified framework based on Dempster–Shafer theory of evidence. The Monte Carlo simulation method is used for uncertainty propagation to calculate the exceedance probabilities of flood quantities (depth and volume) of the case study sewer network. This study demonstrates the suitability of the Gumbel copula in simulating the dependence of rainfall depth and duration, and also shows that the unified framework can effectively integrate the copula-based probabilistic representation of random variables and fuzzy representation of model parameters for flood analysis.
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Baigorria, Guillermo A. y James W. Jones. "GiST: A Stochastic Model for Generating Spatially and Temporally Correlated Daily Rainfall Data". Journal of Climate 23, n.º 22 (15 de noviembre de 2010): 5990–6008. http://dx.doi.org/10.1175/2010jcli3537.1.
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Abstract Weather generators are tools that create synthetic daily weather data over long periods of time. These tools have also been used for downscaling monthly to seasonal climate forecasts, from global and regional circulation models to daily values for use as inputs for crop and other environmental models. One main limitation of most weather generators is that they do not take into account the spatial structure of weather. Spatial correlation of daily rainfall is important when one aggregates, for example, simulated crop yields or hydrology in a watershed or region. A method was developed to generate realizations of daily rainfall for multiple sites in an area while preserving the spatial and temporal correlations among sites. A two-step method generates rainfall events at multiple sites followed by rainfall amounts at sites where generated rainfall events occur. The generation of rainfall events was based on a new orthogonal Markov chain for discrete distributions. For generating rainfall amounts, a vector of random numbers (from a uniform distribution), of order equal to the number of locations with rainfall events that were generated to occur in a day, was matrix-multiplied by the corresponding factorized correlation matrix to create spatially correlated random numbers. Elements from the resulting vector were transformed to a gamma distribution using cumulative probability functions for each location and rescaled to rainfall amounts. One study area was located in north-central Florida, where correlated rainfall data were generated for seven weather stations to evaluate its performance versus a widely used single-site weather generator. A second area was in North Carolina, where rainfall was generated for 25 weather stations to evaluate the effects of a larger number of stations in other regions. One thousand yearlong replications of daily rainfall data were generated for each area. Monthly spatial correlations of generated daily rainfall events and amounts among all pairs of weather stations closely matched their observed counterparts. For daily rainfall amounts the correlation coefficients between the observed pairwise correlation coefficients and the ones estimated from synthetic data among weather stations were 0.977 for Florida and 0.964 for North Carolina. The performance of the geospatial–temporal (GiST) weather generator was also analyzed by comparing the distributions of lengths of dry and wet spells, joint probabilities, Markov transitional probabilities, distance decay of correlation functions, and regionwide days without rainfall at any station. Multiannual mean and standard deviation of the number of rainy days per month and mean monthly rainfall were also calculated. All comparisons between observed and generated rainfall events and amounts using the GiST weather generator were highly correlated. The root-mean-square errors of pairwise correlation values ranged from 0.05 to 0.11 for rainfall events and from 0.03 to 0.06 for amounts.