Journal articles on the topic 'Rainfall percentile'
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Shrestha, Sushban, Xing Fang, and Wesley C. Zech. "What Should Be the 95th Percentile Rainfall Event Depths?" Journal of Irrigation and Drainage Engineering 140, no. 1 (January 2014): 06013002. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0000658.
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Thangjai, Warisa, Sa-Aat Niwitpong, and Suparat Niwitpong. "Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions." PeerJ 10 (December 7, 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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Cao, Fuqiang, Tao Gao, Li Dan, Lian Xie, and Xiang Gong. "Variability of Summer Precipitation Events Associated with Tropical Cyclones over Mid-Lower Reaches of Yangtze River Basin: Role of the El Niño–Southern Oscillation." Atmosphere 10, no. 5 (May 9, 2019): 256. http://dx.doi.org/10.3390/atmos10050256.
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Based on tropical cyclone (TC) track data and gridded observational rainfall data of CN05.1 during the period of 1961 to 2014, we examine the contribution of TCs on three metrics of summertime rainfall regimes and identify the connection between TC-induced precipitation events and El Niño–Southern Oscillation (ENSO) in middle–lower reaches of Yangtze River Basin (MLYRB). At the regional scale, TCs are responsible for approximately 14.4%, 12.5%, and 6.9% of rainfall events for normal, 75th, and 95th percentile precipitation cases, respectively. There is no evidence of significant long-term trends of the three type events linked with TCs, while their interdecadal variability is remarkable. Fractionally, larger proportions of TC-induced events occur along southeast coastal regions of MLYRB for normal rainfall events, and they are recorded over southwest and central-east MLYRB for 95th percentile cases. Moreover, a larger contribution of 95th percentile precipitation events to summer total rainfall is found than that for 75th percentile cases, suggesting that TCs may exert stronger impacts on the upper tail of summertime precipitation distribution across MLYRB. The TC-induced normal rainfall events tend to occur more frequency over central-west MLYRB during negative phase of ENSO in summer. However, the higher likelihood of TC-induced rainfall for three defined metrics are found over the majority of areas over MLYRB during negative ENSO phase in spring. In preceding winter, La Niña episode plays a crucial role in controlling the frequency of both normal and 75th percentile precipitation events.
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Ramezani Ziarani, Maryam, Bodo Bookhagen, Torsten Schmidt, Jens Wickert, Alejandro de la Torre, and Rodrigo Hierro. "Using Convective Available Potential Energy (CAPE) and Dew-Point Temperature to Characterize Rainfall-Extreme Events in the South-Central Andes." Atmosphere 10, no. 7 (July 8, 2019): 379. http://dx.doi.org/10.3390/atmos10070379.
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The interactions between atmosphere and steep topography in the eastern south–central Andes result in complex relations with inhomogenous rainfall distributions. The atmospheric conditions leading to deep convection and extreme rainfall and their spatial patterns—both at the valley and mountain-belt scales—are not well understood. In this study, we aim to identify the dominant atmospheric conditions and their spatial variability by analyzing the convective available potential energy (CAPE) and dew-point temperature ( T d ). We explain the crucial effect of temperature on extreme rainfall generation along the steep climatic and topographic gradients in the NW Argentine Andes stretching from the low-elevation eastern foreland to the high-elevation central Andean Plateau in the west. Our analysis relies on version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) data and TRMM (Tropical Rainfall Measuring Mission) data. We make the following key observations: First, we observe distinctive gradients along and across strike of the Andes in dew-point temperature and CAPE that both control rainfall distributions. Second, we identify a nonlinear correlation between rainfall and a combination of dew-point temperature and CAPE through a multivariable regression analysis. The correlation changes in space along the climatic and topographic gradients and helps to explain controlling factors for extreme-rainfall generation. Third, we observe more contribution (or higher importance) of T d in the tropical low-elevation foreland and intermediate-elevation areas as compared to the high-elevation central Andean Plateau for 90th percentile rainfall. In contrast, we observe a higher contribution of CAPE in the intermediate-elevation area between low and high elevation, especially in the transition zone between the tropical and subtropical areas for the 90th percentile rainfall. Fourth, we find that the parameters of the multivariable regression using CAPE and T d can explain rainfall with higher statistical significance for the 90th percentile compared to lower rainfall percentiles. Based on our results, the spatial pattern of rainfall-extreme events during the past ∼16 years can be described by a combination of dew-point temperature and CAPE in the south–central Andes.
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Krishnamurthy, Chandra Kiran B., Upmanu Lall, and Hyun-Han Kwon. "Changing Frequency and Intensity of Rainfall Extremes over India from 1951 to 2003." Journal of Climate 22, no. 18 (September 15, 2009): 4737–46. http://dx.doi.org/10.1175/2009jcli2896.1.
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Abstract Using a 1951–2003 gridded daily rainfall dataset for India, the authors assess trends in the intensity and frequency of exceedance of thresholds derived from the 90th and the 99th percentile of daily rainfall. A nonparametric method is used to test for monotonic trends at each location. A field significance test is also applied at the national level to assess whether the individual trends identified could occur by chance in an analysis of the large number of time series analyzed. Statistically significant increasing trends in extremes of rainfall are identified over many parts of India, consistent with the indications from climate change models and the hypothesis that the hydrological cycle will intensify as the planet warms. Specifically, for the exceedance of the 99th percentile of daily rainfall, all locations where a significant increasing trend in frequency of exceedance is identified also exhibit a significant trend in rainfall intensity. However, extreme precipitation frequency over many parts of India also appears to exhibit a decreasing trend, especially for the exceedance of the 90th percentile of daily rainfall. Predominantly increasing trends in the intensity of extreme rainfall are observed for both exceedance thresholds.
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Niyogi, Dev, Ming Lei, Chandra Kishtawal, Paul Schmid, and Marshall Shepherd. "Urbanization Impacts on the Summer Heavy Rainfall Climatology over the Eastern United States." Earth Interactions 21, no. 5 (June 1, 2017): 1–17. http://dx.doi.org/10.1175/ei-d-15-0045.1.
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Abstract The relationship between rainfall characteristics and urbanization over the eastern United States was examined by analyzing four datasets: daily rainfall in 4593 surface stations over the last 50 years (1958–2008), a high-resolution gridded rainfall product, reanalysis wind data, and a proxy for urban land use (gridded human population data). Results indicate that summer monthly rainfall amounts show an increasing trend in urbanized regions. The frequency of heavy rainfall events has a potential positive bias toward urbanized regions. Most notably, consistent with case studies for individual cities, the climatology of rainfall amounts downwind of urban–rural boundaries shows a significant increasing trend. Analysis of heavy (90th percentile) and extreme (99.5th percentile) rainfall events indicated decreasing trends of heavy rainfall events and a possible increasing trend for extreme rainfall event frequency over urban areas. Results indicate that the urbanization impact was more pronounced in the northeastern and midwestern United States with an increase in rainfall amounts. In contrast, the southeastern United States showed a slight decrease in rainfall amounts and heavy rainfall event frequencies. Results suggest that the urbanization signature is becoming detectable in rainfall climatology as an anthropogenic influence affecting regional precipitation; however, extracting this signature is not straightforward and requires eliminating other dynamical confounding feedbacks.
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Widi, Herlia, Dea Nisa Rahma Lani, and Faridatul Hasanah. "Determining Agricultural Premium Insurance in Malang City using Black Scholes Model." International Journal of Global Operations Research 2, no. 2 (May 7, 2021): 80–87. http://dx.doi.org/10.47194/ijgor.v2i2.81.
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This study examines the determination of rainfall-based agricultural insurance premium prices using the Black-Scholes model. The Black-Scholes model was originally used to determine the price of European-type options. The research method used is a literature study with secondary data collection. The data used in this study are rainfall data and rice production results in the city of Malang from 2015 to 2020. Based on the results and discussion, rainfall which is strongly correlated with rice production results is in quarter 2. The premium results obtained are different according to the desired percentile. In addition to percentiles, taking R_0 also affects the premium price. When R_0=322, the premium price tends to be cheaper than R_0=271.
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Dave, Hiren, and James M. E. "Characteristics of intense rainfall over Gujarat State (India) based on percentile criteria." Hydrological Sciences Journal 62, no. 12 (August 18, 2017): 2035–48. http://dx.doi.org/10.1080/02626667.2017.1357818.
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Zahiri, Eric-Pascal, Modeste Kacou, Marielle Gosset, and Sahouarizié Adama Ouattara. "Modeling the Interdependence Structure between Rain and Radar Variables Using Copulas: Applications to Heavy Rainfall Estimation by Weather Radar." Atmosphere 13, no. 8 (August 15, 2022): 1298. http://dx.doi.org/10.3390/atmos13081298.
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In radar quantitative precipitation estimates (QPE), the progressive evolution of rainfall algorithms has been guided by attempts to reduce the uncertainties in rainfall retrieval. However, because most of the algorithms are based on the linear dependence between radar and rain variables and designed for rain rates ranging from light to moderate rainfall, they result in misleading estimations of intense or strong rainfall rates. In this paper, based on extensive data gathered during the AMMA and Megha-Tropiques data campaigns, we provided a way to improve the estimation of intense rainfall rates from radar measurements. To this end, we designed a formulation of the QPE algorithm that accounts for the co-dependency between radar observables and rainfall rate using copula simulation synthetic datasets and using the quantile regression features for a more complete picture of covariate effects. The results show a clear improvement in heavy rainfall retrieval from radar data using copula-based R(KDP) algorithms derived from a realistic simulated dataset. For a better performance, Gaussian copula-derived algorithms require a 0.8 percentile distribution to be considered. Conversely, lower percentiles are better for Student’s, Gumbel and HRT copula estimators when retrieving heavy rainfall rates (R > 30). This highlights the need to investigate the entire conditional distribution to determine the performance of radar rainfall estimators.
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Liu, Tao, Luke A. McGuire, Nina Oakley, and Forest Cannon. "Temporal changes in rainfall intensity–duration thresholds for post-wildfire flash floods in southern California." Natural Hazards and Earth System Sciences 22, no. 2 (February 10, 2022): 361–76. http://dx.doi.org/10.5194/nhess-22-361-2022.
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Abstract. Rainfall intensity–duration (ID) thresholds are commonly used to assess flash flood potential downstream of burned watersheds. High-intensity and/or long-duration rainfall is required to generate flash floods as landscapes recover from fire, but there is little guidance on how thresholds change as a function of time since fire. Here, we force a hydrological model with radar-derived precipitation to estimate ID thresholds for post-fire flash floods in a 41.5 km2 watershed in southern California, USA. Prior work in this study area constrains temporal changes in hydrological model parameters, allowing us to estimate temporal changes in ID thresholds. The results indicate that ID thresholds increase by more than a factor of 2 from post-fire year 1 to post-fire year 5. Thresholds based on averaging rainfall intensity over durations of 15–60 min perform better than those that average rainfall intensity over shorter time intervals. Moreover, thresholds based on the 75th percentile of radar-derived rainfall intensity over the watershed perform better than thresholds based on the 25th or 50th percentile of rainfall intensity. Results demonstrate how hydrological models can be used to estimate changes in ID thresholds following disturbance and provide guidance on the rainfall metrics that are best suited for predicting post-fire flash floods.
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Mazvimavi, D. "Investigating changes over time of annual rainfall in Zimbabwe." Hydrology and Earth System Sciences 14, no. 12 (December 22, 2010): 2671–79. http://dx.doi.org/10.5194/hess-14-2671-2010.
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Abstract. There is increasing concern in southern Africa about the possible decline of rainfall as a result of global warming. Some studies concluded that average rainfall in Zimbabwe had declined by 10% or 100 mm during the last 100 years. This paper investigates the validity of the assumption that rainfall is declining in Zimbabwe. Time series of annual rainfall, and total rainfall for (a) the early part of the rainy season, October-November-December (OND), and (b) the mid to end of the rainy season, January-February-March (JFM) are analysed for the presence of trends using the Mann-Kendall test, and for the decline or increase during years with either high or low rainfall using quantile regression analysis. The Pettitt test has also been utilized to examine the possible existence of change or break-points in the rainfall time series. The analysis has been done for 40 rainfall stations with records starting during the 1892–1940 period and ending in 2000, and representative of all the rainfall regions. The Mann-Kendal test did not identify a significant trend at all the 40 stations, and therefore there is no proof that the average rainfall at each of these stations has changed. Quantile regression analysis revealed a decline in annual rainfall less than the tenth percentile at only one station, and increasing of rainfall greater than the ninetieth percentile at another station. All the other stations had no changes over time in both the low and high rainfall at the annual interval. Climate change effects are therefore not yet statistically significant within time series of total seasonal and annual rainfall in Zimbabwe. The general perception about declining rainfall is likely due to the presence of multidecadal variability characterized by bunching of years with above (e.g. 1951–1958, 1973–1980) and below (e.g. 1959–1972, 1982–1994 ) average rainfall.
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Ansah, S. O., M. A. Ahiataku, C. K. Yorke, F. Otu-Larbi, Bashiru Yahaya, P. N. L. Lamptey, and M. Tanu. "Meteorological Analysis of Floods in Ghana." Advances in Meteorology 2020 (March 24, 2020): 1–14. http://dx.doi.org/10.1155/2020/4230627.
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The first episodes of floods caused by heavy rainfall during the major rainy season in 2018 occurred in Accra (5.6°N and 0.17°W), a coastal town, and Kumasi (6.72°N and 1.6°W) in the forest region on the 18th and 28th of June, respectively. We applied the Weather Research and Forecasting (WRF) model to investigate and examine the meteorological dynamics, which resulted in the extreme rainfall and floods that caused 14 deaths, 34076 people being displaced with damaged properties, and economic loss estimated at $168,289 for the two cities according to the National Disaster Management Organization (NADMO). The slow-moving thunderstorms lasted for about 8 hours due to the weak African Easterly Wave (AEW) and Tropical Easterly Jet (TEJ). Results from the analysis showed that surface pressures were low with significant amount of moisture influx aiding the thunderstorms intensification, which produced 90.1 mm and 114.6 mm of rainfall over Accra and Kumasi, respectively. We compared the rainfall amount from this event to the historical rainfall data to investigate possible changes in rainfall intensities over time. A time series of annual daily maximum rainfall (ADMR) showed an increasing trend with a slope of 0.45 over Accra and a decreasing trend and a slope of –0.07 over Kumasi. The 95th percentile frequencies of extreme rainfall with thresholds of 45.10 mm and 42.16 mm were analyzed for Accra and Kumasi, respectively, based on the normal distribution of rainfall. Accra showed fewer days with more heavy rainfall, while Kumasi showed more days with less heavy rainfalls.
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Panthou, Gérémy, Alain Mailhot, Edward Laurence, and Guillaume Talbot. "Relationship between Surface Temperature and Extreme Rainfalls: A Multi-Time-Scale and Event-Based Analysis*." Journal of Hydrometeorology 15, no. 5 (September 25, 2014): 1999–2011. http://dx.doi.org/10.1175/jhm-d-14-0020.1.
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Abstract Recent studies have examined the relationship between the intensity of extreme rainfall and temperature. Two main reasons justify this interest. First, the moisture-holding capacity of the atmosphere is governed by the Clausius–Clapeyron (CC) equation. Second, the temperature dependence of extreme-intensity rainfalls should follow a similar relationship assuming relative humidity remains constant and extreme rainfalls are driven by the actual water content of the atmosphere. The relationship between extreme rainfall intensity and air temperature (Pextr–Ta) was assessed by analyzing maximum daily rainfall intensities for durations ranging from 5 min to 12 h for more than 100 meteorological stations across Canada. Different factors that could influence this relationship have been analyzed. It appears that the duration and the climatic region have a strong influence on this relationship. For short durations, the Pextr–Ta relationship is close to the CC scaling for coastal regions while a super-CC scaling followed by an upper limit is observed for inland regions. As the duration increases, the slope of the relationship Pextr–Ta decreases for all regions. The shape of the Pextr–Ta curve is not sensitive to the percentile or season. Complementary analyses have been carried out to understand the departures from the expected Clausius–Clapeyron scaling. The relationship between dewpoint temperature and extreme rainfall intensity shows that the relative humidity is a limiting factor for inland regions, but not for coastal regions. Using hourly rainfall series, an event-based analysis is proposed in order to understand other deviations (super-CC, sub-CC, and monotonic decrease). The analyses suggest that the observed scaling is primarily due to the rainfall event dynamic.
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Amoussou, Ernest, Hervé Awoye, Henri S. Totin Vodounon, Salomon Obahoundje, Pierre Camberlin, Arona Diedhiou, Kouakou Kouadio, Gil Mahé, Constant Houndénou, and Michel Boko. "Climate and Extreme Rainfall Events in the Mono River Basin (West Africa): Investigating Future Changes with Regional Climate Models." Water 12, no. 3 (March 16, 2020): 833. http://dx.doi.org/10.3390/w12030833.
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This study characterizes the future changes in extreme rainfall and air temperature in the Mono river basin where the main economic activity is weather dependent and local populations are highly vulnerable to natural hazards, including flood inundations. Daily precipitation and temperature from observational datasets and Regional Climate Models (RCMs) output from REMO, RegCM, HadRM3, and RCA were used to analyze climatic variations in space and time, and fit a GEV model to investigate the extreme rainfalls and their return periods. The results indicate that the realism of the simulated climate in this domain is mainly controlled by the choice of the RCMs. These RCMs projected a 1 to 1.5 °C temperature increase by 2050 while the projected trends for cumulated precipitation are null or very moderate and diverge among models. Contrasting results were obtained for the intense rainfall events, with RegCM and HadRM3 pointing to a significant increase in the intensity of extreme rainfall events. The GEV model is well suited for the prediction of heavy rainfall events although there are uncertainties beyond the 90th percentile. The annual maxima of daily precipitation will also increase by 2050 and could be of benefit to the ecosystem services and socioeconomic activities in the Mono river basin but could also be a threat.
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Pitman, A. J., and S. E. Perkins. "Regional Projections of Future Seasonal and Annual Changes in Rainfall and Temperature over Australia Based on Skill-Selected AR4 Models." Earth Interactions 12, no. 12 (August 1, 2008): 1–50. http://dx.doi.org/10.1175/2008ei260.1.
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Abstract Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection.
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Sahlu, Dejene, Semu A. Moges, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Dereje Hailu. "Evaluation of High-Resolution Multisatellite and Reanalysis Rainfall Products over East Africa." Advances in Meteorology 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/4957960.
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The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons.
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Li, Haobo, Xiaoming Wang, Suelynn Choy, Chenhui Jiang, Suqin Wu, Jinglei Zhang, Cong Qiu, et al. "Detecting heavy rainfall using anomaly-based percentile thresholds of predictors derived from GNSS-PWV." Atmospheric Research 265 (January 2022): 105912. http://dx.doi.org/10.1016/j.atmosres.2021.105912.
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Mazvimavi, D. "Investigating possible changes of extreme annual rainfall in Zimbabwe." Hydrology and Earth System Sciences Discussions 5, no. 4 (July 10, 2008): 1765–85. http://dx.doi.org/10.5194/hessd-5-1765-2008.
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Abstract. There is increasing concern about the perceived decline in rainfall which is sometimes attributed to global warming. Some studies have concluded that average rainfall in Zimbabwe has declined by 10% or 100 mm/yr during the last 100 yrs. This paper investigates the validity of the assumption that rainfall is declining in Zimbabwe. Time series of annual rainfall, and total rainfall for a) the early party of the rainy season, October-November-December (OND), and b) the mid to end of the rainy season, January-February-March (JFM) are analysed for the presence of trends using the Mann-Kendall test, and changes in extreme rainfall using quantile regression analysis. The analysis has been done for 40 rainfall stations with records starting during the 1892–1940 period and ending in 2000, and representative of the major rainfall regions. The Mann-Kendal test did not identify a significant trend at all the 40 stations, and therefore there is no proof that the average rainfall at each of these stations has changed. Quantile regression analysis revealed a decline in annual rainfall less than the tenth percentile at only one station, and increasing rainfall for rainfall greater than the ninetieth percentile at another station. All the other stations revealed no changes over time in both the extreme low and high rainfall at the annual interval. Therefore, there is no evidence that the frequency and severity of droughts has changed during the 1892 to 2000 period. The general perception about declining rainfall is likely shaped by a comparison of the recent drought years (1980's–1990's) to recent wet periods (1970's). There have however been periods with similar dry years beyond the recallable memory, e.g. 1926–1936, 1940's. Crop failures and livestock losses attributed to declining rainfall are most likely due to poor agricultural practices such as production of crops in unsuitable climatic regions, degradation of rangelands partly due to increasing livestock populations. Rainfall in Zimbabwe has high inter-annual variability, and currently any change due to global warming is not yet statistically detectable. The annual renewal rate of water resources from rainfall has therefore not changed, and an adaptive water resources management approach is called to overcome problems arising from increasing water demand, and variability of available water resources.
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Ramezani Ziarani, Maryam, Bodo Bookhagen, Torsten Schmidt, Jens Wickert, Alejandro de la Torre, Zhiguo Deng, and Andrea Calori. "A Model for the Relationship between Rainfall, GNSS-Derived Integrated Water Vapour, and CAPE in the Eastern Central Andes." Remote Sensing 13, no. 18 (September 21, 2021): 3788. http://dx.doi.org/10.3390/rs13183788.
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Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.
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Aditya Prasetyo, Dimas, and Luki Setiawan. "Price Determination of Agricultural Insurance Premium Based on Rainfall Index With Black-Scholes Model." Operations Research: International Conference Series 1, no. 4 (December 5, 2020): 118–23. http://dx.doi.org/10.47194/orics.v1i4.153.
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This article discusses the use of the Black-Scholes model to calculate the price of agricultural insurance premiums based on the rainfall index. The Black-Scholes model is one of the models used to determine option prices. The research method used is studying the material through mathematics books and journals and collecting secondary data. The data used in this study are rainfall data and rice production data in Magelang City from 2019 to 2020. Based on the results and discussion, the quarterly rainfall data that has a strong correlation are quarterly rainfall one and three. For the reference size with the latest rainfall data (319.5 mm) the premium obtained is IDR 3,557,321.00 per hectare, while for the reference size with the average rainfall data (1094,725 mm) the premium obtained is IDR 623,387. ,00 per hectare. Based on the calculation results, the higher the percentile value, the higher the premium value for the two reference sizes.
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Diakhaté, M., B. Rodríguez-Fonseca, I. Gómara, E. Mohino, A. L. Dieng, and A. T. Gaye. "Oceanic Forcing on Interannual Variability of Sahel Heavy and Moderate Daily Rainfall." Journal of Hydrometeorology 20, no. 3 (March 1, 2019): 397–410. http://dx.doi.org/10.1175/jhm-d-18-0035.1.
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Abstract This article analyzes SST remote forcing on the interannual variability of Sahel summer (June–September) moderate (below 75th percentile) and heavy (above 75th percentile) daily precipitation events during the period 1981–2016. Evidence is given that interannual variability of these events is markedly different. The occurrence of moderate daily rainfall events appears to be enhanced by positive SST anomalies over the tropical North Atlantic and Mediterranean, which act to increase low-level moisture advection toward the Sahel from the equatorial and north tropical Atlantic (the opposite holds for negative SSTs anomalies). In contrast, heavy and extreme daily rainfall events seem to be linked to El Niño–Southern Oscillation (ENSO) and Mediterranean variability. Under La Niña conditions and a warmer Mediterranean, vertical atmospheric instability is increased over the Sahel and low-level moisture supply from the equatorial Atlantic is enhanced over the area (the reverse is found for opposite-sign SST anomalies). Further evidence suggests that interannual variability of Sahel rainfall is mainly dominated by the extreme events. These results have implications for seasonal forecasting of Sahel moderate and heavy precipitation events based on SST predictors, as significant predictability is found from 1 to 4 months in advance.
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JHA, T. N., and R. D. RAM. "Study of rainfall departure over catchments of Bihar plains." MAUSAM 61, no. 2 (November 27, 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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Lai, Yangchen, Jianfeng Li, Xihui Gu, Yongqin David Chen, Dongdong Kong, Thian Yew Gan, Maofeng Liu, Qingquan Li, and Guofeng Wu. "Greater flood risks in response to slowdown of tropical cyclones over the coast of China." Proceedings of the National Academy of Sciences 117, no. 26 (June 15, 2020): 14751–55. http://dx.doi.org/10.1073/pnas.1918987117.
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The total amount of rainfall associated with tropical cyclones (TCs) over a given region is proportional to rainfall intensity and the inverse of TC translation speed. Although the contributions of increase in rainfall intensity to larger total rainfall amounts have been extensively examined, observational evidence on impacts of the recently reported but still debated long-term slowdown of TCs on local total rainfall amounts is limited. Here, we find that both observations and the multimodel ensemble of Global Climate Model simulations show a significant slowdown of TCs (11% in observations and 10% in simulations, respectively) from 1961 to 2017 over the coast of China. Our analyses of long-term observations find a significant increase in the 90th percentile of TC-induced local rainfall totals and significant inverse relationships between TC translation speeds and local rainfall totals over the study period. The study also shows that TCs with lower translation speed and higher rainfall totals occurred more frequently after 1990 in the Pearl River Delta in southern China. Our probability analysis indicates that slow-moving TCs are more likely to generate heavy rainfall of higher total amounts than fast-moving TCs. Our findings suggest that slowdown of TCs tends to elevate local rainfall totals and thus impose greater flood risks at the regional scale.
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Sierks, Michael D., Julie Kalansky, Forest Cannon, and F. M. Ralph. "Characteristics, Origins, and Impacts of Summertime Extreme Precipitation in the Lake Mead Watershed." Journal of Climate 33, no. 7 (April 1, 2020): 2663–80. http://dx.doi.org/10.1175/jcli-d-19-0387.1.
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AbstractThe North American monsoon (NAM) is the main driver of summertime climate variability in the U.S. Southwest. Previous studies of the NAM have primarily focused on the Tier I region of the North American Monsoon Experiment (NAME), spanning central-western Mexico, southern Arizona, and New Mexico. This study, however, presents a climatological characterization of summertime precipitation, defined as July–September (JAS), in the Lake Mead watershed, located in the NAME Tier II region. Spatiotemporal variability of JAS rainfall is examined from 1981 to 2016 using gridded precipitation data and the meteorological mechanisms that account for this variability are investigated using reanalyses. The importance of the number of wet days (24-h rainfall ≥1 mm) and extreme rainfall events (95th percentile of wet days) to the total JAS precipitation is examined and shows extreme events playing a larger role in the west and central basin. An investigation into the dynamical drivers of extreme rainfall events indicates that anticyclonic Rossby wave breaking (RWB) in the midlatitude westerlies over the U.S. West Coast is associated with 89% of precipitation events >10 mm (98th percentile of wet days) over the Lake Mead basin. This is in contrast to the NAME Tier I region where easterly upper-level disturbances such as inverted troughs are the dominant driver of extreme precipitation. Due to the synoptic nature of RWB events, corresponding impacts and hazards extend beyond the Lake Mead watershed are relevant for the greater U.S. Southwest.
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Iqbal, Zafar, Shamsuddin Shahid, Tarmizi Ismail, Zulfaqar Sa’adi, Aitazaz Farooque, and Zaher Mundher Yaseen. "Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods." Sustainability 14, no. 11 (May 28, 2022): 6620. http://dx.doi.org/10.3390/su14116620.
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Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events.
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Qi, Haixia, Xiefei Zhi, Tao Peng, Yongqing Bai, and Chunze Lin. "Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data." Atmosphere 10, no. 10 (October 9, 2019): 608. http://dx.doi.org/10.3390/atmos10100608.
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Based on the ensemble precipitation forecast data in the summers of 2014–2018 from the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), a comparative study of two multi-model ensemble methods, the Bayesian model average (BMA) and the logistic regression (LR), was conducted. Meanwhile, forecasts of heavy precipitation from the two models over the Wujiang River Basin in China for the summer of 2018 were compared to verify their performances. The training period sensitivity test results show that a training period of 2 years was the best for BMA probability forecast model. Compared with the BMA method, the LR model required more statistical samples and its optimal length of the training period was 5 years. According to the Brier score (BS), for precipitation events exceeding 10 mm with lead times of 1–7 days, the BMA outperformed the LR and the raw ensemble prediction system forecasts (RAW) except for forecasts with a lead time of 1 day. Furthermore, for heavy rainfall events exceeding 25 and 50 mm, the RAW and the BMA performed much the same in terms of prediction. The reliability diagram of the two multi-model ensembles (i.e., BMA and LR) was more reliable than the RAW for heavy and moderate rainfall forecasts, and the BMA model had the best performance. The BMA probabilistic forecast can produce a highly concentrated probability density function (PDF) curve and can also provide deterministic forecasts through analyzing percentile forecast results. With regard to the heavy rainfall forecast in mountainous areas, it is recommended to refer to the forecast with a larger percentile between the 75th and 90th percentiles. Nevertheless, extreme events with low probability forecasts may occur and cannot be ignored.
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Tang, Qiuhong, Andrew W. Wood, and Dennis P. Lettenmaier. "Real-Time Precipitation Estimation Based on Index Station Percentiles*." Journal of Hydrometeorology 10, no. 1 (February 1, 2009): 266–77. http://dx.doi.org/10.1175/2008jhm1017.1.
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Abstract Operational hydrologic models are typically calibrated using meteorological inputs derived from retrospective station data that are commonly not available in real time. Inconsistencies between the calibration and (generally sparser) real-time station datasets can be a source of bias, which can be addressed by expressing real-time hydrological model forcings (primarily precipitation) as percentiles for a set of index stations that report both in real time and during the retrospective calibration period, and by using the real-time percentiles to create adjusted precipitation forcings. Although hydrological model precipitation forcings typically are required at time steps of one day or shorter, percentiles can be calculated for longer averaging periods to reduce the percentile estimation errors. The authors propose an index station percentile method (ISPM) to estimate precipitation at the models input time step using percentiles, relative to a climatological period, for a set of index stations that report in real time. In general, this approach is most appropriate to situations in which the spatial correlation of precipitation is high, such as cold season rainfall in the western United States. The authors evaluate the ISPM approach, including performance sensitivity to the choice of percentile estimation period length, using the Klamath River basin, Oregon, as a case study. Relative to orographically adjusted interpolation of the real-time index station values, ISPM gives better estimates of precipitation throughout the basin. The authors find that ISPM performs best for percentile estimation periods longer than 10 days, with diminishing returns for averaging periods longer than 30 days. They also evaluate the performance of ISPM for a reduced station scenario and find that performance is relatively stable, relative to the competing methods, as the number of real-time stations diminishes.
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Fitrianto, Anwar, Reski Wahyu Yanti, and Muhammad Nur Aidi. "CHARACTERISTICS OF JOINT DISTRIBUTION MODELS AND RETURN PERIOD OF EXTREME RAINFALL VOLUME AND INTENSITY." Journal of Southwest Jiaotong University 57, no. 2 (April 30, 2022): 1–13. http://dx.doi.org/10.35741/issn.0258-2724.57.2.1.
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This article highlights a new approach to finding a copula model that most closely matches extreme rainfall characteristics in South Sulawesi, Indonesia. In climatology research, data that does not meet normal assumptions are often found due to extreme observations; therefore, a method is needed to overcome the dependency of variables that do not meet the normality assumption. The copula approach is a method that can determine the relationship between such variables. Copula can describe the dependency structure between variables with different margins and model its tail dependencies. This study discusses the application of Archimedean copula in modeling the dependency structure of both variables, namely the intensity of the 75th percentile (I75) and volume of the 75th percentile (P75). For explaining the dependencies of both variables, the best copula model was selected using the empirical copula method. The results showed that 16 stations followed the Clayton copula model, 23 stations followed the Gumbel copula model, and 14 stations followed the Frank copula model. After knowing the distribution model characteristics and the intensity and volume of extreme rainfall, the return period value on the intensity and volume of extreme rainfall was calculated. The return period value can be useful as information and evaluation valuable material for preparing natural hazard mitigation programs.
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Onyutha, Charles, and Patrick Willems. "Uncertainty in calibrating generalised Pareto distribution to rainfall extremes in Lake Victoria basin." Hydrology Research 46, no. 3 (July 16, 2014): 356–76. http://dx.doi.org/10.2166/nh.2014.052.
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Uncertainty in the calibration of the generalised Pareto distribution (GPD) to rainfall extremes is assessed based on observed and large number of global climate model rainfall time series for nine locations in the Lake Victoria basin (LVB) in Eastern Africa. The class of the GPD suitable for capturing the tail behaviour of the distribution and extreme quantiles is investigated. The best parameter estimation method is selected following comparison of the method of moments, maximum likelihood, L-moments, and weighted linear regression in quantile plots (WLR) to quantify uncertainty in the extreme intensity quantiles by employing the Jackknife method and nonparametric percentile bootstrapping in a combined way. The normal tailed GPD was found suitable. Although the performance of each parameter estimation method was acceptable in a number of evaluation criteria, generally the WLR technique appears to be more robust than others. The difference between upper and lower limits of the 95% confidence intervals expressed as a percentage of the empirical 10-year rainfall intensity quantile ranges from 9.25 up to 59.66%. The assessed uncertainty will be useful in support of risk based planning, design and operation of water engineering and water management applications related to floods in the LVB.
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Hamada, Atsushi, Yuki Murayama, and Yukari N. Takayabu. "Regional Characteristics of Extreme Rainfall Extracted from TRMM PR Measurements." Journal of Climate 27, no. 21 (October 24, 2014): 8151–69. http://dx.doi.org/10.1175/jcli-d-14-00107.1.
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Abstract Characteristics and global distribution of regional extreme rainfall are presented using 12 yr of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) measurements. By considering each rainfall event as a set of contiguous PR rainy pixels, characteristic values for each event are obtained. Regional extreme rainfall events are defined as those in which maximum near-surface rainfall rates are higher than the corresponding 99.9th percentile on a 2.5° × 2.5° horizontal-resolution grid. The geographical distribution of extreme rainfall rates shows clear regional differences. The size and volumetric rainfall of extreme events also show clear regional differences. Extreme rainfall rates show good correlations with the corresponding rain-top heights and event sizes over oceans but marginal or no correlation over land. The time of maximum occurrence of extreme rainfall events tends to be during 0000–1200 LT over oceans, whereas it has a distinct afternoon peak over land. There are also clear seasonal differences in which the occurrence over land is largely coincident with insolation. Regional extreme rainfall is classified by extreme rainfall rate (intensity) and the corresponding event size (extensity). Regions of “intense and extensive” extreme rainfall are found mainly over oceans near coastal areas and are likely associated with tropical cyclones and convective systems associated with the establishment of monsoons. Regions of “intense but less extensive” extreme rainfall are distributed widely over land and maritime continents, probably related to afternoon showers and mesoscale convective systems. Regions of “extensive but less intense” extreme rainfall are found almost exclusively over oceans, likely associated with well-organized mesoscale convective systems and extratropical cyclones.
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Hu, Huancui, L. Ruby Leung, and Zhe Feng. "Understanding the Distinct Impacts of MCS and Non-MCS Rainfall on the Surface Water Balance in the Central United States Using a Numerical Water-Tagging Technique." Journal of Hydrometeorology 21, no. 10 (October 1, 2020): 2343–57. http://dx.doi.org/10.1175/jhm-d-20-0081.1.
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ABSTRACTWarm-season rainfall associated with mesoscale convective systems (MCSs) in the central United States is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multiparameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April–August (1997–2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface–subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher-intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10th percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lower-intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture–precipitation feedbacks and ecohydrology.
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Sarr, Alioune Badara, and Moctar Camara. "Evolution Des Indices Pluviométriques Extrêmes Par L'analyse De Modèles Climatiques Régionaux Du Programme CORDEX: Les Projections Climatiques Sur Le Sénégal." European Scientific Journal, ESJ 13, no. 17 (June 30, 2017): 206. http://dx.doi.org/10.19044/esj.2017.v13n17p206.
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This study aims at characterizing the extreme rainfall events over West Africa particularly in the Sahel region and Senegal by 2100 (far future) under the greenhouse gas emission scenario RCP8.5 by analyzing the simulations of four (4) regional climate models (RCMs) of CORDEX (Regional COordinated climate Downscaling Experiment) program. The study of these extreme climate indices is crucial for the understanding of the impacts of climate change on some vital socio-economic sectors such as the agriculture in Sahel and Senegal. The results show that almost all the RCMs predict a decrease of the rainfall over most parts of the Sahel region particularly over the Western Sahel. The analysis of the climate indices such as the highest one day precipitation amount, the 99th percentile and the maximum dry spell length (CDD) shows that the RCMs (except CanRCM4) project an increase of these exceptional rainfall events over the Sahel (especially over the Western Sahel) by 2100. In Senegal, the RCMs (except RCA4) agree on a decrease of the precipitation and the number of wet days by 2100. When considering the evolution of rainfall events intensity, the highest one day precipitation amount and the 99th percentile, the RCMs (except CanRCM4) predict an increase of the extreme events which may translate into strong floods in Senegal. As for the dry and wet sequences, the RCMs projections (except those of RCA4) show an increase (respectively a decrease) of the maximum dry spell length (respectively of the maximum wet spell length) in Senegal. This increase in extreme rainfall indices may translate into a strengthening of natural disasters such as floods and drought. This work can be considered as a support for the policymakers in West Africa and particularly in Senegal for the better long-term planning of water resources and disaster management as wells as the build of a resilient agricultural system.
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Bąk, Bogdan, and Leszek Łabędzki. "Prediction of precipitation deficit and excess in Bydgoszcz Region in view of predicted climate change/ Prognoza niedoboru i nadmiaru opadu w rejonie Bydgoszczy w świetle przewidywanej zmiany klimatu." Journal of Water and Land Development 23, no. 1 (December 1, 2014): 11–19. http://dx.doi.org/10.1515/jwld-2014-0025.
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Abstract The paper presents the prediction of rainfall shortage and excess in Bydgoszcz region in the growing seasons (April-September) in 2011-2050 in the perspective of climate change. Based on the predicted monthly sum of precipitations for the percentile 50%, calculated by the regional climate model RM5.1 for Poland with boundary values taken from global model ARPEGE, a decrease in the amount of rainfall during the growing season by approximately 55 mm is predicted, compared to 1971-2000 taken as a reference period. The qualification of rainfall shortage and excess was made using the standardised precipitation index (SPI). According to the predicted values of SPI, the occurrence of 38 months of rainfall excess and 40 months of rainfall deficit in the period 2011-2050 is predicted. Dry months will constitute 16% of all months, wet months - 13%, and normal months - 71%. The occurrence of 13 several-month long periods of rainfall excess and 14 such periods of drought are predicted. The longest periods of both wet and dry weather will last 5 months. So long wet periods are expected in 2020, 2022 and 2031, and drought periods in 2017-2018, 2023-2024 and from 2046 to 2049.
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GHADEKAR, S. R., and K. K. THAKARE. "Some studies on rainfall climatology Of the Nagpur region." MAUSAM 42, no. 1 (February 28, 2022): 57–64. http://dx.doi.org/10.54302/mausam.v42i1.2841.
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Characterisation of rainfall of the Nagpur region for crop production and cropping patterns is studied. Percentile , Index based on five parameters to show adequacy of rainfall for crop production is developed and round to describe monsoon performance satisfactorily, These studies have revealed that the mean rainfall of kharif season is 861.5 mm(CV20.8 %) with S2.3(CV) 7 .3 %) rainy days, Cropping season of 13weeks (25-37MW) with dependable rainfall at 75, 80, 85, 90% probability levels was round to be most assured and risk-free The cropping period at 75% and 50% probability levels can be extended up to 18 weeks (23-40MW) with marginal
risk, In rabi and summer growing seasons, there was no rainfall at all at any feasible, dependable level and this situation asked for Irrigation, Strategy for stabilizing rainfed crop yields Included the adaptation of short to medium duration crops (16-18 weeks) with water requirements 50~-900 mm, viz., groundnut, soybean, sunflower, maize, sorghum. Long duration crops like cotton and rice with high water requirements can be adapted with certain risk.
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Camberlin, Pierre, Marc Kpanou, and Pascal Roucou. "Classification of Intense Rainfall Days in Southern West Africa and Associated Atmospheric Circulation." Atmosphere 11, no. 2 (February 11, 2020): 188. http://dx.doi.org/10.3390/atmos11020188.
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Daily rainfall in southern West Africa (4–8° N, 7° W–3° E) is analyzed with the aim of documenting the intense rainfall events which occur in coastal Ivory Coast, Ghana, Togo, and Benin. The daily 99th percentile (P99) shows that the coastline experiences higher intensity rainfall than inland areas. Using Tropical Rainfall Measuring Mission (TRMM) rainfall data for 1998–2014, a novel way of classifying the intense events is proposed. We consider their space-time structure over a window of 8° latitude-longitude and five days centered on the event. A total 39,680 events (62 at each location) are classified into three major types, mainly found over the oceanic regions south of 5° N, the Bight of Benin, and the inland regions respectively. These types display quite distinct rainfall patterns, propagation features, and seasonal occurrence. Three inland subtypes are also defined. The atmospheric circulation anomalies associated with each type are examined from ERA-interim reanalysis data. Intense rainfall events over the continent are mainly a result of westward propagating disturbances. Over the Gulf of Guinea, many intense events occur as a combination of atmospheric disturbances propagating westward (mid-tropospheric easterly waves or cyclonic vortices) and eastward (lower tropospheric zonal wind and moisture anomalies hypothesized to reflect Kelvin waves). Along the coast, there is a mixture of different types of rainfall events, often associated with interacting eastward- and westward-moving disturbances, which complicates the monitoring of heavy precipitation.
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Chu, Pao-Shin, Xin Zhao, Ying Ruan, and Melodie Grubbs. "Extreme Rainfall Events in the Hawaiian Islands." Journal of Applied Meteorology and Climatology 48, no. 3 (March 1, 2009): 502–16. http://dx.doi.org/10.1175/2008jamc1829.1.
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Abstract Heavy rainfall and the associated floods occur frequently in the Hawaiian Islands and have caused huge economic losses as well as social problems. Extreme rainfall events in this study are defined by three different methods based on 1) the mean annual number of days on which 24-h accumulation exceeds a given daily rainfall amount, 2) the value associated with a specific daily rainfall percentile, and 3) the annual maximum daily rainfall values associated with a specific return period. For estimating the statistics of return periods, the three-parameter generalized extreme value distribution is fit using the method of L-moments. Spatial patterns of heavy and very heavy rainfall events across the islands are mapped separately based on the aforementioned three methods. Among all islands, the pattern on the island of Hawaii is most distinguishable, with a high frequency of events along the eastern slopes of Mauna Kea and a low frequency of events on the western portion so that a sharp gradient in extreme events from east to west is prominent. On other islands, extreme rainfall events tend to occur locally, mainly on the windward slopes. A case is presented for estimating return periods given different rainfall intensity for a station in Upper Manoa, Oahu. For the Halloween flood in 2004, the estimated return period is approximately 27 yr, and its true value should be no less than 13 yr with 95% confidence as determined from the adjusted bootstrap resampling technique.
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Lashkari, Hassan, Neda Esfandiari, and Abbas Kashani. "Identification and Synoptic Analysis of the Highest Precipitation Linked to Ars in Iran." European Journal of Geosciences 3, no. 2 (March 10, 2021): 20–32. http://dx.doi.org/10.34154/2021-ejcc-0020/euraass.
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Atmospheric rivers are long, narrow, concentrated structures of water vapour that are highly associated with rainfall and floods. To identify and introduce the highest rainfall occurring during the presence of atmospheric rivers from November to April (2007-2018) while showing the importance of this phenomenon in creating super heavy rainfall and introducing the areas affected by it, analyzed the synoptic factors affecting them slowly. In order to identify atmospheric rivers, vertical integral data of water vapour flow were used and thresholds were documented on them. The date of occurrence of each atmospheric river with their daily rainfall was examined and ten of the highest rainfall events Station (equivalent to the 95th percentile of maximum rainfall) related to atmospheric rivers was introduced and analyzed. It is found that the South Gram has been directly and indirectly the main source of atmospheric rivers associated with heavy rainfall. The source of most of these atmospheric rivers is at the peak of the Red Sea, the Gulf of Aden and the Horn of Africa. Synonymously, the origins of 7 cases from Atmospheric rivers have been of the Sudanese low pressure and in the remaining three cases have been integrated systems. In Sudanese systems, the predominant structure of the meridional inclination jet and in Integration systems has been oriented. Due to the dominance of a strong upstream current in the vicinity of the highest flux, moisture of heavy convective currents has caused super heavy rainfall and the station with the highest rainfall in the east and North West of the negative omega field or upstream streams.
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Watterson, Ian G., and Tony Rafter. "The distribution of daily rainfall in Australia and simulated future changes." Journal of Southern Hemisphere Earth Systems Science 67, no. 3 (2017): 160. http://dx.doi.org/10.1071/es17010.
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This study extends recent projections of monthly and daily precipitation over Australia by analysing the full frequency distribution of daily rain amounts and making projections of the new statistics wet-day fraction and top percentile of rain. Simulations from an ensemble of 33 CMIP5 models are used, together with six simulations from the downscaling model CCAM, with the data analysed on the model grids. Consistent with its higher resolution (0.5°), CCAM provides a more skilful simulation for the extreme grid point rainfall than most CMIP5 models. CCAM compares well with AWAP gridded data for wet-day fraction, while there is a wide range of CMIP5 results. In the future climate of 2080–2099 under the RCP8.5 scenario, changes in mean rainfall of both signs occur within the CMIP5 ensemble for most regions and seasons, although mean winter rainfall in southern Australia declines 5 to 30 per cent in most models and in CCAM. CCAM simulates increases in summer, and also more wet days, in contrast to CMIP5. Aside from the north in winter, the changes from CMIP5 become increasingly positive, on stepping from mean to top percentile to twenty-year extreme rainfall, a contrast of typically 25 per cent. There is much less contrast between these statistics from CCAM. The distributions of rain amounts shed light on these different projections. Averaged over Australia and four seasons, CCAM produces a broader distribution than the CMIP5 ensemble mean. However much of the future increase is in the 2 to 8 mm daily range, whereas CMIP5 distributions tend to shift towards amounts in the range 30 mm to 200 mm. Further assessment of such distributions in both these and newer versions of CCAM, ACCESS and other GCMs is recommended.
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Acharya, Suwash Chandra, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg. "Ability of an Australian reanalysis dataset to characterise sub-daily precipitation." Hydrology and Earth System Sciences 24, no. 6 (June 4, 2020): 2951–62. http://dx.doi.org/10.5194/hess-24-2951-2020.
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Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.
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Kim, Dong-Ik, Hyun-Han Kwon, and Dawei Han. "Bias correction of daily precipitation over South Korea from the long-term reanalysis using a composite Gamma-Pareto distribution approach." Hydrology Research 50, no. 4 (April 4, 2019): 1138–61. http://dx.doi.org/10.2166/nh.2019.127.
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Abstract Long-term precipitation data plays an important role in climate impact studies, but the observation for a given catchment is very limited. To significantly expand our sample size for the extreme rainfall analysis, we considered ERA-20c, a century-long reanalysis daily precipitation provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Preliminary studies have already indicated that ERA-20c can reproduce the mean reasonably well, but rainfall intensity is underestimated while wet-day frequency is overestimated. Thus, we first adopted a relatively simple approach to adjust the frequency of wet-days by imposing an optimal threshold. Moreover, we introduced a quantile mapping approach based on a composite distribution of a generalized Pareto distribution for the upper tail (e.g. 95th and 99th percentile), and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases over the conventional method for the extremes. We suggested an interpolation method for the set of parameters of bias correction approach in ungauged catchments. A comparison of the corrected precipitation using spatially interpolated parameters shows that the proposed modelling scheme, particularly with the 99th percentile, can reliably reduce the systematic bias.
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DIAS, Vanessa Rakel de Moraes, Fernando da Silva SALLO, Luciana SANCHES, and Rivanildo DALLACORT. "GEOSTATISTICAL MODELING OF THE TEN-DAY RAINFALL IN MATO GROSSO STATE." GEOGRAFIA 42, no. 3 (March 8, 2018): 99–112. http://dx.doi.org/10.5016/geografia.v42i3.13092.
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Mapping the spatial-temporal distribution of rainfall allows the analysis of tendencies and changes on local and regional scale, which is crucial for the development of environmental and agricultural projects. The objective of this paper is to adjust and choose semivariographic mathematical models to analyze the spatial variability of the 75 percentile rainfall in Mato Grosso State, covering October to March, to subsequently represent it on maps using Kriging techniques. Data from 155 weather stations in Mato Grosso S and around were used, with data for over ten years provided by the National Water Agency. The semivariograms were adjusted using the least squares method and chosen among spherical, exponential, and Gaussian models. The exponential model adjusted best to the experimental semivariograms using the criterion of standard deviation in reduced errors obtained from cross validation and level of dependence. The spatial variability of the rainfall was then mapped using Kriging techniques
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López Díez, Abel, Pablo Máyer Suárez, Jaime Díaz Pacheco, and Pedro Dorta Antequera. "Rainfall and Flooding in Coastal Tourist Areas of the Canary Islands (Spain)." Atmosphere 10, no. 12 (December 13, 2019): 809. http://dx.doi.org/10.3390/atmos10120809.
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Coastal spaces exploited for tourism tend to be developed rapidly and with a desire to maximise profit, leading to diverse environmental problems, including flooding. As the origin of flood events is usually associated with intense precipitation episodes, this study considers the general rainfall characteristics of tourist resorts in two islands of the Canary Archipelago (Spain). Days of intense rainfall were determined using the 99th percentile (99p) of 8 daily precipitation data series. In addition, the weather types that generated these episodes were identified, the best-fitting distribution functions were determined to allow calculation of probable maximum daily precipitation for different return periods, and the territorial and economic consequences of flood events were analysed. The results show highly irregular rainfall, with 99p values ranging 50–80 mm. The weather types associated with 49 days of flooding events were predominantly cyclonic and hybrid cyclonic. The Log Pearson III distribution function best fitted the data series, with a strong likelihood in a 100-year return period of rainfall exceeding 100 mm in a 24 h period. However, values below 30 mm have already resulted in significant flood damage, while intense rainfall events in the period 1998–2016 saw over 11.5 million euros paid out in damages for insured goods. Such flood-induced damages were found to be caused more by inadequate urban planning than by rainfall intensity.
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Young, Hannah R., and Nicholas P. Klingaman. "Skill of Seasonal Rainfall and Temperature Forecasts for East Africa." Weather and Forecasting 35, no. 5 (October 1, 2020): 1783–800. http://dx.doi.org/10.1175/waf-d-19-0061.1.
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AbstractSkillful seasonal forecasts can provide useful information for decision-makers, particularly in regions heavily dependent on agriculture, such as East Africa. We analyze prediction skill for seasonal East African rainfall and temperature one to four months ahead from two seasonal forecasting systems: the U.S. National Centers for Environmental Prediction (NCEP) Coupled Forecast System Model, version 2 (CFSv2), and the Met Office (UKMO) Global Seasonal Forecast System, version 5 (GloSea5). We focus on skill for low or high temperature and rainfall, below the 25th or above the 75th percentile, respectively, as these events can have damaging effects in this region. We find skill one month ahead for both low and high rainfall from CFSv2 for December–February in Tanzania, and from GloSea5 for September–November in Kenya. Both models have higher skill for temperature than for rainfall across Ethiopia, Kenya, and Tanzania, with skill two months ahead in some cases. Performance for rainfall and temperature change in the two models during certain El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) phases, the impacts of which vary by country, season, and sometimes by model. While most changes in performance are within the range of uncertainty due to the relatively small sample size in each phase, they are significant in some cases. For example, La Niña lowers performance for Kenya September–November rainfall in CFSv2 but does not affect skill in GloSea5.
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Sun, Zhou, Shen, Chai, Chen, Liu, Shi, Wang, Wang, and Zhou. "Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China." Remote Sensing 11, no. 15 (August 1, 2019): 1805. http://dx.doi.org/10.3390/rs11151805.
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Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product.
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Littlewood, I. G. "Improved unit hydrograph characterisation of the daily flow regime (including low flows) for the River Teifi, Wales: towards better rainfall-streamflow models for regionalisation." Hydrology and Earth System Sciences 6, no. 5 (October 31, 2002): 899–911. http://dx.doi.org/10.5194/hess-6-899-2002.
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Abstract. An established rainfall-streamflow modelling methodology employing a six-parameter unit hydrograph-based rainfall-runoff model structure is developed further to give an improved model-fit to daily flows for the River Teifi at Glan Teifi. It is shown that a previous model of this type for the Teifi, which (a) accounted for 85% of the variance in observed streamflow, (b) incorporated a pure time delay of one day and (c) was calibrated using a trade-off between two model-fit statistics (as recommended in the original methodology), systematically over-estimates low flows. Using that model as a starting point the combined application of a non-integer pure time delay and further adjustment of a temperature modulation parameter in the loss module, using the flow duration curve as an additional model-fit criterion, gives a much improved model-fit to low flows, while leaving the already good model-fit to higher flows essentially unchanged. The further adjustment of the temperature modulation loss module parameter in this way is much more effective at improving model-fit to low flows than the introduction of the non-integer pure time delay. The new model for the Teifi accounts for 88% of the variance in observed streamflow and performs well over the 5 percentile to 95 percentile range of flows. Issues concerning the utility and efficacy of the new model selection procedure are discussed in the context of hydrological studies, including regionalisation. Keywords: unit hydrographs, rainfall-runoff modelling, low flows, regionalisation.
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Ricko, Martina, Robert F. Adler, and George J. Huffman. "Climatology and Interannual Variability of Quasi-Global Intense Precipitation Using Satellite Observations." Journal of Climate 29, no. 15 (July 11, 2016): 5447–68. http://dx.doi.org/10.1175/jcli-d-15-0662.1.
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Abstract Climatology and variations of recent mean and intense precipitation over a near-global (50°S–50°N) domain on a monthly and annual time scale are analyzed. Data used to derive daily precipitation to examine the effects of spatial and temporal coverage of intense precipitation are from the current Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 version 7 precipitation product, with high spatial and temporal resolution during 1998–2013. Intense precipitation is defined by several different parameters, such as a 95th percentile threshold of daily precipitation, a mean precipitation that exceeds that percentile, or a fixed threshold of daily precipitation value (e.g., 25 and 50 mm day−1). All parameters are used to identify the main characteristics of spatial and temporal variation of intense precipitation. High correlations between examined parameters are observed, especially between climatological monthly mean precipitation and intense precipitation, over both tropical land and ocean. Among the various parameters examined, the one best characterizing intense rainfall is a fraction of daily precipitation ≥ 25 mm day−1, defined as a ratio between the intense precipitation above the used threshold and mean precipitation. Regions that experience an increase in mean precipitation likely experience a similar increase in intense precipitation, especially during the El Niño–Southern Oscillation (ENSO) events. Improved knowledge of this intense precipitation regime and its strong connection to mean precipitation given by the fraction parameter can be used for monitoring of intense rainfall and its intensity on a global to regional scale.
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Gimbel, K. F., K. Felsmann, M. Baudis, H. Puhlmann, A. Gessler, H. Bruelheide, Z. Kayler, et al. "Drought in forest understory ecosystems – a novel rainfall reduction experiment." Biogeosciences Discussions 11, no. 10 (October 7, 2014): 14319–58. http://dx.doi.org/10.5194/bgd-11-14319-2014.
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Abstract. Climate change is predicted to severely affect precipitation patterns across central Europe. This may reduce water availability during the plant-growing season and hence affect the performance and vitality of forest ecosystems. We established a novel rainfall reduction experiment on nine sites in Germany to investigate drought effects on soil-forest-understory-ecosystems. A realistic, but extreme annual drought with a return period of 40 years, which corresponds to the 2.5% percentile of the annual precipitation, was imposed. At all sites, we were able to reach the target values of rainfall reduction, while other important ecosystem variables like air temperature, humidity and soil temperature remained unaffected due to the novel design of a flexible roof. The first year of drought showed considerable changes in the soil moisture dynamics relative to the control sites, which affected leaf stomatal conductance of understory species as well as evapotranspiration rates of the forest understory.
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Chan, Alisha Yee, Honghyok Kim, and Michelle L. Bell. "Culex Mosquitoes at Stormwater Control Measures and Combined Sewer Overflow Outfalls after Heavy Rainfall." Water 14, no. 1 (December 23, 2021): 31. http://dx.doi.org/10.3390/w14010031.
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Mosquito borne diseases are increasingly problematic as climate change continues to alter patterns of precipitation, flooding, and temperatures that may favor mosquito habitats. Stormwater control measures (SCMs), ecologically sustainable methods of stormwater management, may have varying impacts on Culex mosquitoes, such as in areas with combined sewer overflows (CSOs). We studied spatial and temporal associations of SCMs and Culex mosquito counts surrounding the SCMs, stratifying our examination amongst those that do/do not use pooling and/or vegetation, as well as surrounding CSO outfalls after heavy rainfall (≥95th percentile) during summer 2018. Results indicate Culex mosquito counts after heavy rainfall were not significantly different at SCMs that use vegetation and/or ponding from at those that do not. We also found a 35.5% reduction in the increase of Culex mosquitoes the day of, and 77.0% reduction 7–8 days after, heavy rainfall at CSO outfalls treated with medium SCM density compared to those without SCMs. Our results suggest that SCMs may be associated with a reduction in the increase of Culex mosquitoes at the CSO outfalls after heavy rainfall. More research is needed to study how the impacts of SCMs on mosquito populations may affect human health.
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Cafaro, Carlo, Beth J. Woodhams, Thorwald H. M. Stein, Cathryn E. Birch, Stuart Webster, Caroline L. Bain, Andrew Hartley, Samantha Clarke, Samantha Ferrett, and Peter Hill. "Do Convection-Permitting Ensembles Lead to More Skillful Short-Range Probabilistic Rainfall Forecasts over Tropical East Africa?" Weather and Forecasting 36, no. 2 (April 2021): 697–716. http://dx.doi.org/10.1175/waf-d-20-0172.1.
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AbstractConvection-permitting ensemble prediction systems (CP-ENS) have been implemented in the midlatitudes for weather forecasting time scales over the past decade, enabled by the increase in computational resources. Recently, efforts are being made to study the benefits of CP-ENS for tropical regions. This study examines CP-ENS forecasts produced by the Met Office over tropical East Africa, for 24 cases in the period April–May 2019. The CP-ENS, an ensemble with parameterized convection (Glob-ENS), and their deterministic counterparts are evaluated against rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated compared to observations. Pairwise comparisons between the different configurations reveal that the CP-ENS is generally the most skillful forecast for both 3- and 24-h accumulations of heavy rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic forecasts of heavy rainfall, verified using a neighborhood approach, show that the CP-ENS is skillful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good as found in the midlatitudes. Skill decreases with lead time and varies diurnally, especially for CP forecasts. The CP-ENS is underspread both in terms of forecasting the locations of heavy rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific suggestions for further research and development, including probabilistic forecast guidance.
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Hacker, R. B., and W. J. Smith. "An evaluation of the DDH/100mm stocking rate index and an alternative approach to stocking rate estimation." Rangeland Journal 29, no. 2 (2007): 139. http://dx.doi.org/10.1071/rj07001.
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Long-term data for (simulated) daily pasture growth and moving 12-monthly rainfall totals were used to examine the performance of the DDH/100 mm stocking rate index under theoretically ideal management for three locations in the Western Division of NSW. Stocking rate was adjusted either monthly or biannually based on rolling 12-monthly values for either pasture growth or rainfall. Under these ‘ideal’ conditions, monthly values of the index fluctuated widely around the carrying capacity benchmark. In practice, such comparisons would not provide a reliable assessment of the sustainability of the current stocking rate or of the need to adjust stock number to match seasonal conditions. Stocking rates calculated from pasture growth estimates were similar to those derived simply from rainfall and the carrying capacity benchmark, and produced similar levels of pasture utilisation. This ‘benchmark method’ of stocking rate determination thus provides a readily calculated, dynamic benchmark against which actual stocking rate may be compared. Due to lag effects, application of calculated proper stocking rates may lead to excessive pasture utilisation under low rainfall conditions (12-monthly totals less than 120–150 mm for the locations studied or, as a rule of thumb, the 10th percentile). Continuous paddock monitoring and projection of 12-monthly rainfall totals are therefore essential components of sustainable management. Short-term trends in the stocking rate index, driven by rainfall at constant stocking rate, will not provide any generally reliable indication of impending dry spells or feed deficits.