Relevant bibliographies by topics / Daily rainfall

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Daily rainfall'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Daily rainfall"

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Thompson, S., I. M. Sanni, U. A. Abubakar, and B. S. Sani. "Preliminary Analysis of Daily Rainfall Data from Kano State using Statistical Techniques." October 2022 6, no. 2 (2022): 317–24. http://dx.doi.org/10.36263/ijest.2022.02.0366.

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A 35-year annual rainfall was collected from NIMET to determine the Standard Precipitation Index (SPI) and also to study the trend of rainfall parameter in the Kano State, Northern Nigeria. The paper captures the average rainfall (normal rainfall) data for thirty-five years period which implies a rainfall of about 1081.56mm. The positive values (above zero) signify rainfalls that were higher than normal (wet); while the negative values (below zero) imply rainfalls that were lower than normal (dry). From the rainfall anomaly, 18 years (52.9%) recorded wet due to the fact that rainfall occurred
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Thompson, S., I. M. Sanni, U. A. Abubakar, and B. S. Sani. "Preliminary Analysis of Daily Rainfall Data from Kano State using Statistical Techniques." October 2022 6, no. 2 (2022): 317–24. http://dx.doi.org/10.36263/nijest.2022.02.0366.

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A 35-year annual rainfall was collected from NIMET to determine the Standard Precipitation Index (SPI) and also to study the trend of rainfall parameter in the Kano State, Northern Nigeria. The paper captures the average rainfall (normal rainfall) data for thirty-five years period which implies a rainfall of about 1081.56mm. The positive values (above zero) signify rainfalls that were higher than normal (wet); while the negative values (below zero) imply rainfalls that were lower than normal (dry). From the rainfall anomaly, 18 years (52.9%) recorded wet due to the fact that rainfall occurred
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Ledingham, Jamie, David Archer, Elizabeth Lewis, Hayley Fowler, and Chris Kilsby. "Contrasting seasonality of storm rainfall and flood runoff in the UK and some implications for rainfall-runoff methods of flood estimation." Hydrology Research 50, no. 5 (2019): 1309–23. http://dx.doi.org/10.2166/nh.2019.040.

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Abstract Using data from 520 gauging stations in Britain and gridded rainfall datasets, the seasonality of storm rainfall and flood runoff is compared and mapped. Annual maximum (AMAX) daily rainfall occurs predominantly in summer, but AMAX floods occur most frequently in winter. Seasonal occurrences of annual daily rainfall and flood maxima differ by more than 50% in dry lowland catchments. The differences diminish with increasing catchment wetness, increase with rainfalls shorter than daily duration and are shown to depend primarily on catchment wetness, as illustrated by variations in mean
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Hershenhorn, J., and D. A. Woolhiser. "Disaggregation of daily rainfall." Journal of Hydrology 95, no. 3-4 (1987): 299–322. http://dx.doi.org/10.1016/0022-1694(87)90008-4.

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

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Daily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return
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Haddad, Khaled, and Ataur Rahman. "Derivation of short-duration design rainfalls using daily rainfall statistics." Natural Hazards 74, no. 3 (2014): 1391–401. http://dx.doi.org/10.1007/s11069-014-1248-7.

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

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Abstract. A stochastic model based on a non-homogeneous Poisson process, characterised by a time-dependent intensity of rainfall occurrence, is employed to explain seasonal effects of daily rainfalls exceeding prefixed threshold values. The data modelling has been performed with a partition of observed daily rainfall data into a calibration period for parameter estimation and a validation period for checking on occurrence process changes. The model has been applied to a set of rain gauges located in different geographical areas of Southern Italy. The results show a good fit for time-varying in
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I Gusti Ngurah Putu Dharmayasa, Cathleen Ariella Simatupang, and Doni Marisi Sinaga. "NASA Power’s: an alternative rainfall data resources for hydrology research and planning activities in Bali Island, Indonesia." Journal of Infrastructure Planning and Engineering (JIPE) 1, no. 1 (2022): 1–7. http://dx.doi.org/10.22225/jipe.1.1.2022.1-7.

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Rainfall data is critical for planning and research in the field of hydrology. Rainfall data must be available continuously, which means it must be recorded continuously. This recording will continue since numerous projects in the field of hydrology require continuous rainfall data. Although rainfall data are collected and recorded daily, some stations frequently have insufficient rainfall records, particularly in developing countries such as Bali, Indonesia. These issues may impair the quality of rainfall data, resulting in inaccuracies in the analysis results. To address this issue, we need
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Connolly, R. D., J. Schirmer, and P. K. Dunn. "A daily rainfall disaggregation model." Agricultural and Forest Meteorology 92, no. 2 (1998): 105–17. http://dx.doi.org/10.1016/s0168-1923(98)00088-4.

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

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Dissertations / Theses on the topic "Daily rainfall"

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To, Chun-hung. "Stochastic model of daily rainfall." Click to view the E-thesis via HKUTO, 1989. http://sunzi.lib.hku.hk/hkuto/record/B31976098.

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To, Chun-hung, and 杜振雄. "Stochastic model of daily rainfall." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31976098.

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HELLIES, MATTEO. "Extreme rainfall regime characterization in Sardinia using daily rainfall data." Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266863.

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For the design of hydraulic structures for flood conveyance and discharge, or protection of territory against flood is fundamental the knowledge of the ``extreme rainfall regime'' in the area where the hydraulic structures must be set up. Indeed the design flood is commonly evaluated as output of rainfall-runoff models that receive as input the quantitative description of a rainfall extreme event with a given exceedance probability. This dissertation assesses the performance of different statistical approaches in characterizing extreme rainfall in the island of Sardinia (Italy). After
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Elsenbeer, Helmut, Keith Cassel, and W. Tinner. "A daily rainfall erosivity model for Western Amazonia." Universität Potsdam, 1993. http://opus.kobv.de/ubp/volltexte/2008/1696/.

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Rainfall erosivities as defined by the R factor from the universal soil loss equation were determined for all events during a two-year period at the station La Cuenca in western Amazonia. Three methods based on a power relationship between rainfall amount and erosivity were then applied to estimate event and daily rainfall erosivities from the respective rainfall amounts. A test of the resulting regression equations against an independent data set proved all three methods equally adequate in predicting rainfall erosivity from daily rainfall amount. We recommend the Richardson model for testing
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Jimoh, Onemayin David. "Modelling the occurrence of daily rainfall in Nigeria." Thesis, University of Birmingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418270.

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Szyniszewska, Anna Maria. "Determining the daily rainfall characteristics from the monthly rainfall totals in central and northeastern Thailand." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0025162.

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Harrold, Timothy Ives Civil &amp Environmental Engineering Faculty of Engineering UNSW. "Stochastic generation of daily rainfall for catchment water management studies." Awarded by:University of New South Wales. School of Civil and Environmental Engineering, 2002. http://handle.unsw.edu.au/1959.4/18640.

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This thesis presents an approach for generating long synthetic sequences of single-site daily rainfall which can incorporate low-frequency features such as drought, while still accurately representing the day-to-day variations in rainfall. The approach is implemented in a two-stage process. The first stage is to generate the entire sequence of rainfall occurrence (i.e. whether each day is dry or wet). The second stage is to generate the rainfall amount on all wet days in the sequence. The models used in both stages are nonparametric (they make minimal general assumptions rather than specific a
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Mahbub, S. M. Parvez Bin, and s. mahbub@qut edu au. "Stochastic Disaggregation of Daily Rainfall for Fine Timescale Design Storms." Central Queensland University. Centre for Railway Engineering, 2008. http://library-resources.cqu.edu.au./thesis/adt-QCQU/public/adt-QCQU20080813.151345.

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Rainfall data are usually gathered at daily timescales due to the availability of daily rain-gauges throughout the world. However, rainfall data at fine timescale are required for certain hydrologic modellings such as crop simulation modelling, erosion modelling etc. Limited availability of such data leads to the option of daily rainfall disaggregation. This research investigates the use of a stochastic rainfall disaggregation model on a regional basis to disaggregate daily rainfall into any desired fine timescale in the State of Queensland, Australia. With the incorporation of seasonality int
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Young, Andrew Richard. "Regionalising a daily rainfall runoff model within the United Kingdom." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340664.

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Alajarmeh, Ramiaah Mohammad Saleh. "Generation of daily rainfall time series using a hybrid stochastic model." Thesis, Queen's University Belfast, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675943.

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Rainfall occurrence and intensity are the most important drivers of the surface runoff process. The knowledge of the occurrence and intensity of rainfall events is a crucial concern for water resources planners and designers. Stochastic rainfall generators are considered a robust tool that can generate the rainfall intensity of any time length at the interested locations. The validity of the stochastic daily rainfall generators for the Middle East in general and Jordan in specific has not been well researched. The aim of the present research is to estimate daily rainfall time series in differe
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Books on the topic "Daily rainfall"

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Jimoh, Onemayin David. Modelling the occurrence of daily rainfall in Nigeria. University of Birmingham, 1996.

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Nepal. Dept. of Hydrology and Meteorology., ed. Daily precipitation records of Nepal, 1999-2000. H.M.G of Nepal, Ministry of Science and Technology, Dept. of Hydrology and Meteorology, 2002.

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rring, Lars Ba. Aspects of daily rainfall climate relevant to soil erosion in Kenya. Lunds Universitet, 1988.

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Lawler, D. M. Changes in the frequency of heavy daily rainfalls: Some preliminary observations from Wales and the West Midlands. Dept. of Geography, University of Birmingham, 1985.

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Ranatunge, Edmound Ranga. A statistical analysis of the spatial and temporal organization of daily rainfall over Sri Lanka. [s.n.], 1994.

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Groen, Maria Margaretha de. Modelling interception and transpiration at monthly time steps: Introducing daily variability through Markov chains. Swets & Zeitlinger, 2002.

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J, Štekl, ed. Extrémni denní srážky na území České Republiky v období 1879-2000 a jejich synoptické příčny =: Extreme daily precipitation on the territory of the Czech Republic in the period 1879-2000 and their synoptic causes. [Český hydrometeorologický ústav], 2001.

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Stabile, Scott. The very rainy day. HarperCollins/Harper Entertainment, 2003.

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Milne, A. A. Kubus puchatek. Wydawnictwo Nasza Ksiegarnia, 2004.

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Milne, A. A. Winnie-the-Pooh. Dutton Children's Books, 1988.

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Book chapters on the topic "Daily rainfall"

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Zucchini, Walter, Iain L. MacDonald, and Roland Langrock. "Daily rainfall occurrence." In Hidden Markov Models for Time Series. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b20790-16.

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Suroso, Fatimatus Sholihah Marush, Purwanto Bekti Santoso, Irfan Sudono, Edvin Aldrian, and Nelly Florida Riama. "Hourly Rainfall Simulation Using Daily Data." In Proceedings of the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9348-9_86.

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Auestad, Bjørn H., Andreas Henriksen, and Hans A. Karlsen. "Modeling and Analysis of Daily Rainfall Data." In Geostatistics Oslo 2012. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4153-9_40.

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Nigussie, Tewodros Assefa, Abebe Fanta, Assefa M. Melesse, and Shoeb Quraishi. "Modeling Rainfall Erosivity From Daily Rainfall Events, Upper Blue Nile Basin, Ethiopia." In Nile River Basin. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02720-3_17.

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Jaafar, J., A. Baki, I. A. Abu Bakar, W. Tahir, H. Awang, and F. Ismail. "Evaluation of Stochastic Daily Rainfall Data Generation Models." In ISFRAM 2015. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0500-8_17.

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Hermance, John F. "Intra-Seasonal Patterns of Rainfall from Daily Values." In SpringerBriefs in Earth Sciences. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00575-1_4.

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Le, Vuong Minh, Binh Thai Pham, Tien-Thinh Le, Hai-Bang Ly, and Lu Minh Le. "Daily Rainfall Prediction Using Nonlinear Autoregressive Neural Network." In Micro-Electronics and Telecommunication Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2329-8_22.

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Kassem, Youssef, Hüseyin Gökçekuş, and Mohamed Elmustafa Elmubarak Elawad Hassan. "Daily Rainfall Characteristics in Sudan: Statistical Analysis and Modeling." In Environmental Earth Sciences. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65960-7_12.

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Paria, Abhirup, Arindam Giri, Subrata Dutta, and Sarmistha Neogy. "AcoNeural: A Hybrid Neural Network Model for Predicting Daily Rainfall." In Springer Proceedings in Information and Communication Technologies. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-5157-0_18.

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Onishi, Ryo, H. A. A. I. S. Bandara, and Koki Matsumoto. "High-Resolution Rainfall Simulations for Early Warning of Rain-Induced Rapid Long-Traveling Landslides in Sri Lanka." In Progress in Landslide Research and Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-72736-8_10.

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AbstractReliable rainfall prediction is a key for successful early warning of rain-induced long-traveling landslides. This study assesses the rainfall prediction skill of a high-resolution weather prediction model named the Multi-Scale Simulator for the Geoenvironment (MSSG). The rainfall accumulation is more crucial rather than hour-to-hour rainfall intensity for long-traveling landslide. Three heavy rain events, which caused devastating landslide events in Sri Lanka, are chosen for the assessment: (i) Aranayaka in 2016, (ii) Rilpola in 2017 and (iii) Kiribathgala in 2014. The raingaunge obse
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Conference papers on the topic "Daily rainfall"

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Sukhavasi, Vidyullatha, AP Chaitanyasri Mouli, Dev Vikas Juneja, Yadala Sucharitha, Sumedh Sameer Joshi, and Samir Dey. "Establishing Ensemble Learning Models for Daily Rainfall Forecasting." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910429.

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Prasetya, Ratih, Dede Djuhana, Adhi Harmoko Saputro, and Donaldi Sukma Permana. "Daily Rainfall Prediction based on Gradient Boosting Regression Model using NEX-GDDP-CMIP6." In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2024. https://doi.org/10.1109/isriti64779.2024.10963581.

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Kumar, Sushil, Arun Kumar, Amarendra Singh, Amar Pratap Bhaskar, Anurag Singh Baghel, and Jagabandhu Panda. "Analysis of Extreme Rainfall Trends Over 75 Locations (District Wise) of Uttar Pradesh, India using Daily Gridded Rainfall Data (1973-2023)." In 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI). IEEE, 2025. https://doi.org/10.1109/ic3ecsbhi63591.2025.10990961.

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Kumar, Barun, Punam Kumari, Ajit Kumar, and Sanjeev Kumar Prasad. "Predicting Daily Rainfall Amount Using Random Forest, Multivariate Linear Regression, and Extreme Gradient Boosting." In 2025 10th International Conference on Signal Processing and Communication (ICSC). IEEE, 2025. https://doi.org/10.1109/icsc64553.2025.10968568.

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Jha, Keshav, and Prashant Kumar. "Comparing Different Machine Learning and Deep Learning Models for Daily Rainfall Prediction at Kerala Point Location." In 2025 3rd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT). IEEE, 2025. https://doi.org/10.1109/dicct64131.2025.10986746.

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Kozel, Tomas, and Ruzena Pavelkova. "POSSIBILITIES FOR DAY-STEP FLOOD FORECASTING IN SMALLER CATCHMENTS USING MACHINE LEARNING METHODS." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024v/3.2/s11.11.

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Today, it is possible to work with a wide range of freely available data, often on a daily basis. These data can be used to create an early warning system for estimating the approximate hazard. It can also be used to develop models of long-term catchment behaviour. Most applications are generally carried out on catchments larger than 200 km2. For this reason, areas between 20 and 350 km2 were selected to test the hypothesis using models based on machine learning methods. How good results can be achieved when using daily data to predict increased flows caused by previous precipitation (summer)
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Hin, Pooi Ah. "Prediction of daily rainfall." In PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4882616.

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Zhou, Lei, Yuanhang Chen, Nian Liang, and Yong Ni. "Daily rainfall model to merge TRMM and ground based observations for rainfall estimations." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729150.

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Moses, Oliver. "Heavy Daily Rainfall Characteristics over the Eastern Botswana." In Environment and Water Resource Management / 837: Health Informatics / 838: Modelling and Simulation / 839: Power and Energy Systems. ACTAPRESS, 2016. http://dx.doi.org/10.2316/p.2016.836-014.

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"Daily rainfall data infilling with a stochastic model." In 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.h3.jin.

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Reports on the topic "Daily rainfall"

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Wagner, Anna, Christopher Hiemstra, Glen Liston, Katrina Bennett, Dan Cooley, and Arthur Gelvin. Changes in climate and its effect on timing of snowmelt and intensity-duration-frequency curves. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41402.

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Snow is a critical water resource for much of the U.S. and failure to account for changes in climate could deleteriously impact military assets. In this study, we produced historical and future snow trends through modeling at three military sites (in Washington, Colorado, and North Dakota) and the Western U.S. For selected rivers, we performed seasonal trend analysis of discharge extremes. We calculated flood frequency curves and estimated the probability of occurrence of future annual maximum daily rainfall depths. Additionally, we generated intensity-duration-frequency curves (IDF) to find r
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Rinehart, Aaron, Wendy Wright, and M. Gregory. Fixed-station water-quality monitoring at Congaree National Park: Data Summary 2012. National Park Service, 2013. https://doi.org/10.36967/2195210.

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In 2006 Congaree National Park (CONG), in conjunction with the Southeast Coast Network (SECN) Inventory and Monitoring Program, began collecting water-quality data at Cedar Creek as part the NPS Vital Signs Monitoring Program. The continuous-monitoring data station is located approximately one mile south of the visitor center and collects water temperature, dissolved oxygen, pH, specific conductance, turbidity and water-level data every 30 minutes. This publication summarizes data collected from January 1, 2012 to December 31, 2012. The CONG fixed-station water-quality monitoring site is part
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Näslund-Hadley, Emma, María Clara Ramos, Juan Roberto Paredes, Ángela Bolivar, and Gustavo Wilches-Chaux. Our Climate is Changing. Inter-American Development Bank, 2015. http://dx.doi.org/10.18235/0006273.

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People used to take the weather and climate for granted -butnot anymore! They are both now studied intensively by scientists and discussed by celebrities and newscasters as they become increasingly important to our own daily lives. Every place on Earth has a climate. A climate includes factors that remain fairly constant for at least 30 years, such as temperature, humidity, the amount of water present in the air, and rainfall. The region between the Tropic of Cancer and the Tropic of Capricorn is called the intertropical (or equatorial) region. This is Earth's "waist" where there are no season
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Wright, Wendy, and M. Gregory. Fixed-station water-quality monitoring at Canaveral National Seashore: 2005 to 2009 Data Summary. National Park Service, 2013. https://doi.org/10.36967/2195998.

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In 2005 the National Park Service (NPS) Southeast Coast Network (SECN) began collecting water-quality data in the estuarine waters of Canaveral National Seashore as part the NPS Vital Signs monitoring program. The scope of the monitoring program includes Mosquito Lagoon and is comprised of continuous water-quality monitoring conducted by the SECN at one site and is augmented with monthly data collected at five stations by St. Johns River Water Management District (SJRWMD). The continuous-monitoring data station is located at the Canaveral National Seashore visitor center dock and collects pH,
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Gregory, M., Aaron Rinehart, and Wendy Wright. Fixed-station water-quality monitoring at Congaree National Park: Data Summary 2011. National Park Service, 2012. https://doi.org/10.36967/2193850.

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In 2006 Congaree National Park (CONG), in conjunction with the Southeast Coast Network (SECN) Inventory and Monitoring Program, began collecting water-quality data at Cedar Creek as part the NPS Vital Signs Monitoring Program. The continuous monitoring data station is located approximately one mile south of the visitor center and collects water temperature, dissolved oxygen, pH, salinity, specific conductance, turbidity and water-level data every 30 minutes following the methods of Wenner and Geist (2001). This publication summarizes data collected from January 1, 2011 to December 31, 2011. Th
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Rinehart, Aaron, M. Gregory, and Wendy Wright. Fixed-station water-quality monitoring at Fort Matanzas National Monument: 2012 data summary. National Park Service, 2013. https://doi.org/10.36967/2195211.

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In 2010 the National Park Service (NPS) Southeast Coast Network (SECN) Inventory and Monitoring Program began collecting water-quality data in the estuarine waters near Fort Matanzas National Monument (FOMA) as part the NPS Vital Signs monitoring program. The continuous-monitoring data station is located at the visitors center dock. This station collects pH, dissolved oxygen, temperature, salinity, specific conductance, turbidity and water-level data every 30 minutes. The FOMA fixed-station water-quality monitoring site is part of the SECN estuarine water-quality monitoring program, which rout
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Rinehart, Aaron, M. Gregory, and Wendy Wright. Fixed-station water-quality monitoring at Cape Lookout National Seashore: 2012 data summary. National Park Service, 2013. https://doi.org/10.36967/2195313.

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In 2007, the National Park Service (NPS) Southeast Coast Network (SECN) Inventory and Monitoring Program began a partnership with the North Carolina National Estuarine Research Reserve (NCNERR) to collect water-quality data in the estuarine waters near Cape Lookout National Seashore (CALO). Two continuous-monitoring stations have been operational since 2008 in the Shackleford Banks Dock and in the Middle Marshes area. Methods used are adapted from the water-quality monitoring program established by the National Estuarine Research Reserve Program. The Cape Lookout National Seashore fixed-statio
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Moreda, Fekadu, Benjamin Lord, Mauro Nalesso, Pedro Coli Valdes Daussa, and Juliana Corrales. Hydro-BID: New Functionalities (Reservoir, Sediment and Groundwater Simulation Modules). Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0009312.

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Abstract:
The Inter-American Development Bank (IDB) provides financial and technicalsupport for infrastructure projects in water and sanitation, irrigation, flood control, transport, and energy, and for development projects in agriculture, urban systems, and natural resources. Many of these projects depend upon water resources and may be affected negatively by climate change and other developments that alter water availability, such as population growth and shifts in land use associated with urbanization, industrial growth, and agricultural practices. Assessing the potential for future changes in water
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9

Accounting for Changes in Extreme Daily Rainfall Intensity in Pacific Island Countries. Asian Development Bank, 2021. http://dx.doi.org/10.22617/arm210446-2.

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