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: 15 June 2025

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Journal articles on the topic "Daily rainfall"

1

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 in those years were greater than the normal rainfall of 1081.56; while 16 years (47.1%) recorded dry because the rainfalls that occurred in those years were below normal rainfall. In the study, Weilbul method was used to determine the return period in order to predict the year of occurrence of maximum rainfall. In addition, Standard Precipitation Index was used to determine periods of dry, normal or wet temperatures. September 1991 recorded the minimum SPI value of -0.86 (moderately dry), while October 2011 recorded the maximum SPI value of 1.88 (moderately wet). This study is carried out because of the importance of agriculture in the region and to Nigeria at large. Kano state is well known to support food production in the country. Also, the presence of dams further buttresses this study. Dams have many purposes; one is agriculture during dry and wet season. It is observed that the rainfall in the basin has no definite pattern.
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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 in those years were greater than the normal rainfall of 1081.56; while 16 years (47.1%) recorded dry because the rainfalls that occurred in those years were below normal rainfall. In the study, Weilbul method was used to determine the return period in order to predict the year of occurrence of maximum rainfall. In addition, Standard Precipitation Index was used to determine periods of dry, normal or wet temperatures. September 1991 recorded the minimum SPI value of -0.86 (moderately dry), while October 2011 recorded the maximum SPI value of 1.88 (moderately wet). This study is carried out because of the importance of agriculture in the region and to Nigeria at large. Kano state is well known to support food production in the country. Also, the presence of dams further buttresses this study. Dams have many purposes; one is agriculture during dry and wet season. It is observed that the rainfall in the basin has no definite pattern.
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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 annual rainfall. Over the whole dataset, only 34% of AMAX daily flood events are matched to daily rainfall annual maxima (and only 20% for 6-hour rainfall maxima). The discontinuity between rainfall maxima and flooding is explained by the consideration of coincident soil moisture storage. The results have serious implications for rainfall-runoff methods of flood risk estimation in the UK where estimation is based on a depth–duration–frequency model of rainfall highly biased to summer. It is concluded that inadequate treatment of the seasonality of rainfall and soil moisture seriously reduces the reliability of event-based flood estimation in Britain.
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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 values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall methodwere applied to maximum rainfall records of 47 stations across the study area. Due to complex geographicand hydro-climatological characteristics of the region, an important research issue focused on breaking downthe large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneousregions, based on the cluster analysis of site characteristics and tests for the regional homogeneity.The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneousregions. The difference may be a result of the distinctive climatic and geographic conditions. The estimatedregional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that theestimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when returnperiods are less than 100 years. But, for higher return periods, rainfall estimates should be treated withcaution. More station years, either from longer records or more stations in the regions, would be required forrainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site averagemaximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestanprovince. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfallsat ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequencyanalysis technique could be applied successfully in deriving design rainfall estimates for northeasternIran. The approach utilized in this study and the findings are of great scientific and practical merit, particularlyfor the purpose of planning for weather-related emergencies and design of hydraulic engineering structures
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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 intensity of rainfall occurrence process by 2-harmonic Fourier law and no statistically significant evidence of changes in the validation period for different threshold values.
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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 a reliable source of rainfall data, one of which is NASA Power, which provides rainfall data for free. NASA Power rainfall data is then compared to observed rainfall data. The comparison of the two rainfalls is measured by a statistical parameter, namely the correlation coefficient. Based on the comparison between lowland and highland areas, the average daily rainfall from NASA Power tends to be lower than the average daily rainfall from observation stations. Meanwhile, from the correlation coefficient value (r) of the comparison of rainfall observations and NASA Power, a considerably high correlation coefficient value (> 0.7) was observed. Thus, it can be suggested that the rainfall data from NASA Power for hydrology research and planning activities in Bali Island, Indonesia.
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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 a detailed review of the theoretical bases of existing methodologies, we compare the results obtained from the use of: a) a Generalized Extreme value (GEV) distribution model, and a Two component Extreme Value (TCEV) distribution model, both applied to yearly maxima of daily rainfall, and b) a Generalized Pareto (GP) distribution model applied to rainfall excesses above a properly specified threshold. For the latter purpose, we use the Multiple Threshold Method (MTM) developed by Deidda(2010), which demonstrate good performance also in the case of quantized records. In order to describe the spatial variation of TCEV, GEV and GP model parameters a regional approach based on homogeneous regions, and two versions of Kriging (a commonly used geostatistical approach) i.e. ordinary Kriging (OK), and Kriging for uncertain Data (KUD), are compared. The obtained results are very promising, pointing towards the use of: a)a GEV distribution model for yearly rainfall maxima, and a KUD model to describe the spatial variation of model parameters, and b)a GP model for rainfall excesses and either an OK or a KUD model for the spatial variation of model parameters. The reason why the OK and KUD approaches lead to the same results in the GP case, is attributed to the robustness of the MTM method.
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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 in the Amazon Basin, and its use with the coefficient from La Cuenca in western Amazonia.
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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 assumptions about the distributional and dependence characteristics of the variables involved), and ensure an appropriate representation of the seasonal variations in rainfall. A key aspect in formulation of the models is selection of the predictor variables used to represent the historical features of the rainfall record. Methods for selection of the predictors are presented here. The approach is applied to daily rainfall from Sydney and Melbourne. The models that are developed use daily-level, seasonal-level, annual-level, and multi-year predictors for rainfall occurrence, and daily-level and annual-level predictors for rainfall amount. The resulting generated sequences provide a better representation of the variability associated with droughts and sustained wet periods than was previously possible. These sequences will be useful in catchment water management studies as a tool for exploring the potential response of catchments to possible future rainfall.
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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 into the variance relationship and capping of the fine timescale maximum intensities, the model was found to be a useful tool for disaggregating daily rainfall in the regions of Queensland. The degree of model complexity in terms of binary chain parameter calibration was also reduced by using only three parameters for Queensland. The resulting rainfall Intensity-Frequency-Duration (IFD) curves better predicted the intensities at fine timescale durations compared with the existing Australian Rainfall and Runoff (ARR) approach. The model has also been linked to the SILO Data Drill synthetic data to disaggregate daily rainfall at sites where limited or no fine timescale observed data are available. This research has analysed the fine timescale rainfall properties at various sites in Queensland and established sufficient confidence in using the model for Queensland.
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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 different climates with particular focus on semiarid and arid regions. As an important result of the present research, a hybrid single-site stochastic daily rainfall generator (the hybrid model) has been developed using both a two-state first-order Markov chain based model and enhancing the simulation of long dry series using a modified serial approach. Long-term daily rainfall time series from rainfall stations under two different precipitation-hydrological regimes; namely, a frontal dominated precipitation-hydrological regime (Northern Ireland) and a semiarid to arid regime driven predominantly by convective rainfall (Jordan) have been used in testing and evaluation of the hybrid model, in addition to the two original approaches (Markov chain based model and Serial approach represented by LARS-WG). Standard statistical analysis and tests have been used to evaluate the performance of the single-site stochastic daily rainfall generators in both regimes. These single-site stochastic daily rainfall generators then have been assessed at multiple sites using a network of daily rainfall stations within the Lough Neagh basin in Northern Ireland and the Mujib basin in Jordan in order to evaluate their ability to correlate the generated time series of the neighbouring stations. According to the present research results of the comparative performance of the single-site stochastic daily rainfall generators, the hybrid model performance in both regimes was superior.
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Books on the topic "Daily rainfall"

1

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 observatory data are compared with the 2 km-resolution MSSG simulation and the Global Satellite Precipitation Map (GSMaP) data with approximately 10 km resolution. Comparison results show that the high-resolution MSSG simulation can represent the 3 day and 24 h rainfall accumulations and also the maximum rainfall intensities better than the GSMaP data. In addition to the assessment for rather short-term prediction, we investigate the exceedance probabilities (EPs) of daily maximum of hourly rainfall intensities at two adjacent raingauge points. The EPs show large difference between the mountainous point and plain point even though their separation is just 4.6 km. The 2 km-resolution MSSG simulation can capture the difference in the two EPs. This confirms the MSSG’s reliability in capturing the topographic enhancement of rainfall intensities.
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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) or a combination of snowmelt and precipitation (winter). The one day step was chosen for availability data (free data) and for testing area size limit for this step. The results showed that floods caused by a combination of rain and snowmelt were significantly better than those caused by rain alone. Two methods were compared. The neural networks ANN and fuzzy model. For both methods were founded the best architecture in training period. The results of the experiment showed that the limit of applicability of the data is above (around) 130 km2 in the case of pure rainfall. In the case of floods caused by a combination of rain and snow, the daily step can be used even for catchments of about 20 km2 with a one-day time shift.
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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 rainfall intensities at several return levels. Generally, our results showed a decreasing trend in historical and future snow duration, rain-on-snow events, and snowmelt runoff. This decreasing trend in snowpack could reduce water resources. A statistically significant increase in maximum streamflow for most rivers at the Washington and North Dakota sites occurred for several months of the year. In Colorado, only a few months indicated such an increase. Future IDF curves for Colorado and North Dakota indicated a slight increase in rainfall intensity whereas the Washington site had about a twofold increase. This increase in rainfall intensity could result in major flood events, demonstrating the importance of accounting for climate changes in infrastructure planning.
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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 of the SECN water-quality monitoring program. Information collected by this monitoring program may be used to help park managers make better-informed decisions by understanding trends and variability related to water-quality conditions in park waters. Summary data are presented in annual data reports published by the SECN. Significant findings for 2012 are as follows: Water temperature ranged 5.7–30.4 °C (42.2–86.7 °F). The lowest monthly average temperature of 10.9°C (51.6°F) occurred in January, and the highest monthly average of 27.7 °C (81.7 °F) occurred in July. Specific conductance values ranged from 19 to 43 µs/cm. There were no major spikes in specific conductance during 2012. Dissolved oxygen concentrations ranged from a low of 4.8 mg/L in July to a high of 11.8 mg/L in February. No daily average concentrations were lower than the South Carolina freshwater standard for daily averages of 5.0 mg/L during 2012. During 2012, values for pH ranged between 5.47 and 6.14, with approximately 99% of measurements (10,455 / 10,608) below the 6.0 unit standard. The monthly averages for pH ranged from 5.8 to 5.9 throughout the year. Daily averages for pH were below 6.0 for the entire year. Turbidity values were generally below 10 NTUs in Cedar Creek throughout much of the year (83% of readings [11,407 / 13,818]). Mean daily turbidity values above 50 NTUs were observed three times during 2012, occurring potentially during, and after, summer rainfall events. The lowest monthly average for turbidity occurred during November (1.7 NTUs,) during an extended period of stable low-flow conditions. The highest monthly average for turbidity was 24.5 NTUs in August, with high variability throughout the month. In 2012, approximately 19% of data were excluded from analysis because it was missing or considered anomalous. An approximate 20-day period of missing data during January was due to loss of power to the sonde during deployment. A second 20-day period of missing data in late March to early April was also due to power failure. pH and turbidity values were the most frequently flagged parameter during 2012.
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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 seasons. The weather tends to remain constant throughout the year. When discussing areas in these regions, instead of referring to seasonal weather changes, we might refer to them as having a rainy climate, meaning it rains frequently all year long. The weather in countries above and below the intertropical region are affected by seasons. For example, these climates may be rainy in the winter and dry in the summer. Weather refers to short-term conditions in a particular area that can quickly change, sometimes within minutes. Climate, however, does not normally change at a rapid pace. In fact, it can take years, decades, or even longer before changes in the climate can be felt.
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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, dissolved oxygen, temperature, salinity, turbidity and water-level data every 30 minutes. The five stations located throughout Mosquito Lagoon are sampled by Volusia County for the SJRWMD as part of the District’s Indian River Lagoon Water-Quality Monitoring Network; data at these five stations include monthly measurements of water-clarity conditions, nutrients, and chlorophyll a levels. Both the SECN and the SJRWMD programs use methods adapted from, or compatible with, the water-quality monitoring program established by the National Estuarine Research Reserve Program. The Canaveral National Seashore fixed-station water-quality monitoring site is part of the SECN estuarine water- and sediment-quality monitoring efforts, which routinely collects data in the vicinity of seven park units along the southeastern U.S. coast. Information collected by this monitoring program will be used to help managers make better-informed decisions by understanding trends and variability related to water-quality conditions in park waters. This report only addresses the continuous data collected at one site at the Canaveral National Seashore Visitors Center. Mean monthly temperatures ranged from 16.6 °C in January and February of 2009 to 31.4 °C in August 2007. Highest water temperatures at CANA were typically observed during the late summer months of July and August with the coolest temperatures in January and February. Mean monthly salinity values averaged 35.5 ppt for this period and ranged from 27.6 ppt in June 2009 to 44.0 ppt in June 2008. Typically, lower salt concentrations were common in the winter and higher during the summer months. Rainfall events, tropical storms, and periods of drought influence salinities. Mean monthly dissolved oxygen levels ranged from 3.8 mg/L in June 2007 to 8.1 mg/L during January 2008. Mean monthly dissolved oxygen levels were typically lowest from June to October. Mean daily dissolved oxygen levels occasionally dropped below 3 mg/L during summer 2007 and fall 2009. Mean monthly pH values averaged 8.01 during this period. Almost all daily average values were between 7.5 and 8.5 except for several days during August when pH values briefly dropped below 7.0. Mean monthly turbidity ranged from 1.6 NTU in January 2008 to 34 NTUs in June 2008. Mean monthly average turbidity level for the period was 11.4 NTUs; however, mean daily turbidity values were highly variable. Water depth at the sampling site varied by 0.5 meters during the period of monitoring. From July 2005 until April 2007 and during July 2007all data collected were flagged due to lack of proper calibration procedures and instrument failures and are not shown in this report. Excluding this period, the average percentage of valid data (all parameters) collected monthly ranged from 14 % to 100 %. During this same period approximately 83 % of all data collected at this site is available for use.
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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. The CONG fixed-station water-quality monitoring site is part of the SECN water-quality monitoring program, which collects data in the vicinity of eight parks located along the southeastern U.S. coast (DeVivo et al. 2008). Information collected by this monitoring program may be used to help park managers make better-informed decisions by understanding trends and variability related to water-quality conditions in park waters. Summary data are presented in annual data reports published by the SECN. Significant findings for 2011 are as follows: The 2011 continuous data record includes several periods of flagged data, primarily due to equipment malfunctions. Dissolved oxygen values were the most often flagged parameter. Water temperature ranged 1.6–30.5°C (34.88–86.9°F). The lowest monthly average temperature of 6.0°C (42.8°F) occurred in January, and the highest monthly average of 28.2°C (82.76°F) occurred in July. Specific conductance values ranged from 21 to 70 µs/cm. The baseline level for specific conductance in Cedar Creek is approximately 25 µs/cm. A majority of spikes in specific conductance were associated with rain and/or runoff events. A period of elevated specific conductance was noted during the first week of April and coinciding with near-bankfull flows on the Congaree River indicating possible backwater effects at the station. Dissolved oxygen concentrations ranged from a low of 4.7 mg/L in July to 11.1 mg/L in February. Forty-one individual readings were below 5.0 mg/L, but only one daily average was lower than the South Carolina freshwater standard of 5.0 mg/L; the daily average dissolved oxygen reading for July 22 was 4.98 mg/L. The lowest monthly average was in June (5.1 mg/L) and the highest monthly average was in January (10.6 mg/L): however, only 2% of readings were used during the month of January (the remaining data were flagged). The next highest monthly average was observed in December (9.8 mg/L) where 63% of the readings were used. pH values ranged between 5.6 and 6.6, with 55% of measurements (5,839/10,595) below the South Carolina standard of 6.0 for freshwater. The lowest monthly average for pH was 5.9, occurring in May, August, September and December. The highest monthly average was 6.2 in January. Elevated pH values were noted during the first week of April due possibly to backwater from the mainstem of the Congaree River during a high flow event. Turbidity values were generally low in Cedar Creek throughout much of the year. Elevated mean daily turbidity values above 50 NTUs (the South Carolina turbidity standard for freshwater) were observed six times during 2011, with all exceedences occurring during, and after, summer rainfall events. The lowest monthly average for turbidity was 2.1 NTUs in January, and the highest monthly average was 22.8 NTUs in June.
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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 routinely collects data in the vicinity of seven coastal parks located along the southeastern U.S. coast (DeVivo et al. 2008). Summary data are presented in annual data reports published by the SECN. Information collected by this monitoring program will be used to help managers make better-informed decisions by understanding trends and variability related to water-quality conditions in park waters. Findings for 2012 are as follows: Continuously-collected dissolved oxygen concentration data indicated good conditions, with the lowest monthly average level occurring in July (5.8mg/L). During the year there were no occurrences of the average daily value for dissolved oxygen dropping below 5 mg/L. Turbidity levels were generally low (<10 NTUs) throughout the year with no daily averages exceeding 35 NTUs. Monthly turbidly averages were highest in the summer, and did not exceed 14.6 NTUs. Salinity levels ranged from 26.96 to 38.57 parts per thousand (ppt) throughout the year, with monthly averages ranging from 34.9 in September to 36.9 in July. Periods of low salinity observed in July and September corresponded to periods of high rainfall and runoff during the preceding months. Mean monthly pH values ranged between 8.0 in August and September, and 8.4 in December. Monthly averages for water temperature ranged from 17.1ºC (62.8ºF) in February to 27.2ºC (81.0ºF) in July. Individual readings for water temperature ranged from 11.4ºC (52.5ºF) in January, to 30.4ºC (86.7ºF) in July. Discrete sampling of water clarity, nutrients, and chlorophyll a indicated fair to good conditions throughout the year. September was the only month were dissolved inorganic nitrogen levels rated fair; the rest of the year was rated as good. Eight of the 12 months rated fair levels for dissolved inorganic phosphorus; the other four months were rated as good. There were no poor results from any water clarity, nutrients, or chlorophyll a sampling in 2012. In 2012, roughly 10% of data were excluded from analysis because they were considered anomalous. Anomalous data was most commonly associated with problems with pH for a variety of reasons, primarily due to unknown sonde malfunctions or probe drift. Additionally, approximately 19 days of data were missing from January 26 to and February 14 due to loss of memory within the data sonde.
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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-station water-quality monitoring sites are part of the SECN estuarine water-quality monitoring program, which routinely collects data in the vicinity of seven coastal parks along the southeastern U.S. coast. Salinity, dissolved oxygen, pH, turbidity, water temperature, specific conductance (to compute salinity), and water-level data were collected at two stations at CALO. Monthly measurements of water clarity conditions, and the collection of a grab sample analyzed for total dissolved phosphorus, dissolved inorganic phosphorus, total dissolved nitrogen, dissolved inorganic nitrogen, and chlorophyll a levels were also made at both sites. Discrete water-quality samples collected monthly at both sites indicated good conditions with respect to water clarity with one fair reading in February at CALOshak. Chlorophyll a, readings were also good throughout the year, with one fair reading in October at CALOshak, where data were available. Dissolved inorganic nitrogen and phosphorus levels were good at CALOshak. Orthophosphate levels were slightly elevated at CALOshak2 during November. Salinity concentrations followed the same general pattern throughout the year at both sites, generally declining after periods of high rainfall and runoff. Individual and monthly mean low readings at CALOshak occurred in November at 25.82 and 39.9 ppt, respectively, compared to individual and monthly mean lows at CALOshak2 in September (22.20 and 27.8 ppt respectively). Conversely, individual and monthly high readings at CALOshak were 36.98 and 36.2 ppt respectively in May, whereas individual and monthly high readings at CALOshak2 were 38.41 and 35.6 ppt respectively in July. Mean monthly dissolved oxygen levels were similar between sites ranging from 5.4 to 8.8 mg/L at CALOshak and from 6.1 to 9.0 mg/L at CALOshak2. The season lows at each site occurred during the summer months; however, individual readings below 5.0 mg/L were common at CALOshak2 occurring between March and October. Mean daily dissolved oxygen (DO) levels were never below 5.0 mg/L at CALOshak2, but were below 5.0 mg/L at CALOshak for six days in August. Monthly mean pH values were similar between sites ranging from 8.0 to 8.1 at CALOshak and from 7.9 to 8.1 at CALOshak2. Average monthly turbidity at both CALOshak and CALOshak2 never exceeded 8.0NTUs. Mean daily turbidity values at CALOshak did not exceed 36 NTUs, and CALOshak2 values did not exceed 26 NTUs. Water temperatures at CALOshak2 were more variable than those at CALOshak. Individual readings at CALOshak2 ranged from 3.1°C (37.5°F) in January to 33.5°C (92.2°F) in August, while temperatures at CALOshak ranged from 7.3°C (45.1°F) in January to 30.8°C (87.5°F) in August. Monthly mean water temperatures ranged from 11.0°C (51.8°F) to 28.8°C (83.84°F) from January to July at CALOshak2, versus 11.3°C (52.34°F) to 28.2°C (82.76°F) from January to July and August at CALOshak.
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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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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 availability is an important step toward ensuring that infrastructure and other development projects meet their operational, financial, and economic goals. It is also important to examine the implications of such projects for the future allocation of available water among competing users and uses to mitigate potential conflict and to ensure such projects are consistent with long-term regional development plans and preservation of essential ecosystem services. As part of its commitment to help member countries adapt to climate change, the IDB is sponsoring work to develop and apply the Regional Water Resources Simulation Model for Latin America and the Caribbean, an integrated suite of watershed modeling tools known as Hydro-BID. Hydro-BID is a highly scalable modeling system that includes hydrology and climate analysis modules to estimate the availability of surface water (stream flows) at the regional, basin, and sub-basin scales. The system includes modules for incorporating the effects of groundwater and reservoirs on surface water flows and for estimating sediment loading. Data produced by Hydro-BID are useful for water balance analysis, water allocation decisions, and economic analysis and decision support tools to help decision-makers make informed choices among alternative designs for infrastructure projects and alternative policies for water resources management. IDB sponsored the development of Hydro-BID and provides the software and basic training free of charge to authorized users; see hydrobidlac.org. The system was developed by RTI International as an adaptation of RTI's proprietary WaterFALL® modeling software, based on over 30 years of experience developing and using the U.S. National Hydrography Dataset (NHDPlus) in support to the U.S. Geological Survey and the U.S. Environmental Protection Agency. In Phase I of this effort, RTI prepared a working version of Hydro-BID that includes: (1) the Analytical Hydrography Dataset for Latin America and the Caribbean (LAC AHD), a digital representation of 229,300 catchments in Central America, South America, and the Caribbean with their corresponding topography, river, and stream segments; (2) a geographic information system (GIS)-based navigation tool to browse AHD catchments and streams with the capability of navigating upstream and downstream; (3) a user interface for specifying the area and period to be modeled and the period and location for which water availability will be simulated; (4) a climate data interface to obtain rainfall and temperature inputs for the area and period of interest; (5) a rainfall-runoff model based on the Generalized Watershed Loading Factor (GWLF) formulation; and (6) a routing scheme for quantifying time of travel and cumulative flow estimates across downstream catchments. Hydro-BID generates output in the form of daily time series of flow estimates for the selected location and period. The output can be summarized as a monthly time series at the user's discretion. In Phase II of this effort, RTI has prepared an updated version of Hydro-BID that includes (1) improvements to the user interface; (2) a module to simulate the effect of reservoirs on downstream flows; (3) a module to link Hydro-BID and groundwater models developed with MODFLOW and incorporate water exchanges between groundwater and surface water compartments into the simulation of sur
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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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