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 February 2022

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Journal articles on the topic "Daily rainfall"

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

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

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

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

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

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

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

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Abstract Using data from 520 gauging stations in Britain and gridded rainfall datasets, the seasonality of storm rainfall and flood runoff is compared and mapped. Annual maximum (AMAX) daily rainfall occurs predominantly in summer, but AMAX floods occur most frequently in winter. Seasonal occurrences of annual daily rainfall and flood maxima differ by more than 50% in dry lowland catchments. The differences diminish with increasing catchment wetness, increase with rainfalls shorter than daily duration and are shown to depend primarily on catchment wetness, as illustrated by variations in mean annual rainfall. Over the whole dataset, only 34% of AMAX daily flood events are matched to daily rainfall annual maxima (and only 20% for 6-hour rainfall maxima). The discontinuity between rainfall maxima and flooding is explained by the consideration of coincident soil moisture storage. The results have serious implications for rainfall-runoff methods of flood risk estimation in the UK where estimation is based on a depth–duration–frequency model of rainfall highly biased to summer. It is concluded that inadequate treatment of the seasonality of rainfall and soil moisture seriously reduces the reliability of event-based flood estimation in Britain.
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JOLLIFFE, IAN T., and PETER B. HOPE. "REPRESENTATION OF DAILY RAINFALL DISTRIBUTIONS USING NORMALIZED RAINFALL CURVES." International Journal of Climatology 16, no. 10 (October 1996): 1157–63. http://dx.doi.org/10.1002/(sici)1097-0088(199610)16:10<1157::aid-joc71>3.0.co;2-r.

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

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Daily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall methodwere applied to maximum rainfall records of 47 stations across the study area. Due to complex geographicand hydro-climatological characteristics of the region, an important research issue focused on breaking downthe large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneousregions, based on the cluster analysis of site characteristics and tests for the regional homogeneity.The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneousregions. The difference may be a result of the distinctive climatic and geographic conditions. The estimatedregional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that theestimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when returnperiods are less than 100 years. But, for higher return periods, rainfall estimates should be treated withcaution. More station years, either from longer records or more stations in the regions, would be required forrainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site averagemaximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestanprovince. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfallsat ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequencyanalysis technique could be applied successfully in deriving design rainfall estimates for northeasternIran. The approach utilized in this study and the findings are of great scientific and practical merit, particularlyfor the purpose of planning for weather-related emergencies and design of hydraulic engineering structures
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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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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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Bärring, Lars. "Aspects of daily rainfall climate relevant to soil erosion in Kenya summary /." Lund, Sweden : Dept. of Physical Geography, University of Lund, 1988. http://catalog.hathitrust.org/api/volumes/oclc/19252214.html.

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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. Birmingham: University of Birmingham, 1996.

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Daily rainfall at Roma, Lesotho for the period 1964-2008. Roma, Lesotho: House 9 Publications, National University of Lesotho, 2008.

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rring, Lars Ba. Aspects of daily rainfall climate relevant to soil erosion in Kenya. Lund: 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. Birmingham: Dept. of Geography, University of Birmingham, 1985.

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

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

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Milne, A. A. Gấu Pooh xinh xắn. Hà Nội: Nhà xuất b̉an Hội nhà văn, 2007.

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Milne, A. A. Winnie-the-Pooh. New York: Dutton Children's Books, 2001.

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Milne, A. A. Winnie-the-Pooh. Toronto: McClelland & Stewart, 2000.

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Milne, A. A. Winnie-the-Pooh. New York: E.P. Dutton, 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, 207–11. Second edition / Walter Zucchini, Iain L. MacDonald, and Roland Langrock. | Boca Raton : Taylor & Francis, 2016. | Series: Monographs on statistics and applied probability ; 150 | “A CRC title.”: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b20790-16.

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Auestad, Bjørn H., Andreas Henriksen, and Hans A. Karlsen. "Modeling and Analysis of Daily Rainfall Data." In Geostatistics Oslo 2012, 493–503. Dordrecht: 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, 307–35. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02720-3_17.

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Hermance, John F. "Intra-Seasonal Patterns of Rainfall from Daily Values." In SpringerBriefs in Earth Sciences, 59–75. Cham: 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, 213–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2329-8_22.

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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, 203–20. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0500-8_17.

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Sedrique, Zoyem Tedonfack, and Julius Tata Nfor. "Rainfall Variability and Quantity of Water Supply in Bamenda I, Northwest Region of Cameroon." In African Handbook of Climate Change Adaptation, 713–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_139.

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AbstractBamenda I municipality found in the humid tropic is endowed with a dense hydrological network which makes it a water catchment for the entire region. Paradoxically, the region still suffers problems of water shortage. This is due to the spatial and temporal variability in rainfall that greatly affects water supply through its impacts on surface and groundwater. For this reason, we came up with the research topic “Rainfall variability and quantity of water supply in Bamenda 1, Northwest Region of Cameroon.” The objective of this study is to examine the manifestations of rainfall variability, and how it affects quantity of water supply in the humid tropics. Rainfall data use for this study comprised of annual, monthly, and daily rainfall over a period of 55 years. Water supply data was made of monthly and annual supply. With these data, a Pearson’s correlation was computed, and it gave a value of 0.701, with a rainfall proportion of 49.14% and 50.86% for other factors. The seasonality and the Standardized Precipitation Index were equally analyzed. At the end of the study, results showed that rainfall events in Bamenda I fluctuates with time and in space. It equally presented a reduction in the number of rainy days from 204 days in 1663 to 155 in 2018. This led to a reduction in length of rainy season and in rainfall amounts. In addition, the area has witnessed sedimentation of riverbeds and water reservoirs due to erosion and deposition during high rainfall peaks. Equally, floods observed during high rainfall episodes have become a potential threat to water infrastructures imposing exceptional water shortages during the rainy seasons. Due to these, actors in the water supply sector are putting in measures to remedy the situation.
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Wu, Jiansheng, and Long Jin. "Daily Rainfall Prediction with SVR Using a Novel Hybrid PSO-SA Algorithms." In Communications in Computer and Information Science, 508–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25002-6_71.

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Peng, Yuzhong, Huasheng Zhao, Jie Li, Xiao Qin, Jianping Liao, and Zhiping Liu. "A Soft Computing-Based Daily Rainfall Forecasting Model Using ELM and GEP." In Proceedings in Adaptation, Learning and Optimization, 328–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_35.

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Malyse, Majoumo Christelle. "Rainfall Variability and Adaptation of Tomatoes Farmers in Santa: Northwest Region of Cameroon." In African Handbook of Climate Change Adaptation, 699–711. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_138.

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AbstractThe Santa agrarian basin being one of the main market gardening basins in Cameroon and one of the producers of tomatoes in the country is vulnerable to the impact of rainfall variability. The spatiotemporal variability of rainfall through the annual, monthly, and daily fluctuations has greatly affected the market gardening sector in general and tomatoes production in particular. Thus, given rise to the research topic “Rainfall variability and adaptation of tomatoes farmers in Santa North west region of Cameroon,” its principal objective is to contribute to better understanding of the recent changes occurring in tomatoes production and productivity in Santa. To attain this objective, a principal hypothesis was formulated that rainfall variability instead of unnatural conditions or human constraints justifies changes observed in tomatoes production in Santa and resulting adaptation strategies developed by peasants and stakeholders.Our study came out with several findings, among which includes rainfall events in Santa fluctuate in time and in space with reduction in the number of rainy day and increase in the intensity of rainfall events causing soil erosion, infertility, and frequent crop diseases, insects, and pests. Extreme events such as drought and flooding have equally become frequent in the area especially during the different cycles of tomatoes production disrupting the agricultural calendar and causing crop failure and decrease in yields with Pearson’s correlation of 0.017. This positive value shows that there is a relationship between annual rainfall and tomatoes output in Santa. Tomatoes farmers in Santa are struggling to adapt locally to this situations, but their efforts are still limited especially due to their low level of education and poverty. Finally, it was seen that the output of tomatoes over the years in Santa has a strong correlation with rainfall. Based on the findings of this study, the government is called upon to assist farmers in their adaptation options.
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Conference papers on the topic "Daily rainfall"

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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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"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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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. Calgary,AB,Canada: ACTAPRESS, 2016. http://dx.doi.org/10.2316/p.2016.836-014.

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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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"Statistical downscaling of daily rainfall for hydrological impact assessment." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.f6.fu.

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"Filling gaps in daily rainfall data: a statistical approach." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand, 2013. http://dx.doi.org/10.36334/modsim.2013.a9.hasan.

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Zhan, Choujun, Fujian Wu, Zhengdong Wu, and Chi K. Tse. "Daily Rainfall Data Construction and Application to Weather Prediction." In 2019 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2019. http://dx.doi.org/10.1109/iscas.2019.8702124.

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Sari, Yuana Ratna, Esmeralda Contessa Djamal, and Fikri Nugraha. "Daily Rainfall Prediction Using One Dimensional Convolutional Neural Networks." In 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE). IEEE, 2020. http://dx.doi.org/10.1109/ic2ie50715.2020.9274572.

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Srivastava, P. K., Saha Dauji, Kapilesh Bhargava, K. Agarwal, and S. Basu. "Simulation of Maximum Hourly Rainfall from Daily Rainfall of Varied Durations for a Typical Monsoon." In 5th International Congress on Computational Mechanics and Simulation. Singapore: Research Publishing Services, 2014. http://dx.doi.org/10.3850/978-981-09-1139-3_152.

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Xu, Qin, Liliang Ren, Zhongbo Yu, Bang Yang, and Guizuo Wang. "Rainfall-Runoff Modeling at Daily Scale with Artificial Neural Networks." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.559.

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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.), August 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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