Abstract
Mountainous terrain poses a challenge in estimating rainfall, because of high variability, both in space and time, associated with the mesoscale process. Accurate rainfall data are essential for various hydrological analyses and studies. A well-designed network with optimal rain gauge density provides critical input for designing, managing, and operating projects focused on water resources management. A dense rain gauge network can help capture the spatiotemporal variations in precipitation. Traditionally, rain gauge networks are designed based on accessibility and economic constraints. Studies have found that networks designed using traditional techniques are sub-optimal at monitoring extreme weather events like flash floods, especially in mountainous terrain. In comparison, satellite and radar-based rainfall estimates have higher spatiotemporal accuracies, making them very effective for monitoring local weather events. However, these alternate datasets require bias correction through ground observations due to sensor and modeling errors. This review evaluates the various techniques for designing optimal rain gauge networks and their effectiveness in capturing hyperlocal extreme weather events in mountainous regions. Our goal is to analyze existing networks in Arizona (USA), Switzerland, Peru, Iran, Taiwan, England, and a sparse network in the Himalayan region, as well as suggest improvements to current approaches employed in these terrains. This will aid in the development of more robust methodologies and the improvement of extreme weather prediction skills. Finally, we conclude this review with the future direction of rain gauge network design for sparse data regions.
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Data analyzed in this study were a re-analysis of existing data, which are openly available at locations cited in the reference section.
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References
Adhikary SK, Yilmaz AG, Muttil N (2015) Optimal design of rain gauge network in the middle yarra river catchment australia. Hydrol Process 29(11):2582–2599. https://doi.org/10.1002/hyp.10389
Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J (2003) Guidelines on Climate Metadata and Homogenization, WCDMP-No. 53, WMO-TD No. 1186. World Meteorological Organization, Geneva, p 55
Andiego G, Waseem M, Usman M, Mani N (2017) The influence of rain gauge network density on the performance of a hydrological model. Comput Water, Energ Environ Eng 7(1):27–50. https://doi.org/10.4236/cweee.2018.81002
Arnaud P, Bouvier C, Cisneros L, Dominguez R (2002) Influence of rainfall spatial variability on flood prediction. J Hydrol 260(1–4):216–230. https://doi.org/10.1016/S0022-1694(01)00611-4
Bárdossy A, Das T (2008) Influence of rainfall observation network on model calibration and application. Hydrol Earth Syst Sci 12(1):77–89. https://doi.org/10.5194/hess-12-77-2008
Barros A, Joshi M, Putkonen J, Burbank D (2000) A study of 1999 monsoon rainfall in a mountainous region in central Nepal using TRMM products and rain gauge observations. Geophys Res Lett 27:3683–3686. https://doi.org/10.1029/2000GL011827
Begert M, Schlegel T, Kirchhofer W (2005) Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. Int J Climatol 25(1):65–80
Bookhagen B, Burbank DW (2010) Toward a complete Himalayan hydrological budget: spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J Geophys Res Earth Surf 115(F3):F03019. https://doi.org/10.1029/2009JF001426
Borga M, Anagnostou EN, Blöschl G, Creutin JD (2011) Flash flood forecasting, warning and risk management: the HYDRATE project. Environ Sci Policy 14(7):834–844. https://doi.org/10.1016/j.envsci.2011.05.017
Bradley AA, Peters-Lidard C, Nelson BR, Smith JA, Young CB (2002) Raingage network design using NEXRAD precipitation estimates 1. J Am Water Resour Assoc 38(5):1393–1407
Buytaert W, Celleri R, Willems P, De Bievre B, Wyseure G (2006) Spatial and temporal rainfall variability in mountainous areas: a case study from the south Ecuadorian Andes. J Hydrol 329(3–4):413–421. https://doi.org/10.1016/j.jhydrol.2006.02.031
Caussinus H, Mestre O (2004) Detection and correction of artificial shifts in climate series. Appl Stat 53:405–425
Chen H, Guo S, Xu CY, Singh VP (2007) Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin. J Hydrol 344(3–4):171–184. https://doi.org/10.1016/j.jhydrol.2007.06.034
Cheng K-S, Lin Y-C, Liou J-J (2008) Rain-gauge network evaluation and augmentation using geostatistics. Hydrol Process 22:2554–2564. https://doi.org/10.1002/hyp.6851
Dai Q, Bray M, Zhuo L, Islam T, Han D (2017) A scheme for rain gauge network design based on remotely sensed rainfall measurements. J Hydrometeorol 18(2):363–379. https://doi.org/10.1175/JHM-D-16-0136.1
di Wendy Zichao, Maggioni Viviana, Mei Yiwen, Vazquez Marilyn, Houser Paul, Emelianenko Maria (2020) Centroidal Voronoi tessellation based methods for optimal rain gauge location prediction. J Hydrol 584:124651. https://doi.org/10.1016/j.jhydrol.2020.124651
Dimri AP, Chevuturi A, Niyogi D, Thayyen RJ, Ray K, Tripathi SN, Pandey AK, Mohanty UC (2017) Cloudbursts in Indian Himalayas: a review. Earth-Sci Rev 168:1–23. https://doi.org/10.1016/j.earscirev.2017.03.006
Domonkos P, Poza R, Efthymiadis D (2011) Newest developments of ACMANT. Adv Sci Res 6:7–11
Espinoza Villar JC, Ronchail J, Guyot JL, Cochonneau G, Naziano F, Lavado W, De Oliveira E, Pombosa R, Vauchel P (2009) Spatio-temporal rainfall variability in the Amazon basin countries (Brazil, Peru, Bolivia, Colombia, and Ecuador). Int J Climatol 29(11):1574–1594
Fox J, Weisberg S (2011) A precipitation climatology of the Alps from high-resolution rain-gauge observations. Int J Climatol 18:873–900
Fujihara Y, Tanaka K, Watanabe T, Nagano T, Kojiri T (2008) Assessing the impacts of climate change on the water resources of the Seyhan River Basin in Turkey: use of dynamically downscaled data for hydrologic simulations. J Hydrol 353(1–2):33–48. https://doi.org/10.1016/j.jhydrol.2008.01.024
Gaume E, Bain V, Bernardara P, Newinger O, Barbuc M, Bateman A, Blaškovičová L, Blöschl G, Borga M, Dumitrescu A, Daliakopoulos I (2009) A compilation of data on European flash floods. J Hydrol 367(1–2):70–78. https://doi.org/10.1016/j.jhydrol.2008.12.028
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press on Demand
Grimes DIF, Pardo-Iguzquiza E, Bonifacio R (1999) Optimal areal rainfall estimation using raingauges and satellite data. J Hydrol 222(1–4):93–108. https://doi.org/10.1016/S0022-1694(99)00092-X
Gubler S, Hunziker S, Begert M, Croci-Maspoli M, Konzelmann T, Brönnimann S, Schwierz C, Oria C, Rosas G (2017) The influence of station density on climate data homogenization. Int J Climatol 37(13):4670–4683. https://doi.org/10.1002/joc.5114
Gupta H, Sorooshian S, Gao X, Imam B, Hus KL, Bastidas L, Li J, Mahani S (2002) The challenge of predicting flash floods from thunderstorm rainfall. Philos Trans A Math Phys Eng Sci. 360(1796):1363–1371. https://doi.org/10.2307/3066446
Hanna E (1995) How effective are tipping-bucket raingauges? A Review Weather 50(10):336–342. https://doi.org/10.1002/j.1477-8696.1995.tb05501.x
Hughes DA (2006) Comparison of satellite rainfall data with observations from gauging station networks. J Hydrol 327(3–4):399–410. https://doi.org/10.1016/j.jhydrol.2005.11.041
Jena P, Garg S, Azad S (2020) Performance analysis of IMD highresolution gridded rainfall and satellite estimates for detecting cloudburst events over the northwest himalayas. J Hydrometeorol. 21(7):1549–1569. https://doi.org/10.1175/JHM-D-19-0287.1
Jonkman SN (2005) Global perspectives on loss of human life caused by floods. Nat Hazards 34(2):151–175. https://doi.org/10.1007/s11069-004-8891-3
Kagan RL (1972) Planning the spatial distribution of hydrometeorological stations to meet an error criterion. In: Casebook on Hydrological Network Design Practice, III-1.2, vol 324. WMO Publication, Geneva, Switzerland
Khaliq MN, Ouarda TB, Ondo JC, Gachon P, Bobée B (2006) Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: a review. J Hydrol 329(3–4):534–552. https://doi.org/10.1016/j.jhydrol.2006.03.004
Kitchen M, Jackson PM (1993) Weather radar performance at long range simulated and observed. J Appl Meteorol Climatol 32(5):975–985. https://doi.org/10.1175/1520-0450(1993)032%3c0975:WRPALR%3e2.0.CO;2
Krajewski, W., and Georgakakos K., 1994: Editorial. J. Appl. Meteorol. (1988–2005), 33(12), 1381–1381. Retrieved April 9, 2021, from http://www.jstor.org/stable/26186778
Krajewski WF, Smith JA (2002) Radar hydrology: rainfall estimation. Adv Water Resour 25(8–12):1387–1394. https://doi.org/10.1016/S0309-1708(02)00062-3
Lee JH, Jun HD (2014) A methodology for rain gauge network evaluation considering the altitude of rain gauge. J Wet Res. 16(1):113–124. https://doi.org/10.17663/JWR.2014.16.1.113
Liang S, Wang J (2019) Chapter 16 - precipitation advanced remote sensing: terrestrial information extraction and applications. Academic Press. https://doi.org/10.1016/B978-0-12-815826-5.00016-7
López-Moreno JI, Morán-Tejeda E, Vicente-Serrano SM, Bazo J, Azorin-Molina C, Revuelto J, Sanchez-Lorenzo A, Navarro-Serrano F, Aguilar E, Chura O (2016) Recent temperature variability and change in the Altiplano of Bolivia and Peru. Int J Climatol 36(4):1773–1796. https://doi.org/10.1002/joc.4459
Luhunga, P.M., Mutayoba, E. and Ng’ongolo, H.K., 2014. Homogeneity of monthly mean air temperature of the United Republic of Tanzania with HOMER. https://doi.org/10.4236/acs.2014.41010.
Martha TR, Roy P, Govindharaj KB, Kumar KV, Diwakar PG, Dadhwal VK (2015) Landslides triggered by the June 2013 extreme rainfall event in parts of Uttarakhand state India. Landslides. 12(1):135–146. https://doi.org/10.1007/s10346-014-0540-7
Michaud J, Sorooshian S (1994) Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed. Water Resour Res 30(3):593–605. https://doi.org/10.1029/93WR03218
Mishra AK (2013) Effect of rain gauge density over the accuracy of rainfall: a case study over Bangalore. India Springerplus 2(1):1–7. https://doi.org/10.1186/2193-1801-2-311
Mishra AK, Coulibaly P (2009) Developments in hydrometric network design: a review. Rev Geophys 47:RG2001. https://doi.org/10.1029/2007RG000243
Montz BE, Gruntfest E (2002) Flash flood mitigation: recommendations for research and applications. Environ Hazards 4(1):15–22. https://doi.org/10.1016/S1464-2867(02)00011-6
Moore RJ, Jones DA, Cox DR, Isham VS (2000) Design of the HYREX raingauge network. Hydrol Earth Syst Sci 4(4):521–530
Nazaripour H, Mansouri Daneshvar MR (2017) Rain gauge network evaluation and optimal design using spatial correlation approach in arid and semi-arid regions of Iran. Theor Appl Climatol 129:1255–1261. https://doi.org/10.1007/s00704-016-1853-3
New M, Hulme M, Jones P (1999) Representing twentieth-century space–time climate variability. Part I. Development of a 1961–90 mean monthly terrestrial climatology. J Clim 12:829–856
Onof C, Chandler RE, Kakou A, Northrop P, Wheater HS, Isham V (2000) Rainfall modelling using Poisson-cluster processes: a review of developments. Stoch Environ Res Risk Assess 14(6):384–411. https://doi.org/10.1007/s004770000043
Pai DS, Sridhar L, Rajeevan M, Sreejith OP, Satbhai NS, Mukhopadhyay B (2014) Development of a new high spatial resolution (0.25× 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam. 65(1):1–18
Pardo-Igúzquiza E (1998a) Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. J Hydrol 210(1–4):206–220. https://doi.org/10.1016/S0022-1694(98)00188-7
Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N, Plummer N, Torok S, Auer I, Boehm R, Gullett D, Vincent L (1998) Homogeneity adjustments of in situ atmospheric climate data: a review. Int J Climatol 18(13):1493–1517
Picard F, Lebarbier E, Hoebeke M, Rigaill G, Thiam B, Robin S (2011) Joint segmentation, calling and normalization of multiple CGH profiles. Biostatistics 12:413–428
Salio P, Hobouchian MP, Skabar YG, Vila D (2015) Evaluation of high-resolution satellite precipitation estimates over southern South America using a dense rain gauge network. Atmos Res 163:146–161. https://doi.org/10.1016/j.atmosres.2014.11.017
Seiler C, Hutjes RW, Kabat P (2013) Climate variability and trends in Bolivia. J Appl Meteorol Climatol 52(1):130–146
Seo DJ (1998) Real-time estimation of rainfall fields using radar rainfall and rain gage data. J Hydrol 208(1–2):37–52. https://doi.org/10.1016/S0022-1694(98)00141-3
Seo DJ, Breidenbach JP, Johnson ER (1999) Real-time estimation of mean field bias in radar rainfall data. J Hydrol 223(3–4):131–147. https://doi.org/10.1016/S0022-1694(99)00106-7
Smith JA, Seo DJ, Baeck ML, Hudlow MD (1996) An intercomparison study of NEXRAD precipitation estimates. Water Resour Res 32(7):2035–2045. https://doi.org/10.1029/96WR00270
Sreedharan KE, James EJ (1983) Design of rain gauge network for the Chaliyar River basin. J Inst Eng 64:170–174
Trewin B (2010) Exposure, instrumentation, and observing practice effects on land temperature measurements. Wiley Interdiscip Rev Clim Change 1(4):490–506. https://doi.org/10.1002/wcc.46
Tsintikidis D, Georgakakos KP, Sperfslage JA, Smith DE, Carpenter TM (2002) Precipitation uncertainty and raingauge network design within Folsom Lake watershed. J. Hydrol. Eng. 7(2):175–184. https://doi.org/10.1061/(ASCE)1084-0699(2002)7:2(175)
Tsintikidis D, Georgakakos KP, Sperfslage JA, Smith DE, Carpenter TM (2002b) Precipitation uncertainty and raingauge network design within Folsom Lake watershed. J Hydrol Eng 7(2):175–184
Venema VKC, Mestre O, Aguilar E, Auer I, Guijarro JA, Domonkos P, Vertacnik G, Szentimrey T, Stepanek P, Zahradnicek P, Viarre J, Müller-Westermeier G, Lakatos M, Williams CN, Menne MJ, Lindau R, Rasol D, Rustemeier E, Kolokythas K, Marinova T, Andresen L, Acquaotta F, Fratianni S, Cheval S, Klancar M, Brunetti M, Gruber C, Prohom Duran M, Likso T, Esteban P, Brandsma T (2012) Benchmarking homogenization algorithms for monthly data. Clim past 8:89–115. https://doi.org/10.5194/cp-8-89-2012
Vertačnik G, Dolinar M, Bertalanič R, Klančar M, Dvoršek D, Nadbath M (2015) Ensemble homogenization of Slovenian monthly air temperature series. Int J Climatol 35(13):4015–4026. https://doi.org/10.1002/joc.4265
Vicente-Serrano SM, El Kenawy A, Azorin-Molina C, Chura O, Trujillo F, Aguilar E, Martín-Hernández N, López-Moreno JI, Sanchez-Lorenzo A, Morán-Tejeda E, Revuelto J (2016) Average monthly and annual climate maps for Bolivia. J Maps 12(2):295–310. https://doi.org/10.1080/17445647.2015.1014940
Villarini G, Mandapaka PV, Krajewski WF. Moore RJ 2008 Rainfall and sampling uncertainties: a rain gauge perspective. J Geophys. Res. Atmos., 113(D11). doi:https://doi.org/10.1029/2007JD009214
Volkmann TH, Lyon SW, Gupta HV, Troch PA (2010) Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain. Water Resour Res 46(11):46. https://doi.org/10.1029/2010WR009145
Wilson JW, Brandes EA (1979) Radar measurement of rainfall—a summary. Bull Amer Meteor Soc 60:1048–1060. https://doi.org/10.1175/1520-0477(1979)0602.0.CO;2
Xu H, Xu CY, Chen H, Zhang Z, Li L (2013) Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China. J Hydrol 505:1–12. https://doi.org/10.1016/j.jhydrol.2013.09.004
Yatheendradas S Wagener T Gupta H Unkrich C Goodrich D Schaffner M Stewart A 2008 Understanding uncertainty in distributed flash flood forecasting for semiarid regions. Water Resour Res 44(5). https://doi.org/10.1029/2007WR005940
Young CB, Nelson BR, Bradley AA, Smith JA, Peters-Lidard CD, Kruger A, Baeck ML (1999) An evaluation of NEXRAD precipitation estimates in complex terrain. J Geophys Res Atmos J Geophys Res Atmos 104(D16):19691–19703. https://doi.org/10.1029/1999JD900123
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A.S. collected data and wrote the manuscript. S.A designed the study and made corrections. Both the authors have contributed equally to the data analysis and interpretation of the results.
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Highlights
Significant weather changes are common in mountainous regions over short distances. As a result, finding the best placement and density for a rain gauge network that monitors rainfall variations over time is crucial.
Sparse networks in mountainous areas with severe climate gradients frequently have missing and low-quality data. Using a Neural Network (NN) technique, we propose to create high confidence rainfall estimations for complex hilly terrain.
To propose a set of recommendations for improving observational capabilities that will help us develop our understanding of the best rain gauge network design.
To promote well-designed networks that can aid in the obtainability of effective rainfall forecasts and early warning systems for hilly terrains prone to extreme events such as flash floods.
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Suri, A., Azad, S. Optimal placement of rain gauge networks in complex terrains for monitoring extreme rainfall events: a review. Theor Appl Climatol 155, 2511–2521 (2024). https://doi.org/10.1007/s00704-024-04856-3
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DOI: https://doi.org/10.1007/s00704-024-04856-3