Improved Precipitation Information for Hydrological Problem Solving - Focus on Open Data and Simulation

Tero Niemi

Improved Precipitation Information for Hydrological Problem Solving - Focus on Open Data and Simulation

Julkaisun otsikon käännös: Parannettu sadantainformaatio hydrologisessa ongelmanratkaisussa – painopisteinä avoin data ja simulointi

Tero Niemi

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Sadanta näyttelee suurta roolia lähes kaikilla hydrologian osa-alueilla toimien hydrologisten prosessien alkuunpanijana ja keskeisenä ylläpitävänä elementtinä. Erityisesti sadanta-valuntamallit tarvitsevat tarkkaa sadedataa syötetiedoksi tuottaakseen luotettavia valuntatuloksia. Vaatimukset datan laadulle korostuvat kaupunkivaluma-alueilla niiden pienen koon ja äärevän valuntavasteen vuoksi. Viime vuosina avoimen sadedatan määrä on lisääntynyt. Kuitenkin edelleen on tarve sateen simulointimalleille, sillä riippumatta saatavilla olevan (avoimen) datan määrästä mittaaminen kaikissa tilanteissa on mahdotonta. Tässä työssä tutkittiin paremmasta sadantainformaatiosta saatavia hyötyjä hydrologisessa tutkimuksessa keskittymällä seuraaviin tutkimuskysymyksiin: 1) Miten avointa sadedataa voidaan hyödyntää aiempaa kattavammin hydrologisessa tutkimuksessa? 2) Miten sateen simulointia voidaan kehittää kuvaamalla sadekenttien muoto aiempaa realistisemmin? Avoimen säätutka- ja sademittaridatan soveltuvuutta kaupunkihydrologiseen tutkimukseen selvitettiin korkean resoluution sadanta-valuntasimulaatioilla pienillä suomalaisilla valuma-alueilla hyödyntäen sekä avointa sadedataa että valuma-alueilla kerättyä sade- ja virtaamadataa. Sademittaridata antaa hyviä tuloksia, kun mittari on sijoitettu valuma-alueelle tai sen välittömään läheisyyteen. Etäisyyden kasvaessa sademittaridatalla korjattu tutkadata voi olla sademittaria parempi vaihtoehto jopa tilanteissa, joissa tutkittava alue on paljon tutkadatan paikkaresoluutiota pienempi. Sadekenttien muodon määrittämiseksi ja muodon kehityksen seuraamiseksi sadetapahtuman aikana kehitettiin uusi Generalized Scale Invariance -konseptiin pohjautuva menetelmä. Sadekenttien muodon kuvaus lisättiin stokastiseen sateen simulointimalliin, joka mahdollisti aiempaa realistisempien sadekenttien luomisen. Sadekentän muodon vaikutusta valuma-alueen hydrologiseen vasteeseen tutkittiin sadanta-valuntamallin avulla rekonstruoimalla havaittu voimakas sadetapahtuma. Vaikka kenttien muodon kuvauksesta stokastisessa sademallissa oli hyötyä halutun sademäärän tuottamisessa, muodon vaikutus vaimeni tutkittaessa valuma-alueen vastetta sateeseen. Työn tulokset tarjoavat perustan sadeprosessien ymmärrykselle karkeasta tutkamittakaavasta yksityiskohtaiseen kaupunkimittakaavaan avoimen datan entistä kokonaisvaltaisemman hyödyntämisen kautta. Esimerkiksi sateen ominaisuuksien tutkiminen eri mittakaavoissa helpottuu. Menetelmä sadekenttien muodon määrittämiseen auttaa aiempaa parempien stokastisten sademallien kehityksessä entistä realistisempien sadekenttien luomiseksi tilanteisiin, joista ei ole saatavilla mitattua dataa.

Julkaisun otsikon käännös	Parannettu sadantainformaatio hydrologisessa ongelmanratkaisussa – painopisteinä avoin data ja simulointi
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Koivusalo, Harri, Vastuuprofessori Kokkonen, Teemu, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-60-7512-9
Sähköinen ISBN	978-952-60-7511-2
Tila	Julkaistu - 2017
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

avoin data
GSI
kaupunkihydrologia
sadanta
sademittari
sateen simulointi
suuunnittelusade
säätutka

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-60-7511-2Lisenssi: Määrittelemätön

Student prize: Aalto University School of Engineering Award for Best Doctoral Dissertation 2017
Niemi, Tero (Student prize recipient), 28 helmik. 2018
Palkinto: Palkinto tai huomionosoitus tuotoksesta

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title = "Improved Precipitation Information for Hydrological Problem Solving - Focus on Open Data and Simulation",

abstract = "Precipitation acts as the starting point and the driving force in nearly every field of hydrology. Rainfall-runoff models in particular require accurate rainfall input data in order to provide accurate runoff results. The data requirements are emphasized in urban environments due to small sizes and rapid runoff responses of urban catchments. In recent years, the amount of open precipitation data has increased due to changes in governmental policies and legislations. However, since measuring everything is ultimately impossible, there remains a need for precipitation simulation models no matter how much data is (openly) available. This thesis studied the benefits of improved precipitation information in hydrological assessments by addressing the following questions: 1) How can open precipitation data be utilized more extensively in hydrological research? 2) How can simulation models be improved via more realistic spatial description of precipitation fields? The feasibility of open weather radar and rain gauge data for urban hydrological assessments was studied by conducting high-resolution rainfall-runoff simulations at small Finnish catchments utilizing open precipitation data and rainfall-runoff data collected at the catchments. The open gauge data performs well, given that the gauge is located at the studied catchment or close to it. When the distance to the gauge increases, gauge corrected radar data can give superior results even when the studied catchment is much smaller than the radar data spatial resolution. A new method was developed to quantify the anisotropic shape of precipitation fields and the evolution of the shape during storm events utilizing the linear Generalized Scale Invariance formalism. The shape description was implemented into a state-of-the art stochastic precipitation generator to provide a parsimonious way for a more realistic description of precipitation features. Impact of the field shape on the catchment response was studied by conducting rainfall-runoff simulations replicating an extreme storm event. While the description of anisotropy allows for creating stochastic precipitation events that produce the desired rainfall accumulations without sacrificing other event characteristics such as storm advection or storm evolution, its effect was attenuated when exploring the catchment response. This thesis lays groundwork for future advances in understanding the precipitation process from coarse radar scales to detailed urban scales by utilizing the open precipitation data more comprehensively. Amongst other things, the open data enables studying precipitation features across scales. The presented anisotropy quantification method allows for building better stochastic precipitation simulation models capable of reproducing more realistic precipitation fields for situations where measurement data, open or not, is unavailable.",

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author = "Tero Niemi",

year = "2017",

language = "English",

isbn = "978-952-60-7512-9",

series = "Aalto University publication series DOCTORAL DISSERTATIONS",

publisher = "Aalto University",

number = "126",

address = "Finland",

school = "Aalto University",

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TY - BOOK

T1 - Improved Precipitation Information for Hydrological Problem Solving - Focus on Open Data and Simulation

AU - Niemi, Tero

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N2 - Precipitation acts as the starting point and the driving force in nearly every field of hydrology. Rainfall-runoff models in particular require accurate rainfall input data in order to provide accurate runoff results. The data requirements are emphasized in urban environments due to small sizes and rapid runoff responses of urban catchments. In recent years, the amount of open precipitation data has increased due to changes in governmental policies and legislations. However, since measuring everything is ultimately impossible, there remains a need for precipitation simulation models no matter how much data is (openly) available. This thesis studied the benefits of improved precipitation information in hydrological assessments by addressing the following questions: 1) How can open precipitation data be utilized more extensively in hydrological research? 2) How can simulation models be improved via more realistic spatial description of precipitation fields? The feasibility of open weather radar and rain gauge data for urban hydrological assessments was studied by conducting high-resolution rainfall-runoff simulations at small Finnish catchments utilizing open precipitation data and rainfall-runoff data collected at the catchments. The open gauge data performs well, given that the gauge is located at the studied catchment or close to it. When the distance to the gauge increases, gauge corrected radar data can give superior results even when the studied catchment is much smaller than the radar data spatial resolution. A new method was developed to quantify the anisotropic shape of precipitation fields and the evolution of the shape during storm events utilizing the linear Generalized Scale Invariance formalism. The shape description was implemented into a state-of-the art stochastic precipitation generator to provide a parsimonious way for a more realistic description of precipitation features. Impact of the field shape on the catchment response was studied by conducting rainfall-runoff simulations replicating an extreme storm event. While the description of anisotropy allows for creating stochastic precipitation events that produce the desired rainfall accumulations without sacrificing other event characteristics such as storm advection or storm evolution, its effect was attenuated when exploring the catchment response. This thesis lays groundwork for future advances in understanding the precipitation process from coarse radar scales to detailed urban scales by utilizing the open precipitation data more comprehensively. Amongst other things, the open data enables studying precipitation features across scales. The presented anisotropy quantification method allows for building better stochastic precipitation simulation models capable of reproducing more realistic precipitation fields for situations where measurement data, open or not, is unavailable.

AB - Precipitation acts as the starting point and the driving force in nearly every field of hydrology. Rainfall-runoff models in particular require accurate rainfall input data in order to provide accurate runoff results. The data requirements are emphasized in urban environments due to small sizes and rapid runoff responses of urban catchments. In recent years, the amount of open precipitation data has increased due to changes in governmental policies and legislations. However, since measuring everything is ultimately impossible, there remains a need for precipitation simulation models no matter how much data is (openly) available. This thesis studied the benefits of improved precipitation information in hydrological assessments by addressing the following questions: 1) How can open precipitation data be utilized more extensively in hydrological research? 2) How can simulation models be improved via more realistic spatial description of precipitation fields? The feasibility of open weather radar and rain gauge data for urban hydrological assessments was studied by conducting high-resolution rainfall-runoff simulations at small Finnish catchments utilizing open precipitation data and rainfall-runoff data collected at the catchments. The open gauge data performs well, given that the gauge is located at the studied catchment or close to it. When the distance to the gauge increases, gauge corrected radar data can give superior results even when the studied catchment is much smaller than the radar data spatial resolution. A new method was developed to quantify the anisotropic shape of precipitation fields and the evolution of the shape during storm events utilizing the linear Generalized Scale Invariance formalism. The shape description was implemented into a state-of-the art stochastic precipitation generator to provide a parsimonious way for a more realistic description of precipitation features. Impact of the field shape on the catchment response was studied by conducting rainfall-runoff simulations replicating an extreme storm event. While the description of anisotropy allows for creating stochastic precipitation events that produce the desired rainfall accumulations without sacrificing other event characteristics such as storm advection or storm evolution, its effect was attenuated when exploring the catchment response. This thesis lays groundwork for future advances in understanding the precipitation process from coarse radar scales to detailed urban scales by utilizing the open precipitation data more comprehensively. Amongst other things, the open data enables studying precipitation features across scales. The presented anisotropy quantification method allows for building better stochastic precipitation simulation models capable of reproducing more realistic precipitation fields for situations where measurement data, open or not, is unavailable.

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KW - GSI

KW - open data

KW - precipitation

KW - precipitation simulation

KW - rainfall

KW - rain gauge

KW - urban hydrology

KW - weather radar

KW - avoin data

KW - GSI

KW - kaupunkihydrologia

KW - sadanta

KW - sademittari

KW - sateen simulointi

KW - suuunnittelusade

KW - säätutka

KW - design storm

KW - GSI

KW - open data

KW - precipitation

KW - precipitation simulation

KW - rainfall

KW - rain gauge

KW - urban hydrology

KW - weather radar

M3 - Doctoral Thesis

SN - 978-952-60-7512-9

T3 - Aalto University publication series DOCTORAL DISSERTATIONS

PB - Aalto University

ER -

Improved Precipitation Information for Hydrological Problem Solving - Focus on Open Data and Simulation

Abstrakti

Tutkimusalat

Pääsy asiakirjaan

Sormenjälki

Palkinnot

Student prize: Aalto University School of Engineering Award for Best Doctoral Dissertation 2017

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