Flood frequency analyses based on streamflow time series, historical information and paleohydrological data

Beregning av dimensjonerende flomverdier (20-1000-årsflommer) er et krav ved bygging av dammer, infrastrukturer og arealplanlegging. Robuste og pålitelige beregninger er viktig for korrekte risikovurderinger og for å ta best mulige beslutninger. En av de anbefalte metodene for å beregne dimensjonerende flommer er basert på årlige maksimalverdier fra en tidsserie med vannføring. Disse seriene tilpasses så en statistisk fordeling, vanligvis den generelle ekstremverdifordelingen (GEV-fordeling). En utfordring med denne tilnærmingen er at man som regel har relativt korte tidsserier med vannføring, de fleste er kortere enn 50 år. Estimat av en 200- eller 1000-årsflom er derfor basert på ekstrapolering av data, noe som inneholder store usikkerheter i beregningene. For å utvide datagrunnlaget for estimering av dimensjonerende flommer er det i denne studien benyttet informasjon om flommer fra før systematiske observasjoner av vannføring ble igangsatt; (i) historiske flomkilder (f.eks. flomsteiner) og (ii) paleohydrologi – flominformasjon fra sedimentprøver er undersøkt. I dette studiet er kjerneprøve FLS113 (18.0 cm lang, representerer omtrent de siste 65 årene) og FLS213 (516 cm lang, representerer trolig de siste 10 000 årene) fra Flyginnsjøen brukt. Ved å studere sedimentene i FLS113 kan man finne igjen karakteristiske flomlag for når Glomma var i flom og vannføringen oversteg terskelen, som i dag er beregnet til 1500 m3/s. Resultatene viser at det er en sammenheng mellom bifurkasjonshendelser i Glomma ved Kongsvinger og sedimentlag i kjerneprøver fra Flyginnsjøen. Dette gir grunnlag for å bruke paleohydrologi til å forlenge flomhistorien og dermed basere flomfrekvensanalysen på lengre datagrunnlag utover det instrumentelle målinger kan gi. De tidligste instrumentelle målingene startet rundt 1870. Historisk informasjon brukt i dette studiet legger til ni flommer i perioden 1650-1850 og paleohydrologisk informasjon legger til 155 flommer siden år 1200. Nye flomfrekvenskurver er laget på bakgrunn av denne utvidede flominformasjonen og man kan, ved å sammenligne disse med tidligere flomfrekvenskurver, se at det utgjør en forskjell. I diskusjons-kapittelet diskuteres det hvorvidt de ulike informasjonskildene og lengden på perioden med informasjon, har av betydning for flomfrekvensanalysene. Resultatene viser generelt at ved å inkludere historiske flomhendelser øker vannføringen for forventede gjentaksintervaller, mens ved å inkludere paleohydrologisk flomdata minker vannføringen for forventede gjentaksintervaller, sammenlignet med flomfrekvensanalyser basert på systematisk data. Bruken av historisk informasjon i flomfrekvensanalyse anses å være av verdi, da beregningene blir gjort på utvidet grunnlag om flomhistorien. Spesielt er det nyttig i beregninger av lengre gjentaksintervaller der det kun finnes korte instrumentelle måleserier. Å bruke paleohydrologisk flominformasjon i flomfrekvensanalyse er en nyere og meget spennende metode som det trengs å forskes mer på

New flood frequency estimates for the largest river in Norway based on the combination of short and long time series

The Glomma River is the largest in Norway, with a catchment area of 154 450 km2. People living near the shores of this river are frequently exposed to destructive floods that impair local cities and communities. Unfortunately, design flood predictions are hampered by uncertainty since the standard flood records are much shorter than the requested return period and the climate is also expected to change in the coming decades. Here we combine systematic historical and paleo information in an effort to improve flood frequency analysis and better understand potential linkages to both climate and non-climatic forcing. Specifically, we (i) compile historical flood data from the existing literature, (ii) produce high-resolution X-ray fluorescence (XRF), magnetic susceptibility (MS), and computed tomography (CT) scanning data from a sediment core covering the last 10 300 years, and (iii) integrate these data sets in order to better estimate design floods and assess non-stationarities. Based on observations from Lake Flyginnsjøen, receiving sediments from Glomma only when it reaches a certain threshold, we can estimate flood frequency in a moving window of 50 years across millennia revealing that past flood frequency is non-stationary on different timescales. We observe that periods with increased flood activity (4000–2000 years ago and <1000 years ago) correspond broadly to intervals with lower than average summer temperatures and glacier growth, whereas intervals with higher than average summer temperatures and receding glaciers overlap with periods of reduced numbers of floods (10 000 to 4000 years ago and 2200 to 1000 years ago). The flood frequency shows significant non-stationarities within periods with increased flood activity, as was the case for the 18th century, including the 1789 CE (“Stor-Ofsen”) flood, the largest on record for the last 10 300 years at this site. Using the identified non-stationarities in the paleoflood record allowed us to estimate non-stationary design floods. In particular, we found that the design flood was 23 % higher during the 18th century than today and that long-term trends in flood variability are intrinsically linked to the availability of snow in late spring linking climate change to adjustments in flood frequency

New flood frequency estimates for the largest river in Norway based on the combination of short and long time series

The Glomma River is the largest in Norway, with a catchment area of 154 450 km2. People living near the shores of this river are frequently exposed to destructive floods that impair local cities and communities. Unfortunately, design flood predictions are hampered by uncertainty since the standard flood records are much shorter than the requested return period and the climate is also expected to change in the coming decades. Here we combine systematic historical and paleo information in an effort to improve flood frequency analysis and better understand potential linkages to both climate and non-climatic forcing. Specifically, we (i) compile historical flood data from the existing literature, (ii) produce high-resolution X-ray fluorescence (XRF), magnetic susceptibility (MS), and computed tomography (CT) scanning data from a sediment core covering the last 10 300 years, and (iii) integrate these data sets in order to better estimate design floods and assess non-stationarities. Based on observations from Lake Flyginnsjøen, receiving sediments from Glomma only when it reaches a certain threshold, we can estimate flood frequency in a moving window of 50 years across millennia revealing that past flood frequency is non-stationary on different timescales. We observe that periods with increased flood activity (4000–2000 years ago and <1000 years ago) correspond broadly to intervals with lower than average summer temperatures and glacier growth, whereas intervals with higher than average summer temperatures and receding glaciers overlap with periods of reduced numbers of floods (10 000 to 4000 years ago and 2200 to 1000 years ago). The flood frequency shows significant non-stationarities within periods with increased flood activity, as was the case for the 18th century, including the 1789 CE (“Stor-Ofsen”) flood, the largest on record for the last 10 300 years at this site. Using the identified non-stationarities in the paleoflood record allowed us to estimate non-stationary design floods. In particular, we found that the design flood was 23 % higher during the 18th century than today and that long-term trends in flood variability are intrinsically linked to the availability of snow in late spring linking climate change to adjustments in flood frequency

New flood frequency estimates for the largest river in Norway based on the combination of short and long time series

Abstract. The Glomma River is the largest in Norway, with a catchment area of 154 450 km2. People living near the shores of this river are frequently exposed to destructive floods that impair local cities and communities. Unfortunately, design flood predictions are hampered by uncertainty since the standard flood records are much shorter than the requested return period and the climate is also expected to change in the coming decades. Here we combine systematic historical and paleo information in an effort to improve flood frequency analysis and better understand potential linkages to both climate and non-climatic forcing. Specifically, we (i) compile historical flood data from the existing literature, (ii) produce high-resolution X-ray fluorescence (XRF), magnetic susceptibility (MS), and computed tomography (CT) scanning data from a sediment core covering the last 10 300 years, and (iii) integrate these data sets in order to better estimate design floods and assess non-stationarities. Based on observations from Lake Flyginnsjøen, receiving sediments from Glomma only when it reaches a certain threshold, we can estimate flood frequency in a moving window of 50 years across millennia revealing that past flood frequency is non-stationary on different timescales. We observe that periods with increased flood activity (4000–2000 years ago and &lt;1000 years ago) correspond broadly to intervals with lower than average summer temperatures and glacier growth, whereas intervals with higher than average summer temperatures and receding glaciers overlap with periods of reduced numbers of floods (10 000 to 4000 years ago and 2200 to 1000 years ago). The flood frequency shows significant non-stationarities within periods with increased flood activity, as was the case for the 18th century, including the 1789 CE (“Stor-Ofsen”) flood, the largest on record for the last 10 300 years at this site. Using the identified non-stationarities in the paleoflood record allowed us to estimate non-stationary design floods. In particular, we found that the design flood was 23 % higher during the 18th century than today and that long-term trends in flood variability are intrinsically linked to the availability of snow in late spring linking climate change to adjustments in flood frequency