Modellert vannavrenning sensitivitet for snø parametrisering -en studie for Øvre Beas Basin i Himachal Pradesh, India

For elver med utspring i Himalaya , er snø og bresmelting store bidragsytere til vannføring . Tidspunktet og størrelsen er viktig for tilgjengeligheten av vann i disse nedbørsfeltene. For hydrologiske modellering er ulike metoder i bruk for å beregne snøsmelte. Målet med denne studien er å evaluere følsomheten i vannbalanse, avrenning i særdeleshet , for hydrologiske modeller med ulik kompleksitet i snøsmelting rutinen . Tre spesifikke deloppgave ble definert . 1 ) Etablere en fordelt hydrologisk modell for Øvre Beas nedbørfelt basert på lokale og globale datasett , 2 ) Utføre en systematisk analyse av modell sensitivitet i forhold til avrenning og snø / isbre dekket området , for tre modeller med varierende kompleksitet i snøsmelte rutinen og 3 ) Analyser variasjonen i sesong avrenning for Øvre Beas nedbørfelt . Tre modeller ; en temperatur-indeks -modell , en forbedret temperatur - indeks modell inkluderer en strålings ledd og en energibalansemodell, ble satt opp og anvendt for analyse. Input data er observert vannføring, nedbør , luftfuktighet og temperatur . Globale datasett ble brukt som input for vind og kortbølget stråling . Det viste seg å være vanskelig å definere et mønster mellom modellenes ytelse. Høy korrelasjonskoeffisienter for alle nedbørfeøt , høye Nash - Sutcliffe effektivitet for kalibrering , lavere for validering. Det var store volum avvik. I nord forårsaker Manali store avvik også nedstrøms . Alle modellene hadde problemer med forutsi den negative utviklingen i vannføring, observert for de høyere liggende nedbørfelt, Sainj og Parvati, i senere deler av perioden. Romlig analyse viste at modellene interpolerer store nedbørsmengder i høyt liggende områder og nord, Manali . Meget høy avrenning finnes nær snøakkumulerings områder . Reduksjon i magasiner opp til 6000 mm / år , tyder på at overestimering i avrenning kan forklares i disse områder. Det var noen forskjeller mellom modellprediksjoner , en generell tendens var at for årlig evaluering den forbedrede temperatur - indeks modellen hadde en høyere avrenning prediksjon for de fleste nedbørfelt bortsett Tirthan hvor full energi -modellen hadde høyest skjevhet

Hydrological ensemble prediction systems: From evaluating daily streamflow forecasts to exploring the impact of selected flood events in a future climate

Worldwide, floods are the natural disaster causing the highest economic losses and casualties. Flood warnings are issued to inform of potential threats, and allow flood managing authorities time to take appropriate actions to prevent and reduce the impact. Flood forecasts are often uncertain, and statistical techniques are used to improve the forecasts. Climate studies indicate that flood magnitude and frequency will change. More knowledge to improve flood forecasts and to assess the outcome of future floods is needed. The aim of this thesis is to provide improved operational flood forecasts and raise awareness of future floods. An experimental setup of the Norwegian flood forecasting systems was used and included 145 river catchments. First, techniques to improve flood forecasts were applied and revealed seasonal and regional patterns in flood forecast performance. By exposing areas with low forecast performance, targeted and tailored corrections can be applied. Secondly, extreme flood events caused by atmospheric rivers in a future climate were compared to the most extreme events of the present climate. The future extreme events showed an increase in both flood magnitude and more rivers flooded concurrently

Streamflow forecast sensitivity to air temperature forecast calibration

In this study, we used meteorological ensemble forecasts as input to hydrological models to quantify the uncertainty in forecasted streamflow, with a particular focus on the effect of temperature forecast calibration on the streamflow ensemble forecast skill. In catchments with seasonal snow cover, snowmelt is an important flood-generating process. Hence, high-quality air temperature data are important to accurately forecast streamflows. The sensitivity of streamflow ensemble forecasts to the calibration of temperature ensemble forecasts was investigated using ensemble forecasts of temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) covering a period of nearly 3 years, from 1 March 2013 to 31 December 2015. To improve the skill and reduce biases of the temperature ensembles, the Norwegian Meteorological Institute (MET Norway) provided parameters for ensemble calibration, derived using a standard quantile mapping method where HIRLAM, a high-resolution regional weather prediction model, was used as reference. A lumped HBV (Hydrologiska Byråns Vattenbalansavdelning) model, distributed on 10 elevation zones, was used to estimate the streamflow. The results show that temperature ensemble calibration affected both temperature and streamflow forecast skill, but differently depending on season and region. We found a close to 1:1 relationship between temperature and streamflow skill change for the spring season, whereas for autumn and winter large temperature skill improvements were not reflected in the streamflow forecasts to the same degree. This can be explained by streamflow being less affected by subzero temperature improvements, which accounted for the biggest temperature biases and corrections during autumn and winter. The skill differs between regions. In particular, there is a cold bias in the forecasted temperature during autumn and winter along the coast, enabling a large improvement by calibration. The forecast skill was partly related to elevation differences and catchment area. Overall, it is evident that temperature forecasts are important for streamflow forecasts in climates with seasonal snow cover

The role of spatial and temporal model resolution in a flood event storyline approach in western Norway

We apply a physical climate storyline approach to an autumn flood event in the West Coast of Norway caused by an atmospheric river to demonstrate the value and challenges of higher spatial and temporal resolution in simulating flood impacts. We use a modelling chain whose outputs are familiar and used operationally, for example to issue flood warnings. With two different versions of a hydrological model, we show that (1) the higher spatial resolution between the global and regional climate model is necessary to realistically simulate the high spatial variability of precipitation in this mountainous region and (2) only with hourly data are we able to capture the fast flood-generating processes leading to the peak streamflow. The higher resolution regional atmospheric model captures the fact that with the passage of an atmospheric river, some valleys receive high amounts of precipitation and others not, while the coarser resolution global model shows uniform precipitation in the whole region. Translating the event into the future leads to similar results: while in some catchments, a future flood might be much larger than a present one, in others no event occurs as the atmospheric river simply does not hit that catchment. The use of an operational flood warning system for future events is expected to facilitate stakeholder engagement