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  • 1.
    Olsson, Jonas
    et al.
    SMHI, Research Department, Hydrology.
    Södling, Johan
    SMHI, Professional Services.
    Wetterhall, Fredrik
    SMHI, Research Department, Hydrology.
    Högupplösta nederbördsdata för hydrologisk modellering: en förstudie2013Report (Other academic)
    Abstract [en]

    Hydrological modeling at SMHI is generally done with a daily time step. However, today simulation and forecasting with a shorter time step is possible, through a spatially highly resolved hydrological model (S-HYPE) as well as high-resolution input data. In this preliminary study, different types of observation-based, high-resolution input data (mainly precipitation) have been invented, compiled and evaluated at different temporal and spatial scales: automatic stations, PTHBV, MESAN, radar data. A new product called PTHBV-radar has been developed by distributing the daily precipitation in PTHBV over the day using radar observations. The different types of data were tested in hydrological simulation by the HYPE model in a small catchment.For long accumulation times (year, month) PTHBV gives higher values than MESAN. Radar data have distinct artifacts, e.g. in the border between radars, but regional mean values agree with other sources. Concerning 1-h precipitation, the overall agreement with automatic station data is best in MESAN, followed by PTHBV-radar and radar. The spatial smoothing in MESAN however generates lower values of maximum intensities, in this respect PTHBV-radar and radar are closer to the station data.The hydrological 1-h simulations with MESAN and PTHBV-radar as input data improved performance evaluated on a daily basis, as compared with a reference simulation with PTHBV as input data. Using radar precipitation as input generated an overestimated discharge. The differences between 1-d and 1-h simulations were illustrated for single high flows and in terms of maximum daily values.

  • 2. Parkes, B. L.
    et al.
    Wetterhall, Fredrik
    SMHI, Research Department, Hydrology.
    Pappenberger, F.
    He, Y.
    Malamud, B. D.
    Cloke, H. L.
    Assessment of a 1-hour gridded precipitation dataset to drive a hydrological model: a case study of the summer 2007 floods in the Upper Severn, UK2013In: HYDROLOGY RESEARCH, ISSN 1998-9563, Vol. 44, no 1, p. 89-105Article in journal (Refereed)
    Abstract [en]

    In this study a gridded hourly 1-km precipitation dataset for a meso-scale catchment (4,062 km(2)) of the Upper Severn River, UK was constructed using rainfall radar data to disaggregate a daily precipitation (rain gauge) dataset. The dataset was compared to an hourly precipitation dataset created entirely from rainfall radar data. Results found that when assessed against gauge readings and as input to the Lisflood-RR hydrological model, the rain gauge/radar disaggregated dataset performed the best suggesting that this simple method of combining rainfall radar data with rain gauge readings can provide temporally detailed precipitation datasets for calibrating hydrological models.

  • 3. Teutschbein, Claudia
    et al.
    Wetterhall, Fredrik
    SMHI, Research Department, Hydrology.
    Seibert, Jan
    Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale2011In: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 37, no 9-10, p. 2087-2105Article in journal (Refereed)
    Abstract [en]

    Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output cannot be used directly for hydrological impact studies but rather needs to be downscaled. In this study, we investigated the variability of seasonal streamflow and flood-peak projections caused by the use of three statistical approaches to downscale precipitation from two GCMs for a meso-scale catchment in southeastern Sweden: (1) an analog method (AM), (2) a multi-objective fuzzy-rule-based classification (MOFRBC) and (3) the Statistical DownScaling Model (SDSM). The obtained higher-resolution precipitation values were then used to simulate daily streamflow for a control period (1961-1990) and for two future emission scenarios (2071-2100) with the precipitation-streamflow model HBV. The choice of downscaled precipitation time series had a major impact on the streamflow simulations, which was directly related to the ability of the downscaling approaches to reproduce observed precipitation. Although SDSM was considered to be most suitable for downscaling precipitation in the studied river basin, we highlighted the importance of an ensemble approach. The climate and streamflow change signals indicated that the current flow regime with a snowmelt-driven spring flood in April will likely change to a flow regime that is rather dominated by large winter streamflows. Spring flood events are expected to decrease considerably and occur earlier, whereas autumn flood peaks are projected to increase slightly. The simulations demonstrated that projections of future streamflow regimes are highly variable and can even partly point towards different directions.

  • 4.
    Wetterhall, Fredrik
    et al.
    SMHI, Research Department, Hydrology.
    Bardossy, Andras
    Chen, Deliang
    Halldin, Sven
    Xu, Chong-Yu
    Daily precipitation-downscaling techniques in three Chinese regions2006In: Water resources research, ISSN 0043-1397, E-ISSN 1944-7973, Vol. 42, no 11, article id W11423Article in journal (Refereed)
    Abstract [en]

    [ 1] Four methods of statistical downscaling of daily precipitation were evaluated on three catchments located in southern, eastern, and central China. The evaluation focused on seasonal variation of statistical properties of precipitation and indices describing the precipitation regime, e. g., maximum length of dry spell and maximum 5-day precipitation, as well as interannual and intra-annual variations of precipitation. The predictors used in this study were mean sea level pressure, geopotential heights at 1000, 850, 700, and 500 hPa, and specific humidity as well as horizontal winds at 850, 700, and 500 hPa levels from the NCEP/NCAR reanalysis with 2.5 degrees x 2.5 degrees resolution for 1961 - 2000. The predictand was daily precipitation from 13 stations. Two analogue methods, one using principal components analysis (PCA) and the other Teweles-Wobus scores (TWS), a multiregression technique with a weather generator producing precipitation (SDSM) and a fuzzy-rule-based weather-pattern-classification method (MOFRBC), were used. Temporal and spatial properties of the predictors were carefully evaluated to derive the optimum setting for each method, and MOFRBC and SDSM were implemented in two modes, with and without humidity as predictor. The results showed that ( 1) precipitation was most successfully downscaled in the southern and eastern catchments located close to the coast, ( 2) winter properties were generally better downscaled, ( 3) MOFRBC and SDSM performed overall better than the analogue methods, ( 4) the modeled interannual variation in precipitation was improved when humidity was added to the predictor set, and ( 5), the annual precipitation cycle was well captured with all methods.

  • 5.
    Wetterhall, Fredrik
    et al.
    SMHI, Research Department, Hydrology.
    Bardossy, Andras
    Chen, Deliang
    Halldin, Sven
    Xu, Chong-yu
    Statistical downscaling of daily precipitation over Sweden using GCM output2009In: Journal of Theoretical and Applied Climatology, ISSN 0177-798X, E-ISSN 1434-4483, Vol. 96, no 1-2, p. 95-103Article in journal (Refereed)
    Abstract [en]

    A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070-2100 compared to 1961-1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns.

  • 6.
    Wetterhall, Fredrik
    et al.
    SMHI, Research Department, Hydrology.
    Graham, Phil
    SMHI, Research Department, Climate research - Rossby Centre.
    Andreasson, Johan
    SMHI, Professional Services.
    Rosberg, Jörgen
    SMHI, Research Department, Hydrology.
    Yang, Wei
    SMHI, Research Department, Hydrology.
    Using ensemble climate projections to assess probabilistic hydrological change in the Nordic region2011In: Natural hazards and earth system sciences, ISSN 1561-8633, E-ISSN 1684-9981, Vol. 11, no 8, p. 2295-2306Article in journal (Refereed)
    Abstract [en]

    Assessing hydrological effects of global climate change at local scales is important for evaluating future hazards to society. However, applying climate model projections to local impact models can be difficult as outcomes can vary considerably between different climate models, and including results from many models is demanding. This study combines multiple climate model outputs with hydrological impact modelling through the use of response surfaces. Response surfaces represent the sensitivity of the impact model to incremental changes in climate variables and show probabilies for reaching a priori determined thresholds. Response surfaces were calculated using the HBV hydrological model for three basins in Sweden. An ensemble of future climate projections was then superimposed onto each response surface, producing a probability estimate for exceeding the threshold being evaluated. Site specific impacts thresholds were used where applicable. Probabilistic trends for future change in hazards or potential can be shown and evaluated. It is particularly useful for visualising the range of probable outcomes from climate models and can easily be updated with new results as they are made available.

  • 7.
    Wetterhall, Fredrik
    et al.
    SMHI, Research Department, Hydrology.
    Pappenberger, F.
    Alfieri, L.
    Cloke, H. L.
    Thielen-del Pozo, J.
    Balabanova, S.
    Danhelka, J.
    Vogelbacher, A.
    Salamon, P.
    Carrasco, I.
    Cabrera-Tordera, A. J.
    Corzo-Toscano, M.
    Garcia-Padilla, M.
    Garcia-Sanchez, R. J.
    Ardilouze, C.
    Jurela, S.
    Terek, B.
    Csik, A.
    Casey, J.
    Stankunavicius, G.
    Ceres, V.
    Sprokkereef, E.
    Stam, J.
    Anghel, E.
    Vladikovic, D.
    Eklund, C. Alionte
    Hjerdt, N.
    Djerv, H.
    Holmberg, F.
    Nilsson, J.
    Nystrom, K.
    Susnik, M.
    Hazlinger, M.
    Holubecka, M.
    HESS Opinions "Forecaster priorities for improving probabilistic flood forecasts"2013In: Hydrology and Earth System Sciences, ISSN 1027-5606, E-ISSN 1607-7938, Vol. 17, no 11, p. 4389-4399Article in journal (Refereed)
    Abstract [en]

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (> 3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium-and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  • 8.
    Yang, Wei
    et al.
    SMHI, Research Department, Hydrology.
    Andreasson, Johan
    SMHI, Professional Services.
    Graham, Phil
    SMHI, Research Department, Climate research - Rossby Centre.
    Olsson, Jonas
    SMHI, Research Department, Hydrology.
    Rosberg, Jörgen
    SMHI, Research Department, Hydrology.
    Wetterhall, Fredrik
    SMHI, Research Department, Hydrology.
    Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies2010In: HYDROLOGY RESEARCH, ISSN 1998-9563, Vol. 41, no 3-4, p. 211-229Article in journal (Refereed)
    Abstract [en]

    As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961-2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

1 - 8 of 8
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