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  • 1.
    Berg, Peter
    et al.
    SMHI, Research Department, Hydrology.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Yang, Wei
    SMHI, Research Department, Hydrology.
    Model Consistent Pseudo-Observations of Precipitation and Their Use for Bias Correcting Regional Climate Models2015In: CLIMATE, ISSN 2225-1154, Vol. 3, no 1, p. 118-132Article in journal (Refereed)
    Abstract [en]

    Lack of suitable observational data makes bias correction of high space and time resolution regional climate models (RCM) problematic. We present a method to construct pseudo-observational precipitation data by merging a large scale constrained RCM reanalysis downscaling simulation with coarse time and space resolution observations. The large scale constraint synchronizes the inner domain solution to the driving reanalysis model, such that the simulated weather is similar to observations on a monthly time scale. Monthly biases for each single month are corrected to the corresponding month of the observational data, and applied to the finer temporal resolution of the RCM. A low-pass filter is applied to the correction factors to retain the small spatial scale information of the RCM. The method is applied to a 12.5 km RCM simulation and proven successful in producing a reliable pseudo-observational data set. Furthermore, the constructed data set is applied as reference in a quantile mapping bias correction, and is proven skillful in retaining small scale information of the RCM, while still correcting the large scale spatial bias. The proposed method allows bias correction of high resolution model simulations without changing the fine scale spatial features, i.e., retaining the very information required by many impact models.

  • 2.
    Bosshard, Thomas
    et al.
    SMHI, Research Department, Hydrology.
    Carambia, M.
    Goergen, K.
    Kotlarski, S.
    Krahe, P.
    Zappa, M.
    Schaer, C.
    Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections2013In: Water resources research, ISSN 0043-1397, E-ISSN 1944-7973, Vol. 49, no 3, p. 1523-1536Article in journal (Refereed)
    Abstract [en]

    The quantification of uncertainties in projections of climate impacts on river streamflow is highly important for climate adaptation purposes. In this study, we present a methodology to separate uncertainties arising from the climate model (CM), the statistical postprocessing (PP) scheme, and the hydrological model (HM). We analyzed ensemble projections of hydrological changes in the Alpine Rhine (Eastern Switzerland) for the near-term and far-term scenario periods 2024-2050 and 2073-2099 with respect to 1964-1990. For the latter scenario period, the model ensemble projects a decrease of daily mean runoff in summer (-32.2%, range [-45.5% to -8.1%]) and an increase in winter (+41.8%, range [+4.8% to +81.7%]). We applied an analysis of variance model combined with a subsampling procedure to assess the importance of different uncertainty sources. The CMs generally are the dominant source in summer and autumn, whereas, in winter and spring, the uncertainties due to the HMs and the statistical PP gain importance and even partly dominate. In addition, results show that the individual uncertainties from the three components are not additive. Rather, the associated interactions among the CM, the statistical PP scheme, and the HM account for about 5%-40% of the total ensemble uncertainty. The results indicate, in distinction to some previous studies, that none of the investigated uncertainty sources are negligible, and some of the uncertainty is not attributable to individual modeling chain components but rather depends upon interactions. Citation: Bosshard, T., M. Carambia, K. Goergen, S. Kotlarski, P. Krahe, M. Zappa, and C. Schar (2013), Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections, Water Resour. Res., 49, 1523-1536, doi: 10.1029/2011WR011533.

  • 3.
    Bosshard, Thomas
    et al.
    SMHI, Research Department, Hydrology.
    Kotlarski, Sven
    Zappa, Massimiliano
    Schaer, Christoph
    Hydrological Climate-Impact Projections for the Rhine River: GCM-RCM Uncertainty and Separate Temperature and Precipitation Effects2014In: Journal of Hydrometeorology, ISSN 1525-755X, E-ISSN 1525-7541, Vol. 15, no 2, p. 697-713Article in journal (Refereed)
    Abstract [en]

    Climate change is expected to affect the hydrological cycle, with considerable impacts on water resources. Climate-induced changes in the hydrology of the Rhine River (Europe) are of major importance for the riparian countries, as the Rhine River is the most important European waterway, serves as a freshwater supply source, and is prone to floods and droughts. Here regional climate model data from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project is used to drive the hydrological model Precipitation-Runoff-Evapotranspiration-Hydrotope (PREVAH) and to assess the impact of climate change on the hydrology in the Rhine basin. Results suggest increases in monthly mean runoff during winter and decreases in summer. At the gauge Cologne and for the period 2070-99 under the A1B scenario of the Special Report on Emissions Scenarios, projected decreases in summer vary between -9% and -40% depending on the climate model used, while increases in winter are in the range of +4% to +51%. These projected changes in mean runoff are generally consistent with earlier studies, but the derived spread in the runoff projections appears to be larger. It is demonstrated that temperature effects (e.g., through altered snow processes) dominate in the Alpine tributaries, while precipitation effects dominate in the lower portion of the Rhine basin. Analyses are also presented for selected extreme runoff indices.

  • 4. Gutierrez, J. M.
    et al.
    Maraun, D.
    Widmann, M.
    Huth, R.
    Hertig, E.
    Benestad, R.
    Roessler, O.
    Wibig, J.
    Wilcke, Renate
    SMHI, Research Department, Climate research - Rossby Centre.
    Kotlarski, S.
    San Martin, D.
    Herrera, S.
    Bedia, J.
    Casanueva, A.
    Manzanas, R.
    Iturbide, M.
    Vrac, M.
    Dubrovsky, M.
    Ribalaygua, J.
    Portoles, J.
    Raty, O.
    Raisanen, J.
    Hingray, B.
    Raynaud, D.
    Casado, M. J.
    Ramos, P.
    Zerenner, T.
    Turco, M.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Stepanek, P.
    Bartholy, J.
    Pongracz, R.
    Keller, D. E.
    Fischer, A. M.
    Cardoso, R. M.
    Soares, P. M. M.
    Czernecki, B.
    Page, C.
    An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment2019In: International Journal of Climatology, ISSN 0899-8418, E-ISSN 1097-0088, Vol. 39, no 9, p. 3750-3785Article in journal (Refereed)
  • 5. Kotlarski, Sven
    et al.
    Szabo, Peter
    Herrera, Sixto
    Raty, Olle
    Keuler, Klaus
    Soares, Pedro M.
    Cardoso, Rita M.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Page, Christian
    Boberg, Fredrik
    Gutierrez, Jose M.
    Isotta, Francesco A.
    Jaczewski, Adam
    Kreienkamp, Frank
    Liniger, Mark A.
    Lussana, Cristian
    Pianko-Kluczynska, Krystyna
    Observational uncertainty and regional climate model evaluation: A pan-European perspective2019In: International Journal of Climatology, ISSN 0899-8418, E-ISSN 1097-0088, Vol. 39, no 9, p. 3730-3749Article in journal (Refereed)
  • 6. Olesen, Jorgen E.
    et al.
    Borgesen, Christen D.
    Hashemi, Fatemeh
    Jabloun, Mohamed
    Bar-Michalczyk, Dominika
    Wachniew, Przemyslaw
    Zurek, Anna J.
    Bartosova, Alena
    SMHI, Research Department, Hydrology.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Hansen, Anne L.
    Refsgaard, Jens C.
    Nitrate leaching losses from two Baltic Sea catchments under scenarios of changes in land use, land management and climate2019In: Ambio, ISSN 0044-7447, E-ISSN 1654-7209, Vol. 48, no 11, p. 1252-1263Article in journal (Refereed)
  • 7. Pechlivanidis, Ilias G.
    et al.
    Olsson, Jonas
    SMHI, Research Department, Hydrology.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Sharma, Devesh
    Sharma, K. C.
    Multi-Basin Modelling of Future Hydrological Fluxes in the Indian Subcontinent2016In: Water, ISSN 2073-4441, E-ISSN 2073-4441, Vol. 8, no 5, article id 177Article in journal (Refereed)
    Abstract [en]

    The impact of climate change on the hydro-climatology of the Indian subcontinent is investigated by comparing statistics of current and projected future fluxes resulting from three RCP scenarios (RCP2.6, RCP4.5, and RCP8.5). Climate projections from the CORDEX-South Asia framework have been bias-corrected using the Distribution-Based Scaling (DBS) method and used to force the HYPE hydrological model to generate projections of evapotranspiration, runoff, soil moisture deficit, snow depth, and applied irrigation water to soil. We also assess the changes in the annual cycles in three major rivers located in different hydro-climatic regions. Results show that conclusions can be influenced by uncertainty in the RCP scenarios. Future scenarios project a gradual increase in temperature (up to 7 degrees C on average), whilst changes (both increase and decrease) in the long-term average precipitation and evapotranspiration are more severe at the end of the century. The potential change (increase and decrease) in runoff could reach 100% depending on the region and time horizon. Analysis of annual cycles for three selected regions showed that changes in discharge and evapotranspiration due to climate change vary between seasons, whereas the magnitude of change is dependent on the region's hydro-climatic gradient. Irrigation needs and the snow depth in the Himalayas are also affected.

  • 8. Pechlivanidis, Ilias G.
    et al.
    Olsson, Jonas
    SMHI, Research Department, Hydrology.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Sharma, Devesh
    Sharma, K. C.
    Multi-Basin Modelling of Future Hydrological Fluxes in the Indian Subcontinent2016In: Water, ISSN 2073-4441, E-ISSN 2073-4441, Vol. 8, no 5, p. 177-177Article in journal (Refereed)
    Abstract [en]

    The impact of climate change on the hydro-climatology of the Indian subcontinent is investigated by comparing statistics of current and projected future fluxes resulting from three RCP scenarios (RCP2.6, RCP4.5, and RCP8.5). Climate projections from the CORDEX-South Asia framework have been bias-corrected using the Distribution-Based Scaling (DBS) method and used to force the HYPE hydrological model to generate projections of evapotranspiration, runoff, soil moisture deficit, snow depth, and applied irrigation water to soil. We also assess the changes in the annual cycles in three major rivers located in different hydro-climatic regions. Results show that conclusions can be influenced by uncertainty in the RCP scenarios. Future scenarios project a gradual increase in temperature (up to 7 degrees C on average), whilst changes (both increase and decrease) in the long-term average precipitation and evapotranspiration are more severe at the end of the century. The potential change (increase and decrease) in runoff could reach 100% depending on the region and time horizon. Analysis of annual cycles for three selected regions showed that changes in discharge and evapotranspiration due to climate change vary between seasons, whereas the magnitude of change is dependent on the region's hydro-climatic gradient. Irrigation needs and the snow depth in the Himalayas are also affected.

  • 9.
    Pechlivanidis, Ilias
    et al.
    SMHI, Research Department, Hydrology.
    Gupta, H.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    An Information Theory Approach to Identifying a Representative Subset of Hydro-Climatic Simulations for Impact Modeling Studies2018In: Water resources research, ISSN 0043-1397, E-ISSN 1944-7973, Vol. 54, no 8, p. 5422-5435Article in journal (Refereed)
  • 10.
    Pechlivanidis, Ilias
    et al.
    SMHI, Research Department, Hydrology.
    Olsson, Jonas
    SMHI, Research Department, Hydrology.
    Sharma, D.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Sharma, K. C.
    ASSESSMENT OF THE CLIMATE CHANGE IMPACTS ON THE WATER RESOURCES OF THE LUNI REGION, INDIA2015In: GLOBAL NEST JOURNAL, ISSN 1790-7632, Vol. 17, no 1, p. 29-40Article in journal (Refereed)
    Abstract [en]

    Climate change is expected to have a strong impact on water resources at the local, regional and global scales. In this study, the impact of climate change on the hydro-climatology of the Luni region, India, is investigated by comparing statistics of current and projected future fluxes resulting from three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5). The use of different scenarios allows for the estimation of uncertainty of future impacts. The projections are based on the CORDEX-South Asia framework and are bias-corrected using the DBS method before being entered into the HYPE (HYdrological Predictions for the Environment) hydrological model to generate predictions of runoff, evapotranspiration, soil moisture deficit, and applied irrigation water to soil. Overall, the high uncertainty in the climate projections is propagated in the impact model, and as a result the spatiotemporal distribution of change is subject to the climate change scenario. In general, for all scenarios, results show a -20 to +20% change in the long-term average precipitation and evapotranspiration, whereas more pronounced impacts are expected for runoff (-40 to +40% change). Climate change can also affect other hydro-climatic components, however, at a lower impact. Finally, the flow dynamics in the Luni River are substantially affected in terms of shape and magnitude.

  • 11. Raty, Olle
    et al.
    Raisanen, Jouni
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Donnelly, Chantal
    SMHI, Research Department, Hydrology.
    Intercomparison of Univariate and Joint Bias Correction Methods in Changing Climate From a Hydrological Perspective2018In: Climate, ISSN 2053-7565, E-ISSN 2225-1154, Vol. 6, no 2, article id 33Article in journal (Refereed)
  • 12. Raty, Olle
    et al.
    Virta, Hanna
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Donnelly, Chantal
    SMHI, Research Department, Hydrology.
    Regional climate model and model output statistics method uncertainties and the effect of temperature and precipitation on future river discharges in Scandinavia2017In: Hydrology Research, ISSN 1998-9563, E-ISSN 2224-7955, Vol. 48, no 5, p. 1363-1377Article in journal (Refereed)
  • 13. Widmann, Martin
    et al.
    Bedia, Joaquin
    Gutierrez, Jose M.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Hertig, Elke
    Maraun, Douglas
    Casado, Maria J.
    Ramos, Petra
    Cardoso, Rita M.
    Soares, Pedro M. M.
    Ribalaygua, Jamie
    Page, Christian
    Fischer, Andreas M.
    Herrera, Sixto
    Huth, Radan
    Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment2019In: International Journal of Climatology, ISSN 0899-8418, E-ISSN 1097-0088, Vol. 39, no 9, p. 3819-3845Article in journal (Refereed)
  • 14.
    Yang, Wei
    et al.
    SMHI, Research Department, Hydrology.
    Gardelin, Marie
    SMHI, Professional Services.
    Olsson, Jonas
    SMHI, Research Department, Hydrology.
    Bosshard, Thomas
    SMHI, Research Department, Hydrology.
    Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden2015In: Natural hazards and earth system sciences, ISSN 1561-8633, E-ISSN 1684-9981, Vol. 15, no 9, p. 2037-2057Article in journal (Refereed)
    Abstract [en]

    As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically down-scaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

1 - 14 of 14
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