1 / 10

Submitted to Journal of Hydrometeorology

Hydrologic implications of different large-scale meteorological model forcing data sets in mountainous regions. Naoki Mizukami, Martyn Clark, Andrew Slater, Levi Brekke, Marketa Elsner, Jeffrey Arnold, Subhrendu Gangopadhyay. Submitted to Journal of Hydrometeorology. Methods. Forcing data

sheila-harris
Télécharger la présentation

Submitted to Journal of Hydrometeorology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Hydrologic implications of different large-scale meteorological model forcing data sets in mountainous regions Naoki Mizukami, Martyn Clark, Andrew Slater, Levi Brekke, Marketa Elsner, Jeffrey Arnold, Subhrendu Gangopadhyay Submitted to Journal of Hydrometeorology

  2. Methods Forcing data Cosgrove03 - reanalysis Maurer02 - Observation Study basin Upper Colorado River basin Hydrologic simulation Model: CLM4.0 Period: 10/1980 – 9/2008 Analysis area Low elev. band 1 – 1398 pxls Mid elev. band 2 – 1878 pxls High elev. band 3- 244 pxls

  3. Daily T and PRCP comparison –Maurer02 vs. SNOTEL • Temperature extrapolation method for Maurer02 dataset • Tmax and Tmin from CO-OP station (lower elevation) • Use constant lapse rate (6.5 C°/km) and elevation difference btw station & pixel

  4. SW radiation – Climatological annual cycle • Cosgrove03 – Reanalysis • Maurer02 – Daily T and P (MTCLIM) Difference in SW increases with elevation

  5. SW radiation comparison with in-situ measurements Cosgrove03 Maurer02 Observed data from A. Slater (2012)

  6. Climatological annual cycle- SWE, ET, and Runoff

  7. Difference in Aridity and water partitioning High elevation: Large difference in precipitation partitioning associated with aridity transition (energy vs. water limited regime). Low elevation: Both datasets are strong aridity, therefore precipitation partitioning is similar. Cosgrove03

  8. Difference in hydrologic sensitivity to T & P variability • Relationships btw climate variables (P & T) vs. hydrologic state (ET & RO) T P High elevation: Different hydrologic sensitivity to climate due to different precipitation partitionings (see previous slide) Low elevation: Hydrologic sensitivity to climate is similar for both datasets ET RO High elevation – energy limited Lower elevation – water limited

  9. Summary • Examined physical process base model (i.e. LSM) simulation forced with different climate datasets • Observed T and P + radiative fluxes, humidity estimated with empirical algorithm with P & T • Reanalysis • Estimation of SW radiation impact on hydrologic simulations of SWE, ET, and Runoff. • Impact of high elevation temperature estimation on shortwave radiation derived from empirical algorithm with P & T. • Implications • Different hydrologic sensitivity to climate variability (assessment of climate change impact on runoff) • Model calibration is likely to be impacted by choice of forcing datasets

  10. Monthly average SW comparison – Maurer02 vs. SNOTEL

More Related