Simulations of a boreal grassland hydrology at Valdai, Russia: PILPS phase2(D)

Citation
Ca. Schlosser et al., Simulations of a boreal grassland hydrology at Valdai, Russia: PILPS phase2(D), M WEATH REV, 128(2), 2000, pp. 301-321
Citations number
50
Categorie Soggetti
Earth Sciences
Journal title
MONTHLY WEATHER REVIEW
ISSN journal
00270644 → ACNP
Volume
128
Issue
2
Year of publication
2000
Pages
301 - 321
Database
ISI
SICI code
0027-0644(200002)128:2<301:SOABGH>2.0.ZU;2-3
Abstract
The Project for the Intercomparison of Land-Surface Parameterization Scheme s (PILPS) aims to improve understanding and modeling of land surface proces ses. PILPS phase 2(d) uses a set of meteorological and hydrological data sp anning 18 yr (1966-83) from a grassland catchment at the Valdai water-balan ce research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs' sensitivity to down ward longwave radiative forcing, timescales of simulated hydrologic variabi lity, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the perform ance of LSSs at a site with a well-defined seasonal snow cover and frozen s oil. Considerable model scatter for the control simulations exists. However , nearly all the LSS scatter in simulated root-zone soil moisture is contai ned within the spatial variability observed inside the catchment. In additi on, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, me ltwater infiltration, and subsequent evapotranspiration. A greater sensitiv ity of the ablation, compared to the accumulation, of the winter snowpack t o the choice of snow parameterization is found. Sensitivity simulations sta rting at prescribed conditions with no spinup demonstrate that the treatmen t of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared t o the corresponding year of the control run, that can persist through the e ntire year and underscore the importance of performing multiyear simulation s.