A synthesis of modeling and observa4onal data for an integrated assessment of the catchment-‐scale
energy and water cycle Mauro Sulis
Meteorological Ins4tute, University of Bonn
Workshop on Coupled Hydrological Modeling Padova, September 23-‐24 2015
Collaborators
Prabhakar Shrestha (MIUB) Sandra Steinke (Uni-‐Köln) Susanne Crewell (Uni-‐Köln) Clemens Simmer (MIUB) Stefan Kollet (IBG3)
Introduc4on
The hydrological and meteorological community have recently converged toward a new integrated simula5on paradigm.
Holis5c and physically-‐based view of the energy, water, and ma=er cycle across a range of spa5al and temporal scales.
New opportuni5es and grand challenges:
Integrated diagnosis of the catchment-‐scale energy and water cycle using fully-‐coupled simula5ons and observa5ons.
Mo#va#ons of the work:
• Powerful tools to test scien5fic hypothesis. • Integrated assessment of the water cycle for long-‐term climate
projec5ons and short-‐ and medium-‐term weather forecasts. • Improved monitoring networks (e.g., mul5ple co-‐located
measurements) that cover the SVA con5nuum.
Study area
North-‐Rhine Westphalia (NRW) domain
Land use classes:
Topography:
Al4tude range: 15 – 700 m
• Cropland (~34 %) • Evergreen forest (~14 %) • Deciduous forest (~17%) • Grassland (~25 %)
Observa4onal dataset – descrip4on
1HD(CP)2 Observa4onal Prototype Experiment (HOPE);2TERrestrial ENvironmental Observatories (TERENO) 3Jülich ObservatorY for Cloud Evolu4on (JOYCE);4Transregional Collabora4ve Research Centre – 32 (TR32)
Data sources: TERENO2, JOYCE3, Er` Verband, and TR324
Time period: April – May 2013 HOPE1 campaign
Variables:
States, fluxes, and diagnos5cs across the subsurface, land surface, and atmosphere compartments of the terrestrial system.
• Radia4on balance composites (radiometers)
• Energy fluxes (eddy covariance measurements) • Soil moisture (cosmic-‐ray probes)
• Precipita4on (X-‐band composites)
• Boundary layer height • Water table depth
• Humidity and temperature profiles (mul4ple meas.)
Observa4onal dataset – temporal distribu4on
Average data coverage: 70%
56%
64% 70%
67%
67%
66%
67%
86%
76%
76%
Latent heat
Sensible heat
2m humidty
Incoming longwave
Emiged longwave
Incoming shortwave
Reflected shortwave
2m temperature
10m u-‐wind
10m v-‐wind
TerrSysMP
COSMO Convec4on permihng configura4on (COSMO-‐DE) (Baldauf et al. 2011).
CLM Land surface scheme (Oleson et al. 2008).
ParFlow Integrated surface-‐subsurface flow model with terrain following coordinates (Kollet and Maxwell, 2006; Maxwell, 2012).
OASIS3 – OASIS-‐MCT External coupler with mul4ple executable approach (Valcke 2013).
Model developments, improvements, and applicaLons:
Shrestha et al., 2014 MWR; Gasper et al., 2014 GMD; Sulis et al., 2015 JHM; Rahman et al., 2015 AWR
Shrestha et al., 2014 MWR
Model setup
SpaLal resoluLon: • COSMO: ΔX = ΔY = 1000 m • ParFlow-‐CLM: ΔX = ΔY = 500m
Temporal resoluLon: • COSMO: Δt = 10 sec • ParFlow-‐CLM: Δt = 900 sec
Coupling frequencies: • COSMO-‐CLM: CPL1 = 900 sec • CLM-‐ParFlow: CPL2 = 900 sec
Boundary condiLons: • COSMO: Hourly reanalysis COSMO-‐DE forcing • ParFlow: No-‐flux condi4ons
Results – Radia4on balance
*bias = (Xsim — Xobs) / Xobs
Systema4c overes4ma4on of the net shortwave radia4on by TerrSysMP. Beger match of the net longwave,with the excep4on of Wuestbach.
Results – Radia4on balance
Analysis of the shortwave radia5on composites:
screening for “clear-‐sky” days
Overes4ma4on of incoming shortwave: cloudiness effect. Underes4ma4on of reflected shortwave: albedo parameterizaLon.
Results – Radia4on balance
Analysis of the longwave radia5on composites:
screening for “clear-‐sky” days
Underes4ma4on of incoming longwave: liquid water path. Good agreement in the emiged longwave: land surface temperature.
Results – Atmospheric states
Analysis of the integrated water vapor (IWV):
Slight underes4ma4on of the simulated IWV, especially with respect to MWR, and late in the a`ernoon. TerrSysMP response is consistent with COSMO-‐DE lateral BCs.
Conclusions
• Need of an accurate assessment of the radia4on balance. • Dras4c influence of local features in the soil moisture
dynamics and par44oning of land surface energy fluxes.
• Soil moisture dynamics generally well reproduced.
• Es4mate the integrated water balance.
• Perform ensemble simula4ons (e.g., COSMO-‐DE-‐EPS).
• Extend the simula4on to longer 4me periods.
Preliminary results:
Next steps:
• Coherence in observa4ons and modeling results.