Opportunities to evaluate a Landscape hydrological model (AWRA-L) using global data sets

The Australian Water Resources Assessment system Landscape model (AWRA-L) aims to produce interpretable water balance component estimates covering all of Australia, and as much as possible agree with water balance observations, including point gauging data and satellite observations. The opportunities to evaluate AWRA-L water balance predictions in Australia are severely limited by the limited amount of field data (e.g. flux tower observations, soil moisture measurements) and the limited range of environments and conditions for which observations are available. Opportunities exist to further evaluate and improve AWRA-L model predictions by using global collations of in situ soil moisture, flux tower, and streamflow data available from the broader scientific community. To evaluate AWRA-L against these observations, global input data are required. We reviewed and compared results of published studies about meteorological data that could be used to parameterise AWRA-L globally. Review findings include: • Satellite-based rainfall performs better during warm seasons and in the tropics, although overestimating total rainfall. Reanalysis data outperforms satellite-based rainfall during winter and in higher latitudes. Gauge bias-corrected TRMM 3B42V6 reduces observed bias in many areas globally. A blending approach may enhance rainfall quality estimates on a global scale, using rainfall from reanalysis in higher latitudes and satellite estimates such as TRMM 3B42V6 in mid-latitudes. •Global monthly, annual and climatological surface temperature anomalies from reanalysis had very similar values. At the daily scale, compared daily maximum and minimum temperature probability density functions from ERA-40, JRA-25 and NCEP-DOE were dissimilar with large regional differences, but overall no reanalysis showed more skill than the other two when compared against regional observational temperature data. •Surface shortwave radiation derived from satellite data generally has smaller biases than reanalysis because they are more constrained by observations. Of the three satellite-based incoming shortwave radiation estimates, GEWEX-SRB appeared superior to the other two. Globally, the biases in the climatology of the re-analyses are considerable. The 60 year (1948-2008) Princeton Global Forcing (PGF) dataset with a spatial resolution of 1° and daily time step was considered an adequate compromise for trial simulations. PGF is based on the NCEP/NCAR reanalysis but uses several additional data sources to constrain and reduce bias in the meteorological variables. We implemented a prototype 1° resolution global hydrological model based on AWRA-L - referred to here as the World-Wide Water Resources Assessment system (W3RA). W3RA was parameterized with the same set of parameters used in AWRA-L except for baseflow coefficient, which was derived from a global analysis of base flow recession. In addition, a snow module was added to simulate snowmelt and snow accumulation. Other data used included land cover maps based on MODIS reflectance data, albedo climatology derived from white-sky albedo and wind speed climatology. As part of preliminary evaluation of W3RA, runoff estimates were compared against a Global Runoff Data Centre (GRDC) blend of observations and modeled runoff climatology. Ongoing evaluation will include comparisons against a quality controlled gauged daily flows in 167 unimpaired catchments located mostly in the tropics, a global data set of collated site soil moisture measurements, and evapotranspiration from a global network of flux towers

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

Key Points Global bimonthly streamflow forecasts show potentially valuable skill Initial catchment conditions are responsible for most skill Skill can be estimated from model performance and theoretical skill Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1° resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 k

Multi-decadal trends in global terrestrial evapotranspiration and its components

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981-2012, and its three components: transpiration from vegetation (E t), direct evaporation from the soil (E s) and vaporization of intercepted rainfall from vegetation (E i). During this period, ET over land has increased significantly (p<0.01), caused by increases in E t and E i, which are partially counteracted by E s decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in E t over land is about twofold of the decrease in E s. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle