John C. Schaake

Modeling of land surface evaporation by four schemes and comparison with FIFE observations

We tested four land surface parameterization schemes against long-term (5 months) area-averaged observations over the 15 km × 15 km First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) area. This approach proved to be very beneficial to understanding the performance and limitations of different land surface models. These four surface models, embodying different complexities of the evaporation/hydrology treatment, included the traditional simple bucket model, the simple water balance (SWB) model, the Oregon State University (OSU) model, and the simplified Simple Biosphere (SSiB) model. The bucket model overestimated the evaporation during wet periods, and this resulted in unrealistically large negative sensible heat fluxes. The SWB model, despite its simple evaporation formulation, simulated well the evaporation during wet periods, but it tended to underestimate the evaporation during dry periods. Overall, the OSU model ably simulated the observed seasonal and diurnal variation in evaporation, soil moisture, sensible heat flux, and surface skin temperature. The more complex SSiB model performed similarly to the OSU model. A range of sensitivity experiments showed that some complexity in the canopy resistance scheme is important in reducing both the overestimation of evaporation during wet periods and underestimation during dry periods. Properly parameterizing not only the effect of soil moisture stress but also other canopy resistance factors, such as the vapor pressure deficit stress, is critical for canopy resistance evaluation. An overly simple canopy resistance that includes only soil moisture stress is unable to simulate observed surface evaporation during dry periods. Given a modestly comprehensive time-dependent canopy resistance treatment, a rather simple surface model such as the OSU model can provide good area-averaged surface heat fluxes for mesoscale atmospheric models.

A parameterization of snowpack and frozen ground intended for NCEP weather and climate models

Extensions to the land surface scheme (LSS) in the National Centers for Environmental Prediction, regional, coupled, land-atmosphere weather prediction model, known as the mesoscale Eta model, are proposed and tested off-line in uncoupled mode to account for seasonal freezing and thawing of soils and snow-accumulation-ablation processes. An original model assumption that there is no significant heat transfer during redistribution of liquid water was relaxed by including a source/sink term in the heat transfer equation to account for latent heat during phase transitions of soil moisture. The parameterization uses the layer-integrated form of heat and water diffusion equations adopted by the original Eta-LSS. Therefore it simulates the total ice content of each selected soil layer. Infiltration reduction under frozen ground conditions was estimated by probabilistic averaging of spatially variable ice content of the soil profile. Off-line uncoupled tests of the new and original Eta-LSS were performed using experimental data from Rosemount, Minnesota. Simulated soil temperature and unfrozen water content matched observed data reasonably well. Neglecting frozen ground processes leads to significant underestimation/overestimation of soil temperature during soil freezing/thawing periods and underestimates total soil moisture content after extensive periods of soil freezing.

Cabauw Experimental Results from the Project for Intercomparison of Land-Surface Parameterization Schemes

In the Project for Intercomparison of Land-Surface Parameterization Schemes phase 2a experiment, meteorological data for the year 1987 from Cabauw, the Netherlands, were used as inputs to 23 land-surface flux schemes designed for use in climate and weather models. Schemes were evaluated by comparing their outputs with long-term measurements of surface sensible heat fluxes into the atmosphere and the ground, and of upward longwave radiation and total net radiative fluxes, and also comparing them with latent heat fluxes derived from a surface energy balance. Tuning of schemes by use of the observed flux data was not permitted. On an annual basis, the predicted surface radiative temperature exhibits a range of 2 K across schemes, consistent with the range of about 10 W m22 in predicted surface net radiation. Most modeled values of monthly net radiation differ from the observations by less than the estimated maximum monthly observational error (6 10 Wm 2 2). However, modeled radiative surface temperature appears to have a systematic positive bias in most schemes; this might be explained by an error in assumed emissivity and by models’ neglect of canopy thermal heterogeneity. Annual means of sensible and latent heat fluxes, into which net radiation is partitioned, have ranges across schemes of

Simple water balance model for estimating runoff at different spatial and temporal scales

A parametric water balance model was developed based on statistical averaging of the main hydrological processes. The model has a two-layer structure with both a physical and statistical basis for the model parameters. It was developed to fill a need for models with a small number of parameters and of intermediate complexity between a one-parameter simple bucket and more complex hydrologically oriented models with many parameters such as the Sacramento model. The focus was to improve the representation of runoff relative to the simple bucket without introducing the full complexity of the Sacramento model. The model was designed to operate over a range of time steps to facilitate coupling to an atmospheric model. The model can be used for catchment scale simulations in hydrological applications and for simple representation of runoff in coupled atmospheric/hydrological models. An important role for the simple water balance (SWB) model is to assist in understanding how much complexity in representing land surface processes is needed and can be supported with available data to estimate model parameters. The model is tested using rainfall, runoff, and surface meteorological data for three catchments from different climate regimes. Model performance is compared to performance of a simple bucket model, the Sacramento model, and the Oregon State University land surface model. Finally, a series of tests were conducted to evaluate the sensitivity of SWB performance when it is operated at time steps different from the time step for which it was calibrated.

The Project for Intercomparison of Land-surface Parameterization / / Schemes PILPS Phase 2 c Red-Arkansas River basin experiment: 1. Experiment description and summary intercomparisons

Abstract Sixteen land-surface schemes participating in the Project for the Intercomparison of Land-surface Schemes (PILPS) Phase 2(c) were run using 10 years (1979–1988) of forcing data for the Red–Arkansas River basins in the Southern Great Plains region of the United States. Forcing data (precipitation, incoming radiation and surface meteorology) and land-surface characteristics (soil and vegetation parameters) were provided to each of the participating schemes. Two groups of runs are presented. (1) Calibration–validation runs, using data from six small catchments distributed across the modeling domain. These runs were designed to test the ability of the schemes to transfer information about model parameters to other catchments and to the computational grid boxes. (2) Base-runs, using data for 1979–1988, designed to evaluate the ability of the schemes to reproduce measured energy and water fluxes over multiple seasonal cycles across a climatically diverse, continental-scale basin. All schemes completed the base-runs but five schemes chose not to calibrate. Observational data (from 1980–1986) including daily river flows and monthly basin total evaporation estimated through an atmospheric budget analysis, were used to evaluate model performance. In general, the results are consistent with earlier PILPS experiments in terms of differences among models in predicted water and energy fluxes. The mean annual net radiation varied between 80 and 105 W m −2 (excluding one model). The mean annual Bowen ratio varied from 0.52 to 1.73 (also excluding one model) as compared to the data-estimated value of 0.92. The run-off ratios varied from a low of 0.02 to a high of 0.41, as compared to an observed value of 0.15. In general, those schemes that did not calibrate performed worse, not only on the validation catchments, but also at the scale of the entire modeling domain. This suggests that further PILPS experiments on the value of calibration need to be carried out.

The project for intercomparison of land-surface parameterization schemes (PILPS) phase 2(c) Red-Arkansas River basin experiment: 3. Spatial and temporal analysis of water fluxes

The energy components of sixteen Soil-Vegetation Atmospheric Transfer (SVAT) schemes were analyzed and intercompared using 10 years of surface meteorological and radiative forcing data from the Red-Arkansas River basin in the Southern Great Plains of the United States. Comparisons of simulated surface energy fluxes among models showed that the net radiation and surface temperature generally had the best agreement among the schemes. On an average (annual and monthly) basis, the estimated latent heat fluxes agreed (to within approximate estimation errors) with the latent heat fluxes derived from a radiosonde-based atmospheric budget method for slightly more than half of the schemes. The sensible heat fluxes had larger differences among the schemes than did the latent heat fluxes, and the model-simulated ground heat fluxes had large variations among the schemes. The spatial patterns of the model-computed net radiation and surface temperature were generally similar among the schemes, and appear reasonable and consistent with observations of related variables, such as surface air temperature. The spatial mean patterns of latent and sensible heat fluxes were less similar than for net radiation, and the spatial patterns of the ground heat flux vary greatly among the 16 schemes. Generally, there is less similarity among the models in the temporal (interannual) variability of surface fluxes and temperature than there is in the mean fields, even for schemes with similar mean fields.

Simulations of a Boreal Grassland Hydrology at Valdai, Russia: PILPS Phase 2(d)

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966‐83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs’ sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpack to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

Effects of frozen soil on soil temperature, spring infiltration, and runoff: results from the PILPS 2(d) experiment at Valdai, Russia

The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated. It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1mo fsoil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snowcover fraction as a function of snow depth is observed at the catchment but not in any of the models.

Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains

[1] Atmospheric forcing used by land surface models is a critical component of the North American Land Data Assimilation System (NLDAS) and its quality crucially affects the final product of NLDAS and our work on model improvement. A three-year (September 1996-September 1999) retrospective forcing data set was created from the Eta Data Assimilation System and observations and used to run the NLDAS land surface models for this period. We compared gridded NLDAS forcing with station observations obtained from networks including the Oklahoma Mesonet and Atmospheric Radiation Measurement/Cloud and Radiation Testbed at the southern Great Plains. Differences in all forcing variables except precipitation between the NLDAS forcing data set and station observations are small at all timescales. While precipitation data do not agree very well at an hourly timescale, they do agree better at longer timescales because of the way NLDAS precipitation forcing is generated. A small high bias in downward solar radiation and a low bias in downward longwave radiation exist in the retrospective forcing. To investigate the impact of these differences on land surface modeling we compared two sets of model simulations, one forced by the standard NLDAS product and one with station-observed meteorology. The differences in the resulting simulations of soil moisture and soil temperature for each model were small, much smaller than the differences between the models and between the models and observations. This indicates that NLDAS retrospective forcing provides an excellent state-of-the-art data set for land surface modeling, at least over the southern Great Plains region.

Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performan...