The successful application of a conceptual rainfall‐runoff (CRR) model depends on how well it is calibrated. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain unique optimal values for their parameters using automatic calibration methods. Unless the best set of parameters associated with a given calibration data set can be found, it is difficult to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. Results are presented that establish clearly the nature of the multiple optima problem for the research CRR model SIXPAR. These results suggest that the CRR model optimization problem is more difficult than had been previously thought and that currently used local search procedures have a very low probability of successfully finding the optimal parameter sets. Next, the performance of three existing global search procedures are evaluated on the model SIXPAR. Finally, a powerful new global optimization procedure is presented, entitled the shuffled complex evolution (SCE‐UA) method, which was able to consistently locate the global optimum of the SIXPAR model, and appears to be capable of efficiently and effectively solving the CRR model optimization problem.

[1] Results are presented from the multi-institution partnership to develop a real-time and retrospective North American Land Data Assimilation System (NLDAS). NLDAS consists of (1) four land models executing in parallel in uncoupled mode, (2) common hourly surface forcing, and (3) common streamflow routing: all using a 1/8°grid over the continental United States. The initiative is largely sponsored by the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP). As the overview for nine NLDAS papers, this paper describes and evaluates the 3-year NLDAS execution of 1 October 1996 to 30 September 1999, a period rich in observations for validation. The validation emphasizes (1) the land states, fluxes, and input forcing of four land models, (2) the application of new GCIP-sponsored products, and (3) a multiscale approach. The validation includes (1) mesoscale observing networks of land surface forcing, fluxes, and states, (2) regional snowpack measurements, (3) daily streamflow measurements, and (4) satellite-based retrievals of snow cover, land surface skin temperature (LST), and surface insolation. The results show substantial intermodel differences in surface evaporation and runoff (especially over nonsparse vegetation), soil moisture storage, snowpack, and LST. Owing to surprisingly large intermodel differences in aerodynamic conductance, intermodel differences in midday summer LST were unlike those expected from the intermodel differences in Bowen ratio. Last, anticipating future assimilation of LST, an NLDAS effort unique to this overview paper assesses geostationary-satellite-derived LST, determines the latter to be of good quality, and applies the latter to validate modeled LST.

[1] Results are presented from the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS-2) research partnership. In NLDAS, the Noah, Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic land surface models (LSMs) are executed over the conterminous U.S. (CONUS) in realtime and retrospective modes. These runs support the drought analysis, monitoring and forecasting activities of the National Integrated Drought Information System, as well as efforts to monitor large-scale floods. NLDAS-2 builds upon the framework of the first phase of NLDAS (NLDAS-1) by increasing the accuracy and consistency of the surface forcing data, upgrading the land surface model code and parameters, and extending the study from a 3-year (1997)(1998)(1999)) to a 30-year (1979-2008) time window. As the first of two parts, this paper details the configuration of NLDAS-2, describes the upgrades to the forcing, parameters, and code of the four LSMs, and explores overall model-to-model comparisons of land surface water and energy flux and state variables over the CONUS. Focusing on model output rather than on observations, this study seeks to highlight the similarities and differences between models, and to assess changes in output from that seen in NLDAS-1. The second part of the two-part article focuses on the validation of model-simulated streamflow and evaporation against observations. The results depict a higher level of agreement among the four models over much of the CONUS than was found in the first phase of NLDAS. This is due, in part, to recent improvements in the parameters, code, and forcing of the NLDAS-2 LSMs that were initiated following NLDAS-1. However, large inter-model differences still exist in the northeast, Lake Superior, and western mountainous regions of the CONUS, which are associated with cold season processes. In addition, variations in the representation of sub-surface hydrology in the four LSMs lead to large differences in modeled evaporation and subsurface runoff. These issues are important targets for future research by the land surface modeling community. Finally, improvement from NLDAS-1 to NLDAS-2 is summarized by comparing the streamflow measured from U.S. Geological Survey stream gauges with that simulated by four NLDAS models over 961 small basins.

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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.

The Model Parameter Estimation Experiment (MOPEX) is an international project aimed at developing enhanced techniques for the a priori estimation of parameters in hydrologic models and in land surface parameterization schemes of atmospheric models. The MOPEX science strategy involves three major steps: data preparation, a priori parameter estimation methodology development, and demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrologic basins in the United States (US) and in other countries. This database is being continuously expanded to include more basins in all parts of the world. A number of international MOPEX workshops have been convened to bring together interested hydrologists and land surface modelers from all over world to exchange knowledge and experience in developing a priori parameter estimation techniques. (Q. Duan). This paper describes the results from the second and third MOPEX workshops. The specific objective of these workshops is to examine the state of a priori parameter estimation techniques and how they can be potentially improved with observations from well-monitored hydrologic basins. Participants of the second and third MOPEX workshops were provided with data from 12 basins in the southeastern US and were asked to carry out a series of numerical experiments using a priori parameters as well as calibrated parameters developed for their respective hydrologic models. Different modeling groups carried out all the required experiments independently using eight different models, and the results from these models have been assembled for analysis in this paper. This paper presents an overview of the MOPEX experiment and its design. The main experimental results are analyzed. A key finding is that existing a priori parameter estimation procedures are problematic and need improvement. Significant improvement of these procedures may be achieved through model calibration of well-monitored hydrologic basins. This paper concludes with a discussion of the lessons learned, and points out further work and future strategy. q

Abstract. 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.