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Science Strategy

Introduction:

A critical step in applying a hydrologic model to a watershed or a land surface parameterization scheme (LSPS) of an atmospheric model to a specific grid element is to estimate the coefficients or constants in the model or LSPS known as parameters. These parameters are inherent in every model. They vary spatially so they are unique to each watershed or a grid point. Some model parameters may also vary seasonally as well as spatially. How to estimate model parameters has been receiving increasing attention from the hydrology and land surface modeling community.

Presently a priori relationships linking model parameters and land surface characteristics such as soil and vegetation classes are available for many hydrologic models and LSPSs. But these relationships have not been fully validated through rigorous testing using retrospective hydrometeorological data and corresponding land surface characteristics data. This is partly because the necessary database needed for such testing has not been available. Moreover, there still exist a gap in our understanding of the links between model parameters and the land surface characteristics. Generally available information about soils (e.g., texture) and vegetation (e.g., type or vegetation index) only indirectly relates to model parameters such as hydraulic properties of soils and rooting depths of vegetation. Also it is not clear how heterogeneity associated with spatial land surface characteristics data affects those characteristics at the scale of a basin or a grid cell. Consequently, there is a considerable degree of uncertainty associated with the parameters given by existing a priori procedures.

Recent studies have illustrated clearly that existing a priori parameter estimation procedures do not produce proper parameter values and that improper model parameters result in poor model performance. Figure 1 shows modeled partition of annual runoff and evapotranspiration for many different land surface models (LSMs) participated in the Project for Intercomparison of Land-surface Parameterization Schemes Phase 2c (PILPS-2c). These LSMs were driven by the same meteorological forcing data. More interestingly, they were required to prescribe the same values for commonly named parameters such as soil hydraulic properties and vegetation phenology parameters. The large scattering of model performance can be partly be explained by the uncertainty in the values of the best parameters to use in each model. Figure 2 is a scattergram of Nash-Sutcliffe efficiency of daily streamflow simulation from different models using a priori parameters and using calibrated parameters for one of the MOPEX test basins. The efficiency for calibrated cases is generally much higher than the a priori cases, indicating a priori parameters are problematic.

Clearly there is a need to improve the existing a priori parameter estimation procedure. A project known as Model Parameter Estimation Experiment (MOPEX) has been funded by NOAA Office of Global Programs to investigate techniques for the a priori estimation of the parameters used in land surface parameterization schemes of atmospheric models and in hydrological models. A first major step by MOPEX project is the development of a comprehensive database that contain many years of historical hydrometeorological time series data and land surface characteristics data for many basins in the United States and from other countries. MOPEX project has been truly an international collaborative effort with involvement of international scientists and data assembled from different countries.



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MOPEX Goals and Objectives:

MOPEX has the following science goals and objectives

1: To develop improved a priori model parameter estimation techniques for large scale modeling applications and for ungaged basins

2: To develop an international database of retrospective hydrometeorological data and basin characteristics data for a wide of climate and geophysiological conditions

3: To develop objective measures to evaluate the parameter estimate techniques and to understand parameter uncertainty

4: To develop diagnostic tools to foster improved understanding of natural hydrologic processes at basin scales and related behaviour of hydrologic models

5: To promote and facilitate the exchange of ideas and experiences on approaches to model parameter estimation for different climatic regimes

 

MOPEX Science Strategy:

The overall strategy of the land surface model experiments is illustrated in Figure 3. The first step of the MOPEX strategy is to develop the necessary data sets. These data are then used to study individual models using three parallel paths. The first path is to make control runs with model parameters estimated using existing a priori parameter estimation procedures. The second path is to make model runs using calibrated or tuned values of selected model parameters. Then, relationships would be developed between the calibrated parameters and basin climate, soils, vegetation and topographic characteristics. These relationships are used to define the new a priori parameters. The third path is to make new model runs using the new a priori parameter estimates. Achievement of the parameter estimation goal is then established in two steps. The first is to measure how much of the potential improvement in model performance when operated in calibration path is obtained when the model is operated using new a priori parameters. This step uses the same data sets as were used to develop the new a priori parameter estimates. The second step is to demonstrate that new a priori techniques produce better model results than existing a priori techniques for basins not used to develop the new a priori techniques. The outcome of this step has very strong implications for the Prediction in Ungaged Basins (PUBs), a major initiative undertaken in International Association of Hydrological Sciences (IAHS).

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Introduction

Goals/Objectives

Strategy



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