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Diagnosis of streamflow prediction skills in Oregon using Hydrologic Landscape Classification
Citation:
Patil, S., J. Wigington, S. Leibowitz, AND R. Comeleo. Diagnosis of streamflow prediction skills in Oregon using Hydrologic Landscape Classification. Presented at American Geophysical Union, December 03 - 07, 2012.
Impact/Purpose:
The ability to forecast when, where, and how much water will be available for use is critical to the decision making of water resource managers. Increasingly, water resources planning and allocation decisions will have to be made in places where little or no past data exists, and the future effects of climate and land-use change on water availability are highly uncertain. Physics-based models of hydrology play a critical role in our understanding of how river basins function and provide a sound scientific basis for decision making under uncertainty. These hydrologic models are primarily used for future predictions of streamflow in river basins. However, research has shown that the forecast skill of hydrologic models differs from region to region. Unfortunately, the underlying causes for these differences are still poorly understood. This research investigates whether the new hydrologic classification system of landscapes, developed by scientists at EPA, can improve our understanding of where and why models fail to predict. Oregon Hydrologic Landscape (OHL) classification was used in conjunction with a spatially lumped hydrologic model to analyze the spatial patterns of streamflow predictability. We found that the model shows a tendency to fail in regions classified with spring snowmelt-dominated flow regime, high aquifer permeability, and medium to high soil permeability. Our results indicate that poor characterization of the snow processes and difficulty in estimating external gains and losses of deep groundwater are the primary reasons for poor model predictability in Oregon. We will use these results to (1) develop a framework for improving the scientific soundness of hydrologic models, (2) provide estimates of the forecast skill at river basins with little or no past data, and (3) assess the potential impacts of climate and land-use change on streamflow predictability.
Description:
A complete understanding of why rainfall-runoff models provide good streamflow predictions at catchments in some regions, but fail to do so in other regions, has still not been achieved. Here, we argue that a hydrologic classification system is a robust conceptual tool that is well equipped to characterize the success or failure of a rainfall-runoff model at regional scales. We use a spatially lumped rainfall-runoff model to predict daily streamflow at 88 catchments in Oregon and analyze its performance within the context of Oregon Hydrologic Landscape (OHL) classification developed by scientists at EPA. OHL classification is used to better understand the physio-climatic conditions that potentially favor high (or low) hydrologic predictability within Oregon. Results show that high predictability catchments (Nash-Sutcliffe efficiency NS > 0.75) are predominantly classified as having very wet climate, winter seasonality of water surplus (rain dominated), low aquifer permeability, and low to medium soil permeability. Most of these catchments are located in the western part of Oregon (west of the Cascade Mountain Range). Conversely, low predictability catchments (NS < 0.6) show propensity towards spring seasonality of water surplus (snow dominated), high aquifer permeability, and medium to high soil permeability. They are mainly located in the volcano-influenced regions near the High Cascades. Results suggest that poor characterization of snow processes and difficulty in estimating external gains and losses of deep groundwater are the primary reasons for low predictability in Oregon. We recommend that low predictability catchments must be dealt with on a case-by-case basis, where a combination of increased model complexity and additional input data is likely to improve streamflow predictions.