Improving Distributed Hydrologic Modeling and Global Land Cover Data

Abstract

Distributed models of the land surface are essential for global climate models because of the importance of land-atmosphere exchanges of water, energy, momentum. They are also used for high resolution hydrologic simulation because of the need to capture non-linear responses to spatially variable inputs. Continued improvements to these models, and the data which they use, is especially important given ongoing changes in climate and land cover. In hydrologic models, important aspects are sometimes neglected due to the need to simplify the models for operational simulation. For example, operational flash flood models do not consider the role of snow and are often lumped (i.e. do not discretize a watershed into multiple units, and so do not fully consider the effect of intense, localized rainstorms). To address this deficiency, an overland flow model is coupled with a subsurface flow model to create a distributed flash flood forecasting system that can simulate flash floods that involve rain on snow. The model is intended for operational use, and there are extensive algorithms to incorporate high-resolution hydrometeorologic data, to assist in the calibration of the models, and to run the model in real time. A second study, which is designed to improve snow simulation in forested environments, demonstrates the importance of explicitly representing a near canopy environment in snow models, instead of only representing open and canopy covered areas (i.e. with % canopy fraction), as is often done. Our modeling, which uses canopy structure information from Aerial Laser Survey Mapping at 1 meter resolution, suggests that areas near trees have more net snow water input than surrounding areas because of the lack of snow interception, shading by the trees, and the effects of wind. In addition, the greatest discrepancy between our model simulations that explicitly represent forest structure and those that do not occur in areas with more canopy edges. In addition, two value-added Land Cover products (land cover type and maximum green vegetation fraction; MGVF) are developed and evaluated. The new products are good successors to current generation land cover products that are used in global models (many of which rely on 20 year old AVHRR land cover data from a single year) because they are based on 10 years of recent MODIS data. There is substantial spurious interannual variability in the MODIS land cover type data, and the MGVF product can vary substantially from year to year depending on climate conditions, suggesting the importance of using climatologies for land cover data. The new land cover type climatology also agrees better with validation sites, and the MGVF climatology is more consistent with other measures of vegetation (e.g. Leaf Area Index) than the older land cover data.