Patrick D. Broxton

A Global Land Cover Climatology Using MODIS Data

AbstractGlobal land cover data are widely used in weather, climate, and hydrometeorological models. The Collection 5.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) product is found to have a substantial amount of interannual variability, with 40% of land pixels showing land cover change one or more times during 2001–10. This affects the global distribution of vegetation if any one year or many years of data are used, for example, to parameterize land processes in regional and global models. In this paper, a value-added global 0.5-km land cover climatology (a single representative map for 2001–10) is developed by weighting each land cover type by its corresponding confidence score for each year and using the highest-weighted land cover type in each pixel in the 2001–10 MODIS data. The climatology is validated by comparing it with the System for Terrestrial Ecosystem Parameterization database as well as additional pixels that are identified from the Google Earth proprietar...

A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling

Earths terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsec (∼1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. We anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models.

A MODIS-Based Global 1-km Maximum Green Vegetation Fraction Dataset

AbstractGlobal land-cover data are widely used in regional and global models because land cover influences land–atmosphere exchanges of water, energy, momentum, and carbon. Many models use data of maximum green vegetation fraction (MGVF) to describe vegetation abundance. MGVF products have been created in the past using different methods, but their validation with ground sites is difficult. Furthermore, uncertainty is introduced because many products use a single year of satellite data. In this study, a global 1-km MGVF product is developed on the basis of a “climatology” of data of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index and land-cover type, which removes biases associated with unusual greenness and inaccurate land-cover classification for individual years. MGVF shows maximum annual variability from 2001 to 2012 for intermediate values of average MGVF, and the standard deviation of MGVF normalized by its mean value decreases nearly monotonically as MGV...

Intercomparison of Seven NDVI Products over the United States and Mexico

Satellites have provided large-scale monitoring of vegetation for over three decades, and several satellite-based Normalized Difference Vegetation Index (NDVI) datasets have been produced. Here we intercompare four long-term NDVI datasets based largely on the AVHRR sensor (NDVIg, NDVI3g, STAR, VIP) and three datasets based on newer sensors (SPOT, Terra, Aqua) and evaluate the effectiveness of homogenizing the datasets using the green vegetation fraction (GVF) and the impact it has on phenology trends. Results show that all NDVI datasets are highly correlated with each other. However, there are significant differences in the regression slopes that vary spatially and temporally. There is a general trend towards higher maximum annual NDVI over much of the temperate forests of the US and a longer greening period due mostly to a delayed end of the season. These trends are less well-defined over rainfall dependent ecosystems in Mexico and the southwest US Compared with the NDVI datasets, the derived GVF datasets show more one-to-one relationships, have reduced interannual variation, preserve their relationships better over the entire time period and are characterized by weaker trends. Finally, weak agreement between the trends in the datasets stresses the importance of using multiple datasets to evaluate changes in vegetation and its phenology.

A hybrid-3D hillslope hydrological model for use in Earth system models

Hillslope-scale rainfall-runoff processes leading to a fast catchment response are not explicitly included in land surface models (LSMs) for use in earth system models (ESMs) due to computational constraints. This study presents a hybrid-3D hillslope hydrological model (h3D) that couples a 1-D vertical soil column model with a lateral pseudo-2D saturated zone and overland flow model for use in ESMs. By representing vertical and lateral responses separately at different spatial resolutions, h3D is computationally efficient. The h3D model was first tested for three different hillslope planforms (uniform, convergent and divergent). We then compared h3D (with single and multiple soil columns) with a complex physically based 3-D model and a simple 1-D soil moisture model coupled with an unconfined aquifer (as typically used in LSMs). It is found that simulations obtained by the simple 1-D model vary considerably from the complex 3-D model and are not able to represent hillslope-scale variations in the lateral flow response. In contrast, the single soil column h3D model shows a much better performance and saves computational time by 2-3 orders of magnitude compared with the complex 3-D model. When multiple vertical soil columns are implemented, the resulting hydrological responses (soil moisture, water table depth, and base flow along the hillslope) from h3D are nearly identical to those predicted by the complex 3-D model, but still saves computational time. As such, the computational efficiency of the h3D model provides a valuable and promising approach to incorporating hillslope-scale hydrological processes into continental and global-scale ESMs.

Testing the hybrid‐3‐D hillslope hydrological model in a controlled environment

Hillslopes are important for converting rainfall into runoff, influencing the terrestrial dynamics of the Earths climate system. Recently, we developed a hybrid-3-D (h3D) hillslope hydrological model that gives similar results as a full 3-D hydrological model but is up to 2–3 orders of magnitude faster computationally. Here h3D is assessed using a number of recharge-drainage experiments within the Landscape Evolution Observatory (LEO) with accurate and high-resolution (both temporally and spatially) observations of the inputs, outputs, and storage dynamics of several hillslopes. Such detailed measurements are generally not available for real-world hillslopes. Results show that the h3D model captures the observed storage, base flow, and overland flow dynamics of both the larger LEO and the smaller miniLEO hillslopes very well. Sensitivity tests are also performed to understand h3Ds difficulty in representing the height of the saturated zone close to the seepage face of the miniLEO hillslope. Results reveal that a temporally constant parameters set is able to simulate the response of the miniLEO for each individual event. However, when one focuses on the saturated zone dynamics at 0.15 m from the seepage face, a stepwise evolution of the optimal model parameter for the saturated lateral conductivity parameter of the gravel layer occurs. This evolution might be related to the migration of soil particles within the hillslope. However, it is currently unclear whether and where this takes place (in the seepage face or within the parts of the loamy sand soil).

Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5)

This work was supported by DOE (DE-SC0006773), NASA (NNX13AK82A), and NSF (AGS-0944101). L. R. Leung was supported by the DOE Office of Science Biological and Environmental Research Earth System Modeling program. Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. We thank the Jet Propulsion Laboratory for providing the GRACE data, which were processed by Sean Swenson under support from the NASA MEaSUREs Program. High-performance computing support was provided by NCARs Computational and Information Systems Laboratory, sponsored by the National Science Foundation, through computing time on Yellowstone (http://n2t.net/ark:/85065/d7wd3xhc) and on The University of Arizona Research Computing

Why Do Global Reanalyses and Land Data Assimilation Products Underestimate Snow Water Equivalent

AbstractThere is a large uncertainty of snow water equivalent (SWE) in reanalyses and the Global Land Data Assimilation System (GLDAS), but the primary reason for this uncertainty remains unclear. Here several reanalysis products and GLDAS with different land models are evaluated and the primary reason for their deficiencies are identified using two high-resolution SWE datasets, including the Snow Data Assimilation System product and a new dataset for SWE and snowfall for the conterminous United States (CONUS) that is based on PRISM precipitation and temperature data and constrained with thousands of point snow observations of snowfall and snow thickness. The reanalyses and GLDAS products substantially underestimate SWE in the CONUS compared to the high-resolution SWE data. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimat...

An evaluation of snow initializations in NCEP global and regional forecasting models

AbstractSnow plays a major role in land–atmosphere interactions, but strong spatial heterogeneity in snow depth (SD) and snow water equivalent (SWE) makes it challenging to evaluate gridded snow quantities using in situ measurements. First, a new method is developed to upscale point measurements into gridded datasets that is superior to other tested methods. It is then utilized to generate daily SD and SWE datasets for water years 2012–14 using measurements from two networks (COOP and SNOTEL) in the United States. These datasets are used to evaluate daily SD and SWE initializations in NCEP global forecasting models (GFS and CFSv2, both on 0.5° × 0.5° grids) and regional models (NAM on 12 km × 12 km grids and RAP on 13 km × 13 km grids) across eight 2° × 2° boxes. Initialized SD from three models (GFS, CFSv2, and NAM) that utilize Air Force Weather Agency (AFWA) SD data for initialization is 77% below the area-averaged values, on average. RAP initializations, which cycle snow instead of using the AFWA SD, ...

Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth

It is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.