1. Relationship between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing in the Wind River Range, Wyoming MODIS-derived snow cover measured on 30 April in any given year explains ~79% of the variance in stream discharge for maximum monthly streamflow in that year. Observed changes in streamflow appear to be related to increasing maximum air temperatures over the last four decades causing lower spring snow-cover extent. Figure 1: Snow-cover depletion curves from the MODIS cloud-gap-filled (CGF) 500-m resolution product Figure 2: Daily discharge of the Wind River near Dubois in the Wind River Range, Wyoming Dorothy K. Hall, James L. Foster, Nicolo E. DiGirolamo & George A. Riggs, Code 614.1, NASA/GSFC and SSAI Wind River Near Dubois

2. Name: Dorothy K. Hall, James L. Foster, Nicolo E. DiGirolamo & George A. Riggs, NASA/GSFC and SSAI E-mail: Dorothy.K.Hall@nasa.gov Phone: 301-614-5771 References: Hall, D.K., J.L. Foster, N.E. DiGirolamo and G.A. Riggs, in press: “Relationship between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing and Stream Power in the Wind River Range, Wyoming,” Geomorphology. Hall, D.K., G.A. Riggs, J.L. Foster and S. Kumar, 2010: “Development and validation of a cloud-gap filled MODIS daily snow-cover product,” Remote Sensing of Environment, 114:496-503, doi:10.1016/j.rse.2009.10.007. Data Sources: Moderate-Resolution Imaging Spectroradiometer (MODIS) data; meteorological data from NWS co-op observer network; streamflow data from USGS Technical Description of Image: Figure 1: Snow-cover depletion curves from the MODIS cloud-gap-filled (CGF) 500-m resolution product (Hall et al., 2010) derived from the fractional snow-cover product, MOD10A1, in the Wind River Range, Wyoming. There are breaks in the plots in some years due to sporadic missing MODIS data. (a) Depletion curves in the elevation range ≥3500 m are shown (Hall et al., in press). Figure 2: Daily discharge of the Wind River near Dubois in the Wind River Range, Wyoming, shown by decade (Hall et al., in press); note that stream discharge declines each decade from 1970-1979 to 2000-2009. [Data were missing between 1992 and 2001, so the decade of 1990-1999 included only two years, and the decade of 2000-2009 included only nine years (discharge data for the Wind River near Dubois for the decade of the 2000s began on 1 May 2001 so data from the year 2000 is not available).] Scientific significance: The majority (>70%) of the water supply in the western United States comes from snowmelt, thus analysis of the declining spring snowpack (and resulting declining stream discharge) has important implications for streamflow management in the drought-prone western U.S. Less total stream discharge or a change in the date of maximum streamflow discharge affects the management and planning of the region’s scarce water reservoirs. The strong relationship between percent of basin covered and streamflow indicates that MODIS snow maps should be useful for predicting streamflow, leading to improved reservoir management. We have demonstrated that MODIS snow-cover maps can be used for predicting stream discharge on both gauged and ungauged basins once a relationship is established between snow-cover extent and discharge on gauged basins. Streamflow data from the six streams in the WRR drainage basins studied show lower annual discharge and earlier snowmelt in the decade of the 2000s than in the previous three decades, though no trend of either lower streamflow or earlier snowmelt was observed using MODIS snow-cover maps within the decade of the 2000s. Relevance for future science and relationship to Decadal Survey: This research relates both to Cryosphere research and Water and Energy Cycle research. We show a strong relationship between percent of basin covered by snow as determined from MODIS snow-cover maps and amount of streamflow during the melt season. This indicates that MODIS snow maps are useful for predicting the amount of streamflow. Knowledge of amount of water (from melting snow) just before the snowmelt season is essential for improved reservoir management. This work relates to more than one of the Decadal Survey Big Questions, for example, “What are the consequences of change in the Earth system for human civilization?”. A logical extension of this work is to study the MODIS record of snow cover and snowmelt timing on larger basins in the western United States.

3. Satellite Retrievals of Phytoplankton Community Composition along the U.S. East Coast The objectives of this study were (1) to develop satellite algorithms to retrieve phytoplankton pigments from the ocean’s surface, (2) apply these satellite-derived pigments to estimate the abundances of phytoplankton community composition and (3) examine the seasonal variability of phytoplankton distributions on the continental shelf and slope of the northeastern U.S. using observations from MODIS-Aqua and SeaWiFS. Coastal waters are optically complex due the presence of multiple absorbing and scattering constituents including sediments, phytoplankton, and dissolved organic matter. This poses a challenge for satellite retrieval of various surface ocean products including the primary phytoplankton pigment chlorophyll a, which is used to compute primary production in the ocean. Figure 1: MODIS-Aqua retrievals of seasonal pigment distributions for 2006. Figure 2: MODIS-Aqua retrievals of phytoplankton community composition for the northeastern U.S. Antonio Mannino, Code 614.2, NASA/GSFC

4. Name: Antonio Mannino, NASA/GSFC E-mail: Antonio.Mannino@nasa.gov Phone: 301-286-0182 References: Pan, X., A. Mannino, M.E. Russ, S.B. Hooker, L.W. Harding, Jr. (2010) Remote Sensing of Phytoplankton Pigment Distribution in the United States Northeast Coast, in Press, Remote Sensing of Environment, doi:10.1016/j.rse.2010.05.015. Pan, X., A. Mannino, K.C. Filippino, M.R. Mulholland, H.G. Marshall (2010) Remote Sensing of Phytoplankton Community Composition along the North American Middle Atlantic Bight Continental Shelf, submitted 2010, Estuaries and Coasts. Data Sources: The CliVEC project is a joint effort between NASA-GSFC (project lead, carbon field measurements, validation of ocean color satellite algorithms, and climate variability analysis), Old Dominion University (field sampling including primary productivity and N 2 fixation measurements), and NOAA (research vessel and sampling logistics, primary productivity model development, and satellite time-series data processing). Surface ocean chlorophyll a observations (Figure 1) derived from merged NASA SeaWiFS and MODIS-Aqua images provided by the NOAA team [K. Hyde, NOAA NEMFS] during the field campaign. Surface ocean chlorophyll distributions (Figure 2) [Pan et al. 2009]. Northeastern U.S. coastal ocean primary productivity (PP) for April 2002 from the NOAA team (Figure 3) [J. O’Reilly and K. Hyde, NOAA NEMFS]. Technical Description of Image: Figure 1: Pigments distributions for chorophyll_a (TChl_a), fucoxanthin (Fuco), peridinin (Perid), and zeaxanthin (Zea) in the U.S. northeast coast in 2006. The [TChl_a] derived from the operational algorithm (OC3M) is also shown for comparison. Each image represents an 8-day mean derived from MODIS- Aqua. The unit of pigment is mg m−3, while the scales of the color bar are 0.03–30 for TChl_a, 0.01–10 for Fuco, and 0.001–1 for Perid and Zea. Figure 2: Distributions of phytoplankton taxonomic groups in terms of chlorophyll_a biomass: diatoms, cryptophytes (Crypt), dinoflagellates (Dino), golden-brown algae (Hapt), green algae (Prasino/Chlo), and cyanobacteria (Cyano). Each image represents a MODIS-Aqua 8-day mean from 2006: February 11-18, May 13-20, August 1-8 and October 29–November 5. The abundance of each phytoplankton group is represented in units mg m-3, while the scales of the color bar are 0.01-10 (S1) for Diatom, Crypt, Hapt, and Prasino/Chlo, and 0.01-1 (S2) for Dino and Cyano. Scientific significance: Current operational satellite algorithms for chlorophyll_a (e.g., OC3M – developed for global distributions) do not work well in optically complex coastal waters. Therefore, regionally-tuned algorithms for retrieval of chlorophyll_a and other pigments are necessary to improve our estimates of phytoplankton distributions and coastal primary production. Satellite products and field observations generated from this work will be crucial for developing and evaluating biogeochemical models while also providing some context to evaluate climate change scenarios for ecosystem variability. These publications demonstrate that ocean color sensors such as MODIS and SeaWiFS can be used to determine phytoplankton community composition along the northeastern U.S. Relevance for future science and relationship to Decadal Survey: Coastal marine ecosystems play an important role in the balance of air-sea CO 2 exchange and are vulnerable to climate variability and anthropogenic activities. Ocean color satellite algorithm refinement and validation of phytoplankton pigments and community composition in coastal ocean regions are relevant for future ocean color missions such as the Tier 2 ACE and GEO-CAPE missions. The techniques and algorithms developed in this work will provide some of the necessary tools and data products to apply future ocean color satellite observations to the coastal ocean.