Amanda G. Grimm

A new method to generate a high-resolution global distribution map of lake chlorophyll

A new method was developed, evaluated, and applied to generate a global dataset of growing-season chlorophyll-a (chl) concentrations in 2011 for freshwater lakes. Chl observations from freshwater lakes are valuable for estimating lake productivity as well as assessing the role that these lakes play in carbon budgets. The standard 4 km NASA OceanColor L3 chlorophyll concentration products generated from MODIS and MERIS sensor data are not sufficiently representative of global chl values because these can only resolve larger lakes, which generally have lower chl concentrations than lakes of smaller surface area. Our new methodology utilizes the 300 m-resolution MERIS full-resolution full-swath (FRS) global dataset as input and does not rely on the land mask used to generate standard NASA products, which masks many lakes that are otherwise resolvable in MERIS imagery. The new method produced chl concentration values for 78,938 and 1,074 lakes in the northern and southern hemispheres, respectively. The mean chl for lakes visible in the MERIS composite was 19.2 ± 19.2, the median was 13.3, and the interquartile range was 3.90–28.6 mg m−3. The accuracy of the MERIS-derived values was assessed by comparison with temporally near-coincident and globally distributed in situ measurements from the literature (n = 185, RMSE = 9.39, R2 = 0.72). This represents the first global-scale dataset of satellite-derived chl estimates for medium to large lakes.

Assessing the influence of watershed characteristics on chlorophyll a in waterbodies at global and regional scales

Abstract Predictions of chlorophyll a (Chl-a) in lentic waterbodies (lakes and reservoirs) are valuable to researchers and resource managers alike but have been rarely conducted at the global scale. With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of Chl-ain waterbodies at global and regional scales, we first developed a database of 227 globally distributed waterbodies and watersheds with corresponding Chl-a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and selected models that most parsimoniously related Chl-ato predictor variables for all 227 waterbodies and for a subset of 51 within the Laurentian Great Lakes region. Our best global model contained 3 hydrogeomorphic variables (waterbody area, shoreline development index, and watershed to waterbody area ratio) and a climate variable (mean temperature in the warmest quarter) that explained about 30% of variation in Chl-a. Our regional model contained one hydrogeomorphic variable (watershed area), the same climate variable, and a nutrient variable (percent of watershed area cover by waterbodies) that explained 58% of variation in Chl-a. Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl-athan a global approach and that nearly a third of global variation in Chl-amay be explained using hydrogeomorphic and climate variables.