Lake Chlorophyll Research Articles

Predicting lake chlorophyll from stream phosphorus concentrations

AbstractExcess nutrient loads from streams drive primary production in downstream lakes, and managing these loads is key to achieving desired conditions in lakes. However, quantifying nutrient loads requires intensive sampling of both nutrient concentrations and streamflow. Total phosphorus measurements collected during routine stream monitoring are broadly available, but these data are thought to provide little information on annual nutrient loads because they are typically collected during low, baseflow conditions. Here, we demonstrate that these routine phosphorus measurements are correlated with annual nutrient loads. We also show that the average of these routine measurements of stream phosphorus within a watershed predict the average lake chlorophyll concentration in that same watershed. These relationships can then be used to set targets for stream phosphorus concentrations to achieve desired conditions in lakes.

Causal feedback loops modify lake chlorophyll a–nutrient relationships over two decades of nutrient reductions and climate warming

AbstractUnderstanding how the causal feedback between phytoplankton and environmental drivers controlling the chlorophyll a (Chl a, as a proxy of phytoplankton biomass)–nutrient relationships are modulated under different ecosystem conditions is a major challenge in aquatic ecology. Using an empirical dynamic model (convergent cross mapping) on a 20‐yr dataset on 20 Danish lakes, we quantified hypothesized causal feedback networks for each lake and related them to lake system properties (e.g., mean water depth, nutrient concentrations and extent of reduction, climate warming) vs. the Chl a–nutrient relationship (estimated from generalized least square models). The results showed prevalent causal feedback across the studied lakes, which demonstrated clear patterns for the tested ecosystem variations. Weaker causal feedbacks were found in deeper lakes and lakes with larger warming trends, while stronger causal feedbacks appeared in lakes experiencing greater reductions of TP (total phosphorus) and TN (total nitrogen). Moreover, these causal feedbacks showed a strong and positive coupled pattern. Most of the causal feedbacks worked as enhancement loops, which promote the sensitivity of phytoplankton to TP, not least in shallow lakes with a high TP reduction, and as regulatory loops, which force a shift in the Chl a–TN relationship from a more negative slope in lakes experiencing a high nutrient reduction and weak warming to a positive slope in lakes with low nutrient reduction and stronger warming. Our findings suggest a mechanistic explanation of how internal feedbacks regulate the Chl a–nutrient relationships across a broad gradient of nutrient reductions, climate warming, and lake morphologies.

Machine learning modeling of lake chlorophyll content in a data scarce region (Northern Patagonia, Chile): insights for environmental monitoring

Among South America, Chile is highly susceptible to climate change impacts on water resources and ecosystems. Chilean lakes and rivers have been impacted by anthropogenic activities leading to chemical pollution and eutrophication. Concerns for conservation and management of water resources has led to the current development of secondary norms for environmental quality of Northern Patagonian lakes. In this context, we analyze historical limnological databases (1979-2022) for these lakes utilizing Random Forest (RF) models. After filtering, we retained data for 11 lakes including key variables of: dissolved oxygen, electric conductivity, transparency, temperature, pH, total nitrogen, total phosphorus and chlorophyll-a. This dataset yielded robust results, accurately predicting chlorophyll-a content. Furthermore, we added lake geomorphological parameters, enhancing the performance of the model. Our study demonstrates the need to improve long-term monitoring programs, optimizing environmental data recording and decreasing costs. We conclude that the studied lakes generally maintain their oligotrophic characteristics, however further analysis suggests that these lakes are more sensitive to nitrogen loading than phosphorus. Our results highlight the need to implement adaptative management plans at the watershed level to regulate anthropogenic nitrogen contamination (from agriculture, pisciculture and urbanization). The features selected by RF, coupled with the assessment of historical trophic state variation, allow the establishment of permissible concentration thresholds for major nutrients and other sentinel parameters, informing the development of regulations such as the secondary norms for environmental quality. Lastly, the enhanced performance of RF modeling when including geographical parameters unveils the need to standardize and integrate geographical data in monitoring practices.

Prediction of lake chlorophyll concentration using the BP neural network and Sentinel-2 images based on time features.

One of the most important indicators of lake eutrophication is chlorophyll-a (Chl-a) concentration, which is also an essential component of lake water quality monitoring. It is an efficient, economical and convenient method to monitor the Chl-a concentration through remote sensing images. Taking the Wuliangsuhai Lake as an example, the relevant bands of Sentinel-2 images were used as the input and the Chl-a concentration as the output to build neural network models. In the process of building the model, we mainly studied and tested the impact of adding time features to the model input on the model accuracy. Through the experiment, it was found that the month and day difference features of remote sensing images and Chl-a measurement could significantly improve the prediction accuracy of Chl-a concentration in varying degrees. Finally, it was determined that the neural network prediction model with 12 bands of Sentinel-2 images combined month features as inputs and one hidden layer, eight neurons and Chl-a concentration as outputs was the best. Then, the accuracy of the model was validated when the test set accounts for 20 and 30%, and good results were obtained.

Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations

Eutrophication and excessive algal growth pose a threat on aquatic organisms and the health of the public, environment, and the economy. Understanding what drives excessive algal growth can inform mitigation measures and aid in advance planning to minimize impacts. We demonstrate how simulated data from weather, hydrological, and agroecosystem numerical prediction models can be combined with machine learning (ML) to assess and predict chlorophyll a (chl a) concentrations, a proxy for lake eutrophication and algal biomass. The study area is Lake Erie for a 16-year period, 2002–2017. A total of 20 environmental variables from linked and coupled physical models are used as input features to train the ML model with chl a observations from 16 measuring stations. Included are meteorological variables from the Weather Research and Forecasting (WRF) model, hydrological variables from the Variable Infiltration Capacity (VIC) model, and agricultural management practice variables from the Environmental Policy Integrated Climate (EPIC) agroecosystem model. The consolidation of these variables is conducive to a successful prediction of chl a. Aside from the synergistic effects that weather, hydrology, and fertilizers have on eutrophication and excessive algal growth, we found that the application of different forms of both P and N fertilizers are highly ranked for the prediction of chl a concentration. The developed ML model successfully predicts chl a with a coefficient of determination of 0.81, bias of −0.12 μg/l and RMSE of 4.97 μg/l. The developed ML-based modeling approach can be used for impact assessment of agriculture practices in a changing climate that affect chl a concentrations in Lake Erie.

Total phosphorus and climate are equally important predictors of water quality in lakes

Water quality degradation is one of the largest threats to freshwater ecosystems. Nutrient inputs, land use changes, and climate are expected to be the most important drivers of water quality degradation. Here, we quantify the relative influence of nutrient inputs, climate, and lake geomorphometry on primary production in freshwater lakes globally, using chlorophyll a (chla) as a proxy. We used a large lake chlorophyll database that included chla and total phosphorus, in addition to lake geomorphometric variables (mean depth, watershed area, elevation, surface area, volume, residence time) and climate (air temperature, precipitation, cloud cover, solar radiation) for 2561 freshwater lakes around the globe. Our model was able to explain 60% of the variation in chla concentrations. Of that, total phosphorus (TP) explained 42%, a combination of climate variables explained 38%, and geomorphometrics explained 20% of the variation. Although there have been increased efforts and regulations in place for land use and farming, nutrient inputs continue to be the leading cause of primary production in lakes. However, the influence of climatic variables acting synergistically (temperature, precipitation, cloud cover and solar radiation) is nearly equal to that of total phosphorus, suggesting nutrient management efforts are not sufficient alone to mitigate water quality degradation. Our findings underscore the critical need to incorporate climate factors into water quality management given current climate change.

A database of chlorophyll and water chemistry in freshwater lakes

Measures of chlorophyll represent the algal biomass in freshwater lakes that is often used by managers as a proxy for water quality and lake productivity. However, chlorophyll concentrations in lakes are dependent on many interacting factors, including nutrient inputs, mixing regime, lake depth, climate, and anthropogenic activities within the watershed. Therefore, integrating a broad scale dataset of lake physical, chemical, and biological characteristics can help elucidate the response of freshwater ecosystems to global change. We synthesized a database of measured chlorophyll a (chla) values, associated water chemistry variables, and lake morphometric characteristics for 11,959 freshwater lakes distributed across 72 countries. Data were collected based on a systematic review examining 3322 published manuscripts that measured lake chla, and we supplemented these data with online repositories such as The Knowledge Network for Biocomplexity, Dryad, and Pangaea. This publicly available database can be used to improve our understanding of how chlorophyll levels respond to global environmental change and provide baseline comparisons for environmental managers responsible for maintaining water quality in lakes.

Satellite remote sensing reveals impacts from dam‐associated hydrological changes on chlorophyll‐a in the world's largest desert lake

AbstractWe present an approach that uses satellite products to derive models for predicting lake chlorophyll from environmental variables, and for investigating impacts of changing environmental flows. Lake Turkana, Kenya, is the world's largest desert lake, and environmental flows from the Omo River have been modified since 2015 by the Gibe III dam in Ethiopia. Using satellite remote sensing, we have evaluated the influence of these altered hydrological patterns on large‐scale lake phytoplankton concentrations for the first time. Prior to dam completion, strong seasonal cycles and large spatial gradients in chlorophyll have been observed, related to natural fluctuations in the Omo River's seasonal discharge. During this period, mean lake chlorophyll showed a strong relationship with both river inflows and lake levels. Empirical models were derived which considered multiple hydro‐climatic drivers, but the best model for predicting chlorophyll‐a was a simple model based on Omo River discharge. Application of this model to data for 2015–2016 estimated that during the filling of Gibe III annual mean Lake Turkana chlorophyll declined by 30%. Future water management scenarios based on Gibe III operations predict reduced seasonal chlorophyll‐a variability, while irrigation scenarios showed marked declines in chlorophyll‐a depending on the level of abstraction. These changes demonstrate how infrastructure developments such as dams can significantly alter lake primary production. Our remote sensing approach is easy to adapt to other lakes to understand how their phytoplankton dynamics may be affected by water management scenarios.

Calibration of in situ chlorophyll fluorometers for organic matter

Organic matter (OM) other than living phytoplankton is known to affect fluorometric in situ assessments of chlorophyll in lakes. For this reason, calibrating fluorometric measurements for OM error is important. In this study, chlorophyll (Chl) fluorescence was measured in situ in multiple Finnish lakes using two sondes equipped with Chl fluorometers (ex.470/em.650–700 nm). OM absorbance (A420) was measured from water samples, and one of the two sondes was also equipped with in situ fluorometer for OM (ex.350/em.430 nm). The sonde with Chl and OM fluorometers was also deployed continuously on an automated water quality monitoring station on Lake Konnevesi. For data from multiple lakes, inclusion of water colour estimates into the calibration model improved the predictability of Chl assessments markedly. When OM absorbance or in situ OM fluorescence was used in the calibration model, predictability between the in situ Chl and laboratory Chl a assessments was also enhanced. However, correction was not superior to the one done with the water colour estimate. Our results demonstrated that correction with water colour assessments or in situ measurements of OM fluorescence offers practical means to overcome the variation due to OM when assessing Chl in humic lakes in situ.

Organic matter supply and bacterial community composition predict methanogenesis rates in temperate lake sediments

AbstractThe objective of our study was to identify environmental conditions that structure lake sediment microbial communities and determine whether community composition explained inter‐lake variation in potential methanogenesis rates. We performed a comparative analysis of microbial communities and methanogenesis rates in 14 lake sediments along gradients of pH and primary productivity. Variation in methanogen community composition and non‐methanogen microbial community composition was best explained by pH and sediment organic matter content. However, these regulators of methanogen community structure were not associated with differences in methanogenesis rates. Instead, variation in lake methanogenesis rates was best explained by proxies for organic matter supplied to sediments (lake chlorophyll a concentration and sediment pore‐water total phosphorus) and the composition of the non‐methanogen microbial community. Our results suggest a role for sediment bacterial community in influencing methanogenesis via the supply of growth substrates.

Evaluating historical trends and influences of meteorological and seasonal climate conditions on lake chlorophyll a using remote sensing

Hansen CH, Burian SJ, Dennison PE, Williams GP. 2019. Evaluating historical trends and influences of meteoorological and seasonal climate conditions on lake chlorophyll a using remote sensing. Lake Reserv Manage. 36:45–63.Evaluations of long-term water quality trends and patterns in lakes and reservoirs are often inhibited by irregular historical records. This study uses historical Landsat satellite imagery to construct a more complete historical record of algal biomass (measured via chlorophyll a [Chl-a]) and presents a framework for developing seasonal algal estimation models using open source tools for processing and model development. This approach is both physically based (using observed patterns of variability and algal succession in the lake) and data driven (relying on statistical methods for model development). We use a generalized linear regression modeling technique to develop lake-specific, seasonal models for each lake in the multilake Great Salt Lake system in Utah. The 32-yr constructed history of estimated Chl-a enables analysis of long-term trends within the lake system as well as evaluations of local climate influences on Chl-a concentrations. The estimated historical record exhibits a shift in seasonality (i.e., maximum Chl-a occurs earlier in the growing season), as well as increasing trends of extreme Chl-a concentrations. We also evaluated relationships between meteorological conditions and Chl-a using the enhanced historical record and found localized sensitivity to short-term weather events such as high wind, high temperatures, or precipitation events. Seasonal climate conditions including high winter precipitation, summer temperatures, and early spring snow water equivalent are consistent with higher Chl-a extremes in the historical record. Improved understanding of the trends and climate influences provides useful context and guidance for future monitoring efforts and management strategies.

A Bayesian network model for estimating stoichiometric ratios of lake seston components

ABSTRACTThe elemental composition of seston provides insights into how lake food webs function and how nutrients cycle through the environment. Here, we describe a Bayesian network model that simultaneously estimates relationships between dissolved and particulate nutrients, suspended volatile and nonvolatile sediments, and algal chlorophyll. The model provides direct estimates of the phosphorus (P) and nitrogen (N) content of phytoplankton, suspended non-living organic matter, and suspended inorganic sediment. We applied this model to data collected from reservoirs in Missouri, USA, to test the validity of our assumed relationships. The results indicate that, on average among all samples, the ratio of N and P (N:P) in phytoplankton and non-living organic matter in these reservoirs were similar, although under nutrient replete conditions, N:P in phytoplankton decreased. P content of inorganic sediment was lower than in phytoplankton and non-living organic matter. The analysis also provided a means of tracking changes in the composition of whole seston over time. In addition to informing questions regarding seston stoichiometry, this modeling approach may inform efforts to manage lake eutrophication because it can improve traditional models of relationships between nutrients and chlorophyll in lakes.

Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales

AbstractEmpirical nutrient models that describe lake nutrient, productivity, and water clarity relationships among lakes play a prominent role in limnology. Landscape‐based regressions are also used to understand macroscale variability of lake nutrients, clarity, and productivity (hereafter referred to as nutrient‐productivity). Predictions from both models are used to inform eutrophication management globally. To date, these two classes of models are generally conducted separately, which ignores the known dependencies among nutrient‐productivity variables. We present a statistical model that integrates nutrient‐productivity and landscape‐based regressions—where lake nutrients, productivity, and clarity variables are modeled jointly. We fitted a joint nutrient‐productivity model to over 7000 lakes with three nutrients (total phosphorus, total nitrogen, nitrate concentrations), chlorophyll a concentrations, and Secchi disk depth as response variables and landscape features as predictor variables. Because lakes in different regions respond to landscape features differently, we focused our analysis on two subregions with different dominant land uses, the agricultural Midwest and the forested Northeast U.S. Predictive performance was enhanced by modeling nutrient‐productivity variables jointly. We also found strong evidence that nutrient‐productivity variables were coupled, and that only nitrate may be decoupled from other nutrient‐productivity variables in the forested region. We speculate that these regional differences may be related to differences in the strength of biogeochemical cycles and stoichiometric controls between these regions. Jointly modeling nutrient‐productivity variables in lakes effectively integrates the two dominant approaches for studying lakes nutrient‐productivity relationships and provides novel insight into macroscale patterns of the coupling of nutrients, chlorophyll, and water clarity in lakes.

A Small Temperate Lake in the 21st Century: Dynamics of Water Temperature, Ice Phenology, Dissolved Oxygen, and Chlorophyll a

AbstractIt is unclear how small temperate lakes will evolve physically and biologically in the whole water column under future climate because previous modeling studies usually focused on only one or two physical or biological state variables in the surface waters. Here we used a well‐validated lake biogeochemistry model driven by different climate scenarios of the 21st century to predict the dynamics of ice phenology, water temperature, dissolved oxygen (DO), and chlorophyll a in a small Canadian temperate lake (0.714 km2) that is oligotrophic and strongly stratified in summer, considering the influence of catchment hydrology. The ice season and thickness of the lake are projected to shrink substantially under warming, resulting in a positive energy feedback between climate and the lake. Due to the reduced heat diffusion and water mixing, the dynamics of water temperature in surface and deep waters of the lake are considerably different, with surface waters warmed dramatically but deep waters muted to warming. DO depletion is predicted to occur in the whole water column of the lake under warming, but the controlling processes are depth dependent. Unexpectedly, the predicted growth of the lake's chlorophyll a is small under warming, due to the weakened convection and the mismatch of the timings of favorable solar radiation, thermal, and nutrient conditions. For the examined state variables, our prediction shows that only the dynamics of DO is significantly impacted by the changing catchment hydrology. This study suggests that similar temperate lakes will have diverse physical and biological responses to climate change.

The potential of Earth Observation in modelling nutrient loading and water quality in lakes of southern Québec, Canada

Phosphorus and nitrogen are key nutrients that affect abundance and growth of aquatic primary producers but cannot be directly remotely sensed as their dissolved or organic forms do not interact with the remote sensing signal. In addition, other lake water quality variables such as chlorophyll a and Secchi disk depth, have been previously successfully estimated with remote sensing, but the retrieval algorithms are site-, season-, and/or scene-specific. Such algorithms do not take into account lake typological features, which can affect the sensitivity of lake to change, or catchment characteristics, for example, land cover that is a major driver of lake water quality change. Here we propose a novel approach that utilises remotely sensed land cover information in the catchment to estimate phosphorus, nitrogen and chlorophyll a concentrations in lake waters. We use land cover type-specific nutrient export coefficients and the NASA MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Type product showing that nutrient loading based on remote sensing can explain up to 75% of variability in lake nutrient concentrations and 58% of variability in lake chlorophyll a concentrations. In addition, we show that land cover information, supplemented by satellite measurements and lake morphometry data are good predictors of chlorophyll a (R2 = 0.77) and Secchi disk depth (R2 = 0.87) in lakes with different trophic statuses and in different months and years.

Patterns and drivers of deep chlorophyll maxima structure in 100 lakes: The relative importance of light and thermal stratification

AbstractThe vertical distribution of chlorophyll in stratified lakes and reservoirs frequently exhibits a maximum peak deep in the water column, referred to as the deep chlorophyll maximum (DCM). DCMs are ecologically important hot spots of primary production and nutrient cycling, and their location can determine vertical habitat gradients for primary consumers. Consequently, the drivers of DCM structure regulate many characteristics of aquatic food webs and biogeochemistry. Previous studies have identified light and thermal stratification as important drivers of summer DCM depth, but their relative importance across a broad range of lakes is not well resolved. We analyzed profiles of chlorophyll fluorescence, temperature, and light during summer stratification from 100 lakes in the Global Lake Ecological Observatory Network (GLEON) and quantified two characteristics of DCM structure: depth and thickness. While DCMs do form in oligotrophic lakes, we found that they can also form in eutrophic to dystrophic lakes. Using a random forest algorithm, we assessed the relative importance of variables associated with light attenuation vs. thermal stratification for predicting DCM structure in lakes that spanned broad gradients of morphometry and transparency. Our analyses revealed that light attenuation was a more important predictor of DCM depth than thermal stratification and that DCMs deepen with increasing lake clarity. DCM thickness was best predicted by lake size with larger lakes having thicker DCMs. Additionally, our analysis demonstrates that the relative importance of light and thermal stratification on DCM structure is not uniform across a diversity of lake types.

A Bayesian network assessment of macroinvertebrate responses to nutrients and other factors in streams of the Eastern Corn Belt Plains, Ohio, USA

Over the past several years, the United States Environmental Protection Agency has urged states to adopt numeric nutrient criteria to protect water quality. In a number of states, new numeric nutrient criteria have incorporated both a nutrient (nitrogen and/or phosphorus) criterion and a biological endpoint (e.g., chlorophyll a in lakes or benthic macroinvertebrates in streams). While the causal relationship between nutrient levels and chlorophyll in lakes is well-established, quantifying causal relationships between nutrients, chlorophyll a, and benthic macroinvertebrate communities in rivers and streams has been more elusive. This is especially true in highly agricultural ecoregions of the upper midwest United States where a number of confounding factors may be present. Predictive relationships derived from field-collected data can provide important support for setting numeric criteria and identifying management alternatives that can achieve and sustain water quality goals. In this paper, we examine the empirical basis for a causal relationship between nutrients, chlorophyll, and benthic macroinvertebrates in the context of other potential macroinvertebrate stressors in a highly agricultural region. We developed a Bayesian network (BN) model for the number of Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT) present in streams of the Eastern Corn Belt Plains Ecoregion of Ohio using Ohio Environmental Protection Agency data collected over roughly a decade (2005–2013). For the data evaluated in this study, useful relationships between nutrients, chlorophyll a, and EPT were not found. An alternative BN model including total Kjeldahl nitrogen with habitat quality and dissolved oxygen appeared to reflect stronger causal influences on this indicator of stream macroinvertebrate quality. However, the predictive power of the BN was relatively low, suggesting that other factors not accounted for in the model may contribute significantly to EPT taxa abundance in these streams.

Spatial Variation in Nutrient and Water Color Effects on Lake Chlorophyll at Macroscales.

The nutrient-water color paradigm is a framework to characterize lake trophic status by relating lake primary productivity to both nutrients and water color, the colored component of dissolved organic carbon. Total phosphorus (TP), a limiting nutrient, and water color, a strong light attenuator, influence lake chlorophyll a concentrations (CHL). But, these relationships have been shown in previous studies to be highly variable, which may be related to differences in lake and catchment geomorphology, the forms of nutrients and carbon entering the system, and lake community composition. Because many of these factors vary across space it is likely that lake nutrient and water color relationships with CHL exhibit spatial autocorrelation, such that lakes near one another have similar relationships compared to lakes further away. Including this spatial dependency in models may improve CHL predictions and clarify how well the nutrient-water color paradigm applies to lakes distributed across diverse landscape settings. However, few studies have explicitly examined spatial heterogeneity in the effects of TP and water color together on lake CHL. In this study, we examined spatial variation in TP and water color relationships with CHL in over 800 north temperate lakes using spatially-varying coefficient models (SVC), a robust statistical method that applies a Bayesian framework to explore space-varying and scale-dependent relationships. We found that TP and water color relationships were spatially autocorrelated and that allowing for these relationships to vary by individual lakes over space improved the model fit and predictive performance as compared to models that did not vary over space. The magnitudes of TP effects on CHL differed across lakes such that a 1 μg/L increase in TP resulted in increased CHL ranging from 2–24 μg/L across lake locations. Water color was not related to CHL for the majority of lakes, but there were some locations where water color had a positive effect such that a unit increase in water color resulted in a 2 μg/L increase in CHL and other locations where it had a negative effect such that a unit increase in water color resulted in a 2 μg/L decrease in CHL. In addition, the spatial scales that captured variation in TP and water color effects were different for our study lakes. Variation in TP–CHL relationships was observed at intermediate distances (~20 km) compared to variation in water color–CHL relationships that was observed at regional distances (~200 km). These results demonstrate that there are lake-to-lake differences in the effects of TP and water color on lake CHL and that this variation is spatially structured. Quantifying spatial structure in these relationships furthers our understanding of the variability in these relationships at macroscales and would improve model prediction of chlorophyll a to better meet lake management goals.

Assessing the feasibility of integrating remote sensing and in-situ measurements in monitoring water quality status of Lake Chivero, Zimbabwe

This work investigates the likelihood of integrating the cheap and readily-available broadband multispectral MODIS data and in-situ measurements in quantifying and monitoring water quality status of an inland lake within Upper Manyame Catchment in Zimbabwe. Specifically we used MODIS images to quantify inland lake chlorophyll_a concentrations, as a proxy for predicting lake pollution levels. The findings of this study show a high chlorophyll_a concentration of 0.101 ± 0.128 μg/L within the Lake. The results further demonstrated that the chlorophyll_a concentration levels did not significantly vary (p = 0.788) between sites, except among depths (p = 0.05). Further, prediction results based on the relationship between observed and predicted chlorophyll_a produced a high R2 value of 0.89 and a root mean square error (RMSE) value of 0.003 μg/L. Moreover, the derived landuse maps of Upper Manyame Catchment indicated a significant variation in the percentage settlement in 1985, 1994 and 2010 change from 1985 to 2010. For instance, 8% increase in settlement in the period between 1994 and 2010 and over 12% increase from 1985 to 2010 and a decline in percent forest coverage (i.e. 9.8% in 1985 to 2.0% in the year 2010) in the catchment was observed. Overall, the findings of this study highlights the importance of free and readily-available satellite datasets (such as the multispectral MODIS and Landsat) in quantifying and monitoring water quality across inland lakes especially in data-scarce areas like Sub-Saharan Africa.

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.