Aline Jaimes

The importance of lake-specific characteristics for water quality across the continental United States.

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agencys 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.

How to Gauge Accuracy of Measurements and of Expert Estimates: Beyond Normal Distributions

To properly process data, we need to know the accuracy of different data points, i.e., accuracy of different measurement results and expert estimates. Often, this accuracy is not given. For such situations, we describe how this accuracy can be estimated based on the available data.

Selecting the Best Location for a Meteorological Tower: A Case Study of Multi-objective Constraint Optimization

Using the problem of selecting the best location for a meteorological tower as an example, we show that in multi-objective optimization under constraints, the traditional weighted average approach is often inadequate. We also show that natural invariance requirements lead to a more adequate approach – a generalization of Nash’s bargaining solution.

Scale-invariant approach to multi-criterion optimisation under uncertainty, with applications to optimal sensor placement, in particular, to sensor placement in environmental research

How, within a given budget, can we design a sensor network that would provide us with the largest amount of useful information? There are two important aspects to this question: (a) how to best distribute the sensors over the large area, i.e. how to best divide the area of interest into zones corresponding to different sensors, and (b) what is the best location of each sensor in the corresponding zone. There is some research on the first aspect to the problem. In this paper, we show that the second aspect can be naturally formalised as a particular case of a general problem of scale-invariant multi-criterion optimisation under uncertainty, and we provide a solution to this general problem. As an illustrative case study, we consider the selection of locations for the Eddy towers, an important micrometeorological instrument.

How to Describe and Propagate Uncertainty When Processing Time Series: Metrological and Computational Challenges, with Potential Applications to Environmental Studies

Time series comes from measurements, and often, measurement inaccuracy needs to be taken into account, especially in such volatile application areas as meteorology and economics. Traditionally, when we deal with an individual measurement or with a sample of measurement results, we subdivide a measurement error into random and systematic components: systematic error does not change from measurement to measurement while random errors corresponding to different measurements are independent. In time series, when we measure the same quantity at different times, we can also have correlation between measurement errors corresponding to nearby moments of time. To capture this correlation, environmental science researchers proposed to consider the third type of measurement errors: periodic. This extended classification of measurement error may seem ad hoc at first glance, but it leads to a good description of the actual errors. In this paper, we provide a theoretical explanation for this semi-empirical classification, and we show how to efficiently propagate all types of uncertainty via computations.

Image inpainting in micrometeorological analysis

Digital image inpainting is the process by which corrupted or defective areas in an image are systematically corrected. New digital image inpainting techniques have been developed in recent years, leading to numerous successful applications, particularly in the area of image restoration. We propose a new image inpainting algorithm based on wavelet sparse representation, and extend its applicability as a new approach for gap-filling in micrometeorological data. Our approach consists of treating the incomplete data set as a structured image that has a sparse representation in the wavelet domain. Therefore, an ℓ1 minimization problem is formulated in order to characterize the sparsest solution associated with the complete data set. A numerical experimentation on a real micrometeorological data set is conducted, demonstrating the effectiveness of the proposed approach.

Multi-objective optimization under positivity constraints, with a meteorological example

In many practical situations, we need to optimize several objectives under the positivity constraints. For example, in meteorological and environmental studies, it is important to collect various types of data, such as temperature and wind speed and direction, from weather stations. For maintenance purposes, it is convenient to place instruments that collect different weather data on the same weather station. Thus, we need to find the “best” location for a weather station. The “best” means, for example, that the external influences, such as flux of cars passing on nearby road, have a minimal impact on the measurement results. There are several such criteria, so we face a multi-objective optimization problem. In this paper, we show that traditional approaches for solving such problems — such as the weighted sum approach — are not fully adequate for solving our problem. We show that fuzzy heuristics lead to a more adequate approach — of using a generalized form of Nash bargaining solution. We then prove that under reasonable assumptions of scale-invariance, the generalized Nash bargaining solution is the only adequate solution for the general problem of multi-objective optimization under positivity constraints — and, in particular, for the problem of selecting an optimal location for a weather station.