Geology topics

Bernhard Lehner

Publications and source records attributed to Bernhard Lehner.

4 recordsLinked to original sources

USGS2024

Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales

Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g., at the catchment and stream reach level). We applied the SPAtially Referenced Regression On Watershed attributes (SPARROW) model in three regions: the Upper Midwest part of the United States, New Zealand, and the Grande River Basin in southeastern Brazil. For each region, we compared predictions of nitrogen delivery between models developed using novel large-scale datasets and those developed using local-scale datasets. Large-scale models tended to underperform the local-scale models in poorly monitored areas. Despite this, large-scale models are well suited to generate hypotheses about relative effects of different nutrient source categories (point and urban, agricultural, native vegetation) and to identify knowledge gaps across spatial scales when data are scarce. Regardless of the spatial resolution of the predictors used in the models, a representative network of water quality monitoring stations is key to improve the performance of large-scale models used to estimate loads and yields. We discuss avenues of research to understand how this large-scale modelling approach can improve decision making for managing catchments at local scales, particularly in data poor regions.

Water Research↗

USGS2023

Inland water greenhouse gas budgets for RECCAP2: 2. Regionalization and homogenization of estimates

Inland waters are important sources of the greenhouse gasses (GHGs) carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) to the atmosphere. In the framework of the 2 nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we synthesize existing estimates of GHG emissions from streams, rivers, lakes and reservoirs, and homogenize them with regard to underlying global maps of water surface area distribution and the effects of seasonal ice cover. We then produce regionalized estimates of GHG emissions over 10 extensive land regions. According to our synthesis, inland water GHG emissions have a global warming potential of an equivalent emission of 13.5 (9.9-20.1) and 8.3 (5.7-12.7) Pg CO 2 -eq. yr -1 at a 20 and 100 year horizon (GWP 20 and GWP 100 ), respectively. Contributions of CO 2 dominate GWP 100 , with rivers being the largest emitter. For GWP 20 , lakes and rivers are equally important emitters, and the warming potential of CH 4 is more important than that of CO 2 . Contributions from N 2 O are about two orders of magnitude lower. Normalized to the area of RECCAP-2 regions, S-America and SE-Asia show the highest emission rates, dominated by riverine CO 2 emissions.

Global Biogeochemical Cycles↗

USGS2023

Inland water greenhouse gas budgets for RECCAP2: 1. State-of-the-art of global scale assessments

Inland waters are important sources of the greenhouse gasses (GHGs) carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) to the atmosphere. In the framework of the 2 nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we review the state of the art in estimating inland water GHG budgets at global scale, which has substantially advanced since the first phase of RECCAP nearly ten years ago. The development of increasingly sophisticated upscaling techniques, including statistical prediction and process based models, allows for spatially explicit estimates which are needed for regionalized assessments of continental GHG budgets such as those established for RECCAP. A few recent estimates also resolve the seasonal and/or interannual variability in inland water GHG emissions. Nonetheless, the global-scale assessment of inland water emissions remains challenging because of limited spatial and temporal coverage of observations and persisting uncertainties in the abundance and distribution of inland water surface areas. To decrease these uncertainties, more empirical work on the contributions of hot-spots and hot-moments to overall inland water GHG emissions is particularly needed.

Global Biogeochemical Cycles↗

USGS2019

A multidisciplinary framework to derive global river reach classifications at high spatial resolution

Projected climate and environmental change are expected to increase the pressure on global freshwater resources. To prepare for and cope with the related risks, stakeholders need to devise plans for sustainable management of river systems, which in turn requires the identification of management-appropriate operational units, such as groups of rivers that share similar environmental and biological characteristics. Ideally, these units are of a manageable size, and are biotically or abiotically distinguishable across a variety of river types. Here, we aim to address this need by presenting a new global river classification framework (GloRiC) to establish a common vocabulary and standardized approach to the development of globally comprehensive and integrated river classifications that can be tailored to different goals and requirements. We define the GloRiC conceptual framework based on five categories of variables: (1) hydrology; (2) physiography and climate; (3) fluvial geomorphology; (4) water chemistry; and (5) aquatic biology. We then apply the framework using hydro-environmental attributes provided by a seamless high-resolution river reach database to create initial instances of three sub-classifications (hydrologic, physio-climatic, and geomorphic) which we ultimately combine into 127 river reach types at the global scale. These supervised classifications utilize a mix of statistical analyses and expert interpretation to identify the classifier variables, the number of classes, and their thresholds. In addition, we also present an unsupervised, multivariable k-means statistical clustering of all river reaches into 30 groups. These first-of-their-kind global river reach classifications at high spatial resolution provide baseline information for a total of 35.9 million kilometers of rivers that have been assessed in this study, and are expected to be particularly useful in remote or data-poor river basins. The GloRiC framework and associated data are primarily designed for broad and rapid applicability in assessments that require stratified analyses of river ecosystem conditions at global and regional scales; smaller-scale applications could follow the same conceptual framework yet use more detailed data sources.

Environmental Research Letters↗