River flood inundation mapping in the Bago River Basin, Myanmar

Flood inundation maps were generated in the Bago River Basin, Myanmar. Although the design of our study was not new, it is one of very few to have analyzed a flood inundation area in Myanmar. Nine flood events were applied to calibrate and validate the results. The flood-inundated area was validated with satellite image for the year 2006. The flood inundation maps with different return periods were delineated. Considering the 50- and 100-year return period flood scenario, the highest depth of inundation may affect the urban area of Bago. The information derived from this study can contribute to assessments of potential flood damage for the local region and for other locations where data is limited

Nature-based solutions for flood risk reduction : a probabilistic modeling framework

Flooding is the most frequent and damaging natural hazard globally. While nature-based solutions can reduce flood risk, they are not part of mainstream risk management. We develop a probabilistic risk analysis framework to quantify these benefits that (1) accounts for frequent small events and rarer large events, (2) can be applied to large basins and data-scarce contexts, and (3) quantifies economic benefits and reduction in people affected. Measuring benefits in terms of avoided losses enables the integration of nature-based solutions in standard cost-benefit analysis of protective infrastructure. Results for the Chindwin River basin in Myanmar highlight the potential consequences of deforestation on long-term flood risk. We find that loss reduction is driven by small but frequent storms, suggesting that current practice relying on large storms may underestimate the benefits of nature-based solutions. By providing average annual losses, the framework helps mainstream nature-based solutions in infrastructure planning or insurance practice.Ministry of Education (MOE)National Research Foundation (NRF)Published versionWe acknowledge funding support from the National Research Foundation, Prime Minister’s Office, Singapore under awards NRF-NRFF2018-06 and NRF-NRFF12-2020-0009, as well as from the Nanyang Technological University, the Stanford Woods Institute for the Environment, the Stanford Urban Resilience Initiative, the Natural Capital Project, the Earth Observatory of Singapore, and the Peruilh Scholarship from the School of Engineering of the University of Buenos Aires. This research was partly supported by the Earth Observatory of Singapore via its funding from the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative. This work comprises EOS contribution number 396

How many microplastics do you need to (sub)sample?

Analysis of microplastics in the environment requires polymer characterization as a confirmation step for suspected microplastic particles found in a sample. Material characterization is costly and can take a long time per particle. When microplastic particle counts are high, many researchers cannot characterize every particle in their sample due to time or monetary constraints. Moreover, characterizing every particle in samples with high plastic particle counts is unnecessary for describing the sample properties. We propose an a priori approach to determine the number of suspected microplastic particles in a sample that should be randomly subsampled for characterization to accurately assess the polymer distribution in the environmental sample. The proposed equation is well-founded in statistics literature and was validated using published microplastic data and simulations for typical microplastic subsampling routines. We report values from the whole equation but also derive a simple way to calculate the necessary particle count for samples or subsamples by taking the error to the power of negative two. Assuming an error of 0.05 (5 %) with a confidence interval of 95 %, an unknown expected proportion, and a sample with many particles (> 100k), the minimum number of particles in a subsample should be 386 particles to accurately characterize the polymer distribution of the sample, given the particles are randomly characterized from the full population of suspected particles. Extending this equation to simultaneously estimate polymer, color, size, and morphology distributions reveals more particles (620) would be needed in the subsample to achieve the same high absolute error threshold for all properties. The above proposal for minimum subsample size also applies to the minimum count that should be present in samples to accurately characterize particle type presence and diversity in a given environmental compartment

Eriksen2015_OceanSurfacePlastic

Data on surface ocean plastics from Eriksen et al. 2015

Los Angeles Clean Streets Index

Street observations from the LA City government ranking streets by level of cleanliness

WADE: Trash Object Detection AI and Labeled Images

A collaboration between Let's Do It World and The Gray Lab at UC Riverside. Open access trash detection AI and labeled images

World Waste Status Project

World Waste Status project shows the current state of waste issue and its connection to the climate change to the global society in a simple and understandable way and stimulates it for action towards the “zero waste world” vision

Litterati Trash Data

Data from Litterati for the State of California and for a research project called Our Clean Community

Ocean Conservancy Clean Swell

Line shapefile data from the Clean Swell app, collected by volunteers during cleanups