Tracking elusive and shifting identities of the global fishing fleet

Illegal, unreported, and unregulated (IUU) fishing costs billions of dollars per year and is enabled by vessels obfuscating their identity. Here, we combine identities of ~35,000 vessels with a decade of GPS data to provide a global assessment of fishing compliance, reflagging patterns, and fishing by foreign-owned vessels. About 17% of high seas fishing is by potentially unauthorized or internationally unregulated vessels, with hot spots of this activity in the west Indian and the southwest Atlantic Oceans. In addition, reflagging, a tactic often used to obscure oversight, occurs in just a few ports primarily by fleets with high foreign ownership. Fishing by foreign-owned vessels is concentrated in parts of high seas and certain national waters, often flying flags of convenience. These findings can address the global scope of potential IUU fishing and enable authorities to improve oversight

The Snowpack Toolbox: Water Research in Far-Flung Places

The Snowpack Toolbox: Water Research in Far-Flung Places Mountain snowpack provides essential water for people and economies around the world with over two billion people relying on snowmelt water for some portion of their daily needs. To understand the mechanisms that connect snow to the streamflow that supports the world’s cities, fields, and industries, we must take a hard look at the tools and data at our disposal. Snow water equivalent (SWE) – or how deep the water would be if the snow were to melt – is the most prevalent variable in analyzing the snowmelt-streamflow connection and provides an accurate measurement of snow water content at a given point. However, measuring SWE faces significant challenges due to limited access, harsh climate, expensive equipment, and difficult maintenance. In addition, many places with a substantial dependence on snowmelt, such as India and Nepal, have few to no weather stations that measure SWE. A lack of data will not change our dependence on snowmelt, so what are our options? Satellite-based measurements of snow allow researchers to study these remote and treacherous regions to gain new insight into snowpack-streamflow dynamics. Snow cover extent (SCE) is a measurement of the presence of snow within an area as a proportion of the total area and is available daily with near-global coverage from two of NASA’s satellites. So, if an area is 95% covered in snow, how does that correlate to SWE measurements in the area? How does the snowpack in that area compare to one with only 30% coverage? Can SCE be used to recreate expected natural patterns such as shorter snow seasons in hotter climates? How can we harness SCE to learn more about snowmelt-streamflow dynamics in areas without traditional snow data? SCE and SWE are both unique representations of the state of mountain snow, each with their own benefits and limitations; But perhaps SCE can provide us with another tool for our toolbox as we continue to learn about the planet we live on

Assessing the power of remotely-sensed snow cover extent to improve understanding of snowpack-streamflow dynamics: an application of MODIS snow cover in Western U.S. mountain watersheds

Mountain snowpacks provide essential water for socio-economic systems around the world, with nearly two billion people living in snow sensitive regions. Therefore, methods for characterizing the snowpack-streamflow dynamics at the watershed level are essential for understanding how changes in temperature and precipitation due to climate change will affect the water supply in these regions. However, in-situ snowpack measurements, such as snow water equivalent (SWE), are often unavailable or insufficient due to the financial and logistical constraints of installing snowpack monitoring systems. Remotely-sensed snow cover extent (SCE), or the proportion of a watershed that is covered in snow, has been previously integrated into snowmelt models and used to assess the relationships between snowmelt and streamflow. However, no research was found that provided a comprehensive analysis of the ability of SCE to characterize snowpack-streamflow dynamics in a way that supports analysis between and within watersheds. This study develops and tests a methodology for characterizing the snowpack-streamflow dynamics of a watershed using SCE-based metrics that capture the shape and key temporal inflections in the SCE curve – start of snow season, start of snow melt, end of snow season, and average SCE. The results demonstrate that SCE and streamflow sufficiently characterize snowpack-streamflow dynamics to allow for inter-watershed comparison and intra-watershed pattern recognition. The techniques developed and tested in this study allow for the characterization of snowpack-streamflow dynamics in remote and unmonitored watersheds to support future research into how those dynamics may change under future climate change scenarios

Hot spots of unseen fishing vessels

Illegal, unreported, and unregulated (IUU) fishing incurs an annual cost of up to US$25 billion in economic losses, results in substantial losses of aquatic life, and has been linked to human rights violations. Vessel tracking data from the automatic identification system (AIS) are powerful tools for combating IUU, yet AIS transponders can be disabled, reducing its efficacy as a surveillance tool. We present a global dataset of AIS disabling in commercial fisheries, which obscures up to 6% (>4.9 M hours) of vessel activity. Disabling hot spots were located near the exclusive economic zones (EEZs) of Argentina and West African nations and in the Northwest Pacific, all regions of IUU concern. Disabling was highest near transshipment hot spots and near EEZ boundaries, particularly contested ones. We also found links between disabling and location hiding from competitors and pirates. These inferences on where and why activities are obscured provide valuable information to improve fisheries management