Browsing by Author "Lin, Peirong"
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- Assessing placement bias of the global river gauge networkKrabbenhoft, Corey A.; Allen, George H.; Lin, Peirong; Godsey, Sarah E.; Allen, Daniel C.; Burrows, Ryan M.; DelVecchia, Amanda G.; Fritz, Ken M.; Shanafield, Margaret; Burgin, Amy J.; Zimmer, Margaret A.; Datry, Thibault; Dodds, Walter K.; Jones, C. Nathan; Mims, Meryl C.; Franklin, Catherin; Hammond, John C.; Zipper, Sam; Ward, Adam S.; Costigan, Katie H.; Beck, Hylke E.; Olden, Julian D. (Nature Portfolio, 2022-07)Hydrologic data collected from river gauges inform critical decisions for allocating water resources, conserving ecosystems and predicting the occurrence of droughts and floods. The current global river gauge network is biased towards large, perennial rivers, and strategic adaptations are needed to capture the full scope of rivers on Earth. Knowing where and when rivers flow is paramount to managing freshwater ecosystems. Yet stream gauging stations are distributed sparsely across rivers globally and may not capture the diversity of fluvial network properties and anthropogenic influences. Here we evaluate the placement bias of a global stream gauge dataset on its representation of socioecological, hydrologic, climatic and physiographic diversity of rivers. We find that gauges are located disproportionally in large, perennial rivers draining more human-occupied watersheds. Gauges are sparsely distributed in protected areas and rivers characterized by non-perennial flow regimes, both of which are critical to freshwater conservation and water security concerns. Disparities between the geography of the global gauging network and the broad diversity of streams and rivers weakens our ability to understand critical hydrologic processes and make informed water-management and policy decisions. Our findings underscore the need to address current gauge placement biases by investing in and prioritizing the installation of new gauging stations, embracing alternative water-monitoring strategies, advancing innovation in hydrologic modelling, and increasing accessibility of local and regional gauging data to support human responses to water challenges, both today and in the future.
- A Framework for Estimating Global River Discharge From the Surface Water and Ocean Topography Satellite MissionDurand, Michael; Gleason, Colin J.; Pavelsky, Tamlin M.; Frasson, Renato Prata de Moraes D. M.; Turmon, Michael; David, Cedric H.; Altenau, Elizabeth H.; Tebaldi, Nikki; Larnier, Kevin; Monnier, Jerome; Malaterre, Pierre Olivier; Oubanas, Hind; Allen, George H.; Astifan, Brian; Brinkerhoff, Craig; Bates, Paul D.; Bjerklie, David; Coss, Stephen; Dudley, Robert; Fenoglio, Luciana; Garambois, Pierre-Andre; Getirana, Augusto; Lin, Peirong; Margulis, Steven A.; Matte, Pascal; Minear, J. Toby; Muhebwa, Aggrey; Pan, Ming; Peters, Daniel; Riggs, Ryan; Sikder, Md Safat; Simmons, Travis; Stuurman, Cassie; Taneja, Jay; Tarpanelli, Angelica; Schulze, Kerstin; Tourian, Mohammad J.; Wang, Jida (American Geophysical Union, 2023-04-06)The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two-thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage-constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.
- Inland Water Greenhouse Gas Budgets for RECCAP2: 1. State-Of-The-Art of Global Scale AssessmentsLauerwald, Ronny; Allen, George H.; Deemer, Bridget R.; Liu, Shaoda; Maavara, Taylor; Raymond, Peter; Alcott, Lewis; Bastviken, David; Hastie, Adam; Holgerson, Meredith A.; Johnson, Matthew S.; Lehner, Bernhard; Lin, Peirong; Marzadri, Alessandra; Ran, Lishan; Tian, Hanqin; Yang, Xiao; Yao, Yuanzhi; Regnier, Pierre (American Geophysical Union, 2023-05-05)Inland waters are important emitters of the greenhouse gasses (GHGs) carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) to the atmosphere. In the framework of the 2nd 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 10 years ago. The development of increasingly sophisticated upscaling techniques, including statistical prediction and process-based models, allows for spatially explicit estimates that 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.