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1. Determining flow directions in river channel networks using planform morphology and topology

   https://doi.org/10.5194/esurf-8-87-2020  

   Schwenk, Jon ; Piliouras, Anastasia ; Rowland, Joel C. ( January 2020 , Earth Surface Dynamics)

   Abstract. The abundance of global, remotely sensed surface water observations has accelerated efforts toward characterizing and modeling how water moves across the Earth's surface through complex channel networks. In particular, deltas and braided river channel networks may contain thousands of links that route water, sediment, and nutrients across landscapes. In order to model flows through channel networks and characterize network structure, the direction of flow for each link within the network must be known. In this work, we propose a rapid, automatic, and objective method to identify flow directions for all links of a channel network using only remotely sensed imagery and knowledge of the network's inlet and outletlocations. We designed a suite of direction-predicting algorithms (DPAs),each of which exploits a particular morphologic characteristic of thechannel network to provide a prediction of a link's flow direction. DPAswere chained together to create “recipes”, or algorithms that set all theflow directions of a channel network. Separate recipes were built for deltasand braided rivers and applied to seven delta and two braided river channelnetworks. Across all nine channel networks, the recipe-predicted flowdirections agreed with expert judgement for 97 % of all tested links, andmost disagreements were attributed to unusual channel network topologiesthat can easily bemore »accounted for by pre-seeding critical links with knownflow directions. Our results highlight the (non)universality ofprocess–form relationships across deltas and braided rivers.« less
2. Thermal infrared images of groundwater discharge zones in the Farmington and Housatonic River watersheds (Connecticut and Massachusetts, 2019)

   https://doi.org/10.5066/P915E8JY  

   M, E Moore ; E, K Jackson ; B, A Haynes ; M, A Helton ; Briggs, Martin ( January 2021 )

   Abstract

   Locations of groundwater discharging to surface water are hydrologically and ecologically important for nutrient processing and thermal refugia, yet little is known about the spatial distribution of groundwater discharges at the river network scale. Groundwater discharge locations can be used to identify anomalous groundwater discharging to surface water as colder groundwater interfaces with warmer surface water in late summer. This data release contains GPS locations, thermal infrared images, and direct temperature measurements of groundwater discharges throughout the Farmington and Housatonic River watersheds. These data were collected in late summer/ early fall 2019 to characterize the spatial distribution of groundwater discharges throughout the Farmington and Housatonic River networks. The initial data release contains groundwater discharge locations and associated thermal images along the Salmon Brook River in the Farmington River watershed. Additional data for the Farmington and Housatonic River watersheds will be added to this dataset in the future. This dataset contains 3 files: 1) SalmonBrook_FLIR.zip is a zipped directory containing thermal infrared and real color images. 2) SalmonBrook_Image_Details.csv contains attribute information for each thermal image. 3) SalmonBrook_Seeps.shp is an ESRI shapefile of the groundwater discharge locations with FLIR thermal images and field notes. Files associated with this shapefile include: the database file SalmonBrook_Seeps.dbf, the projection file SalmonBrook_Seeps.prj, and the geodatabse file SalmonBrook_Seeps.shx. 4) LegacyN_FLIR_2019_readme is a high level readme text file that describes all of the files on the root landing page.
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3. Optimizing Amazonian dams for nature

   https://doi.org/10.1126/science.abn8311  

   Holtgrieve, Gordon W. ; Arias, Mauricio E. ( February 2022 , Science)

   Large river systems, particularly those shared by developing nations in the tropics, exemplify the interconnected and thorny challenges of achieving sustainability with respect to food, energy, and water ( 1 ). Numerous countries in South America, Africa, and Asia have committed to hydropower as a means to supply affordable energy with net-zero emissions by 2050 ( 2 ). The placement, size, and number of dams within each river basin network have enormous consequences for not only the ability to produce electricity ( 3 ) but also how they affect people whose livelihoods depend on the local river systems ( 4 ). On page 753 of this issue, Flecker et al. ( 5 ) present a way to assess a rich set of environmental parameters for an optimization analysis to efficiently sort through an enormous number of possible combinations for dam placements and help find the combination(s) that can achieve energy production targets while minimizing environmental costs in the Amazon basin.
4. Unprecedented acceleration of winter discharge of Upper Yenisei River inferred from tree rings

   https://doi.org/10.1088/1748-9326/ac3e20  

   Panyushkina, Irina P ; Meko, David M ; Shiklomanov, Alexander ; Thaxton, Richard D ; Myglan, Vladimir ; Barinov, Valentin V ; Taynik, Anna V ( December 2021 , Environmental Research Letters)

   Abstract The Yenisei River is the largest contributor of freshwater and energy fluxes among all rivers draining to the Arctic Ocean. Modeling long-term variability of Eurasian runoff to the Arctic Ocean is complicated by the considerable variability of river discharge in time and space, and the monitoring constraints imposed by a sparse gauged-flow network and paucity of satellite data. We quantify tree growth response to river discharge at the upper reaches of the Yenisei River in Tuva, South Siberia. Two regression models built from eight tree-ring width chronologies of Larix sibirica are applied to reconstruct winter (Nov–Apr) discharge for the period 1784–1997 (214 years), and annual (Oct–Sept) discharge for the period 1701–2000 (300 years). The Nov–Apr model explains 52% of the discharge variance whereas Oct–Sept explains 26% for the calibration intervals 1927–1997 and 1927–2000, respectively. This new hydrological archive doubles the length of the instrumental discharge record at the Kyzyl gauge and resets the temporal background of discharge variability back to 1784. The reconstruction finds a remarkable 80% upsurge in winter flow over the last 25 years, which is unprecedented in the last 214 years. In contrast, annual discharge fluctuated normally for this system, with only a 7% increase overmore »the last 25 years. Water balance modeling with CRU data manifests a significant discrepancy between decadal variability of the gauged flow and climate data after 1960. We discuss the impact on the baseflow rate change of both the accelerating permafrost warming in the discontinuous zone of South Siberia and widespread forest fires. The winter discharge accounts for only one third of the annual flow, yet the persistent 25 year upsurge is alarming. This trend is likely caused by Arctic Amplification, which can be further magnified by increased winter flow delivering significantly more fresh water to the Kara Sea during the cold season.« less
5. Physics-Guided Recurrent Graph Model for Predicting Flow and Temperature in River Networks

   Jia, X. ; Zwart, J. ; Sadler, J.: Appling ; Oliver, S. ; Markstrom, S. ; Willard, J. ; Xu, S. ; Steinbach, M. ; Read, J. ; Kumar, V. ( April 2021 , Proceedings of the 2021 SIAM International Conference on Data Mining)

   Demeniconi, Carlotta ; Davidson, Ian (Ed.)

   This paper proposes a physics-guided machine learning approach that combines machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we transfer knowledge from physics-based models to guide the learning of the machine learning model. We also propose a new loss function that balances the performance over different river segments. We demonstrate the effectiveness of the proposed method in predicting temperature and streamflow in a subset of the Delaware River Basin. In particular, the proposed method has brought a 33%/14% accuracy improvement over the state-of-the-art physics-based model and 24%/14% over traditional machine learning models (e.g., LSTM) in temperature/streamflow prediction using very sparse (0.1%) training data. The proposed method has also been shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges.