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1. Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies

   https://doi.org/10.5194/esurf-9-105-2021  

   Barnes, Richard ; Callaghan, Kerry L. ; Wickert, Andrew D. ( January 2021 , Earth Surface Dynamics)

   null (Ed.)

   Abstract. Depressions – inwardly draining regions – are common to many landscapes. When there is sufficient moisture, depressions take the form of lakes and wetlands; otherwise, they may be dry. Hydrological flow models used in geomorphology, hydrology, planetary science, soil and water conservation, and other fields often eliminate depressions through filling or breaching; however, this can produce unrealistic results. Models that retain depressions, on the other hand, are often undesirably expensive to run. In previous work we began to address this by developing a depression hierarchy data structure to capture the full topographic complexity of depressions in a region. Here, we extend this work by presenting the Fill–Spill–Merge algorithm that utilizes our depression hierarchy data structure to rapidly process and distribute runoff. Runoff fills depressions, which then overflow and spill into their neighbors. If both a depression and its neighbor fill, they merge. We provide a detailed explanation of the algorithm and results from two sample study areas. In these case studies, the algorithm runs 90–2600 times faster (with a reduction in compute time of 2000–63 000 times) than the commonly used Jacobi iteration and produces a more accurate output. Complete, well-commented, open-source code with 97 % test coverage is available on GitHub and Zenodo. 

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2. The Role of the Critical Zone Structure on the Hydrology and Pool Patterning of Boreal Peatlands

   Comas, X ; Slater, L. ; Reeve, A. ( October 2021 , FastTIMES)

   Northern peatlands are unique ecosystems typically located in boreal latitudes that sustain unique habitats and species. They are a critical component of the global carbon cycle, acting both as a carbon reservoir (via peat accumulation after sequestration of carbon dioxide from the atmosphere) and as a carbon source to the atmosphere (by releasing methane and carbon dioxide, two greenhouse gases produced by microbes that thrive in the conditions found in peatlands). Although the ecology of peatlands has been well studied for many decades, the geological controls on peatland development and groundwater flow patterns are not completely understood. In Maine (USA), peatlands began forming about 10,000 years ago following the retreat of the ice sheets at the end of the last ice age. They formed in depressions, often starting as lakes or wetlands, within the landscape carved by glaciers and draped with sediments. Almost two decades of research in several peatlands in Maine suggest that glacial landforms buried beneath peatlands may play a key role in regulating both the hydrology (including the presence of open water pools at the surface) and release of methane gasses from peatlands. Subsurface images using an array of hydrogeophysical methods (including ground-penetrating radar, GPR) constrained with direct coring has revealed the presence of buried esker complexes beneath (or close to) surface pools. Hydrological measurements further suggest that the presence of these permeable esker deposits (mainly gravel and sand) may enhance the connection of peatland water to underlying groundwater and help sustain these pools. Whereas geological maps show that the presence of esker systems in Maine are widespread, satellite-derived digital elevation models (DEMs) reveal that they are often proximal to peatland boundaries, further suggesting that they may commonly extend below the peat formation and control hydrology and carbon dynamics in more peatlands than previously thought. Better understanding of how the critical zone influences coupled water and carbon cycling in northern peatlands may improve the understanding of their contribution to radiative forcing of climate as the climate warms. 

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3. Characterization of Flowpath Using Geochemistry and 87Sr/86Sr Isotope Ratios in the Yalahau Region, Yucatan Peninsula, Mexico

   https://doi.org/10.3390/w12092587  

   McKay, Jessica ; Lenczewski, Melissa ; Leal-Bautista, Rosa Maria ( September 2020 , Water)

   null (Ed.)

   The Yalahau region, located in the northeastern portion of the Yucatán Peninsula, hosts a series of elongated depressions trending north/south in the direction of Isla Holbox, identified as the Holbox Fracture Zone. Previous studies have explored the geomorphology and various hydrologic characteristics of the Yucatán Peninsula; however, there is a paucity of data concerning the interior region where the fractures are located. Strontium isotope ratios and major ion geochemistry data of the surface water and groundwater of this region serve as a hydrogeochemical fingerprint, aiding in constraining the hydrological boundaries, determining flow paths, and characterizing hydrogeochemical processes that impact the composition of the groundwater within the region. 87Sr/86Sr isotope ratios indicate a different signature than the surrounding bedrock Sr ratio, suggesting that the flow throughout the Yalahau region is moving through channels faster than that of much of the Yucatán. Through major ion geochemistry and 87Sr/86Sr isotope ratios, we were able to delineate at least two flow paths within the Yalahau region and identify a point of saline intrusion at least 35 km from the coast. Gaining an understanding of the hydrogeochemistry and water flow regions is crucial in determining the impact of various activities (e.g., extensive tourism, drinking water withdrawal, wastewater discharge/injection) that occur within the Yucatán Peninsula. 

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4. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework

   https://doi.org/10.1029/2022WR033091  

   Botterill, Tom E. ; McMillan, Hilary K. ( March 2023 , Water Resources Research)

   Hydrologic signatures are quantitative metrics that describe a streamflow time series. Examples include annual maximum flow, baseflow index and recession shape descriptors. In this paper, we use machine learning (ML) to learn encodings that are optimal ML equivalents of hydrologic signatures, and that are derived directly from the data. We compare the learned signatures to classical signatures, interpret their meaning, and use them to build rainfall‐runoff models in otherwise ungauged watersheds. Our model has an encoder–decoder structure. The encoder is a convolutional neural net mapping historical flow and climate data to a low‐dimensional vector encoding, analogous to hydrological signatures. The decoder structure includes stores and fluxes similar to a classical hydrologic model. For each timestep, the decoder uses current climate data, watershed attributes and the encoding to predict coefficients that distribute precipitation between stores and store outflow coefficients. The model is trained end‐to‐end on the U.S. CAMELS watershed data set to minimize streamflow error. We show that learned signatures can extract new information from streamflow series, because using learned signatures as input to the process‐informed model improves prediction accuracy over benchmark configurations that use classical signatures or no signatures. We interpret learned signatures by correlation with classical signatures, and by using sensitivity analysis to assess their impact on modeled store dynamics. Learned signatures are spatially correlated and relate to streamflow dynamics including seasonality, high and low extremes, baseflow and recessions. We conclude that process‐informed ML models and other applications using hydrologic signatures may benefit from replacing expert‐selected signatures with learned signatures.

    

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5. Wetlandscape hydrologic dynamics driven by shallow groundwater and landscape topography

   https://doi.org/10.1002/hyp.13661  

   Bertassello, Leonardo E. ; Rao, P. Suresh C. ; Jawitz, James W. ; Aubeneau, Antoine F. ; Botter, Gianluca ( January 2020 , Hydrological Processes)

   Wetlands play an important role in watershed eco‐hydrology. The occurrence and distribution of wetlands in a landscape are affected by the surface topography and the hydro‐climatic conditions. Here, we propose a minimalist probabilistic approach to describe the dynamic behaviour of wetlandscape attributes, including number of inundated wetlands and the statistical properties of wetland stage, surface area, perimeter, and storage volume. The method relies on two major assumptions: (a) wetland bottom hydrologic resistance is negligible; and (b) groundwater level is parallel to the mean terrain elevation. The approach links the number ofinundatedwetlands (depressions with water) to the distribution of wetland bottoms and divides, and the position of the shallow water table. We compared the wetlandscape attribute dynamics estimated from the probabilistic approach to those determined from a parsimonious hydrologic model for groundwater‐dominated wetlands. We test the reliability of the assumptions of both models using data from six cypress dome wetlands in the Green Swamp Wildlife Management Area, Florida. The results of the hydrologic model for groundwater‐dominated wetlands showed that the number of inundated wetlands has a unimodal dependence on the groundwater level, as predicted by the probabilistic approach. The proposed models provide a quantitative basis to understand the physical processes that drive the spatiotemporal hydrologic dynamics in wetlandscapes impacted by shallow groundwater fluctuations. Emergent patterns in wetlandscape hydrologic dynamics are of key importance not only for the conservation of water resources, but also for a wide range of eco‐hydrological services provided by connectivity between wetlands and their surrounding uplands.

    

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