Water Resources Research最新文献

{"title":"Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators","authors":"Alexander Y. Sun, Peishi Jiang, Pin Shuai, Xingyuan Chen","doi":"10.1029/2024wr037555","DOIUrl":"https://doi.org/10.1029/2024wr037555","url":null,"abstract":"Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sediment Transport and Flood Risk: Impact of Newly Constructed Embankments on River Morphology and Flood Dynamics in Kathmandu, Nepal","authors":"Saraswati Thapa, Hugh D. Sinclair, Maggie J. Creed, Alistair G. L. Borthwick, C. Scott Watson, Manoranjan Muthusamy","doi":"10.1029/2024wr037742","DOIUrl":"https://doi.org/10.1029/2024wr037742","url":null,"abstract":"Floodplain encroachment by embankments heightens flood risk. This is exacerbated by climate change and land-use modifications. This paper assesses the impact of embankments on sediment transport, channel geometry, conveyance capacity, and flood inundation of a reach of the Nakkhu River, Nepal. Using the CAESAR-Lisflood landscape evolution model based on a 2-m digital elevation model, we simulate four flood scenarios with and without embankments and sediment transport: a historical 25-year return period flood event used to design the embankments, 50-year, 100-year, and 1000-year return period flood events forecast using the Generalized Logistic Model (using data from 1992 to 2017). Our results indicate that flow confinement by embankments reduces inundation by 99% (from 22.5 to 0.3 ha) for the historical 25-year flood discharge of 42.23 <span data-altimg=\"/cms/asset/e8aa5bf6-5a8c-4281-ae9f-df9d782c62f2/wrcr27519-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"85\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27519-math-0001.png\"><mjx-semantics><mjx-mrow data-semantic-children=\"2,4\" data-semantic-content=\"3\" data-semantic- data-semantic-role=\"division\" data-semantic-speech=\"normal m cubed divided by normal s\" data-semantic-type=\"infixop\"><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"superscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: 0.363em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup><mjx-mo data-semantic- data-semantic-operator=\"infixop,/\" data-semantic-parent=\"5\" data-semantic-role=\"division\" data-semantic-type=\"operator\" rspace=\"1\" space=\"1\"><mjx-c></mjx-c></mjx-mo><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27519:wrcr27519-math-0001\" display=\"inline\" location=\"graphic/wrcr27519-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow data-semantic-=\"\" data-semantic-children=\"2,4\" data-semantic-content=\"3\" data-semantic-role=\"division\" data-semantic-speech=\"normal m cubed divided by normal s\" data-semantic-type=\"infixop\"><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-parent=\"5\" data-semantic-role=","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the Wind-Induced Bias of Rainfall Measurements for the Thies CLIMA Optical Disdrometer","authors":"E. Chinchella, A. Cauteruccio, L. G. Lanza","doi":"10.1029/2024wr037366","DOIUrl":"https://doi.org/10.1029/2024wr037366","url":null,"abstract":"The wind-induced bias of rainfall measurements obtained from non-catching instruments is addressed in this work with reference to the Laser Precipitation Monitor (LPM) optical disdrometer manufactured by Thies CLIMA. A numerical simulation approach is adopted to quantify the expected bias, involving three different models with increasing complexity. Computational Fluid-Dynamics simulation of the airflow field around the instrument with an embedded Lagrangian particle-tracking module to obtain raindrop trajectories are performed by solving the Unsteady Reynolds Averaged Navier-Stokes (URANS) equations and a Large Eddy Simulation (LES) model. URANS-uncoupled, LES-uncoupled, and LES-coupled approaches are tested to assess the impact of modeling the airflow turbulent fluctuations in detail. Due to the non-radially symmetric external shape of the instrument, various combinations of the wind speed and direction are considered. Catch ratios for monodisperse rain are obtained as a function of the particle Reynolds number and the wind direction and fitted to obtain site-independent curves to support application of the simulation results. Based on literature expressions to link the drop size distribution of real rainfall events with the rainfall intensity (which instead depend on the local rainfall climatology at the measurement site), sample collection efficiency curves are obtained from the catch ratios of monodisperse rain. The resulting adjustment curves allow rainfall measurements to be corrected using either a real-time or post-processing approach. However, at high wind speed and assuming that the wind blows parallel to the instrument sensing area, the instrument may fail to report precipitation altogether.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing Long-Term Hydrographs for River Climate-Resilience: A Novel Approach for Studying Centennial to Millennial River Behavior","authors":"Mohamed M. Fathi, Virginia Smith, Ayman G. Awadallah, Anjali M. Fernandes, Michael T. Hren, Dennis O. Terry","doi":"10.1029/2024wr037666","DOIUrl":"https://doi.org/10.1029/2024wr037666","url":null,"abstract":"Studying the centennial or millennial timescale response of large rivers to changing patterns in precipitation, discharge, flood intensity and recurrence, and associated sediment erosion is critical for understanding long-term fluvial geomorphic adjustment to climate. Long hydrographs, maintaining reliable Flow Duration Curves (FDCs), are a fundamental input for such simulations; however, recorded discharge series rarely span more than a few decades. The absence of robust methodologies for generating representative long-term hydrographs, especially those incorporating coarse temporal resolution or lacking continuous simulations, is therefore a fundamental challenge for climate resilience. We present a novel approach for constructing multi-century hydrographs that successfully conserve the statistical, especially frequency analysis, and stochastic characteristics of observed hydrographs. This approach integrates a powerful combination of a weather generator with a fine disaggregation technique and a continuous rainfall-runoff transformation model. We tested our approach to generate a statistically representative 300-year hydrograph on the Ninnescah River Basin in Kansas, using a satellite precipitation data set to address the considerable gaps in the available hourly observed data sets. This approach emphasizes the similarities of FDCs between the observed and generated hydrographs, exhibiting a reasonably acceptable range of average absolute deviation between 6% and 18%. We extended this methodology to create projected high-resolution hydrographs based on a range of climate change scenarios. The projected outcomes present pronounced increases in the FDCs compared to the current condition, especially for more distant futures, which necessitates more efficient adaptation strategies. This approach represents a paradigm shift in long-term hydrologic modeling.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Informed Neural Networks for the Augmented System of Shallow Water Equations With Topography","authors":"Susanna Dazzi","doi":"10.1029/2023wr036589","DOIUrl":"https://doi.org/10.1029/2023wr036589","url":null,"abstract":"Physics-informed neural networks (PINNs) are gaining attention as an alternative approach to solve scientific problems governed by differential equations. This work aims at assessing the effectiveness of PINNs to solve a set of partial differential equations for which this method has never been considered, namely the augmented shallow water equations (SWEs) with topography. Differently from traditional SWEs, the bed elevation is considered as an additional conserved variable, and therefore one more equation expressing the fixed-bed condition is included in the system. This approach allows the PINN model to leverage automatic differentiation to compute the bed slopes by learning the topographical information during training. PINNs are here tested for different one-dimensional cases with non-flat topography, and results are compared with analytical solutions. Though some limitations can be highlighted, PINNs show a good accuracy for the depth and velocity predictions even in the presence of non-horizontal bottom. The solution of the augmented system of SWEs can therefore be regarded as a suitable alternative strategy to deal with flows over complex topography using PINNs, also in view of future extensions to realistic problems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Frequency Monitoring and Nitrate Sourcing Reveals Baseflow and Stormflow Controls on Total Dissolved Nitrogen and Carbon Export Along a Rural-Urban Gradient","authors":"Joseph M. Delesantro, Jonathan M. Duncan, Diego Riveros-Iregui, Keridwen M. Whitmore, Lawrence E. Band","doi":"10.1029/2023wr036750","DOIUrl":"https://doi.org/10.1029/2023wr036750","url":null,"abstract":"Efforts to reduce nitrogen and carbon loading from developed watersheds typically target specific flows or sources, but across gradients in development intensity there is no consensus on the contribution of different flows to total loading or sources of nitrogen export. This information is vital to optimize management strategies leveraging source reductions, stormwater controls, and restorations. We investigate how solute loading and sources vary across flows and land-use using high frequency monitoring and stable nitrate isotope analysis from five catchments with different sanitary infrastructure, along a gradient in development intensity. High frequency monitoring allowed estimation of annual loading and attribution to storm versus baseflows. Nitrate loads were 16 kg/km²/yr. from the forested catchment and ranged from 68 to 119 kg/km²/yr., across developed catchments, highest for the septic served site. Across developed catchments, baseflow contributions ranged from 40% of N loading to 75% from the septic served catchment, and the contribution from high stormflows increased with development intensity. Stormflows mobilized and mixed many surface and subsurface nitrate sources while baseflow nitrate was dominated by fewer sources which varied by catchment (soil, wastewater, or fertilizer). To help inform future sampling designs, we demonstrate that grab sampling and targeted storm sampling would likely fail to accurately predict annual loadings within the study period. The dominant baseflow loads and subsurface stormflows are not treated by surface water management practices primarily targeted to surface stormflows. Using a balance of green and gray infrastructure and stream/riparian restoration may target specific flow paths and improve management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting Barlow Twins Reduced Order Modeling for Machine Learning-Based Surrogate Models in Multiphase Flow Problems","authors":"T. Kadeethum, V. L. S. Silva, P. Salinas, C. C. Pain, H. Yoon","doi":"10.1029/2023wr035778","DOIUrl":"https://doi.org/10.1029/2023wr035778","url":null,"abstract":"We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT-ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT-ROM builds upon Barlow Twins reduced order modeling that leverages self-supervised learning to effectively handle linear and nonlinear manifolds by constructing well-structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT-ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT-ROM, we conduct three three-dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT-ROM excels at predicting non-wetting phase saturation (e.g., oil or <span data-altimg=\"/cms/asset/5c0cf0e9-c30f-45e8-b121-258a56d3eb6f/wrcr27508-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"391\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27508-math-0001.png\"><mjx-semantics><mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,3\" data-semantic-content=\"4\" data-semantic- data-semantic-role=\"implicit\" data-semantic-speech=\"normal upper C normal upper O 2\" data-semantic-type=\"infixop\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"5\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-msub data-semantic-children=\"1,2\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"3\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: -0.15em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"3\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msub></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27508:wrcr27508-math-0001\" display=\"inline\" location=\"graphic/wrcr27508-math-0001.png\" xmlns=\"http://www.w3.org/1998/Ma","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extending GLUE With Multilevel Methods to Accelerate Statistical Inversion of Hydrological Models","authors":"Max Gustav Rudolph, Thomas Wöhling, Thorsten Wagener, Andreas Hartmann","doi":"10.1029/2024wr037735","DOIUrl":"https://doi.org/10.1029/2024wr037735","url":null,"abstract":"Inverse problems aim at determining model parameters that produce observed data to subsequently understand, predict or manage hydrological or other environmental systems. While statistical inversion is especially popular, its sampling-based nature often inhibits its application to computationally costly models, which has compromised the use of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, for example, for spatially distributed (partial) differential equation based models. In this study we introduce multilevel GLUE (MLGLUE), which alleviates the computational burden of statistical inversion by utilizing a hierarchy of model resolutions. Inspired by multilevel Monte Carlo, most parameter samples are evaluated on lower levels with computationally cheap low-resolution models and only samples associated with a likelihood above a certain threshold are subsequently passed to higher levels with costly high-resolution models for evaluation. Inferences are made at the level of the highest-resolution model but substantial computational savings are achieved by discarding samples with low likelihood already on levels with low resolution and low computational cost. Two example inverse problems, using a rainfall-runoff model and groundwater flow model, demonstrate the substantially increased computational efficiency of MLGLUE compared to GLUE as well as the similarity of inversion results. Findings are furthermore compared to inversion results from Markov-chain Monte Carlo (MCMC) and multilevel delayed acceptance MCMC, a corresponding multilevel variant, to compare the effects of the multilevel extension. All examples demonstrate the wide-range suitability of the approach and include guidelines for practical applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drag Force on Submerged Flexible Vegetation in an Open-Channel Flow","authors":"Jianyu Wang, Guojian He, Lei Huang, Subhasish Dey, Hongwei Fang","doi":"10.1029/2023wr036879","DOIUrl":"https://doi.org/10.1029/2023wr036879","url":null,"abstract":"The movement of submerged flexible vegetation leads to an increase in resistance to the stream flow. In this study, a formula that can directly calculate the drag force on a highly flexible submerged vegetation, called <i>Ceratophyllum</i>, by using the vegetation swaying characteristics and the flow field information in a steady-uniform open-channel flow is derived. The drag force on submerged flexible vegetation is characterized by the time-averaged flow velocity, turbulence intensity, and the additional force arising from the vegetation swaying. Based on the results of the numerical models in the previous studies (Wang et al., 2022a, 2022b, https://doi.org/10.1017/jfm.2022.598, https://doi.org/10.1017/jfm.2022.899), the drag coefficient is determined. It is revealed that the drag coefficient is influenced by a combination of factors, including the flow conditions, and the distribution and movement characteristics of vegetation. The drag coefficient decreases with an increase in velocity and is approximately linearly related to the cubic power of the bulk flow velocity. In the case of an inter-plant spacing of 0.5 times the initial plant height, the drag coefficient ranges from 10.72 to 2.11, as the Reynolds number varies from 20,000 to 50,000. Besides, the vegetation distribution density and the relative submergence influence the drag coefficient. In this context, the drag coefficient decreases linearly with an increase in the inter-plant spacing. For the Reynolds number equaling 50,000, the drag coefficient ranges from 2.11 to 2.02, when the inter-plant spacing varies from 0.5 to 2 times the plant height, and from 2.47 to 1.79, when the flow depth varies from 1.5 to 3 times the plant height.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Pivotal Role of Evaporation in Lake Water Isotopic Variability Across Space and Time in a High Arctic Periglacial Landscape","authors":"Pete D. Akers, Ben G. Kopec, Eric S. Klein, Hannah Bailey, Jeffrey M. Welker","doi":"10.1029/2023wr036121","DOIUrl":"https://doi.org/10.1029/2023wr036121","url":null,"abstract":"Rapidly changing climate is disrupting the High Arctic's water systems. As tracers of hydrological processes, stable water isotopes can be used for high quality monitoring of Arctic waters to better reconstruct past changes and assess future environmental threats. However, logistical challenges typically limit the length and scope of isotopic monitoring in High Arctic landscapes. Here, we present a comprehensive isotopic survey of 535 water samples taken in 2018 and 2019 of the lakes and other surface waters of the periglacial Pituffik Peninsula in far northwest Greenland. The <i>δ</i>¹⁸O, <i>δ</i>²H, and deuterium-excess values of these samples, representing 196 unique sites, grant unprecedented insight into the environmental drivers of the regional hydrology and water isotopic variability. We find that the spatial variability of lake water isotopes can best be explained through evaporation and the hydrological ability of a lake to replace evaporative water losses with precipitation and snowmelt. Temporally, summer-long evaporation can drive lake water isotopes beyond the isotopic range observed in precipitation, and wide interannual changes in lake water isotopes reflect annual weather differences that influenced evaporation. Following this, water isotope samples taken at individual times or sites in similar periglacial landscapes may have limited regional representativeness, and increasing the spatiotemporal extent of isotopic sampling is critical to producing accurate and informative High Arctic paleoclimate reconstructions. Overall, our survey highlights the diversity of isotopic compositions in Pituffik surface waters, and our complete isotopic and geospatial database provides a strong foundation for future researchers to study hydrological changes at Pituffik and across the Arctic.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}