Water Resources Research最新文献

{"title":"Exploring the Spatiotemporal Heterogeneity of Stream Nitrogen Concentrations in a Typical Human-Activity-Influenced Headwater Watershed in South China","authors":"Congsheng Fu, Haixia Zhang, Huawu Wu, Haohao Wu, Yang Cao, Ye Xia, Zichun Zhu","doi":"10.1029/2024wr038050","DOIUrl":"https://doi.org/10.1029/2024wr038050","url":null,"abstract":"Stream nitrogen concentrations significantly impact nitrogen loads and greenhouse gas emissions, but their spatiotemporal heterogeneity and human influences remain highly uncertain. This study thoroughly explored the spatiotemporal variations in stream nitrogen concentrations in a typical headwater watershed in South China. Spatially distributed measurements were conducted during 2020–2022, and mathematical modeling was implemented based on incorporating these data. More than 4,400 data points were collected for water temperature and concentrations of ammonium nitrogen (NH₄-N), nitrate nitrogen (NO_x-N), dissolved total nitrogen (DTN), total nitrogen (TN), and dissolved oxygen. Results showed that NO_x-N was the largest component of TN, with average concentrations of 1.20 and 1.66 mg L⁻¹, respectively. The stream N₂O concentration could be predicted using NH₄-N and NO_x-N concentrations via the Michaelis-Menten equation. Significant downstream decreases in NH₄-N, NO_x-N, DTN, and TN concentrations were identified in the largest river in the watershed, and clear spatial differences in these nitrogen concentrations existed among the three main rivers. Clear seasonal and annual variations in stream nitrogen concentrations were observed. NH₄-N, NO_x-N, DTN, and TN concentrations correlated with cumulative precipitation from the preceding 8–12 days, while stream N₂O concentrations correlated over 13–20 days. Stream N₂O concentrations and emissions averaged 12.77 nmol L⁻¹ and 1.12 nmol m⁻² s⁻¹, respectively, and were lower in summer than in other seasons. Upstream tea plantations, villages, and adjacent agricultural lands significantly affected nitrogen concentrations, while overflow dams did not. These findings highlight nitrogen cycle's complexity and the need for high-resolution data to guide effective watershed management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317411","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":"Onset for Active Swimming of Microorganisms to Shape Their Transport in Turbulent Open Channel Flows","authors":"Zi Wu, Li Zeng, Guangmiao Li, Zheng Gong, Jie Zhan, Weiquan Jiang, Mengzhen Xu, Xudong Fu","doi":"10.1029/2024wr037586","DOIUrl":"https://doi.org/10.1029/2024wr037586","url":null,"abstract":"Research on active particles has primarily focused on transport in relatively weak flows, during which their active swimming plays a significant role. However, in natural or manmade waterways, the ambient flow velocity and water depth can be on the order of approximately 1 m/s and 1 m, respectively, generating turbulent diffusion that may be strong enough to potentially dominate the transport process, so that the active swimming might be negligible. In this paper, we propose a theoretical framework aiming at identifying the threshold at which the effects of active swimming become significant, under conditions of insufficient data for motion statistics of swimmers. While deriving the governing equation, we find that only the vertical component of the mean swimming has the potential to significantly influence the transport process. This manifests as the characteristic of inducing a non-uniform vertical concentration distribution, in competition with the mechanism of turbulent diffusion, which leads to a uniform distribution. We obtain the analytical solution for the vertical concentration distribution, with the key dimensionless parameter <i>α</i> representing the interplay between the active swimming and turbulent diffusion. The threshold is found to be approximately at the order of magnitude of <i>α</i> ∼ 0.1, below which active swimming is considered negligible. The theoretical predictions are validated by numerical simulations employing Direct Numerical Simulation and particle tracking methods. Applying the theory to two types of microorganisms transported under different flow conditions suggests that there are typical scenarios where the active swimming is negligible, and the swimmers can be treated as passive particles.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321871","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":"Reconstruction of the Dynamics of a Catastrophic Crater Lake Outburst Flood, Changbaishan-Tianchi Volcano","authors":"Shengwu Qin, Jingyu Yao, Guangjie Li, Lingshuai Zhang, Xiaowei Liu, Chaobiao Zhang, Li Li","doi":"10.1029/2024wr037085","DOIUrl":"https://doi.org/10.1029/2024wr037085","url":null,"abstract":"Reconstruction of the catastrophic drainage following the Millennium Eruption (ME) of Changbaishan-Tianchi volcano in 946 ± 20 CE is of great significance, as it contributes to improving the regional maximum flood record and develop rare flood risk analysis. However, limited knowledge exists concerning the failure mode, magnitude, and transport processes of the outburst flooding. In this work, we present a whole system model that describes the paleohydrology of catastrophic drainage using geological records along the downstream valley. The model encompasses the crater lake dynamics, an approximation of the breach erosion process and flood propagation downstream. The boulder competence method was used to constrain by reasonable flow parameters, while mitigating the uncertainty caused by the ambiguous geological paleostage indicators. Paleohydrologic analysis indicates that at least 1 km³ of water was released from the caldera, with the vertical breach erosion rates as high as 34 m/hr. Volcanic activity during the ME II may have directly contributed to triggering of the flood event. The local hydrodynamic response of the downstream riverbed captures the dynamic migration patterns of sediments across spatio-temporal scales, offering a comprehensive interpretation of the specific scouring surfaces observed in the geological profile. The analysis of simulated inundation boundaries reveals that not all recorded inundations can be attributed to the crater lake outburst event. Reconstructions of megafloods based on downstream constraints on flood stage, velocity and discharge can help to infer and constrain the dynamics of dam failure mechanisms, and also contribute to our understanding of these complex paleohydrologic events.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306216","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":"Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region","authors":"Jie Chen, Xingchen Lin, Tonghua Wu, Junming Hao, Xiaodong Wu, Defu Zou, Xiaofan Zhu, Guojie Hu, Yongping Qiao, Dong Wang, Sizhong Yang, Lina Zhang","doi":"10.1029/2023wr036700","DOIUrl":"https://doi.org/10.1029/2023wr036700","url":null,"abstract":"Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze-thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze-thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large-scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai-Tibet Plateau (QTP), covering 18,500 km². We developed dedicated machine learning (ML) models that integrate these InSAR-derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full-coverage deformation map for permafrost terrains, achieving an <i>R</i>² value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost-related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306236","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":"Attributing Urban Evapotranspiration From Eddy-Covariance to Surface Cover: Bottom-Up Versus Top-Down","authors":"H. J. Jongen, S. Vulova, F. Meier, G. J. Steeneveld, F. A. Jansen, D. Tetzlaff, B. Kleinschmit, N. Haacke, A. J. Teuling","doi":"10.1029/2024wr037508","DOIUrl":"https://doi.org/10.1029/2024wr037508","url":null,"abstract":"Evapotranspiration &lt;span data-altimg=\"/cms/asset/5d18cc63-d71d-49d9-9c2a-db2a954fa1fb/wrcr27473-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"216\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27473-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-children=\"4\" data-semantic-content=\"0,5\" data-semantic- data-semantic-role=\"leftright\" data-semantic-speech=\"left parenthesis upper E upper T right parenthesis\" data-semantic-type=\"fenced\"&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"6\" data-semantic-role=\"open\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mrow data-semantic-annotation=\"clearspeak:simple;clearspeak:unit\" data-semantic-children=\"1,2\" data-semantic-content=\"3\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"4\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;/mjx-mrow&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"6\" data-semantic-role=\"close\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:00431397:media:wrcr27473:wrcr27473-math-0001\" display=\"inline\" location=\"graphic/wrcr27473-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow data-semantic-=\"\" data-semantic-children=\"4\" data-semantic-content=\"0,5\" data-semantic-role=\"leftright\" data-semantic-speech=\"left parenthesis upper E upper T right parenthesis\" data-semantic-type=\"fenced\"&gt;&lt;mo data-semantic-=\"\" data-semantic-operator=\"fenced\" data-semantic-parent=\"6\" data-semantic-role=\"open\" data-semantic-type=\"fence\" stretchy=\"false\"&gt;(&lt;/mo&gt;&lt;mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple;clearspeak:unit\" data-semantic-children=\"1,2\" data-semantic-content=\"3\" data-semantic-parent=\"6\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"4\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;E&lt;/mi&gt;&lt;mo data-semantic-=\"\" data","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273666","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":"Predictor Importance for Hydrological Fluxes of Global Hydrological and Land Surface Models","authors":"João Paulo L. F. Brêda, Lieke A. Melsen, Ioannis Athanasiadis, Albert Van Dijk, Vinícius A. Siqueira, Anne Verhoef, Yijian Zeng, Martine van der Ploeg","doi":"10.1029/2023wr036418","DOIUrl":"https://doi.org/10.1029/2023wr036418","url":null,"abstract":"Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the understanding of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest (RF) feature importance to detect the input variables that most influence simulated hydrological fluxes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX, and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoff. RF models functioned as a metamodel and could reproduce GHM/LSMs outputs with a coefficient of determination (<i>R</i>²) over 0.85 in all cases and often considerably better. The GHM/LSMs agreed that precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff, while topographic slope and soil texture have no influence on the total variance of the water balance. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regard to the relative importance of soil texture and topographic slope. Finally, the selection of soil maps was only important for target variables of which soil is a relevant predictor. We conclude that estimating feature importance is a useful diagnostic approach for model intercomparison projects.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142247158","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":"Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning","authors":"Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja","doi":"10.1029/2024wr037122","DOIUrl":"https://doi.org/10.1029/2024wr037122","url":null,"abstract":"Current machine learning methods for discharge prediction often employ aggregated basin-wide hydrometeorological data (lumped modeling) for parametric and non-parametric training. This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating spatiotemporal hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. To test this hypothesis, we designed experiments comparing the performance of identical Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models forced with either lumped or distributed features. We gather meteorological forcing and static attributes for the Mackenzie basin in Canada- a large and unique basin. Importantly, discharge performance is assessed out-of-sample with k-fold replication across gauges. Training LSTMs with disaggregated data significantly improved model accuracy. Specifically, there was a 9.6% increase in the mean Nash-Sutcliffe Efficiency and a 4.6% increase in the mean Kling-Gupta Efficiency, indicating a better agreement between predicted and actual observations in terms of mean, variability, and correlation. These experiments and results demonstrate the importance of integrating topologically guided geomorphologic and hydrologic information (distributed modeling) in data-driven discharge predictions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246266","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":"Analysis of Agricultural Water Security Based on Network Invulnerability: A Case Study in China's Virtual Water Trade Networks","authors":"X. Liang, A. Long, X. Han, X. Lai, Y. Meng","doi":"10.1029/2023wr036497","DOIUrl":"https://doi.org/10.1029/2023wr036497","url":null,"abstract":"“Invulnerability” of complex network was firstly introduced to virtual water (VW) research, aiming to broaden the scope of studies on water use and management. Beginning with the construction of China's virtual water trade networks (VWTNs) of major grain crops, <i>Node Degree</i> (<i>K</i>) and <i>Betweenness Centrality</i> (<i>B</i>) are employed to evaluate and rank the importance of China's 31 regions. Regions with high values for both indicators are identified as playing pivotal roles in the VWTNs: Jiangsu (ranking 1st for both <i>K</i> and <i>B</i>), Hubei (2nd for <i>K</i>, 3rd for <i>B</i>), Henan (3rd for <i>K</i>, 6th for <i>B</i>), Hebei (4th for <i>K</i>, 4th for <i>B</i>), Hunan (4th for <i>K</i>, 5th for <i>B</i>). Using this ranking to simulate the invulnerability of VWTNs under random and intentional attacks. The results reveal a rapid decrease in both <i>Network Efficiency</i> (<i>E</i>) and <i>Maximum Connectivity</i> (<i>C</i>) under intentional attack. In comparison to seven random attacks, <i>E</i> falls below 0.1 and <i>C</i> drops below 0.5 after only three intentional attacks, and the network completely collapsed after 10 intentional attacks. This highlights the VWTN's vulnerability in maintaining food supply and agricultural water security when key regions are subjected to man-made destruction, such as military blockades or occupations. Future work should include integrating climate change models, crops yield models, and water resource allocation models to protect the key areas. Furthermore, interdisciplinary approaches are crucial for overcoming the limitations of VW research and these findings will provide valuable insights to enhance the optimal regulation of VWTNs.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142247173","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":"In-Stream Nitrogen Dynamics in a Point Source Influenced Headwater Stream During Baseflow Conditions","authors":"Caroline Spill, Lukas Ditzel, Matthias Gassmann","doi":"10.1029/2023wr036672","DOIUrl":"https://doi.org/10.1029/2023wr036672","url":null,"abstract":"Hydrochemical signatures are often traced back to their original sources using data collected at catchment outlets. However, this approach introduces uncertainties, as signals may add up, cancel each other out, or be subject to transformation processes. Specifically rural point sources, such as communal wastewater treatment plants (WWTPs), are often overlooked and remain poorly understood in terms of their (local) impact, on water quality and quantity dynamics. We equipped a point source-influenced headwater catchment with a comprehensive measurement setup, to directly trace the different hydrochemical signals. Statistical approaches were used to address c-Q relationships and hydrochemical drivers for nutrient export upstream, downstream and within the WWTP during baseflow conditions. Groundwater infiltration into the old and leaky sewer system as well as rainwater collected via the combined sewer system were found to significantly alter processes within the WWTP, resulting in highly variable effluent nutrient concentrations. Ammonium introduced by the WWTP is rapidly transformed in the stream, leading to increasing nitrate concentrations further downstream. The combination of processes introduced by the WWTP overlap the dilution and (non-significant) chemostatic patterns of the upstream nitrate-discharge relationship, leading to enrichment patterns shortly after, and mainly diluting patterns 290 m downstream of the WWTP. Regarding maximum nutrient concentrations, dry periods during autumn were particularly critical, as the WWTP introduced high ammonium concentrations, which coincided with high nitrate concentrations from the catchment and a minimal dilution potential of the stream. Our study demonstrates the importance of incorporating all nutrient sources into catchment analyses, to facilitate successful management decisions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246267","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 Aspect-Dependent Snowpack Response to High-Elevation Wildfire in the Southern Rocky Mountains","authors":"Wyatt Reis, Daniel McGrath, Kelly Elder, Stephanie Kampf, David Rey","doi":"10.1029/2023wr036539","DOIUrl":"https://doi.org/10.1029/2023wr036539","url":null,"abstract":"Increasing wildfire frequency and severity in high-elevation seasonal snow zones presents a considerable water resource management challenge across the western United States (U.S.). Wildfires can affect snowpack accumulation and melt patterns, altering the quantity and timing of runoff. While prior research has shown that wildfire generally increases snow melt rates and advances snow disappearance dates, uncertainties remain regarding variations across complex terrain and the energy balance between burned and unburned areas. Utilizing paired in situ data sources within the 2020 Cameron Peak burn area on the Front Range of Colorado, U.S., during the 2021–2022 winter, we found no significant difference in peak snow water equivalent (SWE) magnitude between burned and unburned areas. However, the burned south aspect reached peak SWE 22 days earlier than burned north. During the ablation period, burned south melt rates were 71% faster than unburned south melt rates, whereas burned north melt rates were 94% faster than unburned north aspects. Snow disappeared 7–11 days earlier in burned areas than unburned areas. Net energy differences at the burned and unburned weather station sites were seasonally variable, the burned area snowpack lost more net energy during the winter, but gained more net energy during the spring. Increased incoming shortwave radiation at the burned site was 6<i>x</i> more impactful in altering the net shortwave radiation balance than the decline in surface albedo. These findings emphasize the need for post-wildfire water resource planning that accounts for aspect-dependent differences in energy and mass balance to accurately predict snowpack storage and runoff timing.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246257","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}