Journal of Hydrology X最新文献

{"title":"Air temperature data source affects inference from statistical stream temperature models in mountainous terrain","authors":"Daniel J. Isaak,&nbsp;Dona L. Horan,&nbsp;Sherry P. Wollrab","doi":"10.1016/j.hydroa.2024.100172","DOIUrl":"https://doi.org/10.1016/j.hydroa.2024.100172","url":null,"abstract":"<div><p>Instream temperatures control numerous biophysical processes and are frequently the subject of modeling efforts to understand and predict responses to watershed conditions, habitat alterations, and climate change. Air temperature (AT) is regularly used in statistical temperature models as a covariate proxy for physical processes and because it correlates strongly with spatiotemporal variability in water temperatures (T_w). Air temperature data are broadly available and sourced from sensors paired with T_w sites, remote weather stations, and gridded climate data sets—often with limited recognition of the tradeoffs these sources present and how microclimatic variation in topographically complex mountain environments could affect model inference. To address these issues, we collected daily T_w records at 13 sites throughout a mountain river network, linked the records to AT data from 11 sources available across much of North America, and fit linear regression models to assess predictive performance and the consistency of parameter estimation. Although the predictive accuracy of these models was generally high, estimates of the AT slope parameter, which is commonly interpreted as thermal sensitivity, varied substantially depending on the AT data source. These results have implications for the comparability of estimates among T_w studies and highlight the challenges that modeling stream temperatures in mountain landscapes presents. Although no AT data source is ideal, some are more advantageous than others for specific use cases and we provide general recommendations on this topic.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"22 ","pages":"Article 100172"},"PeriodicalIF":4.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000026/pdfft?md5=11fd939c8e5f90acdaf3ccb06e410169&pid=1-s2.0-S2589915524000026-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139718427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic reservoir rule curves – Their creation and utilization","authors":"Nesa Ilich","doi":"10.1016/j.hydroa.2023.100166","DOIUrl":"10.1016/j.hydroa.2023.100166","url":null,"abstract":"<div><p>This paper presents a methodology for the creation of dynamic reservoir rule curves on the basis of the results of implicit stochastic optimization coupled with optimized demand hedging embedded as constraints to optimization. The novelty of the method is a dynamic rule curve that always starts from the current storage level and projects a range of anticipated target levels in the immediate future based on the statistical analyses of the results of implicit stochastic optimization. The method is particularly useful in dry years when storage is not completely filled at the end of wet seasons. Such situations cannot be addressed with standard traditional rule curves, thus causing reservoir operators to base their decisions on mere judgment. The proposed method can be helpful in such situations. The method has been demonstrated on the Tawa reservoir in the Narmada River Basin in India.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"22 ","pages":"Article 100166"},"PeriodicalIF":4.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915523000202/pdfft?md5=bf7c35088f989619546d164e0ec600bf&pid=1-s2.0-S2589915523000202-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139020682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can local drain flow measurements be utilized to improve catchment scale modelling?","authors":"Ida Karlsson Seidenfaden ,&nbsp;Xin He ,&nbsp;Anne Lausten Hansen ,&nbsp;Bo V. Iversen ,&nbsp;Anker Lajer Højberg","doi":"10.1016/j.hydroa.2023.100170","DOIUrl":"https://doi.org/10.1016/j.hydroa.2023.100170","url":null,"abstract":"<div><p>Tile drains constitute a shortcut from agricultural fields to surface water systems, significantly altering the transport pathways and fate of nitrate during transport. A correct representation of tile drainage flow is thus crucial for estimating nitrate load at the catchment scale and to identify optimal locations for N-mitigation measures. Drainage is a local process, controlled by local properties and drain configurations, which are rarely known for individual fields, making drainage flow and transport a challenging task in catchment scale models. This study tests the potential for improving drainage flow dynamics at catchment scale, by utilising local drainage flow measurements in a spatial calibration scheme. A distributed hydrological model, MIKE SHE, for the agricultural-dominated Norsminde catchment (145 km²) in Denmark, was calibrated using spatially distributed surrogate parameters (pilot points) to represent heterogeneity in the soil (top 3 m) and the deeper geology below 3 m. The model was calibrated using hydraulic heads, stream discharge, and measured drainage flow from eight drain catchments. Drain measurements were very important in guiding the calibration of top 3 m and subsurface pilot points located in the drainage fields, showing that drain flow hold information on both local (shallow) and regional (deeper) flow patterns. Contrarily, pilot points located outside the drained fields were mainly sensitive to the hydraulic head measurements and the summer water balance of the stream discharge on a catchment scale. Consequently, incorporation of the drain data improved local performance, but did not improve the parameterization and drain description of the entire catchment. Exploitation of the drain flow information is thus difficult beyond the drain catchments, and other approaches are needed to extrapolate and exploit the local data.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"22 ","pages":"Article 100170"},"PeriodicalIF":4.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258991552300024X/pdfft?md5=5bf47525c4cb97a6f33d60e6f7e95813&pid=1-s2.0-S258991552300024X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139100480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal changes in the frequency of flood types and their impact on flood statistics","authors":"Svenja Fischer,&nbsp;Andreas H. Schumann","doi":"10.1016/j.hydroa.2024.100171","DOIUrl":"https://doi.org/10.1016/j.hydroa.2024.100171","url":null,"abstract":"<div><p>Standard flood frequency analysis assumes stationarity of flood conditions, i.e., no change of the distribution over time. However, long-term variability in climate and anthropogenic impacts question this assumption. Consequently, more and more non-stationary models are considered in flood frequency analyses. Yet, most of them only consider a change-point or trend in the magnitude of flood peaks while ignoring changes in the underlying flood geneses. Recent climate reports suggest such a change in frequency of certain flood-generating factors, e.g., the increase of frequency of heavy-rainfall events. In this study, flood types are applied to detect changes in the meteorological drivers of flood regimes. By application of a robust change-point test for the variance based on Gini’s Mean Difference, significant changes in the frequency of occurrence of certain flood types are detected. A clear tendency to more frequent heavy-rainfall floods and less snowmelt-induced floods is observed for many catchments in Central Europe. A special focus is laid on the shifts in winter floods, which occur less often and are replaced by rainfall-driven floods. The impacts of such changes on flood statistics are demonstrated by several approaches. Though the magnitude of flood peaks does not (necessarily) change, the changing frequency of floods leads to changing flood quantiles. Quantile estimations from traditional statistical analyses of annual series are compared to results of type-based flood statistics. It is shown how standard models are more affected by these changes because they are not able to compensate for changes in the frequency of individual flood types.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"22 ","pages":"Article 100171"},"PeriodicalIF":4.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000014/pdfft?md5=1dfa2f5c9390efc831275ba982ec4595&pid=1-s2.0-S2589915524000014-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139505364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying and valuing irrigation in energy and water limited agroecosystems","authors":"Mehmet Evren Soylu ,&nbsp;Rafael L. Bras","doi":"10.1016/j.hydroa.2023.100169","DOIUrl":"10.1016/j.hydroa.2023.100169","url":null,"abstract":"<div><p>Agriculture in regions with limited water availability is possible because of irrigation. Irrigated croplands are expanding, and irrigation water demand is increasing. Nevertheless, there is a limited understanding of how much water is consumed for irrigation and how effective irrigation increases crop productivity in various climates. In this study, we aim to understand how irrigation water affects crop productivity in different climates. To achieve this goal, we developed a simple approach to quantify irrigation quantities from SMAP satellite soil moisture observations based on a zero-dimensional bucket-type hydrology model. The central assumption is that irrigation quantities can be estimated from the gap between the modeled and observed soil moisture by iteratively providing irrigation as a model input until the soil moisture simulations agree well with the observations. We then used the estimated amount of irrigation to simulate water, energy, and carbon fluxes at two agricultural sites on the west coast of the US: one that was water-limited (Central Valley, CA) and one that was energy-limited (Eugene, OR). An agroecosystem model, AgroIBIS-VSF, was used to conduct simulations. To verify our simulations, we used data from two AmeriFlux Eddy covariance towers at each site. We found that incorporating estimated irrigation amounts into our simulations improved the accuracy of energy balance components and soil moisture predictions, reducing the root-mean-square error of soil moisture predictions by up to 22%. We also discovered that the irrigation value, in terms of increased productivity of actual irrigation water used, is more than five times more valuable at the energy-limited site than at the water-limited site. Soil hydraulic properties have a strong influence on irrigation water valuation. Our study highlights the potential of satellite soil moisture observations to improve our understanding of water productivity in different climates. By better understanding the efficiency of resources used for crop production, we can ensure the sustainability and resilience of agricultural systems, leading to better management practices.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"22 ","pages":"Article 100169"},"PeriodicalIF":4.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915523000238/pdfft?md5=d68724b3a72462813474ca5aedef051b&pid=1-s2.0-S2589915523000238-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138989782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data driven real-time prediction of urban floods with spatial and temporal distribution","authors":"Simon Berkhahn,&nbsp;Insa Neuweiler","doi":"10.1016/j.hydroa.2023.100167","DOIUrl":"https://doi.org/10.1016/j.hydroa.2023.100167","url":null,"abstract":"<div><p>The increase in extreme rainfall events due to climate change, combined with urbanisation, leads to increased risks to urban infrastructure and human life. Physically based urban flood models capable of producing water depth maps with sufficient spatial and temporal resolution are generally too slow for decision makers to react in time during an extreme event. We present a surrogate model with high temporal and spatial resolution for real-time prediction of water levels during a pluvial urban flood. We used machine learning techniques to achieve short computation times. The recursive approach used in this work combines convolutional and fully coupled multilayer architectures. The database for the machine learning was pre-simulated results from a physically based urban flood model. The forcing input of the prediction is precipitation and the output is water level maps with a temporal resolution of 5 min and a spatial resolution of 6 x 6 meters. The prediction performance can be considered promising for testing the model in real operational applications.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"22 ","pages":"Article 100167"},"PeriodicalIF":4.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915523000214/pdfft?md5=18cd45b2333732f44ad4fe4186167d55&pid=1-s2.0-S2589915523000214-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139038431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting groundwater levels using machine learning methods: The case of California’s Central Valley","authors":"Gabriela May-Lagunes ,&nbsp;Valerie Chau ,&nbsp;Eric Ellestad ,&nbsp;Leyla Greengard ,&nbsp;Paolo D'Odorico ,&nbsp;Puya Vahabi ,&nbsp;Alberto Todeschini ,&nbsp;Manuela Girotto","doi":"10.1016/j.hydroa.2023.100161","DOIUrl":"10.1016/j.hydroa.2023.100161","url":null,"abstract":"<div><p>Groundwater, the second largest stock of freshwater on the planet, is an important water source used for municipal water supply, irrigation, or industrial needs. For instance, California’s arid Central Valley relies on groundwater resources to produce a quarter of the United States’ food demand as farmers rely on this precious resource when surface water is scarce. Despite its importance, the nexus between groundwater dynamics and climate drivers remains difficult to quantify, model, and predict because of the lack of a comprehensive observation network. In this study, machine learning techniques were used to predict groundwater levels with a 3-month forecasting horizon for the Sacramento River Basin. For this, publicly available meteorological and hydrological datasets and in-situ well-level measurements were used. Time series, ensemble-based, and deep-learning models including transformers were all tested, with an ensemble-based, XGBoost model, producing the best mean standard deviation percent error (MSPE) of 32.23% and a root mean squared error (RMSE) of 1.05 m (m) when using a 3- month forecasting horizon and when tested using a monthly rolling window over the years 2017–2020. The model proved to be better at predicting into wet months than the dry summer months and was found to be better at extracting seasonality than explaining well-level residuals, with well-specific features, as opposed to exogenous meteorological features specific to the hydrological unit of the well, ranking as the most important features to the model. Though other forecasting horizons were tested, a 3-month look-ahead window resulted in the best balance of precision and accuracy, where smaller forecasting horizons resulted in smaller RMSE but larger MSPE scores and vice-versa for larger forecasting horizons.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"21 ","pages":"Article 100161"},"PeriodicalIF":4.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915523000147/pdfft?md5=aab140af4d0a28517df303e628b13bca&pid=1-s2.0-S2589915523000147-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136127854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Optimizing nature-based solutions by combining social equity, hydro-environmental performance, and economic costs through a novel Gini coefficient” [J. Hydrol. 16 (2022) 100127]","authors":"C.V. Castro","doi":"10.1016/j.hydroa.2023.100164","DOIUrl":"10.1016/j.hydroa.2023.100164","url":null,"abstract":"","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"21 ","pages":"Article 100164"},"PeriodicalIF":4.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915523000184/pdfft?md5=3803d1624812749128b1ab9a5e1d900f&pid=1-s2.0-S2589915523000184-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135654324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Optimizing nature-based solutions by combining social equity, hydro-environmental performance, and economic costs through a novel Gini coefficient” [J. Hydrol. 16 (2022) 100127]","authors":"C.V. Castro","doi":"10.1016/j.hydroa.2023.100162","DOIUrl":"10.1016/j.hydroa.2023.100162","url":null,"abstract":"","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"21 ","pages":"Article 100162"},"PeriodicalIF":4.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915523000160/pdfft?md5=5630650189e9e0ceb5da8f97949d8751&pid=1-s2.0-S2589915523000160-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135410762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the distribution of headwater streams using topoclimatic indices, remote sensing and machine learning.","authors":"Joshua L. Erickson ,&nbsp;Zachary A. Holden ,&nbsp;James A. Efta","doi":"10.1016/j.hydroa.2023.100165","DOIUrl":"https://doi.org/10.1016/j.hydroa.2023.100165","url":null,"abstract":"<div><p>Headwater streams (HWS) are ecologically important components of montane ecosystems. However, they are difficult to map and may not be accurately represented in existing spatial datasets. We used topographically resolved climatic water balance data and satellite indices retrieved from Google Earth Engine to model the occurrence (presence or absence) of HWS across Northwest Montana. A multi-scale feature selection (MSFS) procedure and boosted regression tree models/machine learning algorithms were used to identify variables associated with HWS occurrence. In final model evaluation, models that included climatic water balance deficit were more accurate (83.5% ranging from 82.9% to 83.7%) than using only terrain indices (81.1% ranging from 80.7% to 81.4%) and improved upon estimates of stream extent represented by the National Hydrography Dataset Plus High Resolution (NHDPlus HR) (82.7% ranging from 82.5% to 83.1%). Including topoclimate captured the varying effect of upslope accumulated area across a strong moisture gradient. Multi-scale cross-validation, coupled with a MSFS algorithm allowed us to find a parsimonious model that was not immediately evident using standard cross-validation procedures. More accurate spatial model predictions of HWS have potential for immediate application in land and water resource management, where significant field time can be spent identifying potential stream impacts prior to contracting and planning.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"21 ","pages":"Article 100165"},"PeriodicalIF":4.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915523000196/pdfft?md5=f57e063afc97ddaf4df4a2eb4731152d&pid=1-s2.0-S2589915523000196-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138395458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}