{"title":"Comparison of decadal water storage trends from common GRACE releases (RL05, RL06) using spatial diagnostics and a modified triple collocation approach","authors":"Emad Hasan , Aondover Tarhule","doi":"10.1016/j.hydroa.2021.100108","DOIUrl":"10.1016/j.hydroa.2021.100108","url":null,"abstract":"<div><p>GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On) satellites have provided unique insights into the evolution of Terrestrial Water Storage (TWS) in space and time. Despite such advancements, various GRACE solutions produced by different data centers display uneven spatial attributes with varying associated uncertainties. Via spatial diagnostics tools and a modified triple collocation (MTC) approach, this research evaluates the TWS (terrestrial water storage) trend estimations “<em>on the grid-scale</em>” from 11 gridded GRACE products of RL05 and RL06 releases between 2002 and 2017. Distinct from classic TCA (triple collocation analysis), the MTC employs a GWR (geographically weighted regression) scaling scheme with distinctive spatial coefficients. The spatial diagnostics analyses identified different autocorrelation patterns, clustering tendencies of hot (positive) and cold (negative) spots agglomeration at varying spatial width, and unique frequency distributions. The results indicated that within a 10-degree spatial radius the SHs (Spherical Harmonics) of RL05 and RL06 are highly autocorrelated compared to the mascons (mass concentration blocks) solutions. The spatial clustering results revealed that many solutions agreed on the overall directions and distribution of the hot and cold spots. The clustering among mascon products, however, reflected more localized mass anomalies. At the scale of drainage basins, the trend magnitude, as well as their associated uncertainties appeared to be driven by the occurrence of spatial clusters within the basin area. The MTC results showed that the uncertainty patterns follow the same spatial extent within each cluster. The MTC analysis underscored the added benefits of cluster analysis and the GWR scaling over the classic OLS approach.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"13 ","pages":"Article 100108"},"PeriodicalIF":4.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915521000365/pdfft?md5=6ce9aaaf74d311e197f2f5e18b658f20&pid=1-s2.0-S2589915521000365-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48188143","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":"Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective","authors":"Kue Bum Kim, H. Kwon, Dawei Han","doi":"10.1016/j.hydroa.2021.100109","DOIUrl":"https://doi.org/10.1016/j.hydroa.2021.100109","url":null,"abstract":"","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42196952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Withdrawal notice to “Experimental evidence of the wind-induced bias of precipitation gauges using Particle Image Velocimetry and particle tracking in the wind tunnel” [HYDROA 12 (2021) 100081]","authors":"Arianna Cauteruccio , Elia Brambill , Mattia Stagnaro , Luca G. Lanza , Daniele Rocchi","doi":"10.1016/j.hydroa.2021.100094","DOIUrl":"10.1016/j.hydroa.2021.100094","url":null,"abstract":"","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"12 ","pages":"Article 100094"},"PeriodicalIF":4.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hydroa.2021.100094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44366126","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}
Ico Broekhuizen , Santiago Sandoval , Hanxue Gao , Felipe Mendez-Rios , Günther Leonhardt , Jean-Luc Bertrand-Krajewski , Maria Viklander
{"title":"Performance comparison of green roof hydrological models for full-scale field sites","authors":"Ico Broekhuizen , Santiago Sandoval , Hanxue Gao , Felipe Mendez-Rios , Günther Leonhardt , Jean-Luc Bertrand-Krajewski , Maria Viklander","doi":"10.1016/j.hydroa.2021.100093","DOIUrl":"10.1016/j.hydroa.2021.100093","url":null,"abstract":"<div><p>Green roofs can be valuable components in sustainable urban drainage systems, and hydrological models may provide useful information about the runoff from green roofs for planning purposes. Various models have been proposed in the literature, but so far no papers have compared the performance of multiple models across multiple full-size green roofs. This paper compared 4 models: the conceptual models Urbis and SWMM and the physically-based models Hydrus-1D and Mike SHE, across two field sites (Lyon, France and Umeå, Sweden) and two calibration periods for each site. The uncertainty and accuracy of model predictions were dependent on the selected calibration site and period. Overall model predictions from the simple conceptual model Urbis were least accurate and most uncertain; predictions from SWMM and Mike SHE were jointly the best in terms of raw percentage observations covered by their flow prediction intervals, but the uncertainty in the predictions in SWMM was smaller. However, predictions from Hydrus were more accurate in terms of how well the observations conformed to probabilistic flow predictions. Mike SHE performed best in terms of total runoff volume. In Urbis, SWMM and Hydrus uncertainty in model predictions was almost completely driven by random uncertainty, while parametric uncertainty played a significant role in Mike SHE. Parameter identifiability and most likely parameter values determined with the DREAM Bayesian algorithm were found to be inconsistent across calibration periods in all models, raising questions about the generalizability of model applications. Calibration periods where rainfall retention was highly variable between events were more informative for parameter values in all models.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"12 ","pages":"Article 100093"},"PeriodicalIF":4.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hydroa.2021.100093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42850351","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}
Amina Nouhou Bako , Carine Lucas , Frédéric Darboux , François James , Noémie Gaveau
{"title":"A unifying model framework for soil erosion, river bedload and chemical transport","authors":"Amina Nouhou Bako , Carine Lucas , Frédéric Darboux , François James , Noémie Gaveau","doi":"10.1016/j.hydroa.2021.100082","DOIUrl":"10.1016/j.hydroa.2021.100082","url":null,"abstract":"<div><p>A unified framework for simulating various transport processes in the environment is presented. It consists in a single set of partial differential equations. The main feature of this model framework is its exchange layer, which allows to treat several types of transfer between the soil and the surface water.</p><p>The model framework equations, termed transfer equations, is shown to reproduce three independently-published models developed for soil erosion, river bedload, and chemical transport respectively. By allowing the different processes to be represented within a single model framework, the transfer equations are therefore unifying the representation of particles and chemical fluxes in the environment. The transfer equations are implemented into the open-source software FullSWOF_1D. The code is verified against the approximation of an exact solution, assuring its proper functioning. A good adequacy is found between our numerical results and those published in the literature, attesting the capability of the transfer equations to unify modeling of soil erosion, river bedload, and chemical transport. Hence, the transfer equations can decrease the number of models to be used for simulating transfer of materials in the environment, and limit the number of computer codes to be developed and maintained. The transfer equations could also help in drawing parallels between different fields of hydrology.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"12 ","pages":"Article 100082"},"PeriodicalIF":4.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hydroa.2021.100082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45630692","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}
A. Cauteruccio, E. Brambilla, M. Stagnaro, L. Lanza, D. Rocchi
{"title":"WITHDRAWN: Experimental evidence of the wind-induced bias of precipitation gauges using Particle Image Velocimetry and particle tracking in the wind tunnel","authors":"A. Cauteruccio, E. Brambilla, M. Stagnaro, L. Lanza, D. Rocchi","doi":"10.1016/j.hydroa.2021.100081","DOIUrl":"https://doi.org/10.1016/j.hydroa.2021.100081","url":null,"abstract":"","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hydroa.2021.100081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46350415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"WITHDRAWN: Efficient simulation of groundwater solute transport using the multipoint flux approximation method with arbitrary polygon grids","authors":"Yulong Gao, Shuping Yi, C. Zheng","doi":"10.1016/j.hydroa.2021.100083","DOIUrl":"https://doi.org/10.1016/j.hydroa.2021.100083","url":null,"abstract":"","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hydroa.2021.100083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42162582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhipeng Zhu , Asphota Wasti , Trent Schade , Patrick A. Ray
{"title":"Techniques to evaluate the modifier process of National Weather Service flood forecasts","authors":"Zhipeng Zhu , Asphota Wasti , Trent Schade , Patrick A. Ray","doi":"10.1016/j.hydroa.2020.100073","DOIUrl":"https://doi.org/10.1016/j.hydroa.2020.100073","url":null,"abstract":"<div><p>The operational hydrologists of the United States’ National Weather Service (NWS) develop river forecasts as guidance for those at risk of flood damage and update those flood forecasts in real-time as more information becomes available. To do so they rely on experience and intuition to adjust the inputs, state variables, and parameters of hydrologic models. NWS hydrologists use the term “modifiers” to refer collectively to these adjustments. This paper demonstrates the development and application of tools (statistical and graphical) to aid operational hydrologists in the achievement of accurate flood forecasts. Analysis of variance (ANOVA) identifies the relative contribution to forecast uncertainty of each modifier. Heat map visualizations illustrate for operational hydrologists the basin, lead-time, and season-specific effects of their modifiers choices. The tools provide operational hydrologists with insight into which of three commonly applied modifiers (precipitation, soil moisture, and unit hydrograph shape) are most likely to provide improvement in flood forecast accuracy. The tools are demonstrated for a case study of four watersheds within in the Ohio River Valley, using data for flood events sampled from 1990 to 2018. The findings of this research show that operational hydrologists in the Ohio River Basin would do well apply no modifiers in the winter (leaving hydrologic input variables and parameters at baseline values). And though the forecast might be improved by real-time adjustments to the unit hydrograph in summer months, recommendations for particular unit hydrograph modification levels cannot be made with confidence. These findings call into question the modifier adjustment program as a standard process. In the evaluated cases, modifiers do not systematically improve flood forecasts. Improvement may be more efficiently achieved through better calibration of hydrologic models or techniques for reduction of precipitation uncertainty.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"11 ","pages":"Article 100073"},"PeriodicalIF":4.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hydroa.2020.100073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72119545","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}
Jannis Epting , Adrien Michel , Annette Affolter , Peter Huggenberger
{"title":"Climate change effects on groundwater recharge and temperatures in Swiss alluvial aquifers","authors":"Jannis Epting , Adrien Michel , Annette Affolter , Peter Huggenberger","doi":"10.1016/j.hydroa.2020.100071","DOIUrl":"10.1016/j.hydroa.2020.100071","url":null,"abstract":"<div><p>Climate change will have both quantitative and qualitative effects on groundwater resources. These impacts differ for aquifers in solid and unconsolidated rock, in urban or rural locations, and in the principal processes of groundwater recharge.</p><p>Having knowledge about the intrinsic key parameters (aquifer geometries, storage properties, groundwater renewal rates, residence times, etc.), the principal groundwater recharge processes, and the temperature imprinting makes it possible to compare and forecast the sensitivity of individual aquifers to climate change.</p><p>The sensitivity of future groundwater temperature development for selected climate projections was qualitatively investigated for representative Swiss unconsolidated rock groundwater resources in the Central Plateau as well as the Jura and Alpine region.</p><p>For non-urban and rural areas, climate change is expected to have a strong overall impact on groundwater temperatures. In urban areas, however, direct anthropogenic influences are likely to dominate. Increased thermal subsurface use and waste heat from underground structures, as well as adaptation strategies to mitigate global warming, increase groundwater temperatures. Likewise, measurements for the city of Basel show that groundwater temperatures increased by an average of 3.0 ± 0.7 °C in the period from 1993 to 2016, and that they can exceed 18 °C, especially in densely urbanized areas. Similarly, regarding shallow aquifers with low groundwater saturated zone thicknesses, such as in Davos (Canton Grisons), groundwater temperatures will strongly be influenced by changes in groundwater recharge regimes. In contrast, groundwater temperature changes within deep aquifers with large groundwater saturated zone thicknesses, such as in Biel/Bienne (Canton Bern), or in some cases in aquifers with large distances from the land surface to the groundwater table and extended unsaturated zones, such as in Winterthur (Canton Zurich), are strongly attenuated and can only be expected over long time periods.</p><p>In the context of the presented research we hypothesized that quantitative groundwater recharge and the associated temperature imprinting of aquifers is primarily determined by infiltrating surface waters (i.e. “river-fed aquifers”). We show that seasonal shifts in groundwater recharge processes could be an important factor affecting future groundwater temperatures. Moreover, the interaction with surface waters and increased groundwater recharge during high runoff periods are likely to strongly influence groundwater temperatures. Accordingly, for the “business as usual” climate change scenario and for the end of the century, a shift in precipitation and river flood events from summer to winter months could be accompanied by an increase in groundwater recharge in comparatively cool seasons, which would be accompanied by a tendency to “cool down” groundwater resources.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"11 ","pages":"Article 100071"},"PeriodicalIF":4.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hydroa.2020.100071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46095074","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}
Olivia L. Miller , Annie L. Putman , Jay Alder , Matthew Miller , Daniel K. Jones , Daniel R. Wise
{"title":"Changing climate drives future streamflow declines and challenges in meeting water demand across the southwestern United States","authors":"Olivia L. Miller , Annie L. Putman , Jay Alder , Matthew Miller , Daniel K. Jones , Daniel R. Wise","doi":"10.1016/j.hydroa.2021.100074","DOIUrl":"https://doi.org/10.1016/j.hydroa.2021.100074","url":null,"abstract":"<div><p>Society and the environment in the arid southwestern United States depend on reliable water availability, yet current water use outpaces supply. Water demand is projected to grow in the future and climate change is expected to reduce supply. To adapt, water managers need robust estimates of future regional water supply to support management decisions. To address this need, we estimate future streamflow in seven water resource regions in the southwestern U.S. using a new SPAtially Referenced Regressions On Watershed attributes (SPARROW) streamflow model. We present streamflow projections corresponding to input data from seven climate models and two greenhouse gas Representative Concentration Pathways (RCP4.5 and 8.5) for three, thirty-year intervals centered on the 2030s, 2050s, and 2080s, and for a historical thirty year interval centered on the 1990s. Across water resource regions, about half of the RCP4.5 models (51%) and two thirds of the RCP8.5 models (67%) indicate decreases in streamflow in the 2080s relative to the historical period. Models project maximum decreases in streamflow of 36–80% in all water resource regions for all periods and RCPs relative to historical streamflow, and maximum streamflow decreases of up to 20–45% in the 2080s at sites along the Colorado River used for measuring compliance with interstate and international water agreements. Headwaters are projected to experience the greatest declines, with substantial downstream implications. Among these estimates, the streamflows from models forced with RCP8.5 tend to be lower than those forced with RCP4.5. Not all climate models, times, and RCPs project widespread streamflow declines. The most ubiquitous streamflow increases are projected to occur in the 2030s under RCP4.5. Later time periods and enhanced greenhouse gas forcings indicate smaller regions of streamflow increase and lower accumulated streamflows, suggesting that limiting or reducing greenhouse gas concentrations could support future water availability. Although some possible streamflow increases are promising, the modest and spatially limited increases in streamflow projected for later time periods are still unlikely to be sufficient to meet the projected water demand. These results inform the likelihood of future water agreement compliance, and support developing strategies to balance water supply and demand.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"11 ","pages":"Article 100074"},"PeriodicalIF":4.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hydroa.2021.100074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72119544","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}