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Journal of Hydrology
Elsevier
Journal of Hydrology

Elsevier

0022-1694

Journal of Hydrology/Journal Journal of HydrologySCIISTPEIAHCI
正式出版
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    Robust clustering for assessing the spatiotemporal variability of groundwater quantity and quality

    Nourani, VahidGhaneei, ParnianKantoush, Sameh A.
    21页
    查看更多>>摘要：The long-term spatiotemporal assessment of groundwater resources through robust clustering techniques can be used to promote remediation measures for groundwater depletion and contamination. To fully understand the variability of groundwater quantity and quality due to anthropogenic activities and climate changes, a new ensemble clustering framework based on the Combining Multiple Clusters via Similarity Graph (COMUSA) method was developed. This new approach was applied and evaluated in the context of groundwater well systems on the Ghorveh-Dehgolan Plain (GDP), which is located in western Iran, for groundwater level (GWL) and 13 physicochemical parameters during four periods (the average of data from 1988-1990, 1997-1999, 2006-2008, and 2015-2017). The classification was confirmed by using the cluster validity index of the silhouette coefficient (SC), which indicated that the cluster ensemble method could improve the performance of individual clustering methods for groundwater quantity and quality by up to 12% and 20%, respectively. Piper plots, US Salinity Laboratory Staff (USSL) diagrams, and the pollution index of groundwater (PIG) were assessed for all clusters of physicochemical variables to analyse groundwater suitability for drinking and irrigation purposes. The results of the cluster ensemble showed that a critical pattern of groundwater depletion occurred in the western half of the GDP, while the eastern part was recognized as the most polluted zone on the plain. It could be concluded that the decline in GWL was not the only reason for the increase in groundwater quality variables, but other factors, such as noticeable cropland expansion and the overuse of chemical fertilizers and pesticides, were also influential factors related to these patterns. Taken together, the results of this study contribute to better recognizing the spatiotemporal changes in groundwater quantity and quality under the intense pressure of anthropogenic activities.
      原文链接:
    • NSTL
    • Elsevier

    Nation-scale reference evapotranspiration estimation by using deep learning and classical machine learning models in China

    Bai, ChenyunTang, XiaodiDong, JuanZhu, Yuanjun...
    15页
    查看更多>>摘要：Accurately estimating the reference evapotranspiration (ET0) is a basic requirement for precision irrigation and the correct planning of regional water resources. This study aimed to investigate the spatiotemporal variations in ET0 in China and to improve the accuracy of ET0 calculations on different spatiotemporal scales. Meteorological data collected at 100 stations in China during 1961 to 2019 were used to calculate ET0 with the Penman-Monteith model, and the temporal and spatial patterns in ET0-PM were analyzed with the Mann-Kendall nonparametric trend test method. Three machine learning models comprising convolutional neural network (CNN), extreme learning machine (ELM), and multiple adaptive regression splines (MARS), and seven empirical models calibrated with mind evolutionary algorithm (MEA) were compared to assess their suitability for calculating ET0 on different spatiotemporal scales in China. The results showed that the annual mean ET0-PM value (413.29-2772.35 mm) in China gradually increased from north to south and from west to east. ET0 exhibited an upward trend in the temperate continental zone (TCZ) and mountain plateau zone (MPZ) but a downward trend in the temperate monsoon zone (TMZ) and subtropical monsoon region (SMZ). By comparing the global performance indicators (GPI), the machine learning models generally performed better than the empirical models at different spatiotemporal scales. And CNN was the best model for calculating ET0 in terms of the model accuracy and stability. On the daily scale, MARS performed well in MPZ, whereas ELM performed well in TMZ and TCZ. On the monthly scale, MARS performed well in TMZ, whereas ELM performed well in SMZ and MPZ. At the annual scale, the accuracy of ELM was higher than that of MARS.
      原文链接:
    • NSTL
    • Elsevier

    Understanding the impact of the built environment mosaic on rainfall-runoff behaviour

    Macdonald, N.Redfern, T.Miller, J.Kjeldsen, T. R....
    13页
    查看更多>>摘要：Despite the importance of urban flooding, there are surprisingly few experimental studies of observed flood events from high-resolution hydrological data in urban systems to inform model development and parameterisation. The aim of this study was to understand how the interaction between rainfall and the layout of the built environment influences rainfall-runoff behaviour. A 2-year field campaign was undertaken to monitor rainfall runoff behaviour of two small (<1 ha) catchments representing different types of residential developments in southern England. Statistical analysis of 34 events captured in both catchments was undertaken to investigate the link between key event characteristics (peak flow and percentage runoff) and event drivers (event causing rainfall and antecedent soil moisture). The results show that peak flow is most sensitive to 10 min rainfall intensity while antecedent soil moisture is less important. The sensitivity to rainfall is strongest on the most densely urban catchment. In contrast, no relationship between percentage runoff and neither rainfall nor antecedent soil moisture could be detected in the densely urban catchment, while both factors were found to be significant in the less urbanised catchment. These results reported here demonstrate that the layout of the build environment exerts a strong influence on the hydrological characteristics at the local scale of relevance in urban hydrology and further model development of important when planning flood mitigation measures in urban areas.
      原文链接:
    • NSTL
    • Elsevier

    Interaction between gravel mining pits and river curvature on maximum scour depth through 2D hydraulic modelling

    Mohammad-Hosseinpour, AbedinMolina, Jose-LuisJabbari, Ebrahim
    10页
    查看更多>>摘要：To investigate the interacting effects of rivers curvature and gravel mining pits on rivers morphology, channels with different curvatures were analyzed using a 2D-3D modelling approach implemented through the code CCHE2D. A parameter (Delta : the maximum channel scour depth with existence of pit minus the maximum scour depth without pit) was defined as the net effect of gravel mining pits. The results indicated that the net effect of a pit on the maximum scour depth, from a straight channel to a channel with the curvature (C/L) of 2.38, decreases, and then, increases with higher degrees of curvature. The study of influential parameters showed that the cause of this event is due to the simultaneous effects of two opposing phenomena; while one (smoothing) reduces the Delta, the other (secondary flow) increases it. The reducing effect (smoothing) stems from the asymmetry of the flow velocity in the channel bend. This causes bed deformation in a way that channel experience a gradual decrease in velocity and as a result, less sediment loss. Furthermore, the boosting effect goes back to the existence of secondary flow in the bends, which grows stronger as the amount of curvature increases.
      原文链接:
    • NSTL
    • Elsevier

    Daily suspended sediment forecast by an integrated dynamic neural network

    Li, ShichengXie, QianchengYang, James
    16页
    查看更多>>摘要：Suspended sediment is of importance in river and dam engineering. Due to its high nonlinearity and stochasticity, sediment prediction by conventional methods is a challenging task. Consequently, this paper establishes a new hybrid model for an improved forecast of suspended sediment concentration (SSC). It is a nonlinear autoregressive network with exogenous inputs (NARX) integrated with a data pre-processing framework (thereafter INARX). In this model, wavelet transformation (WT) is used for time series decomposition and multigene genetic programing (MGGP) for details scaling. The two incorporated modules improve time and frequency domain analysis, allowing the network to unveil the embedded characteristics and capture the non-stationarity. At a hydrological station on the upper reaches of the Yangtze River, the records of daily water stage, flow discharge and suspended sediment are collected and refer to a nine-year period during 2004-2012. The data are used to evaluate the models. Several wavelets are explored, showing that the Coif3 leads to the most accurate prediction. Compared to the sediment rating curve (SRC), the conventional MGGP, multilayer perceptron neural network (MLPNN) and NARX, the INARX demonstrates the best forecast performance. Its mean coefficient of determination (CD) increases by 7.7%-38.6% and the root mean squared error (RMSE) reduces by 15.1%-54.5%. The INARX with the Coif3 wavelet is further evaluated for flood events and multistep forecasts. Under flood conditions, the model generates satisfactory results, with CD > 0.83 and 84.7% of the simulated data falling within the +/- 0.1 kg/m3 error. For the multistep forecast, at a one-week lead time, the network also yields predictions with acceptable accuracy (mean CD = 0.78). The model performance deteriorates if the lead time becomes larger. The established framework is robust and reliable for real-time and multistep SSC forecasts and provides reference for time series modeling, e.g. streamflow, river temperature and salinity.
      原文链接:
    • NSTL
    • Elsevier

    Seasonal trends and cycles of lake-level variations over the Tibetan Plateau using multi-sensor altimetry data

    Xu, FenglinZhang, GuoqingYi, ShuangChen, Wenfeng...
    14页
    查看更多>>摘要：The large number of lakes, little influenced by humans, on the Tibetan Plateau, form a natural laboratory for exploring cryosphere-hydrosphere-atmosphere processes under global warming. The water levels of these alpine lakes respond directly to water balance, but are mainly known only on the interannual scale. Seasonal variations are still poorly described due to limited gauge measurements. Here, the seasonal trends and cycles of lake-level changes on the Tibetan Plateau were examined using altimetry data from ICESat (132 available lakes), Cryosat-2 (244 lakes), Sentinel-3A (125 lakes), Sentinel-3B (120 lakes), and ICESat-2 (356 lakes). Bias between altimetry data sets was removed, and a validation against in-situ lake-level measurements from Qinghai Lake between 2003 and 2020 revealed a good performance (R-2 > 0.80, RMSE < 0.12 m). The mean annual trend of lake level between 2003 and 2020 is 0.20 +/- 0.01 m/yr, with similar rates (0.18-0.20 m/yr) in three different seasons. The rate was slightly greater (0.22 +/- 0.02 m/yr) between 2010 and 2020, but seasonal trends were again similar in this period (0.21-0.25 m/yr). Declining water levels for lakes around Nam Co were identified, with a mean negative rate of -0.09 +/- 0.02 m/yr. Furthermore, during 2016-2020, an accelerated lake-level rise was found, with a mean rate of 0.43 +/- 0.05 m/yr (0.47-0.53 m/yr in four seasons). Lake levels in the southern TP are shown to have increased during 2016-2019: a reversal of the pre-2016 observations of declining levels. The seasonal cycles of lake-level variations show that the majority of lakes have a peak water level in August-September, followed by October-November. The features of the peaks vary distinctly in different climate regions, with the timing of the peak occurring gradually later as one moves from southwest to northeast. The seasonal cycles of water level and terrestrial water storage from GRACE data are highly consistent. The spatial pattern of lake-level changes during the seasonal period of rising lake levels also matches precipitation variations well, as does lake volume change with terrestrial water storage budget. The study reveals the tremendous potential of multi-sensor altimetry data for detecting seasonal features of lake-level variation, which is a great asset for understanding Earth's water dynamics and balance.
      原文链接:
    • NSTL
    • Elsevier

    Hydrogeochemical changes before and during the 2019 Benevento seismic swarm in central-southern Italy

    Gori, FrancescaBarberio, Marino Domenico
    10页
    查看更多>>摘要：Insights into seismic precursors have been obtained in the last decades. However, a detailed understanding of hydrogeochemical anomalies prior to earthquakes still remains the aim of many research teams worldwide. In order to investigate the earthquake-groundwater relationship, between 2018 and 2020, we performed sampling surveys coupled with continuous multiparametric monitoring in Grassano spring fed by the Matese aquifer (central-southern Apennines, Italy). Hydrogeochemical changes were observed before the onset and during the 2019 Benevento seismic sequence, including dissolved CO2 increase, pH lowering, and anomalies in major ions (i.e., Ca2+, Na+, HCO3-) that later recovered to their typical concentrations. We suggest that variations in groundwater geochemistry were induced by dilatative preparatory phases of earthquakes, typical of the extensional setting. This condition allowed the deep CO2 upwelling along tectonic discontinuities, as testified by the C-ext (carbon from external sources) behaviour detected in Grassano groundwater during the 2019 year. Despite the small-intermediate magnitude of the mainshock, results highlight and confirm the occurrence of a potential pre-seismic geochemical process in the fractured carbonate aquifers, similar to the one proposed in literature for the stronger 2016-2017 Amatrice-Norcia seismic sequence.
      原文链接:
    • NSTL
    • Elsevier

    Fate and transport modelling framework for assessing risks to soil and groundwater from chemicals accidentally released during surface operations: An Australian example application from shale gas developments

    Mallants, DirkDoble, RebeccaBeiraghdar, Yousef
    16页
    查看更多>>摘要：Shale and tight gas developments in the Beetaloo (28,000 km(2)) and Cooper (139,000 km(2)) basins of Australia are subject to stringent State and Federal Government controls and assessments. Several scientific investigations are ongoing to improve the scientific basis of the risks from unconventional gas developments to water and the environment. In this study a framework was developed to derive estimates of chemical dilution associated with leakage to groundwater from accidental release of chemicals used for shale and tight gas extraction in Australia. The quantitative assessment accounted for key landscape parameters that determine natural attenuation: soil type, depth to groundwater and groundwater velocity. Both basins were discretised into 1000 x 1000 m(2) grids for which the unsaturated zone and groundwater dilution factors were derived. Migration of chemicals through deep unsaturated zones was calculated with the HYDRUS-1D simulator, taking account of best-available hydraulic properties from a digital soil database. A three-dimensional analytical solution of the advection-dispersion equation provided estimates of dilution in groundwater after solutes travelled 500 m from the centre (source location) to the edge of every grid cell. The combined vadose zone-groundwater dilution factors were used to determine under which conditions concentrations of hydraulic fracturing chemicals or flowback water accidentally released into the environment would decrease to levels that are no longer considered harmful to the environment. When the method was applied to 39 hydraulic fracturing chemicals scheduled for stimulation of a shale gas well, ecotoxicological risk quotients (RQ) were calculated to indicate which chemicals were of no environmental concern. This work contributes to increasing the efficiency of quantitative impact assessments and provides a framework to develop dedicated monitoring and management practices to support regulation and management of the gas industry in Australia.
      原文链接:
    • NSTL
    • Elsevier

    Quantile-based Bayesian Model Averaging approach towards merging of precipitation products

    Yumnam, KarismaGuntu, Ravi KumarRathinasamy, MaheswaranAgarwal, Ankit...
    14页
    查看更多>>摘要：Precipitation is a fundamental input for many hydrological and water management studies. Nowadays, a number of satellite precipitation products are easily accessible online at free of cost. Despite so, the utility of such products is still limited owing to their lack of accuracy in capturing the ground truth. To improve the reliability of the satellite precipitation products, we have developed a quantile based Bayesian model averaging (QBMA) approach to merge the satellite precipitation products. QBMA approach was compared with traditional methods, namely, simple model averaging and one outlier removed. We have considered three SPPs (TRMM, PERSIANN-CDR, CMORPH) for QBMA merging during the monsoon season over India's coastal Vamsadhara river basin. QBMA optimal weights were trained using 2001 to 2013 daily monsoon precipitation data and validated for 2014 to 2018. Results indicated that the bias-corrected QBMA outperformed the other methods. On monthly evalu-ation, it is observed that all the products perform better during July and September than that in June and August. The QBMA approaches do not have any significant improvement over the SMA approach in terms of POD. However, the bias-corrected QBMA products have lower FAR. The developed QBMA approach with bias-corrected inputs outperforms the IMERG product in terms of RMSE.
      原文链接:
    • NSTL
    • Elsevier

    Hydrological variability in southern Siberia and the role of permafrost degradation

    Han, LiMenzel, Lucas
    14页
    查看更多>>摘要：Changes in the cryosphere caused by global warming are expected to alter the hydrological cycle, with consequences to freshwater availability for humans and ecosystems. Here, we combine data assimilation, cross correlation analysis, simulation techniques, and the conceptual steady-state Budyko framework to examine the driving mechanisms of historical hydrological changes at annual, seasonal, and monthly scales. We focus on two southern Siberian basins with different landscape properties: the semi-arid Selenga, characterized by discontinuous, sporadic, and isolated permafrost; and the boreal Aldan, which is underlain by continuous permafrost. Our results indicate that the two basins show divergent trends in river runoff over the period 1954-2013. In Selenga, runoff exhibits a significant decreasing trend (-1.3 km(3)/10yrs, p<0.05), whereas a remarkable increasing trend (4.4 km3/10yrs, p<0.05) occurs in Aldan. Given the negligible trends in precipitation over both basins, we attribute these contrasting changes to different impacts from warming-induced permafrost degradation. The Selenga basin, which is dominated by lateral degradation (i.e., decreasing permafrost extent), suffers from severe water loss via the enhanced infiltration of water that was previously stored close to the surface. This leads to a water-deficit surface condition. In the Aldan basin, in contrast, vertical degradation prevails: the thickened active layer is still underlain by a frozen layer with low permeability that sustains water rich surface conditions. Furthermore, summer runoff shows contrasting oscillations, with wet-dry-wet-dry and dry-wet-dry-wet state evolutions in the Selenga and Aldan basins, respectively. We attribute such variabilities to the "seesaw-like" oscillations in summer precipitation associated with the propagation of Rossby wave trains across the Eurasian continent. We also find that warming-induced permafrost degradation over the 30-year period from 1984 to 2013 has led to strong regime shifts in river runoff in both basins. Our study highlights the importance of examining the mechanisms that drive changes in water availability from an integrated land hydrology-atmosphere system perspective.
      原文链接:
    • NSTL
    • Elsevier