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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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    Multimodel quantification of green and blue water components and its error propagations through parameter transferability approach across input choices

    Dey, AiendrilaRemesan, Renji
    18页
    查看更多>>摘要：A robust quantitative assessment of water security through efficient water accounting concepts like blue and green water footprints has gained a lot of attention for water resources management at river basin scale. However, such water accounting tools and models have dependencies to the choice of precipitation data used to drive the model, as the input bias propagate into simulated streamflow through interaction with different hy-drological processes. This study performs a holistic investigation of sensitivity of water accounting fractions like blue water flow (BWF), green water flow (GWF), green water storage (GWS), and their dependencies on sec-ondary precipitation datasets (SPDs) choices using a lumped conceptual rainfall-runoff model [Hydrological Simulation model (HYSIM)] and a physically based semi-distributed hydrological model [Soil and Water Assessment Tool (SWAT)] in the Damodar River basin, India. These models were driven by seven gridded pre-cipitation datasets, including India Meteorological Department (IMD) data, Indian Monsoon Data Assimilation and Analysis (IMDAA) data, Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data, Watch forcing ERA-Interim data, Climate Hazard Group Infrared Precipitation with Station data (CHIRPS), PRINCETON precipitation data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Records (PERSIANN-CDR) data. The reference IMD based multimodel results revealed that the annual average blue and green water components of the Damoder River were in the range of 558-656 mm/year, 574-728 mm/year and 34-112 mm/year for BWF, GWF, and GWS respectively during 1994-2010 period. The results obtained after applying parameter transferability approach across the input space have revealed exact quantification of choice dependent annual variations in spatial and temporal characteristics of BWF, GWF, and GWS. A further evaluation based on error propagation ratios (gamma CC, gamma rRMSE, gamma B) has revealed a quantification of input choice induced dampening/magnification effects on flow estimations of both the HYSIM and SWAT and these quantifications were free from the compensation effects of recalibration. The findings in this study demonstrate that the change in annual average BWF varies between-46.97% to +41.38% in comparison to benchmark IMD simulations, and the corresponding changes for GWF is between-19.63% and +17.06% and for GWS is between-29.97% to +35.5% with different choices of SPD scenarios. The systematic (gamma B) and random (gamma rRMSE) error propagation factors during monsoon season are larger for SWAT model ([0.21-0.78], [0.69-0.89] respectively) than that of HYSIM model ([0.18-0.74], [0.66-0.81], respectively) and it is varying with choice of different SPDs.
      原文链接:
    • NSTL
    • Elsevier

    A modelling framework to track phosphorus sources of the drinking water intakes in a large eutrophic lake

    Yang, HongweiHan, JichaoZhang, QimouYan, Renhua...
    10页
    查看更多>>摘要：Excess phosphorus (P) in freshwater lakes is a global challenge due to its potential cause of harmful algal blooms threating drinking water safety. However, quantifying the P sources for a specific site in a large lake is extremely challenging due to the complex interaction between internal and external P loading to surface water. To address this challenge, this study developed a modelling framework to track P sources of two drinking water intakes in a large shallow lake (Lake Taihu) in China. The framework proposed a new-developed index (PSCI, P source contribution index) to quantify the contributions of various P sources for the drinking water intakes. PSCI was derived by a three-dimensional hydrodynamic and water quality model that describes the complex processes of P sources, sinks and transportation in both horizontal and vertical directions within the lake. Application of the framework to these two drinking water intakes (Jinshu and Shangshan) achieved a surprising finding that in-ternal P loading from sediment was a significant P source with a contribution as high as 47.1% and 30.4%, respectively. Central Lake Taihu had a large contribution of 49.5% and 68.3% for Jinshu and Shangshan, and inflow river (Wangyu River) had a contribution of < 5%. The high contribution of internal P loading was mainly due to the sediment P accumulation for years and large external P loading. Our study highlighted the important role of internal P loading affecting the P concentration of drinking water intake in a large eutrophic lake, and demonstrated the high value of the modelling framework in quantifying the P sources for a specific site (e.g., drinking water intake) in a lake.
      原文链接:
    • NSTL
    • Elsevier

    Identifying the facts and driving factors of deceleration of groundwater table decline in Beijing during 1999-2018

    Zhang, XiaoWu, XiongZhao, RongMu, Wenping...
    15页
    查看更多>>摘要：Domestic water supply and agricultural irrigation are highly dependent on groundwater in Beijing. Groundwater table (GWT) decline and ecological issues caused by groundwater overexploitation have been of growing concern. However, some positive measures such as groundwater extraction control, improvement of water use efficiency, and South-to-North Water Diversion Middle Route Project (SNWDMRP) have alleviated severe groundwater shortage situation in recent years. Therefore, quantifying the contributions of different factors to the deceleration of groundwater table decline contributes to guide groundwater resources. In this study, in-situ groundwater table data, climate data, and water use data were used to identify the deceleration and driving factors of GWT decline by comprehensively using water table fluctuation (WTF) method, Mann-Kendall (MK) trend test, wavelet analysis methods, and Random Forest Regression (RFR). Firstly, the MK trend test evaluated the trend of the defined groundwater stress index (GSI) and groundwater table decline rate (GTDR) changes, indicating the fact that GWT decline was decelerated during 1999-2018. Then, the WTF method combined with in-situ groundwater level data and specific yield were used to derive the monthly groundwater storage anomalies (GWSA), which revealed a slight GWS recovery in the past decade, especially a significant increase of GWS with a rate of 5.76 mm/month since the operation of South-to-North Water Diversion Middle Route Project (SNWDMRP). Wavelet analysis identified a correlation and coherence between GWSA and precipitation in the time-frequency domain. Furthermore, correlation analysis indicated that the deceleration of GWT decline may be attributed to climatic factors (e.g., precipitation, temperature), the SNWDMRP, and water supply and use structure. The RFR quantitatively identified the greatest contribution of water use (e.g., agricultural and domestic water use) to GWT dynamics. Moreover, the effect of SNWDMRP is greater than climatic factors (e.g., precipitation, temperature). These findings have important implications for groundwater resource management in Beijing and other areas suffering from severe groundwater depletion.
      原文链接:
    • NSTL
    • Elsevier

    Long-term riverine nitrogen dynamics reveal the efficacy of water pollution control strategies

    Wu, KaibinHu, MinpengZhang, YufuZhou, Jia...
    11页
    查看更多>>摘要：Identification of long-term water quality trends in response to watershed anthropogenic interventions is crucial for developing and adapting water pollution control strategies. This study represents the first use of the Weighted Regressions on Time, Discharge, and Season (WRTDS) model to evaluate trends and sources of riverine nitrogen (N) levels over the 1980-2019 period in the Yongan River watershed of eastern China. The WRTDS model showed satisfactory accuracies for predicting daily riverine total N (TN), NH4+ and NO(3)(-)concentrations/loads (R-2 > 0.55, n = 366). Modeled flow-normalized riverine NH4+ concentration increased by 789% from 1980 to 2009 and then decreased by 63% in 2010-2019. This changing trend for riverine NH4+ concentration was mainly attributed to a 43% decrease of wastewater NH4+ discharge load in 2010-2019 due to establishment of three new WWTPs in urban areas and enhanced rural domestic sewage collection/treatment. Although chemical N fertilizer use decreased by 49% and domestic animal numbers decreased by 73% in 2000-2019, flow-normalized riverine TN and NO3? concentrations progressively increased by 161% and 232% in 1980-2019, respectively. The paradox between decreasing N inputs and increasing riverine TN/NO3- concentrations is attributed to inputs of legacy N from soil and groundwater. This is supported by the 3.8-fold increase of riverine NO3- concentration in 1980-2019 (86% increase in 2000-2019) following 10-days with no-precipitation (representing groundwater contributions to baseflow) and a 4.1-fold increase of riverine NO3- concentration in 1980-2019 (91% increase in 2000-2019) following the first rainstorm after 10-days of no-precipitation (representing soil flushing). These results document that point-source pollution control efforts were effective, whereas benefits from nonpointsource pollution control were masked by inputs from legacy N pollution. The WRTDS model was demonstrated to be a useful tool for assessing long-term riverine N pollution dynamics and sources, thereby providing decision-makers with critical information to guide watershed N pollution control strategies.
      原文链接:
    • NSTL
    • Elsevier

    An integrated hydrodynamic and multicriteria evaluation Cellular Automata-Markov model to assess the effects of a water resource project on waterbird habitat in wetlands

    Chen, QiuwenZhang, JianyunLi, YuekangZeng, Yuhong...
    13页
    查看更多>>摘要：Large water resource projects may severely alter hydrological regimes and thus the habitat conditions for biota in wetlands. Plenty of habitat models for fish, vegetation, and invertebrates have been developed and widely used; however, there is a lack of effective models to assess the effects of water resource projects on waterbird habitat in wetlands. This study developed a habitat model by integrating two-dimensional hydrodynamic, multicriteria evaluation Cellular Automata-Markov and habitat suitability modules (HMH) to evaluate the effects of hydraulic structures on the habitat of waterbirds in wetlands. The developed HMH model was applied to the Duchang Provincial Nature Reserve (DPNR) of Poyang Lake in China, which is an important conservation wetland in the Ramsar Convention. For demonstration, the HMH model analyzed the habitat suitability for Grus leucogeranus, a rare species in the region, with a scheduled sluice under two different regulating rules (named Rule 1 and Rule 2), and compared it to the habitat suitability without the sluice. Results showed that the sluice operations would increase the water body during dry seasons, and thus increase the area of shallow water, a preferential habitat to Grus leucogeranus. The sluice operation in Rule 1 would either change the location or remarkably reduced the area of suitable habitat in the northern DPNR. The sluice operation in Rule 2 would either slightly increase the area or change the location of suitable habitats in the northern DPNR. The study demonstrated that the developed method for habitat suitability assessment could provide a viable approach for eco-hydrological management of wetlands.
      原文链接:
    • NSTL
    • Elsevier

    Application of a modern multi-level ensemble approach for the estimation of critical shear stress in cohesive sediment mixture

    Singh, Umesh K.Jamei, MehdiKarbasi, MasoudMalik, Anurag...
    22页
    查看更多>>摘要：Exploration of incipient motion study is significantly important for the river hydraulics community. The present study, along with experimental investigation, considered a new multi-level ensemble machine learning (ML) to determine critical shear stress (CSS) of gravel particles in a cohesive mixture of clay-silt-gravel, clay-silt-sand gravel, and clay-sand-gravel. The multi-level ensemble ML included a voting-based ensemble meta-estimator integrated with three modern standalone ensemble techniques, namely extreme gradient boosting (XGBoost), Adaptive boosting (Adaboost), and Random Forest (RF), and performance is compared with three standalone ensemble models for prediction of CSS values. Besides, the optimum input combinations were explored using the forward stepwise selection method, as a correlation-based feature selection, and mutual information theory. The outcomes of simulation indicated that the multi-level ensemble machine learning (voting) model in terms of correlation coefficient (R = 0.9641), and root mean square error (RMSE = 0.2022) was superior to the standalone ensemble techniques, i.e., XGBoost (R = 0.9482, and RMSE = 0.2375), Adaboost (R = 0.9496, and RMSE = 0.2387), and RF (R = 0.9392, and RMSE = 0.2739) for accurate estimation of CSS.
      原文链接:
    • NSTL
    • Elsevier

    A short-term flood prediction based on spatial deep learning network: A case study for Xi County, China

    Chen, ChenJiang, JiangeLiao, ZhanZhou, Yang...
    11页
    查看更多>>摘要：Floods cause substantial damage across the world every year. Accurate and timely prediction of floods can significantly minimize the loss of life and property. Recently, numerous machine learning models have been used for flood prediction, showing that their performance is preferable to traditional statistical models. However, the existing models neglect the spatial features of floods, which drive flood generation and concentration. In this paper, the area of interest is divided into grids based on longitude and latitude, and the rainfall and discharge collected by stations are combined into tensors according to station coordinates. Different from one-dimensional time series, our input feature is a two-dimensional time series with spatial information. Hence, combining a Convolutional Neural Network (CNN) with a Long Short Term Memory Network (LSTM), we propose the convolution LSTM (ConvLSTM) to extract spatiotemporal features of hydrological information. The methodology is demonstrated using the hydrological data collected at the Xi County stations, located on the Huai River in Henan Province, China. Numerical results indicate that the relative error of arrival time is within 30%, and the relative error of peak discharge is within 20%, satisfying the 2005 Chinese Water Resource Standard on flood prediction permit error. The experiments also show that the ConvLSTM outperforms the recent models in terms of flood arrival time and peak discharge, thereby proving a promising alternative.
      原文链接:
    • NSTL
    • Elsevier

    A critical review of real-time modelling of flood forecasting in urban drainage systems

    Piadeh, FarzadBehzadian, KouroshAlani, Amir M.
    16页
    查看更多>>摘要：There has been a strong tendency in recent decades to develop real-time urban flood prediction models for early warning to the public due to a large number of worldwide urban flood occurrences and their disastrous consequences. While a significant breakthrough has been made so far, there are still some potential knowledge gaps that need further investigation. This paper presents a comprehensive review of the current state-of-the-art and future trends of real-time modelling of flood forecasting in urban drainage systems. Findings showed that the combination of various real-time sources of rainfall measurement and the inclusion of other real-time data such as soil moisture, wind flow patterns, evaporation, fluvial flow and infiltration should be more investigated in real-time flood forecasting models. Additionally, artificial intelligence is also present in most of the new RTFF models in UDS and consequently further developments of this technique are expected to appear in future works.
      原文链接:
    • NSTL
    • Elsevier

    Multiscale relationships between monthly sediment load and pertinent factors in a typical karst mountainous watershed

    Zhu, KunhengLi, ZhenweiDuan, LiangxiaLi, Yuanchen...
    12页
    查看更多>>摘要：At different time scales, the potential factors influencing changes in sediment transport vary. Because the processes of each impact factor overlaps on different time scales, it is challenging to evaluate the complex multiscale relationships between monthly sediment load and its potential impact factors. The objective of this study was to investigate the scale-specific main factors influencing monthly sediment loads using the multivariate empirical mode decomposition (MEMD) method. Monthly sediment loads and five potential influencing factors (runoff, precipitation, air temperature, potential evapotranspiration, and enhanced vegetation index) during 2003-2017 were collected in the Wujiang karst watershed of southwest China. The MEMD method was used to decompose the temporal series of monthly sediment load into seven intrinsic mode functions (IMF) and residuals using a Hilbert transform. The sum of variance contribution rates of IMF1 (3.2 months), IMF2 (5.5 months) and IMF3 (12.1 months) was more than 90%. Although temperature and potential evapotranspiration significantly affect monthly sediment loads at the observation scale, no significant relationships were observed between them at some specific scales after MEMD. The accuracy of the prediction model after MEMD was better than that of the prediction model using original data. Runoff and precipitation were important predictors in the prediction model. This study shows the advantages of the MEMD method for analyzing non-stationary and nonlinear hydrological processes, and this useful tool is recommended for application in other karst watersheds.
      原文链接:
    • NSTL
    • Elsevier

    Influence of seawater intrusion on the hot springs in a coastal area: The case of the Anak-Sinchon Uplift, Korean Peninsula

    Ri, KilsangRi, GunhyangRi, MyongcholGuo, Huaming...
    11页
    查看更多>>摘要：Temperatures and chemical compositions of hot springs are decisive of their roles in heating, power-generation, therapeutic applications and recreational activities. However, the influence of seawater intrusion on hot springs are still not fully understood. Typical ten hot springs in and around the Anak-Sinchon Uplift in a coastal area of the northern Korean Peninsula were investigated. All the hot springs were near-neutral to weakly alkaline. High TDS value of 25300 mg/L was found at the hot spring closest to the coastline. TDS values, and concentrations of Na+, K+, Ca2+, Cl- and SO(4)(2-& nbsp;)in the hot spring waters near the coast showed decreasing trends with the distance from the coastline. According to the calculation of seawater fraction, three hot springs near the coast were contaminated by seawater. Temperatures of the geothermal reservoir were estimated to range from 103 to 170 ?, which were interpolated by thin plate spline to predict the local trend of the reservoir temperature distribution. Mg2+ concentration was used to determine the time order between the heating process and seawater intrusion. Among the three seawater-contaminated hot springs, very low Mg2+ concentrations in two hot spring waters probably indicate that they were mixed with seawater before being heated in the geothermal reservoir and not affected by seawater intrusion again afterwards; relatively high Mg2+ concentration in the hot spring water closest to the coastline likely indicates that it was mixed with seawater again during the ascent from the geothermal reservoir. The geochemical modeling performed with PHREEQC indicates that dolomitization of calcite and precipitation of anhydrite were the dominant water-rock interactions for the seawater-contaminated hot spring waters.
      原文链接:
    • NSTL
    • Elsevier