An integrated assessment of surface water dynamics in the Irtysh River Basin during 1990–2019 and exploratory factor analyses

2021 ◽  
Vol 593 ◽  
pp. 125905
Author(s):  
Wenjing Huang ◽  
Weili Duan ◽  
Daniel Nover ◽  
Netrananda Sahu ◽  
Yaning Chen
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Integrated Assessment ◽  
Water Dynamics ◽  
Factor Analyses ◽  
Exploratory Factor Analyses ◽  
Irtysh River ◽  
The Irtysh River

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

scholarly journals Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of earth observation data

2015 ◽  
Vol 12 (11) ◽  
pp. 11847-11903 ◽  
Author(s):  
V. Heimhuber ◽  
M. G. Tulbure ◽  
M. Broich
Keyword(s):  
Time Series ◽  
Surface Water ◽  
River Basin ◽  
Earth Observation ◽  
Water Dynamics ◽  
Observation Data ◽  
Grid Cells ◽  
Modeling Framework ◽  
Basin Scale ◽  
Lag Times

Abstract. The usage of time series of earth observation (EO) data for analyzing and modeling surface water dynamics (SWD) across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWD from a unique validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWD as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET) and rainfall (P). To enable a consistent modeling approach across space, we modeled SWD separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework (SMF) to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWD. Based on these automatically quantified flow lag times and variable combinations, SWD on 233 (64 %) out of 363 floodplain grid cells were modeled with r2 ≥ 0.6. The contribution of P, ET and SM to the models' predictive performance differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The SMF presented here is suitable for modeling SWD on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

scholarly journals Examining regional groundwater–surface water dynamics using an integrated hydrologic model of the San Joaquin River basin

2017 ◽  
Vol 21 (2) ◽  
pp. 923-947 ◽  
Author(s):  
James M. Gilbert ◽  
Reed M. Maxwell
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Regional Scale ◽  
Power Spectra ◽  
Central Valley ◽  
Hydrologic Model ◽  
Water Dynamics ◽  
Irrigated Agriculture ◽  
San Joaquin River ◽  
Basin Scale

Abstract. Widespread irrigated agriculture and a growing population depend on the complex hydrology of the San Joaquin River basin in California. The challenge of managing this complex hydrology hinges, in part, on understanding and quantifying how processes interact to support the groundwater and surface water systems. Here, we use the integrated hydrologic platform ParFlow-CLM to simulate hourly 1 km gridded hydrology over 1 year to study un-impacted groundwater–surface water dynamics in the basin. Comparisons of simulated results to observations show the model accurately captures important regional-scale partitioning of water among streamflow, evapotranspiration (ET), snow, and subsurface storage. Analysis of this simulated Central Valley groundwater system reveals the seasonal cycle of recharge and discharge as well as the role of the small but temporally constant portion of groundwater recharge that comes from the mountain block. Considering uncertainty in mountain block hydraulic conductivity, model results suggest this component accounts for 7–23 % of total Central Valley recharge. A simulated surface water budget guides a hydrograph decomposition that quantifies the temporally variable contribution of local runoff, valley rim inflows, storage, and groundwater to streamflow across the Central Valley. Power spectra of hydrograph components suggest interactions with groundwater across the valley act to increase longer-term correlation in San Joaquin River outflows. Finally, model results reveal hysteresis in the relationship between basin streamflow and groundwater contributions to flow. Using hourly model results, we interpret the hysteretic cycle to be a result of daily-scale fluctuations from precipitation and ET superimposed on seasonal and basin-scale recharge and discharge.

scholarly journals Long-Term Surface Water Dynamics Analysis Based on Landsat Imagery and the Google Earth Engine Platform: A Case Study in the Middle Yangtze River Basin

2018 ◽  
Vol 10 (10) ◽  
pp. 1635 ◽  
Author(s):  
Chao Wang ◽  
Mingming Jia ◽  
Nengcheng Chen ◽  
Wei Wang
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Minimal Surface ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Google Earth ◽  
Water Dynamics ◽  
Maximal Surface ◽  
Google Earth Engine

Dynamics of surface water is of great significance to understand the impacts of global changes and human activities on water resources. Remote sensing provides many advantages in monitoring surface water; however, in large scale, the efficiency of traditional remote sensing methods is extremely low because these methods consume a high amount of manpower, storage, and computing resources. In this paper, we propose a new method for quickly determining what the annual maximal and minimal surface water extent is. The maximal and minimal water extent in the year of 1990, 2000, 2010 and 2017 in the Middle Yangtze River Basin in China were calculated on the Google Earth Engine platform. This approach takes full advantage of the data and computing advantages of the Google Earth Engine’s cloud platform, processed 2343 scenes of Landsat images. Firstly, based on the estimated value of cloud cover for each pixel, the high cloud covered pixels were removed to eliminate the cloud interference and improve the calculation efficiency. Secondly, the annual greenest and wettest images were mosaiced based on vegetation index and surface water index, then the minimum and maximum surface water extents were obtained by the Random Forest Classification. Results showed that (1) the yearly minimal surface water extents were 14,751.23 km2, 14,403.48 km2, 13,601.48 km2, and 15,697.42 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (2) The yearly maximal surface water extents were 18,174.76 km2, 20,671.83 km2, 19,097.73 km2, and 18,235.95 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (3) The accuracies of surface water classification ranged from 86% to 93%. Additionally, the causes of these changes were analyzed. The accuracy evaluation and comparison with other research results show that this method is reliable, novel, and fast in terms of calculating the maximal and minimal surface water extent. In addition, the proposed method can easily be implemented in other regions worldwide.

scholarly journals Examining regional groundwater-surface water dynamics using an integrated hydrologic model of the San Joaquin River basin

2016 ◽  
Author(s):  
James M. Gilbert ◽  
Reed M. Maxwell
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Regional Scale ◽  
Power Spectra ◽  
Central Valley ◽  
Hydrologic Model ◽  
Water Dynamics ◽  
Irrigated Agriculture ◽  
San Joaquin River ◽  
Basin Scale

Abstract. Widespread irrigated agriculture and a growing population depend on the complex hydrology of the San Joaquin River basin in California. The challenge of managing this complex hydrology hinges, in part, on understanding and quantifying how processes interact to support the groundwater and surface water systems. Here, we use the integrated hydrologic platform ParFlow-CLM to simulate hourly 1-km gridded hydrology over one year to study unimpacted groundwater-surface water dynamics in the basin. Comparisons of simulated results to observations show the model accurately captures important regional-scale partitioning of water among streamflow, evapotranspiration (ET), snow, and subsurface storage. Analysis of this simulated Central Valley groundwater system reveals the seasonal cycle of recharge and discharge as well as the role of the small but temporally constant portion of groundwater recharge that comes from the mountain block. Considering uncertainty in mountain block hydraulic conductivity, model results suggest this component accounts for 7 %–23 % of total Central Valley recharge. A simulated surface water budget guides a hydrograph decomposition that quantifies the temporally variable contribution of local runoff, valley rim inflows, storage, and groundwater to streamflow across the Central Valley. Power spectra of hydrograph components suggest interactions with groundwater across the valley act to increase long-term correlation in San Joaquin River outflows. Finally, model results reveal hysteresis in the relationship between basin streamflow and groundwater contributions to flow. Using hourly model results, we interpret the hysteretic cycle to be a result of daily-scale fluctuations from precipitation and ET superimposed on seasonal and basin-scale recharge and discharge.

scholarly journals Satellite-based estimates of surface water dynamics in the Congo River Basin

2018 ◽  
Vol 66 ◽  
pp. 196-209 ◽  
Author(s):  
M. Becker ◽  
F. Papa ◽  
F. Frappart ◽  
D. Alsdorf ◽  
S. Calmant ◽  
...  
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Water Dynamics ◽  
Congo River ◽  
Congo River Basin

Identification and Utility of a Short Form of the Pediatric Symptom Checklist-Youth Self-Report (PSC-17-Y)

2020 ◽  
Vol 36 (1) ◽  
pp. 56-64
Author(s):  
Paul Bergmann ◽  
Cara Lucke ◽  
Theresa Nguyen ◽  
Michael Jellinek ◽  
John Michael Murphy
Keyword(s):  
Psychosocial Functioning ◽  
Short Form ◽  
Parent Report ◽  
Self Report ◽  
Symptom Checklist ◽  
Factor Analyses ◽  
Youth Self Report ◽  
Confirmatory Factor Analyses ◽  
Pediatric Symptom Checklist ◽  
Exploratory Factor Analyses

Abstract. The Pediatric Symptom Checklist-Youth self-report (PSC-Y) is a 35-item measure of adolescent psychosocial functioning that uses the same items as the original parent report version of the PSC. Since a briefer (17-item) version of the parent PSC has been validated, this paper explored whether a subset of items could be used to create a brief form of the PSC-Y. Data were collected on more than 19,000 youth who completed the PSC-Y online as a self-screen offered by Mental Health America. Exploratory factor analyses (EFAs) were first conducted to identify and evaluate candidate solutions and their factor structures. Confirmatory factor analyses (CFAs) were then conducted to determine how well the data fit the candidate models. Tests of measurement invariance across gender were conducted on the selected solution. The EFAs and CFAs suggested that a three-factor short form with 17 items is a viable and most parsimonious solution and met criteria for scalar invariance across gender. Since the 17 items used on the parent PSC short form were close to the best fit found for any subsets of items on the PSC-Y, the same items used on the parent PSC-17 are recommended for the PSC-Y short form.

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