Spatiotemporal Shifts in Key Hydrological Variables and Dominant Factors over China

2021 ◽  
Author(s):  
Shanlei Sun ◽  
Zaoying Bi ◽  
Shujia Zhou ◽  
Hongzhou Wang ◽  
Qingqing Li ◽  
...  
Keyword(s):  
Hydrological Variables

Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models

2021 ◽  
Vol 193 (11) ◽  
Author(s):  
Mohamad Basel Al Sawaf ◽  
Kiyosi Kawanisi ◽  
Mohamad Nazieh Jlilati ◽  
Cong Xiao ◽  
Masoud Bahreinimotlagh
Keyword(s):  
Data Driven ◽  
Using Data ◽  
Hydrological Variables

The Optimality of Potential Rescaling Approaches in Land Data Assimilation

2013 ◽  
Vol 14 (2) ◽  
pp. 650-660 ◽  
Author(s):  
M. Tugrul Yilmaz ◽  
Wade T. Crow
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Least Squares ◽  
Optimal Solution ◽  
Least Squares Regression ◽  
Linear Least Squares ◽  
Optimal Analysis ◽  
Land Data Assimilation ◽  
Temporal Statistics ◽  
Hydrological Variables

Abstract It is well known that systematic differences exist between modeled and observed realizations of hydrological variables like soil moisture. Prior to data assimilation, these differences must be removed in order to obtain an optimal analysis. A number of rescaling approaches have been proposed for this purpose. These methods include rescaling techniques based on matching sampled temporal statistics, minimizing the least squares distance between observations and models, and the application of triple collocation. Here, the authors evaluate the optimality and relative performances of these rescaling methods both analytically and numerically and find that a triple collocation–based rescaling method results in an optimal solution, whereas variance matching and linear least squares regression approaches result in only approximations to this optimal solution.

scholarly journals Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

2016 ◽  
Vol 20 (12) ◽  
pp. 4717-4729 ◽  
Author(s):  
Martin Durocher ◽  
Fateh Chebana ◽  
Taha B. M. J. Ouarda
Keyword(s):  
Target Location ◽  
Projection Pursuit ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Projection Pursuit Regression ◽  
Index Flood ◽  
Regional Flood Frequency Analysis ◽  
Quantile Estimates ◽  
Target Locations ◽  
Hydrological Variables

Abstract. This study investigates the utilization of hydrological information in regional flood frequency analysis (RFFA) to enforce desired properties for a group of gauged stations. Neighbourhoods are particular types of regions that are centred on target locations. A challenge for using neighbourhoods in RFFA is that hydrological information is not available at target locations and cannot be completely replaced by the available physiographical information. Instead of using the available physiographic characteristics to define the centre of a target location, this study proposes to introduce estimates of reference hydrological variables to ensure a better homogeneity. These reference variables represent nonlinear relations with the site characteristics obtained by projection pursuit regression, a nonparametric regression method. The resulting neighbourhoods are investigated in combination with commonly used regional models: the index-flood model and regression-based models. The complete approach is illustrated in a real-world case study with gauged sites from the southern part of the province of Québec, Canada, and is compared with the traditional approaches such as region of influence and canonical correlation analysis. The evaluation focuses on the neighbourhood properties as well as prediction performances, with special attention devoted to problematic stations. Results show clear improvements in neighbourhood definitions and quantile estimates.

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