hydrological variables
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Hydrobiologia ◽  
2021 ◽  
Author(s):  
Katie Irving ◽  
Sonja C. Jähnig ◽  
Mathias Kuemmerlen

AbstractLotic freshwater macroinvertebrate species distribution models (SDMs) have been shown to improve when hydrological variables are included. However, most studies to date only include data describing climate or stream flow-related surrogates. We assessed the relative influence of climatic and hydrological predictor variables on the modelled distribution of macroinvertebrates, expecting model performance to improve when hydrological variables are included. We calibrated five SDMs using combinations of bioclimatic (bC), hydrological (H) and hydroclimatic (hC) predictor datasets and compared model performance as well as variance partition of all combinations. We investigated the difference in trait composition of communities that responded better to either bC or H configurations. The dataset bC had the most influence in terms of proportional variance, however model performance was increased with the addition of hC or H. Trait composition demonstrated distinct patterns between associated model configurations, where species that prefer intermediate to slow-flowing current conditions in regions further downstream performed better with bC–H. Including hydrological variables in SDMs contributes to improved performance, it is however, species-specific and future studies would benefit from hydrology-related variables to link environmental conditions and diverse communities. Consequently, SDMs that include climatic and hydrological variables could more accurately guide sustainable river ecosystem management.


Author(s):  
Adriana Márquez‐Romance ◽  
Nereida López‐Calatayud ◽  
Bettys Farías‐De Márquez ◽  
Edilberto Guevara‐Pérez

2021 ◽  
Vol 34 ◽  
pp. 100393
Author(s):  
Hao-jie Xu ◽  
Xin-ping Wang ◽  
Chuan-yan Zhao ◽  
Shu-yao Shan ◽  
Jin Guo

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1361
Author(s):  
Ruting Yang ◽  
Bing Xing

Profiling the hydrological response of watershed precipitation and streamflow to large-scale circulation patterns and astronomical factors provides novel information into the scientific management and prediction of regional water resources. Possible contacts of El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), sunspot activity to precipitation and streamflow in the upper Yangtze River basin (UYRB) were investigated in this work. Monthly precipitation and streamflow were utilized as well as contemporaneous same-scale teleconnections time series spanning a total of 70 years from 1951 to 2020 in precipitation and 121 years from 1900 to 2020 in streamflow. The principal component analysis (PCA) method was applied so as to characterize the dominant variability patterns over UYRB precipitation time series, with the temporal variability of first two modes explaining more than 80% of total variance. Long-term evolutionary pattern and periodic variation characteristics of precipitation and streamflow are explored by applying continuous wavelet transform (CWT), cross-wavelet transform (XWT) and wavelet coherence (WTC), analyzing multi-scale correlation between hydrological variables and teleconnections in the time-frequency domain. The results manifest that ENSO exhibits multiple interannual period resonance with precipitation and streamflow, while correlations are unstable in time and phase. PDO and sunspot effects on precipitation and streamflow at interannual scales vary with time-frequency domains, yet significant differences are exhibited in their effects at interdecadal scales. PDO exhibits a steady negative correlation with streamflow on interdecadal scales of approximately 10 years, while the effect of sunspot on streamflow exhibits extremely steady positive correlation on longer interdecadal scales of approximately 36 years. Analysis reveals that both PDO and sunspot have significantly stronger effects on streamflow variability than precipitation, which might be associated with the high spatiotemporal variability of precipitation.


2021 ◽  
Vol 193 (11) ◽  
Author(s):  
Mohamad Basel Al Sawaf ◽  
Kiyosi Kawanisi ◽  
Mohamad Nazieh Jlilati ◽  
Cong Xiao ◽  
Masoud Bahreinimotlagh

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 364-384
Author(s):  
Nidhal Khaleel Ajeel

Regional frequency analysis (AFR) brings together a variety of statistical methods aimed at predicting the behavior of extreme hydrological variables at ungauged sites. Regression techniques, geostatistical methods and classification are among the statistical tools frequently encountered in the literature. Methodologies based on these tools lead to regional models that offer a simple, but very useful description of the relationship between extreme hydrological variables and physiometeorological characteristics of a site. These regional models then make it possible to predict the behavior of variables of interest at places where no hydrological information is available. These methods are generally based on restrictive theoretical assumptions, including linearity and normality. These do not reflect the reality of natural phenomena. The general objectives of this paper are to identify the methods affected by these hypotheses, evaluate their impacts and propose improvements aimed at obtaining more realistic and fairer representations. Projection pursuit regression is a non-parametric method similar to generalized additive models and artificial neural networks that are considered in AFR to take into account the non-linearity of hydrological processes. In a comparative study, this paper shows that regression with revealing directions makes it possible to obtain more parsimonious models while preserving the same predictive power as the other nonparametric methods. Canonical Correlation Analysis (ACC) is used to create neighborhoods within which a model (e.g. multiple regression) is used to predict hydrologic variables at ungagged sites on the other hand, ACC strongly depends on the assumptions of normality and linearity. A new methodology for delineating neighborhoods is proposed in this paper and uses revealing direction regression to predict a reference point representing hydrological and physiometeorological information that is relevant to these groupings. The results show that the new methodology generalizes that of ACC, improves the homogeneity of neighborhoods and leads to better performance. In AFR, kriging techniques on transformed spaces are suggested in order to predict extreme hydrological variables. However, a transformation is required so that the hydrological variables of interest derive approximately from a multidimensional normal distribution. This transformation introduces a bias and leads to suboptimal predictions. Solutions have been proposed, but have not been tested in AFR. This paper proposes the approach of spatial copulas and shows that this approach provides satisfactory solutions to the problems encountered with kriging techniques. Max-stable processes are a theoretical formalization of spatial extremes and correspond to a more faithful representation of hydrological processes on the other hand; their characterization of extreme dependence poses technical problems which slow down their adoption. In this paper, the approximate Bayesian calculus is examined as a solution. The results of a simulation study show that the approximate Bayesian computation is superior to the standard approach of compound likelihood. In addition, this approach is more appropriate in order to take into account specification errors.


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