Special Issue "Statistics in Hydrology"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 10135

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Special Issue Editors

Prof. Dr. Yuanfang Chen
E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: hydrological design; stochastic hydrology; long-term HYDROLOGIC forecasting; risk analysis; water resource assessment and management; water resource protection
Prof. Dr. Dong Wang
E-Mail Website
Guest Editor
Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210046, P. R. China
Interests: stochastic hydrology; uncertainty and risk analysis; water resource management
Prof. Dr. Dedi Liu
E-Mail Website
Guest Editor
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
Interests: statistical hydrology; hydrological model; water resources management
Prof. Dr. Binquan Li
E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: hydrological uncertainty; stochastic hydrology; hydrological forecasting; hydrological model; snow hydrology
Prof. Dr. Ashish Sharma
E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, University of New South Wales, Sydney NSW2052, Australia
Interests: stochastic hydrology; probabilistic forecasting and downscaling; radar hydrology; Bayesian hydrology; water resource management

Special Issue Information

Dear Colleagues,

Statistical methods have a long history in the analysis of hydrological data for designing, planning, infilling, forecasting, and specifying better models to assess scenarios of land use and climate change in catchments. The effectiveness of statistical descriptions of hydrological processes reflects the enormous complexity of hydrological systems, which makes a purely deterministic description ineffective.

This Special Issue is envisioned to showcase the state of the art in statistical hydrology. Potential papers would be selected from the presentations delivered at the 10th International Workshop on Statistical Hydrology, which was organized by the International Commission on Statistical Hydrology, International Association of Hydrological Sciences (ICSH-IAHS), and took place in Nanjing, China on 19–20 October 2019.

A total of 132 participants from 13 countries including China, the United States, Canada, Australia, Italy, Germany, Switzerland, Belgium, Spain, Poland, etc. registered to participate in the conference, and more than 300 graduate students from the organizer (Hohai University) attended the conference. During the conference, a number of international hydrological academics shared their latest research results. These scholars included Prof. Eric Wood from Princeton University (Academician of the American Academy of Engineering), Prof. András Bárdossy from the University of Stuttgart (Foreign Academician of the Hungarian Academy of Sciences), Prof. Salvatore Grimaldi (Vice President of IAHS), Prof. Ashish Sharma (President of ICSH-IAHS), Prof. Qingyun Duan from Hohai University, etc. This conference has inspired many new academic innovations and achievements in the past year. Therefore, it is now a good time to organize a Special Issue to share these achievements.

This potential issue will comprise high-quality papers submitted by the participants of the conference and also welcomes contributions from other scholars that match the topics of this Special Issue.

The issue will mainly cover the following topics:

  1. Big data, data mining, and assimilation in hydrology;
  2. Extreme hydrological and meteorological events under climate change;
  3. Hydrological prediction and its uncertainty;
  4. Hydrological design and risk assessment under changing environment;
  5. Eco-hydrological regime changes and uncertainty assessment under human activities.

Prof. Dr. Yuanfang Chen
Prof. Dr. Dong Wang
Prof. Dr. Dedi Liu
Prof. Dr. Binquan Li
Prof. Dr. Ashish Sharma
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistical hydrology
  • stochastic hydrology
  • hydrological uncertainty
  • big data, data analysis
  • extreme hydrological and meteorological events
  • hydrological design and risk assessment
  • bayesian hydrology
  • eco-hydrological uncertainty assessment
  • non-stationary hydrological frequency analysis

Published Papers (12 papers)

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Editorial

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Editorial
Statistics in Hydrology
Water 2022, 14(10), 1571; https://doi.org/10.3390/w14101571 - 13 May 2022
Viewed by 647
Abstract
Statistical methods have a long history in the analysis of hydrological data for designing, planning, infilling, forecasting, and specifying better models to assess scenarios of land use and climate change in catchments [...] Full article
(This article belongs to the Special Issue Statistics in Hydrology)

Research

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Article
Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
Water 2022, 14(8), 1261; https://doi.org/10.3390/w14081261 - 13 Apr 2022
Cited by 1 | Viewed by 719
Abstract
This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed [...] Read more.
This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed post-processor were compared to the traditional MCP and MCP using a truncated Normal distribution (MCPt) by applying multiple deterministic and probabilistic verification indices. This research also assesses the GMCP’s capacity to estimate the predictive uncertainty of the monthly streamflow under different climate conditions in the “Second Workshop on Model Parameter Estimation Experiment” (MOPEX) catchments distributed in the SE part of the USA. The results indicate that all three post-processors showed promising results. However, the GMCP post-processor has shown significant potential in generating more reliable, sharp, and accurate monthly streamflow predictions than the MCP and MCPt methods, especially in dry catchments. Moreover, the MCP and MCPt provided similar performances for monthly streamflow and better performances in wet catchments than in dry catchments. The GMCP constitutes a promising solution to handle heteroscedastic errors in monthly streamflow, therefore moving towards a more realistic monthly hydrological prediction to support effective decision-making in planning and managing water resources. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
Investigating the Linkage between Extreme Rainstorms and Concurrent Synoptic Features: A Case Study in Henan, Central China
Water 2022, 14(7), 1065; https://doi.org/10.3390/w14071065 - 28 Mar 2022
Cited by 1 | Viewed by 611
Abstract
Extraordinary floods are linked with heavy rainstorm systems. Among various systems, their synoptic features can be quite different. The understanding of extreme rainstorms by their causative processes may assist in flood frequency analysis and support the evaluation of any changes in flood occurrence [...] Read more.
Extraordinary floods are linked with heavy rainstorm systems. Among various systems, their synoptic features can be quite different. The understanding of extreme rainstorms by their causative processes may assist in flood frequency analysis and support the evaluation of any changes in flood occurrence and magnitudes. This paper aims to identify the most dominant meteorological factors for extreme rainstorms, using the ERA5 hourly reanalysis dataset in Henan, central China as a case study. Past 72 h extreme precipitation events are investigated, and six potential factors are considered in this study, including precipitable water (PW), the average temperature (Tavg) of and the temperature difference (Tdiff) between the value at 850 hPa and 500 hPa, relative humidity (RH), convective available potential energy (CAPE), and vertical wind velocity (Wind). The drivers of each event and the dominant factor at a given location are identified using the proposed metrics based on the cumulative distribution function (CDF). In Henan, central China, Wind and PW are dominant factors in summer, while CAPE and Wind are highly related factors in winter. For Zhengzhou city particularly, Wind is the key driver for summer extreme rainstorms, while CAPE plays a key role in winter extreme precipitation events. It indicates that the strong transport of water vapor in summer and atmospheric instability in winter should receive more attention from the managers and planners of water resources. On the contrary, temperature-related factors have the least contribution to the occurrence of extreme events in the study area. The analysis of dominant factors can provide insights for further flood estimations and forecasts. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
Statistical Evaluation of the Influences of Precipitation and River Level Fluctuations on Groundwater in Yoshino River Basin, Japan
Water 2022, 14(4), 625; https://doi.org/10.3390/w14040625 - 17 Feb 2022
Cited by 1 | Viewed by 447
Abstract
Precise evaluation of the correlations among precipitation, groundwater and river water enhance our understanding on regional hydrological circulation and water resource management. The innovative and efficient use of wavelet analysis has been able to identify significant interactions in the spatial and temporal domains [...] Read more.
Precise evaluation of the correlations among precipitation, groundwater and river water enhance our understanding on regional hydrological circulation and water resource management. The innovative and efficient use of wavelet analysis has been able to identify significant interactions in the spatial and temporal domains and to estimate the recharge travel time. In this paper, a wavelet analysis was utilized to analyse 43 years of monthly, and 2 years of daily, precipitation, river level and groundwater level data in the Yoshino River Basin, Japan. There were two main results: (1) There was a significant influence of precipitation and river on groundwater, with a periodicity of 4–128 days, 1 year and 2–7 years. The periodicity of 1 year was correlated with seasonal variability. The significant interaction at 4–128 days mainly occurred in the rainy season. The 2–7-year oscillation of aquifer water levels was determined by precipitation. (2) The recharge-water travel times in the study area estimated from the arrow patterns in the precipitation–groundwater wavelet coherence (WTC) were consistent for each observation well. The response times of the aquifer to precipitation were 1 day and 3–6 days in 2013 and 2014, respectively. The different time lags were likely determined by the timing of maximum daily precipitation. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
Bivariate Nonstationary Extreme Flood Risk Estimation Using Mixture Distribution and Copula Function for the Longmen Reservoir, North China
Water 2022, 14(4), 604; https://doi.org/10.3390/w14040604 - 16 Feb 2022
Cited by 1 | Viewed by 627
Abstract
Recently, the homogenous flood generating mechanism assumption has become questionable due to changes in the underlying surface. In addition, flood is a multifaced natural phenomenon and should be characterized by both peak discharge and flood volume, especially for flood protection structures. Hence, in [...] Read more.
Recently, the homogenous flood generating mechanism assumption has become questionable due to changes in the underlying surface. In addition, flood is a multifaced natural phenomenon and should be characterized by both peak discharge and flood volume, especially for flood protection structures. Hence, in this study, data relating to the 55-year reservoir inflow, annual maximum flood peak (AMFP), and annual maximum flood volume (AMFV) for the Longmen Reservoir in North China have been utilized. The 1-day AMFV exhibits a significant correlation with AMFP. The extreme flood peak-volume pairs are then used to detect the heterogeneity and to perform nonstationary flood risk assessment using mixture distribution as the univariate marginal distribution. Moreover, a copula-based bivariate nonstationary flood frequency analysis is developed to investigate environmental effects on the dependence of flood peak and volume. The results indicate that the univariate nonstationary return period is between the joint OR and the AND return periods. The conditional probabilities of 1-day AMFV, when AMFP exceeds a certain threshold, are likely to be high, and the design flood values estimated by joint distribution are larger than the ones in the univariate nonstationary context. This study can provide useful information for engineers and decision-makers to improve reservoir flood control operations. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China
Water 2022, 14(3), 492; https://doi.org/10.3390/w14030492 - 07 Feb 2022
Cited by 1 | Viewed by 613
Abstract
The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning [...] Read more.
The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R2, RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
Spatiotemporal Characteristics Analysis and Driving Forces Assessment of Flash Floods in Altay
Water 2022, 14(3), 331; https://doi.org/10.3390/w14030331 - 24 Jan 2022
Cited by 2 | Viewed by 755
Abstract
Flash floods are devastating natural disasters worldwide. Understanding their spatiotemporal distributions and driving factors is essential for identifying high risk areas and predicting hydrological conditions. In this study, several methods were used to analyze the changing patterns and driving factors of flash floods [...] Read more.
Flash floods are devastating natural disasters worldwide. Understanding their spatiotemporal distributions and driving factors is essential for identifying high risk areas and predicting hydrological conditions. In this study, several methods were used to analyze the changing patterns and driving factors of flash floods in the Altay region. Results indicate that the number of flash floods each year increased in 1980–2015, with two sudden change points (1996 and 2008), and April, June, and July presented the highest frequency of events. Habahe and Jeminay were known to have high flash flood incidences; however, currently, Altay City, Fuhai, Fuyun, and Qinghe are most affected. In terms of driving force analysis, precipitation and altitude performance have a key impact on flash flood occurrence in this settlement compared to other subregions, with a high percentage increase in the mean squared error value of 39, 37, 37, 37, and 33 for 10 min precipitation in a 20-year return period, elevation, 60 min precipitation in a 20-year return period, 6 h precipitation in a 20-year return period, and 24 h precipitation in a 20-year return period, respectively. The study results provide insights into spatial–temporal dynamics of flash floods and a scientific basis for policymakers to set improvement targets in specific areas. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
Frequency Analysis of the Nonstationary Annual Runoff Series Using the Mechanism-Based Reconstruction Method
by and
Water 2022, 14(1), 76; https://doi.org/10.3390/w14010076 - 02 Jan 2022
Cited by 2 | Viewed by 374
Abstract
Due to climate change and human activities, the statistical characteristics of annual runoff series of many rivers around the world exhibit complex nonstationary changes, which seriously impact the frequency analysis of annual runoff and are thus becoming a hotspot of research. A variety [...] Read more.
Due to climate change and human activities, the statistical characteristics of annual runoff series of many rivers around the world exhibit complex nonstationary changes, which seriously impact the frequency analysis of annual runoff and are thus becoming a hotspot of research. A variety of nonstationary frequency analysis methods has been proposed by many scholars, but their reliability and accuracy in practical application are still controversial. The recently proposed Mechanism-based Reconstruction (Me-RS) method is a method to deal with nonstationary changes in hydrological series, which solves the frequency analysis problem of the nonstationary hydrological series by transforming nonstationary series into stationary Me-RS series. Based on the Me-RS method, a calculation method of design annual runoff under the nonstationary conditions is proposed in this paper and applied to the Jialu River Basin (JRB) in northern Shaanxi, China. From the aspects of rationality and uncertainty, the calculated design value of annual runoff is analyzed and evaluated. Then, compared with the design values calculated by traditional frequency analysis method regardless of whether the sample series is stationary, the correctness of the Me-RS theory and its application reliability is demonstrated. The results show that calculation of design annual runoff based on the Me-RS method is not only scientific in theory, but also the obtained design values are relatively consistent with the characteristics of the river basin, and the uncertainty is obviously smaller. Therefore, the Me-RS provides an effective tool for annual runoff frequency analysis under nonstationary conditions. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China
Water 2022, 14(1), 66; https://doi.org/10.3390/w14010066 - 31 Dec 2021
Cited by 1 | Viewed by 431
Abstract
The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, [...] Read more.
The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
Forecasting Summer Rainfall and Streamflow over the Yangtze River Valley Using Western Pacific Subtropical High Feature
Water 2021, 13(18), 2580; https://doi.org/10.3390/w13182580 - 18 Sep 2021
Cited by 1 | Viewed by 749
Abstract
The western Pacific subtropical high (WPSH) is one of the key systems affecting the summer rainfall over the Yangtze River Valley in China. In this study, the forecasting capacity of the WPSH for summer rainfall and streamflow is evaluated based on the WPSH [...] Read more.
The western Pacific subtropical high (WPSH) is one of the key systems affecting the summer rainfall over the Yangtze River Valley in China. In this study, the forecasting capacity of the WPSH for summer rainfall and streamflow is evaluated based on the WPSH index (WPSHI) derived from the NCEP/NCAR reanalysis dataset. It has been found that WPSHI can identify extreme flood years with a higher skill than normal wet years. Specifically, exceedance probability forecasting based on WPSHI has higher skills for higher thresholds of rainfall. For streamflow, adding WPSHI as a predictor only enhances the skill for higher thresholds of streamflow relative to models based on antecedent streamflow. Under the same framework, performances of two postprocessing approaches for dynamical forecasts, i.e., the model output statistics (MOS) approach and the reanalysis-based (RAN) approach are compared. Hindcasts from Climate Forecast System version 2 from the National Center for Environmental Prediction (CFSv2) are used to calculate WPSHI, which is used as the predictor for rainfall and streamflow. The result shows that the RAN approach performs better than the MOS approach. This study emphasizes the fact that the forecasting skill of exceedance probability would largely depend on the selected threshold of the predictand, and this fact should be noticed in future studies in the long-term forecasting field. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
MLE-Based Parameter Estimation for Four-Parameter Exponential Gamma Distribution and Asymptotic Variance of Its Quantiles
Water 2021, 13(15), 2092; https://doi.org/10.3390/w13152092 - 30 Jul 2021
Cited by 1 | Viewed by 831
Abstract
The choice of a probability distribution function and confidence interval of estimated design values have long been of interest in flood frequency analysis. Although the four-parameter exponential gamma (FPEG) distribution has been developed for application in hydrology, its maximum likelihood estimation (MLE)-based parameter [...] Read more.
The choice of a probability distribution function and confidence interval of estimated design values have long been of interest in flood frequency analysis. Although the four-parameter exponential gamma (FPEG) distribution has been developed for application in hydrology, its maximum likelihood estimation (MLE)-based parameter estimation method and asymptotic variance of its quantiles have not been well documented. In this study, the MLE method was used to estimate the parameters and confidence intervals of quantiles of the FPEG distribution. This method entails parameter estimation and asymptotic variances of quantile estimators. The parameter estimation consisted of a set of four equations which, after algebraic simplification, were solved using a three dimensional Levenberg-Marquardt algorithm. Based on sample information matrix and Fisher’s expected information matrix, derivatives of the design quantile with respect to the parameters were derived. The method of estimation was applied to annual precipitation data from the Weihe watershed, China and confidence intervals for quantiles were determined. Results showed that the FPEG was a good candidate to model annual precipitation data and can provide guidance for estimating design values. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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Article
Revision of Frequency Estimates of Extreme Precipitation Based on the Annual Maximum Series in the Jiangsu Province in China
Water 2021, 13(13), 1832; https://doi.org/10.3390/w13131832 - 30 Jun 2021
Cited by 1 | Viewed by 1209
Abstract
Frequency estimates of extreme precipitation are revised using a regional L-moments method based on the annual maximum series and Chow’s equation at lower return periods for the Jiangsu area in China. First, the study area is divided into five homogeneous regions, and the [...] Read more.
Frequency estimates of extreme precipitation are revised using a regional L-moments method based on the annual maximum series and Chow’s equation at lower return periods for the Jiangsu area in China. First, the study area is divided into five homogeneous regions, and the optimum distribution for each region is determined by an integrative assessment. Second, underestimation of quantiles and the applicability of Chow’s equation are verified. The results show that quantiles are underestimated based on the annual maximum series, and that Chow’s formula is applicable for the study area. Next, two methods are used to correct the underestimation of frequency estimation. A set of rational and reliable frequency estimations is obtained using the regional L-moments method and the two revised methods, which can indirectly provide a robust basis for flood control and water resource management. This study extends previous works by verifying underestimation of the quantiles and the provision of two improved methods for obtaining reliable quantile estimations of extreme precipitation at lower recurrence intervals, especially in solving reliable estimates for a 1-year return period from the integral lower limit of the frequency distribution. Full article
(This article belongs to the Special Issue Statistics in Hydrology)
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