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Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models

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

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 12086

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Dr. Wei Zhang

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Guest Editor

Department of Plants, Soils and Climate, Utah State University, Logan, UT 84322, USA
Interests: hydrometeorology; climate change; numerical models
Special Issues, Collections and Topics in MDPI journals

Dr. Nancy A. Barth

E-Mail Website
Guest Editor

Dakota Water Science Center, U.S. Geological Survey, Bismarck, ND 58503, USA
Interests: hydrology; flood frequency; mixed populations; nonstationarity; hydrometeorology

Dr. Hamid Karimi

E-Mail Website
Guest Editor

Department of Computer Science, Utah State University, Logan, UT 84322, USA
Interests: machine learning; data mining; data science; social network analysis; social media mining; educational data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are calling for submissions for the Special Issue “Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models”.

Hydrometeorological extremes such as droughts and floods due to climate change present an urgent issue. Long-lasting droughts in the western U.S. are such an example, along with more frequent floodings across the globe. These extremes are associated with increasing hydroclimatic intensity and more moisture held in air due to Clausius–Clapeyron scaling. While major efforts have been made to characterize and predict hydrometeorological extremes, this phenomenon remains a challenge due to the lack of proper approaches and metrics. This Special Issue aims to develop novel approaches and metrics to characterize and predict hydrometeorological extremes. We encourage submissions that are focused on leveraging machine learning techniques and numerical models. All related manuscripts are welcome. Topics of interest include, but are not limited to: the application of machine learning and numerical models for advancing the prediction skill of hydrometeorological extremes; the development of new approaches and metrics to quantify and predict hydrometeorological extremes; and the application of machine learning or other novel methods to improve climate models and hydrological models. Review articles are also encouraged.

Dr. Wei Zhang
Dr. Nancy A. Barth
Dr. Hamid Karimi
Guest Editors

Manuscript Submission Information

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Keywords

hydrometeorological extremes
machine learning
approaches and metrics

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Published Papers (5 papers)

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Research

21 pages, 9130 KiB

Open AccessArticle

Extreme Runoff Estimation for Ungauged Watersheds Using a New Multisite Multivariate Stochastic Model MASVC

by Joel Hernández-Bedolla, Liliana García-Romero, Chrystopher Daly Franco-Navarro, Sonia Tatiana Sánchez-Quispe and Constantino Domínguez-Sánchez

Water 2023, 15(16), 2994; https://doi.org/10.3390/w15162994 - 19 Aug 2023

Cited by 3 | Viewed by 2637

Abstract

Precipitation is influential in determining runoff at different scales of analysis, whether in minutes, hours, or days. This paper proposes the use of a multisite multivariate model of precipitation at a daily scale. Stochastic models allow the generation of maximum precipitation and its association with different return periods. The modeling is carried out in three phases. The first is the estimation of precipitation occurrence by using a two-state multivariate Markov model to calculate the non-rainfall periods. Once the rainfall periods of various storms have been identified, the amount of precipitation is estimated through a process of normalization, standardization of the series, acquisition of multivariate parameters, and generation of synthetic series. In comparison, the analysis applies probability density functions that require fewer data and, consequently, represent greater certainty. The maximum values of surface runoff show consistency for different observed return periods, therefore, a more reliable estimation of maximum surface runoff. Our approach enhances the use of stochastic models for generating synthetic series that preserve spatial and temporal variability at daily, monthly, annual, and extreme values. Moreover, the number of parameters reduces in comparison to other stochastic weather generators. Full article

(This article belongs to the Special Issue Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models)

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14 pages, 8370 KiB

Open AccessArticle

Characterizing the Synoptic-Scale Precursors of Extreme Precipitation Events in the Southeastern Edge of the Tibetan Plateau: Anomalous Evolution of Atmospheric Dynamic-Thermal Structure

by Longguang Chen, Bin Chen, Ruiyu Zhao and Xiangde Xu

Water 2023, 15(7), 1407; https://doi.org/10.3390/w15071407 - 4 Apr 2023

Cited by 2 | Viewed by 2102

Abstract

Extreme precipitation events frequently occur at the southeastern edge of the Tibetan Plateau (SETP), causing severe disasters. In this study, we selected the top 100 regional extreme precipitation events over the SETP region during the period of 2001–2020, and analyzed their evolutionary characteristics of large-scale thermodynamic anomalies prior to the extreme precipitation events occurring, with the aim of exploring their precursor signals. The results show that, accompanying the wave train propagating across the Eurasian continent and reaching East Asia, the extreme events over SETP during the summer season are dominated by the background large-scale atmospheric circulations characterized by the strengthened Southern Asia high (SAH), the westward-extended Western Pacific subtropical high (WPSH), and an intensified eastern Asia trough. Additionally, an analogue of low-level vortex embedded in the background large-scale circulations is developed at least 4 days prior to the occurrence of extreme events. Under the combined effects of these anomalies, the warm and cold air converge in the SETP area. Further analysis also suggests that the upper-troposphere divergence aloft combined with lower pressures at surface level lead to the upward vertical motion of circulations, along with the enhanced water-vapor transport conveyed both by the East Asian summer monsoon and the Indian summer monsoon. All anomalies mentioned above provide the favorable environment for the occurrence of precipitation extremes in the SETP region. Full article

(This article belongs to the Special Issue Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models)

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24 pages, 2531 KiB

Open AccessArticle

Evaluating the Effect of Deforestation on Decadal Runoffs in Malaysia Using the Revised Curve Number Rainfall Runoff Approach

by Jen Feng Khor, Steven Lim and Lloyd Ling

Water 2023, 15(7), 1392; https://doi.org/10.3390/w15071392 - 4 Apr 2023

Cited by 3 | Viewed by 2086

Abstract

This study presents a revised and calibrated Soil Conservation Service (SCS) curve number (CN) rainfall runoff model for predicting runoff in Malaysia using a new power correlation I_a = S^L, where L represents the initial abstraction coefficient ratio. The traditional SCS-CN model with the proposed relation I_a = 0.2S is found to be unreliable, and the revised model exhibits improved accuracy. The study emphasizes the need to design flood control infrastructure based on the maximum estimated runoff amount to avoid underestimation of the runoff volume. If the flood control infrastructure is designed based on the optimum CN_0.2 values, it could lead to an underestimation of the runoff volume of 50,100 m³ per 1 km² catchment area in Malaysia. The forest areas reduced by 25% in Peninsular Malaysia from the 1970s to the 1990s and 9% in East Malaysia from the 1980s to the 2010s, which was accompanied by an increase in decadal runoff difference, with the most significant rises of 108% in Peninsular Malaysia from the 1970s to the 1990s and 32% in East Malaysia from the 1980s to the 2010s. This study recommends taking land use changes into account during flood prevention planning to effectively address flood issues. Overall, the findings of this study have significant implications for flood prevention and land use management in Malaysia. The revised model presents a viable alternative to the conventional SCS-CN model, with a focus on estimating the maximum runoff amount and accounting for land use alterations in flood prevention planning. This approach has the potential to enhance flood management in the region. Full article

(This article belongs to the Special Issue Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models)

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17 pages, 8701 KiB

Open AccessArticle

Evaluating the Feasibility of the Liuxihe Model for Forecasting Inflow Flood to the Fengshuba Reservoir

by Yanjun Zhao, Yangbo Chen, Yanzheng Zhu and Shichao Xu

Water 2023, 15(6), 1048; https://doi.org/10.3390/w15061048 - 9 Mar 2023

Cited by 4 | Viewed by 2006

Abstract

Because of differences in the underlying surface, short flood confluence times, extreme precipitation, and other dynamic parameters, it is difficult to forecast an inflow flood to a basin reservoir, and traditional hydrological models do not achieve the forecast accuracy required for flood control operations. This study of the Fengshuba Reservoir in China evaluated the capacity of the Liuxihe model, which is based on a physically distributed hydrological model, to predict inflow floods in the Fengshuba Reservoir. The results show that the Liuxihe model has good applicability for flood forecasting in the basin. The use of different river classifications influenced the simulation results. The Liuxihe model can take into account the temporal and spatial inhomogeneity of precipitation and model parameters can be optimized using particle swarm optimization; this greatly improves the accuracy. The results show that the Liuxihe model can be used for real-time flood forecasting in the Fengshuba Reservoir watershed. Full article

(This article belongs to the Special Issue Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models)

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15 pages, 7973 KiB

Open AccessArticle

Responses of Extreme Discharge to Changes in Surface-Air and Dewpoint Temperatures in Utah: Seasonality and Mechanisms

by Timothy E. Wright, Jacob Stuivenvolt-Allen, Grace Affram, Nahid A. Hasan, Cody Ratterman and Wei Zhang

Water 2023, 15(4), 688; https://doi.org/10.3390/w15040688 - 9 Feb 2023

Viewed by 2358

Abstract

The changes in stream discharge extremes due to temperature and seasonality are key metrics in assessing the effects of climate change on the hydrological cycle. While scaling is commonly applied to temperature and precipitation due to the physical connections between temperature and moisture (i.e., Clausius–Clapeyron), the scaling rate of stream discharge extremes to air and dewpoint temperatures has not been evaluated. To address this challenge, we assess the scaling rates between stream discharge and air temperature and between stream discharge and dewpoint temperature in Utah using a well-designed statistical framework. While there are deviations from the Clausius–Clapeyron (CC) relationship in Utah using discharge data based on stream gauges and gridded climate data, we identify positive scaling rates of extreme discharge to temperatures across most of the state. Further diagnosis of extreme discharge events reveals that regional factors combined with topography are responsible for the marked seasonality of scaling, with most areas of Utah driven by spring snowmelt tied to high temperatures. The exception is far southwestern areas, being largely driven by winter rain-on-snow events. Our research highlights a measurable portion of stream discharge extremes associated with higher temperatures and dewpoints, suggesting that climate change could facilitate more extreme discharge events despite reductions to mean flows. Full article

(This article belongs to the Special Issue Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models)

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