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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 13432

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Special Issue Editors

Dr. Lei Ye

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Guest Editor

School of Civil & Hydraulic Engineering, Dalian University of Technology, Dalian, China
Interests: watershed flood forecating; deep learning; remotely sensed observations

Prof. Dr. Shuang Zhu

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Guest Editor

School of geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
Interests: water resources management; hydrological forecasting; drought evolution; remote sensing hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Weihong Liao

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Guest Editor

China Institute of Water Resources and Hydropower Research, Beijing 100044, China
Interests: hydrological simulation; flood forecast; extreme events analysis

Special Issue Information

Dear Colleagues,

Hydrological modeling plays an extremely important role in the water resources management, agricultural irrigation, climate and ecological environment change research. Hydrological processes are influenced by complex weather and non-linear infiltration mechanisms, which are difficult to model and thus, reliable hydrological modeling remains a challenge. Research hotspots include large sacle flood forecasting, remotely sensed data use, hydrometeorological extreme analysis, and hydrological simulation in areas with no observation data. Recently new technologies and methods have also been used in hydrological simulation, such as satellite remote sensing technology, big data mining technology, artificial intelligence, etc. We sincerely invite the authors to contribute original review and research manuscripts focused on developing and improving hydrological modeling and investigating their application in water cycle as well as hydrometeorological extremes under changing climate. Potential topics include but are not limited to the following:

Dr. Lei Ye
Dr. Shuang Zhu
Dr. Weihong Liao
Guest Editors

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Keywords

remote sensing observations and their usability in hydrological modeling
integration of in-situ and remotely-sensed hydrological data
calibration and validation studies
modeling of basin drought and flood
monitoring, modeling, predicting and understanding hydrological extremes
calibration of hydrological model parameters
multi-target parameter calibration
uncertainty of hydrological model: model input, structure and parameter
water resources assessment using hydrological modeling
the use of machine learning for hydrological modeling and forecasting

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Published Papers (5 papers)

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Research

19 pages, 3659 KiB

Open AccessArticle

Construction of a Time-Variant Integrated Drought Index Based on the GAMLSS Approach and Copula Function

by Xia Bai, Juliang Jin, Chengguo Wu, Yuliang Zhou, Libing Zhang, Yi Cui and Fang Tong

Water 2023, 15(9), 1653; https://doi.org/10.3390/w15091653 - 23 Apr 2023

Cited by 3 | Viewed by 1969

Abstract

Construction of an integrated drought index is a fundamental task to conducting drought disaster risk management and developing drought resistance planning strategies. Given the evident non-consistent features during the drought evolution process, firstly, the GAMLSS approach was utilized to construct multiple combination scenarios of time-variant parameters and corresponding probability distribution functions. Then, the time-variant comprehensive drought index integrating the variable characteristics of precipitation and soil moisture was established by means of the copula function. Finally, the reliability of the time-variant comprehensive drought index was verified through its application in frequency analysis and return period determination of drought hazard system in Huaibei Plain, China. The application results demonstrated that: (1) The variation of the time-variant integrated drought indicator presented strong consistency with both soil moisture and precipitation during historical years in Huaibei Plain. (2) The overall variation of the drought hazard system characterized by drought duration and severity presented a gradual mitigation trend from west to east and north to south in Huaibei Plain, which agrees with the geographic differences and water resources availability distribution features. (3) Drought recognition results, including the frequency of drought events and typical drought processes with extreme grades, are in agreement with the practical statistics and observed data series. On the whole, the proposed time-variant integrated drought indicator is capable of extracting complex variation characteristics within the drought hazard evolution process, and can be further applied in drought monitoring, recognition and assessment research fields. Full article

(This article belongs to the Special Issue Hydrological Modelling and Hydrometeorological Extreme Prediction)

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19 pages, 3741 KiB

Open AccessArticle

Hydrometeorological Forecast of a Typical Watershed in an Arid Area Using Ensemble Kalman Filter

by Ganchang He, Yaning Chen, Gonghuan Fang and Zhi Li

Water 2022, 14(23), 3970; https://doi.org/10.3390/w14233970 - 6 Dec 2022

Cited by 1 | Viewed by 2413

Abstract

The stationarity test and systematic prediction of hydrometeorological parameters are becoming increasingly important in water resources management. Based on the Ensemble Kalman Filter (EnKF) and wavelet analysis, this study selects precipitation, evaporation, temperature, and runoff as model variables, builds a model, tests and analyzes the stationarity of the hydrometeorological parameters of the Manas River, and forecasts the selected parameters for two years. The results of the study show that during the 2000–2020 study period, precipitation in the Manas River Basin on the northern slope of the Tianshan Mountains shows a significant downward trend from 2016 to 2020, with an annual average decline rate of 23.30 mm/a over five years. The proportion of runoff during the flood season also increases, with the statistical probability of an extremely low value of runoff increasing by 37.62% on average. After using wavelet decomposition to provide input to EnKF, the NSE of the model for the prediction of precipitation, evaporation, temperature, and runoff reached 0.86, 0.89, 0.96, and 0.9 respectively. At the same time, the K-S value increases from 0.28 to 0.40, which means that the wavelet analysis technique has great potential as a preprocessing of the Ensemble Kalman filter. Full article

(This article belongs to the Special Issue Hydrological Modelling and Hydrometeorological Extreme Prediction)

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21 pages, 4578 KiB

Open AccessArticle

Effects of the Long-Term Climate Change and Selective Discharge Schemes on the Thermal Stratification of a Large Deep Reservoir, Xin’anjiang Reservoir, China

by Huiyun Li, Jia Lan, Boqiang Qin, Liancong Luo, Junliang Jin, Guangwei Zhu and Zhixu Wu

Water 2022, 14(20), 3279; https://doi.org/10.3390/w14203279 - 18 Oct 2022

Cited by 4 | Viewed by 2795

Abstract

The effects of global warming and precipitation changes on water temperature and thermocline parameters, such as thermocline depth, thickness, and strength, were assessed. A catchment model, coupled with a reservoir thermal model with meteorological input calculated by a downscaled general circulation model (GCM) projection under three representative concentration pathways (RCPs), was applied to the Xin’anjiang Reservoir, located in southeast China. The results indicate that water temperature in each layer increased (decreased) with the rise (decline) in air temperature, especially the surface water temperature. There was a significant negative (positive) correlation between thermocline depth (strength) and air temperature during the period of stratification weakness. The most sensitive phenomenon of water temperature-to-precipitation changes occurred in the middle layer (depth = 30 m). Additionally, the thermocline depth and thickness increased with decreases in hydraulic residence time, which were caused by precipitation increases. According to the simulation experiments driven by RCP outputs, mean water temperature in each water layer in the future (2096–2100) has a strong response to increases in air temperature, which is projected to increase by 0.11–0.62 °C for RCP2.6, 0.76–1.19 °C for RCP4.5, and 1.50–2.35 °C for RCP8.5, compared to the baseline (2012–2016). However, mean water temperature in each water layer from 2096 to 2100 underwent a slight decrease caused by precipitation changes, with a 0.03–0.25 °C decrease for RCP2.6, 0.07–0.40 °C for RCP4.5, and 0.04–0.29 °C for RCP8.5, compared to 2012–2016. The mean thermocline depth in the future (2096–2100) will be significantly decreased, while the mean thermocline thickness will be slightly increased. Over a multiyear timescale, the impacts of air temperature changes are stronger than those induced by precipitation variations. However, the effects of hydraulic residence time changes caused by precipitation changes (especially rainstorm) should be considered in the management of deep reservoirs. Full article

(This article belongs to the Special Issue Hydrological Modelling and Hydrometeorological Extreme Prediction)

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20 pages, 6556 KiB

Open AccessArticle

A Study on the Determination Methods of Monitoring Point for Inundation Damage in Urban Area Using UAV and Hydrological Modeling

by Youngseok Song, Hyeongjun Lee, Dongho Kang, Byungsik Kim and Moojong Park

Water 2022, 14(7), 1117; https://doi.org/10.3390/w14071117 - 31 Mar 2022

Cited by 5 | Viewed by 3159

Abstract

Recently, unmanned aerial vehicles (UAVs) have been used in various fields, such as military, logistics, transportation, construction, and agriculture, making it possible to apply the limited activities of humans to various and wide ranges. In addition, UAVs have been utilized to construct topographic data that are more precise than existing satellite images or cadastral maps. In this study, a monitoring point for preventing flood damage in an urban area was selected using a UAV. In addition, the topographic data were constructed using a UAV, and the flow of rainwater was examined using the watershed analysis in an urban area. An orthomosaic, a digital surface model (DSM), and a three-dimensional (3D) model were constructed for the topographic data, and a precision of 0.051 m based on the root mean square error (RMSE) was achieved through the observation of ground control points (GCPs). On the other hand, for the watershed analysis in the urban area, the point in which the flow of rainwater converged was analyzed by adjusting the thresholds. A monitoring point for preventing flood damage was proposed by examining the topographic characteristics of the target area related to the inflow of rainwater. Full article

(This article belongs to the Special Issue Hydrological Modelling and Hydrometeorological Extreme Prediction)

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15 pages, 5991 KiB

Open AccessArticle

Investigation of Hyperparameter Setting of a Long Short-Term Memory Model Applied for Imputation of Missing Discharge Data of the Daihachiga River

by Weilisi and Toshiharu Kojima

Water 2022, 14(2), 213; https://doi.org/10.3390/w14020213 - 12 Jan 2022

Cited by 7 | Viewed by 1997

Abstract

Missing observational data pose an unavoidable problem in the hydrological field. Deep learning technology has recently been developing rapidly, and has started to be applied in the hydrological field. Being one of the network architectures used in deep learning, Long Short-Term Memory (LSTM) has been applied largely in related research, such as flood forecasting and discharge prediction, and the performance of an LSTM model has been compared with other deep learning models. Although the tuning of hyperparameters, which influences the performance of an LSTM model, is necessary, no sufficient knowledge has been obtained. In this study, we tuned the hyperparameters of an LSTM model to investigate the influence on the model performance, and tried to obtain a more suitable hyperparameter combination for the imputation of missing discharge data of the Daihachiga River. A traditional method, linear regression with an accuracy of 0.903 in Nash–Sutcliffe Efficiency (NSE), was chosen as the comparison target of the accuracy. The results of most of the trainings that used the discharge data of both neighboring and estimation points had better accuracy than the regression. Imputation of 7 days of the missing period had a minimum value of 0.904 in NSE, and 1 day of the missing period had a lower quartile of 0.922 in NSE. Dropout value indicated a negative correlation with the accuracy. Setting dropout as 0 had the best accuracy, 0.917 in the lower quartile of NSE. When the missing period was 1 day and the number of hidden layers were more than 100, all the compared results had an accuracy of 0.907–0.959 in NSE. Consequently, the case, which used discharge data with backtracked time considering the missing period of 1 day and 7 days and discharge data of adjacent points as input data, indicated better accuracy than other input data combinations. Moreover, the following information is obtained for this LSTM model: 100 hidden layers are better, and dropout and recurrent dropout levels equaling 0 are also better. The obtained optimal combination of hyperparameters exceeded the accuracy of the traditional method of regression analysis. Full article

(This article belongs to the Special Issue Hydrological Modelling and Hydrometeorological Extreme Prediction)

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