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Remote Sensing of Hydrological Extremes: Current Progress and Future Prospect

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 8112

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Prof. Dr. Yue-Ping Xu

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Guest Editor

Department of Hydraulic Engineering, Zhejiang University, Hangzhou, China
Interests: hydrological extreme analysis under environmental changes; flood simulation and prediction
Special Issues, Collections and Topics in MDPI journals

Dr. Martijn J. Booij

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Guest Editor

Department of Water Engineering and Management, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, The Netherlands
Interests: catchment hydrology; hydrological modelling; environmental change impacts; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Qian Zhu

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Guest Editor

School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: hydrologic remote sensing; hydrological modeling; drought/flash drought

Special Issue Information

Dear Colleagues,

Hydrological extremes attract worldwide attention. Extreme hydrological events, especially floods and droughts, are among the costliest natural disasters, causing many environmental, economic, and social problems. However, the identification, monitoring, quantification, and forecasting of hydrological extremes are quite difficult tasks, and their magnitude, timing, and variations are sensitive to ongoing climate change and human activities. Therefore, many challenges exist to understand, monitor, and predict hydrological extremes under environmental changes. For instance, investigating the possible impacts of climate change on hydrological extremes, as well as their key driving factors (e.g., rainfall, soil moisture, and evapotranspiration) globally and regionally, is critical to manage the possible corresponding risks. Furthermore, with the rapid development of remote sensing technology, more and more remote sensing-based datasets are applied to monitor, simulate, and forecast extreme hydrological events. In addition, datasets are inherently uncertain, and this will result in uncertainties in predicted extreme hydrological events which need to be taken into account in risk assessment and management of extreme events.

This Special Issue will focus on newly developed methods and datasets in remote sensing, and their applications in the analysis of hydrological extremes. The aim of this Special Issue is to collect a wide spectrum of papers which illustrate the current progress and future prospects on remote sensing of hydrological extremes.

We welcome novel research and reviews related to the topic “remote sensing of hydrological extremes”, such as possible impacts of climate change on hydrological extremes, uncertainty of datasets, models, and methods to simulate or predict hydrological extremes, and identification of specific hydrological extremes (e.g., flash droughts and floods).

Specific topics include, but are not limited to:

Impacts of climate change and/ or human activities on hydrological extremes;
Identification and forecasting of flash droughts or floods;
Non-stationarity and uncertainty of hydrological extremes and processes related to hydrological extremes;
Data assimilation and data merging methods for simulation of hydrological extremes with remote sensing datasets;
Calibration of hydrological models for high and low flows using remote sensing datasets.

Prof. Dr. Yue-Ping Xu
Dr. Martijn J. Booij
Dr. Qian Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

remote sensing
hydrological extremes
drought
flood
climate change
human activities

Published Papers (4 papers)

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Research

21 pages, 15157 KiB

Open AccessArticle

Evaluation of Remote Sensing-Based Evapotranspiration Datasets for Improving Hydrological Model Simulation in Humid Region of East China

by Suli Pan, Yue-Ping Xu, Haiting Gu, Bai Yu and Weidong Xuan

Remote Sens. 2022, 14(18), 4546; https://doi.org/10.3390/rs14184546 - 11 Sep 2022

Cited by 3 | Viewed by 1676

Abstract

Conventional calibration methods used in hydrological modelling are based on runoff observations at the basin outlet. However, calibration with only runoff often produces reasonable runoff but poor results for other hydrological variables. Multi-variable calibration with both runoff and remote sensing-based evapotranspiration (ET) is developed naturally, due to the importance of ET and its data availability. This study compares two main calibration schemes: (1) calibration with only runoff (Scheme I) and (2) multi-variable calibration with both runoff and remote sensing-based ET (Scheme II). ET data are obtained from three remote sensing-based ET datasets, namely Penman–Monteith–Leuning (PML), FLUXCOM, and the Global Land Evaporation Amsterdam Model (GLEAM). The aforementioned calibration schemes are applied to calibrate the parameters of the Distributed Hydrology Soil Vegetation Model (DHSVM) through ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII). The results show that all three ET datasets have good performance for areal ET in the study area. The DHSVM model calibrated based on Scheme I produces acceptable performance in runoff simulation (Kling–Gupta Efficiency, KGE = 0.87), but not for ET simulation (KGE < 0.7). However, reasonable simulations can be achieved for both variables based on Scheme II. The KGE value of runoff simulation can reach 0.87(0.91), 0.72(0.85), and 0.75(0.86) in the calibration (validation) period based on Scheme II (PML), Scheme II (FLUXCOM), and Scheme II (GLEAM), respectively. Simultaneously, ET simulations are greatly improved both in the calibration and validation periods. Furthermore, incorporating ET data into all three Scheme II variants is able to improve the performance of extreme flow simulations (including extreme low flow and high flow). Based on the improvement of the three datasets in extreme flow simulations, PML can be utilized for multi-variable calibration in drought forecasting, and FLUXCOM and GLEAM are good choices for flood forecasting. Full article

(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes: Current Progress and Future Prospect)

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22 pages, 9121 KiB

Open AccessArticle

Quantitative Assessment of Impact of Climate Change and Human Activities on Streamflow Changes Using an Improved Three-Parameter Monthly Water Balance Model

by Hao Chen, Saihua Huang, Yue-Ping Xu, Ramesh S. V. Teegavarapu, Yuxue Guo, Jingkai Xie and Hui Nie

Remote Sens. 2022, 14(17), 4411; https://doi.org/10.3390/rs14174411 - 05 Sep 2022

Cited by 1 | Viewed by 1927

Abstract

Understanding the impact of climate change and human activities on the hydrological cycle of any watershed can provide a scientific basis for regional water resource planning, flood management, and disaster mitigation. An improved three-parameter hydrological model (CM) based on monthly water balance using an exponential equation to depict the distribution of groundwater storage capacity was developed and evaluated. The model uses Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) rainfall data as input, with the Zhejiang Province as the case application, and the effects of climate change and human activities on streamflow changes were assessed by separating environmental variables in this study. The results indicate that APHRODITE data has excellent monthly accuracy, with a mean correlation coefficient (CC) of more than 0.96 and an average absolute percentage bias (Pbais) of less than 5%. The three models are relatively close in their ability to simulate high flows, but the CM simulated low flow is better than the other two models. Positive and negative Pbais phenomena occur in the CM model in each catchment, and absolute levels are regulated by 5%. Furthermore, the CM model’s average Nash efficiency coefficient (NSE) is greater than 0.9, indicating that it can correctly fulfill the water balance. The results are more consistent throughout multiple catchments in each watershed using Budyko-based and hydrological model technique to evaluate the influence of climate change and human activities on streamflow. Climate change dominated streamflow variations in 18 of the 21 catchments in Zhejiang Province, whereas human activities dominated the rest. The findings of the study will be used to influence the management, development, and usage of water resources in the watershed. Full article

(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes: Current Progress and Future Prospect)

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20 pages, 3378 KiB

Open AccessArticle

Assessment and Hydrological Validation of Merged Near-Real-Time Satellite Precipitation Estimates Based on the Gauge-Free Triple Collocation Approach

by Daling Cao, Hongtao Li, Enguang Hou, Sulin Song and Chengguang Lai

Remote Sens. 2022, 14(15), 3835; https://doi.org/10.3390/rs14153835 - 08 Aug 2022

Cited by 5 | Viewed by 1387

Abstract

Obtaining accurate near-real-time precipitation data and merging multiple precipitation estimates require sufficient in-situ rain gauge networks. The triple collocation (TC) approach is a novel error assessment method that does not require rain gauge data and provides reasonable precipitation estimates by merging data; this study assesses the TC approach for producing reliable near-real-time satellite-based precipitation estimate (SPE) products and the utility of the merged SPEs for hydrological modeling of ungauged areas. Three widely used near-real-time SPEs, including the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) early/late run (E/L) series, and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR) products, are used in the Beijiang basin in south China. The results show that the TC-based merged SPEs generally outperform all original SPEs, with higher consistency with the in-situ observations, and show superiority over the simple equal-weighted merged SPEs used for comparison; these findings indicate the superiority of the TC approach for utilizing the error characteristics of input SPEs for multi-SPE merging for ungauged areas. The validation of the hydrological modeling utility based on the Génie Rural à 4 paramètres Journalier (GR4J) model shows that the streamflow modeled by the TC-based merged SPEs has the best performance among all SPEs, especially for modeling low streamflow because the integration with the PDIR outperforms the IMERG products in low streamflow modeling. The TC merging approach performs satisfactorily for producing reliable near-real-time SPEs without gauge data, showing great potential for near-real-time applications, such as modeling rainstorms and monitoring floods and flash droughts in ungauged areas. Full article

(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes: Current Progress and Future Prospect)

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28 pages, 5451 KiB

Open AccessCommunication

Meta-Analysis and Visualization of the Literature on Early Identification of Flash Floods

by Zhengli Yang, Xinyue Yuan, Chao Liu, Ruihua Nie, Tiegang Liu, Xiaoai Dai, Lei Ma, Min Tang, Yina Xu and Heng Lu

Remote Sens. 2022, 14(14), 3313; https://doi.org/10.3390/rs14143313 - 09 Jul 2022

Cited by 6 | Viewed by 2453

Abstract

Flash flood is one of the extremely destructive natural disasters in the world. In recent years, extreme rainfall events caused by global climate change have increased, and flash flood disasters are becoming the main types of natural disasters in the world. Due to the characteristics of strong suddenness, complex disaster-causing factors, great difficulty in prediction and forecast, and the lack of historical data, it is difficult to effectively prevent and control flash flood disaster. The early identification technology of flash floods is not only the basis of flash flood disaster prediction and early warning, but also an effective means of flash flood prevention and control. The paper makes a meta-analysis and visual analysis of 475 documents collected by the Web of Science Document Platform in the past 31 years by comprehensively using Citespace, Vosviewer, Origin, etc. We systematically summarize the research progress and development trend of early identification technology of flash flood disasters from five key research subfields: (1) precipitation, (2) sediment, (3) sensitivity analysis, (4) risk assessment, (5) uncertainty analysis. In addition, we analyze and discuss the main problems encountered in the current research of several subfields and put forward some suggestions to provide references for the prevention and control of flash flood disasters. Full article

(This article belongs to the Special Issue Remote Sensing of Hydrological Extremes: Current Progress and Future Prospect)

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