E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques"

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

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Guest Editor
Prof. Quazi K. Hassan

Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. N.W., Calgary, AB T2N 1N4, Canada
Website | E-Mail
Phone: +1-403-210-9494
Interests: optical/thermal remote sensing in: (i) forecasting and monitoring of natural hazards/disasters, such as forest fire, drought, and flooding; (ii) comprehending the dynamics of natural resources, such as forestry, agriculture, and water; and (iii) modelling issues related to boreal environment
Guest Editor
Dr. Ashraf Dewan

Department of Spatial Sciences, Curtin University, Kent St, Bentley WA 6102, Australia
Website | E-Mail
Phone: +08 9266 4930
Interests: Climate Change; Disaster and Natural Hazards; Health Geography; Resources Management, Coastal dynamics

Special Issue Information

Dear Colleagues,

Droughts and floods are very common phenomena that frequently occur in various places across the world. Here, the objective is to bring together scientist(s)/researcher(s) working on this topic, aiming to highlight ongoing research investigations and new applications in the field. Within this framework, the editors of this Special Issue would like to invite both applied and theoretical research contributions, submissions of original works furthering knowledge concerned with any aspect of the use of remote sensing and/or geospatial technologies in droughts and/or floods. Note that these manuscripts must be not only unpublished, but also not under consideration for potential publication elsewhere. In the case of the remote sensing data, the manuscripts may use data acquired by optical, thermal, hyperspectral, active and passive microwave platforms using either airborne or spaceborne remote sensing platforms.

Prof. Dr. Quazi K. Hassan
Dr. Ashraf Dewan
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 papers will be 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. Water is an international peer-reviewed open access monthly 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 1600 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

  • Drought monitoring
  • Flood monitoring
  • Forecasting of flood danger/risk
  • Forecasting of drought danger/risk
  • Drought-induced damage assessment
  • Flood-induced damage assessment
  • Geospatial techniques

Published Papers (5 papers)

View options order results:
result details:
Displaying articles 1-5
Export citation of selected articles as:

Research

Open AccessArticle
Analysing the Relationship between Multiple-Timescale SPI and GRACE Terrestrial Water Storage in the Framework of Drought Monitoring
Water 2019, 11(8), 1672; https://doi.org/10.3390/w11081672
Received: 12 July 2019 / Revised: 9 August 2019 / Accepted: 10 August 2019 / Published: 13 August 2019
PDF Full-text (3241 KB) | HTML Full-text | XML Full-text
Abstract
The operational monitoring of long-term hydrological droughts is often based on the standardised precipitation index (SPI) for long accumulation periods (i.e., 12 months or longer) as a proxy indicator. This is mainly due to the current lack of near-real-time observations of relevant hydrological [...] Read more.
The operational monitoring of long-term hydrological droughts is often based on the standardised precipitation index (SPI) for long accumulation periods (i.e., 12 months or longer) as a proxy indicator. This is mainly due to the current lack of near-real-time observations of relevant hydrological quantities, such as groundwater levels or total water storage (TWS). In this study, the correlation between multiple-timescale SPIs (between 1 and 48 months) and GRACE-derived TWS is investigated, with the goals of: (i) evaluating the benefit of including TWS data in a drought monitoring system, and (ii) testing the potential use of SPI as a robust proxy for TWS in the absence of near-real-time measurements of the latter. The main outcomes of this study highlight the good correlation between TWS anomalies (TWSA) and long-term SPI (12, 24 and 48 months), with SPI-12 representing a global-average optimal solution (R = 0.350 ± 0.250). Unfortunately, the spatial variability of the local-optimal SPI underlines the difficulty in reliably capturing the dynamics of TWSA using a single meteorological drought index, at least at the global scale. On the contrary, over a limited area, such as Europe, the SPI-12 is able to capture most of the key traits of TWSA that are relevant for drought studies, including the occurrence of dry extreme values. In the absence of actual TWS observations, the SPI-12 seems to represent a good proxy of long-term hydrological drought over Europe, whereas the wide range of meteorological conditions and complex hydrological processes involved in the transformation of precipitation into TWS seems to limit the possibility of extending this result to the global scale. Full article
Figures

Figure 1

Open AccessArticle
Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data
Water 2019, 11(8), 1653; https://doi.org/10.3390/w11081653
Received: 23 June 2019 / Revised: 7 August 2019 / Accepted: 8 August 2019 / Published: 10 August 2019
PDF Full-text (6709 KB) | HTML Full-text | XML Full-text
Abstract
Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks [...] Read more.
Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms. Full article
Figures

Figure 1

Open AccessArticle
Estimating Real-Time Water Area of Dongting Lake Using Water Level Information
Water 2019, 11(6), 1240; https://doi.org/10.3390/w11061240
Received: 14 May 2019 / Revised: 7 June 2019 / Accepted: 9 June 2019 / Published: 13 June 2019
PDF Full-text (3453 KB) | HTML Full-text | XML Full-text
Abstract
Dongting Lake, the second largest freshwater lake in China, is an important water source for the Yangtze River Basin. The water area of Dongting Lake fluctuates significantly daily, which may cause flooding and other relevant disasters. Although remote sensing techniques may provide lake [...] Read more.
Dongting Lake, the second largest freshwater lake in China, is an important water source for the Yangtze River Basin. The water area of Dongting Lake fluctuates significantly daily, which may cause flooding and other relevant disasters. Although remote sensing techniques may provide lake area estimates with reasonable accuracy, they are not available in real-time and may be susceptible to weather conditions. To address this issue, this paper attempted to examine the relationship between lake area and the water levels at the hydrological stations. Multi-temporal water area data were derived through analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) imagery using the Automatic Water Extraction Index (AWEI). Then we analyzed the inter- and intra-annual variations in the water area of the Dongting Lake. Corresponding water level information at hydrological stations of the Dongting Lake were obtained. Simple linear regression (SLR) models and stepwise multiple linear regression (SMLR) models were constructed using water levels and water level differences from the upstream and downstream hydrological stations. We used the data from 2004 to 2012 and 2012, respectively, to build the model, and applied the data from 2013 to 2015 to evaluate the models. Results suggest that the maximum water area of the Dongting Lake during 2000–2015 has a clear decreasing trend. The variations in the water area were characterized by hydrological seasons, with the annual minimum and maximum water areas occurring in January and September, respectively. The water level at the Chengjingji station, and water level differences between upstream stations and the Chengjingji station, play a major role in estimating the water area. Further, results also show that the SMLR established in 2012 performs the best in estimating water area of the Dongting Lake, especially with high water levels. Full article
Figures

Figure 1

Open AccessArticle
Construction of Comprehensive Drought Monitoring Model in Jing-Jin-Ji Region Based on Multisource Remote Sensing Data
Water 2019, 11(5), 1077; https://doi.org/10.3390/w11051077
Received: 13 April 2019 / Revised: 20 May 2019 / Accepted: 20 May 2019 / Published: 23 May 2019
PDF Full-text (7207 KB) | HTML Full-text | XML Full-text
Abstract
Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are [...] Read more.
Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are focused mainly on monitoring single drought response factors such as soil or vegetation, and the ability to reflect comprehensive information on drought was poor. This study constructed a comprehensive drought-monitoring model considering the drought factors including precipitation, vegetation growth status, and soil moisture balance during the drought process for the Jing-Jin-Ji region, China. The comprehensive drought index of remote sensing (CDIR), a drought indicator deduced by the model, was composed of the vegetation condition index (VCI), the temperature condition index (TCI), and the precipitation condition index (PCI). The PCI was obtained from the Tropical Rainfall Measuring Mission (TRMM) satellite. The VCI and TCI were obtained from a moderate-resolution imaging spectroradiometer (MODIS). In this study, a heavy drought process was accurately explored using the CDIR in the Jing-Jin-Ji region in 2016. Finally, a three-month scales standardized precipitation index (SPI-3), drought affected crop area, and standardized unit yield of wheat were used as validation to evaluate the accuracy of this model. The results showed that the CDIR is closely related to the SPI-3, as well as variations in the drought-affected crop area and standardized unit yield of crop. The correlation coefficient of the CDIR with SPI-3 was between 0.45 and 0.85. The correlation coefficient between the CDIR and drought affected crop was between −0.81 and −0.86. Moreover, the CDIR was positively correlated with the standardized unit yield of crop. It showed that the CDIR index is a decent indicator that can be used for integrated drought monitoring and that it can synthetically reflect meteorological and agricultural drought information. Full article
Figures

Figure 1

Open AccessArticle
Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios
Water 2019, 11(4), 761; https://doi.org/10.3390/w11040761
Received: 23 March 2019 / Revised: 9 April 2019 / Accepted: 9 April 2019 / Published: 12 April 2019
PDF Full-text (4143 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. [...] Read more.
Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. This study focused on the simulation of snowmelt-runoff using Snowmelt-Runoff Model (SRM) under the current and future Representative Concentration Pathways (RCP 2.6, 4.5 and 8.5) climate scenarios in the two main tributaries of the UIB namely the Astore and the Hunza River basins. Remote sensing data from Advanced Land Observation Satellite (ALOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with in-situ hydro-climatic data was used as input to the SRM. Basin-wide and zone-wise approaches were used in the SRM. For the zone-wise approach, basin areas were sliced into five elevation zones and the mean temperature for the zones with no weather stations was estimated using a lapse rate value of −0.48 °C to −0.76 °C/100 m in both studied basins. Zonal snow cover was estimated for each zone by reclassifying the MODIS snow maps according to the zonal boundaries. SRM was calibrated over 2000–2001 and validated over the 2002–2004 data period. The results implied that the SRM simulated the river flow efficiently with Nash-Sutcliffe model efficiency coefficient of 0.90 (0.86) and 0.86 (0.86) for the basin-wide (zone-wise) approach in the Astore and Hunza River Basins, respectively, over the entire simulation period. Mean annual discharge was projected to increase by 11–58% and 14–90% in the Astore and Hunza River Basins, respectively, under all the RCP mid- and late-21st-century scenarios. Mean summer discharge was projected to increase between 10–60% under all the RCP scenarios of mid- and late-21st century in the Astore and Hunza basins. This study suggests that the water resources of Pakistan should be managed properly to lessen the damage to human lives, agriculture, and economy posed by expected future floods as indicated by the climatic projections. Full article
Figures

Figure 1

Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top