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Water
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Recent Advance in Drought Risk Assessment, Monitoring, and Forecasting
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Recent Advance in Drought Risk Assessment, Monitoring, and Forecasting

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 18667

Special Issue Editor

Prof. Dr. Yuei-An Liou

E-Mail Website
Guest Editor
1. Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli District, Taoyuan City 32001, Taiwan
2. Taiwan Group on Earth Observations, Hsinchu, Taiwan
Interests: remote sensing; atmospheric science; environmental monitoring; disaster analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Droughts are very common phenomena which impose serious challenges to ecosystems and human societies. They occur in all types of climate circumstances. However, their characteristics vary considerably from one region to another.

To properly plan and manage water resources, it is important to accurately and timely forecast drought events. Hence, this Special Issue welcomes presentations of significant advancements in drought monitoring and prediction capabilities on regional and global scales. The studies can be based on known drought indicators or new ones. Particularly, the incorporation of machine learning tools and approaches that can improve existing drought forecasts is encouraged.

We also welcome research on exploring the link from multiple information sources, including satellite-based vegetation conditions and evapotranspiration to investigate current or future drought impacts on water resources.

You may choose our Joint Special Issue in Remote Sensing.

Prof. Dr. Yuei-An Liou
Guest Editor

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. Water 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 2600 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 indicators
  • drought forecasting
  • drought monitoring
  • drought risk assessment
  • evapotranspiration
  • machine learning

Published Papers (5 papers)

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Research

13 pages, 3402 KiB  
Article
Impact of Climate Change on Agricultural Droughts in Spain
by María del Pilar Jiménez-Donaire, Juan Vicente Giráldez and Tom Vanwalleghem
Water 2020, 12(11), 3214; https://0-doi-org.brum.beds.ac.uk/10.3390/w12113214 - 17 Nov 2020
Cited by 7 | Viewed by 4309
Abstract
Drought is an important natural hazard that is expected to increase in frequency and intensity as a consequence of climate change. This study aimed to evaluate the impact of future changes in the temperature and precipitation regime of Spain on agricultural droughts, using [...] Read more.
Drought is an important natural hazard that is expected to increase in frequency and intensity as a consequence of climate change. This study aimed to evaluate the impact of future changes in the temperature and precipitation regime of Spain on agricultural droughts, using novel static and dynamic drought indices. Statistically downscaled climate change scenarios from the model HadGEM2-CC, under the scenario representative concentration pathway 8.5 (RCP8.5), were used at a total of 374 sites for the period 2006 to 2100. The evolution of static and dynamic drought stress indices over time show clearly how drought frequency, duration and intensity increase over time. Values of static and dynamic drought indices increase over time, with more frequent occurrences of maximum index values equal to 1, especially towards the end of the century (2071–2100). Spatially, the increase occurs over almost the entire area, except in the more humid northern Spain, and in areas that are already dry at present, which are located in southeast Spain and in the Ebro valley. This study confirms the potential of static and dynamic indices for monitoring and prediction of drought stress. Full article
(This article belongs to the Special Issue Recent Advance in Drought Risk Assessment, Monitoring, and Forecasting)
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14 pages, 2304 KiB  
Article
Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics
by María del Pilar Jiménez-Donaire, Juan Vicente Giráldez and Tom Vanwalleghem
Water 2020, 12(9), 2592; https://0-doi-org.brum.beds.ac.uk/10.3390/w12092592 - 16 Sep 2020
Cited by 3 | Viewed by 2643
Abstract
The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. [...] Read more.
The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Córdoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools). Full article
(This article belongs to the Special Issue Recent Advance in Drought Risk Assessment, Monitoring, and Forecasting)
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14 pages, 2978 KiB  
Article
Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?
by Yang Lang, Lifeng Luo, Aizhong Ye and Qingyun Duan
Water 2020, 12(7), 2010; https://0-doi-org.brum.beds.ac.uk/10.3390/w12072010 - 15 Jul 2020
Cited by 2 | Viewed by 2686
Abstract
Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month [...] Read more.
Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions. Full article
(This article belongs to the Special Issue Recent Advance in Drought Risk Assessment, Monitoring, and Forecasting)
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20 pages, 5902 KiB  
Article
Analysis of Temporal-Spatial Variation Characteristics of Drought: A Case Study from Xinjiang, China
by Qiang An, Huaxiang He, Juanjuan Gao, Qianwen Nie, Yingjie Cui, Chuanjiang Wei and Xinmin Xie
Water 2020, 12(3), 741; https://0-doi-org.brum.beds.ac.uk/10.3390/w12030741 - 08 Mar 2020
Cited by 17 | Viewed by 3398
Abstract
It is of great significance to study the characteristics and change trends of drought in Xinjiang to provide a basis for implementing local strategies. Based on monthly precipitation and temperature data from 95 meteorological stations in Xinjiang, from 1960 to 2018, the Standardized [...] Read more.
It is of great significance to study the characteristics and change trends of drought in Xinjiang to provide a basis for implementing local strategies. Based on monthly precipitation and temperature data from 95 meteorological stations in Xinjiang, from 1960 to 2018, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated, and the characteristics and trends of drought in Xinjiang were analysed, in details. Furthermore, a comprehensive evaluation index, i.e., Regional Drought Severity (RDS), was proposed to analyse the effects of duration of the drought and the extent of the drought affected area. The results from our study suggested: (1) In consideration of global warming, droughts in Xinjiang have intensified during the past 59 years, and the frequency and range of droughts have increased significantly; (2) During the plant growing season, spring, summer, and autumn, a drying trend was observed, while, a wetting trend was identified for winter season; (3) The drought-prone months shifted from January and December to March-November in the 1970s, and April was identified as a month with the highest frequency of droughts; (4) The meteorological change occurred a period near 1997. It can be speculated that the intensified droughts can be triggered by the excessive temperature rise, through comparing the changes in SPEI and the Standardized Precipitation Index (SPI), before and after the meteorological change; (5) After the meteorological change, the frequency of droughts with different levels had significantly increased, in addition, the drought-prone areas shifted from the north-west to the south-east. The results from this research provide important support for drought management in Xinjiang, also offer scientific basis for the formulation of relevant policies on agricultural and animal husbandry production. Full article
(This article belongs to the Special Issue Recent Advance in Drought Risk Assessment, Monitoring, and Forecasting)
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19 pages, 7856 KiB  
Article
Application of Artificial Neural Networks in Forecasting a Standardized Precipitation Evapotranspiration Index for the Upper Blue Nile Basin
by Getachew Mehabie Mulualem and Yuei-An Liou
Water 2020, 12(3), 643; https://0-doi-org.brum.beds.ac.uk/10.3390/w12030643 - 27 Feb 2020
Cited by 39 | Viewed by 4704
Abstract
The occurrence frequency of drought has intensified with the unprecedented effect of global warming. Knowledge about the spatiotemporal distributions of droughts and their trends is crucial for risk management and developing mitigation strategies. In this study, we developed seven artificial neural network (ANN) [...] Read more.
The occurrence frequency of drought has intensified with the unprecedented effect of global warming. Knowledge about the spatiotemporal distributions of droughts and their trends is crucial for risk management and developing mitigation strategies. In this study, we developed seven artificial neural network (ANN) predictive models incorporating hydro-meteorological, climate, sea surface temperatures, and topographic attributes to forecast the standardized precipitation evapotranspiration index (SPEI) for seven stations in the Upper Blue Nile basin (UBN) of Ethiopia from 1986 to 2015. The main aim was to analyze the sensitivity of drought-trigger input parameters and to measure their predictive ability by comparing the predicted values with the observed values. Statistical comparisons of the different models showed that accurate results in predicting SPEI values could be achieved by including large-scale climate indices. Furthermore, it was found that the coefficient of determination and the root-mean-square error of the best architecture ranged from 0.820 to 0.949 and 0.263 to 0.428, respectively. In terms of statistical achievement, we concluded that ANNs offer an alternative framework for forecasting the SPEI drought index. Full article
(This article belongs to the Special Issue Recent Advance in Drought Risk Assessment, Monitoring, and Forecasting)
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