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Application of Space-Time Statistics in Water Resources
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Application of Space-Time Statistics in Water Resources

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

Deadline for manuscript submissions: closed (10 September 2020) | Viewed by 11028

Special Issue Editor

Prof. Dr. Hao Zhang

E-Mail Website
Guest Editor
Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
Interests: spatial statistics; spatio-temporal statistics; kriging; big data; covariance tapering; extreme values; uncertainty analysis

Special Issue Information

Dear Colleagues,

This Special Issue covers the broad topic of the application of space-time statistics to the impact of climate change on water resources and the management of water resources. Relevant topics include but are not
limited to:

  • Methodology for modeling the inhomogenous space-time data that are often correlated and have heavy tailed probability distributions.
  • Statistical tests or methods for comparing precipitation or other climate variables at different time periods or different spatial regions.
  • Analysis to quantify the changes in spatio-temporal patterns of precipitation.
  • Geospatial methods.
  • Extremes of precipitation (drought and flood).
  • Projection of future precipitation and uncertainty analysis.
  • Water resources and economy in the context of climate change.
  • Monitoring of water resources.
  • Data fusion in water resources.

Prof. Dr. Hao Zhang
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

  • data fusion
  • drought
  • extremes
  • flood
  • geospatial methods
  • precipitation
  • spatio-temporal statistics
  • uncertainty analysis

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Published Papers (3 papers)

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Research

18 pages, 6514 KiB  
Article
Changes in Water Surface Area of the Lake in the Steppe Region of Mongolia: A Case Study of Ugii Nuur Lake, Central Mongolia
by Erdenesukh Sumiya, Batsuren Dorjsuren, Denghua Yan, Sandelger Dorligjav, Hao Wang, Altanbold Enkhbold, Baisha Weng, Tianlin Qin, Kun Wang, Tuvshin Gerelmaa, Oyunbaatar Dambaravjaa, Wuxia Bi, Yuheng Yang, Byambabayar Ganbold, Mohammed Gedefaw, Asaminew Abiyu and Abel Girma
Water 2020, 12(5), 1470; https://doi.org/10.3390/w12051470 - 21 May 2020
Cited by 17 | Viewed by 4943
Abstract
The Ugii Nuur Lake is not only one of the small hydrologically closed lakes located in the Orkhon River Basin in Central Mongolia but also the most vulnerable area for global climate change. Therefore, this study aims to investigate the impacts of recent [...] Read more.
The Ugii Nuur Lake is not only one of the small hydrologically closed lakes located in the Orkhon River Basin in Central Mongolia but also the most vulnerable area for global climate change. Therefore, this study aims to investigate the impacts of recent global climate change on the water surface area. The data we analyzed were various measured hydro-meteorological variables of the lake basin and the lake surface area, which was estimated from Landsat series satellite data from 1986 to 2018. The methods we used were Mann-Kendall (MK), Innovative trend analysis method (ITAM), Sen’s slope estimator test, correlation, and regression analysis. The variation of lake water surface area has a strong positive correlation with the change of the lake water level (r = 0.95). The Mann-Kendall trend analysis has indicated that under a significant decrease in total annual precipitation ( Z   = −0.902) and inflow river discharge ( Z   = −5.392) and a considerable increase in total annual evaporation ( Z = 4.385) and annual average air temperature ( Z   = 4.595), the surface area of the Ugii Nuur Lake has decreased sharply ( Z = −6.021). The total annual evaporation (r = −0.64) and inflow river discharge (r = 0.67) were the essential hydro-meteorological factors affecting the surface area of the Ugii Nuur Lake. The lake surface area decreased by 13.5% in 2018 compared with 1986. In the near future, it is vital to conduct scientific studies considering the volume of lake water, groundwater, and the anthropogenic impact. Full article
(This article belongs to the Special Issue Application of Space-Time Statistics in Water Resources)
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16 pages, 6618 KiB  
Article
Trends of Runoff Variation and Effects of Main Causal Factors in Mun River, Thailand During 1980–2018
by Renzhi Li, Heqing Huang, Guoan Yu, Hong Yu, Arika Bridhikitti and Teng Su
Water 2020, 12(3), 831; https://doi.org/10.3390/w12030831 - 15 Mar 2020
Cited by 16 | Viewed by 3500
Abstract
Mun River is the largest tributary of the Mekong River in Thailand and provides abundant water resources not only for an important agricultural area in Thailand but also for the lower Mekong River. To understand how the runoff of Mun River responds to [...] Read more.
Mun River is the largest tributary of the Mekong River in Thailand and provides abundant water resources not only for an important agricultural area in Thailand but also for the lower Mekong River. To understand how the runoff of Mun River responds to climate change and human activities in recent decades, this study performed a detailed examination of the characteristics of runoff variation based on measurements at two hydrological gauging stations on the main stem of Mun River during 1980–2018. Using the Mann-Kendall test, Morlet wavelet transform and Double Cumulative Curve methods, this study identifies that the variation of annual runoff of Mun River encountered an abruption in 1999/2000, with an increased trend taking place since then. Furthermore, a detailed assessment of the effects of the variations in rainfall, temperature, evaporation, and land use types extracted from remote sensing images at the basin scale reveals that a significant reduction in forest area and slight reductions in evaporation and farmland area taking place since 1999 can lead to an increase in the runoff of Mun River, while the dramatic increase in garden area since 1999 tends to make the runoff decrease. Full article
(This article belongs to the Special Issue Application of Space-Time Statistics in Water Resources)
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15 pages, 3365 KiB  
Article
A Study of Precipitation Patterns through Stochastic Ordering
by Nan Ni and Hao Zhang
Water 2020, 12(2), 351; https://doi.org/10.3390/w12020351 - 27 Jan 2020
Cited by 2 | Viewed by 2285
Abstract
The study of spatial and temporal changes in precipitation patterns is important to agriculture and natural ecosystems. These changes can be described by some climate change indices. Because these indices often have skewed probability distributions, some common statistical procedures become either inappropriate or [...] Read more.
The study of spatial and temporal changes in precipitation patterns is important to agriculture and natural ecosystems. These changes can be described by some climate change indices. Because these indices often have skewed probability distributions, some common statistical procedures become either inappropriate or less powerful when they are applied to the indices. A nonparametric approach based on stochastic ordering is proposed, which does not make any assumption on the shape of the distribution. This approach is applied to the average length of the period between two adjacent precipitation days, which is called the average number of consecutive dry days (ACDD). This approach is shown to be able to reveal some patterns in precipitation that other approaches do not. Using daily precipitations at 756 stations in China from 1960 to 2015, this work compares the ACDDs in three periods, 1960–1965, 1985–1990, and 2010–2015 for each province in China. The results show that ACDD increases stochastically from the period 1960–1965 to either the period 1985–1990 or the period 2010–2015, or from the period 1985–1990 to the period 2010–2015 in all but three provinces in China. Full article
(This article belongs to the Special Issue Application of Space-Time Statistics in Water Resources)
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