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Atmosphere
Special Issues
The Hydrologic Cycle in a Changing Climate
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The Hydrologic Cycle in a Changing Climate

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3910

Special Issue Editors

Dr. Diana Meilutytė-Lukauskienė

E-Mail Website
Guest Editor
1. Laboratory of Hydrology, Lithuanian Energy Institute, Breslaujos St. 3, LT-44403 Kaunas, Lithuania
2. Department of Physics, Mathematics and Biophysics, Faculty of Medicine, Lithuanian University of Health Sciences, Eiveniu str. 4, LT-44307 Kaunas, Lithuania
Interests: climate change; extreme hydrological phenomena; low flow indices; hydromorphology; droughts; spring floods
Dr. Vytautas Akstinas

E-Mail Website
Guest Editor
Laboratory of Hydrology, Lithuanian Energy Institute, Breslaujos St. 3, LT-44403 Kaunas, Lithuania
Interests: climatology; climate change; teleconnection patterns; hydrometeorological phenomena; catchment hydrology; hydrological modelling; spring floods

Special Issue Information

Dear Colleagues,

The hydrological cycle is the continuous movement of water in the Earth's hydrosphere. It is continuous process that consists of atmospheric, surface, and groundwater movement. The changing climate directly affects the drivers and components of the hydrological cycle (evapotranspiration, water vapor concentrations, clouds, air temperature, precipitation patterns, surface runoff, stream flow patterns, etc.).

The climate crisis has led to an increase in average global temperatures and an increase in high-temperature-related extreme events such as heat waves. Higher temperatures are also predicted to change the geographic distribution of climate zones. Higher temperatures accelerate evaporation, which increases the risk of severe drought in one region and causes unexpected flooding in another due to transported moisture. Already, as the climate changes, droughts are becoming more frequent and longer lasting in many regions of the World. Drought is an unusual and temporary lack of water resulting from insufficient rainfall and increased evaporation (due to high temperatures). Conversely, over the last century, an increase in evaporation and precipitation is intensifying the hydrological cycle. This is an undesirable consequence of global warming, as higher temperatures encourage evaporation, i.e., the evaporation from the land surface and sea is transporting the moisture as rain and snow to inland areas. Additionally, warmer air can hold more water vapor which can cause risk of heavy rainfall, extreme flooding, etc. Another example of changes in the hydrological cycle is the retreat of glaciers when the water supplied by solid precipitation is not sufficient to replenish the ice lost by melting or sublimation.

In this Special Issue, we invite all colleagues to contribute papers on new insights into any type of process of the hydrologic cycle, its response to climate change, interactions between its components, and many more topics. Research related to any aspect of observations or modelling of the hydrological cycle is welcome, including new or interdisciplinary approaches, feedback processes, various hydro-meteorological phenomena, the human role in the hydrologic cycle, or other topics that improve our understanding about changes in the hydrologic cycle. Review papers will also be considered.

Dr. Diana Meilutytė-Lukauskienė
Dr. Vytautas Akstinas
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. Atmosphere 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 2400 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

  • climate change
  • hydrologic cycle
  • droughts
  • flooding
  • water resourece management
  • river runoff

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

23 pages, 6044 KiB  
Article
Changes in Magnitude and Shifts in Timing of the Latvian River Annual Flood Peaks
by Elga Apsīte, Didzis Elferts, Jānis Lapinskis, Agrita Briede and Līga Klints
Atmosphere 2024, 15(9), 1139; https://doi.org/10.3390/atmos15091139 - 20 Sep 2024
Viewed by 587
Abstract
Climate change is expected to significantly impact temperature and precipitation, as well as snow accumulations and melt in mid-latitudes, including in the Baltic region, ultimately affecting the quantity and seasonal distribution of streamflow. This study aims to investigate the changes in the magnitude [...] Read more.
Climate change is expected to significantly impact temperature and precipitation, as well as snow accumulations and melt in mid-latitudes, including in the Baltic region, ultimately affecting the quantity and seasonal distribution of streamflow. This study aims to investigate the changes in the magnitude and timing of annual maximum discharge for 30 hydrological monitoring stations across Latvia from 1950/51 to 2021/22. Circular statistics and linear mixed effects models were applied to identify the strength of seasonality and timing. Trend analysis of the magnitude and timing of flood peaks were performed by using the Theil–Sen method and Mann–Kendall test. We analyzed regional significance of trends across different hydrological regions and country using the Walker test. Results indicate strong seasonality in annual flood peaks in catchments, with a single peak occurring in spring in the study sub-period of 1950/51–1986/87. Flood seasonality has changed over recent decades (i.e., 1987/88–2021/22) and is seen as a decrease in spring maximum discharge and increase in winter flood peaks. Alterations in annual flood occurrence also point towards a shift in flow regime from snowmelt dominated to mixed snow–rainfall dominated, with consistent changes towards the earlier timing of the flood peak, with a more or less pronounced gradation from west to east. Analysis shows that a significant trend of decrease in the magnitude and timing of annual maximum discharge was detected. Full article
(This article belongs to the Special Issue The Hydrologic Cycle in a Changing Climate)
►▼ Show Figures

Figure 1

Figure 1
<p>Map showing the location of hydrological stations and drainage regions: I—Western; II—Central; III—Northern; and IV—Eastern. The background information used for the map was obtained from the Latvian Geospatial Information Agency. The map was developed using the open-source software QGIS3.34.</p> Full article ">Figure 2
<p>Annual mean air temperature for the period 1961–2020 mean value for Latvia (blue dots and solid line) its corresponding linear trend (dashed line) and the mean air temperature of four consecutive climate normal (thick black lines) (n = 25) Using Sen’s slope, statistically significant (<span class="html-italic">p</span> &lt; 0.001) with a rate of change of 0.4 °C decade<sup>−1</sup> [<a href="#B39-atmosphere-15-01139" class="html-bibr">39</a>].</p> Full article ">Figure 3
<p>River hydrograph in Latvia and four hydrological regions of Latvia [<a href="#B33-atmosphere-15-01139" class="html-bibr">33</a>].</p> Full article ">Figure 4
<p>Distribution of the annual Qmax observations in percentage per months, study periods, hydrological districts and Latvia as a whole.</p> Full article ">Figure 5
<p>Percentage point changes in the distribution of the number of Qmax observations per months comparing years 1987/88−2021/22 to years 1950/51−1986/87 per hydrological regions and in Latvia.</p> Full article ">Figure 6
<p>Results of the circular statistics analysis by using the Rayleigh test. Circular plot (grey bars) showing the mean direction and mean resultant length (from 0 at the center and 1 at the outermost line) of the annual maximum peak discharge data for each river station in a particular time period. The colored bars represent the mean resultant vector for each of the regions and Latvia as a whole (see <a href="#atmosphere-15-01139-t003" class="html-table">Table 3</a>).</p> Full article ">Figure 7
<p>Map showing the results of trends in Mmax magnitude by using the Mann–Kendal test with blue and red symbols (see the legend) and the Theil–Sen approach with the numbers in the study period of 1950/51–2021/22 (if the mean trend value was positive, then all trends at the stations would also be positive and vice versa).</p> Full article ">Figure 8
<p>Box-plot of Theil–Sen slope in Ls<sup>−1</sup> km<sup>−2</sup> decade<sup>−1</sup> for Mmax magnitude and days per decade for Qmax timing for stations (n = 30) showing trends across Latvia.</p> Full article ">Figure 9
<p>Long-term temporal changes in annual Mmax of floods in four hydrological regions and across Latvia in the study period of 1950/51–2021/22. Green lines present Mmax mean values and red lines present median values over the entire drainage region and across Latvia. The green area represents 95% confidence interval for the mean annual Mmax values of the hydrological year. Blue, dashed lines present the linear trend.</p> Full article ">Figure 10
<p>Map showing the results of trends in annual Qmax timing using the Mann–Kendal test with blue and red symbols (see legend) and the Theil–Sen approach with the numbers in the study period of 1950/51–2021/22 (if the mean trend value was positive, then all trends at the stations would also be positive and vice versa).</p> Full article ">Figure 11
<p>Long-term temporal changes in timing of annual Qmax in four drainage regions and across Latvia in the study period of 1950/51–2021/22. Green lines show mean values and red lines show median values timing across drainage regions and the country. The green area represents a 95% confidence interval for the mean timing values of the hydrological year. Blue, dashed lines show a linear decreasing trend.</p> Full article ">Figure 12
<p>Relationship of trends in magnitude of annual Mmax and changes in timing of annual Qmax. Negative values in timing indicate early changes. The grey area represents 95% confidence interval for the period of 1950/51–2021/22.</p> Full article ">
21 pages, 14988 KiB  
Article
An Analysis of Extreme Rainfall Events in Cambodia
by Sytharith Pen, Saeed Rad, Liheang Ban, Sokhorng Brang, Panha Nuth and Lin Liao
Atmosphere 2024, 15(8), 1017; https://doi.org/10.3390/atmos15081017 - 22 Aug 2024
Viewed by 993
Abstract
Extreme rainfall, also known as heavy rainfall or intense precipitation, is a weather event characterized by a significant amount of rainfall within a short period. This study analyzes the trends in extreme precipitation indices at 17 stations in four main regions in Cambodia—the [...] Read more.
Extreme rainfall, also known as heavy rainfall or intense precipitation, is a weather event characterized by a significant amount of rainfall within a short period. This study analyzes the trends in extreme precipitation indices at 17 stations in four main regions in Cambodia—the Tonle Sap, coastal, Mekong Delta, and Upper Mekong regions—between 1991 and 2021. Analyzing the data with RClimDex v1.9 reveals diverse spatial and temporal variations. The statistical analysis of the extreme rainfall indices in Cambodia from 1991 to 2021 reveals significant trends. In the Tonle Sap region, consecutive dry days (CDDs) increased at most stations, except Battabang, Kampong Thmar, and Pursat, while consecutive wet days (CWDs) increased at most stations. These trends align with rising temperatures and reduced forest cover. In the coastal region, particularly at the Krong Khemarak Phummin station, most rainfall indices increased, with a slope value of 89.94 mm/year. The extreme rainfall indices max. 1-day precipitation (RX1day) and max. 5-day precipitation (RX5day) also increased, suggesting higher precipitation on days exceeding the 95th (R95p) and 99th percentiles (R99p). The Kampot station showed a significant increase in CDDs, indicating a heightened drought risk. In the Mekong Delta, the Prey Veng station recorded a decrease in the CDDs slope value by −4.892 days/year, indicating potential drought risks. The Stung Treng station, which is the only station in Upper Mekong, showed a decreasing trend in CDDs with a slope value of −1.183 days/year, indicating a risk of extreme events. These findings underscore the complex interplay between climate change, land use, and rainfall patterns in Cambodia. Full article
(This article belongs to the Special Issue The Hydrologic Cycle in a Changing Climate)
►▼ Show Figures

Figure 1

Figure 1
<p>The location of the stations in the study area (Cambodia).</p> Full article ">Figure 2
<p>Mean annual rainfall data at each station in Cambodia (1991–2021).</p> Full article ">Figure 3
<p>The spatial interpolation of the trends of the extreme rainfall indices in Cambodia: (<b>a</b>) CDDs, (<b>b</b>) CWDs, (<b>c</b>) RX1day, and (<b>d</b>) RX5day. The filled red downward and filled blue inverted triangles indicate decreasing and increasing trends, respectively.</p> Full article ">Figure 4
<p>The spatial interpolation of the trends of the extreme rainfall indices in Cambodia: (<b>a</b>) R10, (<b>b</b>) R20, (<b>c</b>) Rnn, and (<b>d</b>) PRCPTOT. The filled red downward and filled blue inverted triangles indicate decreasing and increasing trends, respectively.</p> Full article ">Figure 5
<p>The spatial interpolation of the trends of the extreme rainfall indices in Cambodia: (<b>a</b>) R95p, (<b>b</b>) R99p, and (<b>c</b>) SDII. The filled red downward and filled blue inverted triangles indicate decreasing and increasing trends, respectively.</p> Full article ">Figure 6
<p>The temporal variation of the increasing trend for the Tonle Sap region of the CDDs extreme rainfall index. The solid red line is the linear trend, the solid blue line is the annual variations, and the dotted blue line is the ten-year smoothing average.</p> Full article ">Figure 7
<p>The temporal variation of the decreasing trends for the Tonle Sap region of extreme rainfall indices: (<b>a</b>) PRCPTOT, (<b>b</b>) CWDs, (<b>c</b>) R10, (<b>d</b>) R20, (<b>e</b>) R151.75, (<b>f</b>) R95p, (<b>g</b>) R99p, (<b>h</b>) RX1day, (<b>i</b>) RX5day, and (<b>j</b>) SDII. The solid red line is the linear trend, the solid blue line is the annual variations, and the dotted blue line is the ten-year smoothing average.</p> Full article ">Figure 8
<p>The temporal variation of the increasing trends for the coastal region of extreme rainfall indices: (<b>a</b>) PRCPTOT, (<b>b</b>) CWDs, (<b>c</b>) R10, (<b>d</b>) R20, (<b>e</b>) R283.55, (<b>f</b>) R99p, (<b>g</b>) R95p, (<b>h</b>) RX1day, and (<b>i</b>) RX5day. The solid red line is the linear trend, the solid blue line is the annual variations, and the dotted blue line is the ten-year smoothing average.</p> Full article ">Figure 9
<p>The temporal variation of the decreasing trends for the coastal region of extreme rainfall indices: (<b>a</b>) SDII, and (<b>b</b>) CDDs. The solid red line is the linear trend, the solid blue line is the annual variations, and the dotted blue line is the ten-year smoothing average.</p> Full article ">Figure 10
<p>The temporal variation of increasing trends for the Mekong Delta of extreme rainfall indices: (<b>a</b>) PRCPTOT, (<b>b</b>) CWDs, (<b>c</b>) R10, and (<b>d</b>) R140.7. The solid red line is the linear trend, the solid blue line is the annual variations, and the dotted blue line is the ten-year smoothing average.</p> Full article ">Figure 11
<p>The temporal variation of decreasing trends for the Mekong Delta of extreme rainfall indices: (<b>a</b>) CDDs, (<b>b</b>) R20, (<b>c</b>) R95p, (<b>d</b>) R99p, (<b>e</b>) RX1day, (<b>f</b>) RX5day, and (<b>g</b>) SDII. The solid red line is the linear trend, the solid blue line is the annual variations, and the dotted blue line is the ten-year smoothing average.</p> Full article ">Figure 12
<p>The temporal variation of the increasing trends for the Upper Mekong of extreme rainfall indices: (<b>a</b>) PRCPTOT, (<b>b</b>) R10, (<b>c</b>) R20, (<b>d</b>) R95p, (<b>e</b>) R99p, (<b>f</b>) RX1day, (<b>g</b>) RX5day, and (<b>h</b>) SDII. The solid red line is the linear trend, the solid blue line is the annual variations, and the dotted blue line is the ten-year smoothing average.</p> Full article ">Figure 13
<p>The temporal variation of the decreasing trends for the Upper Mekong of extreme rainfall indices: (<b>a</b>) CDDs, (<b>b</b>) CWDs, and (<b>c</b>) R157. The solid red line is the linear trend, the solid blue line is the annual variations, and the dotted blue line is the ten-year smoothing average.</p> Full article ">Figure 14
<p>Seasonal extreme rainfall index R95p: (<b>a</b>) wet season; and (<b>b</b>) dry season. The downward red triangle is the negative trend and the upward blue triangle is the positive trend.</p> Full article ">Figure 15
<p>Seasonal extreme rainfall index R99p: (<b>a</b>) wet season; and (<b>b</b>) dry season. The downward red triangle is the negative trend and the upward blue triangle is the positive trend.</p> Full article ">Figure 16
<p>Number of consecutive dry days (CDDs) in both seasons: wet and dry seasons in Tonle Sap region.</p> Full article ">Figure 17
<p>Number of consecutive dry days (CDDs) in both seasons: wet and dry seasons in the coastal region.</p> Full article ">Figure 18
<p>Number of consecutive dry days (CDDs) in both seasons: wet and dry seasons in Mekong Delta.</p> Full article ">Figure 19
<p>Number of consecutive dry days (CDDs) in both seasons: wet and dry seasons in Upper Mekong.</p> Full article ">
26 pages, 21173 KiB  
Article
Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method
by Bernard Twaróg
Atmosphere 2024, 15(8), 898; https://doi.org/10.3390/atmos15080898 - 27 Jul 2024
Cited by 1 | Viewed by 1238
Abstract
This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the [...] Read more.
This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study’s findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation. Full article
(This article belongs to the Special Issue The Hydrologic Cycle in a Changing Climate)
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Figure 1

Figure 1
<p>Schematic of the bootstrap process for estimating Shannon entropy at the 5% significance level for a selected catchment for a seventy-element sequence.</p> Full article ">Figure 2
<p>Shannon entropy trends for values of minimum monthly precipitation totals.</p> Full article ">Figure 3
<p>Shannon entropy trends for values of maximum monthly precipitation totals.</p> Full article ">Figure 4
<p>Shannon entropy trends for minimum values of monthly average temperatures.</p> Full article ">Figure 5
<p>Shannon entropy trends for maximum values of monthly average temperatures.</p> Full article ">Figure 6
<p>Catchments in which the most dynamic Shannon entropy trends for extremes of precipitation and temperature were recognized at the 5% significance level.</p> Full article ">
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