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29 pages, 15304 KiB  
Article
Lake Trafford Nutrients Budget and Influxes After Organic Sediment Dredging (South Florida, USA)
by Serge Thomas, Mark A. Lucius, Jong-Yeop Kim, Edwin M. Everham and Thomas M. Missimer
Water 2024, 16(22), 3258; https://doi.org/10.3390/w16223258 - 13 Nov 2024
Viewed by 665
Abstract
Lake Trafford, a 600-ha subtropical lake in southwestern Florida, has suffered from over 50 years of cultural eutrophication, resulting in the invasion of Hydrilla verticillata and organic sediment accumulation due to herbicide treatments. This study aimed to assess the effects of dredging on [...] Read more.
Lake Trafford, a 600-ha subtropical lake in southwestern Florida, has suffered from over 50 years of cultural eutrophication, resulting in the invasion of Hydrilla verticillata and organic sediment accumulation due to herbicide treatments. This study aimed to assess the effects of dredging on nutrient dynamics. A pre-dredging nutrient budget, developed using land use models and climatic data, estimated nutrient loads of 190 kg d−1 for total nitrogen (TN) and 18.6 kg d−1 for total phosphorus (TP), with total maximum daily loads (TMDLs) of 70.4 kg d−1 for TN and 4.15 kg d−1 for TP. Post-dredging analysis, using detailed spatiotemporal data, showed higher nutrient loads of 274.3 kg d−1 for TN and 24.2 kg d−1 for TP. While dredging reduced legacy nutrient accumulation, it led to increased nutrient influx from groundwater, caused by the exposure of organic sediment, as evidenced by increased lake water electrical conductivity. These findings demonstrate the importance of conducting thorough pre-dredging assessments to mitigate unintended consequences, offering practical insights for managing nutrient loads and improving restoration strategies in eutrophic lakes. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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Figure 1

Figure 1
<p>Sediment accumulation pre- (2004 map) and post- (2012 map) dredging of Lake Trafford. Note the 5 time discrepancies between the scales (from Thomas et al. [<a href="#B1-water-16-03258" class="html-bibr">1</a>]).</p>
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<p>Median specific conductance of Lake Trafford for the various dredging periods: pre-dredging from 1 January 2004, phase I dredging operations from 4 November 2005 to 25 April 2006, phase II dredging operations from 1 December 2006 to 25 April 2006, phase III dredging operations from 1 June 2009 to 28 December 2010 and post-dredging from 1 January 2010 to 31 December 2012. Post-dredging median specific conductance is significantly higher than pre-dredging (<span class="html-italic">p</span>-value &lt; 0.001). Error bars represent the interquartile range (25th–75th percentiles).</p>
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<p>Trafford watershed boundary delineated for the 2008 TMDL report [<a href="#B11-water-16-03258" class="html-bibr">11</a>] and was revised by Wallace in 2017 [<a href="#B30-water-16-03258" class="html-bibr">30</a>]. The map includes the location of the Lake Trafford watershed (<b>right</b>) within the state of Florida ((<b>left</b>) map). State Road 82 (SR 82), State Road 29 (SR 29), and County Road 846 (CR 846) are depicted on the map.</p>
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<p>Diagram depicting typical inputs and outputs for aquatic systems. SW<sub>out</sub> is surface water outflow; SW<sub>in</sub> is surface water inflow; GW<sub>in</sub> is groundwater discharge; GW<sub>out</sub> is groundwater recharge; ET is evapotranspiration; P<sub>gross</sub> is precipitation; I is interception of precipitation; P<sub>net</sub> is the net precipitation; ∆S is change in storage [<a href="#B1-water-16-03258" class="html-bibr">1</a>,<a href="#B31-water-16-03258" class="html-bibr">31</a>].</p>
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<p>Location of the seepage meters (closed dots) within Lake Trafford. Meters 3, 5, 10, and 13 were used to estimate seepage variation for a given location. Meters 1–14 are situated in the littoral zone, while meters 15–20 are situated in open, deeper water. The black star in the central portion of the lake refers to the station used to sample the lake water column.</p>
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<p>Seepage meter and groundwater wells: well design “1” was used for the shallow seepage meter locations 1 through 14 and well design “2” with a ball valve at the apex was used for the deeper seepage meter locations. The deeper wells were sampled via SCUBA (cf. text for more details).</p>
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<p>Water budget of Lake Trafford (from Thomas et al. [<a href="#B1-water-16-03258" class="html-bibr">1</a>]).</p>
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<p>Box and whisker plots of well sample concentrations for TN (<b>top</b>) and TP (<b>bottom</b>) from October 2015 to October 2016. It is important to note that not all wells were sampled during each event.</p>
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<p>Spatial variation in average total phosphorus (<b>left</b>) and nitrogen (<b>right</b>) concentrations (mg L<sup>−1</sup>) of groundwater samples taken at the twenty groundwater seepage locations. Higher concentrations are found in the center samplings stations of the lake. Meters are shown on the map (closed black dots).</p>
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<p>Interpolated Surfer maps of the average loading of TN (<b>left</b>) and TP (<b>right</b>) in mg m<sup>−2</sup> d<sup>−1</sup> for the 28 sampling events. Elevated flow and nutrient concentrations in the center locations are particularly evident, especially for Meter 19. Meter numbers are shown on the map next to their location (closed black dots).</p>
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<p>Composite sample concentrations for TN (<b>top</b>) and TP (<b>bottom</b>) for each canal during the study period. The highlighted portion of the graph in gray (bottom) may be evidence of a first flush for Canal 1 after the first heavy rains of the wet season occurred.</p>
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<p>SRP/TP ratios for Lake Trafford’s five drainage canals and center-lake grab samples. Grab samples in the canals were used to estimate SRP concentrations in composite samples.</p>
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<p>Biweekly loading rates of TN (<b>top</b>) and TP (<b>bottom</b>) for dry (black bars) and wet (white bars) deposition. Bars are absent for wet deposition loading when rainfall did not occur during a given sampling event.</p>
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<p>Relative percentage of mean daily atmospheric deposition accounted for by wet (stacked gray bar) and dry (stacked black bar) deposition for nitrogen (<b>top graph</b>) and phosphorus (<b>bottom graph</b>).</p>
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<p>Time series plots of lake water quality—TN and TP (<b>top</b>), pH and TOC (<b>center</b>), and conductivity and temperature (<b>bottom</b>) for samples taken from the center of Lake Trafford during the study period (October 2015–October 2016).</p>
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<p>The final daily nutrient budget of all measured loadings for Lake Trafford over the course of the study period. The circled thick gray arrows within the lake represent various physical, chemical, and biological in-lake processes.</p>
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<p>Relative percentages of each source of nutrient loading into Lake Trafford for total nitrogen and total phosphorus. Sheet flow was estimated by averaging all positive net sheet flow, and all negative net sheet flow.</p>
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17 pages, 4582 KiB  
Article
Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China
by Ya Zhang, Li Liu, Hua Luo, Wei Wang and Peng Li
Water 2024, 16(22), 3169; https://doi.org/10.3390/w16223169 - 6 Nov 2024
Viewed by 435
Abstract
Carbon dioxide (CO2) flux measurements were conducted throughout the year 2022 utilizing the eddy covariance technique in this study to investigate the characteristics of carbon fluxes and their influencing factors in the Chenhu wetland, a representative subtropical lake-marsh wetland located in [...] Read more.
Carbon dioxide (CO2) flux measurements were conducted throughout the year 2022 utilizing the eddy covariance technique in this study to investigate the characteristics of carbon fluxes and their influencing factors in the Chenhu wetland, a representative subtropical lake-marsh wetland located in the middle reaches of the Yangtze River in China. The results revealed that the mean daily variation of CO2 flux during the growing season exhibited a U-shaped pattern, with measurements ranging from −12.42 to 4.28 μmolCO2·m−2·s−1. The Chenhu wetland ecosystem functions as a carbon sink throughout the growing season, subsequently transitioning to a carbon source during the non-growing season, as evidenced by observations made in 2022. The annual CO2 absorption was quantified at 21.20 molCO2·m−2, a figure that is lower than those documented for specific subtropical lake wetlands, such as Dongting Lake and Poyang Lake. However, this measurement aligns closely with the average net ecosystem exchange (NEE) reported for wetlands across Asia. The correlation between daytime CO2 flux and photosynthetically active radiation (PAR) can be accurately represented through rectangular hyperbola equations throughout the growing season. Vapor pressure deficit (VPD) acts as a constraining factor for daytime NEE, with an optimal range established between 0.5 and 1.5 kPa. Furthermore, air temperature (Ta), relative humidity (RH), and vapor pressure difference (VPD) are recognized as the principal determinants affecting NEE during the nocturnal period. The association between Ta and NEE during the non-growing season conforms to the van’t Hoff model, suggesting that NEE increases in response to elevated Ta during this timeframe. Full article
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<p>Sketch map of the study area.</p>
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<p>The monthly variation of environmental factors in the Chenhu wetland during the year 2022. (<b>a</b>) Air temperature (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math>), soil temperature (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> </mrow> </msub> </mrow> </semantics></math>), and average <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> in Wuhan from 2011 to 2021. (<b>b</b>) Wind speed (WS), photosynthetically active radiation (PAR). (<b>c</b>) Relative humidity (RH), vapor pressure deficit (VPD). (<b>d</b>) Soil water content (SWC), total precipitation (PPT), and average PPT in Wuhan from 2011 to 2021.</p>
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<p>Diurnal variation of NEE in the Chenhu Wetland during 2022 ((<b>a</b>) growing season; (<b>b</b>) non-growing season).</p>
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<p>The daily variation of NEE on an annual basis (<b>a</b>) and the seasonal variation of monthly total NEE (<b>b</b>) for the Chenhu wetland during the year 2022.</p>
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<p>Comparison of annual NEE in subtropical wetlands.</p>
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<p>Path analysis between main environmental factors and NEE. χ<sup>2</sup> = 0.5, <span class="html-italic">p</span> = 0.46 &gt; 0.05, Degrees of freedom = 1. NFI = 0.999, CFI = 1.000, root mean square error of approximation (RMSEA) = 0.</p>
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<p>Scatter plots of daytime NEE against PAR from April to September 2022. The fitting results and correlation coefficients between daytime NEE and PAR are also shown.</p>
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<p>Scatter plots of daytime NEE against PAR under different VPD during the growth season. The fitting results and correlation coefficients between daytime NEE and PAR are also shown.</p>
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<p>Scatter plots of NEE against T<sub>a</sub> of Chenhu wetland during the night in the growing season (<b>a</b>) and throughout the day in the non-growing season (<b>b</b>). The fitting results and correlation coefficients between NEE and T<sub>a</sub> are also shown.</p>
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20 pages, 7366 KiB  
Article
How Severe Was the 2022 Flash Drought in the Yangtze River Basin?
by Liyan Yang and Jia Wei
Remote Sens. 2024, 16(22), 4122; https://doi.org/10.3390/rs16224122 - 5 Nov 2024
Viewed by 465
Abstract
Flash droughts, characterized by their rapid onset and severe impacts, have critical implications for the ecological environment and water resource security. However, inconsistent definitions of flash droughts have hindered scientific assessments of drought severity, limiting efforts in disaster prevention and mitigation. In this [...] Read more.
Flash droughts, characterized by their rapid onset and severe impacts, have critical implications for the ecological environment and water resource security. However, inconsistent definitions of flash droughts have hindered scientific assessments of drought severity, limiting efforts in disaster prevention and mitigation. In this study, we propose a new method for explicitly characterizing flash drought events, with particular emphasis on the process of soil moisture recovery. The temporal and spatial evolution of flash droughts over the Yangtze River Basin was analyzed, and the severity of the extreme flash drought in 2022 was assessed by comparing its characteristics and impacts with those of three typical dry years. Additionally, the driving factors of the 2022 flash drought were evaluated from multiple perspectives. Results indicate that the new identification method for flash droughts is reasonable and reliable. In recent years, the frequency and duration of flash droughts have significantly increased, with the Dongting Lake and Poyang Lake basins being particularly affected. Spring and summer were identified as peak seasons for flash droughts, with the middle reaches most affected in spring, while summer droughts tend to impact the entire basin. Compared to 2006, 2011, and 2013, the flash drought in 2022 affected the largest area, with the highest number of grids experiencing two flash drought events and a development rate exceeding 15%. Moreover, the summer heat in 2022 was more extreme than in the other three years, extending from spring to fall, especially during July–August. Its evolution was driven by the Western Pacific Subtropical High, which suppressed precipitation and elevated temperatures. The divergence of water vapor flux intensified water shortages, while anomalies in latent and sensible heat fluxes increased surface evaporation and heat transfer, further disturbing the regional water cycle. This study provides valuable insights for flash drought monitoring and early warning in the context of a changing climate. Full article
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) The location and the elevation of the Yangtze River Basin. (<b>b</b>) The subbasins and average annual soil moisture of the Yangtze River Basin.</p>
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<p>Schematic diagram of the definition of the onset, development and recovery stages in flash drought events used in this study. v is the development rate, defined as the average rate of decline in soil moisture percentiles.</p>
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<p>Spatial distribution of flash drought characteristics in the Yangtze River Basin from 1950 to 2022. (<b>a</b>) Frequency: the multi-year average number of flash drought events. (<b>b</b>) Mean duration: the average number of days each drought event lasted. (<b>c</b>) Mean development rate: the average speed at which each flash drought event developed. (<b>d</b>) Mean intensity: the average intensity of each drought event. The white grid points indicate that there was no flash drought. The numbers in the (<b>a</b>–<b>d</b>) represent the subbasins.</p>
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<p>Trend changes in flash drought characteristics in the Yangtze River Basin from 1950 to 2022. (<b>a</b>) Regional average frequency of flash drought events. (<b>b</b>) Regional mean duration of flash drought events. (<b>c</b>) Regional average development rate of flash drought events. (<b>d</b>) Regional mean intensity of flash drought events. The black dotted line is the trend line. The solid blue line is the pre-abrupt mean. The solid red line is the post-abrupt mean. The overall trends passed the MK significance test with <span class="html-italic">p</span> &lt; 0.05 in (<b>a</b>,<b>c</b>,<b>d</b>), and the overall trends passed the MK significance test with <span class="html-italic">p</span> &lt; 0.1 in (<b>b</b>).</p>
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<p>Seasonal characteristics of flash droughts in the Yangtze River Basin. (<b>a</b>) The multi-year average frequency of flash drought in different seasons. (<b>b</b>) The multi-year average total days of flash drought in different seasons. (<b>c</b>) Most frequent season of first flash drought event per grid, 1950–2022. The numbers in the figure represent the subbasins.</p>
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<p>(<b>a</b>) The percentage of grid points affected by flash droughts relative to the total number of grid points in the entire basin for each pentad. (<b>b</b>) Spatial distribution of flash drought frequency in the Yangtze River Basin in 2022. (<b>c</b>) Spatial distribution of flash drought duration in the Yangtze River Basin in 2022. (<b>d</b>) Spatial distribution of flash drought development rate in the Yangtze River Basin in 2022. (<b>e</b>) Spatial distribution of flash drought intensity in the Yangtze River Basin in 2022. The numbers in the figure (<b>b</b>–<b>e</b>) represent the subbasins.</p>
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<p>(<b>a</b>) Standardized precipitation anomalies from 2003 to 2022. (<b>b</b>) Comparison of regional average flash drought characteristics in typical drought years. (<b>c</b>) Percentage of different flash drought frequency in typical drought years. (<b>d</b>) Percentage of different flash drought durations in typical drought years. (<b>e</b>) Percentage of different flash drought development rates in typical drought years. (<b>f</b>) Percentage of different flash drought intensities in typical drought years. The red error bar in (<b>c</b>–<b>f</b>) indicates the difference from the multi-year average (2003–2022), and an upward trend indicates that the value is less than the multi-year average.</p>
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<p>(<b>a</b>) Time series of accumulated precipitation in drought years. (<b>b</b>) Time series of maximum temperature in drought years. The horizontal axis represents the pentad of the year, and the black solid line indicates the multi-year average for the past twenty years (2003–2022) in (<b>a</b>,<b>b</b>).</p>
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<p>Spatial patterns of hydrometeorological variables over the Yangtze River Basin in 2022. (<b>a</b>) Precipitation in the Yangtze River Basin in June 2022. (<b>b</b>) Precipitation in the Yangtze River Basin in July 2022. (<b>c</b>) Precipitation in the Yangtze River Basin in August 2022. (<b>d</b>) Temperature in the Yangtze River Basin in June 2022. (<b>e</b>) Temperature in the Yangtze River Basin in July 2022. (<b>f</b>) Temperature in the Yangtze River Basin in August 2022.</p>
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<p>Spatial patterns of climatic variables over the Yangtze River Basin in 2022. (<b>a</b>) Moisture divergence in the Yangtze River Basin in June 2022. (<b>b</b>) Moisture divergence in the Yangtze River Basin in July 2022. (<b>c</b>) Moisture divergence in the Yangtze River Basin in August 2022. (<b>d</b>) Geopotential height of 500 hpa in the Yangtze River Basin in June 2022. (<b>e</b>) Geopotential height of 500 hpa in the Yangtze River Basin in July 2022. (<b>f</b>) Geopotential height of 500 hpa in the Yangtze River Basin in August 2022.</p>
Full article ">Figure 11
<p>Spatial patterns of surface heat flux over the Yangtze River Basin in 2022. (<b>a</b>) Latent heat flux in the Yangtze River Basin in June 2022. (<b>b</b>) Latent heat flux in the Yangtze River Basin in July 2022. (<b>c</b>) Latent heat flux in the Yangtze River Basin in August 2022. (<b>d</b>) Sensible heat flux in the Yangtze River Basin in June 2022. (<b>e</b>) Sensible heat flux in the Yangtze River Basin in July 2022. (<b>f</b>) Sensible heat flux in the Yangtze River Basin in August 2022. The ERA5 dataset convention for vertical fluxes is positive downwards. Negative surface latent heat flux indicates evaporation and negative surface sensible heat flux indicates heat transfer from the surface to the atmosphere.</p>
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13 pages, 2939 KiB  
Article
Comparison of Nitrous Oxide Consumption of Paddy Soils Developed from Three Parent Materials in Subtropical China
by Ling Wang, Man Yang, Jun Li, Zhaohua Li, Alan Wright and Kun Li
Land 2024, 13(10), 1710; https://doi.org/10.3390/land13101710 - 18 Oct 2024
Viewed by 488
Abstract
Paddy soils developed from various parent materials are widely distributed in the subtropical region in China and have a non-negligible but unclear potential to consume nitrous oxide (N2O) due to long-term flooding. This study selected three of the most common paddy [...] Read more.
Paddy soils developed from various parent materials are widely distributed in the subtropical region in China and have a non-negligible but unclear potential to consume nitrous oxide (N2O) due to long-term flooding. This study selected three of the most common paddy soils in subtropical China, developing from quaternary red soil (R), lake sediment sand (S), and alluvial soil (C), to study their total N2O consumption and total nitrogen (N2) production using N2-free microcosm experiments. These paddy soils were treated with N2O addition (N2O treatment) or helium (He) addition (CK treatment) and incubated under flooding and anoxic conditions. The results showed that three alluvial soils (C1, C2, and C3) consumed over 99.93% of the N2O accumulated in the soil profile, significantly higher than R and S soils (p < 0.05). And the N2 production in three C soils was also significantly higher than other soils, accounting for 81.61% of the total N2O consumption. The main soil factors affecting N2O consumption in C, S, and R soils were soil clay content (p < 0.05), soil sand content (R2 = 0.95, p < 0.001), and soil available potassium (AK) (p < 0.01), respectively. These results indicate flooding paddy soils, no matter the parent materials developed, could consume extremely large amount of N2O produced in soil profiles. Full article
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Figure 1
<p>(<b>a</b>) Schematic diagram of the soil incubation device. The bottom space consisted of a disc-shaped sealed silicone tube placed at the bottom of the PVC cylinder and was used to add N<sub>2</sub>O or He gas. (<b>b</b>) Outline of N<sub>2</sub>-free microcosm system [<a href="#B29-land-13-01710" class="html-bibr">29</a>].</p>
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<p>Dynamics of the cumulative concentrations of N<sub>2</sub>O and N<sub>2</sub> in the headspace above the surface of three loam paddy soils (<b>a</b>–<b>c</b>), three sandy loam paddy soils (<b>d</b>–<b>f</b>), and three silt clay loam paddy soils (<b>g</b>–<b>i</b>) in incubation devices. The vertical bars indicate the standard error of the mean (n = 3).</p>
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<p>Changes in (<b>a</b>) soil NH<sub>4</sub><sup>+</sup>-N increment, (<b>b</b>) soil NO<sub>3</sub><sup>−</sup>-N consumption, and (<b>c</b>) soil DOC consumption of the R soils, S soils, and C soils during the 96 h incubation. The vertical bars indicate the standard error of the mean (n = 3). Different lowercase letters indicate significant differences among nine soils in the N<sub>2</sub>O treatment (<span class="html-italic">p</span> &lt; 0.05), and uppercase letters indicate significant differences among nine soils in the CK treatment (<span class="html-italic">p</span> &lt; 0.05). *, **, and *** indicate significant differences at the 0.05, 0.01, and 0.001 levels between the N<sub>2</sub>O treatment and CK treatment of each soil.</p>
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<p>(<b>a</b>) PCA of N<sub>2</sub>O consumption in loam paddy soils (R), sandy loam paddy soils (S), and silt clay loam paddy soils (C) with various soil factors; (<b>b</b>) regression analysis of total N<sub>2</sub>O consumption and soil sand content in S soils, and (<b>c</b>) total N<sub>2</sub>O consumption and available potassium in R soils.</p>
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8 pages, 1688 KiB  
Case Report
Severe Systemic Chromobacterium violaceum Infection: A Case Study of a German Long-Term Resident in French Guyana
by Caroline Klenk, Miriam Schnieders, Melina Heinemann, Christiane Wiegard, Henning Büttner, Michael Ramharter, Sabine Jordan and Maria Sophia Mackroth
Trop. Med. Infect. Dis. 2024, 9(10), 242; https://doi.org/10.3390/tropicalmed9100242 - 15 Oct 2024
Viewed by 948
Abstract
Chromobacterium violaceum is a Gram-negative, facultative anaerobe proteobacterium. Its natural habitat is water and soil in tropical and subtropical regions. Human infections are characterized by rapid dissemination that can lead to high fatality rates. Here, we describe the first case of a C. [...] Read more.
Chromobacterium violaceum is a Gram-negative, facultative anaerobe proteobacterium. Its natural habitat is water and soil in tropical and subtropical regions. Human infections are characterized by rapid dissemination that can lead to high fatality rates. Here, we describe the first case of a C. violaceum infection reported from Germany. A German national with permanent residence in French Guyana contracted a C. violaceum infection presumably while bathing in a barrier lake in Brazil. The patient presented with a high fever and a crusty, erythematous skin lesion at an emergency department in Hamburg, Germany. Ultrasound and a CT scan of the abdomen revealed multiple liver abscesses. C. violaceum was detected in blood and from aspirates of the liver abscesses, using traditional culture methods and modern molecular assays. Prolonged treatment with meropenem and ciprofloxacin led to full recovery. Rapid pathogen detection and treatment initiation are of high importance in C. violaceum infections as mortality rates are overall declining but have still tended to reach up to 25% in recent years in systemic infections. Due to its broad natural drug resistance, antibiotic treatment is challenging. Increased travel activities may lead to more frequent presentation of patients with environmental pathogens of the tropics such as C. violaceum. Full article
(This article belongs to the Section Neglected and Emerging Tropical Diseases)
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Figure 1
<p>Skin finding on day of admission. Chronic skin lesion on abdomen on day of presentation to emergency room.</p>
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<p>Abdominal and contrast-enhanced ultrasound (CEUS) of the liver. On admission: the liver with multiple small echo-poor lesions sharply delineated from the surrounding tissue (<b>a</b>); CEUS: no enhancement of the lesions, but hyperenhancement in the periphery of the abscess (<b>b</b>). Ultrasound-guided diagnostic punction of one lesion to gain 3 ml pus (<b>c</b>). Progress control after 2 weeks (<b>d</b>) and 6 weeks (<b>e</b>). The diameter of the multiple echo-poor lesions decreased over 6 weeks under therapy.</p>
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17 pages, 6425 KiB  
Article
Quantile Regression Illuminates the Heterogeneous Effect of Water Quality on Phytoplankton in Lake Taihu, China
by Lu Wang, Shuo Liu, Shuqin Ma, Zhongwen Yang, Yan Chen, Wei Gao, Qingqing Liu and Yuan Zhang
Water 2024, 16(18), 2570; https://doi.org/10.3390/w16182570 - 10 Sep 2024
Viewed by 748
Abstract
Lake Taihu, a subtropical shallow lake in the Yangtze River Basin, is the third-largest freshwater lake in China. It serves not only as a crucial source of drinking water and an ecological resource but also holds significant economic, tourism, and fisheries value. Phytoplankton, [...] Read more.
Lake Taihu, a subtropical shallow lake in the Yangtze River Basin, is the third-largest freshwater lake in China. It serves not only as a crucial source of drinking water and an ecological resource but also holds significant economic, tourism, and fisheries value. Phytoplankton, a vital component of aquatic ecosystems, plays a critical role in nutrient cycling and maintaining water structure. Its community composition and concentration reflect changes in the aquatic environment, making it an important biological indicator for monitoring ecological conditions. Understanding the impact of water quality on phytoplankton is essential for maintaining ecological balance and ensuring the sustainable use of water resources. This paper focuses on Lake Taihu, with water samples collected in February, May, August, and November from 2011 to 2019. Using quantile regression, a robust statistical analysis tool, the study investigates the heterogeneous effects of water quality on phytoplankton and seasonal variations. The results indicate significant seasonal differences in water quality in Lake Taihu, which substantially influence phytoplankton, showing weakly alkaline characteristics. When phytoplankton concentrations are low, pondus hydrogenii (pH), chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), water temperature (WT), and conductivity significantly affect them. At medium concentrations, COD, TP, TN, and WT have significant effects. At high concentrations, transparency and dissolved oxygen (DO) significantly impact phytoplankton, while TP no longer has a significant effect. These findings provide valuable insights for policymakers and environmental managers, supporting the prevention and control of harmful algal blooms in Lake Taihu and similar aquatic systems. Full article
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
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<p>Location of Lake Taihu in China and sampling sites in Lake Taihu.</p>
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<p>Pearson correlation coefficient matrix between phytoplankton and each water quality parameters. Blue indicates the positive correlation, and red indicates the negative correlation.</p>
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<p>Scatter plot between phytoplankton and eight water quality variables. The fitting lines of univariate linear regression and univariate quantile regression (<math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>) and the <span class="html-italic">t</span>-value and <span class="html-italic">p</span>-value of the significance test are shown. Pink indicates the linear regression results, and blue indicates the quantile regression results.</p>
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<p>Scatter plot between phytoplankton and eight water quality variables. The fitting lines of univariate linear regression and univariate quantile regression (<math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>) and the <span class="html-italic">t</span>-value and <span class="html-italic">p</span>-value of the significance test are shown. Pink indicates the linear regression results, and blue indicates the quantile regression results.</p>
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<p>Quantile regression coefficient plot under different quantiles. Blue line indicates quantile regression coefficients, Blue shade indicates the confidence intervals under different quantiles, and Black indicates linear regression coefficient and confidence interval.</p>
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<p>Violin plot of phytoplankton distribution under different seasons.</p>
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<p>Quantile regression coefficient plot under different seasons.</p>
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32 pages, 7266 KiB  
Article
Evaluation of Seasonal Reservoir Water Treatment Processes in Southwest Florida: Protection of the Caloosahatchee River Estuary
by Thomas M. Missimer, Seneshaw Tsegaye, Serge Thomas, Ashley Danley-Thomson and Peter R. Michael
Water 2024, 16(15), 2145; https://doi.org/10.3390/w16152145 - 29 Jul 2024
Viewed by 870
Abstract
In southwest Florida, the Caloosahatchee River flows from Lake Okeechobee into a biologically productive tidal estuarine system. A combination of excess water during the wet season, insufficient fresh water in the dry season, and poor quality of the river water are damaging the [...] Read more.
In southwest Florida, the Caloosahatchee River flows from Lake Okeechobee into a biologically productive tidal estuarine system. A combination of excess water during the wet season, insufficient fresh water in the dry season, and poor quality of the river water are damaging the estuarine ecosystem. To better control the quality and quantity of the water entering the estuary, reservoirs are being constructed to store excess, poor quality water during the wet season and return it to the river for discharge into the estuary at an appropriate time. This stored water is enriched in nutrients and organic carbon. Because of the subtropical nature of the climate in southwest Florida and potential increases in temperature in the future, the return flow of water from the reservoirs must be treated before it can be returned to the river. Hence, an experimental water treatment system was developed and operated to compare biological treatment processes consisting of solely wetland plants versus adding some engineered processes, including slow sand filtration and a combination of slow sand filtration and ultraviolet (UV) treatment. These three treatment trains were operated and monitored through a seasonal cycle in 2021–2022. All three treatment methods significantly reduced the concentrations of nutrients and total organic carbon. While the enhanced engineered wetlands’ treatment trains did slightly outperform the wetland train, a comparison of the three process trains showed no statistically significant difference. It was concluded that upscaling of the slow sand filtration and UV process could improve the treatment efficiency, but this change would have to be evaluated within a framework of long-term economic benefits. It was also concluded that the Caloosahatchee River water quality is quite enriched in nutrients so that reservoir storage would increase the organic carbon concentrations, making it imperative that it be treated before being returned to the river. It was also discovered that the green alga Cladophora sp. grew rapidly in the biological treatment tubs and will present a significant challenge for the treatment of the reservoir discharge water using the currently proposed alum treatment. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>Location map showing the Caloosahatchee River channel, the drainage basin, the location of the research site (BOMA), and S-79, which is the location of river water entering the estuary.</p>
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<p>Schematic diagram of the initial water treatment test design.</p>
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<p>The layout of the C-43 mesocosms site including the main supply tank, the storage building with the power supply, and the tubs containing the vegetation. Note that the mesocosms will be referred to as tubs in the text.</p>
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<p>Box plots of total (TN) and organic nitrogen (OrgN) before and after sand filtration (SF).</p>
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<p>Box plots of nitrate (NO<sub>3</sub>), nitrite (NO<sub>2</sub>), and ammonia (NH<sub>3</sub>) before and after sand filtration (SF).</p>
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<p>Box plots of total phosphorus (TP) and orthophosphate (PO<sub>4</sub>) before and after slow sand filtration (SF).</p>
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<p>Box plot for variation in total (TN) and organic nitrogen (OrgN) before and after UV treatment.</p>
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<p>Box plots of total and organic nitrogen concentrations before and after passage through both vegetation tanks in the control train.</p>
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<p>Comparison of effectiveness of the full process train C on concentrations of total and organic nitrogen.</p>
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<p>Growth dynamics of emergent plants in tubs #8 (reference treatment), #9 (UV treatment), and #10 (filter treatment). (<b>Top left</b>): change in total number of plants; (<b>top right</b>): change in <span class="html-italic">S. californicus</span>; (<b>bottom left</b>): change in <span class="html-italic">T. domingensis</span>; and (<b>bottom left</b>): change in <span class="html-italic">T domingensis</span> inflorescences. Note: the missing datum for tub 10 is due to a corrupted photograph. This missing datum is not present for the bottom right graph since inflorescences were visible by zooming on the photograph encapsulating all six tubs.</p>
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<p>Box diagram of the changes in the concentrations of total and organic nitrogen in train A (vegetation only, control).</p>
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<p>Box diagram of the changes in the concentrations of total and organic nitrogen in train B (slow sand filtration + vegetation).</p>
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<p>Box diagram of the changes in the concentration of total and organic nitrogen in train C (slow sand filtration + UV + vegetation).</p>
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<p>Box plot of the variation in the concentration of nitrate, nitrite, and ammonia in treatment train A (vegetation only, control).</p>
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<p>Box plot of the variation in the concentration of nitrate, nitrite, and ammonia in treatment train A (slow sand filtration + vegetation).</p>
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<p>Box plot of the variation in the concentration of nitrate, nitrite, and ammonia in treatment train c (slow sand filtration + UV + vegetation).</p>
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<p>Box plot showing the changes in total phosphorus and phosphate in treatment train A (vegetation only, control).</p>
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<p>Box plot showing the changes in total phosphorus and phosphate in treatment train B (slow sand filtration + vegetation).</p>
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<p>Box plot showing the changes in total phosphorus and phosphate in treatment train C (slow sand filtration + UV + vegetation).</p>
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22 pages, 14390 KiB  
Article
Prediction of Adaptability of Typical Vegetation Species in Flood Storage Areas under Future Climate Change: A Case in Hongze Lake FDZ, China
by Liang Wang, Jilin Cheng, Yushan Jiang, Nian Liu and Kai Wang
Sustainability 2024, 16(15), 6331; https://doi.org/10.3390/su16156331 - 24 Jul 2024
Viewed by 790
Abstract
China experiences frequent heavy rainfall and flooding events, which have particularly increased in recent years. As flood storage zones (FDZs) play an important role in reducing disaster losses, their ecological restoration has been receiving widespread attention. Hongze Lake is an important flood discharge [...] Read more.
China experiences frequent heavy rainfall and flooding events, which have particularly increased in recent years. As flood storage zones (FDZs) play an important role in reducing disaster losses, their ecological restoration has been receiving widespread attention. Hongze Lake is an important flood discharge area in the Huaihe River Basin of China. Previous studies have preliminarily analyzed the protection of vegetation zones in the FDZ of this lake, but the future growth trend of typical vegetation in the area has not been considered as a basis for the precise protection of vegetation diversity and introductory cultivation of suitable species in the area. Taking the FDZ of Hongze Lake as an example, this study investigated the change trend of the suitability of typical vegetation species in the Hongze Lake FDZ based on future climate change and the distribution pattern of the suitable areas. To this end, the distribution of potentially suitable habitats of 20 typical vegetation species in the 2040s was predicted under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate scenarios using the latest Coupled Model Intercomparison Project CMIP6. The predicted distribution was compared with the current distribution of potentially suitable habitats. The results showed that the model integrating high-performance random forest, generalized linear model, boosted tree model, flexible discriminant analysis model, and generalized additive model had significantly higher TSS and AUC values than the individual models, and could effectively improve model accuracy. The high sensitivity of these 20 typical vegetation species to temperature and rainfall related factors reflects the climatic characteristics of the study area at the junction of subtropical monsoon climate and temperate monsoon climate. Under future climate scenarios, with reference to the current scenario of the 20 typical species, the suitability for Nelumbo nucifera Gaertn decreased, that for Iris pseudacorus L. increased in the western part of the study area but decreased in the eastern wetland and floodplain, and the suitability of the remaining 18 species increased. This study identified the trend of potential suitable habitat distribution and the shift in the suitability of various typical vegetation species in the floodplain of Hongze Lake. The findings are important for the future enhancement of vegetation habitat conservation and suitable planting in the study area, and have implications for the restoration and conservation of vegetation diversity in most typical floodplain areas. Full article
(This article belongs to the Section Air, Climate Change and Sustainability)
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<p>Overview map of the study area.</p>
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<p>Accuracy test results of 10 single model algorithms (taking <span class="html-italic">Trifolium repens</span> L. as an example).</p>
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<p>Accuracy of each vegetation prediction model.</p>
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<p>The contribution rates of various bio factors to different vegetation types.</p>
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<p>Response curves.</p>
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<p>Response curves.</p>
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<p>Response curves.</p>
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<p>Current distribution of suitable vegetation areas.</p>
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<p>Current distribution of suitable vegetation areas.</p>
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<p>Change rate range of suitable areas for 20 typical vegetation species under different climate scenarios.</p>
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<p>Change rate range of suitable areas for 20 typical vegetation species under different climate scenarios.</p>
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<p>Change rate range of suitable areas for 20 typical vegetation species under different climate scenarios.</p>
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28 pages, 12078 KiB  
Article
Water Budget for Lake Trafford, a Natural Subtropical Lake in South Florida: An Example of Enhanced Groundwater Influx in a Subtropical Lake Subsequent to Organic Sediment Dredging
by Serge Thomas, Mark A. Lucius, Jong-Yeop Kim, Edwin M. Everham, Dana L. Dettmar and Thomas M. Missimer
Water 2024, 16(8), 1188; https://doi.org/10.3390/w16081188 - 22 Apr 2024
Viewed by 1688
Abstract
A very detailed water budget analysis was conducted on Lake Trafford in South Florida. The inflow was dominated by surface water influx via five canals (61%), with groundwater influx constituting 12% and direct rainfall constituting 27%. Lake discharge was dominated by sheet flow [...] Read more.
A very detailed water budget analysis was conducted on Lake Trafford in South Florida. The inflow was dominated by surface water influx via five canals (61%), with groundwater influx constituting 12% and direct rainfall constituting 27%. Lake discharge was dominated by sheet flow (69%) and evapotranspiration (30.5%), with groundwater recharge of the hydraulically connected unconfined aquifer accounting for only 0.5%. The removal of 30 M tons (4.4 × 106 m3) of organic sediment impacted the groundwater influx, causing enhanced groundwater flow into the deeper parts of the lake and mixed flow along the banks, creating a rather unusual pattern. The large number of groundwater seepage meters used during this investigation led to a very reliable set of measurements with occasional failure of only a few meters. A distinctive relationship was found between the wet-season lake stage, heavy rainfall events, and pulses of exiting sheet flow from the lake. Estimation of the evapotranspiration loss using data collected from a weather station on the lake allowed the use of three different models, which, when averaged, produced results comparable to Lake Okeechobee (South Florida). A limitation of this investigation was the inability to directly measure sheet-flow discharges, which had to be estimated as a residual within the calculated water budget. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>Lake Trafford watershed boundary delineated in 2017 [<a href="#B21-water-16-01188" class="html-bibr">21</a>] and revised from Kang and Gilbert’s [<a href="#B22-water-16-01188" class="html-bibr">22</a>] watershed delineation.</p>
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<p>Satellite image of Lake Trafford and the adjacent land to its east. The Immokalee Slough is highlighted in opaque blue. Each canal is delineated with a white line and the sampling stations in those canals are marked with white circles. Canal 1 can be seen in the terminating area of the slough.</p>
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<p>(<b>Top Left</b>) Diagram of the slightly tilted groundwater seepage meter with its collecting bag attached. (<b>Bottom Left</b>) Sontek IQ flow velocimeter for canal water flow measurement. (<b>Right</b>) Location of the seepage meters (closed dots) within Lake Trafford. Meters 3, 5, 10, and 13 were used to estimate the seepage variation for a given location.</p>
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<p>Bathymetric map of Lake Trafford for a surface water elevation of NAVD’88 5.53m. Bathymetry data were used to determine volumes and planar surface areas at various lake levels using Surfer 27.</p>
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<p>Map of the average groundwater flow rates (L m<sup>−2</sup> d<sup>−1</sup>) using all flow data from the 28 sampling events. Note that meters 8, 19, and 20 had high positive flow averages, while meters 2, 3, 5, 6, 10, 12, and 13 had low or negative averages. Meter numbers are shown on the map next to their locations (closed white dots).</p>
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<p>Total groundwater discharge (gray bars) and recharge (white bars) in m<sup>3</sup> d<sup>−1</sup> for each sampling event 1 through 28 (associated dates in parentheses).</p>
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<p>Groundwater discharge (closed black circles) and recharge (open circles) interpolated between biweekly sampling events using a Bessel spline function.</p>
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<p>Time series of cumulative net groundwater flow in both total cubic meters (m<sup>3</sup>) and percent (%) of average lake volume. A total of 26.3% of the average lake volume entered the lake via groundwater net flow over the course of data collection.</p>
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<p>Hydrographs of each of the canal discharges (Q, m<sup>3</sup> d<sup>−1</sup>) from 1 October 2015 to 24 October 2016. Mean daily Q is also reported for comparisons. Estimated portions of the hydrographs appear as dashed lines (cf. text for more information).</p>
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<p>Canal discharge plotted over rainfall for all five canals. Note that the scales for Q are not standardized to best show the response of discharge to rainfall. Canals 1, 4, and 5 appear to be the most responsive to rain events.</p>
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<p>Scatterplots comparing groundwater level or lake stage to canal discharge (Q). Each dashed line indicates the best fit linear or polynomial function. Canal 2 had very low discharge throughout the study period and thus a strong predictive relationship was not found. Note that Canal 3 is not featured because its discharge was around zero.</p>
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<p>Air temperature (red line) and water temperature (blue line) plotted with rainfall (black bars). Water temperature and air temperature reached their lowest points in January, which also unexpectedly saw the highest amounts of rainfall.</p>
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<p>Changes in gross solar radiation, net solar radiation, and reflected solar radiation over time. Note the points of negative reflected solar radiation, where perching birds likely shaded the gross solar radiation sensor.</p>
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<p>Results of the three evaporation models (“E”) applied to Lake Trafford. An expected seasonal trend is present, with decreased evaporation rates in the winter months, increasing throughout the spring. Heavy rains and cloud cover caused several days of minimal evaporation in late June.</p>
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<p>(<b>Top</b>) Time series of lake surface elevation (USGS 02291200) over nine years, with the bold dotted line representing the average stage over that period and the thin dotted lines indicating z-scores of +1 and −1. (<b>Bottom Left</b>) Time series of lake surface elevation during this study. The lake level was highest at the start of the project, in February after the El-Niño rains, and throughout the rainy season (June through October 2016). The lake stage was higher than average throughout the entire study period. A stage–volume curve and a stage–planar surface area curve were established for Lake Trafford to calculate the lake volumes and surface areas based on lake stage (<b>Bottom Right</b>).</p>
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<p>Top diagram: Final daily water budget of all fluxes for Lake Trafford over the course of the study period. The mean net sheet flow was negative and was thus depicted as an efflux. Bottom pie chart: Relative percentage of inputs and outputs for the water budget. Sheet flow is only represented in the outputs chart due to the inability to separate positive and negative flows from the calculated net sheet flow. The discharge from Canal 1 is separated from Canals 2 through 5 to show its magnitude compared to the other incoming water fluxes.</p>
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<p>Time series of both the actual volume in Lake Trafford and the modeled volume as a sum of the measured water budget components. The difference between the modeled volume and the actual volume was used to calculate the net sheet flow. Time periods where the modeled volume is higher than the actual volume indicate a net negative sheet flow moving out of the lake. Time periods where the modeled volume is lower than the true volume indicate a net positive sheet flow.</p>
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<p>Organic sediment accumulation pre (2004 left map) and post (2012 right map) dredging in Lake Trafford. Dredging occurred between 2006 and 2010. Note the five-fold difference between the scales.</p>
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<p>Changes in groundwater (USGS well) and lake elevations over the course of the study, along with the measured groundwater discharge (black dots). Increased groundwater discharge should be expected when the groundwater elevation (dotted line) is higher than the lake water elevation (black line).</p>
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24 pages, 14189 KiB  
Article
Spatiotemporal Evolution Features of the 2022 Compound Hot and Drought Event over the Yangtze River Basin
by Lilu Cui, Linhao Zhong, Jiacheng Meng, Jiachun An, Cheng Zhang and Yu Li
Remote Sens. 2024, 16(8), 1367; https://doi.org/10.3390/rs16081367 - 12 Apr 2024
Cited by 1 | Viewed by 1469
Abstract
A rare compound hot and drought (CHD) event occurred in the Yangtze River Basin (YRB) in the summer of 2022, which brought serious social crisis and ecological disaster. The analysis of the causes, spatiotemporal characteristics and impacts of this event is of great [...] Read more.
A rare compound hot and drought (CHD) event occurred in the Yangtze River Basin (YRB) in the summer of 2022, which brought serious social crisis and ecological disaster. The analysis of the causes, spatiotemporal characteristics and impacts of this event is of great significance and value for future drought warning and mitigation. We used the Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, meteorological data, hydrological data and satellite remote sensing data to discuss the spatiotemporal evolution, formation mechanism and the influence of the CHD event. The results show that the drought severity caused by the CHD event was the most severe during 2003 and 2022. The CHD event lasted a total of five months (from July to November), and there were variations in the damage in different sub-basins. The Wu River Basin (WRB) is the region where the CHD event lasted the longest, at six months (from July to December), while it also lasted four or five months in all the other basins. Among them, the WRB, Dongting Lake Rivers Basin (DLRB) and Mainstream of the YRB (MSY) are the three most affected basins, whose hot and drought severity values are 7.750 and −8.520 (WRB), 7.105 and −9.915 (DLRB) and 6.232 and −9.143 (MSY), respectively. High temperature and low precipitation are the direct causes of the CHD event, and the underlying causes behind this event are the triple La Niña and negative Indian Ocean Dipole event. The two extreme climate events made the Western Pacific Subtropical High (WPSH) unusually strong, and then the WPSH covered a more northerly and westerly region than in previous years and remained entrenched for a long period of time over the YRB and its adjacent regions. Moreover, this CHD event had a devastating impact on local agricultural production and seriously disrupted daily life and production. Our results have implications for the study of extreme disaster events. Full article
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<p>Topographic features and the main sub-basins of the YRB. The name of No. 1-4 hydroelectric power station and hydrological stations are shown in <a href="#remotesensing-16-01367-t001" class="html-table">Table 1</a>.</p>
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<p>The time series of six GRACE TWSCs and fused results in the YRB during 2003 and 2022.</p>
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<p>The spatial distribution of uncertainties of fused TWSC results in the YRB.</p>
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<p>The time series of the WSDI, SPEI-03, SPEI-06, SPEI-12 and SCPDSI in the YRB during 2003 and 2022. In (<b>b</b>): the yellow points and line represent SPEI-03 vs. WSDI; the blue points and line represent SPEI-06 vs. WSDI; the green points and line represent SPEI-12 vs. WSDI; the red points and line represent SCPDSI vs. WSDI.</p>
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<p>Temporal evolution of TEM and TWSC anomalies in the YRB during 2003–2022. (<b>a</b>) TWSC and TEM anomalous events; (<b>b</b>) the WSDI and STI; gray shading indicates compound hot and drought event occurrence.</p>
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<p>The spatiotemporal distribution of monthly STI in the YRB during January and November 2022.</p>
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<p>The spatiotemporal distribution of monthly WSDI in the YRB during January and November 2022.</p>
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<p>The spatiotemporal evolution of the CHD event in the YRB during January and November 2022. Dark red regions indicate the CHD event.</p>
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<p>The temporal evolution of STI (<b>a</b>) and WSDI (<b>b</b>) in the nine sub-basins of the YRB during January and November 2022. The black dotted lines indicate STI = 0.5 (<b>a</b>) and WSDI = 0 (<b>b</b>).</p>
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<p>The center of gravity of hot and drought movement trajectory maps: (<b>a</b>) 1–6 represent the center of gravity of drought from July to December; (<b>b</b>) 1–7 represent the center of gravity of hot from June to December.</p>
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<p>The percentages of PPT and ET anomalies in the YRB during 2003 and 2022. The red box indicates compound hot and drought event occurrence.</p>
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<p>The spatial distribution map of anomalous percentages of PPT (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and ET (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) in the YRB during July and December 2022.</p>
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<p>The spatial distribution map of global SST anomalous events (unit °C) during May and August 2022.</p>
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<p>The temporal evolution of Niño 3.4 index during 2017 and 2022.</p>
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<p>WII (<b>a</b>), WAI (<b>b</b>), Ridge (<b>c</b>) and WRP (<b>d</b>) in 2022. The lines represent the climatological state for the corresponding months from 2003 to 2022.</p>
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<p>The spatial distribution map of SSI in the YRB during July and December 2022.</p>
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<p>The monthly change in water level of four hydroelectric power stations in the YRB. The green and yellow bars indicate the averages for 2019–2021 and 2022, respectively.</p>
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<p>The monthly change in runoff of four hydrological stations in the YRB. The blue and orange bars indicate the averages for 2019–2021 and 2022, respectively.</p>
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18 pages, 5010 KiB  
Article
Synoptic Analysis of Flood-Causing Rainfall and Flood Characteristics in the Source Area of the Yellow River
by Lijun Jin, Changsheng Yan, Baojun Yuan, Jing Liu and Jifeng Liu
Water 2024, 16(6), 857; https://doi.org/10.3390/w16060857 - 16 Mar 2024
Viewed by 1134
Abstract
The source area of the Yellow River (SAYR) in China is an important water yield and water-conservation area in the Yellow River. Understanding the variability in rainfall and flood over the SAYR region and the related mechanism of flood-causing rainfall is of great [...] Read more.
The source area of the Yellow River (SAYR) in China is an important water yield and water-conservation area in the Yellow River. Understanding the variability in rainfall and flood over the SAYR region and the related mechanism of flood-causing rainfall is of great importance for the utilization of flood water resources through the optimal operation of cascade reservoirs over the upper Yellow River such as Longyangxia and Liujiaxia, and even for the prevention of flood and drought disasters for the entire Yellow River. Based on the flow data of Tangnaihai hydrological station, the rainfall data of the SAYR region and NCEP-NCAR reanalysis data from 1961 to 2020, three meteorological conceptual models of flood-causing rainfall—namely westerly trough type, low vortex shear type, and subtropical high southwest flow type—are established by using the weather-type method. The mechanism of flood-causing rainfall and the corresponding flood characteristics of each weather type were investigated. The results show that during the process of flood-causing rainfall, in the westerly trough type, the mid- and high-latitude circulation is flat and fluctuating. In the low vortex shear type, the high pressures over the Ural Mountains and the Okhotsk Sea are stronger compared to other types in the same period, and a low vortex shear line is formed in the west of the SAYR region at the low level. The rain is formed during the eastward movement of the shear line. In the subtropical high southwest flow type, the low trough of Lake Balkhash and the subtropical high are stronger compared to other types in the same period. Flood-causing rainfall generally occurs in areas with low-level convergence, high-level negative vorticity, low-level positive vorticity, convergence of water vapor flux, a certain amount of atmospheric precipitable water, and low-level cold advection. In terms of flood peak increment and the maximum accumulated flood volume, the westerly trough type has a long duration and small flood volume, and the low vortex shear type and the subtropical high southwest flow type have a short duration and large flood volume. Full article
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<p>The geographical location and the distribution map of 12 meteorological stations and Tangnaihai hydrological station in the SAYR.</p>
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<p>The schematic diagram of the method of defining the flood-causing rainfall process.</p>
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<p>(<b>a</b>) Geopotential height field (black line, unit: dagpm), wind field (vector field, unit: m/s), and upper jet stream (green line, unit: m/s) at 200 hPa of a small flood when the type I flood-causing rainfall occurs; (<b>c</b>,<b>e</b>) as in (<b>a</b>), but for a medium flood and a large flood. (<b>b</b>) Geopotential height field (black line, unit: dagpm) and its anomaly (color-filled, unit: dagpm) at 500 hPa of a small flood; (<b>d</b>,<b>f</b>) as in (<b>b</b>), but for a medium flood and a large flood.</p>
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<p>As in <a href="#water-16-00857-f003" class="html-fig">Figure 3</a>, but for when the type II flood-causing rainfall occurs.</p>
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<p>As in <a href="#water-16-00857-f003" class="html-fig">Figure 3</a>, but for when the type III flood-causing rainfall occurs.</p>
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<p>Violin plot of the physical quantity characteristic statistics of various flood types in the SAYR region. (The bottom and top of the vertical black bars in the violin plot indicate the 25th and 75th percentiles, the white circles indicate the mean values, and the different symbols marked on the top of the violin plot indicate that the mean values of physical quantities are significantly different, ***, <span class="html-italic">p</span> &lt; 0.001, **, <span class="html-italic">p</span> &lt; 0.05, *, <span class="html-italic">p</span> &lt; 0.1).</p>
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<p>Violin plot of the physical quantity characteristic statistics of various flood types in the SAYR region. (The bottom and top of the vertical black bars in the violin plot indicate the 25th and 75th percentiles, the white circles indicate the mean values, and the different symbols marked on the top of the violin plot indicate that the mean values of physical quantities are significantly different, ***, <span class="html-italic">p</span> &lt; 0.001, **, <span class="html-italic">p</span> &lt; 0.05, *, <span class="html-italic">p</span> &lt; 0.1).</p>
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<p>Violin plot of the physical quantity characteristic statistics of various flood types in the SAYR region. (The bottom and top of the vertical black bars in the violin plot indicate the 25th and 75th percentiles, the white circles indicate the mean values, and the different symbols marked on the top of the violin plot indicate that the mean values of physical quantities are significantly different, ***, <span class="html-italic">p</span> &lt; 0.001, **, <span class="html-italic">p</span> &lt; 0.05, *, <span class="html-italic">p</span> &lt; 0.1).</p>
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<p>Conceptual models of flood-causing rainfall in the SAYR region.</p>
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22 pages, 15310 KiB  
Article
The Applicability of the Drought Index and Analysis of Spatiotemporal Evolution Mechanisms of Drought in the Poyang Lake Basin
by Zihan Gui, Heshuai Qi, Faliang Gui, Baoxian Zheng, Shiwu Wang and Hua Bai
Water 2024, 16(5), 766; https://doi.org/10.3390/w16050766 - 4 Mar 2024
Viewed by 1388
Abstract
Poyang Lake, the largest freshwater lake in China, is an important regional water resource and a landmark ecosystem. In recent years, it has experienced a period of prolonged drought. Using appropriate drought indices to describe the drought characteristics of the Poyang Lake Basin [...] Read more.
Poyang Lake, the largest freshwater lake in China, is an important regional water resource and a landmark ecosystem. In recent years, it has experienced a period of prolonged drought. Using appropriate drought indices to describe the drought characteristics of the Poyang Lake Basin (PLB) is of great practical significance in the face of severe drought situations. This article explores the applicability of four drought indices (including the precipitation anomaly index (PJP), standardized precipitation index (SPI), China Z-index (CPZI), and standardized precipitation evapotranspiration index (SPEI)) based on historical facts. A systematic study was conducted on the spatiotemporal evolution patterns of meteorological drought in the PLB based on the optimal drought index. The results show that SPI is more suitable for the description of drought characteristics in the PLB. Meteorological droughts occur frequently in the summer and autumn in the PLB, with the frequency of mild drought being 17.29% and 16.88%, respectively. The impact range of severe drought or worse reached 22.19% and 28.33% of the entire basin, respectively. The probability of drought occurrence in the PLB shows an increasing trend in spring, while in most areas, it shows a decreasing trend in other seasons, with only a slight increase in the upper reaches of the Ganjiang River (UGR). One of the important factors influencing drought in the PLB is atmospheric circulation. The abnormal variation of the Western Pacific Subtropical High was one of the key factors contributing to the severe drought in the PLB in 2022. This study is based on a long-term series of meteorological data and selects the drought index for the PLB. It describes the spatiotemporal distribution characteristics and evolution patterns of drought and investigates the developmental path and influencing factors of drought in typical years. This study provides a reliable scientific basis for similar watershed water resource management. Full article
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<p>Water resource zoning map of the Poyang Lake Basin.</p>
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<p>Comparison of drought index in each sub-basin in 2003: (<b>a</b>) UGR; (<b>b</b>) MGR; (<b>c</b>) LGR; (<b>d</b>) FR; (<b>e</b>) XJR; (<b>f</b>) RR; (<b>g</b>) XR; (<b>h</b>) PYL.</p>
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<p>Comparison of drought index in each sub-basin in 2019: (<b>a</b>) UGR; (<b>b</b>) MGR; (<b>c</b>) LGR; (<b>d</b>) FR; (<b>e</b>) XJR; (<b>f</b>) RR; (<b>g</b>) XR; (<b>h</b>) PYL.</p>
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<p>Radar map of drought occurrence probability at different time scales in each sub-watershed.</p>
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<p>Annual frequency of drought in each sub-basin of Poyang Lake.</p>
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<p>Spatial distribution of drought frequency above severe severity at different time scales in Poyang Lake Basin: (<b>a</b>) spring; (<b>b</b>) summer; (<b>c</b>) autumn; (<b>d</b>) winter; (<b>e</b>) annual scale; (<b>f</b>) monthly scale.</p>
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<p>Assessment results of regional meteorological drought in Poyang Lake at different time scales.</p>
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<p>Drought trends at different time scales in Poyang Lake Basin: (<b>a</b>) spring; (<b>b</b>) summer; (<b>c</b>) autumn; (<b>d</b>) winter; (<b>e</b>) annual scale; (<b>f</b>) monthly scale.</p>
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<p>Drought trends at different time scales in Poyang Lake Basin: (<b>a</b>) spring; (<b>b</b>) summer; (<b>c</b>) autumn; (<b>d</b>) winter; (<b>e</b>) annual scale; (<b>f</b>) monthly scale.</p>
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<p>Temporal and spatial distribution characteristics of meteorological drought in Poyang Lake Basin in 2022: (<b>a</b>) January; (<b>b</b>) February; (<b>c</b>) March; (<b>d</b>) April; (<b>e</b>) May; (<b>f</b>) June; (<b>g</b>) July; (<b>h</b>) August; (<b>i</b>) September; (<b>j</b>) October; (<b>k</b>) November; (<b>l</b>) December.</p>
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<p>Temporal and spatial distribution characteristics of meteorological drought in Poyang Lake Basin in 2022: (<b>a</b>) January; (<b>b</b>) February; (<b>c</b>) March; (<b>d</b>) April; (<b>e</b>) May; (<b>f</b>) June; (<b>g</b>) July; (<b>h</b>) August; (<b>i</b>) September; (<b>j</b>) October; (<b>k</b>) November; (<b>l</b>) December.</p>
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<p>Development path of drought in a typical year (2022).</p>
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<p>Correlation of drought center location, SPI, and WPSH index in typical years (The asterisk of the correlation coefficient indicates that the correlation is more significant. “*” represents <span class="html-italic">p</span> &lt; 0.05, and “***” represent <span class="html-italic">p</span> &lt; 0.001.).</p>
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19 pages, 3368 KiB  
Article
Identifying Challenges to 3D Hydrodynamic Modeling for a Small, Stratified Tropical Lake in the Philippines
by Maurice Alfonso Duka, Malone Luke E. Monterey, Niño Carlo I. Casim, Jake Henson R. Andres and Katsuhide Yokoyama
Water 2024, 16(4), 561; https://doi.org/10.3390/w16040561 - 12 Feb 2024
Cited by 1 | Viewed by 3331
Abstract
Three-dimensional hydrodynamic modeling for small, stratified tropical lakes in the Philippines and in Southeast Asia in general is not deeply explored. This study pioneers investigating the hydrodynamics of a small crater lake in the Philippines with a focus on temperature simulation using a [...] Read more.
Three-dimensional hydrodynamic modeling for small, stratified tropical lakes in the Philippines and in Southeast Asia in general is not deeply explored. This study pioneers investigating the hydrodynamics of a small crater lake in the Philippines with a focus on temperature simulation using a Fantom Refined 3D model that has been tested mostly for temperate and sub-tropical lakes. The lake’s monthly temperature during the dry season served as a reference for the model’s initial condition and validation. For the simulation to proceed, input data such as weather, inflow, and bathymetry were prepared. In the absence of hourly meteorological data from local weather stations, this paper adopted the satellite weather data from Solcast. Simple correlation analysis of daily weather data between local stations and Solcast showed valid and acceptable results. Inflow values were estimated using the rational method while the stream temperature was estimated from a regression equation using air temperatures as input. The validated satellite-derived data and runoff model can therefore be employed for 3D modeling. The simulations resulted in extremely higher temperatures compared with those observed when using previous default model settings. Direct modifications were then applied to weather parameters, compromising their integrity but resulting in reasonable profiles. By adding scaling factors to heat flux equations and multiplying their components by 0.75 (shortwave), 1.35 (longwave), 0.935 (air temperature), and 0.80 (wind), better results were achieved. This study identifies several challenges in performing 3D hydrodynamic modeling, such as paucity in input hydro-meteorologic and limnologic data and the need for heat flux model improvement. Overall, this study was successful in employing 3D hydrodynamic modeling in a tropical lake, which can pave directions and serve as an excellent reference for future modeling in the same region. Full article
(This article belongs to the Special Issue Challenges to Interdisciplinary Application of Hydrodynamic Models)
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<p>Map of Sampaloc Lake.</p>
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<p>Map of Sampaloc Lake and locations of the local weather stations (blue dots).</p>
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<p>Comparison of (<b>a</b>,<b>b</b>) Air Temperature, (<b>c</b>) Wind Speed, and (<b>d</b>) Solar Radiation from UPLB NAS, BSWM, and Solcast.</p>
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<p>Temporal variation in (<b>a</b>) rainfall and cloud cover, (<b>b</b>) air temperature, (<b>c</b>) solar radiation, (<b>d</b>) wind speed and wind direction, and its (<b>e</b>) wind rose.</p>
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<p>Temperature profile (<b>a1</b>–<b>c1</b>) and Brunt–Vaisala frequency (<b>a2</b>–<b>c2</b>) of the lake in (<b>a1</b>,<b>a2</b>) March, (<b>b1</b>,<b>b2</b>) April, and (<b>c1</b>,<b>c2</b>) May.</p>
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<p>Comparison of temperature profiles from preliminary simulation in (<b>a</b>) March, (<b>b</b>) April, and (<b>c</b>) May.</p>
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<p>Trends of temperature profile by varying the scaling factors; (<b>a1</b>,<b>a2</b>) shortwave, (<b>b1</b>,<b>b2</b>) longwave, (<b>c1</b>,<b>c2</b>) air temperature, (<b>d1</b>,<b>d2</b>) wind speed, and (<b>e1</b>,<b>e2</b>) light extinction coefficient.</p>
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<p>Final comparison of temperature profiles using different approaches of calibration in (<b>a</b>) March, (<b>b</b>) April, and (<b>c</b>) May.</p>
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14 pages, 16820 KiB  
Article
Extended-Range Forecast of Winter Rainfall in the Yangtze River Delta Based on Intra-Seasonal Oscillation of Atmospheric Circulations
by Fei Xin and Wei Wang
Atmosphere 2024, 15(2), 206; https://doi.org/10.3390/atmos15020206 - 6 Feb 2024
Cited by 1 | Viewed by 1010
Abstract
The Yangtze River Delta (YRD) is an important economic region in China. Heavy winter rainfall may pose serious threats to city operations. To ensure the safe operation of the city, meteorological departments need to provide forecast results for the Spring Festival travel rush [...] Read more.
The Yangtze River Delta (YRD) is an important economic region in China. Heavy winter rainfall may pose serious threats to city operations. To ensure the safe operation of the city, meteorological departments need to provide forecast results for the Spring Festival travel rush weather service. Therefore, the extended-range forecast of winter rainfall is of considerable importance. To solve this problem, based on the analysis of low-frequency rainfall and the intra-seasonal oscillation of atmospheric circulation, an extended-range forecast model for winter rainfall is developed using spatiotemporal projection methods and is applied to a case study from 2020. The results show that: (1) The precipitation in the YRD during the winter has a significant intra-seasonal oscillation (ISO) with a periodicity of 10–30 d. (2) The atmospheric circulations associated with winter rainfall in the YRD have a significant characteristic of low-frequency oscillation. From a 30-day to a 0-day lead, large modifications appear in the low-frequency atmospheric circulations at low, mid, and high latitudes. At low latitudes, strong wet convective activity characterized by a negative OLR combined with a positive RH700 correlation coefficient moves northwestward and covers the entire YRD. Meanwhile, the Western Pacific subtropical high (WPSH) characterized by a positive Z500 anomaly enhances and lifts northward. At mid and high latitudes, the signal of negatively correlated Z500 northwest of Lake Balkhash propagates southeastward, indicating the cold is air moving southward. Multiple circulation factors combine together and lead to the precipitation process in the YRD. (3) Taking the intra-seasonal dynamical evolution process of the atmospheric circulation as the prediction factor, the spatiotemporal method is used to build the model for winter mean extended-range precipitation anomaly tendency in the YRD. The hindcast for the recent 10 years shows that the ensemble model has a higher skill that can reach up to 20 days. In particular, the skill of the eastern part of the YRD can reach 25 days. (4) The rainfall in the 2019/2020 winter has a significant ISO. The ensemble model could forecast the most extreme precipitation for 20 days ahead. Full article
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<p>The distribution of the 62 observatories in the YRD shown as white dots.</p>
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<p>The power spectrum (black curve) of the winter daily rainfall time series averaged over the YRD for the period from 1991 to 2020. (The green line denotes the spectrum for the Markov red noise. The blue and red lines denote the 10% and 90% significance test).</p>
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<p>Temporal correlation coefficients (color-shaded) maps of preceding outgoing longwave radiation (OLR) and 700-hPa relative humidity (RH700) with the intra-seasonal component of daily precipitation averaged from 30- to 0-day leads over the YRD. Cross areas denote values at the 95% confidence level determined by Student’s <span class="html-italic">t</span>-test. The numbers 1−5 denote leads of 30, 25, 15, 10, and 0 days, respectively.</p>
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<p>Temporal correlation coefficient maps of 850-hPa (<b>left</b>) and 200-hPa (<b>right</b>) winds with the intra-seasonal component of daily precipitation averaged from 30- to 0-day leads over the YRD. The bold vector arrows indicate the vector correlation coefficients values at 95% confidence level by <span class="html-italic">t</span>-test. The numbers 1–5 denote leads 30, 25, 15, 10, and 0 days, respectively.</p>
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<p>Temporal correlation coefficient maps of 200 hPa and 500 hPa geopotential height with the intra-seasonal component of daily precipitation averaged from 30- to 0-day leads over the YRD. Cross areas denote values at the 95% confidence level determined by Student’s <span class="html-italic">t</span>-test. Red dashed lines with arrows indicate the movement of the key signals. The numbers 1–5 denote leads 30, 25, 15, 10, and 0 days, respectively.</p>
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<p>Pentadly ACC between hindcast results and ground truths in the winters of 2011–2019 in the YRD.</p>
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<p>TCC between the hindcast results and ground truths with ensemble members in the winters of 2011–2019 in the YRD. It show the results for lead times of 10, 15, 20, 25, 30, and 35 days. (The dotted area denotes the area passing the 90% significance test).</p>
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<p>(<b>a</b>) The power spectrum (black curve) of the winter daily rainfall time series averaged over the YRD for the period during the 2019/2020 winter. (The green line denotes the spectrum for Markov red noise. The blue and red lines denote the 10% and 90% significance tests). (<b>b</b>) Daily precipitation amount (bar), the persistent rainy events (green lines), and the 10–30-day filtered rainfall anomaly (red curve) in Shanghai.</p>
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<p>The predictands based on the forecast model during the next 3−8 pentads starting from 1 January 2020. (<b>a</b>) is the third pentad, (<b>b</b>) is the fourth pentad, (<b>c</b>) is the fifth pentad, (<b>d</b>) is the sixth pentad, (<b>e</b>) is the seventh pentad, (<b>f</b>) is the eighth pentad.</p>
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17 pages, 5468 KiB  
Article
Temporal Distribution Patterns of Cryptic Brachionus calyciflorus (Rotifera) Species in Relation to Biogeographical Gradient Associated with Latitude
by Yuan Xu, Le-Le Ge, Xin-Feng Cheng, Xian-Ling Xiang, Xin-Li Wen, Yong-Jin Wang, Hao Fu, Ya-Li Ge and Yi-Long Xi
Animals 2024, 14(2), 244; https://doi.org/10.3390/ani14020244 - 12 Jan 2024
Viewed by 1122
Abstract
Sympatric distribution and temporal overlap of cryptic zooplankton species pose a challenge to the framework of the niche differentiation theory and the mechanisms allowing competitor coexistence. We applied the methods of phylogenetic analysis, DNA taxonomy, and statistical analysis to study the temporal distribution [...] Read more.
Sympatric distribution and temporal overlap of cryptic zooplankton species pose a challenge to the framework of the niche differentiation theory and the mechanisms allowing competitor coexistence. We applied the methods of phylogenetic analysis, DNA taxonomy, and statistical analysis to study the temporal distribution patterns of the cryptic B. calyciflorus species, an excellent model, in three lakes, and to explore the putative mechanisms for their seasonal succession and temporal overlap. The results showed that in the warm-temperate Lake Yunlong, B. fernandoi and B. calyciflorus s.s. underwent a seasonal succession, which was largely attributed to their differential adaptation to water temperature. In the subtropical Lake Jinghu, B. fernandoi, B. calyciflorus s.s., and B. dorcas exhibited both seasonal succession and temporal overlap. Seasonal successions were largely attributed to their differential adaptation to temperature, and temporal overlap resulted from their differential responses to algal food concentration. In the tropical Lake Jinniu, B. calyciflorus s.s. persisted throughout the year and overlapped with B. dorcas for 5 months. The temporal overlap resulted from their differential responses to copepod predation. These results indicated that the temporal distribution pattern of the cryptic B. calyciforus species and the mechanism that allows competitor coexistence vary with different climate zones. Full article
(This article belongs to the Section Aquatic Animals)
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<p>Temporal fluctuations of the densities of <span class="html-italic">Asplanchna</span>, cladocerans, and copepods in Lake Yunlong, Lake Jinghu, and Lake Jinniu.</p>
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<p>The maximum-likelihood phylogenetic trees and DNA taxonomy results of the Brachionus calyciflorus species complex based on the mtCOI and nuITS1 sequences from Lake Yunlong, Lake Jinghu, and Lake Jinniu. “A”, “C”, and “D” represent <span class="html-italic">B. dorcas</span>, <span class="html-italic">B. calyciflorus</span> s.s., and <span class="html-italic">B. fernandoi</span>, respectively.</p>
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<p>Relative frequencies of cryptic Brachionus calyciflorus groups in Lake Yunlong, Lake Jinghu, and Lake Jinniu. “A”, “C”, and “D” represent <span class="html-italic">B. dorcas</span>, <span class="html-italic">B. calyciflorus</span> s.s., and <span class="html-italic">B. fernandoi</span>, respectively.</p>
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<p>Densities of cryptic Brachionus calyciflorus groups and the <span class="html-italic">B. calyciflorus</span> species complex in Lake Yunlong, Lake Jinghu, and Lake Jinniu. “A”, “C”, and “D” represent <span class="html-italic">B. dorcas</span>, <span class="html-italic">B. calyciflorus</span> s.s., and <span class="html-italic">B. fernandoi</span>, respectively.</p>
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<p>Principal component analyses on the environmental variables (temperature, pH, DO, chl-<span class="html-italic">a</span> concentration, and the densities of <span class="html-italic">Asplanchna</span>, copepods, and cladocerans) in Lake Yunlong, Lake Jinghu, and Lake Jinniu. Three variables (the densities of <span class="html-italic">Asplanchna</span>, copepods, and cladocerans) in both Lake Yunlong and Lake Jinghu, and five variables (water temperature, TP and dissolved oxygen concentrations, and the densities of <span class="html-italic">Asplanchna</span> and cladocerans) in Lake Jinniu were very strongly skewed and were transformed to lg (<span class="html-italic">x</span> + 1) or lg <span class="html-italic">x</span> (only for water temperature). “DO” represents dissolved oxygen, “Asp.” represents <span class="html-italic">Asplanchna</span>, “Cop.” represents copepods, and “Cla.” represents cladocerans. “A”, “C”, and “D” represent <span class="html-italic">B. dorcas</span>, <span class="html-italic">B. calyciflorus</span> s.s., and <span class="html-italic">B. fernandoi</span>, respectively.</p>
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