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The Impact of Water Level Changes (Frequency and Amplitude) on Water Quality in Lakes

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

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 18778

Special Issue Editors

Prof. Dr. Moshe Gophen

E-Mail Website
Guest Editor
MIGAL-Scientific Research Institute, Tel-Hai Academic College, P.O. Box 831, Kiryat Shmone 11016, Israel
Interests: Kinneret; Hula Valley; limnology; wetlands ecology; freshwater plankton and fish ecology; lake and watershed management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thomas L. Crisman

E-Mail Website
Guest Editor
College of Arts and Sciences, University of South Florida, Tampa, FL, USA
Interests: limnology, lake management, water resources; variability; remote sensing; lake level; Qinghai; Tibet

Special Issue Information

Dear Colleagues,

The reality of global changes in climate conditions, particularly global warming, is known worldwide, globally expressed as both dryness and water scarcity in some geographical regions and water luxury accompanied by floods in other parts of the world. Consequently, water scarcity and overwhelming rainfall and river discharge require renovated design approaches to water level management in lakes. The management of water levels in lakes is a key operational factor tool under the circumstances of climate and, consequently, hydrological changes. Water quality protection and supply constrains are, therefore, crucial. Moreover, aquatic recreation along beaches affected by water level fluctuations, growth rates of submerged and emerged aquatic vegetation and fish reproduction capacities in the shallows are critical for the ecological services attributed to lakes. Limnologists and aquatic scientists are invited to contribute papers in the field of zoological, botanical and hydrological aspects of the impact of water level fluctuations on water quality in lakes.

Prof. Dr. Moshe Gophen
Prof. Dr. Thomas L. Crisman
Guest Editors

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Keywords

  • plankton
  • benthos
  • littoral
  • pollution
  • sediments
  • beach vegetation
  • wave action
  • nutrients
  • residence time
  • fish spawning grounds

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Published Papers (5 papers)

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Research

27 pages, 7418 KiB  
Article
Exploring the Effect of Meteorological Factors on Predicting Hourly Water Levels Based on CEEMDAN and LSTM
by Zihuang Yan, Xianghui Lu and Lifeng Wu
Water 2023, 15(18), 3190; https://doi.org/10.3390/w15183190 - 7 Sep 2023
Cited by 4 | Viewed by 1890
Abstract
The magnitude of tidal energy depends on changes in ocean water levels, and by accurately predicting water level changes, tidal power plants can be effectively helped to plan and optimize the timing of power generation to maximize energy harvesting efficiency. The time-dependent nature [...] Read more.
The magnitude of tidal energy depends on changes in ocean water levels, and by accurately predicting water level changes, tidal power plants can be effectively helped to plan and optimize the timing of power generation to maximize energy harvesting efficiency. The time-dependent nature of water level changes results in water level data being of the time-series type and is essential for both short- and long-term forecasting. Real-time water level information is essential for studying tidal power, and the National Oceanic and Atmospheric Administration (NOAA) has real-time water level information, making the NOAA data useful for such studies. In this paper, long short-term memory (LSTM) and its variants, stack long short-term memory (StackLSTM) and bi-directional long short-term memory (BiLSTM), are used to predict water levels at three sites and compared with classical machine learning algorithms, e.g., support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). This study aims to investigate the effects of wind speed (WS), wind direction (WD), gusts (WG), air temperature (AT), and atmospheric pressure (Baro) on predicting hourly water levels (WL). The results show that the highest coefficient of determination (R2) was obtained at all meteorological factors when used as inputs, except at the La Jolla site. (Burlington station (R2) = 0.721, Kahului station (R2) = 0.852). In the final part of this article, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm was introduced into various models, and the results showed a significant improvement in predicting water levels at each site. Among them, the CEEMDAN-BiLSTM algorithm performed the best, with an average RMSE of 0.0759 mh−1 for the prediction of three sites. This indicates that applying the CEEMDAN algorithm to deep learning has a more stable predictive performance for water level forecasting in different regions. Full article
(This article belongs to the Special Issue The Impact of Water Level Changes (Frequency and Amplitude) on Water Quality in Lakes)
►▼ Show Figures

Figure 1

Figure 1
<p>The three water level monitoring stations studied in this paper.</p> Full article ">Figure 2
<p>Violin analysis plots of meteorological (WS, WG, AT)—water level data for two stations, (<b>a</b>,<b>b</b>): Kahului and La Jolla stations, respectively.</p> Full article ">Figure 3
<p>LSTM basic cell structure.</p> Full article ">Figure 4
<p>Two-layer stackLSTM basic cell structure.</p> Full article ">Figure 5
<p>BiLSTM basic cell structure.</p> Full article ">Figure 6
<p>CEEMDAN-LSTM forecasting process; note that the LSTM part of it can be replaced with other models for the prediction task.</p> Full article ">Figure 7
<p>Prediction curves of each model for three stations when all meteorological factors are used as inputs. The right histogram’s L-G, B-L, S-L, and L represent LightGBM, BiLSTM, StackLSTM, and LSTM, respectively.</p> Full article ">Figure 7 Cont.
<p>Prediction curves of each model for three stations when all meteorological factors are used as inputs. The right histogram’s L-G, B-L, S-L, and L represent LightGBM, BiLSTM, StackLSTM, and LSTM, respectively.</p> Full article ">Figure 8
<p>RMSE thermal analysis of each model’s prediction results for all seasons under all combinations of meteorological factors.</p> Full article ">Figure 9
<p>CEEMDAN signal decomposition of water level data at the La Jolla site.</p> Full article ">Figure 10
<p>CEEMDAN-BiLSTM predictions of water level at the La Jolla site based on input combinations of all meteorological factors.</p> Full article ">Figure A1
<p>Residual scatter plots of predicted water levels at the Burlington site under seven models with all meteorological factors as inputs.</p> Full article ">Figure A2
<p>The prediction results for each IMF mode and residual obtained from the CEEMDAN decomposition of water level data at site Burlington.</p> Full article ">Figure A2 Cont.
<p>The prediction results for each IMF mode and residual obtained from the CEEMDAN decomposition of water level data at site Burlington.</p> Full article ">Figure A2 Cont.
<p>The prediction results for each IMF mode and residual obtained from the CEEMDAN decomposition of water level data at site Burlington.</p> Full article ">
15 pages, 6549 KiB  
Article
Spatio-Temporal Variation of Trophic Status and Water Quality with Water Level Fluctuation in a Reservoir
by Wenwen Liao, Hsinan Chen, Meijeng Peng and Tawei Chang
Water 2023, 15(17), 3154; https://doi.org/10.3390/w15173154 - 3 Sep 2023
Cited by 1 | Viewed by 1538
Abstract
Water level fluctuation (WLF) is one of the important factors that affect reservoir water quality, habitat, species, and ecosystems. In this study, an independent sample t-test was used to evaluate the trophic status and water quality of the spatial and temporal variations [...] Read more.
Water level fluctuation (WLF) is one of the important factors that affect reservoir water quality, habitat, species, and ecosystems. In this study, an independent sample t-test was used to evaluate the trophic status and water quality of the spatial and temporal variations with WLF in Shihmen Reservoir, Taiwan. The results of this study show that the Shihmen Reservoir has the lowest mean water level and higher potential of showing eutrophic status in April and May. This may be attributed to a lower water level, water depth, and transparency in this period. However, although there is no statistically significant difference in mean algal abundance in spring compared with other seasons, seasonal mean algae abundance and the seasonal mean Carlson’s trophic status index (CTSI) show as highly and positively correlated. It means that the increase in the CTSI value may not only be caused by effects on the sediment increase but also by algal proliferation. Mean water depth seems to be one of the important key indexes for reservoir management regarding trophic status since it reflects water quality and can be easy to obtain. This study suggests that reservoir administration can use the water level as a reference threshold for controlling CTSI strategies. In proper hydrological conditions, administration should try to hold a higher water level in a reservoir to downgrade CTSI. Full article
(This article belongs to the Special Issue The Impact of Water Level Changes (Frequency and Amplitude) on Water Quality in Lakes)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of the study area and 5 water quality monitoring station locations in Shihmen Reservoir. (The blue area shows storage area of the Shimen Reservoir, and the yellow dots represent the locations of water quality monitoring stations).</p> Full article ">Figure 2
<p>Monthly variations of mean CTSI and other water quality parameters with the water level in Shihmen Reservoir from 2011 to 2021; (<b>a</b>) CTSI, (<b>b</b>) EC, (<b>c</b>) NH<sub>4</sub><sup>+</sup>-N, (<b>d</b>) SS. (J-11 represents January in 2011).</p> Full article ">Figure 3
<p>Variations of mean CTSI and other water quality parameters with the water level in each month in Shihmen Reservoir from 2011 to 2021; (<b>a</b>) CTSI, (<b>b</b>) TP, (<b>c</b>) Chl-a, (<b>d</b>) transparency, (<b>e</b>) algal abundance (seasonal), (<b>f</b>) EC, (<b>g</b>) TOC, (<b>h</b>) turbidity, (<b>i</b>) NH<sub>4</sub><sup>+</sup>-N, (<b>j</b>) NO<sub>3</sub><sup>−</sup>-N, (<b>k</b>) SS, (<b>l</b>) WQI.</p> Full article ">Figure 4
<p>The TSI deviation graph (<b>a</b>) and correlation of mean algae abundance and mean CTSI (<b>b</b>) of all Shihmen Reservoir data during 2011 to 2021.</p> Full article ">Figure 5
<p>Variations of mean CTSI and other water quality parameters with water depth at the 5 monitoring stations in Shihmen Reservoir from 2011 to 2021; (<b>a</b>) CTSI, (<b>b</b>) TP, (<b>c</b>) Chl-a, (<b>d</b>) transparency, (<b>e</b>) algal abundance, (<b>f</b>) EC, (<b>g</b>) TOC, (<b>h</b>) turbidity, (<b>i</b>) NH<sub>4</sub><sup>+</sup>-N, (<b>j</b>) NO<sub>3</sub><sup>−</sup>-N, (<b>k</b>) SS, (<b>l</b>) WQI.</p> Full article ">Figure 6
<p>Regression analysis of mean CTSI with WLF in 5 monitoring stations of Shihmen Reservoir from 2011 to 2021. (<b>○</b> represents data without algae abundance data; ● represents data with algae cells no. under 10,000 cells/mL; <span style="color:#00B0F0">●</span> represents data algae cells no. between 10,000 ~ 20,000 cells/mL; <span style="color:red">●</span> represents data with algae cells no. above 20,000 cells/mL).</p> Full article ">Figure 7
<p>Regression analysis of mean CTSI with WLF in Shihmen Reservoir from 2011 to 2021.</p> Full article ">Figure 8
<p>Relationship of mean CTSI and (<b>a</b>) algal abundance and (<b>b</b>) transparency with WLF in Shihmen Reservoir from 2011 to 2021.</p> Full article ">
18 pages, 12705 KiB  
Article
The Salinity of the Great Salt Lake and Its Deep Brine Layer
by Madeline F. Merck and David G. Tarboton
Water 2023, 15(8), 1488; https://doi.org/10.3390/w15081488 - 11 Apr 2023
Cited by 4 | Viewed by 7537
Abstract
The Great Salt Lake is a highly saline terminal lake with considerable fluctuations in water surface elevation and salinity. The lake is divided into two arms by a railroad causeway. River inflows enter the larger south arm, while the north arm only receives [...] Read more.
The Great Salt Lake is a highly saline terminal lake with considerable fluctuations in water surface elevation and salinity. The lake is divided into two arms by a railroad causeway. River inflows enter the larger south arm, while the north arm only receives minimal surface runoff. Evaporation from both arms and limited exchange of water and salt through causeway openings result in complex water and salinity processes in the lake. The north arm is typically homogeneous and close to saturation. The south arm is typically stratified with periodic occurrences of a deep brine layer. This paper analyzes the lake’s long-term historical salinity and water surface elevation data record. Its purpose is to better document the movement of salt and changes to salinity in time and space within the lake and the occurrence and extent of its deep brine layer. This work is important because of the lake’s salinity-dependent ecosystem and industries as well as the role played by the deep brine layer in the concentration of salt and contaminants. We documented that the deep brine layer in the south arm is intermittent, occurring only when causeway exchange supports flow from the north to the south arms. We found that the overall mass of salt in the lake is declining and quantified this in terms of mineral extraction records and historical density measurements. Full article
(This article belongs to the Special Issue The Impact of Water Level Changes (Frequency and Amplitude) on Water Quality in Lakes)
►▼ Show Figures

Figure 1

Figure 1
<p>Locations of historical UGS (orange) and USGS (green) sampling sites within the GSL. Light blue lines are 0.3 m contours. Dark blue line is the 1272.5 m contour, within which the DBL can be found. Location of GSL in Northern Utah, USA, is shown.</p> Full article ">Figure 2
<p>Timeline indicating when the GSL was sampled over the historical record, from 1966 to early 2023, using UGS north arm (dark orange), UGS south arm (light orange), USGS north arm (dark green), and USGS south arm (light green) sites. Each x represents one sampling event.</p> Full article ">Figure 3
<p>Annual count of individual UGS (orange) and USGS (green) samples over the historical record. Samples taken at different depths at a single site were counted separately.</p> Full article ">Figure 4
<p>Hypothetical lake at WSE <italic>h</italic> with <italic>n</italic> number of measurements at depth on a given day. Cross-sections indicate layers used to calculate average dissolved salt mass and average salinity. Adapted with permission from Ref. [<xref ref-type="bibr" rid="B26-water-15-01488">26</xref>], Copyright American Geophysical Union, 2012.</p> Full article ">Figure 5
<p>Example plots of salinity at depth over the period of record for individual sample sites (<bold>a</bold>) AS2 and (<bold>b</bold>) AIS, both of which are in the south arm (see <xref ref-type="fig" rid="water-15-01488-f001">Figure 1</xref> for site locations). Each dot indicates a salinity calculation from a single sample site measurement. Salinity was calculated using the Naftz et al. [<xref ref-type="bibr" rid="B16-water-15-01488">16</xref>] equation of state, Equation (1). Solid black line represents the WSE from USGS measurements. Dashed gray horizontal line represents the elevation of the bottom of the lake at the specific sample site. Due to drifting of the boat or instrument while being lowered into the water, some measurements have been recorded below the elevation of the lake bottom at the location of the measurement site.</p> Full article ">Figure 6
<p>Salinity at depth over time in the (<bold>a</bold>) north arm and (<bold>b</bold>) south arm for all samples taken over the period of record at the UGS and USGS measurement sites shown in <xref ref-type="fig" rid="water-15-01488-f001">Figure 1</xref>. Each dot indicates a salinity calculation from a single sample site measurement. Salinity was calculated using the Naftz et al. [<xref ref-type="bibr" rid="B16-water-15-01488">16</xref>] equation of state, Equation (1). Solid black line represents the WSE from USGS measurements. Horizontal dashed lines indicate causeway openings invert elevations, culvert base and top elevations, and the elevation separating permeable and impermeable fill in causeway base. Vertical dashed lines indicate date causeway was constructed and periods of west desert pumping and return.</p> Full article ">Figure 6 Cont.
<p>Salinity at depth over time in the (<bold>a</bold>) north arm and (<bold>b</bold>) south arm for all samples taken over the period of record at the UGS and USGS measurement sites shown in <xref ref-type="fig" rid="water-15-01488-f001">Figure 1</xref>. Each dot indicates a salinity calculation from a single sample site measurement. Salinity was calculated using the Naftz et al. [<xref ref-type="bibr" rid="B16-water-15-01488">16</xref>] equation of state, Equation (1). Solid black line represents the WSE from USGS measurements. Horizontal dashed lines indicate causeway openings invert elevations, culvert base and top elevations, and the elevation separating permeable and impermeable fill in causeway base. Vertical dashed lines indicate date causeway was constructed and periods of west desert pumping and return.</p> Full article ">Figure 7
<p>North and south arm average dissolved salt mass over the period of record at GSL measurement sites (<xref ref-type="fig" rid="water-15-01488-f001">Figure 1</xref>). Point values were calculated using all measurements at a single site on a given date. Dashed lines represent the estimated monthly time series.</p> Full article ">Figure 8
<p>North and south arm salinities over the period of record at GSL measurement sites (<xref ref-type="fig" rid="water-15-01488-f001">Figure 1</xref>). Point values were calculated using all measurements at a single site on a given date. Dashed lines represent the estimated monthly time series.</p> Full article ">Figure 9
<p>Salinities in the south arm. Surface (less than 0.3 m depth) and bottom (below 1272.5 m elevation) salinity points were calculated using individual measurements from sample sites within the 1272.5 m contour shown in <xref ref-type="fig" rid="water-15-01488-f001">Figure 1</xref> (including UGS sites RT4, RT2, RT1, NLN, FB2, AS2, AC3, and AC2; and USGS sites 405356112205601, 410637,112270401, and 410644112382601). The dashed line is the estimated monthly time series of average salinity from <xref ref-type="fig" rid="water-15-01488-f008">Figure 8</xref>.</p> Full article ">Figure 10
<p>North and south arm salt mass calculations over the period of record at the UGS and USGS measurement sites shown in <xref ref-type="fig" rid="water-15-01488-f001">Figure 1</xref>. Calculations were only performed for sample events with 3 or more samples at depth. 1 Mg = 1 metric ton.</p> Full article ">
18 pages, 5283 KiB  
Article
Are Water Level Fluctuations and Pelagic Water Quality in Lake Kinneret Directly Related? Perspectives of Nutrient Dynamics
by Moshe Gophen
Water 2023, 15(8), 1473; https://doi.org/10.3390/w15081473 - 10 Apr 2023
Cited by 1 | Viewed by 1666
Abstract
Long-term records of Water Level Fluctuations (WLF) and nutrient dynamics in Lake Kinneret have indicated an independence between them. The winter’s high WLF with nutrient-rich conditions and the summer’s low WLF with nutrient-poor conditions are recurrent states. Are Water Level Fluctuations and Lake [...] Read more.
Long-term records of Water Level Fluctuations (WLF) and nutrient dynamics in Lake Kinneret have indicated an independence between them. The winter’s high WLF with nutrient-rich conditions and the summer’s low WLF with nutrient-poor conditions are recurrent states. Are Water Level Fluctuations and Lake Kinneret’s pelagic water quality related directly or indirectly? Overall, the results found that WLF and nutrient dynamics in the pelagic zone of Lake Kinneret are not co-partners, but independent escorts. The common periodical (monthly) distribution of nutrient concentrations in the epilimnion of Lake Kinneret indicates that a 20 m deep epilimnion formed following a decline in water input, temperature, and evaporation elevation, resulting in the decline of WL. There was a seasonal correlation between summer’s natural conditions and pelagic nutrients’ deficiency. Low WL in summer is the result of natural subtropical climate conditions, whilst dry or high rainfall seasons induce water input modification and consequently, the WL decline of nutrient inputs and independent followers. Full article
(This article belongs to the Special Issue The Impact of Water Level Changes (Frequency and Amplitude) on Water Quality in Lakes)
►▼ Show Figures

Figure 1

Figure 1
<p>Lowess smoother trend of changes of annual (1970–2018) Jordan River discharge.</p> Full article ">Figure 2
<p>Trend of changes (Lowess smoother) of an annual mean of Lake Kinneret water level (WL). (mbsl) during 1934–2018.</p> Full article ">Figure 3
<p>Trend of changes (Lowess smoother, 0.8 bandwidth) of annual lake average of chloride concentration (ppm). Two periods are presented: Left—1934–2005 and Right—1969–2001.</p> Full article ">Figure 4
<p>Linear prediction (95% CI) of annual mean of Lake Kinneret’s chloride concentration (ppm) relative to the annual mean of Lake Kinneret’s WL altitude (mbsl) (1969–2018).</p> Full article ">Figure 5
<p>Fractional polynomial regression between annual (1969–2018) maximal salt load (10<sup>3</sup> ton/lake) and annual mean of WL. Salt load is based on chloride concentration (ppm) and maximal lake volume (10<sup>6</sup> m<sup>3</sup>/year; mcm/y), which is respective to WL altitude as published in the morphometric map of Lake Kinneret by TAHAL in 1961 [<xref ref-type="bibr" rid="B1-water-15-01473">1</xref>].</p> Full article ">Figure 6
<p>Similar computation of salt load and annual means of WL and lake volume was applied for <xref ref-type="fig" rid="water-15-01473-f006">Figure 6</xref> where annual maximal values and means of salinity (ppm) and WL (mbsl) and lake volume (mcm) were plotted vs. time (1969–2001).</p> Full article ">Figure 7
<p>Linear regression (W95% CI) between annual lake means of maximal salt load (10<sup>3</sup> ton) and chloride concentration (ppm) during 1969–2001.</p> Full article ">Figure 8
<p>Trend of changes (Lowess smoother, 0.8 bandwidth) of annual lake averages of TP concentration (ppm scale range: 0.01–0.03) vs. WL (mbsl) during 1969–2001.</p> Full article ">Figure 9
<p>Trend of changes (Lowess Smoother, 0.8 bandwidth) of annual lake averages of orthophosphate concentration (ppm scale range: 0.000–0.015) vs. WL (mbsl) during 1969–2001.</p> Full article ">Figure 10
<p>Trend of changes (Lowess smoother, 0.8 bandwidth) of annual lake averages of total nitrogen (TN) concentration (ppm scale range: 0.4–1.4) with respect to WL (mbsl) during 1969–2001.</p> Full article ">Figure 11
<p>Trend of changes (Lowess smoother, 0.8 bandwidth) of annual lake averages of nitrate (NO<sub>3</sub>) concentration (ppm scale range: 0.0–0.4) with respect to WL (mbsl) during 1969–2001.</p> Full article ">Figure 12
<p>Trend of changes (Lowess smoother, 0.8 bandwidth) of annual lake averages of ammonium concentration (ppm scale range: 0.1–0.25) vs. WL (mbsl) during 1969–2001.</p> Full article ">Figure 13
<p>Trend of changes (Lowess smoother, 0.8 bandwidth) of annual lake averages of sulfate (SO<sub>4</sub>) concentration (ppm) vs. WL (mbsl) during 1969–2001.</p> Full article ">Figure 14
<p>Lowess smoother (0.8 bandwidth) trend of changes plot of monthly changes of WL (mbsl) (1969–2001).</p> Full article ">Figure 15
<p>Lowess smoother (0.8 bandwidth) trend of changes plot of monthly changes of chloride concentration (ppm) (left) and salt loads (10<sup>3</sup> tons) (right) (1969–2001).</p> Full article ">Figure 16
<p>Lowess smoother (0.8 bandwidth) trend of changes plot of monthly changes of nitrate concentrations (ppm scale range: 0.0–0.3) (1969–2001).</p> Full article ">Figure 17
<p>Lowess smoother (0.8 bandwidth) trend of changes plot of monthly changes of ammonium (left) and total orthophosphate (right) concentrations (ppm scale range: Ammonium—0.05–0.3; P-Otho—0.00–0.020) (1969–2001).</p> Full article ">Figure 18
<p>Lowess smoother (0.8 bandwidth) trend of changes plot of monthly changes of total phosphorus (left) and total nitrogen (right) concentrations (ppm scale range: Total Phosphorus—0.018–0.026; Total Nitrogen—0.0–0.3) (1969–2001).</p> Full article ">Figure 19
<p>Trend of changes (LOWESS 0.8 bandwidth) of mean annual concentration of chloride (ppm) in relation to annual mean of WL in Lake Kinneret during 1933–1969.</p> Full article ">Figure 20
<p>Linear regression (95% Cl) (r<sup>2</sup> and p values are given) between monthly means of chloride concentration (ppm) and salt load (10<sup>3</sup> tons) during the hydraulic year (October to next September), 2018–2019 (Data Source: Mekorot National Water Supply Co., Jordan districts, Laboratory and Drainage Basin Unit).</p> Full article ">Figure 21
<p>Linear regression (95% CI) between annual means (1970–2020) of Hydraulic residence time (HRT) (Years) and lake volume (mcm).</p> Full article ">Figure 22
<p>Linear regression (95% CI) between annual means (1970–2020) of Hydraulic residence time (HRT) (Years) and salt load (10<sup>3</sup> tons).</p> Full article ">
13 pages, 4147 KiB  
Article
Historical Review on Water Level Changes in Lake Kinneret (Israel) and Incomparable Perspectives
by Moshe Gophen
Water 2023, 15(5), 837; https://doi.org/10.3390/w15050837 - 21 Feb 2023
Cited by 2 | Viewed by 5473
Abstract
A long-term (1933–2022) record of water level (WL) fluctuations in Lake Kinneret was reviewed. The dependence of the Kinneret WL management on climate change (flood–dryness alternate), dam and National Water Carrier (NWC) constructions constrained by water availability and domestic supply demands were indicated. [...] Read more.
A long-term (1933–2022) record of water level (WL) fluctuations in Lake Kinneret was reviewed. The dependence of the Kinneret WL management on climate change (flood–dryness alternate), dam and National Water Carrier (NWC) constructions constrained by water availability and domestic supply demands were indicated. A short-term range of maximal WL decline of 4–6 m and 4.6–6.5% of the total surface area of lake water shrinkage in Lake Kinneret was documented. Nevertheless, incomparably longer periods and higher amplitudes of WL decline accompanied by a dramatic shrinking of the water surface were documented in Lake Tchad, the Aral Sea and Lake Sivan (SAT). Therefore, the comparative results of WL decline in Lake Kinneret and in other lakes as SAT are not justified. Full article
(This article belongs to the Special Issue The Impact of Water Level Changes (Frequency and Amplitude) on Water Quality in Lakes)
►▼ Show Figures

Figure 1

Figure 1
<p>Geographical chart of the Lake Kinneret Watershed. River Jordan, three major headwaters, (Dan, Snir, and Banyas), Longitude and latitude location, Lake Kinneret, the Hula Valley and geopolitical territories are indicated.</p> Full article ">Figure 2
<p>Ten year (decade) grouped averages of monthly means.</p> Full article ">Figure 3
<p>Temporal (1940–2022) fluctuations of annual rainfall (mm) measured in the northern part of the Hula valley (Dafna): scatter plot (<bold>left</bold>), fractional polynomial regression (<bold>middle</bold>) and lowess smoother trend of changes (<bold>right</bold>).</p> Full article ">Figure 4
<p>Temporal (1940–2022) fluctuations of annual maximal altitude (mbsl) of water level (WL) in lake Kinneret: scatter plot (<bold>left</bold>), fractional polynomial regression (predicted value) (<bold>middle</bold>) and lowess smoother trend of changes (<bold>right</bold>).</p> Full article ">Figure 5
<p>Temporal (1940–2022) fluctuations of annual minimal altitude (mbsl) of water level (WL) in Lake Kinneret: scatter plot (<bold>left</bold>), fractional polynomial regression (predicted value) (<bold>middle</bold>) and lowess smoother trend of changes (<bold>right</bold>).</p> Full article ">Figure 6
<p>Temporal (1940–2022) fluctuations of annual water level increase (m) in Lake Kinneret: scatter plot (<bold>left</bold>), fractional polynomial regression (predicted value) (<bold>middle</bold>) and lowess smoother trend of changes (<bold>right</bold>).</p> Full article ">Figure 7
<p>Temporal (1940–2022) fluctuations of annual water level decline (m) in Lake Kinneret: scatter plot (<bold>left</bold>), fractional polynomial regression (predicted value) (<bold>middle</bold>) and lowess smoother trend of changes (<bold>right</bold>).</p> Full article ">Figure 8
<p>Hypsometric curve of Lake Kinneret: Lake volume (mcm: 10<sup>6</sup> m<sup>3</sup>) vs. depth (<bold>left</bold>) and lake water surface (km<sup>2</sup>) (<bold>right</bold>) (TAHAL 1961).</p> Full article ">Figure 9
<p>Theoretical (Synthetic values) hypsometric curve in shallow lakes: surface change vs. depth (<bold>left</bold>) gradient is flat in the depth and steeper in the shallows. Vice versa of volume changes (<bold>right</bold>): volume change vs. depth-gradient is flat in the shallows and steeper in the depth.</p> Full article ">
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