Conceptualization of the indirect link between climate variability and lake water level using conditional heteroscedasticity

Abstract. A total of 13 satellite missions have been launched since 1985, with different types of radar altimeters on board. This study intends to make a comprehensive evaluation of historic and currently operational satellite radar altimetry missions for lake water level retrieval over the same set of lakes and to develop a strategy for constructing consistent long-term water level records for inland lakes at global scale. The lake water level estimates produced by different retracking algorithms (retrackers) of the satellite missions were compared with the gauge measurements over 12 lakes in four countries. The performance of each retracker was assessed in terms of the data missing rate, the correlation coefficient r, the bias, and the root mean square error (RMSE) between the altimetry-derived lake water level estimates and the concurrent gauge measurements. The results show that the model-free retrackers (e.g., OCOG/Ice-1/Ice) outperform the model-based retrackers for most of the missions, particularly over small lakes. Among the satellite altimetry missions, Sentinel-3 gave the best results, followed by SARAL. ENVISAT has slightly better lake water level estimates than Jason-1 and Jason-2, but its data missing rate is higher. For small lakes, ERS-1 and ERS-2 missions provided more accurate lake water level estimates than the TOPEX/Poseidon mission. In contrast, for large lakes, TOPEX/Poseidon is a better option due to its lower data missing rate and shorter repeat cycle. GeoSat and GeoSat Follow-On (GFO) both have an extremely high data missing rate of lake water level estimates. Although several contemporary radar altimetry missions provide more accurate lake level estimates than GFO, GeoSat was the sole radar altimetry mission, between 1985 and 1990, that provided the lake water level estimates. With a full consideration of the performance and the operational duration, the best strategy for constructing long-term lake water level records should be a two-step bias correction and normalization procedure. In the first step, use Jason-2 as the initial reference to estimate the systematic biases with TOPEX/Poseidon, Jason-1, and Jason-3 and then normalize them to form a consistent TOPEX/Poseidon–Jason series. Then, use the TOPEX/Poseidon–Jason series as the reference to estimate and remove systematic biases with other radar altimetry missions to construct consistent long-term lake water level series for ungauged lakes.

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Lake water level response to drought in a lake-rich region explained by lake and landscape characteristics

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2019-0270 ◽

2020 ◽

Vol 77 (11) ◽

pp. 1836-1845

Author(s):

K. Martin Perales ◽

Catherine L. Hein ◽

Noah R. Lottig ◽

M. Jake Vander Zanden

Keyword(s):

Climate Change ◽

Water Level ◽

Lake Water ◽

Water Levels ◽

Water Level Fluctuations ◽

Lake Area ◽

Lake Water Level ◽

Landscape Characteristics ◽

Lake Ecology ◽

Coarse Woody Habitat

Climate change is altering hydrologic regimes, with implications for lake water levels. While lakes within lake districts experience the same climate, lakes may exhibit differential climate vulnerability regarding water level response to drought. We took advantage of a recent drought (∼2005–2010) and estimated changes in lake area, water level, and shoreline position on 47 lakes in northern Wisconsin using high-resolution orthoimagery and hypsographic curves. We developed a model predicting water level response to drought to identify characteristics of the most vulnerable lakes in the region, which indicated that low-conductivity seepage lakes found high in the landscape, with little surrounding wetland and highly permeable soils, showed the greatest water level declines. To explore potential changes in the littoral zone, we estimated coarse woody habitat (CWH) loss during the drought and found that drainage lakes lost 0.8% CWH while seepage lakes were disproportionately impacted, with a mean loss of 40% CWH. Characterizing how lakes and lake districts respond to drought will further our understanding of how climate change may alter lake ecology via water level fluctuations.

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Bathymetry Development and Flow Analyses Using Two-Dimensional Numerical Modeling Approach for Lake Victoria

Fluids ◽

10.3390/fluids4040182 ◽

2019 ◽

Vol 4 (4) ◽

pp. 182 ◽

Cited By ~ 1

Author(s):

Seema Paul ◽

Jesper Oppelstrup ◽

Roger Thunvik ◽

John Mango Magero ◽

David Ddumba Walakira ◽

...

Keyword(s):

Water Level ◽

Lake Water ◽

Lake Victoria ◽

State Level ◽

Linear Measurement ◽

Two Dimensional ◽

Mean Flow ◽

Lake Water Level ◽

Outflow Boundary Condition ◽

Outflow Boundary

This study explored two-dimensional (2D) numerical hydrodynamic model simulations of Lake Victoria. Several methods were developed in Matlab to build the lake topography. Old depth soundings taken in smaller parts of the lake were combined with more recent extensive data to produce a smooth topographical model. The lake free surface numerical model in the COMSOL Multiphysics (CM) software was implemented using bathymetry and vertically integrated 2D shallow water equations. Validated by measurements of mean lake water level, the model predicted very low mean flow speeds and was thus close to being linear and time invariant, allowing long-time simulations with low-pass filtered inflow data. An outflow boundary condition allowed an accurate simulation to achieve the lake’s steady state level. The numerical accuracy of the linear measurement of lake water level was excellent.

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Robustness lake water level prediction using the search heuristic-based artificial intelligence methods

ISH Journal of Hydraulic Engineering ◽

10.1080/09715010.2018.1424568 ◽

2018 ◽

Vol 25 (3) ◽

pp. 316-324 ◽

Cited By ~ 10

Author(s):

Amir Hossein Zaji ◽

Hossein Bonakdari

Keyword(s):

Artificial Intelligence ◽

Water Level ◽

Lake Water ◽

Search Heuristic ◽

Lake Water Level ◽

Artificial Intelligence Methods ◽

Water Level Prediction

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Statistical analysis of rainfall and streamflow time series in the Lake Tana Basin, Ethiopia

Journal of Water and Climate Change ◽

10.2166/wcc.2018.008 ◽

2018 ◽

Vol 11 (1) ◽

pp. 258-273 ◽

Cited By ~ 2

Author(s):

Tibebe B. Tigabu ◽

Georg Hörmann ◽

Paul D. Wagner ◽

Nicola Fohrer

Keyword(s):

Time Series ◽

Water Level ◽

Lake Water ◽

Land Use Changes ◽

Annual Rainfall ◽

Lake Tana ◽

Agricultural Water Management ◽

Lake Water Level ◽

Rainfall Variation ◽

Sample Data

Abstract This research focuses on the statistical analyses of hydrometeorological time series in the basin of Lake Tana, the largest freshwater lake in Ethiopia. We used autocorrelation, cross-correlation, Mann–Kendall, and Tukey multiple mean comparison tests to understand the spatiotemporal variation of the hydrometeorological data in the period from 1960 to 2015. Our results show that mean annual streamflow and the lake water level are varying significantly from decade to decade, whereas the mean annual rainfall variation is not significant. The decadal mean of the lake outflow and the lake water level decreased between the 1990s and 2000s by 11.34 m3/s and 0.35 m, respectively. The autocorrelation for both rainfall and streamflow were significantly different from zero, indicating that the sample data are non-random. Changes in streamflow and lake water level are linked to land use changes. Improvements in agricultural water management could contribute to mitigate the decreasing trends.

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Estimation of the Change in Lake Water Level by Artificial Intelligence Methods

Water Resources Management ◽

10.1007/s11269-014-0773-1 ◽

2014 ◽

Vol 28 (13) ◽

pp. 4747-4763 ◽

Cited By ~ 40

Author(s):

Meral Buyukyildiz ◽

Gulay Tezel ◽

Volkan Yilmaz

Keyword(s):

Artificial Intelligence ◽

Water Level ◽

Lake Water ◽

Lake Water Level ◽

Artificial Intelligence Methods

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Zooplankton species richness and abiotic conditions in Thirlmere Lakes, New South Wales, Australia, with reference to water-level fluctuations

Australian Zoologist ◽

10.7882/az.2020.020 ◽

2020 ◽

Vol 41 (1) ◽

pp. 107-123

Author(s):

Tsuyoshi Kobayashi ◽

Martin Krogh ◽

Hiroyuki ◽

Russell J. Shiel ◽

Hendrik Segers ◽

...

Keyword(s):

Species Richness ◽

Water Level ◽

Lake Water ◽

New South ◽

Water Levels ◽

Water Level Fluctuations ◽

Zooplankton Species ◽

Abiotic Conditions ◽

Lake Water Level ◽

South Wales

Water-level fluctuations can have significant effects on lake biological communities. Thirlmere Lakes are a group of five interconnected lakes located near Sydney. Water levels in Thirlmere Lakes have fluctuated over time, but there has been a recent decline that is of significant concern. In this study, we examined over one year the species composition and richness of zooplankton (Rotifera, Cladocera and Copepoda) and abiotic conditions in Lakes Nerrigorang and Werri Berri, two of the five Thirlmere lakes, with reference to lake water level. We recorded a total of 66 taxa of zooplankton, with the first report of the rotifer Notommata saccigera from Australia, and the first report of the rotifers Keratella javana, Lecane rhytida and Rousseletia corniculata from New South Wales. There was a marked difference in abiotic conditions between the two lakes, with more variable conditions in Lake Nerrigorang. There was a significant positive correlation between zooplankton species richness and lake water level but only for Lake Nerrigorang. Although the two lakes are closely situated and thought to be potentially connected at high water levels, they show distinct ecological characters and the effect of water-level fluctuations on zooplankton species richness seems to differ between the lakes.

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