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Satellite altimetry has been proven to measure water levels in lakes and rivers effectively. The Sentinel-3A satellite is equipped with a dual-frequency synthetic aperture radar altimeter (SRAL), which allows for inland water levels to be measured with higher precision and improved spatial resolution. However, in regions at middle and high latitudes, where many lakes are covered by ice during the winter, the non-uniformity of the altimeter footprint can substantially impact the accuracy of water level estimates, resulting in abnormal readings when applying standard SRAL synthetic aperture radar (SAR) waveform retracking algorithms (retrackers). In this study, a modified method is proposed to determine the current surface type of lakes, analyzing changes in backscattering coefficients and brightness temperature. This method aligns with ground station observations and ensures consistent surface type classification. Additionally, a dual-threshold algorithm that addresses the limitations of the original bimodal algorithm by identifying multiple peaks without needing elevation correction is introduced. This innovative approach significantly enhances the precision of equivalent water level measurements for ice-covered lakes. The study retrieves and compares the water level data of nine North American lakes covered by ice from 2016–2019 using the dual-threshold and the SAMOSA-3 algorithm with in situ data. For Lake Athabasca, Cedar Lake, Great Slave Lake, Lake Winnipeg, and Lake Erie, the root mean square error (RMSE) of SAMOSA-3 is 39.58 cm, 46.18 cm, 45.75 cm, 42.64 cm, and 6.89 cm, respectively. However, the dual-threshold algorithm achieves an RMSE of 6.75 cm, 9.47 cm, 5.90 cm, 7.67 cm, and 5.01 cm, respectively, representing a decrease of 75%, 79%, 87%, 82%, and 27%, respectively, compared to SAMOSA-3. The dual-threshold algorithm can accurately estimate water levels in ice-covered lakes during winter. It offers a promising prospect for achieving long-term, continuous, and high-precision water level measurements for middle- and high-latitude lakes. [ABSTRACT FROM AUTHOR]

Satellite altimetry has been proven to measure water levels in lakes and rivers effectively. The Sentinel-3A satellite is equipped with a dual-frequency synthetic aperture radar altimeter (SRAL), which allows for inland water levels to be measured with higher precision and improved spatial resolution. However, in regions at middle and high latitudes, where many lakes are covered by ice during the winter, the non-uniformity of the altimeter footprint can substantially impact the accuracy of water level estimates, resulting in abnormal readings when applying standard SRAL synthetic aperture radar (SAR) waveform retracking algorithms (retrackers). In this study, a modified method is proposed to determine the current surface type of lakes, analyzing changes in backscattering coefficients and brightness temperature. This method aligns with ground station observations and ensures consistent surface type classification. Additionally, a dual-threshold algorithm that addresses the limitations of the original bimodal algorithm by identifying multiple peaks without needing elevation correction is introduced. This innovative approach significantly enhances the precision of equivalent water level measurements for ice-covered lakes. The study retrieves and compares the water level data of nine North American lakes covered by ice from 2016–2019 using the dual-threshold and the SAMOSA-3 algorithm with in situ data. For Lake Athabasca, Cedar Lake, Great Slave Lake, Lake Winnipeg, and Lake Erie, the root mean square error (RMSE) of SAMOSA-3 is 39.58 cm, 46.18 cm, 45.75 cm, 42.64 cm, and 6.89 cm, respectively. However, the dual-threshold algorithm achieves an RMSE of 6.75 cm, 9.47 cm, 5.90 cm, 7.67 cm, and 5.01 cm, respectively, representing a decrease of 75%, 79%, 87%, 82%, and 27%, respectively, compared to SAMOSA-3. The dual-threshold algorithm can accurately estimate water levels in ice-covered lakes during winter. It offers a promising prospect for achieving long-term, continuous, and high-precision water level measurements for middle- and high-latitude lakes.

Determining water level changes in lakes is significant for sustainable water supply planning, flood control, water resource management, and economic development with ecosystem sustainability. Trend analysis is a commonly used tool for detecting changes in hydrologic time series of quantities such as lake level, precipitation and temperature. Trend analysis of meteorological variables is very important for assessing the long-term changes in lake levels. This study examines long-term changes in the lakes in the Central Anatolia region of Turkey. Kızören and Timraş sinkhole lakes and Lakes Tuz and Beyşehir are some of the lakes located in this region. Changes in these lakes were examined along with changes in precipitation trends and human effects. The precipitation stations representing these lakes were determined using Thiessen polygons. In this study, the well-known nonparametric Mann–Kendall trend test (MK) was used. Before performing trend analysis, homogeneity and autocorrelation tests were applied for the time series. The standard normal homogeneity test (SNHT) was used to evaluate homogeneity. Trend analysis was performed before and after the change points the inhomogeneous series for the period of the homogeneous series. In addition, the modified Mann–Kendall method (MMK) was performed to examine the strong autocorrelations seen in the entire time series. The results showed that precipitation station data are homogeneous and lake levels are inhomogeneous. Trend analysis was performed by determining the change points for the inhomogeneous lakes. There were no significant trends after the change points for the Lake Tuz and Timraş sinkhole lake. These results are supported by the insignificant trends in precipitation stations. Contrary to the trend analysis results, the increase in the water level of Beyşehir Lake was determined to increment from the water transferred to the lake. The decrease in the water level of Kızören sinkhole was a result of anthropogenic effect rather than precipitation. The results confirm that lake levels have been affected by precipitation trends and anthropogenic effects. [ABSTRACT FROM AUTHOR]

Climate variability and change that have been intensifying since the 1970s are accompanied by changes in hydrology and water balance of inland aquatic systems. These changes, however, have not been well documented with regard to small and shallow aquatic systems that are more vulnerable. Changes in temperature, rainfall and wind speed around Lakes Wamala and Kawi (Uganda) were examined to provide insight on how the increasing variability and change in climate affect water balance and lake water levels. Around Lake Wamala, average air temperature has increased by 0.018°C y−1since 1980. Rainfall increased by 9.01 mm y−1since 1970 and accounted for 79.5% of the water gain during 2011 and 2013 period. However, the gains were exceeded by losses as a result of evaporation that accounted for >85% of the water loss. Despite the increase in rainfall, the mean lake depth of Lake Wamala decreased by 0.015 m y−1, apparently due to high evaporation rates. Around Lake Kawi, average air temperature has increased by 0.036°C y−1since 1980. Rainfall, however, has decreased, although it still dominates the water inflows, accounting for 83% of the total water gains. Interviews with local fishermen on Lake Kawi indicated that the lake shoreline has receded by ∼50 m over the last two decades. These results suggested that most of the water in small shallow lakes is gained through direct rainfall, but more is lost through evaporation. Therefore, increase in rainfall around these lakes is no longer sufficient to sustain normal lake water levels and lake surface area as long as temperature and wind speed, which enhance evaporation, continue increasing. This has implications for lake productivity processes and needs to be understood and incorporated in management of fisheries and the catchment areas as climate warming intensifies. [ABSTRACT FROM AUTHOR]

The growth of forecasting models has resulted in the development of an excellent model known as the support vector machine (SVM). SVMs can find a global optimal solution equipped with kernel functions. This research trains and tests the SVM network and constructs the support vector regression prediction model by using hydrologic data. Six hydrologic time series were calculated by different kernel functions (namely, linear, polynomial, radial basis function (RBF)), to determine which kernel is the more suitable hydrologic time series in practice. A new solution is presented to identify the good parameter (C; g) by using grid-search and cross-validation. Results prove that linear SVM is a superior model to polynomial and RBF and produced the most accurate results for modeling hydrologic time series behavior as complex hydrologic phenomena. The case study also shows that the calculation errors were correlated with data characteristics. More stable raw data will result in a more accurate result, whereas more random data will result in a more inaccurate result. Model performance could also be dependent on base data nonlinearity. [ABSTRACT FROM AUTHOR]

We surveyed and dated the former shorelines of one lake in the Shackleton Range and two lakes in the Pensacola Mountains, situated inland of the Weddell Sea embayment Antarctica between 80° and 85°S. These are amongst the highest latitude lakes in the Antarctic and are located in areas where there is little or no Holocene climate and hydrological information. Surveys of the lake shorelines show that past water levels have been up to 15.7, 17.7 and 69.5 m higher than present in the three study lakes. AMS radiocarbon dating of lake-derived macrofossils showed that there was a sustained period of higher water levels from approximately 4300 and until sometime after 2250 cal. yr BP. This is interpreted as being the result of an increased number of meltwater events and/or degree-days above freezing, relative to the present. The closest comparable ice cores from the Dominion Range in the Transantarctic Mountains (85°S, 166°E) and the Plateau Remote ice core on the continental East Antarctic Ice Sheet (84°S, 43°E) also provide some evidence of a warmer period beginning at c. 4000–3500 yr BP and ending after 2000–1500 yr BP, as does a synthesis of oxygen isotope data from five Antarctic ice cores. This suggests that the well-documented mid- to late-Holocene warm period, measured in many lake and marine sediments around the coast of Antarctica, extended into these regions of the continental interior. [ABSTRACT FROM AUTHOR]

Between 1975 and 2002, diminished breeding success of Ospreys was associated with drought and falling lake levels in the western half of our study area near the town of Loon Lake, west-central Saskatchewan. Only 46% of nest attempts were successful in the west compared to 72% in the east, producing 0.88 young per accessible nest in the west and 1.42 in the east. Breeding success was greater in the eastern half, where water levels were stable, in spite of increased human use of the resort lakes there. Our unique long-term Canadian data base results support Ogden's 1977 prediction that Osprey productivity may decrease when water levels drop and fish populations are reduced. [ABSTRACT FROM AUTHOR]

Summary: The use of echo state network (ESN) for dynamical system modeling is known as Reservoir Computing and has been shown to be effective for a number of applications, including signal processing, learning grammatical structure, time series prediction and motor/system control. However, the performance of Reservoir Computing approach on hydrological time series remains largely unexplored. This study investigates the potential of ESN or Reservoir Computing for long-term prediction of lake water levels. Great Lakes water levels from 1918 to 2005 are used to develop and evaluate the ESN models. The forecast performance of the ESN-based models is compared with the results obtained from two benchmark models, the conventional recurrent neural network (RNN) and the Bayesian neural network (BNN). The test results indicate a strong ability of ESN models to provide improved lake level forecasts up to 10-month ahead – suggesting that the inherent structure and innovative learning approach of the ESN is suitable for hydrological time series modeling. Another particular advantage of ESN learning approach is that it simplifies the network training complexity and avoids the limitations inherent to the gradient descent optimization method. Overall, it is shown that the ESN can be a good alternative method for improved lake level forecasting, performing better than both the RNN and the BNN on the four selected Great Lakes time series, namely, the Lakes Erie, Huron-Michigan, Ontario, and Superior. [Copyright &y& Elsevier]

Satellite radar altimetry has been widely used in the monitoring of water levels of lakes, rivers and wetlands in the past decades. The conventional pulse-limited radar altimeters have a relatively large ground footprint, which limits their capability to retrieve surface elevation information over small and medium-sized water bodies. A new generation of satellite radar altimeter system, a dual-frequency SAR radar altimeter (SRAL) onboard the Copernicus Sentinel-3 satellite, has produced densely sampled elevation measurements with a smaller footprint for the Earth's surfaces since June 2016, owing to the Delay-Doppler processing technique. Four standard SRAL SAR altimetry waveform retracking algorithms (known as retrackers) have been designed to retrieve elevation measurements for different types of surfaces: Ice-Sheet retracker for polar ice sheets, SAMOSA-3 retracker for open ocean and coastal zones, OCOG retracker for sea-ice margins, and Sea-Ice retracker for sea ice. In this research, we evaluated the performances of the Sentinel-3 SRAL SAR altimetry retrackers over lakes, particularly over seasonally ice-covered lakes in one hydrological cycle. For 15 lakes and reservoirs with different sizes and at varying latitudes in the northern hemisphere, we compared the lake water levels estimated by each of standard SRAL SAR retrackers against in - situ water level measurements for different seasons (a full hydrologic cycle) during 2016–2017. Our evaluation shows that Sea-Ice retracker was unable to provide continuous estimates of lake water levels, as a result of the high rate of missing data. Although the precision and relative accuracy of lake water level estimates from these three standard SRAL SAR retrackers are similar, the SAMOSA-3 retracker has the least bias in comparison with ground-based gauge measurements. When the lakes in the mid- and high-latitude regions were covered by ice in the winter season, these three standard SAR retrackers generated erroneous lake water level measurements, significantly lower than the true lake water levels recorded by in - situ gauge stations. The measurement errors of these three standard retrackers increase with the growth of the lake ice thickness. To address the negative effect of the seasonal ice cover, we developed a new bimodal correction algorithm. We demonstrate that our bimodal correction algorithm can retrieve the ice thickness and reliably estimate water levels for the ice-covered lakes in winter, hence enabling the generation of temporally consistent lake water level measurements throughout all seasons for lake hydrological analysis. • The performances of Sentinel-3 SRAL SAR retrackers over inland lakes are evaluated. • The mechanism of how lake ice affects SRAL SAR water level estimates is revealed. • We develop a bimodal algorithm to derive consistent water levels for ice-covered lake. [ABSTRACT FROM AUTHOR]

The paper identifies relationships between lake water levels and indices of macroscale atmospheric circulations: Arctic Oscillation (AO), North Atlantic Oscillation (NAO), East Atlantic (EA), and Scandinavian pattern (SCAND). Correlation coefficients between synchronous and asynchronous series of monthly water levels and 4 circulation indices were calculated. Based on Ward hierarchical grouping considering 156 correlation coefficients, the groups of lakes were designated due to the strength and term of relation of circulation indices with lake water levels. It was found that these links are not strong but noticeable. The strength of relationships varies in space and time, and the designated groups of lakes refer not only to the climatic diversity of the studied area, but also to some extent to the types of water levels regime. The observed relationships are the most important in the case of AO and NAO (particularly in winter period), and slightly weaker for EA and SCAND. The analysis used mean monthly water levels of 15 lakes in northern Poland from 1976–2015. [ABSTRACT FROM AUTHOR]