Lake Level Time Series Research Articles

Daily lake-level time series spectral analysis using EMD, VMD, EWT, and EFD

ABSTRACT This study investigates the dynamics of daily Urmia Lake level (ULL) changes using spectral analysis tools to discover fluctuating patterns in the ULL series. Therefore, in the present research, the empirical mode decomposition (EMD), variational mode decomposition (VMD), empirical wavelet transform (EWT), and empirical Fourier decomposition (EFD) were used to analyze the ULL signal. ULL series were decomposed into subseries, and the optimized outcome was used. All methods concluded that the ULL series has a steep downward trend. Signal reconstruction was performed, and it was inferred that EFD could not estimate the ULL series appropriately and had root-mean-square error (RMSE) = 12.26. Different from EFD, other methods performed better signal construction according to RMSE and error analysis. The mode-mixing issue was the last step in verifying the capabilities of signal-analyzing methods. Based on the power spectral density (PSD), it was seen that EMDs had mode-mixing problems and limitations in signal decomposition, whereas VMD and EWT did not have these issues. Results demonstrated that the present study has some limitations. Overall, it was concluded that VMD performed better in terms of RMSE, error analysis, reconstruction, mode-mixing problems, and PSD analysis while decomposing and extracting features from the ULL signal.

A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data

ABSTRACT Lake water levels are an important indicator of water balance and water cycles, and are essential for climate and environmental change studies and water resource evaluation. Currently, lake level measurements are scarce or inconsistent throughout the country, and traditional gauge measurements of many lakes are not feasible, so satellite altimetry is a vital alternative to gauge lake levels. However, the accuracy and sampling frequency of lake level time series are usually low because of time and space coverage limitations; therefore, it is necessary to utilize multi-altimeter data to monitor lake levels and obtain lake level changes over long time series. In this study, we extracted the water level changes in 988 lakes (>10 km2) in China between 2002 and 2023 based on ICESat/-2, Cryosat-2, Jason-1/2/3, and Sentinel-3A/3B altimetry data using waveform retracking, lake level extraction, lake level time series construction, the fusion of multi-altimeter lake level time series, and outlier removal. A total of 55% of the lakes in this dataset have been monitored for more than 10 years, and 34% have more than 12 times the annual average water level monitoring. At the same time, in situ data from 21 lakes were used for validation, and the average root mean square error (RMSE) for each of the datasets of ICESat/-2, Cryosat-2, Jason-1/2/3, and Sentinel-3A/3B versus the in situ lake levels are 0.223 m, 0.163 m, 0.207 m, 0.596 m, 0.295 m, 0.275 m, 0.243 m, and 0.317 m, respectively, and the mean RMSE of the fused lake levels reaches 0.332 m. During the monitoring period, the water levels in Chinese lakes generally increased. The overall annual average rate of change at the 20 and 10-year scales was 0.123 m/a and 0.151 m/a, respectively, among which the overall water levels in large lakes increased significantly. The lakes with a faster rate of decline in the water level were primarily small. The water storage in each lake region in China shows an upward trend, with the most significant increase in the Tibetan Plateau region, where the average annual water level change rate has remained above 0.15 m/a over the past two decades. This dataset has high spatiotemporal coverage and accuracy and can support the estimation of changes in lake water storage, analysis of lake level trends, plateau flooding, and the relationship between lake ecosystems and water resources.

Accurate estimation of lake levels by the spatio-temporal modeling of satellite altimetry data

Satellite altimetry is currently one of the most widely used techniques for monitoring lake water levels. Raw altimetry measurements are generally delivered in an ellipsoid height system, while lake heights are finally required in a local level height system to ensure the lake surface being strictly flat under the physical constraints of both gravity and non-gravity factors. Therefore, there is always a need for height system transformation of altimetry data. Many of the previous studies have used orthometric heights in an approximate manner in the altimetric data processing. The geoid errors, together with other error sources, can introduce significant biases into water level estimations. In this paper, we propose a spatio-temporal regression method to calculate orthometric height to level height (O2L) differences, which can be used to yield accurate lake levels from satellite altimetry data. In the experiments conducted in this study, we chose ICESat-2 data to train the O2L bias correction model, and evaluated the algorithm performance on two large lakes in central Asian (Issyk-Kul and Baikal lakes) and one lake in North America (Lake Michigan). The experimental results indicate that the spatio-temporal modeling method performs better than the traditional spatial modeling method in terms of regression accuracy, and the water levels derived from the new method are much closer to in-situ gauging measurements. Influenced by O2L bias, there is often a jitter phenomenon in the time series of lake levels, and the results of different satellites are, accordingly, not mutually consistent. A visual analysis reveals that the spatio-temporal modeling method can greatly reduce the high-frequency bias.

Detecting Lake Level Change From 1992 to 2019 of Zhari Namco in Tibet Using Altimetry Data of TOPEX/Poseidon and Jason-1/2/3 Missions

Zhari Namco, a large lake in the Tibetan Plateau (TP), is sensitive to climate and environmental change. However, it is difficult to retrieve accurate and continuous lake levels for Zhari Namco. A robust strategy, including atmospheric delay correction, waveform retracking, outlier deletion, and inter-satellite adjustment, is proposed to generate a long-term series of lake levels for Zhari Namco through multi-altimeter data. Apparent biases are found in troposphere delay correction from different altimeter products and adjusted using an identical model. The threshold (20%) algorithm is employed for waveform retracking. The two-step method combining a sliding median filter and 2σ criterion is used to eliminate outliers. Tandem mission data of altimeters are used to estimate inter-satellite bias. Finally, a 27-year-long lake level time series of Zhari Namco are constructed using the TOPEX/Poseidon-Jason1/2/3 (T/P-Jason1/2/3) altimeter data from 1992 to 2019, resulting in an accuracy of 10.1 cm for T/P-Jason1/2/3. Temperature, precipitation, lake area, equivalent water height, and in situ gauge data are used for validation. The correlation coefficient more than 0.90 can be observed between this result and in situ gauge data. Compared with previous studies and existing database products, our method yields sequences with the best observational quality and the longest continuous monitoring in Zhari Namco. The time series indicates that the lake level in Zhari Namco has increased by ∼ 5.7 m, with an overall trend of 0.14 ± 0.01 m/yr, showing a fluctuating rate (1992–1999: −0.25 ± 0.05 m/yr, 2000–2008: 0.26 ± 0.04 m/yr, 2009–2016: −0.05 ± 0.03 m/yr, 2017–2019: 1.34 ± 0.34 m/yr). These findings will enhance the understanding of water budget and the effect of climate change in the TP.

Retracking Cryosat-2 Data in SARIn and LRM Modes for Plateau Lakes: A Case Study for Tibetan and Dianchi Lakes

This paper estimates lake level variations over two small and adjacent lakes in the Tibetan plateau (TP), namely Gemang Co and Zhangnai Co, as well as the inland Dianchi Lake in China using CryoSat-2 SARIn-mode and LRM 20-Hz waveforms over the period of 2011–2018. Different retrackers and a dedicated data editing procedure have been used to process CryoSat-2 data for determining the lake level time series. The lake level estimations are indirectly validated against those from Jason-2 in TP and from in situ data in Dianchi Lake, both showing good agreement with strong correlation coefficients >0.74. The results of this paper suggest that the official ICE retracker for LRM data and APD-PPT retracker for SARIn-mode waveforms are the most appropriate retrackers over Dianchi Lake and TP lakes, respectively. The trend estimates of the time series derived by both retrackers are 61.0 ± 10.8 mm/yr for Gemang Co and Zhangnai Co in TP, and 30.9 ± 64.9 mm/yr for Dianchi Lake, indicating that the lake levels over three lakes were continuously rising over the study period. The results of this study show that CryoSat-2 SARIn-mode data can be used for monitoring many small lakes that have not been measured by other altimetry missions in TP.

Improved Lake Level Estimation From Radar Altimeter Using an Automatic Multiscale-Based Peak Detection Retracker

Satellite radar altimetry is an important technique for monitoring the water levels of oceans and inland water bodies, especially in areas where in-situ data are sparse or nonexistent. This study presented an automatic multiscale-based peak detection retracker (AMPDR). The retracker can extract a robust threshold level for each track, then the stable lake level can be obtained from the multipeak waveforms using a shortest-path algorithm. Additionally, the retracker can be used for mountain lakes and for flat lakes, and is also suitable for many kinds of altimetry data, such as those of Cryosat-2, Sentinel-3, and Jason-2/3. To validate the lake levels derived by the AMPDR retracker, the in-situ gauge data of seven lakes in the Tibetan Plateau and two lakes in a flat area are used. Moreover, seven existing retrackers are compared to evaluate the performance of the proposed AMPDR retracker. The results suggest that AMPDR can efficiently process many complex multipeak waveforms, and the AMPDR has the lowest mean of all track standard deviations over all lakes. The root-mean-squared error (RMSE) of the lake level time series obtained using AMPDR is the lowest over several lakes: The mean RMSEs of all the lakes overpassed by Cryosat-2, Sentinel-3, and Jason-2/3 are 0.149, 0.139, and 0.181 m, respectively. The AMPDR retracker is easy to implement, computationally efficient, and can give a height estimate for even the most contaminated waveforms.

Monitoring lake level changes in China using multi-altimeter data (2016–2019)

Changes in lake levels are an important indicator of water balance and climate change. However, it is not feasible to perform continuous in situ measurements for a large number of lakes in China. Satellite altimetry has been used successfully to monitor water levels of inland lakes in recent years. In this study, through waveform retracking, lake level estimation, lake level time series construction, multi-altimeter time series fusion and outlier detection, changes in water level of 340 lakes (>10 km2) in China between 2016 and 2019 were monitored using multi-altimeter data from Cryosat-2, Jason-2, Jason-3, and Sentinel-3A. Overall, the water levels of these lakes showed a mean increasing trend of 0.343 m/yr. The water levels of 274 lakes have a mean increasing trend of 0.465 m/yr and those of 66 showed a mean decreasing trend of −0.164 m/yr. The annual rate of change of lake water levels increased from south to north and decreased from west to east in terms of spatial distribution. The changes in lake level varied among different basins owing to their high spatial heterogeneity: most lakes in the Songhua and Liao River Basin and Yellow River Basin showed an increasing trend; all lakes in the Huai River Basin were relatively steady; 14 of 19 lakes in the Yangtze River Basin showed a decreasing trend; the great majority of lakes in the Inland River Basin showed an increasing trend; and the lakes located in the southeastern Qiangtang Basin, near the Nyainqen Tanglha Mountain, showed a decreasing trend. In addition, some lakes in the western Qiangtang Basin also showed a decreasing trend. Factor analysis indicated that the factors driving changes in lake level varied among different areas. However, temperature changes in autumn and winter were significantly correlated with changes in lake level.

Combining multisource satellite data to estimate storage variation of a lake in the Rift Valley Basin, Ethiopia

Integration of remote sensing data sets from multiple satellites is tested to simulate water storage variation of Lake Ziway, Ethiopia for the period 2009-2018. Sixty Landsat ETM+/OLI images served to trace temporal variation of lake surface area using a water extraction index. Time series of lake levels were acquired from two altimetry databases that were validated by in-situ lake level measurements. Coinciding pairs of optical satellite based lake surface area and radar altimetry based lake levels were related through regression and served for simulating lake storage variation. Indices for extracting lake surface area from images showed 91–99 % overall accuracy. Lake water levels from the altimetry products well agreed to in-situ lake level measurements with R2 = 0.92 and root mean square error of 11.9 cm. Based on this study we conclude that integrating satellite imagery and radar altimetry is a viable approach for frequent and accurate monitoring of lake water volume variation and for long-term change detection. Findings indicate water level reduction (4 cm/annum), surface area shrinkage (0.08km2/annum) and water storage loss (20.4Mm3/annum) of Lake Ziway (2009–2018).

Assessment of Cryosat-2 and SARAL/AltiKa altimetry for measuring inland water and coastal sea level variations: A case study on Tibetan Plateau lake and Taiwan Coast

Satellite altimetry has been proven as an effective technology to accurately measure water level, ice elevation, and flat land surface changes since the 1990s. To overcome limitations of pulse-limited altimetry, new altimetric missions such as Cryosat-2 and Satellite with ARgos and AltiKa (SARAL/AltiKa), have been designed to have higher along-track spatial resolution to measure more accurately inland water levels for small water bodies, and coastal sea level changes. In this study, we evaluate the performance of Cryosat-2 low-resolution (LRM) and SARin modes and SARAL/AltiKa Ka-band data on two connected lakes in central Tibetan Plateau, and in the coastal region of Taiwan. Results are compared with in situ tide gauge data in Taiwan and altimetric lake level time series from the CNES Hydroweb database. Our results show that water level change trends observed by Cryosat-2 20-Hz retracked observations, the SARAL/AltiKa 40-Hz Ice-1 retracked data, and the Hydroweb measurements are consistent with the estimated water level trend of ∼0.30 m/y, during 2011–2017, and 2013–2015, for the Tibetan Migriggyangzham Co and Dorsoidong Co, respectively. For the coastal region, the performance of SARAL/AltiKa is better than that of Cryosat-2 LRM data in Taiwan. This finding demonstrates the superiority of the Ka-band over Ku-band radar altimetry.

Robust, Long-term Lake Level Change from Multiple Satellite Altimeters in Tibet: Observing the Rapid Rise of Ngangzi Co over a New Wetland

Satellite altimetry has been successfully applied to monitoring water level variation of global lakes. However, it is still difficult to retrieve accurate and continuous observations for most Tibetan lakes, due to their high altitude and rough terrain. Aiming to generate long-term and accurate lake level time series for the Tibetan lakes using multi-altimeters, we present a robust strategy including atmosphere delay corrections, waveform retracking, outlier removal and inter-satellite bias adjustment. Apparent biases in dry troposphere corrections from different altimeter products are found, and such correctios must be recalculated using the same surface pressure model. A parameter is defined to evaluate the performance of the retracking algorithm. The ICE retracker outperforms the 20% and 50% threshold retrackers in the case of Ngangzi Co, where a new wetland has been established. A two-step algorithm is proposed for outlier removal. Two methods are adopted to estimate inter-satellite bias for different cases of with and without overlap. Finally, a 25-year-long lake level time series of Ngangzi Co are constructed using the TOPEX/Poseidon-family altimeter data from October 1992 to December 2017, resulting in an accuracy of ~17 cm for TOPEX/Poseidon and ~10 cm for Jason-1/2/3. The accuracy of retrieved lake levels is on the order of decimeter. Because of no gauge data available, ICESat and SARAL data with the accuracy better than 7 cm are used for validation. A correlation more than 0.9 can be observed between the mean lake levels from TOPEX/Poseidon-family satellites, ICESat and SARAL. Compared to the previous studies and other available altimeter-derived lake level databases, our result is the most robust and has resulted in the maximum number of continuous samples. The time series indicates that the lake level of Ngangzi Co increased by ~8 m over 1998–2017 and changed with different rates in the past 25 years (-0.39 m/yr in 1992–1997, 1.03 m/yr in 1998–2002 and 0.32 m/yr in 2003–2014). These findings will enhance the understanding of water budget and the effect of climate change.

A reliable linear method for modeling lake level fluctuations

Accurate forecasting of lake level time series (LLTS) is an important but challenging problem with major economic, social and environmental implications. However, in recent years, the level of uncertainty in the existing LLTS forecast methods has increased significantly due to climate change, therefore, the need to develop more accurate models. The main research question for this study is whether it is necessary to use nonlinear methods in LLTS modeling or if linear methods can produce as accurate and reliable forecast tools. We introduce a new linear-based forecast method for LLTS using spectral analysis, seasonal standardization, and stochastic terms. The application of the new LLTS forecast method is tested on two case study Lakes, including the Van Lake, in Turkey and the Michigan-Huron Lake, in North America. A two-step preprocessing techniques based on standardization and differencing was used for the Van Lake, and spectral analysis and differencing was employed for the Michigan-Huron Lake. We then compared the accuracy and uncertainty of the proposed linear method with an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods. The uncertainty of the new linear LLTS forecast model was ±0.00455 and ±0.00264 for the Van Lake and the Michigan-Huron Lake, respectively, compared to ±0.00625 and ±0.00766 for the ANN and the ANFIS (respectively) at the Van Lake and ±0.00312 and ±0.00319 for the ANN and the ANFIS (respectively) at the Michigan-Huron Lake.

A study of Lake Erie seiche and low frequency water level fluctuations in the presence of surface ice

Coastal storms can initiate periodic low-frequency fluctuations of water level particularly in enclosed bodies of water. Such oscillations, known as seiches, can potentially result in an unexpected rise in water level leading to coastal flooding in low lying areas. Low frequency water level motions are also believed to contribute to long-term erosion of coasts. In this paper, low frequency motions of Lake Erie water levels are, analytically and numerically, studied for ice-free and ice-covered lake conditions. For the analytical study, first, a spectral analysis is performed on long-term hourly water level data collected over a 40-year period (1975–2015) at various water levels measuring stations around the lake. The frequencies of the lake's seiching modes are determined and the spatial variations of the energy corresponding to the low frequency fluctuations of the lake are investigated. Secondly, the seasonal variations of the seiching motions as well as the effects of surface ice on the low frequency oscillations of the lake level are assessed by analyzing the spectra and cross-spectra of the hourly water level data at various stations during winter and summer for ice-covered and ice-free conditions. The spatial variations of the spectral energy, phase lag and coherence for the low frequency motions are quantified for the winter and summer data. The lake's low frequency water level variations for a short-term extreme event, i.e. storm, are studied numerically. A historical winter storm taking place under ice-covered lake conditions is simulated using a coupled storm surge and spectral wave model (ADCIRC-SWAN). The model is forced by wind and pressure fields under a hypothetical ice-free conditions. The spectra of the low frequency motions for the simulated and measured lake level time-series are then compared. The spectral analysis of the Lake Erie long-term hourly water level data reveals distinct energy concentrations at the frequencies of approximately 1.7, 2.6, 4.1 and 5.8 cycle/day (CPD) which correspond to the first four modes of seiching oscillations. The energy corresponding to the low frequency motions is found to be at its highest level near Toledo, in the western basin, and lowest in Fairport, located at the nodal point for the first mode. The comparisons of the summer and winter spectra show that surface ice can suppress the low frequency oscillations. In general, the spectra of the lake levels during the summer are more peaked at the seiching modes than those of the winters when the lake is ice-covered. For the low frequency oscillations of the lake level following a short-term extreme event, higher energy levels are associated with the ice-free lake condition. This energy level is higher at the eastern and western ends of the lake, consistent with the findings for the winter and summer lake level oscillations. In general, the effect of surface ice on low frequency fluctuations of the lake is more pronounced in the western basin where typically more extensive surface ice is present during winters.

Monitoring changes in the water volume of Hulun Lake by integrating satellite altimetry data and Landsat images between 1992 and 2010

Lake level and volume are sensitive to climate change, and their changes can affect the sustainable utilization of regional water resources. Satellite radar/laser altimetry has effectively been used for monitoring water-level changes in recent years. In this study, satellite altimetry data and optical images were used to assess the changes in water level, area, and volume of Hulun Lake in north-eastern China. We derived a time series of lake levels for nearly two decades (1992 to 2010) from the altimetry data of two satellite sensors (Topex/Poseidon and Envisat RA-2); additionally, lake surface extent was extracted from Landsat TM/ETM+ images during the same period. The results indicate that the water level, area, and volume of Hulun Lake decreased over the past two decades. The water level shows a significant decrease (−0.36 m/year) of a total of −5.21 m from 1992 to 2010, specifically including a slight decrease (−0.4 m) during 1992 to 1999 and a sudden drop (−4.81 m) during 2000 to 2010. There has also been a consistent and significant reduction in lake area (−355.35 km 2 ) and volume (−12.92 km 3 ) . An integrated examination on changes in temperature, evaporation, precipitation, and runoff during 1992 to 2010 shows that the main changes in the Hulun Lake area are correlated with increasing temperature (0.47°C/year) and evaporation (13.61 mm/year) , as well as decreasing precipitation (−6.58 mm/year) and runoff (−1.04×10 8 m 3 /year) . Thus, we infer that climate warming is likely the main cause of the changes in water level, area, and volume of Hulun Lake. In addition, anthropogenic factors accelerate the degradation of the Hulun Lake wetland to some extent.

Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models

Accurate predicting of lake level fluctuations is essential and basic in water resources management for water supply purposes. The predicting of lake level is complicated because of it is affected by nonlinear hydrological processes. This paper applies integrated wavelet and auto regressive moving average (ARMA), adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) models for forecasting monthly lake level fluctuations. First, lake level time series is decomposed into low and high frequency components by using discrete wavelet transform. Then, each component is separately predicted by using ARMA, ANFIS and SVR models. Finally, the predicted components are summed to obtain estimated original lake level time series. The performance of the proposed WSVR (Wavelet-SVR), WANFIS (Wavelet-ANFIS) and WARMA (Wavelet-ARMA) models is compared with single ARMA, SVR and ANFIS models. Results show that the integrated models give better precision in forecasting lake levels in the study region when compared to single models. WSVR model is found to be slightly better than the other integrated models.

Self-affinity and surface-area-dependent fluctuations of lake-level time series

We performed power-spectral analyses on 133 globally distributed lake-level time series after removing annual variability. Lake-level power spectra are found to be power-law functions of frequency over the range of 20 to 27 , suggesting that lake levels are globally a -type noise. The spectral exponent ( ), i.e., the best-fit slope of the logarithm of the power spectrum to the logarithm of frequency, is a nonlinear function of lake surface area, indicating that lake size is an important control on the magnitude of water-level variability over the range of time scales we considered. A simple cellular model for lake-level fluctuations that reproduces the observed spectral-scaling properties is presented. The model (an adaptation of a surface-growth model with random deposition and relaxation) is based on the equations governing flow in an unconfined aquifer with stochastic inputs and outputs of water (e.g., random storms). The agreement between observation and simulation suggests that lake surface area, spatiotemporal stochastic forcing, and diffusion of the groundwater table are the primary factors controlling lake water-level variability in natural (unmanaged) lakes. Water-level variability is generally considered to be a manifestation of climate trends or climate change, yet our work shows that an input with short or no memory (i.e., weather) gives rise to a long-memory nonstationary output (lake water-level). This work forms the basis for a null hypothesis of lake water-level variability that should be disproven before water-level trends are to be attributed to climate.

Combined ICESat and CryoSat-2 Altimetry for Accessing Water Level Dynamics of Tibetan Lakes over 2003–2014

Long-term observations of lake water level are essential to our understanding of the evolution of Tibetan lake system. CryoSat-2 radar altimetry data over the Tibetan Plateau (2010–2014, P2) is used to extend lake level measurements from ICESat laser altimetry (2003–2009, P1). This study evaluates the performance of CryoSat-2 data by comparing with gauge-based water levels that are calibrated by ICESat-observed water level time series, and quantifies the uncertainty of water-level change rate estimates from satellite altimetry measurements. We completely investigate the 131 lakes that were observed by both ICESat and CryoSat-2. The mean change rate of water level for all of examined lakes in P2 (0.19 ± 0.03 m·year–1) is slightly lower than that (0.21 ± 0.02 m·year–1) observed in P1. The extended lake level time series also indicates that, in the past few years, lakes in the Northern Changtang (especially in Hol Xil) showed accelerated growth; and that the extensive lake level rises north to the Gangdise Mountains, during 2003–2009, were found dampened during the CryoSat-2 observation period. The spatio-temporal heterogeneity of precipitation observed from weather stations can be used to partly explain the observed temporal pattern of lake level changes over different sub-zones of the plateau.

Attribution of Decadal-Scale Lake-Level Trends in the Michigan-Huron System

This study disentangles causes of the Michigan-Huron system lake-level variability. Regional precipitation is identified as the primary driver of lake levels with sub-monthly time lag, implying that the lake-level time series can be used as a proxy for regional precipitation throughout most of the 1865–present instrumental record. Aside from secular variations associated with the Atlantic Multidecadal Oscillation, the lake-level time series is dominated by two near-decadal cycles with periods of 8 and 12 years. A combination of correlation analysis and compositing suggests that the 8-y cycle stems from changes in daily wintertime precipitation amounts associated with individual storms, possibly due to large-scale atmospheric flow anomalies that affect moisture availability. In contrast, the 12-y cycle is caused by changes in the number of instances, or frequency, of summertime convective precipitation due to a preferred upper-air trough pattern situated over the Great Lakes. In recent decades, the lake-level budget exhibited an abnormal—relative to the remainder of the instrumental record—evaporation-driven trend, likely connected to regional signatures of anthropogenic climate change. The latter effect must be accounted for, along with the effects of precipitation, when assessing possible scenarios of future lake-level variability.

Numerical Simulation and Forecasting of Water Level for Qinghai Lake Using Multi-Altimeter Data Between 2002 and 2012

Satellite radar altimetry has effectively been used for monitoring the water level change in recent years. In this study, Qinghai Lake was taken as an example to simulate and forecast water level using the multi-altimeter data from Envisat/RA-2, Cryosat-2/Siral, and Jason-1/Poseidon-2. First, using the robust least square method and system bias correction algorithms, abnormal water levels and the system bias were eliminated, and an accurate lake-level time series was obtained. Then, singular spectrum analysis (SSA) algorithms were used to extract the effective fluctuation signal from the accurate lake-level time series, and the accuracy of the altimetry data was improved. Based on an analysis of SSA algorithms' characteristics, comparison of the SSA-extracted fluctuation signal, and in-situ gauge measurements of Qinghai Lake, the accurate lake-level time series was affected by white noise of zero-mean and 0.5-m variance and colored noise of 0.2202-0.2473-m mean and 0.252-0.2800-m root-mean-square difference. After eliminating the white noise, the accuracy of the altimeter data reached the decimeter level in inland lake monitoring. Next, the SSA-extracted fluctuation signal was decomposed into linear composition, periodic components, and a residual component, and a combined linear-periodic-residual model was established using simple regression, a trigonometric function, and autoregressive-moving-average models. Using the model, the water level change of Qinghai Lake was simulated and forecasted to 2 years, with its accuracy reaching the decimeter level. The experiences of this study can provide an effective reference for the other lakes.

Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models

In this study various ANN and ANFIS models were developed to forecast the lake level fluctuations in Lake Urmia in northwest of Iran. In addition to the time series of lake levels, the time series of three most effective variables in the water budget of the lake namely, the rainfall, evaporation and inflow were also used to find the best input variables to the models. Furthermore the uncertainty due to the error in measuring the hydrological variables and also the uncertainty in the outputs of ANN and ANFIS models which stems from their sensitivity to the training sets used to train these models and also the initial configuration before commencement of training were estimated. Comparing the outputs and confidence intervals of the two types of models it was found that the results of ANFIS model are superior to those of ANN’ in that they are both more accurate and with less uncertainty.

Statistical Changes of Lake Stages in Two Rapidly Urbanizing Watersheds

Understanding changes induced by watershed urbanization is integral to developing an effective long-term management strategy. In this research, the authors study statistical changes of lake water surface levels in two urbanizing watersheds by evaluating serial change in time series parameters, autocorrelation, and variance as well as by developing a regression model to estimate weekly lake level fluctuations. The authors fit a seasonal integrated autoregressive moving average model to lake levels over subperiods of the data record to identify trends in parameter values. The authors fit the regression model with rainfall, lake stage, and temperature components for pre-urbanized and urbanized time periods to identify changes in baseflow. The lakes were located in Pasco County, Florida, USA and have not been significantly influenced by changes in rainfall patterns, pumping, surface water extraction or physical modification. Furthermore, the lakes exhibit consistent watershed urbanization and have sufficiently long and complete records. Based upon the research, the authors reach the following general conclusions about lakes in urbanizing watersheds: (1) the statistical structure of lake level time series is systematically altered, (2) in the absence of other forcing mechanisms, autocorrelation and baseflow decrease, (3) the presence of wetlands adjacent to lakes can offset the reduction in baseflow. These conclusions can be applied globally to similar regions that consist of lakes undergoing urbanization in flat, humid, shallow water table environments with wetlands. Furthermore, the methodology utilized can be applied at lakes in both similar and dissimilar environments to those studied in this research.