scholarly journals Densified multi-mission observations by developed optical water levels show marked increases in lake water storage and overflow floods on the Tibetan Plateau

2019 ◽  
Author(s):  
Xingdong Li ◽  
Di Long ◽  
Qi Huang ◽  
Pengfei Han ◽  
Fanyu Zhao ◽  
...  

Abstract. The Tibetan Plateau (TP) known as Asia's water towers is quite sensitive to climate change, reflected by changes in hydrological state variables such as lake water storage. Given the extremely limited ground observations on the TP due to the harsh environment and complex terrain, we exploited multisource remote sensing, i.e., multiple altimetric missions and Landsat archives to create dense time series (monthly and even higher such as 10 days on average) of lake water level and storage changes across 52 large lakes (> 100 km2) on the TP during 2000–2017 (the data set is available online with a DOI: https://doi.org/10.1594/PANGAEA.898411). Field experiments were carried out in two typical lakes to validate the remotely sensed results. With Landsat archives and partial altimetry data, we developed optical water levels that cover most of TP lakes and serve as an ideal reference for merging multisource lake water levels. The optical water levels show an uncertainty of ~ 0.1 m that is comparable with most altimetry data and largely reduce the lack of dense altimetric observations with systematic errors well removed for most of lakes. The densified lake water levels provided critical and accurate information on the long-term and short-term monitoring of lake water level and storage changes on the TP. We found that the total storage of the 52 lakes increased by 97.3 km3 at two stages, i.e., 6.68 km3/yr during 2000–2012 and 2.85 km3/yr during 2012–2017. The total overflow from Lake Kusai to Lake Haidingnuoer and Lake Salt during Nov 9–Dec 31 in 2011 was estimated to be 0.22 km3, providing critical information on lake overflow flood monitoring and prediction as the expansion of some TP lakes becomes a serious threat to surrounding residents and infrastructure.

2019 ◽  
Vol 11 (4) ◽  
pp. 1603-1627 ◽  
Author(s):  
Xingdong Li ◽  
Di Long ◽  
Qi Huang ◽  
Pengfei Han ◽  
Fanyu Zhao ◽  
...  

Abstract. The Tibetan Plateau (TP), known as Asia's water tower, is quite sensitive to climate change, which is reflected by changes in hydrologic state variables such as lake water storage. Given the extremely limited ground observations on the TP due to the harsh environment and complex terrain, we exploited multiple altimetric missions and Landsat satellite data to create high-temporal-resolution lake water level and storage change time series at weekly to monthly timescales for 52 large lakes (50 lakes larger than 150 km2 and 2 lakes larger than 100 km2) on the TP during 2000–2017. The data sets are available online at https://doi.org/10.1594/PANGAEA.898411 (Li et al., 2019). With Landsat archives and altimetry data, we developed water levels from lake shoreline positions (i.e., Landsat-derived water levels) that cover the study period and serve as an ideal reference for merging multisource lake water levels with systematic biases being removed. To validate the Landsat-derived water levels, field experiments were carried out in two typical lakes, and theoretical uncertainty analysis was performed based on high-resolution optical images (0.8 m) as well. The RMSE of the Landsat-derived water levels is 0.11 m compared with the in situ measurements, consistent with the magnitude from theoretical analysis (0.1–0.2 m). The accuracy of the Landsat-derived water levels that can be derived in relatively small lakes is comparable with most altimetry data. The resulting merged Landsat-derived and altimetric lake water levels can provide accurate information on multiyear and short-term monitoring of lake water levels and storage changes on the TP, and critical information on lake overflow flood monitoring and prediction as the expansion of some TP lakes becomes a serious threat to surrounding residents and infrastructure.


CATENA ◽  
2021 ◽  
Vol 200 ◽  
pp. 105177
Author(s):  
Shuangxiao Luo ◽  
Chunqiao Song ◽  
Pengfei Zhan ◽  
Kai Liu ◽  
Tan Chen ◽  
...  

2020 ◽  
Vol 77 (11) ◽  
pp. 1836-1845
Author(s):  
K. Martin Perales ◽  
Catherine L. Hein ◽  
Noah R. Lottig ◽  
M. Jake Vander Zanden

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.


2020 ◽  
Vol 41 (1) ◽  
pp. 107-123
Author(s):  
Tsuyoshi Kobayashi ◽  
Martin Krogh ◽  
Hiroyuki ◽  
Russell J. Shiel ◽  
Hendrik Segers ◽  
...  

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.


2016 ◽  
Vol 47 (S1) ◽  
pp. 69-83 ◽  
Author(s):  
Bing Li ◽  
Guishan Yang ◽  
Rongrong Wan ◽  
Xue Dai ◽  
Yanhui Zhang

Modeling of hydrological time series is essential for sustainable development and management of lake water resources. This study aims to develop an efficient model for forecasting lake water level variations, exemplified by the Poyang Lake (China) case study. A random forests (RF) model was first applied and compared with artificial neural networks, support vector regression, and a linear model. Three scenarios were adopted to investigate the effect of time lag and previous water levels as model inputs for real-time forecasting. Variable importance was then analyzed to evaluate the influence of each predictor for water level variations. Results indicated that the RF model exhibits the best performance for daily forecasting in terms of root mean square error (RMSE) and coefficient of determination (R2). Moreover, the highest accuracy was achieved using discharge series at 4-day-ahead and the average water level over the previous week as model inputs, with an average RMSE of 0.25 m for five stations within the lake. In addition, the previous water level was the most efficient predictor for water level forecasting, followed by discharge from the Yangtze River. Based on the performance of the soft computing methods, RF can be calibrated to provide information or simulation scenarios for water management and decision-making.


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