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Here, we present a 420‐year‐long winter lake level reconstruction for Lake Erie based primarily on temperature‐sensitive tree‐ring chronologies from Alaska, Oregon, and California. This well‐verified model explains more than 51% of the variance in winter lake levels over a 131‐year calibration period (1860–1990) and shows strong decadal fluctuations related to changes in sea surface temperatures in the North Pacific and the North Atlantic, which alternate in terms of their relative influence. Decadal variability is superimposed on a persistent secular lake level rise that began in the mid‐1900s coinciding with a growing influence of the Atlantic sector. In the context of the last 420 years, the instrumental period experienced extreme lake levels, with the lowest over the entire record during the Dustbowl and the highest in 2020. Fluctuations in Lake Erie water levels are primarily determined by climate, and their variability greatly impacts the region's infrastructure and ecosystems. Plain Language Summary Tree rings are annually resolved records of past climate. Here, we use 49 tree‐ring records from Western North America to reconstruct Lake Erie winter water levels back to CE 1600. The record shows that the lowest and one of the highest stands of the lake have occurred over the past 100 years with the lows recorded during the American Dustbowl years in the 1930s and the high in 2020. The analyses of this extended record can help to better anticipate future lake level changes in a warmer climate, which greatly affect infrastructure and ecosystems along Erie's shorelines. Key Points Western North American tree‐ring records are used to reconstruct changes in Lake Erie winter water levels for the past 420 years One of the highest lake levels in the past 420 years occurred in 2020, which is the highest value in the observational record. The lowest levels occurred during the 1930s Dust Bowl Correlation of lake levels with the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation reveal a shift from a strong North Pacific signal to one in the North Atlantic around 1960

Lake level change in the Tibetan Plateau is an important indicator for regional and global climate changes. We use altimeter data from Cryosat-2, SARAL, ICESat, and Jason-2 to detect lake level changes at different spatial and temporal resolutions over 2003 - 2017 (Jason-3 data in 2017 for validation). Cryosat-2's SARIn mode provides precise water level time series over 59 lakes. SARAL's waveforms are retracked to generate near monthly, high-quality measurements at 31 lakes. Jason-2 provides a reference for removing inter-altimeter biases, enabling coherent records over lakes with Jason-2 passes. After a decade of rise since the ICESat record of 2003, the lake levels of Nam Co, Selin Co, Ngangzi Co, and Chibuzhang Co became flat in 2014-2016 and started to fluctuate or decline after 2016. Such positive-flat-negative trends are consistent with the trend variations of mass change from Gravity Recovery and Climate Experiment (GRACE). SARAL detected persistent lake level declines over 2013-2016 in southern Tibet that may signify the onset of decadal reduced flows of the Yarlung Tsangpo and Brahmaputra River that could affect the water supply for their downstream regions in India and Bangladesh. Cryosat-2 and Jason-2 detected sudden lake level rises and falls around Zhuonai, Kusai, and Salt Lake associated with a 2011 lake outburst, which is confirmed by lake volume changes from two Landsat-7 images. With a careful processing and calibration, multiple altimeters allow for determining and cross-validating long-term and episodic lake level changes unachievable by a single altimeter.

Lake level changes are critical indicators of hydrological balance and climate change, yet long‐term monthly lake level reconstruction is challenging with incomplete or short‐term data. Data‐driven models, while promising, struggle with nonstationary lake level changes and complex dependencies on meteorological factors, limiting their applicability. Here, we introduce the Hydroformer, a frequency domain enhanced multi‐attention Transformer model designed for monthly lake level reconstruction, utilizing reanalysis data. This model features two innovative mechanisms: (a) Frequency‐Enhanced Attention (FEA) for capturing long‐term temporal dependence, and (b) Causality‐based Cross‐dimensional Attention (CCA) to elucidate how specific meteorological factors influence lake level. Seasonal and trend patterns of catchment meteorological factors and lake level are initially identified by a time series decomposition block, then independently learned and refined within the model. Tested across 50 lakes globally, the Hydroformer excelled in reconstruction periods ranging from half to three times the training‐test length. The model exhibited good performance even when training data missing rates were below 50%, particularly in lakes with significant seasonal fluctuations. The Hydroformer demonstrated robust generalization across lakes of varying sizes, from 10.11 to 18,135 km2, with median values for R2, MAE, MSE, and RMSE at 0.813, 0.313, 0.215, and 0.4, respectively. Furthermore, the Hydroformer outperformed data‐driven models, improving MSE by 29.2% and MAE by 24.4% compared to the next best model, the FEDformer. Our method proposes a novel approach for reconstructing long‐term water level changes and managing lake resources under climate change. Plain Language Summary Lake water levels, as key indicators of hydrologic dynamics and catchment balance, are vital for understanding climate impacts and managing water resources. However, the lack of continuous measurements for most global lakes, combined with the inability of traditional data‐driven models to effectively decipher complex interactions with catchment hydrological processes, leads to significant gaps in generalizability, accuracy, and reconstructive length. Given these limitations, accurate monthly reconstructions of lake level remain a persistent challenge. To address this, we develop Hydroformer, an innovative frequency domain enhanced multi‐attention Transformer model, utilizing reanalysis data for monthly lake level reconstruction. It employs two innovative attention mechanisms: Frequency‐Enhanced Attention for capturing long‐term temporal dependencies and Causality‐based Cross‐dimensional Attention for cross‐dimensional causal dependencies between catchment meteorological factors and lake level. Through a decomposition block, the model efficiently recognizes and refines inherent seasonal and trend patterns, leading to a comprehensive understanding of lake behaviors. Through testing on 50 global lakes, the Hydroformer has exhibited exceptional performance in reconstructing water levels for lakes ranging from 10.11 to 18,135 km2, adeptly handling short‐term, long‐term, and varying proportions of data gaps. It notably outperforms supervised data‐driven models. This positions it as a vital instrument for monthly lake level reconstruction, showcasing the power of integrating advanced artificial intelligence techniques in hydrological modeling. Key Points A novel frequency domain enhanced multi‐attention Transformer model, Hydroformer, has been built for reconstructing monthly lake level using reanalysis data The model accurately extends reconstructions 2–3 times the training data length, excelling with less than 50% missing training data Hydroformer surpasses advanced AI‐based models, improving MSE and MAE by over 20% and demonstrating strong generalization across lakes of varying sizes

Future hydroclimate change is expected to generally follow a wet-get-wetter, dry-get-drier (WWDD) pattern, yet key uncertainties remain regionally and over land. It has been previously hypothesized that lake levels of the Last Glacial Maximum (LGM) could map a reverse analog to future hydroclimate changes due to reduction of CO2 levels at this time. Potential complications to this approach include, however, the confounding effects of factors such as the Laurentide Ice Sheet and lake evaporation changes. Using the ensemble output of six coupled climate models, lake energy and water balance models, an atmospheric moisture budget analysis, and additional CO2 sensitivity experiments, we assess the effectiveness of the LGM as a reverse analog for future hydroclimate changes for a transect from the drylands of North America to southern South America. The model ensemble successfully simulates the general pattern of lower tropical lake levels and higher extratropical lake levels at LGM, matching 82% of the lake proxy records. The greatest model-data mismatch occurs in tropical and extratropical South America, potentially as a result of underestimated changes in temperature and surface evaporation. Thermodynamic processes of the mean circulation best explain the direction of lake changes observed in the proxy record, particularly in the tropics and Pacific coasts of the extratropics, and produce a WWDD pattern. CO2 forcing alone cannot account for LGM lake level changes, however, as the enhanced cooling from the Laurentide ice sheet appears necessary to generate LGM dry anomalies in the tropics and to deepen anomalies in the extratropics. LGM performance as a reverse analog is regionally dependent as anti-correlation between LGM and future P − E is not uniformly observed across the study domain.

Heavy rainfall in eastern Africa between late 2019 and mid 2020 caused devastating floods and landslides throughout the region. These rains drove the levels of Lake Victoria to a record-breaking maximum in the second half of May 2020. The combination of high lake levels, consequent shoreline flooding, and flooding of tributary rivers caused hundreds of casualties and damage to housing, agriculture, and infrastructure in the riparian countries of Uganda, Kenya, and Tanzania. Media and government reports linked the heavy precipitation and floods to anthropogenic climate change, but a formal scientific attribution study has not been carried out so far. In this study, we characterize the spatial extent and impacts of the floods in the Lake Victoria basin and then investigate to what extent human-induced climate change influenced the probability and magnitude of the record-breaking lake levels and associated flooding by applying a multi-model extreme event attribution methodology. Using remote-sensing-based flood mapping tools, we find that more than 29 000 people living within a 50 km radius of the lake shorelines were affected by floods between April and July 2020. Precipitation in the basin was the highest recorded in at least 3 decades, causing lake levels to rise by 1.21 m between late 2019 and mid 2020. The flood, defined as a 6-month rise in lake levels as extreme as that observed in the lead-up to May 2020, is estimated to be a 63-year event in the current climate. Based on observations and climate model simulations, the best estimate is that the event has become more likely by a factor of 1.8 in the current climate compared to a pre-industrial climate and that in the absence of anthropogenic climate change an event with the same return period would have led lake levels to rise by 7 cm less than observed. Nonetheless, uncertainties in the attribution statement are relatively large due to large natural variability and include the possibility of no observed attributable change in the probability of the event (probability ratio, 95 % confidence interval 0.8-15.8) or in the magnitude of lake level rise during an event with the same return period (magnitude change, 95 % confidence interval 0-14 cm). In addition to anthropogenic climate change, other possible drivers of the floods and their impacts include human land and water management, the exposure and vulnerability of settlements and economic activities located in flood-prone areas, and modes of climate variability that modulate seasonal precipitation. The attribution statement could be strengthened by using a larger number of climate model simulations, as well as by quantitatively accounting for non-meteorological drivers of the flood and potential unforced modes of climate variability. By disentangling the role of anthropogenic climate change and natural variability in the high-impact 2020 floods in the Lake Victoria basin, this paper contributes to a better understanding of changing hydrometeorological extremes in eastern Africa and the African Great Lakes region.

Recently-developed Holocene lake-level reconstructions from the Rocky Mountains offer a quantitative target for testing the skill of state-of-the-art climate system models in simulating hydroclimate change. Here, we use a combination of hydrologic models of catchment streamflow, lake energy balance, and lake water balance to simulate lake level at Little Windy Hill Pond (LWH) in the Medicine Bow Range of Wyoming for a period of severe drought during the mid-Holocene (MH; approximately 6000 years ago). Using Coupled Model Intercomparison Project (CMIP5) output to drive our hydrologic models, we find that none of our simulations reproduce the significantly lowered lake levels at LWH during the MH. Rather, simulated hydroclimate changes for the MH are modest (< 10% reductions in precipitation and streamflow and generally 10–30% increases in lake evaporation), and LWH lake-level changes are buffered by the large volume of snowmelt runoff that the lake receives. Only when winter precipitation is approximately halved in sensitivity experiments do water inputs to the lake become small enough that lake level can be significantly drawn down by year-over-year negative water balances. Possible explanations for the model-data mismatch could lie in the realism of our hydrological modeling framework or in the accuracy of the CMIP5 output, the latter having important implications for projections of future drying in western North America.

The Ross Sea I glaciation, marked by the northward advance of the Ross Ice Sheet (RIS) in the Ross Sea, east Antarctica, corresponds with the last major expansion of the West Antarctic Ice Sheet during the last glacial period. During its advance, the RIS was grounded along the southern Victoria Land coast, completely blocking the mouths of several of the McMurdo Dry Valleys (MDVs). Several authors have proposed that very large paleolakes, proglacial to the RIS, existed in many of the MDVs. Studies of these large paleolakes have been key in the interpretation of the regional landscape, climate, hydrology, and glacier and ice sheet movements. By far the most studied of these large paleolakes is Glacial Lake Washburn (GLW) in Taylor Valley. Here, we present a comprehensive review of literature related to GLW, focusing on the waters supplying the paleolake, signatures of the paleolake itself, and signatures of past glacial movements that controlled the spatial extent of GLW. We find that while a valley-wide proglacial lake likely did exist in Taylor Valley during the early stages of the Ross Sea I glaciation, during later stages two isolated lakes occupied the eastern and western sections of the valley, confined by an expansion of local alpine glaciers. Lake levels above ~140 m asl were confined to western Taylor Valley, and major lake level changes were likely driven by RIS movements, with climate variables playing a more minor role. These results may have major implications for our understanding of the MDVs and the RIS during the Ross Sea I glaciation.

The Tibetan Plateau (TP), known as the Roof of the World and the Water Tower of Asia, has the largest number of lakes in the world, and, because of its high altitude and the near absence of disturbances by human activity, the plateau has long been an important site for studying global climate change. Hydrological stations cannot be readily set up in this region, and in situ gauge data are not always publicly accessible. Satellite radar altimetry has become a very important alternative to in situ observations as a source of data. Estimation of the water levels of lakes via radar altimetry is often limited by temporal and spatial coverage, and, therefore, multi-altimeter data are often used to monitor lake levels. Restricted by the accuracy of waveform processing and the interval period between different altimetry missions, the accuracy and the sampling frequency of the water level series are typically low. By processing and merging data from eight different altimetry missions (Envisat, ICESat-1, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, and Sentinel-3A), the developed datasets provided the water level changes for 361 lakes (larger than 10 km2) on the TP from 2002 to 2021 (181 lakes for the time series from 2002 to 2021 and 180 lakes for the time series from 2010 to 2021). The lake level change series shows good consistency with in situ measurements, demonstrating a median root-mean-square error (RMSE) of 0.19 m across eight validation gauges. The dataset further exhibits robust agreement with established satellite altimetry products (DAHITI, Hydroweb, and G-REALM), with median RMSE values below 0.30 m in all cross-validation comparisons. The present datasets and associated approaches are valuable for calculating the changes in lake storage, trend analyses of the lake levels, short-term monitoring of the overflow of lakes and flooding disasters on the plateau, and the relationships between changes in the lake ecosystems and changes in the water resources. Data described in this article can be accessed at PANGAEA under https://doi.org/10.1594/PANGAEA.973443 (Chen et al., 2024).

Variations in a lake area constitute an important indicator of the modifications of the lake hydrology. This paper explores the inter-annual variations of the Poyang Lake area during the dry seasons occurring within the 1961 to 2010 period and further quantifies the severity of dryness recently endured during the 2000s. A physically based hydrodynamic model of Poyang Lake established the relation between the lake area and lake level. The lake area was calculated using the observed lake water level. Results indicated the average lake area in the dry seasons was 1,015 km2. There was a considerable inter-annual variation of the minimum lake area that varied from 702.8 km2 to 1,259.7 km2. Poyang Lake experienced the most severe dryness in the 2000s, resulting in an average lake area during 2001 to 2010 of 124 km2 less than that of the preceding period. During the dry seasons, the catchment of the river discharge is likely the primary cause of the changes in lake area. This study evaluated the inter-annual variations of the Poyang Lake over a period of 50 years. Our results may provide support for an integrated management of the lake-catchment system, securing the water supply.

Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test ( α = 0.05 level) for the first moment and the F test for the second moment ( α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.