Geology ReportsSearch

USGS · 70196561

Cyclic heliothermal behaviour of the shallow, hypersaline Lake Hayward, Western Australia

Abstract

Lake Hayward is one of only about 30 hypersaline lakes worldwide that is meromictic and heliothermal and as such behaves as a natural salt gradient solar pond. Lake Hayward acts as a local groundwater sink, resulting in seasonally variable hypersaline lake water with total dissolved solids (TDS) in the upper layer (mixolimnion) ranging between 56 kg m −3 and 207 kg m −3 and the deeper layer (monimolimnion) from 153 kg m −3 to 211 kg m −3 . This is up to six times the salinity of seawater and thus has the highest salinity of all eleven lakes in the Yalgorup National Park lake system. A program of continuously recorded water temperature profiles has shown that salinity stratification initiated by direct rainfall onto the lake’s surface and local runoff into the lake results in the onset of heliothermal conditions within hours of rainfall onset. The lake alternates between being fully mixed and becoming thermally and chemically stratified several times during the annual cycle, with the longest extended periods of heliothermal behaviour lasting 23 and 22 weeks in the winters of 1992 and 1993 respectively. The objective was to quantify the heat budgets of the cyclical heliothermal behaviour of Lake Hayward. During the period of temperature profile logging, the maximum recorded temperature of the monimolimnion was 42.6 °C at which time the temperature of the mixolimnion was 29.4 °C. The heat budget of two closed heliothermal cycles initiated by two rainfall events of 50 mm and 52 mm in 1993 were analysed. The cycles prevailed for 11 and 20 days respectively and the heat budget showed net heat accumulations of 34.2 MJ m −3 and 15.4 MJ m −3 , respectively. The corresponding efficiencies of lake heat gain to incident solar energy were 0.17 and 0.18 respectively. Typically, artificial salinity gradient solar ponds (SGSP) have a solar radiation capture efficiencies ranging from 0.10 up to 0.30. Results from Lake Hayward have implications for comparative biogeochemistry and its characteristics should aid in identification of other hitherto unknown heliothermal lakes.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: -33.121450558365964° to -32.69717735929062° latitude; 115.587158203125° to 115.76156616210938° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jeffrey V. Turner, Michael R. Rosen, Lee Coshell, Robert J. Woodbury. 2018. Cyclic heliothermal behaviour of the shallow, hypersaline Lake Hayward, Western Australia. https://doi.org/10.1016/j.jhydrol.2018.03.056

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Reservoir releases and land cover interact to drive event-scale nitrate export in a large agricultural basin

Understanding the drivers of nitrate export in rivers is critical for developing effective nutrient management strategies. However, few studies have explored event-scale drivers of export in large river basins with human modifications like reservoirs. Here, we analyzed nitrate concentration-discharge (C-Q) relationships from 215 events at the outlet of the Kansas River Basin, USA (155,690 km 2 ) from 2014 to 2022 to (1) characterize event-scale nitrate export behaviors in a large agricultural basin, and (2) determine how different event characteristics are linked to these behaviors. We found that C-Q behaviors varied greatly, with 60% of events producing nitrate enrichment (n = 130) and 40% of events producing nitrate dilution (n = 85). These behaviors were correlated with complex spatial interlinkages between climate and land cover: across most of the basin, nitrate enrichment was correlated with drier antecedent conditions, but in wetter areas with higher proportions of urban and forested land cover, enrichment was more strongly correlated with precipitation magnitude/intensity. This difference in hydroclimatic controls on nitrate export might be related to differential distributions of nitrate sources within these land covers. Upstream of major reservoirs, however, neither variable was strongly correlated with C-Q behavior, suggesting that nitrate attenuation within reservoirs decouples event-scale concentration signals in upstream waters from those downstream. Reservoir outflows had variable impacts on C-Q behavior, reflecting reservoir-specific variations in nitrate attenuation efficiency. Together, these results identify specific complex interactions between hydroclimate, land cover, reservoir positioning, and individual reservoir properties that control event-scale nitrate export from large basins.

Colorado, Kansas, Nebraska

Deep learning error post-processing improves stochastic watershed modeling

Hydrologic extremes, including floods and droughts, pose substantial societal risks that are expected to intensify with climate change. Deterministic watershed models (DWMs) remain a mainstay for modeling these extremes, but lack explicit representation of uncertainty, limiting their utility for risk-informed planning. Stochastic watershed models (SWMs) address this limitation by generating ensembles of streamflow via models of observed DWM residuals. However, most SWMs struggle with the complex dependence between DWM residuals and the underlying hydrologic state, which can complicate stochastic simulations under nonstationary climates. Deep learning (DL) models, whether used as standalone models or post-processors for process-based DWMs, offer a pathway to address this challenge by reducing conditional dependence. In this study, we evaluate SWMs applied to seven models: three process-based models (PRMS, Hymod, and HBV), their hybrid process-DL counterparts, and a pure DL DWM, focusing on daily simulations and extremes under both historical conditions and synthetic climate change scenarios. Results for a case study watershed in Massachusetts show that SWMs applied to hybrid or pure DL DWMs consistently outperform those applied to process-based DWMs. However, an SWM applied to the pure DL model exhibits weaknesses at low flows for this study basin, underscoring the value of hybrid approaches. Extending the analysis across 73 additional basins demonstrates that these improvements are robust and generalizable statewide. This work highlights the potential of a DL-enhanced stochastic watershed modeling framework to advance hydrologic risk prediction under changing climate conditions, offering a scalable methodology for integrating uncertainty into watershed modeling for long-term planning.

Journal of Hydrology

Groundwater drought in the United States: Spatial and temporal variability

Many communities and ecosystems in the United States that are dependent on groundwater are potentially adversely affected by groundwater drought. We computed yearly groundwater-drought metrics and mean groundwater levels at well locations across the conterminous United States (CONUS), using data from wells and remotely sensed and modeled Gravity Recovery and Climate Experiment Drought Monitor Data Assimilation (GRACE-DADM). We also modeled the probability of low or high human impact at each well location. The spatial distribution of groundwater-drought duration and severity from 2001 to 2020 for 1,510 wells shows longer maximum duration and higher maximum severity events in drier regions like the Southwest than in wetter regions like the Northeast. Based on 613 wells in CONUS from 1981 to 2020, there are many significant decreases in drought duration and severity in the Northeast and many significant increases in annual-mean groundwater levels. In contrast, there are many significant increases in drought metrics and decreases in mean water levels in parts of the Southeast. There are major differences in trends from 2001 to 2020 between well-based and GRACE-DADM-based groundwater metrics in some CONUS regions and a very low correlation between trends at individual locations across CONUS. A potential reason for this disparity is the low GRACE-DADM resolution (∼12 km) and the potential for a large amount of groundwater variation at the local scale. Also, GRACE-DADM represents shallow, unconfined aquifers which may not match the screened interval of the monitoring wells we evaluated. Large spatial gaps in long-term, high frequency, and quality-assured groundwater-well monitoring data present a challenge for understanding groundwater-drought variability across CONUS. Remote sensing tools such as GRACE can help but cannot fully replace well monitoring, as highlighted by our study results. Substantially more long-term monitoring wells would more accurately represent groundwater-drought trends and spatial variability across CONUS, particularly in western regions.

conterminous United States