Satellite based Budyko framework reveals the human imprint on long-term surface water partitioning across India

2021 ◽  
pp. 126770
Author(s):  
Anav Vora ◽  
Riddhi Singh
Keyword(s):  
Surface Water ◽  
Water Partitioning

Modelling the Effects of Acid Deposition: Estimation of Long-Term Water Quality Responses in Forested Catchments in Finland

1988 ◽  
Vol 19 (2) ◽  
pp. 99-120 ◽  
Author(s):  
A. Lepistö ◽  
P. G. Whitehead ◽  
C. Neal ◽  
B. J. Cosby
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Base Cations ◽  
Surface Water Quality ◽  
Mineral Weathering ◽  
Co2 Solubility ◽  
Forested Catchments ◽  
Deposition Rates ◽  
Magic Model

A modelling study has been undertaken to investigate long-term changes in surface water quality in two contrasting forested catchments; Yli-Knuutila, with high concentrations of base cations and sulphate, in southern Finland; and organically rich, acid Liuhapuro in eastern Finland. The MAGIC model is based on the assumption that certain chemical processes (anion retention, cation exchange, primary mineral weathering, aluminium dissolution and CO2 solubility) in catchment soils are likely keys to the responses of surface water quality to acidic deposition. The model was applied for the first time to an organically rich catchment with high quantities of humic substances. The historical reconstruction of water quality at Yli-Knuutila indicates that the catchment surface waters have lost about 90 μeq l−1 of alkalinity in 140 years, which is about 60% of their preacidification alkalinity. The model reproduces the declining pH levels of recent decades as indicated by paleoecological analysis. Stream acidity trends are investigated assuming two scenarios for future deposition. Assuming deposition rates are maintained in the future at 1984 levels, the model indicates that stream pH is likely to continue to decline below presently measured levels. A 50% reduction in deposition rates would likely result in an increase in pH and alkalinity of the stream, although not to estimated preacidification levels. Because of the high load of organic acids to the Liuhapuro stream it has been acid before atmospheric pollution; a decline of 0.2 pH-units was estimated with increasing leaching of base cations from the soil despite the partial pH buffering of the system by organic compounds.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

Toxicity analysis of surface water in the Lower Don basin in Rostov-on-Don City by long-term bioassay data

2015 ◽  
Vol 42 (1) ◽  
pp. 70-77
Author(s):  
E. N. Bakaeva ◽  
A. M. Nikanorov ◽  
N. A. Ignatova
Keyword(s):  
Surface Water ◽  
Toxicity Analysis ◽  
Bioassay Data

Establishing past environmental conditions and tracking long-term environmental change in the Canadian Maritime provinces using lake sediments

2013 ◽  
Vol 21 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Jennifer B. Korosi ◽  
Brian K. Ginn ◽  
Brian F. Cumming ◽  
John P. Smol
Keyword(s):  
Nova Scotia ◽  
Surface Water ◽  
Environmental Change ◽  
New Brunswick ◽  
Water Acidification ◽  
Maritime Provinces ◽  
Freshwater Resources ◽  
Anthropogenic Stressors ◽  
Surface Water Acidification

Freshwater lakes in the Canadian Maritime provinces have been detrimentally influenced by multiple, often synergistic, anthropogenically-sourced environmental stressors. These include surface-water acidification (and a subsequent decrease in calcium loading to lakes); increased nutrient inputs; watershed development; invasive species; and climate change. While detailed studies of these stressors are often hindered by a lack of predisturbance monitoring information; in many cases, these missing data can be determined using paleolimnological techniques, along with inferences on the full extent of environmental change (and natural variability), the timing of changes, and linkages to probable causes for change. As freshwater resources are important for fisheries, agriculture, municipal drinking water, and recreational activities, among others, understanding long-term ecological changes in response to anthropogenic stressors is critical. To assess the impacts of the major water-quality issues facing freshwater resources in this ecologically significant region, a large number of paleolimnological studies have recently been conducted in Nova Scotia and southern New Brunswick. These studies showed that several lakes in southwestern Nova Scotia, especially those in Kejimkujik National Park, have undergone surface-water acidification (mean decline of 0.5 pH units) in response to local-source SO2 emissions and the long-range transport of airborne pollutants. There has been no measureable chemical or biological recovery since emission restrictions were enacted. Lakewater calcium (Ca) decline, a recently recognized environmental stressor that is inextricably linked to acidification, has negatively affected the keystone zooplankter Daphnia in at least two lakes in Nova Scotia (and likely more), with critical implications for aquatic food webs. A consistent pattern of increasing planktonic diatoms and scaled chrysophytes was observed in lakes across Nova Scotia and New Brunswick, suggesting that the strength and duration of lake thermal stratification has increased since pre-industrial times in response to warming temperatures (∼1.5 °C since 1870). These include three lakes near Bridgewater, Nova Scotia, that are among the last known habitat for critically endangered Atlantic whitefish (Coregonus huntsmani). Overall, these studies suggest that aquatic ecosystems in the Maritime Provinces are being affected by multiple anthropogenic stressors and paleolimnology can be effective for inferring the ecological implications of these stressors.

scholarly journals Deeper waters are changing less consistently than surface waters in a global analysis of 102 lakes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rachel M. Pilla ◽  
Craig E. Williamson ◽  
Boris V. Adamovich ◽  
Rita Adrian ◽  
Orlane Anneville ◽  
...  
Keyword(s):  
Surface Water ◽  
Average Rate ◽  
Thermal Structure ◽  
Global Analysis ◽  
Regional Climate ◽  
Data Set ◽  
Water Column Stability ◽  
Temperature Trends ◽  
Lake Characteristics

AbstractGlobally, lake surface water temperatures have warmed rapidly relative to air temperatures, but changes in deepwater temperatures and vertical thermal structure are still largely unknown. We have compiled the most comprehensive data set to date of long-term (1970–2009) summertime vertical temperature profiles in lakes across the world to examine trends and drivers of whole-lake vertical thermal structure. We found significant increases in surface water temperatures across lakes at an average rate of + 0.37 °C decade−1, comparable to changes reported previously for other lakes, and similarly consistent trends of increasing water column stability (+ 0.08 kg m−3 decade−1). In contrast, however, deepwater temperature trends showed little change on average (+ 0.06 °C decade−1), but had high variability across lakes, with trends in individual lakes ranging from − 0.68 °C decade−1 to + 0.65 °C decade−1. The variability in deepwater temperature trends was not explained by trends in either surface water temperatures or thermal stability within lakes, and only 8.4% was explained by lake thermal region or local lake characteristics in a random forest analysis. These findings suggest that external drivers beyond our tested lake characteristics are important in explaining long-term trends in thermal structure, such as local to regional climate patterns or additional external anthropogenic influences.

scholarly journals Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

2020 ◽  
Vol 12 (17) ◽  
pp. 2675
Author(s):  
Qianqian Han ◽  
Zhenguo Niu
Keyword(s):  
Surface Water ◽  
Water Area ◽  
Temporal Parameter ◽  
Modis Ndvi ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Spatio Temporal ◽  
Global Surface ◽  
Global Water

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.

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