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Over the past decades, numerous studies have been carried out in understanding causes of Harmful Algal Blooms (HABs) and their dynamics, yielding great knowledge in this field. Lake Erie, the fourth-largest lake of the five Great Lake, is among those highly vulnerable to the impacts of HABs and has received substantial attention from the public, water management sectors, and academic field. Building upon previous work, this study aims to characterize spatiotemporal variability of Chlorophyll a (Chl-a), which is an important indicator of HABs, and to explore relative importance of environmental factors associated with HABs in the west Lake Erie. Ten years of biweekly Chl-a information over western Lake Erie were derived from MERIS data at the pixel scale. Based on the MERIS-derived information high concentrations of Chl-a were observed in the south near shore area in spring and fall and in the west corner area of western Lake Erie in all three seasons except winter. Wavelet analysis suggested that the 0.5- and 1-year periods were dominant modes for the Chl-a series. The Multivariate Adaptive Regression Splines (MARS) analysis was performed to explore factors associated with the dynamics of Chl-a. The results suggested that overall both phenological (e.g. wind) and ecological (e.g. nutrient levels) factors exhibited significant correlations with the remotely-sensed imagery based observations of Chl-a despite spatial and temporal variations. The important phenological and ecological factors include solar radiation and wind speed in spring, water temperature, solar radiation, and total Kjeldahl nitrogen concentration in summer, wind speed in fall, and water temperature and streamflow in winter. Both consistency and differences of findings of the study with others in the region may suggest strengths and limitations of the remotely sensed imagery-based analysis, offering valuable information for future work. [ABSTRACT FROM AUTHOR]

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush–Karakoram–Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann–Kendall test and Sen's slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995–2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of −198.36 km2/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is −0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region. [ABSTRACT FROM AUTHOR]