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Physical and chemical water quality characteristics were studied in six of Lake Tana. The purpose of the study was to explore how different methods describe the “health” of the wetlands and how different approaches relate to each other. The physicochemical parameters were measured in-situ with portable multimeter and nutrients and chlorophyll a were determined by following the standard procedures outlined in the United States Environmental Protection Agency using UV/Visible photometer (Spectrophotometer). The trophic state index (TSI) of wetlands was determined using trophic state variable and Carlson model. The lake water quality index (WQI) was also evaluated using data from multiple water quality parameters into a mathematical equation to express the overall water quality at each study wetland and season. The water quality datasets were subjected to four multivariate statistical techniques, namely, univariate analysis of variance (univariate ANOVA), cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA). Analysis of the physicochemical dataset using univariate analysis indicated a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of dissolved oxygen, electrical conductivity, Secchi depth a.m., and p.m., salinity, nitrate, total ammonia, total nitrogen, total phosphorous, and Chlorophyll-a while water temperature, water depth, soluble reactive phosphorous were not affected (ANOVA, p > 0.05) by the interaction between wetland by season. Spatial diversity and site grouping based on water quality characteristics using CA, PCA and FA analysis grouped the 6-wetlands into four clusters based on the similarity of water quality characteristics. The four clusters displayed in the dendrogram were grouped into least polluted cluster 1 (WO and RA), slightly polluted cluster 2 (MRM). moderately polluted cluster 3 ( GRM and ZG ) and highly polluted cluster 1 (AV). There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of total trophic state index (TOTTSI), total nitrogen trophic state index (TSITN), total phosphorous trophic state index (TSITP,), total chlorophyll-a trophic state index (TSIChla) ,and total Secchi depth trophic state index (TSISTD). However, there was no a significant interaction between wetland and season (ANOVA, p > 0.05) for the mean value of WQI. In conclusion, ranking of the pollution status of wetlands of Lake Tana using different approaches in this study using multivariate statistics, Carlson TSI, and WQI model suggest that some wetlands did not fit completely in the same category The current study on water quality variables of Lake Tana recommends that top priority should be given to regular water quality monitoring, in conjunction with biodiversity and fish health assessment.
Wetlands within dryland regions are highly sensitive to climate change and human activities. Based on three types of land use data sources from satellite images and a spatial data analysis, the spatiotemporal characteristics of wetland evolution in China’s drylands and their relationship with human interference and climate change from 1990 to 2020 were analyzed. The results were as follows: (1) The wetlands within China’s drylands expanded, including rivers, lakes, and artificial wetlands, apart from marshes, which shrunk. Meanwhile, wetland fragmentation increased, with rivers being particularly severely fragmented. (2) Temperature and precipitation showed an increasing trend from 1990 to 2020 in China’s drylands. Lakes and rivers expanded with regional differences due to the uneven distribution of precipitation and rising temperature. (3) Human activities, more than climate change, became the key driving factor for the changes in wetland patterns in China’s drylands. The increased areas of farmland and grassland along with increased levels of drainage and irrigation activities led to the shrinkage of marshes and the fragmentation of rivers. The increase in the number of artificial reservoirs was the main reason for the expansion of artificial wetlands. This study clarifies the specific driving factors of different types of wetlands within China’s drylands, which is of great use for better protecting wetlands and the gradual restoration of degraded wetlands.
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