Publication Date:
2023-11-23
Description:
We investigated controls on the water chemistry of a South Ecuadorian cloud forest catchment which is partly pristine, and partly converted to extensive pasture. From April 2007 to May 2008 water samples were taken weekly to biweekly at nine different subcatchments, and were screened for differences in electric conductivity, pH, anion, as well as element composition. A principal component analysis was conducted to reduce dimensionality of the data set and define major factors explaining variation in the data. Three main factors were isolated by a subset of 10 elements (Ca2+, Ce, Gd, K+, Mg2+, Na+, Nd, Rb, Sr, Y), explaining around 90% of the data variation. Land-use was the major factor controlling and changing water chemistry of the subcatchments. A second factor was associated with the concentration of rare earth elements in water, presumably highlighting other anthropogenic influences such as gravel excavation or road construction. Around 12% of the variation was explained by the third component, which was defined by the occurrence of Rb and K and represents the influence of vegetation dynamics on element accumulation and wash-out. Comparison of base- and fast flow concentrations led to the assumption that a significant portion of soil water from around 30 cm depth contributes to storm flow, as revealed by increased rare earth element concentrations in fast flow samples. Our findings demonstrate the utility of multi-tracer principal component analysis to study tropical headwater streams, and emphasize the need for effective land management in cloud forest catchments.
Keywords:
Aluminium; Aluminium, standard deviation; Area; Arsenic; Arsenic, standard deviation; Barium, standard deviation; Barium 2+; Calcium; Calcium, standard deviation; Calculated; Cerium; Cerium, standard deviation; Chloride; Chloride, standard deviation; Chromium; Chromium, standard deviation; Conductivity, electrical; Conductivity, standard deviation; Conductivity and pH meter, pH/Cond 340i (WTW, Weilheim); Copper; Copper, standard deviation; Dysprosium; Dysprosium, standard deviation; Ecuador; Erbium; Erbium, standard deviation; Gadolinium; Gadolinium, standard deviation; Height above sea level; Human Dimensions; ICP-MS, Agilent 7500c; Ion chromatograph, Dionex Corporation, DX-120; Iron; Iron, standard deviation; Lakes & Rivers; Land Surface; Land use; Lanthanum; Lanthanum, standard deviation; LATITUDE; Lead; Lead, standard deviation; Lithium; Lithium, standard deviation; LONGITUDE; Magnesium; Magnesium, standard deviation; Manganese, standard deviation; Manganese 2+; Neodymium; Neodymium, standard deviation; Nickel; Nickel, standard deviation; Nitrate; Nitrate, standard deviation; pH; pH, standard deviation; Potassium; Potassium, standard deviation; Praseodymium; Praseodymium, standard deviation; Rio_SanFrancisco; River; RIVER; Rubidium; Rubidium, standard deviation; Samarium; Samarium, standard deviation; Sample code/label; Sampling river; Sodium; Sodium, standard deviation; Strontium, standard deviation; Strontium 2+; Sulfate; Sulfate, standard deviation; Uranium; Uranium, standard deviation; Vanadium; Vanadium, standard deviation; Ytterbium; Ytterbium, standard deviation; Yttrium; Yttrium, standard deviation; Zinc; Zinc, standard deviation
Type:
Dataset
Format:
text/tab-separated-values, 730 data points
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