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It is well known that the spatial and temporal patterns in streamflow can be correlated with many teleconnections, e.g., solar activity and climatic phenomena such as El Niño. However, fewer studies have attempted to analyze both the influence of solar activity and large scale climatic phenomena on natural processes, particularly hydrological processes. In this study we examine long term records of solar activity and El Niño for their combined influence on streamflow across southern Canada. Data used in the analysis include sunspot number, sea surface temperature anomaly in Niño region 3.4, and annual mean streamflow from 50 Canadian Reference Hydrometric Basin Network (RHBN) stations with record lengths ≥50 years (14 of them ≥90 years). Analysis is performed using Fourier spectrum analysis (FSA), continuous wavelet transform (CWT), and cross wavelet transform coherence analysis (WTC). Results of FSA show that for almost all the 14 RHBN stations with record lengths of ≥90 years, streamflow exhibits periodicities of approximately 11 and 22 years (which is in accordance with solar activity), as well as shorter term periodicities consistent with El Niño (2–7 years). WTC analysis confirms the correlation between these periodicities (2–7 years, 11 years, 22 years) in streamflow with solar activity and El Niño records. Both solar activity and El Niño's influences on annual mean streamflow in 18–32 year bands are common, while the influence of El Nino is more extensive in the 2–7 and ∼11 year bands. Through examination of correlations between solar activity and streamflow, El Niño and streamflow, and finally El Niño and solar activity, WTC analysis has identified that solar activity affects El Niño first, and this influence is then transferred by El Niño to streamflow. This study expands on earlier efforts examining linkages between El Niño and streamflow across southern Canada to an examination of linkages between solar activity, El Niño, and streamflow.

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Abstract:The use of spectral response curves for estimating nitrogen (N) leaf concentrations generally has been found to be a challenging task for a variety of plant species. In this investigation, leaf N concentration and corresponding laboratory hyperspectral data were examined for two species of mangrove (Avicennia germinans, Rhizophora mangle) representing a variety of conditions (healthy, poor condition, dwarf) of a degraded mangrove forest located in the Mexican Pacific. This is the first time leaf nitrogen content has been examined using close range hyperspectral remote sensing of a degraded mangrove forest. Simple comparisons between individual wavebands and N concentrations were examined, as well as two models employed to predict N concentrations based on multiple wavebands. For one model, an Artificial Neural Network (ANN) was developed based on known N absorption bands. For comparative purposes, a second model, based on the well-known Stepwise Multiple Linear Regression (SMLR) approach, was employed using the entire dataset. For both models, the input data included continuum removed reflectance, band depth at the centre of the absorption feature (BNC), and log (1/BNC). Weak to moderate correlations were found between N concentration and single band spectral responses. The results also indicate that ANNs were more predictive for N concentration than was SMLR, and had consistently higher r value (0.91) was observed in the prediction of black mangrove (A. germinans) leaf N concentration using the BNC transformation. It is thus suggested that artificial neural networks could be used in a complementary manner with other techniques to assess mangrove health, thereby improving environmental monitoring in coastal wetlands, which is of prime importance to local communities. In addition, it is recommended that the BNC transformation be used on the input for such N concentration prediction models.

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