Axel Ritter

Bio: Axel Ritter is an academic researcher from University of La Laguna. The author has contributed to research in topics: Soil water & Cloud forest. The author has an hindex of 22, co-authored 55 publications receiving 1650 citations. Previous affiliations of Axel Ritter include University of Florida.

Fog Water Collection in a Subtropical Elfin Laurel Forest of the Garajonay National Park (Canary Islands): A Combined Approach Using Artificial Fog Catchers and a Physically Based Impaction Model

Abstract: Fog precipitation has long been assumed as an additional water source in the relic laurel ecosystems of the Canary Islands, located at 500–1400 m MSL. However, to what extent fog water can contribute to the laurel forest water balance is not yet clear. Combining data from artificial fog catchers and a physically based impaction model, the authors evaluated the potential contribution of fog water captured by needle-leafed Erica arborea L. trees in a selected watershed of the Garajonay National Park (La Gomera Island) for a 2-yr period (February 2003–January 2005). Fog water collection was measured with artificial catchers at four micrometeorological stations placed at 1145, 1185, 1230, and 1270 m MSL. Average fog water collection was only significant at the highest measurement site (one order of magnitude greater than at lower altitudes), totaling 496 L m−2 yr−1 during the 2-yr period. The average fog water yield in the first and second annual periods ranged between 0.2–5.0 and 0.1–2.1 L m−2 day−1...

Fog reduces transpiration in tree species of the Canarian relict heath-laurel cloud forest (Garajonay National Park, Spain).

Abstract: The ecophysiologic role of fog in the evergreen heath-laurel 'laurisilva' cloud forests of the Canary Islands has not been unequivocally demonstrated, although it is generally assumed that fog water is important for the survival and the distribution of this relict paleoecosystem of the North Atlantic Macaronesian archipelagos. To determine the role of fog in this ecosystem, we combined direct transpiration measurements of heath-laurel tree species, obtained with Granier's heat dissipation probes, with micrometeorological and artificial fog collection measurements carried out in a 43.7-ha watershed located in the Garajonay National Park (La Gomera, Canary Islands, Spain) over a 10-month period. Median ambient temperature spanned from 7 to 15 degrees C under foggy conditions whereas higher values, ranging from 9 to 21 degrees C, were registered during fog-free periods. Additionally, during the periods when fog water was collected, global solar radiation values were linearly related (r2=0.831) to those under fog-free conditions, such that there was a 75+/-1% reduction in median radiation in response to fog. Fog events greatly reduced median diurnal tree transpiration, with rates about 30 times lower than that during fog-free conditions and approximating the nighttime rates in both species studied (the needle-like leaf Erica arborea L. and the broadleaf Myrica faya Ait.). This large decrease in transpiration in response to fog was independent of the time of the day, tree size and species and micrometeorological status, both when expressed on a median basis and in cumulative terms for the entire 10-month measuring period. We conclude that, in contrast to the turbulent deposition of fog water droplets on the heath-laurel species, which may be regarded as a localized hydrological phenomenon that is important for high-altitude wind-exposed E. arborea trees, the cooler, wetter and shaded microenvironment provided by the cloud immersion belt represents a large-scale effect that is crucial for reducing the transpirational water loss of trees that have profligate water use, such as those of the 'laurisilva'.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Analysing Ecological Data

Abstract: Introduction.- Data management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- Introduction to mixed modelling.- Univariate tree models.- Measures of association.- Ordination--first encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- Introduction to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis--Introduction.- Common trends and sudden changes.- Analysis and modelling lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugual.- Crop pollination by honeybees in an Argentinean pampas system using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detection of birds for North Sea wind farms.- Fish stock identification through neural network analysis of parasite fauna.- Monitoring for change: using generalised least squares, nonmetric multidimensional scaling, and the Mantel test on western Montana grasslands.- Univariate and multivariate analysis applied on a Dutch sandy beach community.- Multivariate analyses of South-American zoobenthic species--spoilt for choice.- Principal component analysis applied to harbour porpoise fatty acid data.- Multivariate analysis of morphometric turtle data--size and shape.- Redundancy analysis and additive modelling applied on savanna tree data.- Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico.- Estimating common trends in Portuguese fisheries landings.- Common trends in demersal communities on the Newfoundland-Labrador Shelf.- Sea level change and salt marshes in the Wadden Sea: a time series analysis.- Time series analysis of Hawaiian waterbirds.- Spatial modelling of forest community features in the Volzhsko-Kamsky reserve.

Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria

Abstract: Performance measures (PMs) and corresponding performance evaluation criteria (PEC) are important aspects of calibrating and validating hydrologic and water quality models and should be updated with advances in modeling science. We synthesized PMs and PEC from a previous special collection, performed a meta-analysis of performance data reported in recent peer-reviewed literature for three widely published watershed-scale models (SWAT, HSPF, WARMF), and one field-scale model (ADAPT), and provided guidelines for model performance evaluation. Based on the synthesis, meta-analysis, and personal modeling experiences, we recommend coefficient of determination (R2; in conjunction with gradient and intercept of the corresponding regression line), Nash Sutcliffe efficiency (NSE), index of agreement (d), root mean square error (RMSE; alongside the ratio of RMSE and standard deviation of measured data, RSR), percent bias (PBIAS), and several graphical PMs to evaluate model performance. We recommend that model performance can be judged satisfactory for flow simulations if monthly R2 0.70 and d 0.75 for field-scale models, and daily, monthly, or annual R2 0.60, NSE 0.50, and PBIAS ≤ ±15% for watershed-scale models. Model performance at the watershed scale can be evaluated as satisfactory if monthly R2 0.40 and NSE 0.45 and daily, monthly, or annual PBIAS ≤ ±20% for sediment; monthly R20.40 and NSE 0.35 and daily, monthly, or annual PBIAS ≤ ±30% for phosphorus (P); and monthly R2 0.30 and NSE 0.35 and daily, monthly, or annual PBIAS ≤ ±30% for nitrogen (N). For RSR, we recommend that previously published PEC be used as detailed in this article. We also recommend that these PEC be used primarily for the four models for which there were adequate data, and used only with caution for other models. These PEC can be adjusted within acceptable bounds based on additional considerations, such as quality and quantity of available measured data, spatial and temporal scales, and project scope and magnitude, and updated based on the framework presented herein. This initial meta-analysis sets the stage for more comprehensive meta-analysis to revise PEC as new PMs and more data become available.