Willem Bouten

Modeling water retention curves of sandy soils using neural networks

We used neural networks (NNs) to model the drying water retention curve (WRC) of 204 sandy soil samples from particle-size distribution (PSD), soil organic matter content (SOM), and bulk density (BD). Neural networks can relate multiple model input data to multiple model output data without the need of an a priori model concept. In this way a high performance black-box model is created, which is very useful in a data exploration effort to assess the maximum obtainable prediction accuracy. We used a series of NN models with an increasing parametrization of input and output variables to get a better interpretability of model results. In the first two models we used the nine PSD fractions, BD, and SOM as input, while we predicted the nine points of the water retention curve. These NNs had 12 input and 9 output variables, predicting WRCs with an average root-mean-square residual (RMSR) water content of 0.020 cm3 cm−3. After a few intermediary models with increasing parametrization of PSD and WRC using (adapted) van Genuchten [1980] equations we arrived at a final NN model that used six input variables to predict three van Genuchten [1980] parameters resulting in a RMSR of 0.024 cm3 cm−3. We found saturated and residual water contents to be unrelated to the PSD, BD, or SOM, therefore the saturated water content was considered to be an independent input variable, while the residual water content was set to zero. Sensitivity analyses showed that the PSD had a major influence on the shape of the WRC, while BD and SOM were less important. On the basis of these sensitivity analyses we established more explicit equations that demonstrated similarity relations between PSD and WRC and incorporated effects of SOM and BD in an empirical way. Despite the fact that we considered a large number of linear and nonlinear variants these equations had a weaker performance (RMSR: 0.029 cm3 cm−3) than the NN models, proving the modeling power of that technique.

Gross rainfall and its partitioning into throughfall, stemflow and evaporation of intercepted water in four forest ecosystems in western Amazonia.

The partitioning of gross rainfall into throughfall, stemflow and evaporation of intercepted rainfall was studied in four forest ecosystems in the Middle Caqueta, Colombian Amazonia. Data on climate was collected automatically on an hourly basis during a five-year period. Weekly measurements of rainfall, throughfall and stemflow were carried out during a period of two years, while daily measurements, on an event basis, were carried out during two subsequent years. Throughfall, stemflow and evaporation in each forest were checked for correlations with gross rainfall characteristics, canopy gap fraction, tree crown area and bark texture. Canopy gap fraction differed between forests, ranging from 9% on the flood plain to 17% on the Tertiary sedimentary plain. Rainfall was rather evenly distributed over the year, with one dry period from December to February. 92% of the rain fell in single showers of less than 30 mm and most of the storms (56%) fell in less than one hour, during the afternoon or early night. Throughfall ranged from 82 to 87% of gross rainfall in the forests studied and varied with gross rainfall in all forests. It depended on the amounts and characteristics of rainfall, but differences in throughfall among forests, when comparing similar rainfall events, clearly indicated that throughfall also depends on forest structure. Stemflow contributed little to net precipitation (on average 1.1% of gross rainfall in all forests) and showed a power relation with gross rainfall. Correlations between stemflow per tree, projected crown area and bark texture were very poor as indicated by the low coefficients of determination. Evaporation during rainfall events exhibited a linear relation with rainfall duration and the ratio of evaporation over gross rainfall increased with forest cover (1-gap fraction) in the forests studied. The structure of the forests seemed to vary considerably and given its influence on rainfall partitioning it may explain both differences and similarities between results from this study and those from most other studies within Amazonia.

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

Abstract Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River...

Spatial patterns of throughfall and soil water dynamics in a Douglas fir stand

Measured spatial patterns of water uptake were found to be related to measured throughfall patterns around trees, especially in dry months. To simulate these lateral feedback mechanisms, the one-dimensional soil water model SWIF was modified to a quasi-three-dimensional model allowing preferential water uptake from wet sites. Input-output relations, linking soil physical input parameters to simulation results, showed that soil water contents at field capacity and those after a dry period in summer could be used to parametrize the soil physical characteristics. To assess parameter values for the 2.5-ha research area, soil water contents were mapped using data from vertically installed time domain reflectometry sensors. Model results indicated that the spatial distributions of yearly water uptake and percolation fluxes were strongly affected by throughfall patterns, whereas soil water contents primarily depended on soil physical properties. Results also indicated that refined model parametrization can improve the reliability of model predictions of soil water dynamics at specific sites.

Frequency domain analysis of time domain reflectometry waveforms: 2. A four‐component complex dielectric mixing model for soils

Although time domain reflectometry (TDR) is becoming accepted as an important tool for the measurement of soil water content and bulk soil electrical conductivity, a major part of the method is based on empirical relationships. An improved understanding of dielectric measurements on soils may give more insight into soil properties other than soil water content and bulk soil electrical conductivity. Frequency domain analysis of TDR waveforms enables the measurement of the frequency dependent complex dielectric permittivity of soils. The frequency dependent complex dielectric permittivity of soils can be described with a four-component complex dielectric mixing model based on the volumetric mixing of the refractive indices of the soil components. The four soil components in the model are air, solids, bound water, and free water. Results indicate that the apparent dielectric permittivity obtained from the travel time of the TDR pulse in the soil is the dielectric permittivity at the highest measurement frequency of the cable tester, probe, and soil system. The model based on the volumetric mixing of real permittivities underestimates the measurements in situations with high values of the imaginary part of the dielectric permittivity. Because the model based on the mixing of the complex dielectric permittivities can describe the data, we conclude that the apparent dielectric permittivity is influenced by the imaginary parts in the dielectric, permittivities of the soil components. Combination of the four-component complex dielectric mixing model with the complex dielectric permittivity obtained from the frequency domain analysis of TDR waveforms gives a tool for modeling the bulk soil electrical conductivity by separating the conductivity of the soil water into a bound water conductivity and a free water conductivity.

How Predictability of Feeding Patches Affects Home Range and Foraging Habitat Selection in Avian Social Scavengers

Feeding stations are commonly used to sustain conservation programs of scavengers but their impact on behaviour is still debated. They increase the temporal and spatial predictability of food resources while scavengers have supposedly evolved to search for unpredictable resources. In the Grands Causses (France), a reintroduced population of Griffon vultures Gyps fulvus can find carcasses at three types of sites: 1. “light feeding stations”, where farmers can drop carcasses at their farm (spatially predictable), 2. “heavy feeding stations”, where carcasses from nearby farms are concentrated (spatially and temporally predictable) and 3. open grasslands, where resources are randomly distributed (unpredictable). The impact of feeding stations on vulture’s foraging behaviour was investigated using 28 GPS-tracked vultures. The average home range size was maximal in spring (1272±752 km2) and minimal in winter (473±237 km2) and was highly variable among individuals. Analyses of home range characteristics and feeding habitat selection via compositional analysis showed that feeding stations were always preferred compared to the rest of the habitat where vultures can find unpredictable resources. Feeding stations were particularly used when resources were scarce (summer) or when flight conditions were poor (winter), limiting long-ranging movements. However, when flight conditions were optimal, home ranges also encompassed large areas of grassland where vultures could find unpredictable resources, suggesting that vultures did not lose their natural ability to forage on unpredictable resources, even when feeding stations were available. However during seasons when food abundance and flight conditions were not limited, vultures seemed to favour light over heavy feeding stations, probably because of the reduced intraspecific competition and a pattern closer to the natural dispersion of resources in the landscape. Light feeding stations are interesting tools for managing food resources, but don’t prevent vultures to feed at other places with possibly high risk of intoxication (poison).

Obtaining the Spatial Distribution of Water Content along a TDR Probe Using the SCEM-UA Bayesian Inverse Modeling Scheme

Time domain reflectometry (TDR) has become one of the standard methods for the measurement of the temporal and spatial distribution of water saturation in soils. Current waveform analysis methodology gives a measurement of the average water content along the length of the TDR probe. Close inspection of TDR waveforms shows that heterogeneity in water content along the probe can be seen in the TDR waveform. We present a comprehensive approach to TDR waveform analysis that gives a quantitative estimate of the dielectric permittivity profile along the length of the probe and, therefore, the distribution of water content. The approach is based on the combination of a multisection scatter function model for the TDR measurement system with the shuffled complex evolution Metropolis algorithm (SCEM-UA). This combined approach allows for the estimation of the 40 parameters in the transmission line model using a series of simple calibration measurements. The proof of concept is given with measurements in a layered system consisting of air and water. Finally, TDR waveforms from layered soil samples were analyzed to estimate the distribution of the water content along the length of the probe. Results show that the proposed method provides much more reproducible results than obtained with the traditional travel time method. Because the proposed method can be fully automated, it increases the applicability of the TDR method, especially in applications where detailed (real-time) data are required on heterogeneous infiltration.

Integrating Meteorology into Research on Migration

Atmospheric dynamics strongly influence the migration of flying organisms. They affect, among others, the onset, duration and cost of migration, migratory routes, stop-over decisions, and flight speeds en-route. Animals move through a heterogeneous environment and have to react to atmospheric dynamics at different spatial and temporal scales. Integrating meteorology into research on migration is not only challenging but it is also important, especially when trying to understand the variability of the various aspects of migratory behavior observed in nature. In this article, we give an overview of some different modeling approaches and we show how these have been incorporated into migration research. We provide a more detailed description of the development and application of two dynamic, individual-based models, one for waders and one for soaring migrants, as examples of how and why to integrate meteorology into research on migration. We use these models to help understand underlying mechanisms of individual response to atmospheric conditions en-route and to explain emergent patterns. This type of models can be used to study the impact of variability in atmospheric dynamics on migration along a migratory trajectory, between seasons and between years. We conclude by providing some basic guidelines to help researchers towards finding the right modeling approach and the meteorological data needed to integrate meteorology into their own research.

From Sensor Data to Animal Behaviour: An Oystercatcher Example

Animal-borne sensors enable researchers to remotely track animals, their physiological state and body movements. Accelerometers, for example, have been used in several studies to measure body movement, posture, and energy expenditure, although predominantly in marine animals. In many studies, behaviour is often inferred from expert interpretation of sensor data and not validated with direct observations of the animal. The aim of this study was to derive models that could be used to classify oystercatcher (Haematopus ostralegus) behaviour based on sensor data. We measured the location, speed, and tri-axial acceleration of three oystercatchers using a flexible GPS tracking system and conducted simultaneous visual observations of the behaviour of these birds in their natural environment. We then used these data to develop three supervised classification trees of behaviour and finally applied one of the models to calculate time-activity budgets. The model based on accelerometer data developed to classify three behaviours (fly, terrestrial locomotion, and no movement) was much more accurate (cross-validation error = 0.14) than the model based on GPS-speed alone (cross-validation error = 0.35). The most parsimonious acceleration model designed to classify eight behaviours could distinguish five: fly, forage, body care, stand, and sit (cross-validation error = 0.28); other behaviours that were observed, such as aggression or handling of prey, could not be distinguished. Model limitations and potential improvements are discussed. The workflow design presented in this study can facilitate model development, be adapted to a wide range of species, and together with the appropriate measurements, can foster the study of behaviour and habitat use of free living animals throughout their annual routine.

MICROWAVE TRANSMISSION A NEW TOOL IN FOREST HYDROLOGICAL RESEARCH

Abstract After several decades of interception studies, there are still considerable gaps in the understanding of wet-canopy evaporation. Model development is being obstructed by the lack of techniques for the measurement of state and rate variables which have to be quantified for model validation. The applicability of microwave attenuation measurements for the determination of canopy wetness is examined. The attenuation caused by a single spruce fir in the laboratory and the vertical attenuation profiles of a Douglas fir stand were measured under dry and wet conditions. The results indicate an instant increase of the attenuation upon wetting and a decrease owing to drip and evaporation after rainfall ceased. From the results, conclusions have been drawn on the design of instrumentation for an optimized measuring system which is suitable for unattended automated scanning of canopy water storage. This system has been calibrated, using vertically integrated microwave attenuation profiles and canopy water budgets from precipitation and throughfall measurements. This system will be used for a forest hydrological study in the framework of the Dutch ACIFORN project, a research project on the effect of atmospheric deposition on Douglas fir vitality.