M. Knotters

Sampling for natural resource monitoring

The book presents the statistical knowledge and methodology of sampling and data analysis useful for spatial inventory and monitoring of natural resources. The authors omitted all theory not essential for applications or for basic understanding. This presentation is broader than standard statistical texts, as the authors pay much attention to how statistical methodology can be employed and embedded in real-life spatial inventory and monitoring projects. Thus they discuss in detail how efficient sampling schemes and monitoring systems can be designed in view of the aims and constraints of the project.

Physical basis of time series models for water table depths

The relationship between precipitation excess and water table depth can be described by empirical time series models such as transfer function noise models, autoregressive exogenous variable models (ARX), and threshold autoregressive self-exciting open loop models. In this paper these models are interpreted in terms of the water balance of a soil column. A physically based ARX model is used to predict the effect of an intervention on the water table dynamics at two locations. It is shown that the physically based ARX model predicts the effect of interventions reasonably well.

Space‐time modeling of water table depth using a regionalized time series model and the Kalman Filter

The spatiotemporal variation of shallow water table depth is modeled with a regionalized version of an autoregressive exogenous (ARX) time series model. The ARX model relates the temporal variation of the water table depth at a single location to a time series of precipitation surplus. The ARX model is calibrated first at locations where time series of water table depth are available. ARX parameters at nonvisited locations are estimated through geostatistical interpolation using auxiliary information, resulting in a regionalized ARX model or RARX model. The parameters of the geostatistical model are estimated by embedding the RARX model in a space-time Kalman filter and minimization of a maximum likelihood criterion built from the filter innovations. The resulting state estimator can be used for optimal space-time prediction of water table depth, network optimization, and space-time conditional simulation.

Predicting water table depths in space and time using a regionalised time series model

Abstract A regionalised autoregressive exogenous variable (RARX) model is presented for the relationship between precipitation surplus and water table depth. The parameters of the RARX model are ‘guessed’ at unvisited locations using auxiliary information such as soil profile descriptions, topographic maps and digital elevation models (DEM). In the direct method, the guessed parameters are used to predict time series of water table depth at unvisited locations; observed water table depths are not used in the prediction procedure. In the indirect method, observed water table depths are used to correct the predictions resulting from the direct method for systematic prediction errors. The prediction performance is evaluated by cross-validation. The validation results show small random errors (standard deviation on average is 9.8 cm) but large systematic errors (absolute mean error on average is 18 cm). The root mean squared error of the predicted time series is, on average, 22 cm. Taking the uncertainty of both the future weather conditions and the RARX-model predictions into account, a map reflecting the risk that a critical depth will be exceeded at a critical day in a future year is constructed. Furthermore, maps showing the components of uncertainty in predicted water table depths are given.

Calibration of transfer function–noise models to sparsely or irregularly observed time series

A method is presented to calibrate transfer function-noise (TFN) models, operating at the same frequency as the input (auxiliary) variables, to sparsely or irregularly observed time series of the output (target) variable. Once calibrated, the TFN models can be used to predict or simulate the output variable at the same frequency as the input variable. Consequently, the method provides a useful tool for filling in gaps of irregularly or sparsely observed hydrological time series. Although generic and suitable for any type of time series, the method is described through the modeling of a time series of groundwater head data with precipitation surplus (precipitation minus potential evapotranspiration) as input variable. First, the TFN model is written in vector notation, yielding the state equation of a linear discrete stochastic system. Subsequently, the state equation is embedded in a Kalman filter algorithm. The Kalman filter is then combined with a maximum likelihood criterion to obtain estimates of the parameters of the TFN model for small time steps (e.g., 1 day) while using sparsely (e.g., two times a month) or even irregularly observed time series of groundwater head data. The method is illustrated using (subsets of) time series of groundwater head data with varying regular and irregular observation intervals.

Software for hydrogeologic time series analysis, interfacing data with physical insight

The program Menyanthes combines a variety of functions for managing, editing, visualizing, analyzing and modeling hydrogeologic time series. Menyanthes was initially developed within the scope of the PhD research of the first author, whose primary aim was the integration of data and physically-based methods for modeling time series of groundwater heads. As such, time series analysis forms the heart of Menyanthes. Within Menyanthes, time series can be modeled using both the ARMA and PIRFICT methods. The PIRFICT method is a new method of time series analysis that has practical advantages and facilitates physical interpretation and implementation of knowledge on physical behavior. Analytic solutions to specific hydrogeologic problems may be used as response function, along with their physically-based parameters. A more general approach is possible using Skew-Gaussian distribution functions, which prove to fit the behavior of hydrogeologic (and other) systems well. Use of such functions within the PIRFICT method substantially simplifies the model identification procedure, as compared to the traditional Box-Jenkins procedure. PIRFICT models may be fitted to a large number of time series in batch. Spatial patterns that emerge in the results provide useful, additional, and independent information, which adds another dimension to time series analysis. Their interpretation is supported by the spatial visualization and analysis tools of Menyanthes. The PIRFICT method also facilitates the integration of time series and spatially-distributed models via, e.g., moment-generating differential equations. The PIRFICT method may prove to be of use for other types of time series as well, both within and outside the realm of environmental sciences.

How much would it cost to monitor farmland biodiversity in Europe

To evaluate progress on political biodiversity objectives, biodiversity monitoring provides information on whether intended results are being achieved. Despite scientific proof that monitoring and evaluation increase the (cost) efficiency of policy measures, cost estimates for monitoring schemes are seldom available, hampering their inclusion in policy programme budgets. Empirical data collected from 12 case studies across Europe were used in a power analysis to estimate the number of farms that would need to be sampled per major farm type to detect changes in species richness over time for four taxa (vascular plants, earthworms, spiders and bees). A sampling design was developed to allocate spatially, across Europe, the farms that should be sampled. Cost estimates are provided for nine monitoring scenarios with differing robustness for detecting temporal changes in species numbers. These cost estimates are compared with the Common Agricultural Policy (CAP) budget (2014-2020) to determine the budget allocation required for the proposed farmland biodiversity monitoring. Results show that the bee indicator requires the highest number of farms to be sampled and the vascular plant indicator the lowest. The costs for the nine farmland biodiversity monitoring scenarios corresponded to 0·01%-0·74% of the total CAP budget and to 0·04%-2·48% of the CAP budget specifically allocated to environmental targets. Synthesis and applications. The results of the cost scenarios demonstrate that, based on the taxa and methods used in this study, a Europe-wide farmland biodiversity monitoring scheme would require a modest share of the Common Agricultural Policy budget. The monitoring scenarios are flexible and can be adapted or complemented with alternate data collection options (e.g. at national scale or voluntary efforts), data mobilization, data integration or modelling efforts.

Validatie van grondwaterstandsinformatie over verdroging : Fase 2: opzet en inrichting van een validatiemeetnet in het pilotgebied Boetelerveld

In het natuurgebied Boetelerveld is middels een kanssteekproef een validatiemeetnet ingericht waarmee de kwaliteit van gebiedsdekkende grondwaterstandinformatie kan worden vastgesteld. In dit validatiemeetnet is gedurende een jaar en zeven maanden op tien locaties viermaal daags de grondwaterstand gemeten met automatische drukopnemers, in een tot drie filters op verschillende diepten. Het doel van de metingen was om het validatiemeetnet in de praktijk te toetsen.