411 research outputs found
Stream valley catchments in times of climate change: an ecohydrological approach
No full textAerts, M.A.P.A. [Promotor]Bierkens, M.F.P. [Promotor]Bodegom, P.M. van [Copromotor
Groundwater System Identification through Time Series Analysis
No full textGroundwater, water in the ground. Although it is invisible, it is a vital resource for all terrestrial life (whether direct or indirect). Many processes interact with it. Rain recharges it, as it infiltrates the soil. Plant roots take it up, and their leaves evaporate it. It discharges to rivers and streams, and is abstracted with pumping wells. It is controlled with ditches and drainage means. Such processes and activities leave their traces in the groundwater level fluctuations. Careful analysis of these fluctuations may reveal much of the functioning of systems, and of the effects of individual factors. This is shown by many, but practiced by too few, as traditional time series analysis theory and software are complex. In this thesis, a new method of time series analysis is presented. Its continuous time formulation fits existing physical-hydrologic theory and methods well. It is shown that groundwater level responses generally take the shape of simple distribution functions. This notion, combined with the program Menyanthes that was developed, enable the quick and easy analysis of large numbers of time series. The spatial patterns that emerge in the results of multiple models literally add another dimension to the technique. As time series models are usually accurate also, they may be valuable to every (eco)hydrologist.Water ManagementCivil Engineering and Geoscience
Modular Factorization Pattern M.F.P
No full textModular Factorization Pattern M.F.P
Author: Marlon Fernando Polegato
Date: 04/25/2025
Introduction
This article presents a rigorous analysis of three variants of the Pattern-Based
Factorization Method (MFP), all based on decimal redistribution in the form nk = 10A +
d0. Each approach explores deterministic projections to detect actual divisors without
classical factorization. The three versions were implemented in C++ using parallelism,
optimization, and process prioritization control.
Section 1: Common Mathematical Foundatio
A theoretical framework for discontinuity capturing: Joining variational multiscale analysis and variation entropy theory
No full textIn this paper we show that the variational multiscale method together with the variation entropy concept form the underlying theoretical framework of discontinuity capturing. The variation entropy [M.F.P. ten Eikelder and I. Akkerman, Comput. Methods Appl. Mech. Engrg. 355 (2019) 261-283] is the recently introduced concept that equips total variation diminishing solutions with an entropy foundation. This is the missing ingredient in order to show that the variational multiscale method can capture sharp layers. The novel framework naturally equips the variational multiscale method with a class of discontinuity capturing operators. This class includes the popular YZβ method and methods based on the residual of the variation-entropy. The discontinuity capturing mechanisms do not contain ad hoc devices and appropriate length scales are derived. Numerical results obtained with quadratic NURBS are virtually oscillation-free and show sharp layers, which confirms the viability of the methodology.Accepted Author ManuscriptShip Hydromechanics and Structure
Space-time modeling of water table depth using a regionalized time series model and the Kalman filter
No full textWater authorities in the Netherlands are not only responsible for managing surface water, but also for managing the groundwater reserves. Particularly the water table depth is an important variable, determining agricultural production and the potential for nature development. Knowledge of the spatio-temporal variation of the water table depth is therefore vitally important for regional scale water management. This raises the following question: At what spatial density and which temporal frequency must the water table depth be observed to obtain a complete spatio-temporal picture at a required accuracy and at minimal costs. In this paper this problem is tackled by using a statistical space-time model of the water table depth in combination with a space-time Kalman filter. The statistical model is built as follows. A simple time series model (called ARX model) is used to describe water table depth as a function of precipitation surplus (precipitation minus potential evapotranspiration). The ARX model is calibrated first at locations where time series of water table depth are available (Bierkens et al., 1999). ARX parameters at non-visited locations are estimated through geostatistical interpolation using auxiliary information, such as surface elevation from a digital elevation model (DEM). The result is a so called regionalized ARX model or RARX model (Knotters and Bierkens, 2001). The parameters of the geostatistical model (i.e. the semivariogram) are estimated by embedding the RARX model in a space-time Kalman filter and minimisation of a maximum likelihood criterion built from the filter innovations. The resulting state-space model can be used for optimal space-time prediction of water table depth, space-time conditional simulation and network optimisation (Bierkens, 2001). The parameters of the RARX model can interpreted physically, such that the predicted water table depth can used to predict specific drainage discharge. Hence, it is possible to predict the total discharge from a catchment with predominantly groundwater flow. This way, it is also possible to assimilate discharge measurements to improve predictions of water table depth. A case study is presented where the RARX model and the Kalman filter are used for optimisation of an existing network of 233 piezometers in the water authority Reest and Wieden, the Netherlands. Water table depths are recorded two times a month for all locations. Observation and maintenance costs of this network are high. The accuracy of the existing network is analysed using the RARX model and the Kalman filter. The accuracy is both "modelled" (assuming an additive noise process that is discrete and white in time and continuous, coloured and multiGaussian in space) and estimated with cross-validation. Several options for decreasing observation efforts are analysed. A particularly promising option is observing a limited number of well placed locations with high frequency (i.e. using divers) and the remaining locations only occasionally
Regionalised time series models for water table depths
No full textIndex words: groundwater head, time series analysis, physical interpretation, resampling, stochastic simulation, accuracy, quantified uncertaintyBecause of its shallow depths, the water table is of significant importance for agriculture and nature conservation in the Netherlands. Water management therefore requires accurate information on the spatial and temporal variations of the water table depth. This information is preferably expressed in terms of probabilities, in order to enable risk assessment. Furthermore, to support strategic decisions in water policy, the information on the water table dynamics should reflect the prevailing climatic conditions (say, the average weather over a 30-year period). Since the number of observation wells and the lengths of the time series are limited for regional studies, spatio-temporal prediction methods should be able to incorporate additional measurements and additional information related to the water table depth.Stochastic methods are devised for estimating fluctuation characteristics representing the prevailing climatic and hydrologic conditions. These methods are based on various models for the dynamic relationship between precipitation surplus and water table depth: a physical descriptive, one-dimensional model, SWATRE, supplemented with a univariate time series model for the noise (SWATRE+ARMA), linear transfer function-noise models (TFN), dynamic regression models (DR) or autoregressive exogenous variable models (ARX), and nonlinear threshold autoregressive models (TARSO). These models are applied to extrapolate observed time series of water table depths, by using observed input series on the precipitation surplus having a length of 30 years. Uncertainty is accounted for by generating a large number of realisations using the stochastic model component. The models perform only slightly differently in simulating water table depths, despite their clearly different theoretical starting points. It is shown that a first-order ARX model can easily be expressed in terms of a water balance for a soil column. Moreover, the physically based ARX model can be applied in predicting the effects of human interventions in the hydrological regime on the water table dynamics.The ARX model is regionalised to a RARX model, by making its parameters dependent of the spatial co-ordinates. Because of their physical basis, the RARX model parameters can be guessed from auxiliary information such as a digital elevation model (DEM), digital topographic maps and digitally stored soil profile descriptions. Next, the guessed RARX parameters are used to transform a precipitation surplus series into a series of water table depths. Predictions obtained by this 'direct' method are compared with observed water table depths. The observed errors are used to correct the final predictions for systematic errors, and to perform stochastic simulations ('indirect' method). The RARX model is incorporated into a space-time Kalman filter algorithm, which enables predictions conditional to observed water table depths. A cross-validation experiment shows that Kalman filter approaches predict the temporal variation of the water table depths relatively precise, whereas the 'indirect' method yields relatively accurate estimates of expected water table depths, since systematic errors are small. The uncertainty about the temporal variation of the water table depth is underestimated by all methods evaluated. Given the sampling design, the accuracy of the uncertainty about the mean water table depth could not be assessed. Besides efforts to reduce uncertainty, it would be interesting to optimise sampling designs in order to obtain accurate estimates of uncertainty. </font
Skill and value of global seasonal streamflow forecasts
No full textIn our changing world, humans experience increasingly the negative consequences of
floods and droughts. Seasonal forecasts with lead times of several months, and covering larger areas are necessary to increase global preparedness. This thesis explores the potential of global hydrological models in operational seasonal forecasting applications, assesses the skill and value of global seasonal streamflow forecasts and investigates possible ways to improve the current skill and value.
To assess the prospect of applying a global hydrological model for seasonal forecasting, global terrestrial hydrology is simulated with the model PCR-GLOBWB. The model is forced with a meteorological dataset based on historical observations and model skill is assessed based on monthly discharges for twenty large rivers across the world. PCR-GLOBWB cannot forecast the historical hydrographs adequately for all basins but higher skills can be attained in forecasting the occurrence of monthly anomalies. The prospects for seasonal forecasting with PCR-GLOBWB or other comparable models are assessed to be positive.
The simulated hydrological response depends on both the initial hydrological conditions and the meteorological forcing. Uncertainty in both inputs is evaluated by comparing ESP/revESP forecast ensembles with retrospective model simulations driven by meteorological observations. The results are analysed in the context of prevailing hydroclimatic conditions for larger rivers across the globe. The influence of the initial conditions and meteorological forcing on forecasting skill is found to vary considerably according to location, season and lead time. For arctic and snow fed rivers, forecasts of high flows may benefit from assimilation of snow and ice data. In some snow fed basins where the onset of melting is highly sensitive to temperature changes, forecast skill depends on better climate prediction. Groundwater and surface water states also strongly influence the skill in very large rivers. In monsoonal basins, the variability of the monsoon dominates forecasting skill, except for those where snow and ice contribute to streamflow.
When the total skill is assessed in actual forecasting mode, actual seasonal meteorological
forecasts are used as input into PCR-GLOBWB. The model is forced with S3 seasonal meteorological forecast ensembles from the ECMWF as well as with probabilistic
meteorological ensembles obtained following the ESP procedure. Ensemble forecasts
of monthly discharges for twenty large rivers of the world are produced with lead times of up to six months. Analysis of the results suggest that forecasting skill decreases with increasing lead time and varies considerably by region and season. The performance of ECMWF S3 forecasts is close to that of the ESP forecasts. In the current setup, the forecasting skill is limited and needs to be improved before forecasts can be adopted for water management applications. However, even with little added skill, forecasts may still be useful for end-users, allowing them to decide for themselves if they should take the risk of using the forecast information.
The success of a hydrological forecasting system will ultimately be determined not only by its skill but also by its value to inform decision-making for water management. The interaction between skill and value is explored and possible ways to improve the value of seasonal hydrological forecasts on a global scale for water related applications are discussed with an emphasis on flood and drought mitigation. The ability of seasonal streamflow forecasting systems to predict the right category of an event months ahead is potentially valuable for many water-related applications. Seasonal hydrological forecasting on a global scale could be especially valuable for transboundary river basins as well as for developing regions of the world, where no effective local hydrological forecasting systems exist. The realization of the potential added value depends largely on the collaboration between forecast producers and users
Shedding light on the ‘invisible’ water crisis: Modelling past and future global surface water quality
No full textClean water is essential for supporting human livelihoods and maintaining ecosystem health. However, our knowledge of water quality is severely impaired by a lack of quantitative information. Being under-monitored and often imperceptible to the human eye, water pollution has been branded an “invisible crisis”. Protecting and improving the quality of surface waters globally is contingent upon an improved understanding of the problem and its drivers. Process-based models are tools that can supplement our knowledge of water quality beyond what is possible using in situ measurements alone.
This thesis introduces and applies the Dynamical Surface Water Quality (DynQual) model, a high-resolution global surface water quality model for simulating water temperature and concentrations of salinity (total dissolved solids; TDS), organic (biological oxygen demand; BOD) and pathogen (fecal coliform; FC) pollution. DynQual was used to provide a global assessment of past and current surface water quality. Modelled results demonstrate that surface water quality issues are globally relevant, with exceedances of key concentration thresholds for TDS, BOD and FC pollution occurring across all world regions albeit with different frequencies and magnitudes. Current year-round and multi-pollutant hotspots are located across northern India and eastern China, whereas trends towards surface water quality deterioration in the last ~40 years are most profound in Sub-Saharan Africa and southern Asia.
Process-based models provide unique opportunities to quantitatively assess the impact of future change on the availability and quality of water resources. This includes exploring the effectiveness of management strategies for improving water quality. In this thesis, DynQual was applied to assess the effectiveness of halving the proportion of untreated wastewater entering the environment by 2030 for improving ambient surface water quality. While substantial reductions in organic (BOD) and pathogen (FC) pollution are achieved, changes to the frequency and magnitude of water quality threshold exceedances drastically vary across world regions. Particularly in the developing world, reductions in pollutant loadings are locally effective but the transmission of pollution from upstream areas still leads to water quality issues downstream.
This thesis also presents the first assessment of the impact of global change on water quality, based on state-of-the-art projections of societal change and trajectories of climate change. Results indicate that the proportion of the global population exposed to salinity, organic and pathogen pollution by the end of the century ranges from 17 - 27%, 20 - 37% and 22 - 44%, respectively, with poor surface water quality disproportionately affecting people living in developing countries. Exhibiting the largest increases in both the absolute and relative number of people exposed to polluted surface water, irrespective of climate change and socioeconomic development scenario, this thesis concludes that Sub-Saharan Africa will increasingly become the key hotspot of surface water pollution.
Inability to meet our clean water demands is considered one of the major risks to humankind both in terms of likelihood and potential impacts. This thesis highlights the need to better understand and account for water quality aspects, in addition to water availability aspects, in order to achieve sustainable management of water resources globally
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