On the role of model structure in hydrological modeling: Understanding models

Modeling is an essential part of the science of hydrology. Models enable us to formulate what we know and perceive from the real world into a neat package. Rainfall-runoff models are abstract simplifications of how a catchment works. Within the research field of scientific rainfall-runoff modeling, the focus is shifting from performance to consistency with fundamental principles of hydrological processes. Despite all efforts there are still gaps in our understanding of how real catchments work. Additionally there are enormous endeavors to understand the behavior of rainfall-runoff mechanisms within a catchment. Examples of research items that characterize system behavior are; water residence time, travel time, differences between velocity and celerity, use of tracers and remote sensing techniques. Meanwhile not much attention is given to understanding how all this knowledge should be implemented in a model. This thesis is an attempt to understand the role of model structure and the related assumptions in making better use of the available knowledge on catchment processes. The starting point of this thesis is building a hydrological model based on topographical landscape units, using a newly introduced descriptor called HAND (Height above nearest drainage). The purpose of this part of research was to build a model which contains our perception of how rainfall-runoff mechanisms work for distinct landscapes, classified based on HAND and slope. Three different models have been developed from simple to complex. A simple model has a limited number of constraints while a complex model can have various constraints imposed. To have a realistic behavior of these models a set of constraints has been defined and imposed on the model parameters as well as model fluxes and states in a comparative fashion. After simulations with these models, it is observed that even without calibration and just by constraining, the complex model was able to simulate discharge within an acceptable range. Moreover, without the constraints the complex model shows higher parity with observation compared to the simple model. To refine this method even more, a simple parameter search strategy is proposed to satisfy all imposed constraints (chapters 3, 4 and 5). The fact that adding proper constraints can have such a positive impact on the accuracy of the outcome is the basis for further research into the structural elements and type of constraints imposed on the model. However to study the effect of structural changes in a model, the dominant effect of assumptions on state-discharge relations (parameterizations) had to be diminished. To achieve this, weak parameterization was introduced building on the concept of random functions. Using weak parameterization, it was found that structural complexity can improve themodel’s correspondence with observed data significantly. It was also found that there is a balance between model complexity, imposed constraints and performance of the model. Although increased complexity of a model can improve its performance, an over-constrained model can deviate from observed data. This means that, although a complex model may be structurally more favorable, the constraints, based on expert knowledge, should be set-up in such a way as to minimize the bias of the model outcome. This research can be a basis for the dialog between modelers and experimentalists. This approach can be used to investigate the behavior of the model in relation to its structural configuration, parameterizations and the set of imposed constraints (chapter 6). The last part of this thesis (chapter 7) covers the introduction of the concept of time consistent model parameters. Traditionally a part of the time series is kept for a separate evaluation of the model. However, during so-called SubPeriod calibration (SuPer calibration), parameter sets are evaluated based on their resulting model performance for different sub-periods of the entire observed time series. The parameter sets identified by SuPer calibration are different from the optimal parameter set found by calibration over the same period of data. This thesis concludes with a critical review of this research work together with discussions that took place during the research (chapter 8). This chapter is reflecting the novelty and deficiency of presented work and the battles to be fought in future.Water ManagementCivil Engineering and Geoscience

Kernel Density Independence Sampling based Monte Carlo Scheme (KISMCS) for inverse hydrological modeling

Posterior sampling methods are increasingly being used to describe parameter and model predictive uncertainty in hydrologic modelling. This paper proposes an alternative to random walk chains (such as DREAM-zs). We propose a sampler based on independence chains with an embedded feature of standardized importance weights based on Kernel density estimates. A Markov Chain Monte Carlo sampling algorithm is proposed with Metropolis-Hastings (M-H) updates using an independence sampler. The independence sampler ensures that candidate observations are drawn independently of the current state of a chain, thereby ensuring efficient exploration of the target distribution. The M-H acceptance-rejection criterion is used to sample across 3 chains, which ensures that the chains are well mixed. Kernel density estimation on last 600 samples in a chain is used to calculate standardized importance weights within the independence sampler to ensure fast convergence of sampled points to the target distribution. Its performance is contrasted with a state of the art algorithm, Differential Evolution Adaptive Metropolis (DREAM-zs), based on a toy 10 dimensional bi-modal Gaussian mixture distribution and HYMOD model based synthetic and real world case studies. The comparison of KISMCS and DREAM-zs is done based on their convergence to ‘true’ posterior parameter distributions in case of synthetics case studies and their convergence to a stationary distribution in case of real world hydrological modeling case studies.Water ManagementCivil Engineering and Geoscience

Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment

This paper presents a detailed performance and sensitivity analysis of a recently developed hydrological landscape classification method based on dominant runoff mechanisms. Three landscape classes are distinguished: wetland, hillslope and plateau, corresponding to three dominant hydrological regimes: saturation excess overland flow, storage excess sub-surface flow, and deep percolation. Topography, geology and land use hold the key to identifying these landscapes. The height above the nearest drainage (HAND) and the surface slope, which can be easily obtained from a digital elevation model, appear to be the dominant topographical controls for hydrological classification. In this paper several indicators for classification are tested as well as their sensitivity to scale and resolution of observed points (sample size). The best results are obtained by the simple use of HAND and slope. The results obtained compared well with the topographical wetness index. The HAND based landscape classification appears to be an efficient method to ''read the landscape'' on the basis of which conceptual models can be developed

Land classification based on hydrological landscape units

This paper presents a new type of hydrological landscape classification based on dominant runoff mechanisms. Three landscape classes are distinguished: wetland, hillslope and plateau, corresponding to three dominant hydrological regimes: saturation excess overland flow, storage excess sub-surface flow, and deep percolation. Topography, geology and land use hold the key to identifying these landscapes. The height above the nearest drain (HAND) and the surface slope, which can be readily obtained from a digital elevation model, appear to be the dominant topographical parameters for hydrological classification. In this paper several indicators for classification are tested as well as their sensitivity to scale and sample size. It appears that the best results are obtained by the simple use of HAND and slope. The results obtained compare well with field observations and the topographical wetness index. The new approach appears to be an efficient method to “read the landscape” on the basis of which conceptual models can be developed.Water ManagementCivil Engineering and Geoscience

Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration

Conceptual environmental system models, such as rainfall runoff models, generally rely on calibration for parameter identification. Increasing complexity of this type of models for better representation of hydrological process heterogeneity, typically makes parameter identification more difficult. Although various, potentially valuable, approaches for better parameter estimation have been developed, strategies to impose general conceptual understanding of how a catchment works into the process of parameter estimation has not been fully explored. In this study we assess the effects of imposing semi-quantitative, relational inequality constraints, based on expert-knowledge, for model development and parameter specification, efficiently exploiting the complexity of a semi-distributed model formulation. Making use of a topography driven rainfall-runoff modeling (FLEX-TOPO) approach, a catchment was delineated into three functional units, i.e., wetland, hillslope and plateau. Ranging from simple to complex, three model setups, FLEX^A, FLEX^B and FLEX^C were developed based on these functional units, where FLEX^A is a lumped representation of the study catchment, and the semi-distributed formulations FLEX^B and FLEX^C progressively introduce more complexity. In spite of increased complexity, FLEX^B and FLEX^C allow modelers to compare parameters, as well as states and fluxes, of their different functional units to each other, allowing the formulation of constraints that limit the feasible parameter space. We show that by allowing for more landscape-related process heterogeneity in a model, e.g., FLEX^C, the performance increases even without traditional calibration. The additional introduction of relational constraints further improved the performance of these models

Passive Air Sampling Survey of Polybrominated Diphenyl Ether in Private Cars: Implications for Sources and Human Exposure

In order to characterize polybrominated diphenyl ether (PBDE) contamination in vehicle interiors, airborne concentrations of polybrominated diphenyl ethers were investigated using PUF disk passive air samplers in 25 private cars. Passive air samplers were fixed inside the selected cars for a period of 4 to 6 weeks. EPBDE concentrations (sum of the 10 congeners) ranged between 0.01 and 8.2 ng/m 3 with respective arithmetic and geometric mean concentrations of 0.71 and 0.091 ng/m 3 . High concentrations of polybrominated diphenyl ethers found in cars might provide an important source of human exposure to PBDEs either via inhalation or dust ingestion. A driver spending 8 hours a day inside a contaminated car (the worst scenario) would receive a daily inhalation intake of 54 ng. Age of the vehicles was found to be the most influential factor affecting polybrominated diphenyl ether emission in car interiors (R=0.47, r<0.01). Furthermore, significant variations were observed in polybrominated diphenyl ether concentrations between cars from same manufacturer with similar ages. The median ratio of BDE 47:99 for air samples was 1.7 comparing with the respective values of ~1 and ~0.7 reported for BK 70-5DE and DE-71, suggesting these commercial formulations to be likely sources of polybrominated diphenyl ethers in the car indoor environments

An approach to identify time consistent model parameters: Sub-period calibration

Conceptual hydrological models rely on calibration for the identification of their parameters. As these models are typically designed to reflect real catchment processes, a key objective of an appropriate calibration strategy is the determination of parameter sets that reflect a “realistic” model behavior. Previous studies have shown that parameter estimates for different calibration periods can be significantly different. This questions model transposability in time, which is one of the key conditions for the set-up of a “realistic” model. This paper presents a new approach that selects parameter sets that provide a consistent model performance in time. The approach consists of testing model performance in different periods, and selecting parameter sets that are as close as possible to the optimum of each individual sub-period. While aiding model calibration, the approach is also useful as a diagnostic tool, illustrating tradeoffs in the identification of time-consistent parameter sets. The approach is applied to a case study in Luxembourg using the HyMod hydrological model as an example.Water ManagementCivil Engineering and Geoscience