TY - GEN A1 - Reinhardt, Julia A1 - Liersch, Stefan A1 - Abdeladhim, Mohamed Arbi A1 - Diallo, Mori A1 - Dickens, Chris A1 - Fournet, Samuel A1 - Hattermann, Fred Fokko A1 - Kabaseke, Clovis A1 - Muhumuza, Moses A1 - Mul, Marloes L. A1 - Pilz, Tobias A1 - Otto, Ilona M. A1 - Walz, Ariane T1 - Systematic evaluation of scenario assessments supporting sustainable integrated natural resources management BT - evidence from four case studies in Africa T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Scenarios have become a key tool for supporting sustainability research on regional and global change. In this study we evaluate four regional scenario assessments: first, to explore a number of research challenges related to sustainability science and, second, to contribute to sustainability research in the specific case studies. The four case studies used commonly applied scenario approaches that are (i) a story and simulation approach with stakeholder participation in the Oum Zessar watershed, Tunisia, (ii) a participatory scenario exploration in the Rwenzori region, Uganda, (iii) a model-based prepolicy study in the Inner Niger Delta, Mali, and (iv) a model coupling-based scenario analysis in upper Thukela basin, South Africa. The scenario assessments are evaluated against a set of known challenges in sustainability science, with each challenge represented by two indicators, complemented by a survey carried out on the perception of the scenario assessments within the case study regions. The results show that all types of scenario assessments address many sustainability challenges, but that the more complex ones based on story and simulation and model coupling are the most comprehensive. The study highlights the need to investigate abrupt system changes as well as governmental and political factors as important sources of uncertainty. For an in-depth analysis of these issues, the use of qualitative approaches and an active engagement of local stakeholders are suggested. Studying ecological thresholds for the regional scale is recommended to support research on regional sustainability. The evaluation of the scenario processes and outcomes by local researchers indicates the most transparent scenario assessments as the most useful. Focused, straightforward, yet iterative scenario assessments can be very relevant by contributing information to selected sustainability problems. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 930 KW - Africa KW - global and regional change KW - integrated assessments KW - participatory research KW - sustainability science Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-445784 SN - 1866-8372 IS - 930 ER - TY - JOUR A1 - Reinhardt, Julia A1 - Liersch, Stefan A1 - Abdeladhim, Mohamed Arbi A1 - Diallo, Mori A1 - Dickens, Chris A1 - Fournet, Samuel A1 - Hattermann, Fred Fokko A1 - Kabaseke, Clovis A1 - Muhumuza, Moses A1 - Mul, Marloes L. A1 - Pilz, Tobias A1 - Otto, Ilona M. A1 - Walz, Ariane T1 - Systematic evaluation of scenario assessments supporting sustainable integrated natural resources management BT - evidence from four case studies in Africa JF - Ecology and society : a journal of integrative science for resilience and sustainability N2 - Scenarios have become a key tool for supporting sustainability research on regional and global change. In this study we evaluate four regional scenario assessments: first, to explore a number of research challenges related to sustainability science and, second, to contribute to sustainability research in the specific case studies. The four case studies used commonly applied scenario approaches that are (i) a story and simulation approach with stakeholder participation in the Oum Zessar watershed, Tunisia, (ii) a participatory scenario exploration in the Rwenzori region, Uganda, (iii) a model-based prepolicy study in the Inner Niger Delta, Mali, and (iv) a model coupling-based scenario analysis in upper Thukela basin, South Africa. The scenario assessments are evaluated against a set of known challenges in sustainability science, with each challenge represented by two indicators, complemented by a survey carried out on the perception of the scenario assessments within the case study regions. The results show that all types of scenario assessments address many sustainability challenges, but that the more complex ones based on story and simulation and model coupling are the most comprehensive. The study highlights the need to investigate abrupt system changes as well as governmental and political factors as important sources of uncertainty. For an in-depth analysis of these issues, the use of qualitative approaches and an active engagement of local stakeholders are suggested. Studying ecological thresholds for the regional scale is recommended to support research on regional sustainability. The evaluation of the scenario processes and outcomes by local researchers indicates the most transparent scenario assessments as the most useful. Focused, straightforward, yet iterative scenario assessments can be very relevant by contributing information to selected sustainability problems. KW - Africa KW - global and regional change KW - integrated assessments KW - participatory research KW - sustainability science Y1 - 2018 U6 - https://doi.org/10.5751/ES-09728-230105 SN - 1708-3087 VL - 23 IS - 1 PB - Resilience Alliance CY - Wolfville ER - TY - JOUR A1 - Pilz, Tobias A1 - Delgado, José Miguel Martins A1 - Voss, Sebastian A1 - Vormoor, Klaus Josef A1 - Francke, Till A1 - Cunha Costa, Alexandre A1 - Martins, Eduardo A1 - Bronstert, Axel T1 - Seasonal drought prediction for semiarid northeast Brazil BT - what is the added value of a process-based hydrological model? JF - Hydrology and Earth System Sciences N2 - The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach. Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models. KW - Water Availability KW - Uncertainty Processor KW - Forecasting Framework KW - Sediment Transport KW - Reservoir Networks KW - Jaguaribe Basin KW - Climate KW - Precipitation KW - Nordeste KW - Connectivity Y1 - 2019 U6 - https://doi.org/10.5194/hess-23-1951-2019 SN - 1027-5606 SN - 1607-7938 VL - 23 SP - 1951 EP - 1971 PB - Copernicus Publications CY - Göttingen ER - TY - GEN A1 - Pilz, Tobias A1 - Delgado, José Miguel Martins A1 - Voss, Sebastian A1 - Vormoor, Klaus Josef A1 - Francke, Till A1 - Cunha Costa, Alexandre A1 - Martins, Eduardo A1 - Bronstert, Axel T1 - Seasonal drought prediction for semiarid northeast Brazil BT - what is the added value of a process-based hydrological model? T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach. Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 702 KW - Water Availability KW - Uncertainty Processor KW - Forecasting Framework KW - Sediment Transport KW - Reservoir Networks KW - Jaguaribe Basin KW - Climate KW - Precipitation KW - Nordeste KW - Connectivity Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427950 SN - 1866-8372 IS - 702 ER - TY - THES A1 - Pilz, Tobias T1 - Pursuing the understanding of uncertainties in hydrological modelling N2 - Hydrological models are important tools for the simulation and quantification of the water cycle. They therefore aid in the understanding of hydrological processes, prediction of river discharge, assessment of the impacts of land use and climate changes, or the management of water resources. However, uncertainties associated with hydrological modelling are still large. While significant research has been done on the quantification and reduction of uncertainties, there are still fields which have gained little attention so far, such as model structural uncertainties that are related to the process implementations in the models. This holds especially true for complex process-based models in contrast to simpler conceptual models. Consequently, the aim of this thesis is to improve the understanding of structural uncertainties with focus on process-based hydrological modelling, including methods for their quantification. To identify common deficits of frequently used hydrological models and develop further strategies on how to reduce them, a survey among modellers was conducted. It was found that there is a certain degree of subjectivity in the perception of modellers, for instance with respect to the distinction of hydrological models into conceptual groups. It was further found that there are ambiguities on how to apply a certain hydrological model, for instance how many parameters should be calibrated, together with a large diversity of opinion regarding the deficits of models. Nevertheless, evapotranspiration processes are often represented in a more physically based manner, while processes of groundwater and soil water movement are often simplified, which many survey participants saw as a drawback. A large flexibility, for instance with respect to different alternative process implementations or a small number of parameters that needs to be calibrated, was generally seen as strength of a model. Flexible and efficient software, which is straightforward to apply, has been increasingly acknowledged by the hydrological community. This work further elaborated on this topic in a twofold way. First, a software package for semi-automated landscape discretisation has been developed, which serves as a tool for model initialisation. This was complemented by a sensitivity analysis of important and commonly used discretisation parameters, of which the size of hydrological sub-catchments as well as the size and number of hydrologically uniform computational units appeared to be more influential than information considered for the characterisation of hillslope profiles. Second, a process-based hydrological model has been implemented into a flexible simulation environment with several alternative process representations and a number of numerical solvers. It turned out that, even though computation times were still long, enhanced computational capabilities nowadays in combination with innovative methods for statistical analysis allow for the exploration of structural uncertainties of even complex process-based models, which up to now was often neglected by the modelling community. In a further study it could be shown that process-based models may even be employed as tools for seasonal operational forecasting. In contrast to statistical models, which are faster to initialise and to apply, process-based models produce more information in addition to the target variable, even at finer spatial and temporal scales, and provide more insights into process behaviour and catchment functioning. However, the process-based model was much more dependent on reliable rainfall forecasts. It seems unlikely that there exists a single best formulation for hydrological processes, even for a specific catchment. This supports the use of flexible model environments with alternative process representations instead of a single model structure. However, correlation and compensation effects between process formulations, their parametrisation, and other aspects such as numerical solver and model resolution, may lead to surprising results and potentially misleading conclusions. In future studies, such effects should be more explicitly addressed and quantified. Moreover, model functioning appeared to be highly dependent on the meteorological conditions and rainfall input generally was the most important source of uncertainty. It is still unclear, how this could be addressed, especially in the light of the aforementioned correlations. The use of innovative data products, e.g.\ remote sensing data in combination with station measurements, and efficient processing methods for the improvement of rainfall input and explicit consideration of associated uncertainties is advisable to bring more insights and make hydrological simulations and predictions more reliable. N2 - Hydrologische Modelle sind wichtige Werkzeuge zur Simulation und Quantifizierung des Wasserkreislaufs. Sie helfen bei der explorativen Analyse hydrologischer Prozesse, Abflussvorhersage, Abschätzung der Folgen von Klima- und Landnutzungswandel oder dem Management von Wasserressourcen. Allerdings sind die mit der hydrologischen Modellierung einhergehenden Unsicherheiten noch immer groß. Trotz der zahlreichen Forschungsarbeiten auf dem Gebiet der Quantifizierung und Reduktion der Unsicherheiten gibt es einige Bereiche, die bisher wenig erforscht wurden, wie beispielsweise strukturelle Unsicherheiten, welche sich unter anderem auf die Prozessimplementation in den Modellen beziehen. Dies betrifft vor allem komplexe prozessbasierte Modelle im Gegensatz zu einfacheren konzeptionellen Modellen. Gegenstand dieser Arbeit ist es daher, das Verständnis struktureller Unsicherheiten sowie Methoden für deren Quantifizierung innerhalb prozessbasierter hydrologischer Modellanwendungen zu erweitern. Zur Identifikation typischer Defizite hydrologischer Modelle und Erarbeitung von Lösungsstrategien, um diese zu reduzieren, wurde eine Umfrage unter Modellanwendern durchgeführt. Dabei stellte sich heraus, dass ein hohes Maß an Subjektivität in der Wahrnehmung des Themas unter Modellieren herrscht, beispielsweise bei der Einordnung hydrologischer Modelle in konzeptionelle Klassen. Des Weiteren gibt es Unklarheiten in der Art und Weise, wie ein bestimmtes hydrologisches Modell angewendet werden sollte, wie etwa hinsichtlich der Kalibrierung bestimmter Parameter, sowie vielschichtige Auffassungen bezüglich der Modelldefizite. Letztlich stellte sich jedoch heraus, dass Verdunstungsprozesse vor allem physikalisch basiert abgebildet werden, während Prozesse im Bereich des Grundwassers und der Bodenwasserbewegung häufig vereinfacht abgebildet werden, was von vielen Umfrageteilnehmern als Nachteil empfunden wurde. Generell als Stärke wurde die Flexibilität einiger Modelle empfunden, zum Beispiel wenn diese verschiedene Implementationen eines Prozesses enthalten oder wenn nur eine geringe Zahl an Parametern kalibriert werden muss. Flexible und effiziente Software, die darüber hinaus einfach zu bedienen ist, wird von der hydrologischen Gemeinschaft immer stärker in den Vordergrund gebracht. Daher greift diese Arbeit das Thema in zweifacher Hinsicht auf. Zum einen wurde ein Softwarepaket zur halbautomatischen Landschaftsdiskretisierung entwickelt, welches zudem als Werkzeug zur Modellinitialisierung gedacht ist. Damit einhergehend wurde eine Sensitivitätsanalyse wichtiger und häufig genutzter Diskretisierungsparameter durchgeführt, bei der die Größe hydrologischer Teileinzugsgebiete sowie die Anzahl und Größe hydrologischer Elementarflächen sich als maßgeblicher herausstellte als etwa raumbezogene Informationen zur Charakterisierung der Hangprofile. Zum anderen wurde ein prozessbasiertes hydrologisches Modell in eine flexible Softwareumgebung integriert, der verschiedene alternative Prozessformulierungen sowie numerische Differentialgleichungslöser hinzugefügt wurden. Die Analyse struktureller Unsicherheiten komplexer prozessbasierter Modelle wurde in der Vergangenheit von der hydrologischen Gemeinschaft mit Verweis auf zu lange Rechenzeit oft vernachlässigt. Es zeigte sich jedoch, dass die mittlerweile zur Verfügung stehenden Computerressourcen, vor allem in Kombination mit innovativen statistischen Analyseverfahren, derartige Untersuchungen bereits ermöglichen. In einer weiteren Studie konnte zudem gezeigt werden, dass auch prozessbasierte Modelle für den operationellen Einsatz in der saisonalen Vorhersage geeignet sind. Im Gegensatz zu statistischen Modellen, welche schneller initialisierbar und anwendbar sind, produzieren prozessbasierte Modelle neben der eigentlichen Zielgröße weitere potentiell relevante Informationen in höherer räumlicher und zeitlicher Auflösung und geben zudem tiefere Einblicke in die generelle Wirkungsweise der hydrologischen Prozesse in einem Einzugsgebiet. In der Studie stellte sich jedoch ebenso heraus, dass zuverlässige Niederschlagsvorhersagen für ein prozessbasiertes Modell umso wichtiger sind. Allgemein erscheint es unwahrscheinlich, dass eine einzelne optimale Implementation für einen hydrologischen Prozess, selbst innerhalb eines bestimmten Einzugsgebietes, überhaupt existiert. Die Nutzung flexibler Modellumgebungen mit alternativen Prozessbeschreibungen anstelle eines einzelnen Modells scheint deshalb große Vorteile zu bringen. Mögliche Korrelationen zwischen Prozessbeschreibungen, deren Parametrisierung, sowie anderen Aspekte wie numerischen Lösern und Modellauflösung, können jedoch zu überraschenden Ergebnissen und letztlich falschen Schlussfolgerungen führen. In zukünftigen Studien sollten solche Effekte daher explizit berücksichtigt und quantifiziert werden. Darüber hinaus wird die Leistungsfähigkeit eines Modells maßgeblich von den meteorologischen Randbedingungen beeinflusst. Vor allem der Niederschlag erwies sich innerhalb dieser Arbeit als wichtigste Ursache für Unsicherheiten in der Modellierung. Allerdings ist nicht vollständig klar, wie dieser Umstand berücksichtigt werden kann und inwiefern die zuvor genannten Korrelationen hier einen Einfluss haben. Die Nutzung innovativer Datenprodukte, zum Beispiel Fernerkundungsdaten verbunden mit Stationsmessungen, in Kombination mit effizienten Prozessierungsalgorithmen zur Verbesserung des Niederschlagsinputs und expliziten Beachtung einhergehender Unsicherheiten wird angeraten. Dies verspricht bessere Einblicke in die Zusammenhänge verschiedener Unsicherheitsquellen zu gewinnen und letztlich hydrologische Simulationen und Vorhersagen zuverlässiger zu machen. T2 - Beiträge zum Verständnis der Unsicherheiten in der hydrologischen Modellierung KW - hydrology KW - hydrological modelling KW - uncertainties KW - Hydrologie KW - hydrologische Modellierung KW - Unsicherheiten Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-476643 ER - TY - JOUR A1 - Pilz, Tobias A1 - Francke, Till A1 - Bronstert, Axel T1 - lumpR 2.0.0: an R package facilitating landscape discretisation for hillslope-based hydrological models JF - Geoscientific model development : an interactive open access journal of the European Geosciences Union N2 - The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all. Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation. In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes. Y1 - 2017 U6 - https://doi.org/10.5194/gmd-10-3001-2017 SN - 1991-959X SN - 1991-9603 VL - 10 SP - 3001 EP - 3023 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Pilz, Tobias A1 - Francke, Till A1 - Bronstert, Axel T1 - lumpR 2.0.0: an R package facilitating landscape discretisation for hillslope-based hydrological models N2 - The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all. Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation. In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 389 Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-402880 ER - TY - JOUR A1 - Pilz, Tobias A1 - Francke, Till A1 - Bronstert, Axel T1 - lumpR 2.0.0: an R package facilitating landscape discretisation for hillslope-based hydrological models JF - Geoscientific model development N2 - The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all. Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation. In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes. Y1 - 2017 U6 - https://doi.org/10.5194/gmd-10-3001-2017 SN - 1991-959X SN - 1991-9603 VL - 10 SP - 3001 EP - 3023 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Pilz, Tobias A1 - Francke, Till A1 - Baroni, Gabriele A1 - Bronstert, Axel T1 - How to Tailor my process-based hydrological model? BT - dynamic identifiability analysis of flexible model structures JF - Water resources research N2 - In the field of hydrological modeling, many alternative representations of natural processes exist. Choosing specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. In addition, the numerical integration of the underlying differential equations and parametrization of model structures influence model performance. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build an ensemble of semidistributed, process-based hydrological model configurations with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the framework to identify the most adequate model. While an optimal model configuration could not be clearly distinguished, interesting results were obtained when relating model identifiability with hydro-meteorological boundary conditions. For instance, we tested the Penman-Monteith and Shuttleworth & Wallace evapotranspiration models and found that the former performs better under wet and the latter under dry conditions. Parametrization of model structures plays a dominant role as it can compensate for inadequate process representations and poor numerical solvers. Therefore, it was found that numerical solvers of high order of accuracy do often, though not necessarily, lead to better model performance. The proposed coupled framework proved to be a straightforward diagnostic tool for model building and hypotheses testing and shows potential for more in-depth analysis of process implementations and catchment functioning. KW - identifiability analysis KW - flexible model KW - numerics KW - model structure KW - WASA-SED KW - ECHSE Y1 - 2020 U6 - https://doi.org/10.1029/2020WR028042 SN - 0043-1397 SN - 1944-7973 VL - 56 IS - 8 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Yalew, S. G. A1 - Pilz, Tobias A1 - Schweitzer, C. A1 - Liersch, Stefan A1 - van der Kwast, J. A1 - van Griensven, A. A1 - Mul, Marloes L. A1 - Dickens, Chris A1 - van der Zaag, Pieter T1 - Coupling land-use change and hydrologic models for quantification of catchment ecosystem services JF - Environmental modelling & software with environment data news N2 - Representation of land-use and hydrologic interactions in respective models has traditionally been problematic. The use of static land-use in most hydrologic models or that of the use of simple hydrologic proxies in land-use change models call for more integrated approaches. The objective of this study is to assess whether dynamic feedback between land-use change and hydrology can (1) improve model performances, and/or (2) produce a more realistic quantification of ecosystem services. To test this, we coupled a land-use change model and a hydrologic mode. First, the land-use change and the hydrologic models were separately developed and calibrated. Then, the two models were dynamically coupled to exchange data at yearly time-steps. The approach is applied to a catchment in South Africa. Performance of coupled models when compared to the uncoupled models were marginal, but the coupled models excelled at the quantification of catchment ecosystem services more robustly. KW - Model coupling KW - Ecosystem services KW - Integrated modelling KW - Land and water Y1 - 2018 U6 - https://doi.org/10.1016/j.envsoft.2018.08.029 SN - 1364-8152 SN - 1873-6726 VL - 109 SP - 315 EP - 328 PB - Elsevier CY - Oxford ER -