ORCID Profile
0000-0003-1149-5080
Current Organisation
Uppsala University
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Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/IAHS2022-363
Abstract: & & Water resources management and hydrological risk reduction require anticipation of emergent (unexpected or unintended) phenomena as fundamental dynamics of complex human-water systems. Explaining and characterizing these sociohydrological phenomena is a central focus of Panta Rhei& #8211 Everything Flows, the Scientific Decade of the International Association of Hydrological Sciences (2013-2022). Here we use System Dynamics& #8217 archetypes to describe and classify socio-hydrological phenomena emerging from nonlinear interactions between human and water systems. Archetypes illustrate dynamic behaviours that are frequently observed across (seemingly) different systems, contexts or problem settings. They include intended or unintended actions and reactions with explicit recognition of delays in reaction times. Reinforcing (or positive) and balancing (or negative) feedback loops and system boundaries are key elements for describing archetypes. In the study of human-water systems, the most common archetypes are Limits to Growth, Fixes that Backfire, Success to the Successful, and Tragedy of the Commons. Using system archetypes to generalize the phenomena in coupled human-water systems allows for the comparison among case studies along with the integration of multiple dimensions, thereby contributing to the development of new theories which provide common causes and explanations. We first present several classes of sociohydrological phenomena that have been explored over the past decade, and then discuss their role in explaining the dynamics of human-water systems. Finally, we engage with the implications for integrated water resources management, hydrological risk reduction, and water governance.& &
Publisher: Unpublished
Date: 2018
Publisher: Department of Water Resources Engineering, Lund University
Date: 2013
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2017
Publisher: California Digital Library (CDL)
Date: 04-2019
Publisher: Wiley
Date: 28-11-2018
Publisher: Elsevier BV
Date: 05-2021
Publisher: Wiley
Date: 26-11-2018
Publisher: Center for Open Science
Date: 08-12-2020
Abstract: Catchment models are conventionally evaluated in terms of their response surface or likelihood surface constructed from model runs using different sets of model parameters. Model evaluation methods are mainly based upon the concept of the equifinality of model structures or parameter sets. The operational definition of equifinality is that multiple model structures arameters are equally capable of producing acceptable simulations of catchment processes such as runoff. Examining various aspects of this convention, in this thesis I demonstrate their shortcomings and introduce improvements including new approaches and insights for evaluating catchment models as multiple working hypotheses (MWH). First (Chapter 2), arguing that there is more to equifinality than just model structures arameters, I propose a theoretical framework to conceptualise various facets of equifinality, based on a meta-synthesis of a broad range of literature across geosciences, system theory, and philosophy of science. I distinguish between process-equifinality (equifinality within the real-world systems rocesses) and model-equifinality (equifinality within models of real-world systems), explain various aspects of each of these two facets, and discuss their implications for hypothesis testing and modelling of hydrological systems under uncertainty. Second (Chapter 3), building up on this theoretical framework, I propose that characterising model-equifinality based on model internal fluxes — instead of model parameters which is the current approach to account for model-equifinality — provides valuable insights for evaluating catchment models. I developed a new method for model evaluation — called flux mapping — based on the equifinality of runoff generating fluxes of large ensembles of catchment model simulations (1 million model runs for each catchment). Evaluating the model behaviour within the flux space is a powerful approach, beyond the convention, to formulate testable hypotheses for runoff generation processes at the catchment scale. Third (Chapter 4), I further explore the dependency of the flux map of a catchment model upon the choice of model structure and parameterisation, error metric, and data information content. I compare two catchment models (SIMHYD and SACRAMENTO) across 221 Australian catchments (known as Hydrologic Reference Stations, HRS) using multiple error metrics. I particularly demonstrate the fundamental shortcomings of two widely used error metrics — i.e. Nash–Sutcliffe efficiency and Willmott’s refined index of agreement — in model evaluation. I develop the skill score version of Kling–Gupta efficiency (KGEss), and argue it is a more reliable error metric that the other metrics. I also compare two strategies of random s ling (Latin Hypercube S ling) and guided search (Shuffled Complex Evolution) for model parameterisation, and discuss their implications in evaluating catchment models as MWH. Finally (Chapter 5), I explore how catchment characteristics (physiographic, climatic, and streamflow response characteristics) control the flux map of catchment models (i.e. runoff generation hypotheses). To this end, I formulate runoff generating hypotheses from a large ensemble of SIMHYD simulations (1 million model runs in each catchment). These hypotheses are based on the internal runoff fluxes of SIMHYD — namely infiltration excess overland flow, interflow and saturation excess overland flow, and baseflow — which represent runoff generation at catchment scale. I examine the dependency of these hypotheses on 22 different catchment attributes across 186 of the HRS catchments with acceptable model performance and sufficient parameter s ling. The model performance of each simulation is evaluated using KGEss metric benchmarked against the catchment-specific calendar day average observed flow model, which is more informative than the conventional benchmark of average overall observed flow. I identify catchment attributes that control the degree of equifinality of model runoff fluxes. Higher degree of flux equifinality implies larger uncertainties associated with the representation of runoff processes at catchment scale, and hence pose a greater challenge for reliable and realistic simulation and prediction of streamflow. The findings of this chapter provides insights into the functional connectivity of catchment attributes and the internal dynamics of model runoff fluxes.
Publisher: American Geophysical Union (AGU)
Date: 11-2019
DOI: 10.1029/2018WR023750
Publisher: Copernicus GmbH
Date: 03-11-2018
Publisher: Springer Science and Business Media LLC
Date: 18-01-2015
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-9888
Abstract: & & Question #20 of the UPH aspires to disentangle and reduce model prediction uncertainty. One feasible approach is to first formulate the relationship between variability (of real-world hydrological processes and catchment characteristics) and uncertainty (of model components and variables), which links the UPH theme of & #8220 modelling methods& #8221 to & #8220 time variability and change& #8221 and & #8220 space variability and scaling& #8221 . Building on this premise, we explored the relationship between runoff generation hypotheses, derived from a large ensemble of catchment model simulations, and catchment characteristics (physiographic, climatic, and streamflow response characteristics) across a large s le of 221 Australian catchments. Using ensembles of 10& sup& & & /sup& runs of SIMHYD model for each catchment, runoff generation hypotheses were formulated based on the interaction of 3 runoff generating fluxes of SIMHYD, namely intensity-based, wetness-based, and slow responses. The hypotheses were derived from model runs with acceptable performance and sufficient parameter s ling. For model performance acceptability, we benchmarked Kling-Gupta Efficiency (KGE) skill score against the calendar day average observed flow, a catchment-specific and more informative benchmark than the conventional observed flow mean. The relative parameter s ling sufficiency was also defined based on the comparative efficacy of two common model parameterisation routines of Latin Hypercube S ling and Shuffled Complex Evolution for each catchment. Across 186 catchments with acceptable catchment models, we examined the association of uncertain runoff generation hypotheses (i.e. ensemble of modeled runoff fluxes) with 22 catchment attributes. We used the Flux Mapping method (0.1029/2018WR023750) to characterise the uncertainty of runoff generation hypotheses, and a range of daily and annual summary statistics to characterise catchment attributes. Among the metrics used, Spearman rank correlation coefficient (R& sub& s& /sub& ) was the most informative metric to capture the functional connectivity of catchment attributes with the internal dynamics of model runoff fluxes, compared to linear Pearson correlation and distance correlation coefficients. We found that streamflow characteristics generally have the most important influence on runoff generation hypotheses, followed by climate and then physiographic attributes. Particularly, daily flow coefficient of variability (Qcv) and skewness (Q Skewness), followed by the same summary statistics of precipitation (Pcv and& P Skewness), were most important. These four attributes are strongly correlated with one another, and represent the dynamics of the rainfall-runoff signal within a catchment system. A higher& Pcv& denotes a higher day-to-day variability in rainfall on the catchment, responded by a higher& Qcv& flow response. A higher variability in rainfall propagates through the catchment model and translates into a higher degree of equifinality in model runoff fluxes, which implies larger uncertainties of runoff generation hypotheses at catchment scale, and hence a greater challenge for reliable/realistic simulation and prediction of streamflow.& &
Publisher: Copernicus GmbH
Date: 06-07-2023
DOI: 10.5194/EMS2023-609
Abstract: Advances in impact modelling and numerical weather forecasting have allowed accurate drought monitoring and skillful forecasts that can drive decisions at the regional scale. State-of-the-art drought early warning systems are currently based on statistical drought indicators, which do not account for regional vulnerabilities, and hence neglect the socio-economic impact for initiating actions. The transition from conventional physical forecasts of droughts towards impact-based forecasting (IbF) is a recent paradigm shift in early warning services, to ultimately bridge the gap between science and action. The demand to generate predictions of & #8220 what the weather will do& #8221 underpins the rising interest in drought IbF across all weather-sensitive sectors. Despite the socio-economic benefits are expected to be high, migrating to this new paradigm presents myriad challenges. In this paper, we outline the progress in IbF of drought, and present a road map highlighting current challenges and corresponding needs for advancing this emerging field. More specifically, we identify the main disconnects and biases of the science and practice of IbF of drought, namely contextual bias (inadequate accounting for the spatio-sectoral dynamics of vulnerability and exposure), data bias (mainly textual and lacking collection protocols for all sectors), model bias (mostly reliant on machine learning models), sectoral bias (mainly agriculture in the science), and geography (mainly Europe for the science, and developing regions in the practice). Our vision is to facilitate the IbF progress and its use in making informed and timely decisions on mitigation measures, thus minimizing the impacts of droughts globally.& & & & & &
Publisher: Informa UK Limited
Date: 02-07-2019
Publisher: Wiley
Date: 25-10-2023
DOI: 10.1002/WAT2.1698
Publisher: Elsevier BV
Date: 02-2019
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-6810
Abstract: Climate services are expected to deliver better climate adaptation by providing decision-makers with timely, salient, credible, legitimate, and accessible climate information. Nonetheless, climate services& #8217 impact on long-term adaptation remains poorly understood due to their ambiguous protocols, quality standards, and inadequate monitoring and evaluation processes.The aim of this study is to present the underpinnings of a framework representing the causal mechanisms and feedback interactions between adaptation to hydrometeorological extremes, i.e. floods and droughts, and climate services among the partner living labs of the I-CISK project (icisk.eu). To this end, a qualitative investigation based on interviews and surveys of the living labs& #8217 stakeholders is performed. Following, the findings from the qualitative analysis are iteratively discussed with the stakeholders and presented as a causal loop diagram, highlighting feedback loops in the coupled human-climate system. Finally, the emerging dynamics are described using system archetypes.This research offers a systemic tool for evaluating the long-term dynamics of adaptation to hydrometeorological extremes while building the bases for further research in the living labs. Moreover, it shows the efficacy of system dynamics tools for informing adaptive policy-making.
Publisher: American Geophysical Union (AGU)
Date: 03-2021
DOI: 10.1029/2020EF001843
Abstract: The achievement of global sustainability agendas, such as the Sustainable Development Goals, relies on transformational change across society, economy, and environment that are co‐created in a transdisciplinary exercise by all stakeholders. Within this context, environmental and societal change is increasingly understood and represented via participatory modeling for genuine engagement with multiple collaborators in the modeling process. Despite the ersity of participatory modeling methods to promote engagement and co‐creation, it remains uncertain what the extent and modes of participation are in different contexts, and how to select the suitable methods to use in a given situation. Based on a review of available methods and specification of potential contextual requirements, we propose a unifying framework to guide how collaborators of different backgrounds can work together and evaluate the suitability of participatory modeling methods for co‐creating sustainability pathways. The evaluation of method suitability promises the integration of concepts and approaches necessary to address the complexities of problems at hand while ensuring robust methodologies based on well‐tested evidence and negotiated among participants. Using two illustrative case studies, we demonstrate how to explore and evaluate the choice of methods for participatory modeling in varying contexts. The insights gained can inform creative participatory approaches to pathway development through tailored combinations of methods that best serve the specific sustainability context of particular case studies.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-8211
Abstract: & & A long-standing research issue, in the hydrological sciences and beyond, is that of developing methods to evaluate the predictive/forecasting skill, errors and uncertainties of a model (or model ensembles). Various methods have been proposed for characterising time series residuals, i.e. the differences between observed (or target) and modelled (or estimate) time series. Most notably, the Taylor Diagram summarises model performance via a single plot based on three related metrics: the (linear Pearson) correlation, standard deviation, and root mean squared differences of one or multiple pairs of target and estimate time series. Despite its theoretical elegance and widespread use, the Taylor diagram does not account for bias errors, which is an important summary statistic for evaluating model performance. Further, it is very common to evaluate, compare, and report on model & #8220 skill& #8221 by use of a single aggregate metric value, even when a vector of metrics is used to calibrate/train the model most commonly this is a dimensionless efficiency metric such as Nash-Sutcliffe Efficiency (NSE) or Kling-Gupta Efficiency (KGE). Such & #8220 efficiency& #8221 metrics typically aggregate over multiple types of residual behaviours: for ex le the most commonly used version of KGE is based on correlation, bias, and variability errors, although the authors recommended that it should be applied in a context-dependent fashion based on which model behaviours are deemed to be important to a given situation. Nevertheless, the use of a single summary value fails to account for the interactions among the error component terms, which can be quite informative for the evaluation and benchmarking of models. In this study, we propose a new diagram that is as easy to use and interpret as the Taylor Diagram, while also accounting for bias. We further suggest a new convention for reporting model skill that is based on foundational error terms. Our vision is that this new diagram and convention will enable researchers and practitioners to better interpret and report model performance. We provide multiple numerical ex les to illustrate how this approach can be used for evaluating performance in the context of multi-model and multi-catchment (large-s le) studies.& &
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-11716
Abstract: & & Hydrological models are conventionally evaluated in terms of their response surface or likelihood surface constructed with the model parameter space. To evaluate models as hypotheses, we developed the method of & em& Flux Mapping& /em& to construct a hypothesis space based on model process representation. Here we defined the hypothesis space based on dominant runoff generating mechanisms, and acceptable model runs are defined as total simulated flow with similar (and minimal) model error simulated by distinct combinations of runoff components. We demonstrate that the hypothesis space in each modeling case is the result of interplay between the factors of model structure, parameter s ling, choice of error metric, and data information content. The aim of this study is to disentangle the role of each factor in this interplay. We used two model structures (SACRAMENTO and SIMHYD), two parameter s ling approaches (small s les based on guided-search and large s les based on Latin Hypercube S ling), three widely used error metrics (NSE, KGE, and WIA & #8212 Willmott& #8217 s Index of Agreement), and hydrological data from a range of Australian catchments. First, we characterized how the three error metrics behave under different error regimes independent of any modeling. We then conducted a series of controlled experiments, i.e. a type of one-factor-at-a-time sensitivity analysis, to unpack the role of each factor in runoff simulation. We show that KGE is a more reliable error metric compared to NSE and WIA for model evaluation. We also argue that robust error metrics and sufficient parameter s ling are necessary conditions for evaluating models as hypotheses under uncertainty. We particularly argue that s ling sufficiency, regardless of the s ling strategy, should be further evaluated based on its interaction with other modeling factors determining the model response. We conclude that the interplay of these modeling factors is complex and unique to each modeling case, and hence generalizing model-based inferences should be done with caution particularly in characterizing hydrological processes in large-s le hydrology.& &
Publisher: IEEE
Date: 10-2014
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/IAHS2022-659
Abstract: & & Uncertainties & #8212 of different types, sources, and their characterisation methods & #8212 have been studied for years in environmental sciences and beyond, until Donald Rumsfeld gave it a new kick and audience. The terms & em& known knowns& /em& , & em& known unknowns& /em& , and & em& unknown unknowns& /em& have become more prevalent to map out different types of uncertainties associated with understanding, modelling, and managing complex systems even though these terms had been used in scientific literature before Rumsfeld& #8217 s remarks. The last piece of this quadrant that is less discussed is & em& unknown knowns& /em& , i.e. the knowns that are treated as unknowns, that we have overlooked, forgotten, or ignored. In this work, we would like to emphasis on & em& unknown knowns& /em& by discussing a few ex les of them in scientific inquiries in general, and hydrological sciences in particular from socio-economic factors to unconscious biases in formulating and analysing complex human-water systems. For ex le, in many cases the modellers& #8217 choice of hydrological models is biased toward legacy (of existing models within the repertoire of the modellers) than based on adequacy. While human-water interactions are inherent in catchment processes, most common catchment models do not have a module to account for human activities and policy feedback. Neglecting such basic knowns & #8212 unknown knowns & #8212 can bias our analyses, decision making, and the narratives established to explain or react to a phenomenon such as drought. For instance, the & #8220 Zero Day& #8221 narrative of the Cape Town water crisis (around 2017). While the hydro-climatic multi-year drought was a contributing factor to the water shortage, the regional politics and water management also played a major role. Despite dramatic drop in water supply and hence drastic restrictions in water consumption across multiple sectors, water consumption by industrial users with political connections (such as local licensed Coca-Cola manufacturers) remained disproportionally higher. In effect, the zero day was only & em& zero& /em& for some and not for everybody. To advance hydrological sciences, particularly to integrate socioeconomic and policy feedback interactions into the mainstream hydrological modelling, hydrologists can learn from such advances in neighbouring disciplines (e.g. sustainability science and system dynamics) and account for less conventional types/sources of uncertainties, especially & em& unknown knowns& /em& .& &
Publisher: Wiley
Date: 25-08-2020
Publisher: American Geophysical Union (AGU)
Date: 08-2019
DOI: 10.1029/2019EA000706
No related grants have been discovered for Sina Khatami.