Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

Abstract. Distributed catchment models are widely used tools for predicting hydrologic behavior. While distributed models require many parameters to describe a system, they are expected to simulate behavior that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several behavioral sets that fit observations (typically streamflow). In this study, we investigate the extent to which equifinality impacts a typical distributed modeling application. We outline a hierarchical approach to reduce the number of behavioral sets based on regional, observation-driven, and expert-knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of behavioral parameter sets and altered distributions of spatiotemporal simulations, simulating a well-studied headwater catchment, Stringer Creek, Montana, using the distributed hydrology–soil–vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10 000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series reduced the number of behavioral parameter sets but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioral parameter sets can reduce equifinality and bolster more careful application and simulation of spatiotemporal processes via distributed modeling at the catchment scale.

Download Full-text

Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

10.5194/hess-2016-642 ◽

2016 ◽

Cited By ~ 1

Author(s):

Christa Kelleher ◽

Brian McGlynn ◽

Thorsten Wagener

Keyword(s):

Goodness Of Fit ◽

Snow Water Equivalent ◽

Expert Knowledge ◽

Model Performance ◽

Hierarchical Approach ◽

Catchment Scale ◽

Distributed Models ◽

Distributed Modelling ◽

Spatio Temporal ◽

Behavioural Parameter

Abstract. Distributed catchment models are widely used tools for predicting hydrologic behaviour. While distributed models require many parameters to describe a system, they are expected to simulate behaviour that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several ‘behavioural’ sets that fit observations (typically streamflow). In this study, we investigate the extent to which equifinality impacts a typical distributed modelling application. We outline a hierarchical approach to reduce the number of behavioural sets based on regional, observation-driven, and expert knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of ‘behavioural’ parameter sets and increased certainty in spatio-temporal simulations, simulating a well-studied headwater catchment, Stringer Creek, MT using the distributed hydrology-soil-vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10,000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series further reduced the number of behavioural parameter sets, but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioural parameter sets can reduce equifinality and bolster more careful application and simulation of spatio-temporal processes via distributed modelling at the catchment scale.

Download Full-text

Changes in snow storages and their impact on occurrence and extremity of runoff caused by rain-on-snow events

10.5194/egusphere-egu2020-13431 ◽

2020 ◽

Author(s):

Ondrej Hotovy ◽

Michal Jenicek

Keyword(s):

Air Temperature ◽

Snow Water Equivalent ◽

Hydrological Cycle ◽

Winter Season ◽

Model Performance ◽

Catchment Scale ◽

The Future ◽

Snow Water ◽

The Impact ◽

Catchment Runoff

Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently.The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 59 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1980-2014 and to explain the role of different catchment attributes.The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.

Download Full-text

The importance of topography controlled sub-grid process heterogeneity in distributed hydrological models

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-13301-2015 ◽

2015 ◽

Vol 12 (12) ◽

pp. 13301-13358 ◽

Cited By ~ 1

Author(s):

R. C. Nijzink ◽

L. Samaniego ◽

J. Mai ◽

R. Kumar ◽

S. Thober ◽

...

Keyword(s):

Expert Knowledge ◽

Model Performance ◽

Hydrologic Model ◽

Low Flow ◽

Evaluation Procedure ◽

Base Case ◽

Hydrological Process ◽

Hydrological Models ◽

Knowledge Based ◽

Distributed Hydrological Models

Abstract. Heterogeneity of landscape features like terrain, soil, and vegetation properties affect the partitioning of water and energy. However, it remains unclear to which extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from sub-grid topography heterogeneity was incorporated in the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expert-knowledge based model constraints reduces model uncertainty; and (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both, the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as overall measure for model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 % respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. Besides, it was shown that suitable semi-quantitative prior constraints in combination with the transfer function based regularization approach of mHM, can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

Download Full-text

The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1151-2016 ◽

2016 ◽

Vol 20 (3) ◽

pp. 1151-1176 ◽

Cited By ~ 34

Author(s):

Remko C. Nijzink ◽

Luis Samaniego ◽

Juliane Mai ◽

Rohini Kumar ◽

Stephan Thober ◽

...

Keyword(s):

Expert Knowledge ◽

Model Performance ◽

Hydrologic Model ◽

Low Flow ◽

Evaluation Procedure ◽

Base Case ◽

Hydrological Process ◽

Hydrological Models ◽

Knowledge Based ◽

Distributed Hydrological Models

Abstract. Heterogeneity of landscape features like terrain, soil, and vegetation properties affects the partitioning of water and energy. However, it remains unclear to what extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from sub-grid topography heterogeneity was incorporated into the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expert-knowledge-based model constraints reduces model uncertainty, and whether (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge-based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as an overall measure of model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 %, respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. In addition, it was shown that suitable semi-quantitative prior constraints in combination with the transfer-function-based regularization approach of mHM can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

Download Full-text

ATAT 1.1, the Automated Timing Accordance Tool for comparing ice-sheet model output with geochronological data

Geoscientific Model Development ◽

10.5194/gmd-12-933-2019 ◽

2019 ◽

Vol 12 (3) ◽

pp. 933-953 ◽

Cited By ~ 2

Author(s):

Jeremy C. Ely ◽

Chris D. Clark ◽

David Small ◽

Richard C. A. Hindmarsh

Keyword(s):

Goodness Of Fit ◽

Expert Knowledge ◽

Numerical Models ◽

Ice Sheets ◽

Ice Sheet ◽

Model Performance ◽

Geochronological Data ◽

Improve Model ◽

Geochronological Evidence ◽

Ice Sheet Modelling

Abstract. Earth's extant ice sheets are of great societal importance given their ongoing and potential future contributions to sea-level rise. Numerical models of ice sheets are designed to simulate ice-sheet behaviour in response to climate changes but to be improved require validation against observations. The direct observational record of extant ice sheets is limited to a few recent decades, but there is a large and growing body of geochronological evidence spanning millennia constraining the behaviour of palaeo-ice sheets. Hindcasts can be used to improve model formulations and study interactions between ice sheets, the climate system and landscape. However, ice-sheet modelling results have inherent quantitative errors stemming from parameter uncertainty and their internal dynamics, leading many modellers to perform ensemble simulations, while uncertainty in geochronological evidence necessitates expert interpretation. Quantitative tools are essential to examine which members of an ice-sheet model ensemble best fit the constraints provided by geochronological data. We present the Automated Timing Accordance Tool (ATAT version 1.1) used to quantify differences between model results and geochronological data on the timing of ice-sheet advance and/or retreat. To demonstrate its utility, we perform three simplified ice-sheet modelling experiments of the former British–Irish ice sheet. These illustrate how ATAT can be used to quantify model performance, either by using the discrete locations where the data originated together with dating constraints or by comparing model outputs with empirically derived reconstructions that have used these data along with wider expert knowledge. The ATAT code is made available and can be used by ice-sheet modellers to quantify the goodness of fit of hindcasts. ATAT may also be useful for highlighting data inconsistent with glaciological principles or reconstructions that cannot be replicated by an ice-sheet model.

Download Full-text

Investigating the development of shallow snowpacks on arable land, using comprehensive field observations and spatially distributed snow modelling

Hydrology Research ◽

10.2166/nh.2017.269 ◽

2017 ◽

Vol 49 (1) ◽

pp. 41-59

Author(s):

Torsten Starkloff ◽

Jannes Stolte ◽

Rudi Hessel ◽

Coen Ritsema

Keyword(s):

Overland Flow ◽

Snow Water Equivalent ◽

Arable Land ◽

Model Performance ◽

Field Observations ◽

Catchment Scale ◽

Spatially Distributed ◽

Snow Water ◽

Southern Norway ◽

Agricultural Areas

Abstract Shallow (<1 m deep) snowpacks on agricultural areas are an important hydrological component in many countries, which determines how much meltwater is potentially available for overland flow, causing soil erosion and flooding at the end of winter. Therefore, it is important to understand the development of shallow snowpacks in a spatially distributed manner. This study combined field observations with spatially distributed snow modelling using the UEBGrid model, for three consecutive winters (2013–2015) in southern Norway. Model performance was evaluated by comparing the spatially distributed snow water equivalent (SWE) measurements over time with the simulated SWE. UEBGrid replicated SWE development at catchment scale with satisfactory accuracy for the three winters. The different calibration approaches which were necessary for winters 2013 and 2015 showed the delicacy of modelling the change in shallow snowpacks. Especially the refreezing of meltwater and prevention of runoff and infiltration of meltwater by frozen soils and ice layers can make simulations of shallow snowpacks challenging.

Download Full-text

Changes in seasonal snowpack in mountain catchments in Czechia

10.5194/egusphere-egu2020-9999 ◽

2020 ◽

Author(s):

Ondrej Nedelcev ◽

Michal Jenicek

Keyword(s):

Time Series ◽

Snow Cover ◽

Air Temperature ◽

Snow Water Equivalent ◽

Model Performance ◽

Distributed Hydrological Model ◽

Catchment Scale ◽

Water Balance Components ◽

Variable Model ◽

Snow Cover Duration

Seasonal snowpack is an important part of the water cycle and it has a large influence on runoff regime in mountain catchments of Central Europe. However, snow water equivalent (SWE) is decreasing in many mountain regions over the last decades and spring snowmelt occurs earlier in the year. This study aimed 1) to analyse long-term changes and trends in selected snowpack characteristics, such as SWE, snow cover duration, snowmelt onset and melt-out in 40 mountain catchments in Czechia in the period 1965&#8211;2014 and 2) to relate the detected changes to changes in air temperature and snowfall fraction at different elevations. Since the availability of time series of measured SWE at a catchment scale is limited, a conceptual semi-distributed hydrological model HBV-light was used to simulate daily SWE for defined elevation zones. Besides SWE, the model simulated other water balance components, such as runoff, soil moisture and groundwater recharge. The integrated multi-variable model calibration procedure was used to calibrate the model. Both observed runoff and SWE were used for evaluation of the model performance. Seasonal and monthly mean of SWE, as well as snow cover duration, snowmelt onset, snowmelt rates and melt-out were calculated for individual catchments and elevation zones. The non-parametric Mann-Kendall test was used to detect potential trends in simulated time series. The results showed significant decreasing trends in snowfall fraction for all catchments and elevations in the study period mostly due to an increase in air temperature. This resulted in a decrease in snow storages in most of catchments, especially in western parts of Czechia. However, a lot of regional differences exists and no trends in SWE were detected in some catchments. Decreasing trends in snow cover duration were detected as well, mostly because of earlier snowmelt onset and melt-out.

Download Full-text

ATAT 1.0, an Automated Timing Accordance Tool for comparing ice-sheet model output with geochronological data

10.5194/gmd-2018-12 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jeremy C. Ely ◽

Chris D. Clark ◽

David Small ◽

Richard C. A. Hindmarsh

Keyword(s):

Goodness Of Fit ◽

Expert Knowledge ◽

Numerical Models ◽

Ice Sheets ◽

Ice Sheet ◽

Model Performance ◽

Geochronological Data ◽

Improve Model ◽

Geochronological Evidence ◽

Ice Sheet Modelling

Abstract. Earth's extant ice sheets are of great societal importance given their ongoing and potential future contributions to sea-level rise. Numerical models of ice sheets are designed to simulate ice sheet behaviour in response to climate changes, but to be improved require validation against observations. The direct observational record of extant ice sheets is limited to a few recent decades, but there is a large and growing body of geochronological evidence spanning millennia constraining the behaviour of palaeo-ice sheets. Hindcasts can be used to improve model formulations and study interactions between ice sheets, the climate system and landscape. However, ice-sheet modelling results have inherent quantitative errors stemming from parameter uncertainty and their internal dynamics, leading many modellers to perform ensemble simulations, while uncertainty in geochronological evidence necessitates expert interpretation. Quantitative tools are essential to examine which members of an ice-sheet model ensemble best fit the constraints provided by geochronological data. We present an Automated Timing Accordance Tool (ATAT version 1.0) used to quantify differences between model results and geo-data on the timing of ice sheet advance and/or retreat. To demonstrate its utility, we perform three simplified ice-sheet modelling experiments of the former British-Irish Ice Sheet. These illustrate how ATAT can be used to quantify model performance, either by using the discrete locations where the data originated together with dating constraints or by comparing model outputs with empirically-derived reconstructions that have used these data along with wider expert knowledge. The ATAT code is made available and can be used by ice-sheet modellers to quantify the goodness of fit of hindcasts. ATAT may also be useful for highlighting data inconsistent with glaciological principles or reconstructions that cannot be replicated by an ice sheet model.

Download Full-text

Distributed Modeling Of Building Systems Through Cyber-Physical Integration

Promyshlennoe i Grazhdanskoe Stroitel stvo ◽

10.33622/0869-7019.2019.09.12-17 ◽

2019 ◽

pp. 12-17

Keyword(s):

Data Structure ◽

Operating Conditions ◽

Physical Models ◽

Distributed Model ◽

Physical Processes ◽

Distributed Modeling ◽

Distributed Models ◽

Operating Modes ◽

Building Systems ◽

Important Practical Application

This article describes the proposed approaches to creating distributed models that can, with given accuracy under given restrictions, replace classical physical models for construction objects. The ability to implement the proposed approaches is a consequence of the cyber-physical integration of building systems. The principles of forming the data structure of designed objects and distributed models, which make it possible to uniquely identify the elements and increase the level of detail of such a model, are presented. The data structure diagram of distributed modeling includes, among other things, the level of formation and transmission of signals about physical processes inside cyber-physical building systems. An enlarged algorithm for creating the structure of the distributed model which describes the process of developing a data structure, formalizing requirements for the parameters of a design object and its operating modes (including normal operating conditions and extreme conditions, including natural disasters) and selecting objects for a complete group that provides distributed modeling is presented. The article formulates the main approaches to the implementation of an important practical application of the cyber-physical integration of building systems - the possibility of forming distributed physical models of designed construction objects and the directions of further research are outlined.

Download Full-text