scholarly journals Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints

2018 ◽  
Vol 22 (1) ◽  
pp. 203-220 ◽  
Author(s):  
Simon Höllering ◽  
Jan Wienhöfer ◽  
Jürgen Ihringer ◽  
Luis Samaniego ◽  
Erwin Zehe
Keyword(s):  
Sensitivity Analysis ◽  
Temporal Dynamics ◽  
Methodological Approach ◽  
Regional Analysis ◽  
Parameter Sensitivity ◽  
Model Parameters ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Response Characteristics ◽  
Streamflow Response

Abstract. Diagnostics of hydrological models are pivotal for a better understanding of catchment functioning, and the analysis of dominating model parameters plays a key role for region-specific calibration or parameter transfer. A major challenge in the analysis of parameter sensitivity is the assessment of both temporal and spatial differences of parameter influences on simulated streamflow response. We present a methodological approach for global sensitivity analysis of hydrological models. The multilevel approach is geared towards complementary forms of streamflow response targets, and combines sensitivity analysis directed to hydrological fingerprints, i.e. temporally independent and temporally aggregated characteristics of streamflow (INDPAS), with the conventional analysis of the temporal dynamics of parameter sensitivity (TEDPAS). The approach was tested in 14 mesoscale headwater catchments of the Ruhr River in western Germany using simulations with the spatially distributed hydrological model mHM. The multilevel analysis with diverse response characteristics allowed us to pinpoint parameter sensitivity patterns much more clearly as compared to using TEDPAS alone. It was not only possible to identify two dominating parameters, for soil moisture dynamics and evapotranspiration, but we could also disentangle the role of these and other parameters with reference to different streamflow characteristics. The combination of TEDPAS and INDPAS further allowed us to detect regional differences in parameter sensitivity and in simulated hydrological functioning, despite the rather small differences in the hydroclimatic and topographic setting of the Ruhr headwaters.

scholarly journals Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints

2017 ◽  
Author(s):  
Simon Höllering ◽  
Jan Wienhöfer ◽  
Jürgen Ihringer ◽  
Luis Samaniego ◽  
Erwin Zehe
Keyword(s):  
Sensitivity Analysis ◽  
Temporal Dynamics ◽  
Regional Analysis ◽  
Specific Model ◽  
Parameter Sensitivity ◽  
Distributed Hydrological Model ◽  
Response Characteristics ◽  
Sensitivity Pattern ◽  
Spatial Differences ◽  
Streamflow Response

Abstract. Diagnostics of hydrological models is pivotal for a better understanding of catchment functioning. The analysis of dominating parameters for the simulation of streamflow plays a key role for region specific model diagnostics, model calibration or parameter transfer. A major challenge in this analysis of parameter sensitivity is the assessment of both temporal and spatial differences of parameter influences on simulated streamflow response. A methodical approach is presented, wherein a two-tiered global sensitivity analysis on a spatially distributed hydrological model is applied to 14 mesoscale headwater catchments of the river Ruhr in western Germany. The analysis of parameter sensitivity is geared towards two complementary forms of streamflow response targets. The analysis of the temporal dynamics of parameter sensitivity (TEDPAS) is contrasted with sensitivity analysis directed to hydrological fingerprints, i.e. temporally independent and temporally aggregated characteristics of streamflow (INDPAS). The two-tiered approach allows to discern a clarified sensitivity pattern pinpointed to diverse response characteristics, to detect regional differences and to reveal the regional relevance of the response target. Small local differences in the hydroclimatic and topographic setting of the headwaters lead to slight differences in the hydrological functioning, which was revealed by gradual differences in TEDPAS and INDPAS.

scholarly journals Process verification of a hydrological model using a temporal parameter sensitivity analysis

2015 ◽  
Vol 19 (10) ◽  
pp. 4365-4376 ◽  
Author(s):  
M. Pfannerstill ◽  
B. Guse ◽  
D. Reusser ◽  
N. Fohrer
Keyword(s):  
Hydrological Model ◽  
Temporal Dynamics ◽  
Parameter Sensitivity ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Hydrological Process ◽  
Hydrological Models ◽  
Parameter Sensitivity Analysis ◽  
Process Verification ◽  
Process Dynamics

Abstract. To ensure reliable results of hydrological models, it is essential that the models reproduce the hydrological process dynamics adequately. Information about simulated process dynamics is provided by looking at the temporal sensitivities of the corresponding model parameters. For this, the temporal dynamics of parameter sensitivity are analysed to identify the simulated hydrological processes. Based on these analyses it can be verified if the simulated hydrological processes match the observed processes of the real world. We present a framework that makes use of processes observed in a study catchment to verify simulated hydrological processes. Temporal dynamics of parameter sensitivity of a hydrological model are interpreted to simulated hydrological processes and compared with observed hydrological processes of the study catchment. The results of the analysis show the appropriate simulation of all relevant hydrological processes in relation to processes observed in the catchment. Thus, we conclude that temporal dynamics of parameter sensitivity are helpful for verifying simulated processes of hydrological models.

scholarly journals Climate change impacts model parameter sensitivity – What does this mean for calibration?

2020 ◽  
Author(s):  
Lieke Anna Melsen ◽  
Björn Guse
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
The United States ◽  
Parameter Sensitivity ◽  
Model Parameters ◽  
Hydrological Models ◽  
The Future ◽  
Influence Parameter ◽  
Sensitivity Changes ◽  
Sensitive Parameters

Abstract. Hydrological models are useful tools to explore the hydrological impact of climate change. Many of these models require calibration. A frequently employed strategy is to calibrate the five parameters that were found to be most relevant as identified in a sensitivity analysis. However, parameter sensitivity varies over climate, and therefore climate change could influence parameter sensitivity. In this study we explore the change in parameter sensitivity within a plausible climate change rate, and investigate if changes in sensitivity propagate into the calibration strategy. We employed three frequently used hydrological models (SAC, VIC, and HBV), and explored parameter sensitivity changes across 605 catchments in the United States by comparing a GCM-forced historical and future period. Consistent among all models is that the sensitivity of snow parameters decreases in the future. Which parameters increase in sensitivity is less consistent among the models. In 43 % to 49 % of the catchments, dependent on the model, at least one parameter changes in the future in the top-5 most sensitive parameters. The maximum number of changes in the parameter top-5 is two, in 2–4 % of the investigated catchments. The value of the parameters that enter the top-5 cannot easily be identified based on historical data, because the model is not yet sensitive to these parameters. This requires an adapted calibration strategy for long-term projections, for which we provide several suggestions. The disagreement among the models on processes becoming relevant in future projections also calls for a strict evaluation of the adequacy of the model structure and the model parameters implemented therein.

Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

2021 ◽  
Author(s):  
Rodric Mérimé Nonki ◽  
André Lenouo ◽  
Christopher J. Lennard ◽  
Raphael M. Tshimanga ◽  
Clément Tchawoua
Keyword(s):  
Sensitivity Analysis ◽  
Impact Assessment ◽  
Hydrological Modeling ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Sensitivity Approach ◽  
Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

scholarly journals Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil

2018 ◽  
Author(s):  
Elizabeth Buckingham-Jeffery ◽  
Edward M. Hill ◽  
Samik Datta ◽  
Erin Dilger ◽  
Orin Courtenay
Keyword(s):  
Sensitivity Analysis ◽  
Leishmania Infantum ◽  
Parameter Sensitivity ◽  
Domestic Dogs ◽  
Sand Fly ◽  
Model Parameters ◽  
Infection Prevalence ◽  
Parameter Sensitivity Analysis ◽  
Model Framework ◽  
Stochastic Parameter

AbstractBackgroundThe parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease.Leishmania infantum parasites are transmitted between hosts during blood feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models.MethodsWe have developed a stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fit distributions for these entities to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters.ResultsWe computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with L. infantum parasites.ConclusionsEstablishing the model parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.

scholarly journals Assessing the temporally dynamic parameters in hydrological models: dynamic operations and evolutionary processes

2020 ◽  
Author(s):  
Tian Lan ◽  
Kairong Lin ◽  
Chong-Yu Xu ◽  
Zhiyong Liu ◽  
Huayang Cai
Keyword(s):  
Hydrological Model ◽  
Temporal Dynamics ◽  
Global Optimum ◽  
Structural Defects ◽  
Model Parameters ◽  
Dynamic Parameters ◽  
Hydrological Models ◽  
Evolutionary Processes ◽  
Individual Parameter ◽  
Catchment Characteristics

Abstract. The temporal dynamics of parameters can compensate for structural defects of hydrological models and improve the accuracy and robustness of the streamflow forecast. Given the parameters usually estimated by global optimization algorithms, a critical issue, however, which received little attention in the literature, is that the possible failure in finding the global optimum might lead to unreasonable parameter values. This may cause the poor response of the dynamic parameters to time-varying catchment characteristics (such as seasonal variations of land cover). In this regard, we propose a framework for identifying the difficulty of finding the global optimum for dynamic hydrological model parameters by investigating their evolutionary processes. Specifically, the probability distributions of the violin plots and the divergence measure of the polylines in the parallel coordinates are applied and developed to configure the evolutionary processes in the individual parameter spaces and multi-parameter space, respectively. Also, a complete solution for the dynamic operation of parameters is proposed. Furthermore, clustering operations, calibration scheme and correlation between parameters are further discussed. The results showed that the performance of the hydrological model with dynamic parameters achieves a significant improvement. However, the response of individual parameters (even high-sensitive parameter) to dynamic catchment characteristics is generally poor. The main reasons can be primarily attributed to the complexly linear and nonlinear correlation between parameters and poor ability in finding the global optimum. In this regard, the dynamic parameter set instead of individual dynamic parameters is suggested to extract dynamic catchment characteristics. Importantly, we found that the properties of hydrological-model parameters, including identifiability, sensitivity, correlation and the ability to find global optimum, interact with the response of parameters to the dynamic catchment characteristics. The ability to find global optimum has a significant influence on the hydrological model performance with dynamic parameters. Hence, the ability to find global optimum is suggested as one of the essential properties of the hydrological model parameters. The study provides a valuable benchmark for temporally dynamic parameters in hydrological models.

A meta-model assisted framework of optimization of the Hydrological model parameters for accurate calibration

2020 ◽  
Author(s):  
Ammara Nusrat ◽  
Hamza Farooq Gabriel ◽  
Sajjad Haider ◽  
Muhammad Shahid
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Optimization ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Large River ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Dimensional Parameter ◽  
Input Parameters

<p> Increase in frequency of the floods is one of the noticeable climate change impacts. The efficient and optimized flood analysis system needs to be used for the reliable flood forecasting. The credibility and the reliability of the flood forecasting system is depending upon the framework used for its parameter optimization. Comprehensive framework has been presented to optimize the input parameters of the computationally extensive distributed hydrological model. A large river basin has the high spatio-temporal heterogeneity of aquifer and surface properties.  Estimating the parameters in fully distributed hydrological model is a challenging task. The parameter optimization becomes computationally more demanding when the model input parameters (30 to 100 even greater) have multi-dimensional parameter space, many output parameters which make the optimization problem multi-objective and large number of model simulations requirement for the optimization. Aforementioned challenges are met by introducing the methodology to optimize the input parameters of fully distributed hydrological model, following steps are included (1) screening of the parameters through Morris sensitivity analysis method in different flow periods, so that optimization would be performed for sensitive parameters, different scalar output functions are used in this regard (2) to emulate the hydrologic response of the dynamic model, surrogate models or meta-models are used (3) sampling of parameters values using the optimized ranges obtained from the meta-models; the results are evident that the parameter optimization using the proposed framework is efficient can be effectively performed.  The effectiveness and efficiency of the proposed framework has been demonstrated through the accurate calibration of the model with fewer model runs. This study also demonstrates the importance and use of scalar functions in calculating sensitivity indices, when the model output is temporally variable. In addition, the parameter optimization using the proposed framework is efficient and present study can be used as reference for optimization of distributed hydrological model. </p><p> </p><p><strong>Keywords: </strong>Calibration, parameter ranking, Sensitivity analysis, Hydrological modeling, optimization</p>

scholarly journals Temporal parameter sensitivity guided verification of process dynamics

2015 ◽  
Vol 12 (2) ◽  
pp. 1729-1764 ◽  
Author(s):  
M. Pfannerstill ◽  
B. Guse ◽  
D. Reusser ◽  
N. Fohrer
Keyword(s):  
Hydrological Model ◽  
Temporal Dynamics ◽  
Parameter Sensitivity ◽  
Temporal Parameter ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Hydrological Process ◽  
Time Step ◽  
Process Dynamics ◽  
Model Components

Abstract. To ensure reliable results of a hydrological model, it is essential that the model reproduces the hydrological processes adequately. Information about process dynamics is provided by looking at the temporal sensitivities of the corresponding model parameters. For this, the temporal dynamics of parameter sensitivity are used to describe the dominance of parameters for each time step. The parameter dominance is then related to the corresponding hydrological process, since the temporal parameter sensitivity represents the modelled hydrological process. For a reliable model application it has to be verified that the modelled hydrological processes match the expectations of real-world hydrological processes. We present a framework, which distinguishes between a verification of single model components and of the overall model behaviour. We analyse the temporal dynamics of parameter sensitivity of a modified groundwater component of a hydrological model. The results of the single analysis for the modified component show that the behaviour of the parameters of the modified groundwater component is consistent with the idea of the structural modifications. Additionally, the appropriate simulation of all relevant hydrological processes is verified as the temporal dynamics of parameter sensitivity represent these processes according to the expectations. Thus, we conclude that temporal dynamics of parameter sensitivity are helpful for verifying modifications of hydrological models.

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