Assessing a seasonal calibration approach for a small forest catchment in a Mediterranean region North Central Portugal

2021 ◽  
Author(s):  
Svenja van Schelve ◽  
Diana C. S. Vieira ◽  
Jan J. Keizer ◽  
Martinho António Santos Martins ◽  
Anne-Karine Boulet
Keyword(s):  
Model Performance ◽  
Dry Season ◽  
Validation Period ◽  
North Central ◽  
Climate Conditions ◽  
Seasonal Climate ◽  
Model Predictions ◽  
Forest Catchment ◽  
Central Portugal ◽  
Calibration Approach

<p>Hydrological modeling is nowadays a widely used decision making tool to predict watershed behavior in forest areas. A commonly used processed based watershed model is the Soil and Water Assessment Tool (SWAT). SWAT provides comprehensive forest management operations and offers a diversity of adjustable input parameters to simulate complex processes inside a catchment. Nevertheless, one well-known obstacle of SWAT is the poor model performance during dry periods, characterized by low discharge and/or a dried-out catchment, causes by a possible seasonal dependency of input parameter related to climate conditions. Model predictions inherently goes along with uncertainties, linked to a diversity of unknown input parameters and assumptions. Therefore, to minimize model predictions uncertainties the use of an appropriate calibration technique is indispensable. During a conventional calibration process with SWAT model, inputs do not consider seasonal variabilities, by generally using a single parameter set for simulating discharge in a catchment. Although some studies have shown, a significant improvement while using different parameter sets, according to a wet or dry season [1, 2]. However, there is still a knowledge gap in applying such season-based calibration approach, namely under which conditions such approach could improve model predictions. The aim of this study is to determine the parameters which seem to have higher influence under seasonal climate conditions in contrast to season independent parameters, in a semi-managed eucalyptus forest catchment in North Central Portugal. We will use different parameter sets according to a wet and dry period, to improve the discharge simulation and make a model performance more robust. Further to optimize different model scenarios, such as transport processes, that depending on seasonal flow regimes. The climate of the study area is a warm- summer Mediterranean climate dominated by dry, warm and long summers. The hydrological dataset used for the calibration and validation period comprises the hydrological years 2010 to 2016, with a local metrological dataset and discharge measurements from the outlet of the catchment. Global sensitive analysis (GSA) is performed with the Fourier Amplitude Sensitivity Testing (FAST) in SWATplusR [3], for following defined cases, (i) over the complete data period (conventional), (ii) the wet and the (iii) dry season dataset. Whereas for the calibration and the validation period, the dataset is divided by a 4-year calibration and a 2-year validation period. Respectively, a conventional and a season-based calibration is done while using SWATplusR. The GSA results show that the most influencing parameters for the conventional dataset are the curve number (CN2) with a sensitivity of 0.65, followed by the available water capacity of the soil layer (SOL_AWC) with a sensitivity of 0.008. When using the dry season dataset the sensitivity of the CN2 parameter decreases by a factor of 0.45 and SOL_AWC increases by a factor of 5, confirming the hypothesis of an input dependency on seasonal climate conditions.</p><p>[1] Zhang, D. et al., 2015. https://doi.org/10.1016/j.ecolmodel.2015.01.018<br>[2] Muleta, M.K. et al., 2012. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000421<br>[3] Schürz, C., 2019. doi: 10.5281/zenodo.3373859</p>

Multi-objective calibration of a distributed eco-hydrological model using several remotely sensed information

2020 ◽  
Author(s):  
Félix Francés ◽  
Carlos Echeverría ◽  
Maria Gonzalez-Sanchis ◽  
Fernando Rivas
Keyword(s):  
Remote Sensing ◽  
Model Calibration ◽  
Hydrological Model ◽  
Model Performance ◽  
Temporal Information ◽  
Temporal Data ◽  
Hydrological Models ◽  
Validation Period ◽  
Spatio Temporal ◽  
Calibration Approach

<p>Calibration of eco-hydrological models is difficult to carry on, even more if observed data sets are scarce. It is known that calibration using traditional trial-and-error approach depends strongly of the knowledge and the subjectivity of the hydrologist, and automatic calibration has a strong dependency of the objective-function and the initial values established to initialize the process.</p><p>The traditional calibration approach mainly focuses on the temporal variation of the discharge at the catchment outlet point, representing an integrated catchment response and provides thus only limited insight on the lumped behaviour of the catchment. It has been long demonstrated the limited capabilities of such an approach when models are validated at interior points of a river basin. The development of distributed eco-hydrological models and the burst of spatio-temporal data provided by remote sensing appear as key alternative to overcome those limitations. Indeed, remote sensing imagery provides not only temporal information but also valuable information on spatial patterns, which can facilitate a spatial-pattern-oriented model calibration.</p><p>However, there is still a lack of how to effectively handle spatio-temporal data when included in model calibration and how to evaluate the accuracy of the simulated spatial patterns. Moreover, it is still unclear whether including spatio-temporal data improves model performance in face to an unavoidable more complex and time-demanding calibration procedure. To elucidate in this sense, we performed three different multiobjective calibration configurations: (1) including only temporal information of discharges at the catchment outlet (2) including both temporal and spatio-temporal information and (3) only including spatio-temporal information. In the three approaches, we calibrated the same distributed eco-hydrological model (TETIS) in the same study area: Carraixet Basin, and used the same multi-objective algorithm: MOSCEM-UA. The spatio-temporal information obtained from satellite has been the surface soil moisture (from SMOS-BEC) and the leaf area index (from MODIS).</p><p>Even though the performance of the first calibration approach (only temporal information included) was slightly better than the others, all calibration approaches provided satisfactory and similar results within the calibration period. To put these results into test, we also validated the model performance by using historical data that was not used to calibrate the model (validation period). Within the validation period, the second calibration approach obtained better performance than the others, pointing out the higher reliability of the obtained parameter values when including spatio-temporal data (in this case, in combination with temporal data) in the model calibration. It is also reliable to mention that the approaches considering only spatio-temporal information provided interesting results in terms of discharges, considering that this variable was not used at all for calibration purposes.</p>

scholarly journals Modelling Exposure by Spraying Activities—Status and Future Needs

2021 ◽  
Vol 18 (15) ◽  
pp. 7737
Author(s):  
Stefan Hahn ◽  
Jessica Meyer ◽  
Michael Roitzsch ◽  
Christiaan Delmaar ◽  
Wolfgang Koch ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Model Performance ◽  
Relevant Information ◽  
Systematic Evaluation ◽  
Model Accuracy ◽  
Efficient Manner ◽  
User Perspective ◽  
Model Predictions ◽  
Spray Applications

Spray applications enable a uniform distribution of substances on surfaces in a highly efficient manner, and thus can be found at workplaces as well as in consumer environments. A systematic literature review on modelling exposure by spraying activities has been conducted and status and further needs have been discussed with experts at a symposium. This review summarizes the current knowledge about models and their level of conservatism and accuracy. We found that extraction of relevant information on model performance for spraying from published studies and interpretation of model accuracy proved to be challenging, as the studies often accounted for only a small part of potential spray applications. To achieve a better quality of exposure estimates in the future, more systematic evaluation of models is beneficial, taking into account a representative variety of spray equipment and application patterns. Model predictions could be improved by more accurate consideration of variation in spray equipment. Inter-model harmonization with regard to spray input parameters and appropriate grouping of spray exposure situations is recommended. From a user perspective, a platform or database with information on different spraying equipment and techniques and agreed standard parameters for specific spraying scenarios from different regulations may be useful.

Development of a Stepped Calibration Approach for XAJ Hydrological Model

2013 ◽  
Vol 405-408 ◽  
pp. 2222-2225
Author(s):  
Qian Li ◽  
Wei Min Bao ◽  
Jing Lin Qian
Keyword(s):  
Root Mean Square Error ◽  
Objective Function ◽  
Traditional Method ◽  
Hydrological Model ◽  
Model Performance ◽  
Layer By Layer ◽  
Mean Square ◽  
Objective Functions ◽  
Single Objective ◽  
Calibration Approach

This paper discusses the conceptual stepped calibration approach (SCA) which has been developed for the Xinanjiang (XAJ) model. Multi-layer and multi-objective functions which can make optimization work simpler and more effective are introduced in this procedure. In all eight parameters were considered, they were divided into four layers according to the structure of XAJ model, and then calibrated layer by layer. The SCA procedure tends to improve the performance of the traditional method of calibration (thus, using a single objective function, such as root mean square error RMSE). The compared results demonstrate that the SCA yield better model performance than RMSE.

scholarly journals Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population

2017 ◽  
Vol 28 (1) ◽  
pp. 309-320 ◽  
Author(s):  
Scott Powers ◽  
Valerie McGuire ◽  
Leslie Bernstein ◽  
Alison J Canchola ◽  
Alice S Whittemore
Keyword(s):  
Prediction Models ◽  
Risk Model ◽  
Disease Incidence ◽  
Model Performance ◽  
Target Population ◽  
R Package ◽  
Model Assessment ◽  
Weighted Estimates ◽  
Cancer Prediction ◽  
Model Predictions

Personal predictive models for disease development play important roles in chronic disease prevention. The performance of these models is evaluated by applying them to the baseline covariates of participants in external cohort studies, with model predictions compared to subjects' subsequent disease incidence. However, the covariate distribution among participants in a validation cohort may differ from that of the population for which the model will be used. Since estimates of predictive model performance depend on the distribution of covariates among the subjects to which it is applied, such differences can cause misleading estimates of model performance in the target population. We propose a method for addressing this problem by weighting the cohort subjects to make their covariate distribution better match that of the target population. Simulations show that the method provides accurate estimates of model performance in the target population, while un-weighted estimates may not. We illustrate the method by applying it to evaluate an ovarian cancer prediction model targeted to US women, using cohort data from participants in the California Teachers Study. The methods can be implemented using open-source code for public use as the R-package RMAP (Risk Model Assessment Package) available at http://stanford.edu/~ggong/rmap/ .

The nodular granite of Castanheira, north central Portugal: origin of the nodules and evidence for diapiric mobilization of granite

1993 ◽  
Vol 130 (2) ◽  
pp. 145-153 ◽  
Author(s):  
R. J. Reavy ◽  
D. H. W. Hutton ◽  
A. A. Finch
Keyword(s):  
Chemical Potential ◽  
Structural Data ◽  
Vapour Phase ◽  
Vapour Bubble ◽  
North Central ◽  
Host Granite ◽  
Low Viscosity ◽  
Granite Body ◽  
Central Portugal ◽  
Strong Depletion

AbstractThe Castanheira pluton in north-central Portugal is a small (1000 m × 600 m) granite body of Hercynian age which contains a remarkable abundance of granite-cored, biotite-rimmed nodules. The nodules are interpreted as representing original bubbles in the uppermost volatile-rich zone of a granitic pluton. Strong depletion in K and Rb in the host granite around the nodules suggests that the biotite is magmatic in origin. The nodules may have formed by reaction between chloroferrate(II) complexes in the vapour phase and silicate melt, possibly followed by condensation of the vapour phase to a small granitic core. Motion of the vapour bubble stabilized a gradient in chemical potential with respect to the host granite, giving rise to the nodules. Chemical, petrological and structural data suggest that the pluton was part of a larger granite body, which was forcefully emplaced during synchronous transcurrent shearing. The inferred presence of volatiles, in addition to the pervasive tourmalinization of the roof zone, suggest that the magma was halogen-rich; this may imply that the magma had low viscosity.

Post-fire mobilization of metals in a recently burnt area in North-Central Portugal: a contamination risk for waterbodies?

2021 ◽  
Author(s):  
Dalila Serpa ◽  
Ana Machado ◽  
Martha Santos ◽  
Isabel Campos ◽  
Fátima Jesus ◽  
...  
Keyword(s):  
Overland Flow ◽  
Fire Severity ◽  
Hazardous Substances ◽  
Burnt Area ◽  
North Central ◽  
Contamination Risk ◽  
Freshwater Bodies ◽  
Central Portugal ◽  
Study Sites

<p>Wildfires constitute a diffuse source of contamination to aquatic ecosystems. In burnt hillslopes, ash and sediments transported by overland flow are a source of potentially hazardous substances, like metals, posing a risk for downstream water bodies. In the present study, post-fire metal mobilization by overland flow was evaluated in 16 m<sup>2 </sup>bounded plots at a eucalypt stand in Albergaria-a-Velha (Aveiro district, North-Central Portugal) that burnt with moderate severity in September 2019. Overland flow samples were collected on a weekly to bi-weekly basis, depending on the occurrence of rain, during the first 6 months after fire. Aside from overland flow samples collected at slope scale, water and sediment samples were also collected in a fire-affected stream within the Albergaria burned catchment, to assess the contamination risk posed by the fire. Samples were collected at three sites along the stream: one upstream, one within and another downstream from the burnt area, after major rainfall events. The metals analysed in this study included, vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd) and lead (Pb). Results showed that most metals exhibited a peak in exports immediately after the first significant post-fire rainfall event likely due to the wash-off of the ash layer and high sediment losses, but for some elements like Zn and Cu, exports were more or less constant over time. The fire seems to have had a low impact on the water quality of the affected stream, since metal concentrations were similar between the three study sites. The quality of stream sediments, on the other hand, was clearly affected by the fire, especially after the rainy season. As fire severity and frequency is forecasted to increase in the near future due to climate changes, the results of this work reinforce the importance of water managers to define adaptative strategies to effectively protect freshwater bodies.</p>

Evaluating Model Performance and Significance

2011 ◽  
Author(s):  
A. Townsend Peterson ◽  
Jorge Soberón ◽  
Richard G. Pearson ◽  
Robert P. Anderson ◽  
Enrique Martínez-Meyer ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Model Calibration ◽  
Model Performance ◽  
Future Research ◽  
Commission Error ◽  
Tests Of Significance ◽  
Occurrence Data ◽  
Model Predictions ◽  
Key Concepts

This chapter describes a framework for selecting appropriate strategies for evaluating model performance and significance. It begins with a review of key concepts, focusing on how primary occurrence data can be presence-only, presence/background, presence/pseudoabsence, or presence/absence as well as factors that may contribute to apparent commission error. It then considers the availability of two pools of occurrence data: one for model calibration and another for evaluation of model predictions. It also discusses strategies for detecting overfitting or sensitivity to bias in model calibration, with particular emphasis on quantification of performance and tests of significance. Finally, it suggests directions for future research as regards model evaluation, highlighting areas in need of theoretical and/or methodological advances.

scholarly journals Discharge sensitivity to snowmelt parameterization: a case study for Upper Beas basin in Himachal Pradesh, India

2016 ◽  
Vol 47 (4) ◽  
pp. 683-700 ◽  
Author(s):  
Trine J. Hegdahl ◽  
Lena M. Tallaksen ◽  
Kolbjørn Engeland ◽  
John F. Burkhart ◽  
Chong-Yu Xu
Keyword(s):  
Interpolation Method ◽  
Model Performance ◽  
Shortwave Radiation ◽  
Quality Data ◽  
Data Sets ◽  
Distributed Hydrological Model ◽  
Validation Period ◽  
Index Method ◽  
Temperature Index ◽  
Mountainous Regions

Snow- and glacier melt are important contributors to river discharge in high-elevated areas of the Himalayan region. Thus, it is important that the key processes controlling snow and glacier accumulation and melting, are well represented in hydrological models. In this study, the sensitivity of modelled discharge to different snowmelt parameterizations was evaluated. A distributed hydrological model that operated on a 1 × 1 km2 grid at a daily time resolution was applied to a high-elevated mountainous basin, the Upper Beas basin in Indian Himalaya, including several sub-basins with a varying degree of glacier covered areas. The snowmelt was calculated using (i) a temperature-index method, (ii) an enhanced temperature-index method including a shortwave radiation term, and (iii) an energy balance method. All model configurations showed similar performance at daily, seasonal, and annual timescales and a lower performance for the validation period than for the calibration period; a main reason being the failure to capture the observed negative trend in annual discharge in the validation period. The results suggest that model performance is more sensitive to the precipitation input, i.e. interpolation method than to the choice of snowmelt routine. The paper highlights the challenges related to the lack of high quality data sets in mountainous regions, which are those areas globally with most water resources.

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