A comparative modeling study of soil water dynamics
•Télécharger en tant que PPTX, PDF•
2 j'aime•221 vues
2017 SWCS Annual Conference July 30-August 2, 2017 Monona Terrace Convention Center
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à A comparative modeling study of soil water dynamics
Similaire à A comparative modeling study of soil water dynamics (20)
Plus de Soil and Water Conservation Society
Plus de Soil and Water Conservation Society (20)
Dernier
Dernier (20)
A comparative modeling study of soil water dynamics
- 1. A comparative modeling study of soil water dynamics with different pedotransfer functions Sara Acevedo Godoy S. Acevedo, C. Contreras, N. Nieto, F. Lira, C. Bonilla 1 Department of Hydraulic and Environmental Engineering Pontificia Universidad Católica de Chile
- 2. Field Capacity (FC) and Wilting Point (WP): Available water content (%) 2 Field capacity (FC) Wilting point (WP) FC / WP INPUTS MODEL OUTPUT
- 3. Problem: Missing data 3 Basic soil properties: Sand (%), Clay (%), Silt(%),OM(%), BD (g/cm3) Soil Hydraulic Properties: FC, WP (m3/m3) ? ? ? ? Chilean Soil Survey CIREN (Centro de Investigación de recursos naturales) Basic soil properties Basic soil properties + FC, WP 47% 53%
- 4. Pedotransfer functions (PTF) A pedotransfer function is a predictive function of certain soil properties from other more available, easily, routinely, or cheaply measured properties. Soil hydraulic properties FC and WP = f(Clay (%), Silt (%), OM(%))…) 4
- 5. How do evaluate PTFs? Direct validation: Measured vs estimated data 5 Estimated FC / WP MeasuredFC/WP
- 6. How do evaluate PTFs? Direct validation: Functional validation: Using FC and WP as inputs Water balance HYDRUS-1D (Simunek and Van Genuchten 1999) It solves Richards’ equation for saturated/unsaturated flow Output: Water balance 6
- 7. Objective A comparative modeling study by HYDRUS-1D of soil water dynamics with different pedotransfer functions in order to evaluate them functionally This study evaluated: Water balance Evapotranspiration Water Storage Drainage 7
- 9. Rosetta and Hydrus PTF Field capacity (FC) and Wilting Point (WP) 9 **Estimated by PTF Control: Real data PTF GL**/ PTF R04** / PTF R83**
- 10. HYDRUS 1-D: Annual Water Balance 10 PTF 1/ PTF 2 / PTF 3 M2/ M3/ M4_PTF/M5_PTF/M5 (real)
- 11. 11 Seven soils from Central Chile 32 soil horizons CIREN soil survey
- 12. Results 12
- 13. Goodness of fit – Field capacity and Wilting Point R_83_FC R_83_WP R_GL_FC R_GL_WP R_04_FC R_04_WP ME -0.06 -0.03 -0.04 0 -0.04 -0.04 MAE 0.06 0.03 0.06 0.02 0.05 0.04 MSE 0.01 0 0.01 0 0 0 RMSE 0.08 0.04 0.07 0.02 0.06 0.05 PBIAS (%) -24.6 -27.1 -17.7 2.6 -16.5 -33.2 NSE 0.4 0.33 0.52 0.82 0.61 0.07 r 0.89 0.88 0.92 0.91 0.92 0.89 R2 0.79 0.77 0.84 0.83 0.85 0.79 13 n=32
- 15. Storage (mm) 15
- 20. 20 150% 50%
- 21. 21 150% 50%
- 22. Conclusion Goodness of fit is not an overall predictor of water balance performance Water balance shows high variation in the response to PTF Data adjustment with PTF should be evaluated according to the modeling target Further research Laboratory vs modeling Clayey soils Statistical approaches 22
- 23. Acknowledgements CONICYT FONDECYT Francisco Lira and Natalia Nieto Prof. Carlos Bonilla Research Group 23
- 24. Questions? Torres del Paine, Chile 24
Notes de l'éditeur
- Good morning. Thanks for coming to my presentation. It is titled: TITLE. (25-30)
- Let’s start with the introduction. Probably you know, soil physical properties are very important. In this presentation, I’ll be focused on two main properties: FC and WP WHY FC and WP? Because they are useful descriptors of soil and water relationship. For example: the gap between them is the available water content for plants. Additionally, they also are input for soil and water modeling, as we will see later. (30-40)
- However, although these properties are important, they are commonly missing in, for example, the Chilean Soil Survey. Some reasons for this is their determination is time-consuming and expensive and that is why we have this scenario. As I mentioned before, more than 50% of the chilean soil database, called CIREN, do not have this data available. In order to overcome this problem,
- Our research group, specifically my research work has been exploring the PTFs… The main idea is develop an accurate estimation of, soil hydraulic properties, as a function of basic soil properties (35-45)
- This brings to us the following question: how do evaluate this functions? The first approach is, a direct validation comparing the estimated values versus the measured ones.
- However, we also have considered a functional validation. What does it mean? For example, Due to water balance estimation is imperative in managing water resources, we tested the FC and WP as inputs in a model that describe it. For this, We chose HYDRUS-1D as water-flow model. The main idea is test how relevant is the application of this estimated data in the final ouput.
- To sum up, the objective of this work was:
- Now I am going to describe briefly how the PTFs were used in the model
- First, An HYDRUS-user have several options for getting the model run depending on the data availability. Here shows the Model 2, which requires SSC. Then M3 requres the same, but also the bulk density. However, ias you can see in the diagram, n order to be able to run Model 4 and 5, FC and or WP are needed. In this point, we use the PTFs, (3 of them) and run the model. And for comparison with real data, we also run M5 with the full set of inputs required. Finally, the PTFs are labeled as R04/GL/R83
- This diagram summaris the conceptual frame of this reseach: With the help of the PTFs, the data availablity is increasing, from Model 2 to Model 5. This selected models lead to different parameters for this modeling Using climatic and boundary conditions, HYDRUS was run for water balance as output Finally, and annual WB was obtained
- This Figure represents the soils that were used for modeling. 7 soil from central chile we selected, an the data was obtained from the CIREN soil survey
- Let’s start with some resulst
- Now I am going to show you the direct validation. This table summarizes the goodness of fit of these three PTFs. We can see several error metrics including mean error, nash-sutflice efficiency, determination coefficient It is important to hightlight that the second PTF has better perfonmace. This means that If we only rely in this validation, the 2 PTF would be selected, and the rest of them discarted.
- This graph represents the result of Actual Evaporation for the 7 soils, all grouped toghether. Means with different letters indicate significant differences between soils according to an statistical test. Also the models are arranged according to data availability and the PTF used In this case, differences between Models were not statistically importanr
- Similarly, as evaporation, storage does not show significant differences between models.
- However, for free drainage, we found that M3 is signicanly different in comparison to M5 or control. As I stated before, this analysis merges the studied soils. For this reason, an extra analysis was performed
- However, for free drainage, we found that M3 is signicanly different in comparison to M5 or control. In this case, chosing any PTF in order to use M4 and M5 would be recommended. As I stated before, this analysis merges the studied soils. For this reason, an extra analysis was performed
- First, let me explain this patchwork The results showed here are the same that before, but also they include the individual behavior of the soils. To the right, there is a dendogram , which is a way of grouping soils into clusters based on their similarity. Also, the color key help us to highlight under or overstimation of each output in comparison to Model 5 with real data. For evaporation, overall there is homogeneus, except for Model 5 with GL-PTF, which was the best if only the direct validation were considered.
- First, let me explain this patchwork The results showed here are the same that before, but also they include the individual behavior of the soils. To the right, there is a dendogram , which is a way of grouping soils into clusters based on their similarity. Also, the color key help us to highlight under or overstimation of each output in comparison to Model 5 with real data. For evaporation, overall there is homogeneus, except for Model 5 with GL-PTF, which was the best if only the direct validation were considered.
- For free drainage, it is get more complicated, we cannot see any trend overall, so each soil has its own behavour, and there is no model better that the rest of them
- For storage, we found that for a group of soils every PTF models and M2 and M3 tend to overestimate. However it is still unclear what these soils have in common, in order to produce this grouping trend.
- Finally, in conclusion: -We found that we cannot rely on direct validation for PTFs evaluation -It is clear, that is why we are still working on to understand how soil prop are affecting this type of mdelling -ET is less variable than FD and ST, I strongly recommend you if you are interested in this variables, go to the field and get real data
- Before I finish, I would like to thank to the Soil Biophysics Lab member and to my advisor CB for his feedback and valuable comments. Finally, thanks to my scholarship and government funding which are supporting my graduate studies and research in Chile.