SOIL EROSION MODELING WORKSHOP ISPRA MARCH 2017

Transcription

1 SOIL EROSION MODELING WORKSHOP ISPRA MARCH 2017 EROSION RATE PREDICTIONS FROM PESERA AND RUSLE AT A MEDITERRANEAN SITE BEFORE AND AFTER A WILDFIRE: COMPARISON, IMPLICATIONS AND ONGOING RESEARCH TOWARDS VALIDATION Karamesouti, Μ., Papanikolaou, I.D., Petropoulos, G.P., Kairis, O., Kosmas, K.

2 Aim of study: - theoretical comparison of the performance of the PESERA and RUSLE models in a hilly Mediterranean study site, before and after a wild fire Study site: Greece Attica - Mt. Parnitha experienced a major wildfire on June 28, 2007 suppressed 5 days later, burning a total of > 45 Km² of land Photo Source:

3 Models input data: Input data Meteorological datasets (in daily basis for rainfall, temperature, potential evapotranspiration Soil characteristics (field data collection, semi-detailed mapping 1:20,000) Topographic data (30 m DEM from ASTER sensor) Land use data (CORINE Land Cover 2000) Studied area & Fire characteristics (Landsat TM from USGS) RUSLE Rainfall erosivity factor (Rfactor) Soil erodibility factor (Kfactor) Topographic factor (LS-factor) Crop factor (C-factor) Soil conservation practices actor (P-factor) PESERA (128 layers) Climate data (mean monthly rainfall, mean monthly rainfall per rainy day, mean monthly temperature corrected for altitude, monthly temperature range, Potential Evapotranspiration) Land use-land cover data (Land cover type, initial ground cover, initial surface storage, surface roughness reduction per month, root depth) Soil parameters (Surface crusting, sensitivity to erosion, effective soil water storage capacity, soil water availability to plants over the top 30 cm and cm, scale depth) Topographic data (standard deviation of elevation)

4 Models outputs before the fire: RUSLE PESERA

5 Comments on outputs for the pre-fire state: The average estimates of soil erosion, before fire, using the RUSLE model were about 2.5 times greater than the estimates by the PESERA model. In spatial comparative analysis the models had an agreement (soil erosion difference < 1.5 t ha -1 yr -1 ) in about 38% of the area For very steep slopes ( >60%) the calculated values using RUSLE were about 5.5 times greater than values using the PESERA. RUSLE: for sl > 60%, average soil loss ~100 tn ha - ¹ yr - ¹ For flat to almost flat areas (<2%) the calculated values using PESERA were about 2 times greater than values using the RUSLE. Recommendations needed on how to manage special cases: Steep slopes where soil depth is limited. Flat to almost flat areas. Model RUSLE (tn ha - ¹ yr - ¹ ) 0-2% % % % % % % > 60% Average PESERA (tn ha - ¹ yr - ¹ )

6 Comments on outputs for the pre-fire state: In agricultural areas, RUSLE gives much higher values compared to PESERA In areas characterized as coniferous and -transitional woodland shrubland, the two models had the higher level of agreement Range of values for the two models Model Range of values (tn ha - ¹ yr - ¹) % area till 5 tn ha - ¹ yr - ¹ Recommendations needed on: % area till 10 tn ha - ¹ yr - ¹ RUSLE ~65% ~81% ~17 PESERA ~76% ~84% ~6.5 Overall Average value (tn ha - ¹ yr - ¹ ) Managing Outliers taking into consideration the special cases Improving C-factor Land cover RUSLE pre-fire PESERA pre-fire RUSLE post-fire PESERA post-fire Avg. 1.4 Avg Avg. 5.9 Avg Avg. 6.0 Avg Avg Avg CLC class C-factor C-factor (JRC Panagos et al., 2015) RUSLE (Avg. Value) PESERA (Avg. Value) 211 (non-irrigated arable land) 0.3 NA (complex cultivation) (land principally occupied by agriculture) (coniferous forest) (sclerophyllous vegetation) (transitional woodland shrub)

7 Models outputs after fire: dnbr range of values - Description Class1 ( ): Unburned Class 2 (35 172): Unburned to low Class 3 ( ): Low to moderate Class 4 ( ): Moderate to low Class 5 ( ): Moderate high Class 6 ( ): Moderate high to high Temperature o C 0 95 o C o C o C Parameters affected RUSLE model PESERA model P-Factor* P-Factor* P-Factor* K-Factor P-Factor* Class 7 ( ): High Class 8 ( ): High o C > 460 o C K-Factor C-Factor P-Factor* *only in cases of forested areas Vegetation cover Vegetation cover Vegetation cover Crusting Erodibility

8 Comments on outputs for the post-fire state: The average estimates of soil erosion, after fire, using the RUSLE model were about 5 times greater than the estimates by the PESERA model. The RUSLE model post fire values about 9 times greater than the pre fire estimates. The PESERA model post fire values about 6 times greater than the pre fire estimates. Spatial comparative analysis showed that both models had an agreement (soil erosion difference < 1.5 t ha-1yr-1) in only 15% of the area (in pre-fire it was 38%) During the second period there is less rainfall than in the first period and this has significant impact, despite the impact of fire (rain pre-fire , rain post-fire ) Model RUSLE (tn ha - ¹ yr - ¹ ) PESERA (tn ha - ¹ yr - ¹ ) 0-2% % % % % % % > 60% Average

9 Comments on outputs for the post-fire state: Coniferous forest (312), recorded the bigger difference in the pre and post fire soil erosion estimate, using the RUSLE (pre 1.8, post 663 t ha - 1 yr -1 ) For flat to almost flat areas (<2%) the average calculated values using PESERA were about 16 t ha -1 yr -1 (in pre-fire the average value was 1.5 t ha - 1 yr -1 ) Overall, RUSLE gave locally extreme values, which affected the mean soil erosion predictions Recommendations for discussion: Calibration for post-fire conditions for both models (c-factor, crusting, erodibility) Model Range of values (tn ha - ¹ yr - ¹ ) % area till 5tn ha - ¹ yr - ¹ % area till 10tn ha - ¹ yr - ¹ RUSLE ~64% ~70% ~157 PESERA ~36% ~52% ~37 Land cover Overall Average value (tn ha - ¹ yr - ¹ ) RUSLE pre-fire PESERA pre-fire RUSLE post-fire PESERA post-fire Avg. 1.4 Avg Avg. 5.9 Avg Avg. 6.0 Avg Avg Avg

10 Conclusions: Overall in the current study RUSLE gives more extreme values than the PESERA PESERA provides a smoother spatial distribution in both pre and post-fire case Although RUSLE might seem easy as a process, it becomes really complicated when it should be adjusted in local scale RUSLE is very sensitive to changes in the contributing factors, for this reason special attention is required for: 1. topographic factor (LS-factor, especially for areas with sl > 60%) and rainfall erosivity factors 2. crop factor (C-factor, especially for values referring to burnt and agricultural areas) PESERA showed high sensitivity to the vegetation coverage and to soil characteristics (crusting and erodibility) PESERA gives values for soil erosion in flat almost flat areas (slope < 2%) Further debate is needed for the extreme values Further debate is needed for special cases (i.e. very steep slopes where soil depth is very limited or bedrock is exposed) More details on practical model applicability are provided in paper: Erosion rate predictions from PESERA and RUSLE at a Mediterranean site before and after a wild fire: Comparison & implications (Karamesouti M., Petropoulos G.P., Papanikolaou I.D., Kairis O., Kosmas K. in Geoderma)

11 Post fire soil erosion and deposition Modern soil Post-fire deposition Burned Paleosoil

12 Current test site for post fire soil erosion processes

13 Thank you very much for your attention!!