Author: Marta Angulo-Martinez

1. Trends in daily rainfall erosivity in relation with NAO,
MO and WeMO for NE Spain, during the period
1955-2006.
Marta Angulo-Martinez
PhD student
Estación Experimental Aula Dei-CSIC,
Zaragoza, España
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012

2. Outline
Introduction:
- What’s rainfall erosivity?
- How to measure it
- Database creation
- Study area
Trends in rainfall erosivity at NE Spain, 1955-2006
Analysis of the relationship between some teleconnection
patterns and rainfall erosivity
Influence of the atmospheric teleconnection patterns
evolution in explaining rainfall erosivity trends
Conclusions
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

3. The rainfall erosivity factor in soil erosion (EI)
Measures the rainfall energy or the ability of rainfall to erode soil.
Climate dynamics and
rainfall genetic mechanisms
Splash erosion & runoff erosion
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

4. Measuring and modelling rainfall erosivity
Where:
Empirical models: vr = Rainfall volume;
Universal Equation for Soil Erosion (R)USLE I30 = Maximum intensity in 30 min;
er = Unit rainfall energy.
m
1 n j  o 
R = ∑∑ ( EI 30 ) k EI = EI 30 =  ∑ er vr  I 30 Empirically estimated with
n j =1 k =1  r =1  data from US
er = 0.29 [1 − 0.72 exp( −0.05ir )]
Limitations:
- High resolution temporal data (15’) Other empirical equations
- Empiricism Cerro et al. (1998); Barcelona, Spain
Depends on the physics of rainfall and er ( Cr ) = 0.384 [1 − 0.538 exp( −0.029ir )]
climate dynamics.
Depends on the physico-chemical
characteristics of the soil
Van Dijk et al. (2002); Universal
er (VD ) = 0.283 [1 − 0.52 exp( −0.042ir )]
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

5. Measuring rainfall kinetic energy er and splash
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

6. Relationships between kinetic energy (er) and intensity
Logarithmic functions
Ek = a + b log I
Power-law functions
Ek = a I b
Exponential functions
EK = emax [1 − a exp (−b I )]
USLE proposal
er = 0.29 [1 − 0.72 exp( −0.05ir )]
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

7. Daily Rainfall Erosivity Model
1 n j
m Databases
R = ∑∑( EI 30 ) k
n j =1 k =1 110 rainfall series
15’ time resolution
 o 
EI = EI 30 =  ∑ er vr  I 30 EI m = am P bm + ε Period 1997-2006
 r =1 
156 rainfall series
er = 0.29 [1 − 0.72 exp( −0.05ir )] Daily time resolution
Period 1955-2006
R factor estimated MJ mm ha-1h-1 y-1
R factor observed MJ mm ha-1h-1 y-1
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

8. Daily Rainfall Erosivity databases spatial distribution
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

9. Daily Rainfall Erosivity vs. Rainfall (month by month)
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

10. Trends in rainfall erosivity in relation with NAO, MO &
WeMO
Study case:
Trends in rainfall erosivity at NE Spain
during the period 1955-2006 in relation
with NAO, MO & WeMO
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

11. Rainfall erosivity trends at annual scale
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

12. Rainfall erosivity trends at seasonal scale
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

13. Rainfall erosivity trends at seasonal scale
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

14. Temporal evolution of rainfall erosivity quintile
occurrence
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

15. Rainfall erosivity trends at daily scale; evolution of the
occurrence in low (Q1) and high (Q5) events.
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

16. Rainfall erosivity trends at daily scale; evolution of the
occurrence in low (Q1) and high (Q5) events.
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

17. Rainfall erosivity trends at daily scale; evolution of the
occurrence in low (Q1) and high (Q5) events.
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

18. Rainfall erosivity trends at daily scale; evolution of the
occurrence in low (Q1) and high (Q5) events.
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

19. Could be these trends related with atmospheric
mechanisms and teleconnection patterns evolution?
The characteristics of the rainfall erosivity events—Drop Size Distribution, Kinetic
Energy, Intensity and Duration—depend on the type of rainfall event (Van Dijk
et al. 2002), and in the meteorological and climatic characteristics involved in the
generation of the type of rainfall event:
The leading atmospheric circulation patterns
Sea level pressure fields
Temperature contrasts between high and low atmospheric
layers
Humidity percentage in the air masses
Wind flows
Geographical factors
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

20. Main atmospheric teleconnection patterns affecting the
climate of the study area
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

21. Teleconnection indices circulation patterns in positive
(above) and negative (beneath) phase
NAO+ MO+ WeMO+
NAO- MO- WeMO-
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

22. Are teleconnection indices phases influencing daily
rainfall erosivity?
Differences in daily rainfall erosivity, negative and positive
atmospheric teleconnection indices phases
Positive events = daily indices > 0.5
sd
Negative events = daily indices < -0.5 sd
Relative difference of average daily rainfall erosivity of negative NAO
days with respect to positive NAO days.
EIdif = (EINAO- - EINAO+) / EINAO+
where EINAO- (EINAO+) is the average daily rainfall erosivity of negative (positive) NAO days
Statistical significance checked by the Wilcoxon-Mann-Whitney test
month by month. Significance level α=0.05
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

23. NAO influencing daily rainfall erosivity
Relative difference of average daily rainfall erosivity of negative NAO days with respect to positive NAO days
(MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between
positive and negative NAO phases.
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

24. MO influencing rainfall erosivity
Relative difference of average daily rainfall erosivity of negative MO days with respect to positive MO days
(MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between
positive and negative MO phases.
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

25. WeMO influencing rainfall erosivity
Relative difference of average daily rainfall erosivity of negative WeMO days with respect to positive WeMO
days (MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between
positive and negative WeMO phases.
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

26. Analysis of the probability of occurrence of extreme daily
rainfall erosivity records associated to negative and positive
teleconnection indices phase
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

27. Temporal evolution of atmospheric teleconnection
indices and of rainfall erosivity quintile occurrence
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

28. Conclusions
Rainfall erosivity has decreased in the NE of the Iberian
peninsula during the last 55 years
Decrease occurs at annual and seasonal scale, though little
increase occurs at summer and autumn
Decrease is explained by a reduction in the high and extreme
rainfall erosivity events occurrence , whilst low events have
increased.
This evolution seems related with positive trends in the
atmospheric teleconnection indices: NAO, MO and WeMO.
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec

29. Contact details:
Estación Experimental Aula Dei – CSIC, Zaragoza (ESPAÑA)/Departamento de Suelo y
Agua
Marta Angulo-Martinez
PhD Student
Email: mangulo@eead.csic.es
Thanks!