This work has sought to answer how global environmental change affects the concentration of nutrients in Mediterranean rivers. By means of a modeling effort using data collected by water agencies, this work has contributed to the knowledge of the relevant factors that drive nutrient dynamics in Mediterranean rivers, and how these change in time and space. Furthermore, it has clearly shown the potential modeling and time-series analysis offer to river scientists and managers.

1. 1
Supervised by:
Dr. Rafael Marcé Romero and Dr. Sergi Sabater Cortés
Doctoral Program in Water Science and Technology

2. OUTLINE
 General Introduction
 Main Objectives
 Study Area
 Results
PART I
• Modeling nutrient retention and nutrient load apportionment at the basin scale
PART II
• Detection and attribution of global change effects on nutrient dynamics in a large
Mediterranean basin
2

3. GENERAL INTRODUCTION
Global Environmental Change
-add to the intrinsic natural variability of the Earth system
-counteract or enhance natural changes
Freshwaters are at the forefront of global change phenomena.
3
ANTHROPOGENIC
ACTIVITIES

4. GENERAL INTRODUCTION
Global Change and Mediterranean Basins
Historically among the most heavily impacted by anthropogenic activities.
4
RELATIVE CHANGE IN WATER AVAILABILITY FOR IRRIGATION as projected under the A1B
emission scenario by regional climate model for 2071-2100 relative to 1961-1990.
Regional Assessment of Climate Change in the Mediterranean provided
by Euro-Mediterranean Centre on Climate Change (CMMC).

5. GENERAL INTRODUCTION
RiverWater Quality in Mediterranean Basins
DAMMING
WATER
EXTRACTION
URBANIZATION
Mediterranean rivers can be particularly vulnerable to water
pollution due to the presence of additional pressures:
5

6. GENERAL INTRODUCTION
Nutrient in-stream processes
• NUTRIENT POLLUTION
One of the most common causes of pollution
of freshwater bodies.
• Streams and rivers act as REGULATORS of
exported nutrient loads to downstream
aquatic ecosystems.
• The relative importance of the nutrient
sources at the BASIN SCALE is better
expressed in terms of IN-STREAM
PROCESSES.
DESCRIPTIONOFWATER QUALITY
VARIABILITYWITHINTHE RIVER NETWORK,
RATHERTHAN ON A SITE-BY-SITE BASIS.
6

7. GENERAL INTRODUCTION
Common problems in river water quality studies
• CHALLENGES
 Complex cause-effect relationships
 Spatio-temporal dimension
 Up-scaling processes to basin scale
 Large datasets
 Data requirements
7
Gaps
Length
Frequency
ADEQUATE
METHODS andTOOLS

8. MAIN OBJECTIVES
• To describe IN-STREAM NUTRIENT RETENTION processes at the basin scale, considering
both biological and hydrological factors in IMPAIRED RIVERS .
• To identify and quantify the main NITRATE AND PHOSPHATE SOURCES and link their
variability TO LAND-USE AND CLIMATIC CONDITIONS in a Mediterranean basin.
• To detect and characterize COMMON WATER QUALITY PATTERNS in river basins while
tackling the most commonly encountered challenges in time-series analysis.
• To characterize the spatio-temporal variability of nutrient dynamics in a Mediterranean
basin and ATTRIBUTE THE POTENTIAL DRIVERS behind the underlying patterns in the
context of GLOBAL CHANGE.
8

9. STUDY AREA
Mediterranean River Basins
River water quality monitoring points:
EBRO (n=50), JÚCAR (n=90), and LLOBREGAT (n=20)
9

10. STUDY BASINS
Llobregat River Basin
10
Area: 4,948 km2
Average Rainfall: 610 mm

11. STUDY BASINS
Ebro River Basin
11
Area: 85,500 km2
Average Rainfall: 400-2000 mm

12. Modeling nutrient retention at the basin scale:
does small stream research apply to the whole river network?
Aguilera et al. Journal of Geophysical Research-Biogeosciences (2013) 118: 1-13
PART I:
12

13. PART I
Introduction: Nutrient in-stream processes
• NUTRIENT SPIRALING (Newbold et al. 1981)
Uptake velocity (vf ) [mm min-1]  nutrient removal
downward velocity in the water column
Areal Uptake Rate (U) [mg m-2 min-1] 
vf x Concentration
13
BENTHIC COMPARTMENT
RIVER
WATER
COLUMNNUTRIENT vf

14. PART I
Introduction: Modeling nutrient in-stream retention
• Most models use FIRST-ORDER decay to estimate in-
stream nutrient retention at the basin scale, relying
mainly on hydrological conditions.
 What about available nutrient concentration?
• EFFICIENCY LOSS MODEL (EL)
Log-transformed uptake velocity (vf) decreasing with
log-transformed nutrient concentration (O’Brien et al., 2007).
• BASIN-SCALE NUTRIENT MODEL
Heuristic approach to estimate in-stream processes in a
basin under major anthropogenic stress.
14
LogUptakeVelocity(vf)
LogConcentration
LogUptakeRate(u)
FIRST-ORDER MODEL
EFFICIENCY LOSS MODEL
PARTIAL SATURATION

15. PART I
Nutrient Model Setup
15
LLOBREGAT RIVER BASIN
• Network of 79 river reaches and sub-basins
• 23 monitoring points (Catalan Water Agency)
 NO3
- and PO4
3- concentration (2000-2006)
 River discharge measurements
• WWTP and industrial effluents
• Land uses
Nutrient
Sources
MODEL NUTRIENT LOAD

16. A
B
PART I
SPARROW – Spatially Referenced Regression on Watershed Attributes
(United States Geological Survey; Schwarz et al., 2006)
16
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I. Upstream Load II. Sub-basin Load
LOAD ESTIMATION : SPATIALLY-REFERENCED REGRESSION

17. PART I
Reach Decay Specification in SPARROW
EFFICIENCY LOSS CONCEPT IN SPARROW
UPTAKEVELOCITY (vf )
• biological measure mathematically independent of hydrology (Wollheim et al., 2006)
Reach decay
specification =
exp (- vf × HL
-1)
• where HL is the
hydraulic load and
vf IS CONSTANT
(Schwarz et al., 2006;
Wollheim et al., 2006)
vf = a × Cb
• Power law set to
VARYING vf values
with respect to
available nutrient
concentration
Reach decay
specification =
exp [- (a × Cb) × HL
-1]
• OUR APPROACH
to model nutrient
in-stream decay
in SPARROW
17
1st 2nd 3rd

18. NO3 (mg L-1
)
0.0001 0.001 0.01 0.1 1 10 100 1000
U(mgm-2
min-1
)
1e+0
1e+1
1e+2
1e+3
1e+4
1e+5
1e+6
log
logNO3 (mg L-1
)
0.0001 0.001 0.01 0.1 1 10 100 1000
vf(mmmin-1
)
0.001
0.01
0.1
1
10
100
1000
log
log
PART I
Nitrate Model Results: In-stream decay
UPTAKEVELOCITY (vf) UPTAKE RATE (U)
SPARROW
18

19. PO4 (mg L-1
)
0.0001 0.001 0.01 0.1 1 10 100
U(mgm-2min-1)
1e-2
1e-1
1e+0
1e+1
1e+2
1e+3
1e+4
1e+5
log
log
PO4 (mg L-1
)
0.0001 0.001 0.01 0.1 1 10 100
vf(mmmin-1)
0.001
0.01
0.1
1
10
100
PART I
Phosphate Model Results: In-stream decay
UPTAKEVELOCITY (vf) UPTAKE RATE (U)
SPARROW
log
log
19

20. PART I
Temporal averaging: difference between Literature and Llobregat responses?
20
Nitrate concentration (mg NO3
-
L-1
)
2 3 4 5 6 7 8 9 20 30 40 50 60 7010
vf'(mday-1
)
0.1
1
Resulting vf ' using 0.5C-0.48
as generator curve
Resulting vf ' using 4.6C-1.2
as generator curve
Reference curve-low slope (0.5C-0.48
)
Reference curve-high slope (4.6C-1.2
)
x
REFERENCE SPARROW
FS = FULL
SPECTRUM OF
vf/HL IN A YEAR
FS - Literature power law
FS - SPARROW power law
REFERENCE LITERATURE

21. PART I
Contrasting Literature and Llobregat stream data
• We can discard
temporal averaging
as the generator of
differences.
• Difference in slopes 
BIOGEOCHEMICAL
RESPONSE involved in
nutrient removal in
large impaired rivers.
Streamflow (L s-1
)
100
101
102
103
104
Nitrateconcentration(gN-NO3
-
L-1
)
100
101
102
103
104
105 Field data for this study
Literature review
21

22. HYDROLOGY-DEPENDENT EFFICIENCY LOSS-BASED IMPAIRED RIVERS
HIGH RETENTION
UNDER LARGER RANGE
OF HYDROLOGICAL
CONDITIONS
PART I
Implications for in-stream nutrient retention estimation
22
NUTRIENTRETENTION

23. PART I
Results: MeanTotal Load – Mean Removed Fraction (2000-2006)
23
NITRATE PHOSPHATE

24. PART I
Results: Mean Source Apportionment (2000-2006)
24
FOREST/GRASS
AGRICULTURE
POINT
SOURCES
NITRATE PHOSPHATE

25. PART I – In-stream nutrient retention at basin scale
25
• NUTRIENT RETENTION CAPACITY in the Llobregat River Basin decreased with increasing
nutrient concentration, differing significantly from the Efficiency Loss observed in the
literature.
• Most modeling approaches consider hydrology as the solely factor that shapes nutrient in-
stream retention.
However, BIOLOGICAL UPTAKE variables should also be taken into account,
especially in impaired rivers and streams.
• NUTRIENT APPORTIONMENT varied according to nutrients and followed the gradient of
land use distribution in the Llobregat River Basin.

26. Detection and attribution of global change effects
on nutrient dynamics in a large Mediterranean basin
Aguilera et al. Biogeosciences Discuss., 12, 5259-5291, doi:10.5194/bgd-12-5259-2015, 2015.
PART II:
26

27. PART II
Introduction
27
• TIME-SERIES contain valuable information about the
physical, biological, or socio-economical system that
shaped them (Ghil et al. 2002).
• Time-series can be NOISY and/or contain GAPS
• Tools to extract KEY PROPERTIES while overcoming
CHALLENGES
 DETECT AND ATTRIBUTE the effects of global
change on river water quality patterns.1750 1800 1850 1900 1950 2000
ATMOSPHERIC CO2
TIME 
RIVERNITRATE
CONCENTRATION
Gaps
Length
Frequency
DATA• CAUSE-EFFECT
• SPATIO-TEMPORAL

28. PART II
Dynamic Factor Analysis (DFA)
LINEAR COMBINATION OF COMMON PATTERNS + ERROR (Zuur et al., 2003).
FACTOR LOADINGS = pattern relevance
COMPLEX PATTERNS and DATA GAPS
28
ERROR
P1
P2
P1
P2
TS1
TS2
TS3

29. PART II
Characterizing water quality variability
29
ATTRIBUTION
HOW GLOBAL CHANGE
PHENOMENA AFFECT
RIVER WATER QUALITY
IN A BASIN OR REGION?
DETECTION
TEMPORAL + SPATIAL

30. PART II
Introduction
30
Nutrient dynamics – Ebro River Basin
 Nitrate and Phosphate Concentration
 50 time-series (monthly; 1980-2011)
 Environmental variables
Land uses
Climate-related
Understand how global change may
affect nutrient variability (and hence water
quality) in the Ebro basin.

31. PART II
Results: Pattern Detection
31
COMMON
PATTERNS
BEST
FITS
NITRATE PHOSPHATE
BEST FIT = linear combination of patterns x factor loadings (relevance)

32. PART II
Results: Pattern Attribution
32

33. + HYDROLOGY
PART II
Results: Pattern Detection – Nitrate Concentration
33
FACTOR LOADINGS
 Magnitude = Relevance
 Sign = Behavior
- HYDROLOGY
OPPOSITE

34. PART II
Results: Pattern Attribution – Nitrate Concentration
Identification of regions with coincident
potential cause-effect relationships
Relevance
of patterns
Relevant
explanatory variables
34
NITRATE
 Lower Segre and Mid Ebro rivers
 Upstream tributaries
 Upstream headwaters
 Downstream Ebro River
MEAN PATTERN WEIGHT
 Hydrology-driven
 Temperature-driven
 Fertilizer application

35. CHAPTER 4
Drivers of Nutrient Dynamics in the Ebro River Basin
35
PO4
3-
Climate oscillations
(NAO, ENSO) Climate
change?
Seasonal
climate
oscillations
Streamflow
variability
P load
human
activities
P load
adjacent
ecosystems
Industrial
activity
Synthetic
fertilizers
Unknown
local factors
NO3
-
Climate oscillations
(NAO, ENSO) Climate
change?
Seasonal
climate
oscillations
Streamflow
variability
N load
human
activities
N load
adjacent
ecosystems
Irrigation
Industrial
activity
Synthetic
fertilizers Manure
Unknown
local factors
Dams Dams

36. PART II – Detection and Attribution
• Dynamic Factor Analysis + Complementary methods:
- EXTRACT key properties of the time-series and patterns
- ATTRIBUTE water quality spatio-temporal variability
• Impact of global change on nitrate dynamics in the EBRO BASIN relied mainly on regional and
global factors, whereas the impact on phosphate depended more on local factors.
36

37. 37
Thank you for your attention
ACKNOWLEDGEMENTS
Spanish Ministry of Economy and Competitiveness through the project SCARCE (Consolider Ingenio 2010 CSD2009-00065)
Doctoral Grant (FI-DGR 2012) awarded by the Catalan Government