Hydroclimatic context of the Peruvian Pacific drainage

Get Complete Project Material File(s) Now! »

Regions Characterization

In this section, we documented the precipitation seasonal distribution and interannual variability over the nine identified regions. Some of geographical features (area, latitudinal and altitudinal ranges) are presented in Table 3.2. All regions present a unimodal precipitation seasonal distribution (see Figure 3.7) and differ from their peak calendar month, intensity and duration of the rainy season.
Region 1 extends over northern lowlands including drier areas as the Sechura desert (79°W to 81°W and 5.5°S to 6.5°S) where the average interannual precipitation is about 90 mm/yr. A maximum seasonal precipitation is observed in March (see Figure 3.7a1) with a rainy season from January to May (JFMAM) with values less than 50 mm.month-1 which represent near to 90% of the annual precipitation. The rest of the year is considered as dry due to values near or equal to zero, corroborating the irregularity in the seasonal precipitation pattern (see Figure 3.7a1) and in the interannual variability of monthly precipitation (see Figure 3.7a2) at the coast (Garreaud et al., 2002; Lavado et al., 2012).
Region 2 comprises a large part that belongs to the foothills of the northern Andes covering bi-national river watersheds of Peru and Ecuador. This zone exhibits an irregular seasonal precipitation pattern (see Figure 3.7b1) and an irregular interannual variability of monthly precipitation (see Figure 3.7b2). Average interannual precipitation value is around 370 mm/yr. The wettest period occurs between January and April (JFMA) cumulating near to 90% of total precipitation.
Northern coastal regions as regions 1 and 2 are significantly affected by strong events represented by two peaks reaching 413 mm.month-1 in March 1983 and 299 mm.month-1 in March 1998 for region 1 (see Figure 3.7a2); and 746 mm.month-1 in March 1983 and 708 mm.month-1 in March 1998 for region 2 (see Figure 3.7b2). A summary of precipitation statistics is given in Table 3.2 and a box plot representation of monthly precipitation in Figure 3.8. Outliers from Figure 3.8, represented by small circles, correspond to values exceeding 1.5 times the interquartile range (IQR). All regions have observations that exceed Q3 + 1.5(IQR), however northern coastal regions 1 and 2 differs from the rest for having greater number of outliers values (14% and 17% respectively) with the largest precipitation anomalies reaching 56 and 25 times of mean monthly precipitation for region 1 and 2 respectively. Most of the interannual variability in precipitation, reflected as well in higher CV values (see Table 3.2), is directly due to the occurrence of the strong El Niño events indicating also a high intensity of interannual variability than other regions. This is particularly obvious for region 1 where three extreme precipitation events are observed corresponding to the year 1972, 1982 and 1997, known as strong El Niño years. Interestingly the more inland region 2 exhibits interannual variations of precipitation that does not necessarily corresponds to the strong El Niño years. These events may correspond to local convective events associated with coastal warm oceanic conditions related mainly to Kelvin waves and the Madden and Julian Oscillation (MJO) (Woodman, 1985; Bourrel et al., 2015).
Region 3 covers bi-national river watersheds of Peru and Ecuador bordering with the Amazon Basin by the east. This is also the wettest region (see Figure 3.7c1, 3.7c2 and Figure 3.8). On the other hand, precipitation amount decreases southward with precipitation regularity in the seasonal pattern (see Figure 3.7c1) and in the interannual variability of monthly precipitation (see Figure 3.7c2), with a rainy season from January to April (JFMA) that represents almost 70% of the annual precipitation. Mean interannual precipitation reaches 1024 mm/yr, representing eleven times of the mean interannual precipitation of region 1 and three times of region 2 (see Table 3.2). The precipitation interannual variations are weakly associated with the extreme El Niño events (the correlation between the E index and precipitation is 0.2) but is negatively correlated to the C index (r=-0.4) indicating that the R3 region is sensitive to cool enhanced coastal conditions during Central Pacific El Niño events (Bourrel et al., 2015). The inter-events fluctuations are also noticeable which are related to local convective events not related to ENSO but mostly by the ITCZ and the large-scale atmospheric variability associated with the MJO (Tapley and Waylen, 1990; Takahashi, 2004; Bourrel et al., 2015). Also noteworthy, there is an increase of precipitation peaks frequency over the last two decades (see Figure 3.7c2).
Region 4 is the longest region located between the coastal plain and the foothills of the western Andes and contains some of the major coastal cities as the capital Lima. This region corresponds to a zone influenced by the large-scale mid tropospheric subsidence of the southeastern subtropical Pacific Ocean, enhanced by the coastal upwelling of cold water (Vuille et al., 2000; Garreaud et al., 2002; Lavado et al., 2012) without presenting a relationship between strong precipitation peaks and strong ENSO events. Then, mean interannual precipitation reaches a value of 16 mm/yr defining the driest region in the country (see Table 3.2) with precipitation irregularity in the seasonal pattern (see Figure 3.7d1) and in the interannual variability of monthly precipitation (see Figure 3.7d2) very common in coastal regions (see Figure 3.8). The wet period from January to March (JFM) represents near to 75% of the annual precipitation. In the southern part, drier areas are found like the Nazca desert (74.5°W to 75.5°W and 14.5°S to 15.5°S).
Region 5 comprises a border with region 3 and the Amazon Basin by the east. The mean interannual precipitation reaches 492 mm/yr and the wet period occurs between December and April (DJFMA) cumulating near to 80% of total precipitation. No precipitation peaks were identified during strong El Niño events (see Figure 3.7e2) as those in regions 1 and 2, suggesting that precipitation in regions 4 and 5 are likely to be affected by others processes, either local (e.g. coastal SST) or non-local (e.g., dry air transport from the southern region that reduce the precipitation), resulting in a heterogeneous interannual variability of monthly precipitation (see Figure 3.7e2) with a low value of coefficient of variation around 0.3 (see Table 3.2).
Region 6 borders with the Amazon Basin by the east and shows a heterogeneous precipitation pattern without distinguishing any peak corresponding to the strong El Niño events (see Figure 3.7f2). Precipitation distribution is well defined with a rainy season from December to March (DJFM) that represents near to 85% of the annual precipitation (see Figure 3.7f1) and with a mean interannual precipitation reaching 366 mm/yr.
Region 7 is characterized by lower precipitation regime with a rainy season from January to March (JFM) accounting for 65% of the annual precipitation. Furthermore, this region is one of the driest areas in the country where the interannual precipitation (23 mm/yr) presenting precipitation irregularity in the seasonal pattern (see Figure 3.7g1) and in the interannual variability of monthly precipitation (see Figure 3.7g2). This region could be considered as an extension of region 4, also influenced by the large-scale mid tropospheric subsidence of the southeastern subtropical Pacific Ocean but differing in the increase of precipitation peaks frequency in the last decade unlike region 4 as can be seen in Figure 3.7g2.
Region 8 comprises an area thus belongs to the foothills of the southern Andes. This zone exhibits irregular precipitation in the seasonal pattern (see Figure 3.7h1) and in the interannual variability of monthly precipitation (see Figure 3.7h2). The mean interannual precipitation presents a higher value than region 7, reaching 296 mm/yr. The wettest period occurs between December and March (DJFM) cumulating near to 90% of total precipitation (see Figure 3.7h1).
Finally, region 9 borders with the Titicaca Basin in the south and east and with the Amazon Basin by the east. The mean interannual precipitation reaches 594 mm/yr and the wet period occurs between December and March (DJFM) cumulating near to 80% of total precipitation. Like region 8, region 9 presents a deficit in precipitation during strong El Niño events (see Figure 3.7h2 and 3.7i2). However, unlike region 8, it presents precipitation regularity in the seasonal pattern (see Figure 3.7i1) and in the interannual variability of monthly precipitation (see Figure 3.7i2) associate with a low value of coefficient of variation around 0.2 (see Table 3.2) indicating also the lowest intensity of interannual variability.
Figure 3.7. Monthly precipitation regime (1964-2011) for the nine identified regions. A precipitation time series is shown by region. Regions 4 and 7 are shown in a different precipitation scale.


This chapter proposes a method for the regionalization of the precipitation in the Pd that consists in a two-step procedure: a preliminary cluster analysis (k-means) followed by the regional vector method (RVM) analysis. Using this procedure, 9 regions were identified so as to depict synthetically the relationship between precipitation variability and altitude and latitude. In particular, precipitation variability is higher at the northern latitudes and decreases to the south in high altitudes. The motivation for performing a classification using cluster analysis prior to the regionalisation by RVM stands in the complex of processes influencing precipitation variability over this region. In particular, previous studies (Lavado and Espinoza, 2014; Bourrel et al., 2015) have shown that precipitation along the Pd experiences the influence of both type of El Niño, and due to the strong positive skewness of strong El Niño events, the distribution of precipitation data is not Gaussian, limiting to some extents the linear analysis approach (i.e. RVM).
It was verified that the proposed approach leads to a different definition of the regions than an approach based only on RVM; we inferred 3 regions for lowlands, middle altitude basin and highland in the northern and southern Pd, which was not possible to identify using the method based on the RVM only. The k-means clustering analysis allows for a preliminary grouping of station data that is used as a first guess for the RVM and this step constrains to a large extend the regionalization procedure. The proposed two-step methodology also leads to a slight improvement in the thresholds estimate for evaluating the RVM quality.
This product will provide valuable information for hydrological sensitivity analysis over Peruvian Pacific watersheds (through hydrological modelling) for quantifying the effects of climate variability and human activities on runoff with the aim of improving ecological and water resources management. The ENSO/precipitation relationship based on the nine identified regions is discussed in Chapter 6 incorporating other atmospheric and oceanic key indices (cf. Bourrel et al., 2015).
This chapter presents the results obtained in the first part of the paper entitled: “Hydroclimatic change disparity of Peruvian Pacific drainage catchments” submitted to Theoretical and Applied Climatology in February 12, 2016, accepted in August 23, 2017 (http://dx.doi.org/10.1007/s00704-017-2263-x) and published online in September 5, 2017 (Rau et al., 2017b, see Annex A.2).
– Identification of 11 from 26 catchments with low water balance disparity based on a hydroclimatic balance with the Budyko theory.
– A first approach of the relationship between anthropogenization, evolution of land cover and low/high water balance disparity.
– Documentation of the annual hydroclimatological regime (i.e. precipitation, temperature, evapotranspiration and runoff) at basin scale for the first time of Peruvian Pacific drainage catchments.

Theoretical background

Quantifying and deciphering the effects of climate variability and human activities on hydrological regime represent a major challenge, especially at short scales of time and space (Donohue et al. 2007; Wagener et al. 2010). In order to decipher climate variability and anthropogenic influence on water balance, this chapter is based on the Budyko theory (Budyko 1958, 1974). This theory is widely used and is a well-established global empirical framework within the hydrological community (Donohue et al. 2011; Coron et al. 2015: Greve et al. 2015). This method relates the interannual evaporative index (ratio between actual evapotranspiration and precipitation) and the interannual dryness index (ratio between potential evapotranspiration and precipitation) in a global description called the “Budyko space”. Thereby, all interactions through the hydrological cycle between vegetation, soil and atmosphere create an empirical equilibrium represented by the Budyko curve (van der Velde et al. 2013). To emphasize the impact of other factors on the water balance such as vegetation, an emerging general relationship proposed by Zhang (Zhang et al. 2001) known as the Budyko-Zhang framework has been used. This empirical framework comes from an evaluation of 250 catchments worldwide including dryland regions (Zhang et al. 2001). It has been applied to single catchments and specific areas until nowadays, considering different approaches as the assessment of their sensitivity to climate change (Donohue et al. 2011; Renner et al. 2012; van der Velde et al. 2013). In order to answer properly to the issues raised by the effects of climate change on water resources (Sivapalan et al. 2011), the Budyko curve is recognized “as a much valuable tool to back to the basics, it means, the physical basis of catchment water balance” (Coron et al. 2015).
The degree of anthropogenic influence can be determined using two types of influence on runoff change: human activity with direct influence (soil conservation, water control works, increasing water demand) and human activity with indirect influence (land use and land cover changes) (Wang et al. 2013). They constitute descriptive elements to understand the behaviour of hydroclimatic data series at interannual scale and to identify the catchments presenting a low level of anthropogenization. This selection can be performed through an analysis of water balance disparity by catchment via the Budyko-Zhang framework, which assumes that catchments do not present changes in basin water storage over long-term averages (≥10 year) (Zhang et al. 2001). This steady-state assumption is related to a closed land water balance, which is expected to maintain over catchments with a low water balance disparity.
A general hydroclimatic description of the Pd is achieved based on the available time series of precipitation, temperature, evapotranspiration and streamflow. Then the Budyko-Zhang framework is applied to this dataset in order to identify catchments with a low (high) water balance disparity, which are associated with environments with less (more) climatic and human activity influence.

READ  General paleo environmental interpretations with ostracod tool


Catchment water balance disparity

The water balance for a catchment can be basically described in a general form at annual scale as:
P=AET+R+ΔS (4.1)
where P is precipitation (mm/yr), AET is the actual evapotranspiration (mm/yr), R is runoff (mm/yr) and ΔS is the change in basin water storage (mm/yr). At the annual scale, ΔS can be neglected especially for long periods (≥10 years) (Zhang et al. 2001).
Based on the Budyko theory (Budyko 1974) which considers that the available energy and water are the primary factors for determining the rate of actual evapotranspiration, the approach developed by Zhang et al. (2001) is used here. It is called the Budyko-Zhang curve, which estimates the AET based on a simple model (see equation 4.2) that writes as follows:
where PET (mm/yr) is the potential evapotranspiration and w (non-dimensional) is the plant-available water coefficient related to vegetation type. The details of the relationship can be found in Zhang et al. (2001). The very sensitive parameter “w” is calibrated over the long-term interannual AET from Equation 4.2. The use of the Budyko-Zhang curve over the Pd appears as a valuable method for the interpretation of the water balance considering the importance of vegetation (Donohue et al. 2007) over arid and semi-arid areas. It has been used previously in comparable studies such as Yang et al. (2009), Zhao et al. (2013) and Chen et al. (2013).
Low disparity of a water balance was evaluated in terms of three criteria: a) the shape of the association of points between dryness index (PET/P) and evaporative index (AET/P), must follows a Budyko-Zhang curve with a positive value of “w”, b) the correlation coefficient “r” between the AET estimated using the Budyko-Zhang framework and estimated using the water balance (P-R) must be higher than 0.7 and c) the relative standard error (%RSE) from the curve adjustment should be less than 15%. Any catchment outside these three criteria falls off the Budyko curve and is considered as a catchment with a high water balance disparity. According to Wang et al. (2011), Jones et al. (2012) and Coron et al. (2015), such a catchment is interpreted as being strongly influenced by anthropogenization, a catchment under strong climatic variability conditions especially droughts, a catchment with missing other components of the water balance (such as water demands, groundwater flows alteration) or in the worst case, a catchment where there were inadequate measures of the hydroclimatic variables.

Results and discussion

Hydroclimatic time series

Based on the processing of the original monthly time step database, a complete monthly hydroclimatic dataset of precipitation (P), temperature (T), potential evapotranspiration (PET) and streamflow (Q), over the 1970‒2008 period was computed, over the 26 catchments in a lumped way. The series of annual PET, annual runoff (R) by the ratio between Q and catchment area, and annual actual evapotranspiration (AET) by water-balance (P – R) following the hydrological year (September ‒ August) were determined. Observed annual P, estimated PET and R series from eleven catchments (mostly coveringnthe 1970‒2008 period) are presented for displaying purposes in Figure 4.1 (the choice of these 11 catchments among the whole of the 26 studied catchments is discussed in section 4.3.2).
Mean annual values of hydroclimatic series are given in Table 4.1. For mean annual precipitation, catchments located in northern areas generally present higher values above 600 mm/yr than southern areas with values under around 400 mm/yr. This is because of the influence of the ENSO phenomenon over northern catchments that clearly appears in peaks during 1982/1983 and 1997/1998 events known as years of extreme El Niño events (see Figure 4.1a, 4.1b, 4.1c and 4.1d).
This influence is also present in the runoff variability, decreasing towards southern latitudes in general, but showing high values above 400 mm/yr in catchments located at central areas as Santa upstream (n°7), Santa (n°8) and Rimac (n°15), associated with the relationship of water availability and catchment size. For mean annual temperature, PET and AET, they decrease in general towards southern latitudes. Mean annual PET variability follows the same behaviour of the mean annual temperature variability along the Pd because of the empirical nature of the Oudin method. However, there is a slight increase over arid catchment located in the south where there is a predominance of bare ground and open schrubland areas.
These results corroborate the dryland conditions of the Pd, accentuating towards southern latitudes. This can be explained by the range of the aridity index (P/PET) proposed by Hassan and Dregne (1997) and contrasted with the annual precipitation module, a method recommended by the United Nations Environment Program (UNEP). Table 4.1 provides the values of the aridity index in most of the catchments below “1” and precipitation below 1000 mm in all catchments. Southern catchments present an annual precipitation around below 400 mm and are defined as arid areas. Catchments located in major rainy areas (Santa up (n°7), Santa (n°8), Huarmey (n°10), Huaura (n°12), Chancay Huaral (n°13), Chillon (n°14), Rimac (n°15), Cañete (n°16) and Majes (n°21)) are found in the limits between semi-arid and dry sub-humid areas. The rest of catchments are defined as semi-arid areas.

Catchment water balance disparity

Based on the hydroclimatic time series calculated over the 26 catchments, series of dryness index (PET/P) and evaporative index (AET/P) are generated. Figures 4.2a and 4.2c show the dispersion of these two indices for two catchments (Piura upstream (n°1) and Rimac (n°15) respectively). Piura upstream shows the behaviour of a northern catchment with strong climate variability as a result of ENSO influence and Rimac shows the behaviour of a very anthropized catchment as a result of large hydraulic infrastructure to provide water to the city of Lima in lowlands. Both catchments represent the two main types of associations found in the study area, which were differentiated following the methodology explained in section 4.2.1 in terms of “w” and “%RSE” (i.e. see Figure 4.2a and Figure 4.2c) and “r” (i.e. see Figure 4.2b and Figure 4.2d).

Table of contents :

Chapter 1
1.1. General context
1.2. Motivation ..
1.3. Main and specific objectives
1.4. Organization
Chapter 2
Study area and data
2.1. Hydroclimatic context of the Peruvian Pacific drainage
2.1.1. Climate variability
a. Mean climatic conditions in the South East Pacific
b. Modes of variability
El Niño phenomenon (ENSO) and its diversity Decadal variability Intraseasonal variability (MJO and oceanic Kelvin waves)
2.1.2. Physical landscape
a. Topography
b. Geology
c. Vegetation
2.1.3. Hydrological context
a. Arid and semi-arid conditions
b. Anthropogenization
2.2. Data
2.2.1. Precipitation
2.2.2. Temperature and evapotranspiration
2.2.3. Streamflow
2.2.4. Climatic indices
a. ENSO indices
b. MJO index and Kelvin waves
Chapter 3
Precipitation regime
3.1. Theoretical background
3.2. Methods
3.2.1. Data homogenization and validation
3.2.2. Classification and Regionalization Process
a. K-means clustering technique
b. Regionalization Analysis
c. Precipitation data interpolation
3.3. Results and discussion
3.3.1. Precipitation Classification
3.3.2. Regionalization
3.3.3. Regions Characterization
3.4. Conclusions
Chapter 4
Hydroclimatic balance
4.1. Theoretical background
4.2. Methods
4.2.1. Catchment water balance disparity
4.3. Results and discussion
4.3.1. Hydroclimatic time series
4.3.2. Catchment water balance disparity
4.4. Conclusions
Chapter 5
Runoff regime
5.1. Theoretical background
5.1.1. Hydrological lumped conceptual modelling
5.1.2. Regional runoff .
5.2. Methods
5.2.1. Runoff simulation based on conceptual lumped models
5.2.2. Performance and efficiency of conceptual lumped models
5.2.3. Regional runoff model (RRM) and freshwater estimates
5.3. Results and discussion
5.3.1. Efficiency of the GR1A and GR2M models
5.3.2. Regional runoff model evaluation
5.3.3. Freshwater runoff estimation
5.4. Conclusions
Chapter 6
Impacts of climate variability and hydroclimatic change on precipitation and runoff
6.1. Precipitation and runoff variability associated with ENSO
6.1.1. Theoretical background
6.1.2. Methods
a. Principal Component Analysis (PCA)
b. The wavelets and coherence analysis
c. Correlation analysis
d. Covariance analysis
6.1.3. Results and discussion
a. PCA analysis of ENSO indices
b. Coherence between ENSO indices and precipitation series
c. Low frequency modulation of ENSO and precipitation regime
d. Precipitation variability and sea surface temperature anomalies
e. Low frequency modulation of ENSO and runoff regime
6.1.4. Conclusions
6.2. Trends and hydroclimatic change disparity over catchments
6.2.1. Theoretical background
6.2.2. Methods
a. Characterization of hydroclimatic time series
b. Hydroclimatic change disparity
6.2.3. Results and discussion
a. Characterization of hydroclimatic time series
b. Hydroclimatic change disparity
6.2.4. Conclusions
Chapter 7
General conclusions and perspectives
7.1. Conclusions
7.2. Perspectives
7.2.1. Impact of climate variability over seasonal hydrological regime as a forecasting tool
7.2.2. Impact of climate and catchment change over the hydrological regimes
Conclusions générales et perspectives (version française)


Related Posts