Water dynamics over a Western Patagonian watershed:
land surface changes and human factors

Olivera-Guerra, L.a,b*, Quintanilla, M.a,c, Moletto-Lobos, I.a,c, Pichuante E.a,c, Zamorano-Elgueta, C.d, Mattar, C.c

a Laboratory for Analysis of the Biosphere (LAB), University of Chile, Santiago, Chile

b CESBIO, Université de Toulouse, CNRS/UPS/IRD/CNES/INRAE, Toulouse, France

c Laboratory of Geosciences, University of Aysén, Chile

d Department of Natural Science and Technology, University of Aysén, Chile

* Corresponding author. E-mail address: oliveral@cesbio.cnes.fr (L. Olivera-Guerra)
# 1 Abstract

2 Warming trends in Patagonia and severe droughts in recent decades are still poorly 3 understood in terms of their hydrological effects. The effects of climate change on water 4 dynamics in addition to human water management could generate a future water scarcity 5 scenario in one of the regions with the most abundant water resources of Chile. The aim of 6 this work is to focus on assessing the impacts of warming trends on water dynamics in the 7 Patagonian Simpson River watershed during the last two decades. We estimated anomalies in 8 the main components of water balance such as precipitation (P), snow cover (SC), 9 evapotranspiration (ET) and streamflows (Q) as well as surface variables and meteorological 10 forcing (i.e. air temperature - Ta, solar radiation - RS, land surface temperature - LST). The 11 processed data were obtained from remote sensing, reanalysis and in-situ data. We 12 implemented a trend analysis for each variable in the period 2000-2019 at monthly, seasonal 13 and annual scale. Results showed a warming trend in Ta and LST of about 1.2°C and 2.1°C, 14 respectively, concentrated mainly in the autumn and winter seasons. Although P showed non- 15 significant trends, Q diminished significantly at rates of more than 9.1 m³/s/decade, 16 representing 36% of its historical mean. However, the decreases in Q are seen only in the 17 maximum (spring) and minimum (summer) seasonal flows. These decreases are explained by 18 significant increases in ET, led by a positive feedback of its drivers (LST, Ta and RS), which 19 is directly linked to the impact of warming and an associated vegetation greenness in the 20 watershed, as well as a decrease in SC during winter that feeds the Simpson River during 21 spring and summer. The decrease in Q is reinforced by the intensification of water 22 withdrawals in recent decades, as shown by an accelerated increase in water rights for 23 agricultural and drinking uses. In a context of water scarcity and increasing and extreme 24 droughts, this work contributes to further understanding water dynamics in western
Patagonia, providing support for policy and decision-making when defining sustainable
productive practices at watershed scale.

**Keywords:** Warming effect, water dynamic, trend analysis, vegetation greenness, remote sensing, reanalysis data.

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## 1 Introduction

The impacts of rising temperatures due to climate change could have unpredictable consequences on sub-Antarctic ecosystems. Precipitation distribution, glacier melting rate and future water stress scenario might generate unknown consequences over marine and earth ecosystems deserving of special attention. In fact, Cabré et al. (2016) developed several future simulations on climate change scenarios for South America, in particular for the Patagonia region. They projected that by the end of the 21st century, the severe changes in temperature (i.e. increases ranging from 1 up to 5.5 °C) and the dramatic decrease in precipitation (i.e. 60% for summer and spring seasons) will directly impact the water resource abundance mainly located in ice fields. These trends could lead to increases in the melting rate of glaciers, and also bring about a stressful ecophysiological pattern on native forest, generating losses on water ecosystem services that affect the water footprint. However, climate change is not the only factor having an effect on the water footprint in Patagonia.
During the last century, several repercussions of the Anthropocene on land cover dynamics have devastated more than 6 million hectares due to bad forestry and agricultural practices (Caldentey et al., 2009; Fajardo and Gundale, 2015; Lusk, 1996; Promis and Allen, 2017; Veblen et al., 1989; Zamorano-Elgueta et al., 2012, 2014). Cattle ranching in forests fires, alien invasive species and selective logging, are currently the main anthropogenic threats to Patagonian forest ecosystems, for example on forest resilience in the face of global warming and water scarcity scenarios. (Zamorano-Elgueta et al., 2012, 2014).

The impacts of global warming have also been revealed in extreme southern heat waves events (Jacques-Coper et al., 2016). This is basically related to the interaction between the westerly flow from the Pacific Ocean and the southern Andes, which leads to a temperate and hyper-humid climate. Thus, spatial and temporal variations of temperature contribute to
increase/decrease the humidity content and affects the water dynamics. Furthermore, the projections in western Patagonia tend to be warmer and characterized by extreme heat waves (Feron et al., 2019) causing a variety of effects such as increased risk of flooding as the affected Villa Santalucía a few years ago (Duhart et al., 2019).

To understand the water dynamics in Patagonia in further scenarios, it is necessary to use several information sources. However, the western Patagonian watershed is poorly monitored by meteorological stations networks. Remote sensing fulfills the critical role of estimating various land surface biophysical patterns, such as water demand and its relation to extreme climate and drought events based on its historical data sets (Jiménez-Muñoz et al., 2016). Moderate Resolution Imaging Spectroradiometer (MODIS) products have been mostly used in cases where long term records or scarcity meteorological networks are lacking and optical data are available (Jiménez-Muñoz et al., 2013). In addition, remote sensing is capable of providing historical records of vegetation indicators such as the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI) or thermal parameters such as Land Surface Temperature (LST), which can be combined using energy balance models in order to estimate the vegetation and surface water status such as evapotranspiration and water scarcity indexes (Jimenez et al., 2018; Olivera-Guerra et al., 2017). These surface variables are related to different processes of water and energy flows between soil and atmosphere, which have made it possible to better understand the hydrological cycle at remote regions and larger scales, such as at catchment level (Lara et al., 2018; Olivares-Contreras et al., 2019)

The aim of this work is to provide a comprehensive assessment of the water dynamics of the Patagonian watershed by combining remote sensing and meteorological data in order to disentangle the impact of climate change and the anthropogenic contribution to water dynamics.
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## 2 Study area and data sets

### 2.1 Study area

The study area is located in the Simpson River basin, on the eastern slopes of the Andes Mountains in the Aysén Region, Western Patagonia (45°19′ - 46°10′S, 72°35′W - 71°35′, 570 m a.s.l.) (Figure 1). This area has been historically dominated by deciduous *Nothofagus* forests, mainly *N. pumilio* (lenga) and *N. antarctica* (ñirre). According to the Köppen-Geiger classification, the study area presents a warm temperate climate with i) humid and cool summers and ii) dry and warm summers (Kottek et al., 2006). The precipitation varies between 500 to 1100 mm in the valley and the mean temperatures range between 6 and 11 °C.

The data used in this study were obtained from different sources such as remote sensing (Section 2.2), reanalysis (Section 2.3) and in situ data (Section 2.4) collected during the period between 2000 and 2019, as detailed below.

### 2.2 MODIS data

MODIS data from collection 6 were used to derive critical surface variables analyzed in this study from 2000 to 2019, which have been obtained at 1 km and monthly resolutions (or aggregated when it is not available at such resolutions). Two key variables were derived from MODIS data in order to analyze the water dynamics in the past two decades: evapotranspiration (ET) and snow cover (SC). The ET is obtained from the Penman–Monteith–Leuning V2 product (PML_V2:Gan et al., 2018; Zhang et al., 2016, 2019) available from 2002 to 2017 at 500 m and 8-day resolution. PML_V2 is built in the Google Earth Engine by using MODIS data (specifically leaf area index, albedo and emissivity) together with GLDAS meteorological forcing data. The PML-V2 product has
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been validated against 95 widely-distributed flux towers for 10 plant functional types with a
root mean square error of 0.69 mm d⁻¹ (Zhang et al., 2019).

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The SC is obtained from the MODIS product at 500 m and 8-day resolution (MOD10A2) as
the maximum snow cover extent during an eight-day period. MOD10A2 is a composite of
daily snow cover product identified using the Normalized Difference Snow Index (NDSI)
and with the aid of the Normalized Difference Vegetation Index (NDVI) over densely
forested areas (Riggs et al., 2016).

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The vegetation index and land surface temperature (LST) products are also obtained from
MODIS data in order to analyze the response and forcing of water dynamics inside the
watershed. The 16-day NDVI composites and 8-day LST products are both obtained at 1 km
spatial resolution from the Terra (MOD13A2 and MOD11A2, respectively) and Aqua
(MYD13A2 and MYD11A2, respectively) sensors.

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## 2.3 Reanalysis data

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We used monthly precipitation (P) from meteorological fields from the fifth generation
ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis
data (ERA5; Hersbach et al., 2020) between 2000 and 2019. ERA5 provides aggregated
values for each month of total precipitation at 0.25° spatial resolution. Additionally, the
Global Land Data Assimilation Version 2.1 (GLDAS-2.1; Rodell et al., 2004) at 0.25° spatial
resolution was used to obtain monthly mean air temperature (Ta) and solar radiation (RS) in
order to analyze and explain ET and snow cover dynamics as important meteorological
forcing.

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## 2.4 In situ data

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We used daily streamflow data provided by the Chilean Directorate of Water resources
(DGA) from Simpson Bajo and Huemules stations located downstream on the Simpson River

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and at the upper part of the basin. Additionally, we used daily precipitation and air temperature provided by the National Weather Service (DMC) from Coyhaique and Balmaceda stations located very close to the streamflow stations. The in situ meteorological data were used to complement the reanalysis data. In addition, we analyzed the historical evolution of granted water rights obtained from DGA database per year since the beginning of the current legislation in 1981. This database contains the water rights with the corresponding flow, date of allocation, source (surface or groundwater) and use (e.g. irrigation, industrial, hydroelectric, drinking water).

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## 3 Methodology

Trend analysis was implemented on variables closely linked to the water balance dynamics of the basin (P, ET, SC and Q). To better understand the water dynamics, we also analyzed key surface variables in the exchange of water fluxes between the surface and atmosphere (LST and NDVI) as well as key meteorological forcing (Ta and RS) in this process.

Prior to temporal analysis, in situ measurements, MODIS and reanalysis data were averaged to monthly time series and then averaged at both annual and seasonal scale. The austral seasons are the mean between: March-May (autumn), June-August (winter), September-November (spring) and December-February (summer). The time series were analyzed in terms of standardized anomalies (analogous to z-score) using the differences of each period (month, season and year) and its historical average and standard deviation based on the full time series (2000-2019). The use of standardized anomalies reduces the autocorrelation and seasonality of the time series and allows the comparison between variables that may be related by the coupled water-energy balance.
The trend analysis on the time series of standardized anomalies was performed at pixel and basin scale for seasonal and yearly periods. This trend analysis was based on the Theil-Sen slope method (Theil, 1950; Sen, 1968) for determining the rate at which the variable is changing over time. The Theil-Sen slope method is a robust non-parametric test represented by the median of the slopes among all the combinations of ordered pairs. The significance of trends is then verified by the non-parametric Mann-Kendall test (Mann, 1945; Kendall, 1975) with a significance level of 0.05.

In addition, the standardized anomalies were used to perform a correlation matrix between the studied variables at different temporal scales. For this purpose, the Pearson coefficient was estimated from the time series for the 2000-2019 period at monthly, seasonal and annual scale in order to assess the interaction and coupling between variables. Even though correlations cannot prove links of causality, they can be used as indirect estimations of coupling for relationships for which causality has been established (Seneviratne et al., 2010).

In this vein, correlations allow for a better understanding of the variability and possible trends detected in the variables studied. Finally, in order to assess the impact of water withdrawals in the decrease of streamflows in the Simpson catchment, we analyzed the water use rights granted over the last decades. The concept of water use rights was established by the Chilean legislation on water resources in 1981 (Water Code Decree-Law N°1122), which strengthen private autonomy in water use. The water rights are divided into those referring to actual water consumption (consumptive uses) and those in which the water is simply used to produce something else (non-consumptive uses) (Valdés-Pineda et al., 2014).

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173 **4 Results and Discussion**

174 Figure 2 shows the historical monthly values (medians) all along the year of the studied
175 variables from remote sensing and reanalysis data at basin scale (blue lines) as well from in
176 situ measurements at downstream and upstream for the period from 2000 to 2019. The P
177 from reanalysis are greater than in situ stations, which is explained partially by the fact that
178 the latter are located in the valley where P are much lower than they are in the mountains to
179 the east of the watershed. Similarly, Ta at watershed scale is lower than Ta measured
180 downstream and upstream in the valley. However, both P and Ta from reanalysis exhibit the
181 same behavior as that seen at the stations. Indeed, monthly P from reanalysis data at basin
182 scale presents a Pearson coefficient R of 0.89 and 0.82 with P from the stations at
183 downstream and upstream basin, while monthly Ta presents a R of 0.99 with Ta from the
184 both in situ stations. SC is mainly concentrated in winter (JJA) with high variability while
185 streamflows show their maximum in spring (SON).

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187 **4.1 Monthly analysis**

188 The time series of normalized anomalies are presented in Figure 3 for the eight variables of
189 interest at monthly and basin scale. This figure also shows the Theil-Sen slope regression
190 applied to the normalized anomalies. As can be seen, all variables, with exception of water
191 inflows to the basin (i.e. P and SC), show significant trends (p-value < 0.05). It should be
192 noted that P measurements from the Coyhaique (downstream basin) and Balmaceda
193 (upstream basin) meteorological stations show a non-significant trend (not shown) as well as
194 P from reanalysis data. In addition, in order to assess the representativeness of the studied
195 period (2000-2020) for a longer period, the analysis was performed over the last four decades
196 (1980-2020) using P measurements from the Coyhaique and Balmaceda stations (not shown).
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The temporal analysis for the period 1980-2020 indicates that there is no significant trend in the Coyhaique (p-values = 0.37) and Balmaceda (p-values = 0.14) time series.

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Q shows a significant negative trend at monthly scale with a slope of -0.57 standard deviation units per decade (σ/decade), representing a decrease of 13.3 m³/s/decade in absolute terms.

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Even though there is an important gap of five years in the downstream Q time series (2010-2015), the full upstream Q time series measured at Huemules River (gray line in Figure 3) allows us to represent the gap since both upstream and downstream are highly correlated in both monthly absolute values (R = 0.94) and anomalies (R = 0.90) (not shown). The upstream Q likewise shows a significant negative trend with a slope of -0.39 σ/decade. The significance trends in upstream and downstream Q therefore allow us to conclude that Q at basin scale on a monthly basis has decreased by -0.78 and -1.14 σ in the last two decades, representing between 18.2 m³/s and 26.6 m³/s regarding the measured downstream Q. This decrease is a significant streamflow if we consider the historical (2000-2019) annual mean of 50.5 m³/s (equal to 49.4 m³/s considering all records from 1969), which is equivalent to a drop of between 36% and 53%.

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In terms of ET, another important water outflow at basin scale, we observe a significant positive trend (0.77 σ/decade) that can be explained by a positive feedback of its main drivers (NDVI, LST, Ta and RS), showing similarly significant positive trends. It should be noted that the watershed is mainly characterized by an energy-limited evapotranspiration regime.

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That means that energy (radiation) provides a first-order constraint on ET and evaporative fraction (i.e. the ratio of ET to available energy) is independent of the soil moisture content since it is often above a critical threshold at which vegetation undergoes water stress (Seneviratne et al. 2010). Thus, the positive trend in RS (0.60 σ/decade) and the related warming trend (i.e. significant positive trend in Ta and LST both with a rate greater than 0.50 σ/decade) leads to a significant increase in ET in the Simpson basin due to the radiation

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limitation regime. The warming trend represented a significant increase of 1.2 °C and 2.1 °C over the last two decades in Ta and LST, respectively, on the Simpson catchment scale. These results are in agreement with significant warming trends detected in Ta over the last two decades at downstream and upstream in situ stations of 0.78 and 1.32 °C, respectively. In addition, the warming trend is consistent with a recent study carried out at larger extent over the Patagonian sub-Antarctic forest by Olivares-Contreras et al. (2019), who provided evidence of a trend of 0.78 °C/decade in LST.

In particular, the ET-Ta feedback is explained by the fact that an increase in Ta leads to more evaporative demand by means of a higher vapor pressure deficit and hence an increase in ET. Accordingly, a recent study by Green et al. (2020) carried out in the Amazonian rainforest (humid biome) found increases in photosynthesis, and hence in ET and NDVI, in response to an increased atmospheric dryness (i.e. higher vapor pressure deficit) as a result of global warming. In addition to the control of Ta on ET, the contrary effect can be expected in cold biomes like Patagonian watersheds, where Ta increases are dampened by ET through evaporative cooling and thus reducing the warming effect. Given that temperature is the major constraint on plant development in cold regions, the positive radiation and temperature (Ta and LST) trends also lead to a significant positive trend in NDVI, such as the significant rate of 0.60 σ/decade found in the Simpson watershed for the 2000-2019 period.

The positive feedbacks mentioned above are also evidenced by the high positive correlations between ET and its drivers, as displayed in Figure 4. The drivers of ET, such as atmospheric forcing (Ta and RS) and surface variables (LST and NDVI), are also strongly correlated between each other. The positive correlations between Ta and RS with ET indicate a strong atmospheric control on ET as reported in Seneviratne et al. (2006). Our results are consistent with this study, which also identified positive correlations between Ta and ET in cold climates like in the Patagonian biomes. Note that the positive feedbacks in Figure 4 are
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depicted for both correlations in monthly absolute values and monthly anomalies, although
the latter are much lower than the former but remain significant.

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In the P time series, we can observe the period with steady negative anomalies from
September 2015 to August 2016, which indicates the record-breaking climate anomalies
leading to the severe drought in Western Patagonia recorded since 1948, as also reported by
Garreaud (2018). The extreme precipitation deficit in the studied period is also expressed as
the largest reduction in streamflow (Q) in terms of both magnitude (a drop of 62.5 m³/s in
October 2016) and time period (extending from September 2015 until December 2016 with
steady negative anomalies). It is worth noting that the Q time series exhibited a long period of
28 months from October 2017 with continuous negative anomalies (averaging 15.9 m³/s),
with the exception of 2 months with positive anomalies related to heavy rainfalls in April and
September 2018. According to the P time series, this period with important Q decreases is not
linked to reductions in P, which might indicate increases in water consumption at basin scale.

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Conversely to the strong drought effect on streamflow, vegetation (by means of NDVI) does
not present the largest reduction in this period, but only negative anomalies for 3 months at
the beginning of 2016. The largest negative anomalies in vegetation occurred during the three
periods with the largest positive anomalies in snow cover (i.e. mid-2005, end of 2009 and
mid-2010). The minimum anomalies in vegetation occurred at the end of 2009 due to a
strengthening of the strong negative anomalies in Ta, while the period with anomalies in mid-
2005 is closely related to the largest negative anomalies in both ET and LST.

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## 4.2 Annual and seasonal analysis

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In order to have a comprehensive understanding of the dynamics of water and its drivers, an
analysis of anomalies and trends was carried out at annual and seasonal scale. The time series

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of annual normalized anomalies are presented in Figure 5 for the studied variables at basin scale, together with its trend in terms of Theil-Sen slope regression. The analysis performed using annual anomalies is similar to the one performed by using monthly anomalies with the exception that the ET trend shows a non-significant trend at a 0.05 level of significance but still significant at a level of 0.1 (p-value = 0.079). That can be explained in part by the limited amount of data, as ET from the PML-V2 product is available only between 2002 and 2017 (i.e. 16 annual data), and in turn, the ET time series at monthly scale showed the largest trend (0.77 σ/decade) among all the studied variables.

At annual scale, NDVI depicts the largest trend (1.35 σ/decade) with a p-value lower than 0.001. This trend of vegetation greenness represents an increase of 0.08 in NDVI in the last two decades, equivalent to 18.5% of the historical (2000-2019) annual mean. It is worth noting sustained positive anomalies in NDVI from 2011, with two drops in 2012 and 2017 that are nevertheless still positive, which might be related to positive anomalies in P. During the same period, LST depicts the same NDVI patterns with the highest anomaly occurring during 2016 and a negative anomaly in 2017. As is illustrated in Figure 4 and Figure 7, NDVI shows the highest correlation with LST at both monthly and annual scale (R = 0.92). That is explained by the fact that the watershed characterized by a humid climate and positive water balance presents vegetation with low water use efficiency (Huxman et al., 2004) and vegetation activity may be higher in dry periods than they are in wet ones (Vicente-Serrano et al., 2013) because evapotranspiration and photosynthesis are radiation-limited during the latter. That can be represented by the greening reported in some areas in Amazon forests during the 2005 drought (Saleska et al., 2007; Samanta et al., 2010). Indeed, the Simpson watershed largely greened-up during 2016 (1.68 σ in NDVI anomaly) when a severe drought hit western Patagonia, led by record-breaking P anomalies (Garreaud, 2018), which in our study area results in a decrease of 530 mm (-2.26 σ). During the 2016 drought, the
296 meteorological stations saw a precipitation deficit of 48 % and 45 % relative to their
297 historical means in Coyhaique (downstream basin) and Balmaceda (upstream basin),
298 respectively (Table 1). However, the green-up detected in the Simpson watershed opposes the
299 widespread decline in vegetation activity over western Patagonia reported by Garreaud
300 (2018), which was most intense along the western slope of the Andes, while some areas
301 experienced vegetation greening as shown in our study located on the eastern slopes of the
302 Andes. Nevertheless, in the Simpson watershed, the significant greening during 2016 is
303 directly linked to the extreme anomalies in temperatures (Ta and LST) with negative
304 anomalies in SC, which together allowed for even greater development of the vegetation that
305 intensifies from autumn and winter (see monthly anomalies in Figure 3). That is evidenced by
306 the high correlations of NDVI with Ta and LST at monthly (Figure 4), annual and seasonal
307 scale (Figure 7), and particularly, between NDVI and SC in winter seasons.

308 Figure 6 illustrates the trend analysis at seasonal scale, where P shows no trend for any
309 season, as detected in the annual analysis. Unlike the monthly and annual analysis, SC show
310 a significant negative trend in the winter season (-1.12 σ/decade), which is hidden at annual
311 scale due to non-significant changes in the rest of the seasons. Keeping in mind the large
312 variability observed in Figure 2 during the winter months, the change has to be large in order
313 to be statistically significant, such as the detected decrease of 27 % in snow cover per decade
314 at watershed scale. This significant decrease corresponds to an important increase in Ta and
315 RS during winter and likely to an increase of the isotherm 0°C as reported north of our study
316 area by Aguayo et al. (2019).

317 Seasonal analysis allows us to know when the detected trends on a monthly and annual scale
318 are concentrated. For instance, the analysis indicates significant trends for Q in spring (-1.08
319 σ/decade), representing a decrease of 29.4 m³/s/decade. In summer, a significant trend at a
320 level of 0.1 is detected (-0.77 σ/decade with p-value of 0.06), representing a decrease of 15.7
m³/s/decade. As can be observed in Figure 2, these decreases occur during the maximum (spring) and minimum (summer) seasonal flows, such that, although the decrease in spring is greater than that in summer, the latter represents 60.4 % of the median streamflow in summer (26 m³/s) while the former represents 40.3 % of the median streamflow in spring (73 m³/s).

These significant decreases can be partially explained by the decrease detected in SC during winter, which feeds streamflows during spring and summer. That is further confirmed by the high correlation between SC and Q during summer (R = 0.80), as shown in Figure 7. The decreases in Q may be also related to the growing rise in water withdrawals for agricultural and drinking uses (see Section 4.4). These changes are in line with the global observed trends in streamflows reported recently in Gudmundsson et al. (2021), even though our study area was not included due to the scarcity of data. Note that the seasonal decreases in Q occurred during the seasons with maximum vegetation activity and ET, where both NDVI and ET reveal increases with significant trends in spring that are likely induced by a significance increase in RS. ET presents a significant trend only in the spring season, partly due to the low number of data, as explained at annual scale, while NDVI exhibits significant positive trends for all seasons. Regarding the warming trend detected at annual/monthly scales, from seasonal scale the significant warming is revealed in Ta and LST during autumn and winter, explained mainly by a significant increase in RS. As can be seen in Table 2, the warming trend during the last two decades in autumn represents an increase of 0.78 °C/decade and 1.11 °C/decade in Ta and LST, respectively, while in winter the increase reaches 0.81 °C/decade and 1.33 °C/decade in Ta and LST, respectively. Although RS shows a significant trend in spring, the non-significant positive trend in Ta and LST (p > 0.1) can be explained by the dampening effect of evaporative cooling (as mentioned above) due to greater vegetation activity and the significant trends detected in NDVI and notably in ET.
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Figure 7 illustrates the matrix correlation between studied variable at annual and seasonal scale. A marked red triangle can be seen on the right of every matrix correlation showing the positive feedback between the ET and its drivers and between each other, as commented above for monthly scale. A stronger positive feedback can be observed during winter and spring since regions with energy-limited ET are characterized by a positive correlation between ET and Ta (and thus RS and LST), which is enhanced in both the colder season (winter) and the season with increased vegetation growth (spring). Conversely, during autumn it can be seen that the energy-limited ET regime is weakened and shifted to a soil moisture-limited ET regime as the ET is no longer significantly correlated with Ta, LST and RS (R < 0.5 with p-value > 0.05) and, in addition, ET is positively correlated with P, unlike in the other seasons. The shift of some regions between energy- and soil moisture-limited ET regimes along the year has also been reported by Ryu et al. (2008).

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The matrix correlation also illustrates the Simpson River regime (by means Q), which is predominantly controlled by P at annual scale (R = 0.73) and is, in turn, markedly influenced by the other studied variables along the seasons. For instance, during autumn and winter, Q is mainly and almost exclusively driven by P (with R equal to 0.88 and 0.79, respectively), while in spring it is also driven by ET (R = -0.80) and NDVI (R = -0.74), which at the same time are influenced by LST (R = -0.77) and RS (R = -0.74). In summer, Q is mainly controlled by snow cover (R = 0.80) and P (R = 0.70), revealing the Simpson River regime during the hot season fed by summer thaw and rain. The summer thaw is directly linked with Ta, which is evidenced by the strong correlation between Q and Ta (R = -0.78).

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367 **4.3** *Vegetation responses to climatic variations*

368 Vegetation responses in terms of indicators such as NDVI or EVI to climatic variations are
369 often complex and not straightforward (Fabricante et al., 2009). The influence of air
370 temperature and precipitation on NDVI often depends on biome characteristics, the
371 functional types of dominant species and the phenological stages of the vegetation (Mao et
372 al., 2012; Mohamed et al., 2004). For instance, Bianchi et al. (2020) reported relationships of
373 NDVI with Ta and P over northern Patagonia, finding significant positive correlations in late
374 winter/spring over temperate forests in western North Patagonia (Chilean Patagonia). In our
375 study area, the strong NDVI-Ta relationship is revealed at monthly (R = 0.60) and annual (R
376 = 0.79) scales, while at seasonal scale it is seen in autumn (R = 0.69) and particularly in
377 winter and spring (R = 0.8). This positive relationship is partially explained by the effect of
378 temperature on the onset and length of the growing season, which modulates variations in
379 NDVI as reported over cold grassland, Patagonian steppes and temperate forests (Bianchi et
380 al., 2020; Buermann et al., 2003; Jobbágy et al., 2002). In terms of the NDV-P relationship,
381 we found weaker and negative relationships as other studies have reported for cold grassland
382 and temperate forests (Bianchi et al., 2020; Mao et al., 2012).

383 In the study area, the pasture management and forest plantations of exotic species may
384 increase the water demand at landscape scale, which could cause water scarcity in a scenario
385 of climate change. Previous studies in the central and southern zone of Chile have
386 demonstrated the impact of industrial forest plantations, increasing the water requirements at
387 landscape scale (Alvarez-Garreton et al., 2019; Little et al., 2009; Olivera-Guerra et al.,
388 2014). These changes could have an unknown impact on the functional properties of the
389 ecosystem and its resilience, indicating that further research is needed to fully understand the
390 multiple factors affecting forest ecosystem functions after human-induced disturbances. For
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instance, the case of water withdrawals introduced and additional anthropogenic forcing,
which have unknown consequences on ecosystem services and landscape resilience.

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## 4.4 Human water consumption

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The evolution of water rights granted in the Simpson basin is illustrated in Figure 8,
highlighting the locations of water withdrawals in four different periods: 1980-2005, 2006-
2010, 2011-2015 and 2016-2020. It is worth noting that water withdrawals in the Simpson
basin were non-significant before 2000 with only 92 granted rights (7.7 m³/s). However, the
granted water rights significantly increased after 2000, reaching 2076 water rights (50.2 m³/s)
in 2020, although the flows have remained fairly constant since 2016 (48.9 m³/s). In the
1980-2005 period, it can be seen that water rights are concentrated at the lower part of the
basin while in the next periods the rights are granted for the tributaries of the Simpson River
(south and northwest of the basin). It should be noted that most of the water rights correspond
to surface withdrawals, with 99.75% of flows (50.07 m³/s) concentrated in 90 % of the
number of water rights (1868) regarding the cumulated rights in 2020. In the Simpson basin,
the consumptive uses represent the 93.9 % of the number of water rights (1950), though they
correspond to the 22.7 % of the total rights in water withdrawals (11.4 m³/s). These
consumptive uses are allocated mostly to irrigation and drinking uses, while the non-
consumptive uses are allocated mostly to hydroelectric use.

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Even though the water rights are not exactly the actual water withdrawals, they are a good
indicator of water demand, particularly the consumptive water rights (dark blue in Figure
8.e), and how the pressure on water resources have steadily increased in recent decades.

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Indeed, the increase in water withdrawals for irrigation and drinking uses represent an
important pressure in streamflow (Q) in the Simpson basin, particularly during summer when
water demand is higher due to irrigation uses and when water availability (seasonal Q) is

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lower. Therefore, the significant reductions in Q during summer (Section 4.2) may be closely related to the increase in water rights for irrigation and drinking uses.

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## 4.5 Human impact on water dynamics

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The increase of human water demand, closely linked to water use rights granted in Simpson basin, is a clear anthropogenic pressure on the streamflow, particularly the consumptive uses extracted almost entirely from surface waters. Indeed, during 2020, the total volume granted as consumptive uses represented the 22.6 % of the historical annual mean streamflow at catchment scale (55.5 m³/s) and its consequences on ecosystem services and water reservoirs are still unknown. However, this water management scheme deserves more analysis in terms of those ecological, economic and social impacts of an excessive human water consumption.

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For instance, Barria et al. (2021) recently proposed an index of anthropic intervention (IAI) at basin scale as a diagnosis of water allocation system in Chile. This IAI index is a theoretical approach to assess adequately the water management system to satisfy human and ecological demands for water based in the ratio between the total granted consumptive water volume plus the ecosystem requirements based on the ecological streamflow to the water availability within the basin. The water availability correspond to theoretical volumes of water to be granted as permanently exercisable surface and subsurface (groundwater) water rights on an annual basis. The water availability is calculated at monthly basis as Q with probability of exceedance greater than 85% ($Q_{85\%}$), while the ecological streamflow is calculated between the 20 % of historical mean Q and the 50 % of the $Q_{50\%}$ (Barria et al., 2021; DGA, 2008).

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Barria et al. (2021) also defined two IAI thresholds to assess the allocation water in the catchment, where IAI over 0.75 means a risk of future overallocation and IAI over 1 means that the basin is already overallocated, therefore, under large risk of water scarcity.

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In order to assess the level of the human impact within the Simpson basin, we implement the IAI during the last decades over Simpson catchment. The consumptive water use rights reached to 11.4 m³/s in 2020, the ecological streamflow and water availability are 7.6 m³/s and 28.8 m³/s, respectively, which results in an IAI value of 0.66. This IAI index is very close to the critical value generating a future risk for water allocation in Simpson River.

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Indeed, the water use rights granted until 2020 (11.4 m³/s) are close to reach a critical value of 14 m³/s that represents an IAI threshold (0.75) of risk of future overallocation. The latter estimation is even assuming that the water availability (based on the historical streamflows) remains constant. In another hand, this IAI threshold could also be reached if the mean streamflow (i.e. water availability) decreases to 44.3 m³/s, which is calculated using the water rights granted until year 2020 and by assuming that there are not further water rights granted in Simpson catchment. Therefore, it is very likely that this critical IAI value of 0.75 will be reached during over the next few years due to the decreasing trend in Q (13.3 m³/s/decade) and the steady increase of water consumption during the last two decades (i.e. consumptive water rights with a trend of 4.0 m³/s/decade). Both trends in Q and consumptive water rights decrease the water availability and reinforce the water withdrawals in the basin.

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In addition to the impact of human water consumption on streamflow, water supply by snow during winter and summer thaw are very important in the Patagonian watersheds. Although a significant decreasing trend of snow cover in winter has been detected, accumulation and melt of snowpack should be addressed in order to better quantify and isolate the natural and anthropic impact on water availability. The snow cover in Patagonian watersheds is one of the most important variables which contributes to the water reservoirs, although a lack of in-situ measurements in terms of snow properties and spatial distribution limited the analysis in terms of LST and ET. Recent studies have reported a decrease in snow cover (Pérez et al., 2018), which is consistent with our results, although further approaches have been concerned
465

in terms of spatial and temporal snow monitoring because cloud cover could affect the retrievals from optical remote sensing. In this context, further efforts related to improve the background concept focusing on snow water equivalent and reservoirs which will be driving the Q and ET under a water scarcity scenario. Moreover, it would be interesting to include the impact of land use and land cover changes on the water dynamics since their important impact on global water cycles is well documented, particularly in ET (Li et al., 2020; Wang et al., 2021).

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## 5 Conclusions

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In the last two decades, the Simpson watershed located in western Patagonia showed increases in Ta and LST of 1.2 °C and 2.1 °C, respectively, which are concentrated in the autumn (+1.6 °C in Ta and +2.2 °C in LST) and winter (+1.6 °C in Ta and +2.7 °C in LST) seasons. These warming trends are closely linked to significant increases in RS during autumn and winter. Although RS also shows a significant trend in spring, the non-significant increases in Ta and LST in spring can be explained by the dampening effect of evaporative cooling due to greater vegetation activity and the significant increases detected in NDVI and notably in ET, thus reducing the warming effect during spring. However, the snow trends reveal a strong decrease in winter, which is accompanied by the influences of ET and LST.

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The main input of water, P showed non-significant trends at any temporal scale, however, Q significantly decreased by 9.1 to 13.3 m³/s/decade (i.e. between 36 % and 56 % of its historical streamflow mean). The decreases in Q are concentrated during the maximum (spring) and minimum (summer) seasonal flows, which are linked to the periods of maximum vegetation activity and ET. The significant trends in NDVI and ET revealed in spring are likely induced by a significant increase in RS given the high correlations between them.

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Though NDVI exhibits a significant trend in all seasonal scales, Q reductions in summer may be related to the rise in water withdrawals brought on by an accelerated increase in water rights for agricultural and drinking uses, as well as decreases in SC during winter that feeds streamflows during spring and summer.

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ET exhibits a significant positive trend led by a positive feedback of its drivers (LST, Ta and RS), which is directly linked to the warming impact. Moreover, an associated vegetation greenness in the watershed is detected at monthly, annual and seasonal scales. The detected positive feedback is consistent with the cold and humid climate of the hydrological watershed. Indeed, the greatest greening at watershed scale occurred in 2016 during a severe drought that affected western Patagonia, led by record-breaking P anomalies. The counterintuitive vegetation greenness during the 2016 drought is directly linked to the highest positive anomalies detected in temperatures (Ta and LST) and the lowest negative anomalies in SC, which together allowed for further development of the vegetation that is intensified in the cold seasons (autumn and winter).

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The results of this study contribute to further understanding local water dynamics in western Patagonia, which in a context of water scarcity and more frequent and extreme droughts, provide support for policy and decision-making when defining sustainable productive practices at watershed scale. However, the impact of land-use change on the water dynamics should be addressed in future studies in order to assure water supply and allocate different uses at watershed scale including agriculture, drinking, tourism, hydroelectricity and aquiculture.

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# 6 Acknowledgments

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The authors would like to thank the ECMWF for the ERA-Interim products and the MODIS
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team for the land surface products. This work was supported in part by the FONDECYT

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Regular (CONICYT/ref-1181155). C.Z.E. would like to thank ANID/FONDAP/ 15110009

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program.

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## 8 Tables

<table><caption>Table 1. Precipitations during 2016 drought and historical mean precipitation for period 2000 – 2019 and 1980 – 2010 over in situ stations (Balmaceda and Coyhaique) and at watershed scale from ERA data. The precipitation deficit (%) in 2016 is shown with respect both historical periods.</caption><thead><tr><th rowspan="2"></th><th>P 2016</th><th colspan="2">2000 - 2019</th><th colspan="2">1980 - 2010</th></tr><tr><th>mm</th><th>mm</th><th>Deficit (%)</th><th>mm</th><th>Deficit (%)</th></tr></thead><tbody><tr><td>Balmaceda</td><td>276.3</td><td>504.6</td><td>45.2</td><td>542.7</td><td>49.1</td></tr><tr><td>Coyhaique</td><td>527.9</td><td>1023</td><td>48.4</td><td>995.9</td><td>47.0</td></tr><tr><td>Simpson watershed (ERA)</td><td>1349.0</td><td>1878.9</td><td>28.2</td><td></td><td></td></tr></tbody></table>
<table><caption>Table 2. Trends and corresponding p-value for each variable in absolute units per decade at different temporal scale. P-values of significant trend from the Mann-Kendall test are marked in bold. The variables -down and -up refers to in situ measurements at downstream and upstream basin, respectively.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Month</th><th colspan="2">Year</th><th colspan="2">Autumn</th><th colspan="2">Winter</th><th colspan="2">Spring</th><th colspan="2">Summer</th></tr><tr><th>Trend</th><th>p-value</th><th>Trend</th><th>p-value</th><th>Trend</th><th>p-value</th><th>Trend</th><th>p-value</th><th>Trend</th><th>p-value</th><th>Trend</th><th>p-value</th></tr></thead><tbody><tr><td>P</td><td>-1.64</td><td>0.824</td><td>-37.92</td><td>0.697</td><td>1.57</td><td>1.000</td><td>23.57</td><td>0.119</td><td>-9.31</td><td>0.399</td><td>-10.98</td><td>0.299</td></tr><tr><td>P-down</td><td>-9.25</td><td>0.084</td><td>-114.59</td><td>0.144</td><td>12.73</td><td>0.721</td><td>1.61</td><td>1.000</td><td>-46.85</td><td><strong>0.098*</strong></td><td>-30.66</td><td>0.230</td></tr><tr><td>P-up</td><td>-4.16</td><td>0.164</td><td>-45.39</td><td>0.315</td><td>10.10</td><td>0.626</td><td>-8.26</td><td>0.770</td><td>-39.15</td><td>0.112</td><td>-19.43</td><td>0.119</td></tr><tr><td>SC</td><td>-0.003</td><td>0.126</td><td>-0.010</td><td>0.496</td><td>0.008</td><td>0.284</td><td>-0.270</td><td><strong>0.044</strong></td><td>-0.001</td><td>0.770</td><td>-0.002</td><td>0.234</td></tr><tr><td>Qdown</td><td>-13.29</td><td><strong>&lt; 0.001</strong></td><td>-16.17</td><td><strong>0.009</strong></td><td>-6.58</td><td>0.428</td><td>-0.86</td><td>1.000</td><td>-29.43</td><td><strong>0.009</strong></td><td>-15.69</td><td><strong>0.063*</strong></td></tr><tr><td>Qup</td><td>-1.54</td><td><strong>&lt; 0.001</strong></td><td>-0.93</td><td>0.456</td><td>-0.40</td><td>0.726</td><td>-0.87</td><td>0.538</td><td>-3.79</td><td><strong>0.093*</strong></td><td>-0.76</td><td>0.363</td></tr><tr><td>ET</td><td>0.09</td><td><strong>&lt; 0.001</strong></td><td>0.09</td><td><strong>0.079*</strong></td><td>0.07</td><td>0.113</td><td>0.05</td><td>0.163</td><td>0.23</td><td><strong>0.027</strong></td><td>-0.03</td><td>0.822</td></tr><tr><td>NDVI</td><td>0.030</td><td><strong>&lt; 0.001</strong></td><td>0.042</td><td><strong>&lt; 0.001</strong></td><td>0.028</td><td><strong>0.041</strong></td><td>0.062</td><td><strong>0.007</strong></td><td>0.036</td><td><strong>0.025</strong></td><td>0.020</td><td><strong>0.006</strong></td></tr><tr><td>LST</td><td>1.06</td><td><strong>&lt; 0.001</strong></td><td>1.18</td><td><strong>0.018</strong></td><td>1.11</td><td><strong>0.010</strong></td><td>1.33</td><td><strong>0.041</strong></td><td>0.65</td><td>0.127</td><td>1.55</td><td><strong>0.064*</strong></td></tr><tr><td>Ta</td><td>0.60</td><td><strong>&lt; 0.001</strong></td><td>0.58</td><td><strong>0.010</strong></td><td>0.78</td><td><strong>0.015</strong></td><td>0.81</td><td><strong>0.006</strong></td><td>0.30</td><td>0.183</td><td>0.42</td><td>0.581</td></tr><tr><td>Ta-down</td><td>0.389</td><td><strong>&lt; 0.001</strong></td><td>0.404</td><td><strong>0.030</strong></td><td>0.491</td><td><strong>0.086*</strong></td><td>0.838</td><td><strong>0.030</strong></td><td>0.150</td><td>0.626</td><td>0.169</td><td>0.581</td></tr><tr><td>Ta-up</td><td>0.661</td><td><strong>&lt; 0.001</strong></td><td>0.660</td><td><strong>0.010</strong></td><td>0.880</td><td><strong>0.010</strong></td><td>1.115</td><td><strong>0.015</strong></td><td>0.417</td><td><strong>0.093*</strong></td><td>0.376</td><td>0.294</td></tr><tr><td>RS</td><td>0.008</td><td><strong>&lt; 0.001</strong></td><td>0.008</td><td><strong>0.035</strong></td><td>0.005</td><td><strong>0.098*</strong></td><td>0.006</td><td><strong>0.012</strong></td><td>0.013</td><td><strong>0.041</strong></td><td>0.012</td><td>0.144</td></tr></tbody></table>

<footnote>* Significant trend at significance level of 0.1.</footnote>
9 Figures

<figure><img src="image_1.png" /><figcaption>Figure 1. Study area and location of meteorological stations (Balmaceda and Teniente Vidal) and streamflow stations (1: Simpson Bajo and 4: Huemules).</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>Figure 2. Historical monthly median for studied variables in Simpson watershed for the period 2000-2019. Shaded areas represent the historical lower and upper quartiles. Blue lines represent data from remote sensing (SC, ET, NDVI and LST) or reanalysis (P, Ta and RS) at watershed scale, while red and green lines represent downstream and upstream measurements, respectively.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 3. Monthly normalized anomalies for studied variables averaged in the Simpson watershed. The blue line represents the linear trend for normalized anomalies whose slope (in normalized anomaly per decade) and p-value (p) are indicated for each variable. In the plot for the streamflow (Q), anomalies in filled color represent the downstream Q from the Simpson Bajo station while the grey line represents upstream Q from the Huemules station.</figcaption></figure>
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>Figure 4. Correlation matrix between a) monthly absolute values and b) monthly anomalies of studied variables.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 5. Annual normalized anomalies for the studied variables averaged in the Simpson watershed. The blue line represents the linear trend for normalized anomalies whose slope (in normalized anomaly per decade) and p-value (p) are indicated for each variable.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 6. Seasonal normalized anomalies for studied variables averaged in the Simpson watershed. The blue line represents the linear trend for normalized anomalies whose slope (in normalized anomaly per year) and p-value (p) are indicated for each variable.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure 7. Correlation matrix between normalized anomalies of studied variables for a) annual, and c-f) austral seasonal periods.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Figure 8. Location of water rights granted by DGA in Simpson basin (delimited by red line) in different periods: a) 1980 – 2005 (blue points), b) 2006 – 2010 (green points), c) 2011 – 2015 (yellow points) and d) 2016 – 2020 (red points). Water rights granted in previous periods are kept in order to show the intensification in water withdrawals. The bottom plot shows the cumulated water rights per year highlighting the consumptive uses in dark blue.</figcaption></figure>
# The Role of Land Surfaces Changes, Meteorological Forcing and Human Water Consumption in Patagonia

<figure><img src="image_13.png" /></figure>

## Analysis in 2000-2020 for:

- Precipitation

- Snow Cover

- Streamflow

- ET

- NDVI

- LST

- Air Temperature

- Solar Radiation

<figure><img src="image_14.png" /></figure>

This study provides a **better understanding of water dynamics** in a western Patagonia in a context of warming trends, water scarcity and increasing and extreme droughts.

# Anomalies and Trend Analysis during 2000-2020

<figure><img src="image_15.png" /><figcaption>## Human water demand</figcaption></figure>

<figure><img src="image_16.png" /></figure>

The interactions between the water balance components, land surface changes, meteorological forcing and human factors are reported. It is highlighted the **higher pressure on water availability** as well as the risk of a **water scarcity scenario**.