# Assessing the contribution of meltwater to meet environmental flow requirements during drought events in the Andean Santa River basin

Bryan Marinelli, Arthur Lutz, Lutz Breuer, Björn Weeser, Sonu Khanal, Thomas Condom, Alicia Correa

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Bryan Marinelli, Arthur Lutz, Lutz Breuer, Björn Weeser, Sonu Khanal, et al. Assessing the contribution of meltwater to meet environmental flow requirements during drought events in the Andean Santa River basin. Journal of Hydrology: Regional Studies, 2025, 58, pp.102248. 10.1016/j.ejrh.2025.102248. hal-05450060

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# Assessing the contribution of meltwater to meet environmental flow requirements during drought events in the Andean Santa River basin

Bryan Marinelli<sup>a,b,c,*</sup>, Arthur Lutz<sup>a,d</sup>, Lutz Breuer<sup>b,e</sup>, Björn Weeser<sup>b,e</sup>, Sonu Khanal<sup>d</sup>, Thomas Condom<sup>f</sup>, Alicia Correa<sup>b,e</sup>

## ARTICLE INFO

Key words:

SPHY

Cryospheric-hydrology

Andes

Spatially distributed modelling

Environmental flow requirements

## ABSTRACT

**Study region:** Santa River catchment, Cordillera Blanca, Peruvian Andes.

**Study focus:** The effects of global change on the hydrological cycle are amplified in the highly-dynamic tropics. In the high Andes (identified as a hotspot of climate change), a warming climate impacts the cryosphere, and, therefore, alters the composition of downstream discharge. Quantifying these compositions, and evaluating the contributions of meltwater components, is vital for sustainable water resources management under changing hydrological regimes. For such analyses, we used the spatially-distributed cryosphere-hydrological model 'Spatial Processes in Hydrology,' forced with PISCO and RAIN4PE distributed meteorological data to simulate daily discharge components (rainfall-runoff, snowmelt, glacier melt, baseflow). We then computed a compensation metric to assess how meltwater compensated for the lack of rainfall-runoff during drought events, and further evaluated if the compensating flows were sufficient to maintain environmental flow requirements.

**New hydrological insights for the region:** During 35 drought events, snowmelt contributions tended to decrease, while glacier melt contributions tended to increase. In total, meltwater contributions generally increased, and were able to compensate for reductions in rainfall-runoff. Meltwater proved critical during 85 % of identified drought events to ensure the total discharge reached an environmentally safe threshold.

This research advances the understanding of the compensating effect of meltwater during droughts, which can inform water management in glacier-influenced catchments and shed light on climate change impacts in the tropical Andes.

https://doi.org/10.1016/j.ejrh.2025.102248

Received 16 June 2023; Received in revised form 19 December 2024; Accepted 11 February 2025

Available online 17 February 2025

2214-5818/© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

<footnote><sup>a</sup> Department of Physical Geography, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, Utrecht 3584 CB, Netherlands</footnote>

<footnote><sup>b</sup> Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff Ring 26, Giessen 35392, Germany</footnote>

<footnote><sup>c</sup> Earth Systems and Global Change Group, Wageningen University & Research, Droevendaalsesteeg 3, Wageningen 6700 AA, Netherlands</footnote>

<footnote><sup>d</sup> FutureWater, Costerweg 1V, Wageningen 6702 AA, Netherlands</footnote>

<footnote><sup>e</sup> Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Senckenbergstrasse 3, Giessen 35390, Germany</footnote>

<footnote><sup>f</sup> Université Grenoble Alpes, CNRS, IRD, INRAE, Grenoble-INP, Institut des Géosciences de l'Environnement, CS 40700, Grenoble Cedex 9, Grenoble 38 058, France</footnote>

<footnote>* Corresponding author at: Earth Systems and Global Change Group, Wageningen University & Research, Droevendaalsesteeg 3, Wageningen 6700 AA, Netherlands.

*E-mail address:* [bryan.marinelli@wur.nl](mailto:bryan.marinelli@wur.nl) (B. Marinelli).</footnote>
# 1. Introduction

Important high mountain water resources are currently under pressure due to climate change driving fluctuations in global precipitation and temperature patterns (Immerzeel et al., 2020). This impacts the hydrologic cycle, which then disrupts ecosystems, societies, and economies (Ripple et al., 2019). High altitude regions are particularly affected by temperature increase due to elevation-dependent warming (Pepin et al., 2015).

Moving toward comprehensive scientific evidence to help ensure the availability of water resources for nature and people is not trivial; it requires new capabilities and tools. In earth sciences, combining remote sensing with rainfall-runoff and cryosphere-hydrological models at different complexities is commonly done to analyze runoff response under climate and biophysical conditions (Immerzeel et al., 2014, 2012, 2010; Khaki et al., 2020; Kraaijenbrink et al., 2017). In recent years, supporting tools to include high-resolution data in such models have been developed (Immerzeel et al., 2014; Ragettli et al., 2015). Cryosphere-hydrological models are key tools for assessing streamflow composition in high mountain environments (Kaser et al., 2010). Simulating streamflow responses is only part of the story, however, as these findings have barely been analyzed at the component level and compared with environmental flow requirements (EFR; Buytaert et al., 2017). Meeting environmentally critical flow is vital for maintaining ecological health, as microrefugia – areas supporting species populations that would not be able to survive in surrounding areas – are expected to disappear if drying periods persist (Hannah et al., 2014; McLaughlin et al., 2017; Rull, 2009). Furthermore, the contribution of meltwater to runoff has been simulated mainly by distributed hydrological models in the High Mountain Asia region and the European Alps (Armstrong et al., 2019; Biemans et al., 2019; Farinotti et al., 2016; Lutz et al., 2014). Meltwater propagation and downstream distribution has been less extensively studied in the Andean region, although recent studies have characterized the water, energy, and mass balances of the regional glaciers (Fyffe et al., 2021; Motschmann et al., 2022).

Climate change projections for the tropical Andes estimate an increase in temperature of up to 5°C, along with seasonal changes to precipitation trends that will ultimately reduce annual rainfall and snowfall, by the end of the 21st century (Castellanos et al., 2022; Fyffe et al., 2021). Temperatures will increase more in the high mountains than at lower elevations, and overall increases in maximum temperature are expected to occur in the high mountains of Ecuador, Peru, Bolivia, and northern Chile (Bradley et al., 2006). These changes are particularly important for highly sensitive mountain regions, which are considered water towers and providers of water and ecosystem services for environmental and human demands (Correa et al., 2020; Immerzeel et al., 2020). The amount of water that the Andes in particular supply is disproportionately large in relation to their geographic extent (Buytaert et al., 2017).

In the Andes, glaciers and lakes are important components of the hydrological cycle and substantial contributors to runoff (Farinotti et al., 2019; Junk, 2013; Kaser et al., 2010; Ramón et al., 2021; van Colen et al., 2017). Glacial lakes are densely located in the mountain range, regulating flow rates by buffering the temporal variability of streamflow and persisting in both area and count while glacier retreat has occurred (Brown et al., 2008; Emmer et al., 2020, 2016; Goodman et al., 2011; van Colen et al., 2017). On the other hand, glaciers respond to large-scale annual and multiannual climate variations, such as the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), and the influence of climate change affects glacial dynamics and causes retreat and mass loss (Rabatel et al., 2013; Veettil et al., 2017; Vuille et al., 2018). The glaciers in the Andes exhibit shrinking that is among the fastest rates in the world (Dussaillant et al., 2019). Their rate of mass loss is around $19.43 \pm 0.60 \text{ Gt a}^{-1}$, and is higher in the Patagonian region than in the tropical and extratropical areas (Braun et al., 2019). Observations show that the elevation of the 0°C isotherm has increased, and some glaciers overshot the tipping point of complete melting at the beginning of the 21st century (Bradley et al., 2006; Ragettli et al., 2016). This affects the water storage, hydrological regime, glacier-related hazards, and communities that rely on glacier-fed water supplies (Bradley et al., 2006; Drenkhan et al., 2019; Erwin, 2009; Motschmann et al., 2020).

Several studies focused on individual catchments in Bolivia and Peru based their results on temperature-index approaches (Baraer et al., 2009; Fernández and Mark, 2016; Mark et al., 2005; Soruco et al., 2015). A study covering part of our study region, which coupled a glacier melt model with a water balance model, showed higher contributions of glacier melt towards the Pacific Ocean than in the Amazon watershed (Buytaert et al., 2017). Future scenarios, under RCP4.5 and RCP8.5, projected that runoff influenced by glacier contributions would remain stable until 2030, then decrease in the central Andes (Ragettli et al., 2016). An overall water deficit was forecasted for certain periods through the end of the 21st century (Avilés et al., 2020).

Although a range of cryosphere-hydrological models can be used to estimate streamflow composition in mountain catchments, we used the Spatial Processes in Hydrology (SPHY) model because it can operate under data scarce conditions and has a particularly strong cryospheric module (Terink et al., 2015). This module enables the spatially distributed distinction of glacier melt and snowmelt components in the simulated discharge. These meltwater contributions to streamflow, however, have not been extensively studied, particularly in the Andes. Quantifying their role in compensating for the lack of rainfall-runoff during droughts is vital to understanding the importance of glacier melt and snowmelt, as well as determining the implications for future declines in meltwater generation.

Here, we studied the Santa River catchment in Peru. Water from the Santa River (fed by both glacial and non-glacial sources) is vital to the region's economy and the livelihoods of the population (Bury et al., 2013). The following economic activities occur in the catchment. At altitudes between 2000 and 4000 m a.s.l., Quechua farmers have practiced irrigated hillside agriculture for centuries. This includes an intricate system of small canals adapted to the mountain slopes. Traditional small-scale agricultural practices may be considered suboptimal in terms of efficiency, primarily due to significant infiltration losses resulting from gravitational irrigation systems and inadequately maintained irrigation channels (Motschmann et al., 2022). Furthermore, over the past decade, extensive cultivation areas have been established for fruit production in proximity to Caraz city, situated at an altitude of approximately 2200 m a.s.l. Additionally, over the last decade, very large area cultivations have been developed to grow fruit. Below 2000 m a.s.l., taking
advantage of the extraordinary natural landscape of Cañon del Pato, a hydroelectric project converts the Santa River’s water into electrical energy. The power station has been in operation since 1954. Below the Cañon del Pato power plant, water from the Santa River, at the foot of the Andes, is used to irrigate the vast agricultural areas that have recently emerged in the arid coastal zone. Two major diversion routes divide the basin into irrigated areas north and south of the Santa River mouth, the Chavimochic and Chinecas projects. Accounting for the combined water demand of small-scale agricultural areas situated in the highlands and the Chinecas and Chavimochic irrigation zones near the Pacific coast, agriculture constitutes approximately 54 % of the total water demand (Motschmann et al., 2022). As such, it represents a critical factor influencing overall water availability (ANA, 2015). In addition, the coastal towns of Chimbote and Trujillo further north depend on water from the Santa River for their drinking water supply (Condom et al., 2012).

The increasing probability of central El Niño events since 1940 and their link to rising hydrological drought frequency have im-
plications for ecosystems and water resources. Central El Niño events, characterized by a rapid increase in warm event probabilities,
are associated with shifts in climate patterns that often exacerbate drought conditions (Rojo Hernández et al., 2020). A recent study in
the Santa River catchment highlighted the influence of climate change on the present and future water balance (Motschmann et al.,
2022). Their study provides valuable insight into scenarios of local water use and availability at monthly timesteps, and indicates more

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Santa River catchment boundary including digital elevation model, river network, glacier locations, and discharge stations (Condorcerro, 1; La Balsa, 2; Casa de Agua, 3). [use color in print].</figcaption></figure>
frequent occurrences of hydrological droughts in the basin in the future. Changes in the water demand for domestic and agricultural purposes on the water balance are critical. For instance, future projections of water demand considering crop area extension of ~66 km² by 2050 suggest a potentially severe water deficit (Aste Cannock, 2018). It is important to note that pressure on water resources is particularly dependent on the management approach. Motschmann et al. (2022) demonstrate that, under the same climate model, a difference of up to 30% in water demand can occur. This highlights the considerable sensitivity to changing water demands in the dry season. With an increasing frequency of droughts, the meltwater regime in the Santa River catchment will be altered. The timing and volume of snowmelt and glacier melt runoff, contributing to river discharge, help to support river flows during periods of drought, when rainfall-runoff is reduced. These contributions of meltwater components to discharge, as well as their ability to compensate for reductions in rainfall-runoff during drought events, need to be better understood under a changing climate with increased drought frequency. Our research, therefore, seeks to characterize historical drought events in the Santa River catchment at daily timesteps using SPHY, and address the role of meltwater during these events.

In this water tower, with diverse economic activities, we simulate the hydrology of the Santa River catchment between 1981 and 2015 with the following specific objectives:

i) Quantify the spatial and temporal distribution of river flow components.

ii) Analyze how meltwater components compensate for a lack of rainfall-runoff during drought events.

iii) Evaluate how meltwater components contribute to meeting environmental flow requirements.

## 2. Materials and methods

### 2.1. Study area

The high mountain Santa River catchment (Fig. 1) is located in the Cordillera Blanca of Peru, the region with the largest glacial cover in the tropics. The catchment covers an area of 12,279 km² and contains the second-largest river along the Pacific Coast (Mark et al., 2010). The catchment reaches an elevation of 6768 m a.s.l. at the Huascaran Peak, and the land is sparsely vegetated farther west before the river ultimately discharges into the Pacific Ocean. The south is largely covered by grassland, while shrubland and broadleaved tree cover prevail in the north. Glaciers accounted for 6.6 % of the total land cover in the catchment (in 2000; Seehaus et al., 2019) and are mostly located along the southeastern ridge.

The Condorcerro gauging station, situated on the Santa River (Fig. 1, 479 m a.s.l.), monitors discharge from a catchment area of 9969 km² from 479 to 6768 m a.s.l. and is located at the union of two sub-basins: the middle/upper Santa sub-catchment (6815 km²), and the Tablachaca sub-catchment (3132 km²). The La Balsa gauging station (8.87°S, 77.82°W; 1800 m a.s.l.; drainage area: 4800 km²) is situated near the diversion canal of the Cañón del Pato hydroelectric plant and serves as a long-term reference point for discharge measurements on the Santa River at the base of the Callejón de Huaylas basin. In the upper reaches of the Santa catchment, the Casa de Agua gauging station (9.46°S, 77.37°W; 3948 m a.s.l.; drainage area ~67 km², with 24.8 % glacial cover in 2017) provides additional hydrological data (Mateo et al., 2022).

The middle/upper Santa sub-catchment has a rugged topography with steep slopes in higher parts of the range, containing more than 15 peaks higher than 6000 m a.s.l. and a multitude of glaciers. In contrast, the Tablachaca sub-catchment extends northward, with elevations ranging from 479 to 5000 m a.s.l., and has no glacier cover. The natural setting of the Tablachaca sub-catchment, with its strong altitudinal gradient, creates deeper and smaller rivers than those observed in the Santa sub-catchment (Morera et al., 2013).

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Mean annual precipitation (a) and daily temperature (b) in the Santa River catchment (1981–2015). [use color in print].</figcaption></figure>
Precipitation and temperature vary spatially throughout the Santa River catchment (Fig. 2; Fernandez-Palomino et al., 2021; Huerta et al., 2018). Low precipitation values were present at the river outlet to the Pacific Ocean (47 mm/y), with high values along the eastern glaciated ridge near Casa de Agua gauging station (1165 mm/y; Fig. 1). The average annual precipitation was 754 mm. Daily mean temperatures throughout the catchment ranged from -5°C along the eastern, glaciated ridge of the catchment to 28°C along the river towards the outlet. The mean daily temperature averaged over the whole catchment was 14°C.

The average simulated discharge of the Santa River was 161 m³/s during the study period. The dry season was identified as April through September, when outfluxes exceeded influxes (see Section 3.2 for a detailed explanation).

## 2.2. Spatially distributed cryosphere-hydrological model

We used the cryospheric-hydrological model SPHY at a spatial resolution of 500 × 500 m with daily timesteps. This model was specifically developed for applications under data scarcity (Terink et al., 2015). The daily total runoff generated in each gridcell was calculated as the sum of rainfall-runoff (i.e., the sum of surface runoff and lateral flow), snowmelt, glacier melt, and baseflow. The total runoff and its components were subsequently routed downstream using a simple routing scheme that used a digital elevation model (DEM; Table 1) from HydroSHEDS (Lehner et al., 2008) and a calibrated recession coefficient (0.945; Table 5) to obtain the daily discharge for each gridcell.

To estimate the contribution of snowmelt and glacier melt to runoff, we applied sub-grid variability at 25 x 25 m resolution. For each complete or partial glacier within a model gridcell, we included information about the mean glacier elevation and the glacier type (i.e., debris-free or debris-covered; Lehner et al., 2008; Seehaus et al., 2019). Debris-free glaciers refer to clean ice, while debris-covered glaciers include layers of rock, sediment, and/or organic material on the ice surface. We assume the debris layer as mapped by Seehaus et al. (2019) to be thicker than ~5 cm and thus have an insulating effect on the glacier ice and decreasing melt rates (Östrem, 1959).

Glacier melt was calculated using a degree-day approach with different melt factors assigned to debris-free and debris-covered glaciers (Terink et al., 2015). Degree-day melt approaches have been applied to tropical Andean glaciers successfully in previous (Fuchs et al., 2016; Pellicciotti et al., 2008). Part of the glacier melt subsequently became surface runoff and the remainder became baseflow. This distinction was determined by a calibrated glacier runoff fraction indicating the ratio of surface runoff and baseflow derived from glacial melt.

Snow accumulation was determined for each gridcell using daily precipitation and a temperature threshold to distinguish between rain and snow. The snow module included refreezing and water storage in the snowpack. Snowmelt was generated when the liquid storage in the snowpack exceeded the threshold and simulated using a degree-day approach using a snow melt factor. The liquid and dry snow storage were updated daily after aggregating all incoming and outgoing flows.

Rainfall-runoff was calculated according to the saturation excess runoff principle. For the root zone layer, the Modified Hargreaves reference evapotranspiration equation and a land use dependent crop coefficient were applied to calculate the potential evapo-transpiration (Droogers and Allen, 2002). The actual evapotranspiration equaled the potential evapotranspiration, limited by the soil water content.

Lateral flow of water in the soil between gridcells and vertical exchange of water between soil layers through percolation and capillary rise were also taken into account. Each gridcell had a groundwater reservoir, and depending on the amount of simulated recharge from the subsoil layer, released delayed baseflow to the runoff. More details about the model are described in Terink et al. (2015).

## 2.3. Data collection, processing, and model input set up

Input data necessary for SPHY were collected and pre-processed for uniformity (equal resolution, projection, and extent). These included meteorological forcing maps, static maps, and calibration-validation time series.

Forcing data was derived from PISCO (maximum, minimum temperature) and RAIN4PE (precipitation) at daily timesteps from 1981 to 2015 (Fernandez-Palomino et al., 2021; Huerta et al., 2018). We spatially smoothed the forcing data from their native resolutions (0.1°) to model resolution (500 × 500 m). We calculated mean daily temperatures as the midpoint between the daily minimum and maximum temperature values, and subsequently downscaled the PISCO temperature fields using a DEM at model resolution and a fixed temperature lapse rate of -0.008 °C/m (Schauwecker et al., 2014).

We calibrated and validated simulated river discharge in the catchment using daily observed values at three gauging stations: Condorcerro (station number: 3948350; GRDC, 2024)), Casa de Agua, La Balsa (Mateo et al., 2022).

Static data (Table 1) and soil maps (Table 2) were also required for SPHY. Elevation data was derived from HydroSHEDS and resampled to 500 x 500 m model resolution (Lehner and Grill, 2013).

<table><caption>**Table 1**
Static data maps and their sources.</caption><thead><tr><td>Data</td><td>Source</td></tr></thead><tbody><tr><td>DEM</td><td>HydroSHEDS (Lehner et al., 2008)</td></tr><tr><td>Land use land cover</td><td>ESA Land Cover CCI (European Space Agency, 2017)</td></tr><tr><td>Soil hydraulic properties</td><td>HiHydroSoil v2.0 (Simons et al., 2020)</td></tr></tbody></table>
The DEM served as the base to compute the local drainage directions map (LDD), used to derive the river network for routing of the runoff, as well as the slope map. The water accumulation map was then derived from the LDD. Soil hydraulic properties were taken from the HiHydroSoil dataset, which is based on the SoilGrids250m v2.0 dataset, enriched with soil hydraulic properties based on conversion from soil properties using pedotransfer functions (ISRIC, 2020; Simons et al., 2020; Tóth et al., 2015).

SPHY also required tabular inputs for glacier characteristics (Table 3). The Model IDs were determined by assigning a value to each gridcell in the clone map, and the Glacier IDs were assigned using the outlines from the glacier dataset (Seehaus et al., 2019). The model elevation values were computed as the average elevation of the DEM per model gridcell, and the glacier elevation values were computed at the sub-grid scale for gridcells containing glaciers. A fractional ice cover ranging between 0 (no ice cover) and 1 (complete ice cover) was calculated at the sub-grid scale and assigned to each model gridcell. Sub-gridcells with glacier debris were

given a value of 1, and those without debris a value of 0. The glacier outlines were united with the sub-grid to create shapefiles for each Unique ID, a combination of the Model ID, the Glacier ID, and the debris.

## 2.4. Model calibration, validation, and outputs

We apply a two-step calibration approach to overcome the common equifinality problem of model calibration in mountain catchments where errors in parameters are frequently compensated by errors in other parameters (Pellicciotti et al., 2012). As a first step, we manually calibrated the cryospheric parameters in SPHY by comparing simulated glacier mass balance between 2000 and 2015 and remote sensing geodetic mass balance for the same period (Seehaus et al., 2019). We calibrated the degree-day factors for debris-free glaciers, debris-covered glaciers, and snow (Table 5; Seehaus et al., 2019). We calculated the simulated change in glacier area and volume over time, and selected the model parameterization with results that most accurately reflected the changes in glacier area and volume reported by Seehaus et al. (2019) for our study region. We also used the discharge observations at Casa de Agua, being close to glacier outlets as independent validation of calibrated cryospheric model parameters, to demonstrate the ability of the degree-day model to satisfactorily simulate melt rates.

As second step, we manually calibrated other parameters related to rainfall-runoff by individually adjusting the most sensitive parameters (remaining parameters, excluding those for the cryospheric parameter calibration; Table 5). We statistically evaluated the model performance in discharge simulations for a calibration period (2008–2015) and validation period (2000–2007) by comparing the simulated and observed monthly river discharge at the Condorcerro, Casa de Agua (only calibration due to limited discharge observations), and La Balsa gauging stations, using the Nash–Sutcliffe efficiency, percent bias, and coefficient of determination (Moriasi et al., 2007; Nash and Sutcliffe, 1970).

The modelled discharge was further differentiated based on the source of its contributions into rainfall-runoff, snowmelt, glacier melt, and baseflow components. Daily simulated values of rainfall, snowfall, actual evapotranspiration, runoff, and discharge (separated by contributor) were used in the subsequent analyses of spatiotemporal dynamics of flow components.

## 2.5. Identification of drought events

Drought events were identified as warm/dry events and extremely warm/dry events. Dry events were defined as periods of at least seven consecutive days where the average precipitation in the catchment was less than 2 mm/day (van Tiel et al., 2021). Warm periods and extremely warm periods were derived from the long-term averaged daily temperatures. Temperature values were assigned to each day of the study period using 7-day moving averages of the mean daily temperatures. For each specific day of the year, the 80th (warm) and 90th (extremely warm) percentiles were extracted as thresholds. A day was then considered warm (or extremely warm) if the mean daily temperature exceeded the threshold(s). Combining these approaches, periods of at least 7 consecutive days that were both warm and dry, or extremely warm and dry, were classified.

## 2.6. Compensation metric during drought events

Compensation metrics were calculated for each event to assess how increases in meltwater made up for the lack of flows generated by rainfall. These metrics were derived from the method used by van Tiel et al. (2021), who applied their metric to observed discharge data. Here, we have the advantage of streamflow separated by contributor, and have adapted the compensation metric accordingly (van Tiel et al., 2021). Compensation was calculated as:

<table><caption>**Table 2**
Soil hydraulic properties necessary for SPHY.</caption><thead><tr><td>Property</td><td>Rootzone</td><td>Subsoil</td></tr></thead><tbody><tr><td>Permanent wilting point</td><td>X</td><td></td></tr><tr><td>Field capacity</td><td>X</td><td>X</td></tr><tr><td>Saturated water content</td><td>X</td><td>X</td></tr><tr><td>Wilting point</td><td>X</td><td></td></tr><tr><td>Saturated hydraulic conductivity</td><td>X</td><td>X</td></tr></tbody></table>
<table><caption>**Table 3**
Inputs for the SPHY glacier table.</caption><thead><tr><td>Glacier property</td><td>Description</td></tr></thead><tbody><tr><td>Unique ID</td><td>Unique value determined by combining Glacier ID, Model ID, and debris</td></tr><tr><td>Model ID</td><td>Unique value for each model gridcell</td></tr><tr><td>Glacier ID</td><td>Unique value for each glacier</td></tr><tr><td>Model elevation</td><td>Average elevation (from DEM) for each model gridcell</td></tr><tr><td>Glacier elevation</td><td>Average elevation (from DEM) for glaciers within each model gridcell</td></tr><tr><td>Presence of debris</td><td>Binary value depicting the presence or absence of glacial debris</td></tr><tr><td>Glaciated fraction</td><td>Fraction of model gridcell which has glacier cover</td></tr><tr><td>Ice thickness</td><td>Depth of ice for each unique glacier gridcell</td></tr></tbody></table>

$$C_{event} = \frac{\bar{Q}_{event}}{\bar{Q}_{typical}}$$

C represents the compensation during the drought event, $\bar{Q}_{event}$ represents the mean flows during the dates of the drought event, and $\bar{Q}_{typical}$ represents the long-term mean flows of those dates across all the years in our study period (1981–2015). These calculations were performed for the individual flow components, meltwater runoff, and total runoff.

To further interpret and contrast the compensation metrics of meltwater sources with their respective contribution during the aforementioned events, we calculated spatially distributed component contributions as the ratio of the snowmelt discharge, glacier melt discharge, and total meltwater discharge to the total discharge. This process was carried out for the event flows and the typical flows.

## 2.7. Environmental flow requirements

During drought events, we assessed the ability of compensating flows to meet environmental flow requirements (EFR). We calculated the daily EFR per gridcell as the 10th percentile of the 7-day moving average of daily spatial discharge (Smakhtin et al., 2004; van Tiel et al., 2021). We then determined the percent of days per event where the EFR was met by snowmelt, glacier melt, total meltwater, non-meltwater, and total flows in each stream cell. We then averaged these values spatially to show the average percent of stream cells in which the EFR was met by each component per event. This was done separately for the warm/dry events and the extremely warm/dry events.

Subsequently, we calculated the percent of cells (per event) where the meltwater was critical for meeting the EFR. We did this for each event day by determining the cells in which the EFR was met by the total flows, but not by the non-meltwater flows alone. This implied that meltwater flows were necessary to push the total flows above the EFR threshold. For each event day, we assessed the gridcells in major streams to see if the meltwater was critical. Daily maps were then averaged per event, yielding an output map that dictated the percent of event days when the meltwater was critical in each stream cell. We then calculated the zonally averaged value of the stream cells in this output map to yield a single value that described how critical the meltwater was for meeting the EFR in the catchment per event. This was done separately for the warm/dry and extremely warm/dry events.

# 3. Results

## 3.1. SPHY model calibration and validation

The performance of the model calibration and validation were evaluated statistically (Table 4). For the glacier parameter calibration (Table 5), SPHY underestimated the change in glacier area between 2000 and 2015 by –0.90 km²/y (12% error) and overestimated the change in glacier volume in time by 0.09 km³/y (6% error). Streamflow simulations based on the configuration parameters (Table 5) appropriately reflected the seasonal dynamics, as well as the high-flows, low-flows, peaks, and the recession in flows after the wet season at all three gauging stations (Fig. 3). The acceptable calibration results at Casa de Agua in particular further supported the cryospheric model parameter calibration, as the proximity of the gauging station to glacier outlets signals a relationship between the simulated melt rates of the degree-day model and the discharge observations. The calibration and validation values were considered satisfactory, and the model was run for the full 35-year time series (Moriasi et al., 2007). For the model run over our full study period, we used the static glacier module, meaning glacier retreat (changes in area, volume, thickness) were not simulated. This

<table><caption>**Table 4**
Statistical evaluation of SPHY discharge calibration and validation per gauging station from the model run used for further analyses.</caption><thead><tr><td>Station</td><td>Run</td><td>Percent Bias</td><td>Nash-Sutcliffe Efficiency</td><td>R<sup>2</sup></td></tr></thead><tbody><tr><td rowspan="2">Condorcerro</td><td>Calibration</td><td>14.4 %</td><td>0.58</td><td>0.58</td></tr><tr><td>Validation</td><td>13.1 %</td><td>0.70</td><td>0.70</td></tr><tr><td rowspan="2">Casa de Agua</td><td>Calibration</td><td>-8.9 %</td><td>0.60</td><td>0.60</td></tr><tr><td>Calibration</td><td>-4.1 %</td><td>0.72</td><td>0.72</td></tr><tr><td>La Balsa</td><td>Validation</td><td>-12.5 %</td><td>0.83</td><td>0.83</td></tr></tbody></table>
<caption>**Table 5**</caption>

SPHY configuration parameters with the range of tested values. Parameters without minimum and maximum values were fixed.

<table><thead><tr><th>Parameter</th><th>Description</th><th>Min</th><th>Max</th><th>Value</th><th>Unit</th></tr></thead><tbody><tr><td>RootFrac</td><td>Rootzone hydraulic property scaling factor</td><td>-</td><td>-</td><td>0.25</td><td>-</td></tr><tr><td>KsatFrac</td><td>Saturated hydraulic conductivity scaling factor</td><td>0.25</td><td>0.5</td><td>0.25</td><td>-</td></tr><tr><td>RootDepthFlat</td><td>Thickness of rootzone</td><td>100</td><td>1000</td><td>200</td><td>cm</td></tr><tr><td>SubDepthFlat</td><td>Thickness of subsoil</td><td>400</td><td>2800</td><td>2800</td><td>cm</td></tr><tr><td>CapRiseMax</td><td>Maximum allowed capillary rise</td><td>1</td><td>3</td><td>1</td><td>mm/day</td></tr><tr><td>GwDepth</td><td>Groundwater depth</td><td>250</td><td>3000</td><td>500</td><td>cm</td></tr><tr><td>GwSat</td><td>Saturated groundwater content</td><td>250</td><td>2000</td><td>500</td><td>cm</td></tr><tr><td>deltaGW</td><td>Groundwater recharge delay</td><td>149.85</td><td>350</td><td>350</td><td>day</td></tr><tr><td>alphaGW</td><td>Parameter of baseflow days</td><td>0.05</td><td>1.1</td><td>0.05</td><td>day<sup>-1</sup></td></tr><tr><td>Gw</td><td>Initial groundwater storage</td><td>250</td><td>2000</td><td>500</td><td>cm</td></tr><tr><td>H_gw</td><td>Initial height of groundwater table</td><td>0.501</td><td>3.001</td><td>3</td><td>m</td></tr><tr><td>DDFG</td><td>Degree-day factor debris-free glaciers</td><td>0.4</td><td>1</td><td>0.4</td><td>mm/°C/day</td></tr><tr><td>DDFGD</td><td>Degree-day factor debris-covered glaciers</td><td>0.2</td><td>0.5</td><td>0.2</td><td>mm/°C/day</td></tr><tr><td>GlacF</td><td>Glacier melt runoff factor</td><td>-</td><td>-</td><td>0.25</td><td>-</td></tr><tr><td>DDFS</td><td>Degree-day factor snow</td><td>0.4</td><td>1</td><td>0.4</td><td>mm/°C/day</td></tr><tr><td>kx</td><td>Routing recession coefficient</td><td>0.8</td><td>0.95</td><td>0.945</td><td>-</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Daily hydrographs including modelled flow components for the calibration (2008–2015; a-c) and validation periods (2000–2007; d-e). Note that observed data at Casa de Agua was limited to a smaller time frame than our calibration period. [use color in print].</figcaption></figure>

was due to the limited overlap of available glacier data (2000–2016) and the meteorological forcing (1981–2015). We used the glacier data from 2000 as our static inputs, as it was close to the midpoint of our study period.

## 3.2. Simulated temporal and spatial dynamics of hydrological fluxes

Monthly flows clearly depicted the seasonal change in rainfall, as well as the resulting influence on runoff (Fig. 4). Rainfall and snowfall followed a similar pattern, with maximum values in the first quarter of the calendar year and minimums between June and August. Evapotranspiration showed a similar, albeit less extreme, trend. During the wetter period, October through March, the storage in the catchment increased (inflows exceeded outflows); while in the drier period, April through September, when inflows decreased, baseflow and glacier outflows were maintained. The latter implies a dual process: the important role of soils and glaciers in maintaining the streamflow, and that water stored in the wetter months was delayed and left the system in the drier months.

The spatial variability of total discharge and percent contributions for each outflow component during the dry season showed that rainfall-runoff (Fig. 5a) was the dominant component of streamflow, with high contributions in the northern part of the catchment and less in the glacierized zone. Baseflow (Fig. 5d) was the second most relevant component, with less contribution to streamflow in the north, and more in the glaciated area. Snowmelt contributions (Fig. 5b) were the smallest, and its spatial variability was barely evident throughout the catchment. Glacier melt contributions (Fig. 5c) were slightly higher in the surrounding mountain area than
<figure><img src="image_8.png" /><figcaption>Fig. 4. Average monthly fluxes over the entire Santa River catchment, separated by source (1981–2015). [use color in print].</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 5. Average dry-season discharge and percent contributions from rainfall-runoff (a), snowmelt runoff (b), glacier melt runoff (c), and baseflow runoff (d) from the full study period. [use color in print].</figcaption></figure>

downstream. The catchment outlet was largely influenced by rainfall-runoff, followed by baseflow.

The coefficients of variation (CV) for annual and monthly streamflow components were also computed. The annual rainfall-runoff CV had a range of 0.19–0.89, with a mean of 0.36; the rainfall-runoff CV during dry months had a range of 1.27–1.77, with a mean of 1.56. This implies that there was greater intra-annual variability than inter-annual variability, highlighting the influence of seasonality in the catchment.

### 3.3. Compensation metric during drought events

We identified 35 warm/dry events and 13 extremely warm/dry events (Table A1 and Table A2). Boxplots detailing the spatially
averaged compensation and contributions to streamflow throughout the catchment, per warm/dry and extremely warm/dry event, are shown in Fig. 6. A compensation value of 1 implies that the event flows were equal to the typical flows, while values less than 1 imply the event flows were unable to compensate for the typical flows. Typical flows are defined here as the long-term averaged flows for any given day(s) of the year, whether or not a drought occurred.

Snowmelt runoff during drought events was generally unable to compensate, while the glacier melt runoff was able to compensate (Fig. 6a). The relative difference between the values of these flows resulted in the meltwater runoff, as well as the total flows, generally compensating for the lack of rainfall.

Fig. 6b shows a similar trend, as the snowmelt contributions tended to decrease during events (likely due to melting prior to the official start of the drought period), while the glacier melt contributions tended to increase. Ultimately, this resulted in an increase in meltwater contributions during event periods, relative to the meltwater contributions during the same dates of other years when drought events did not occur. The relationships were present for warm/dry events and extremely warm/dry events, with slightly more amplified trends during the latter.

Similar results were seen spatially, where meltwater compensation had a greater spatial coverage during extremely warm/dry events than warm/dry events (Fig. 7). In both cases, meltwater runoff was generally able to compensate for the lack of rainfall-runoff in the central portion of the catchment (downstream of the glaciers), but was less capable in the far north and far south of the catchment. Furthermore, meltwater contributions to streamflow tended to increase in these regions downstream of the glaciers as well. The mean compensation value for warm/dry events was 6%, while the mean compensation values for extremely warm/dry events was 8%, indicating a slight increase in river discharge due to greater meltwater runoff during drought events.

### 3.4. Environmental flow requirements

The average percent of days during an event where the EFR was met by various flow components per stream cell are presented in Fig. 8. It shows that the meltwater was insufficient on its own to meet the EFR, but that the non-meltwater was often sufficient. When put together, the total flows during events were generally sufficient to meet the environmental flow requirements. By taking the

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Spatially averaged compensation of event flows to ‘typical flows’ (a) and the relative change in percent contributions from ‘typical flows’ to event flows (b) per contributor (snowmelt runoff, glacier melt runoff, total meltwater runoff, total river discharge) in stream cells. Data is presented for all warm/dry and extremely warm/dry events over the study period (i.e., *Snow* represents the snowmelt runoff during warm/dry events, *xSnow* represents the snowmelt runoff during extremely warm/dry events).</figcaption></figure>
<figure><img src="image_11.png" /></figure>

**Fig. 7.** Mean compensation and mean change in meltwater contributions during all warm/dry events (a) and extremely warm/dry events (b) relative to the 'typical flows' during the event dates over the full study period. Cells in white are locations where the mean event flows did not compensate for the mean 'typical flows.' [use color in print].

<figure><img src="image_12.png" /></figure>

**Fig. 8.** Percent of stream cells per event where each various components met the EFR.

difference between the non-meltwater flows and the total flows, we simulated that meltwater was, on average, necessary to meet environmental flows in 21.97 % of the stream network during drought events and 27.78 % of the stream network during extreme drought events.

Fig. 9 presents boxplots displaying the percent of cells where meltwater was critical for meeting the EFR per event. Criticality here

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

**Fig. 9.** Percent of stream cells per drought event where the meltwater was critical for meeting the EFR.
is used to define cells where the meltwater was necessary for the total flows to meet the EFR (i.e., the non-meltwater flows were insufficient, but the total flows were sufficient). The plots show that meltwater was, on average, critical for meeting the EFR in 22 % of the stream network during warm/dry events and 28 % during extremely warm/dry events. There were, however, several events (7 warm/dry, 4 extremely warm/dry) when the majority of stream cells required meltwater to meet the EFR. Conversely, there were 6 warm/dry events and 1 extremely warm/dry event where the meltwater was never critical for meeting the EFR. Importantly, meltwater was critical at some point of the stream network during 85 % of identified drought events.

## 4. Discussion

Higher temperatures during warm periods caused increased melting of glaciers, leading to more meltwater, and the lack of rainfall during dry periods reduced the contributions of rainfall-runoff to discharge. When these trends were coupled during drought events, the result was an increase in the prevalence of meltwater. Additionally, the criticality of meltwater, while not pervasive throughout the catchment, occurred during the vast majority of drought events. This exemplifies the greater influence of meltwater during drought periods than non-drought periods, when environmental flow requirements to ensure ecological integrity are less likely to be met. The trends identified in this study, showing the increasing compensation of meltwater flows during drought periods, indicate an increasing importance of meltwater (particularly glacier melt) for maintaining the EFR during such events.

Future depletion in glacier volumes under climate change will likely affect the compensating role glacier melt currently plays in the catchment. In a warming climate, yields from glacier melt runoff initially increase, reach a peak, then decline once glaciers have shrunk below a certain mass. A global study addressing the moment when this 'peak water' occurs indicated that the peak for the Santa River catchment likely occurred between 2010 and 2020, albeit with a large range in uncertainty (Huss and Hock, 2018), and a local study suggests moment for the dry season in the catchment was crossed sooner (Baraer et al., 2012). Thus, the amount of glacier melt will likely decrease in the coming decades, which could result in more frequent occurrences of hydrological droughts (Vuille et al., 2018).

Although the compensating effect of snowmelt in the catchment is limited, continued warming will lead to a further shift from snowfall towards rainfall, resulting in less snow accumulation (Vuille et al., 2018). This will then further reduce the compensating effect of snowmelt, thereby contributing to more frequent hydrological droughts.

Increasing El Niño events and hydrological drought frequency is expected to impact ecosystems by compromising environmental flow requirements, leading to negative consequences for the provision of water to ecosystems and human uses. Ecosystems will suffer from unmet EFRs. Human sectors will not be spared, as prevalent needs such as mining, potable water use, hydroelectric power generation, and coastal irrigation will be put at risk. Additionally, traditional smallholder agriculture, which previously focused on subsistence crops like alfalfa, barley, potatoes, and wheat, is notably declining. In contrast, there is a shift towards commercial agricultural practices, with increasing production of export crops such as asparagus, rice, and sugar. Smallholder agriculture is expected to be impacted, as well as the important coastal irrigation projects in the region (Chavimochic and Chinecas). This transition reflects broader changes in agricultural practices and these changes to the hydrological cycle are occurring concurrently with increases to the population and water demand (Bury et al., 2013). Energy production at the Cañon del Pato hydroelectric station, located in the middle reaches of the Santa River, will be directly impacted by changes to the water flows. Since the 1940s, population growth in the Santa River catchment has generated an increase of potable water use. For example, total water delivery in Huaraz rose from 3.2 million m³ in 1999 to 4.8 million m³ in 2010, while Caraz saw an increase from 502,000 m³ to 896,000 m³ in the same period. This rising demand for potable water, driven by urbanization, will be impacted by drought conditions affecting water availability in communities. Available water and the resultant implication for water consumption in communities (e.g., Trujillo on the coast, and several others spread throughout the catchment) will be directly impacted by the droughts. A recent study conducted in the upper part of the Santa River catchment details the complex interaction between water services and human activities and policies (Dextre et al., 2022). Clearly, there is a need for effective management and planning of water resources to ensure a continued provision of the environmental flows necessary to sustain riverine ecosystems and support human uses in the face of changing environmental flow conditions – as emphasized in the paper by Stein et al. (2022).

We compared our simulated discharge values at Condorcerro with those from Motschmann et al. (2022) for the period 2000–2015. Our results over this whole period were 19 % greater (mean discharge of 165 m³/s vs 138 m³/s). Specifically for the dry months of July, August, and September, our results were 47 % smaller (22 m³/s vs 42 m³/s), and specifically for the wet months of January, February, and March, our results were 41 % greater (328 m³/s vs 233 m³/s). These trends imply a more extreme simulated hydrological regime from our study, as the peak flows are greater and the low-flows are lesser.

The model parameters were subject to uncertainties. They were partly calibrated and partly transferred from studies in other catchments (Khanal et al., 2021; Lutz et al., 2016). A full uncertainty analysis can provide more detailed information about the magnitude of the uncertainties and the sensitivity of the results to these uncertainties. This is, however, beyond the scope of our study, where our objective is to demonstrate the concept of compensation by meltwater components in meeting environmental flow requirements.

One limitation of our study is that large catchment-scale modelling may restrict the representation of spatial and temporal heterogeneity of small-scale hydrological processes, and its simplification, which are crucial for the functioning of ecosystems such as bofedales. These small-scale high-Andean wetlands are characterized by saturated soils, dense cushion vegetation, and a critical role in water storage and regulation, as well as their support of unique biodiversity. Due to their limited spatial extent compared to larger landscape units, bofedales are particularly susceptible to being overlooked or oversimplified in large-scale hydrological models. This simplification arises from the challenges of integrating multiple scales and interactive processes, potentially reducing the accuracy of
predictions related to these ecosystems (Kirchner, 2009).

In our study, meltwater contributions were only established using a hydrological model. To improve confidence in these estimates, they would ideally be validated through comparison with another method of estimate streamflow contributions. This could, for example, be done with isotope tracking (Mark and Seltzer, 2003). This, however, requires taking water samples in the field, and unfortunately, contributions estimated with this method are only valid at one moment in time.

We used a range of globally available datasets, such as a map of soil hydraulic properties and a land use map. Possible improvements could be made by replacing or validating some of these input data with more local or regional datasets.

We now ran SPHY statically over our full study period (using glacier data from 2000), despite calibrating the glacier parameters dynamically (from 2000 to 2015). This was due to the limited temporal availability of the glacier outlines, relative to the meteorological forcing data. As such, there are limitations in the model outputs we simulated. The application of a dynamic glacier module over the entire model period with evolving glacial areas, volumes, and ice thicknesses using a mass balance approach would enhance the implications of our findings. Future work could also include additional inventories, which could be used to obtain a more accurate calibration of glacier melt parameters (e.g., Racoviteanu et al., 2008).

When parameterizing SPHY, we set the subsoil depth to 28 m to ensure that the routed baseflow runoff was meeting the observed low-flows. While this is a deep soil depth, it provided the most accurate simulations of dry-season discharge in the catchment. Furthermore, our discharge calibration results had an NSE of 0.63 (averaged over all three calibration gauging stations). While this value is satisfactory for simulating discharge in a river basin, a better agreement between the simulated and observed values would yield improved results for the subsequent analyses conducted in our study.

## 5. Conclusions

1) The SPHY model accurately simulated changes in glacier area and volume over time.

2) The SPHY model accurately simulated discharge components at multiple gauging stations and spatial processes in the Santa River catchment.

3) During drought events, meltwater runoff dynamics compensated for the lack of rainfall-runoff in the catchment.

4) In the majority of drought events (85 %), meltwater was necessary at some point in space and time to meet the environmental flow requirements.

5) Our research defined spatially distributed compensation zones to better understand the impacts of climate change on meltwater dynamics, supporting informed management of glaciated catchments.

Our parameterization of SPHY accurately simulated glacier dynamics compared to remotely sensed data, estimating similar losses in both glacier area and volume over a 16-year period. Furthermore, SPHY accurately simulated discharge at multiple gauging stations in the Santa River catchment. This simulated discharge measured four separate runoff components; rainfall-runoff, snowmelt runoff, glacier melt runoff, and baseflow runoff. Throughout the catchment, rainfall-runoff was the most dominant; however, in both the dry season and during drought events, baseflow made greater contributions to total runoff.

During drought events, meltwater runoff dynamics were also altered. Snowmelt runoff did not compensate, yet glacier melt did, and offset the decreases in snowmelt. The result was that meltwater runoff was generally compensating. Total runoff also tended to be compensated. Similar trends were seen regarding the contributions of these meltwater components during drought events; snowmelt contributions decreased, but glacier melt (and meltwater) contributions increased. While meltwater runoff increased during drought events, it was generally insufficient to meet EFRs; however, non-meltwater and total runoff were usually able to maintain the EFR. In most circumstances, the majority of stream cells did not rely on meltwater to meet the EFR; however, in the majority of drought events, meltwater was necessary at some point in space and time to maintain the EFR.

The findings of this study are important to gain deeper insight into the critical role of meltwater to maintain environmental flows in the Santa River catchment. As drought frequency continues to increase, the role of glacier melt and snowmelt will likewise grow, albeit towards a tipping point. As seen due to glacier retreat in the catchment, the ability of meltwater to continue buffering for decreases in rainfall-runoff during drought events can only persist while these stores of ice and snow remain. This is why it is crucial to better understand the importance of meltwater for environmental protection, as it is a necessary component of river discharge in the Santa River catchment, but also a dwindling resource.

Our study defined spatially distributed compensation zones to better understand the meltwater dynamics during drought events in glaciated catchments. The findings of our research can be used to support informed management of such catchments, aimed at mitigating these impacts. Further research that can advance our findings includes the application of future climate projections to better understand how these compensation zones will evolve over time under various socioeconomic and climatic scenarios. Additionally, we recommend the use of multiple spatial scales, different models, and dynamic downscaling to better infer future hydrological droughts and their derived impacts on environmental flow requirements.

## CRediT authorship contribution statement

**Marinelli Bryan:** Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. **Khanal Sonu:** Software, Writing – review & editing. **Lutz Arthur:** Conceptualization, Formal analysis, Methodology, Writing – review & editing. **Weeser Björn:** Writing – review & editing. **Breuer Lutz:** Conceptualization, Funding acquisition, Project administration, Writing – review & editing. **Correa Alicia:** Conceptualization, Formal analysis,
Methodology, Supervision, Writing – original draft, Writing – review & editing. **Condom Thomas:** Investigation, Writing – review & editing.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

Alicia Correa, Björn Weeser, and Lutz Breuer acknowledge the funding by the German Academic Exchange Service (DAAD) from funds of the Federal Ministry for Economic Cooperation (BMZ), SDGnexus Network (Grant No. 57526248), program "exceed - Hochschulexzellenz in der Entwicklungszusammenarbeit."

## Appendix

Table A1 and Table A2 show the start and end dates of each event, as well as the durations. 35 warm/dry events and 13 extreme warm/dry events were identified. The longest warm/dry event was 35 days, with a mean of 11 days and a median of 8 days. The longest extreme warm/dry event was 15 days, with a mean of 10 days and a median of 11 days.

<table><caption>**Table A1**
Dates and durations of warm/dry events in the Santa River catchment (1981–2015)</caption><thead><tr><td>Event</td><td>Start Date</td><td>End Date</td><td>Duration</td></tr></thead><tbody><tr><td>1</td><td>April 12, 1983</td><td>April 19, 1983</td><td>8</td></tr><tr><td>2</td><td>May 2, 1983</td><td>May 17, 1983</td><td>16</td></tr><tr><td>3</td><td>June 4, 1983</td><td>June 19, 1983</td><td>16</td></tr><tr><td>4</td><td>June 24, 1983</td><td>July 8, 1983</td><td>15</td></tr><tr><td>5</td><td>July 15, 1983</td><td>July 21, 1983</td><td>7</td></tr><tr><td>6</td><td>August 7, 1983</td><td>August 24, 1983</td><td>18</td></tr><tr><td>7</td><td>September 30, 1983</td><td>October 6, 1983</td><td>7</td></tr><tr><td>8</td><td>August 18, 1987</td><td>August 24, 1987</td><td>7</td></tr><tr><td>9</td><td>September 7, 1987</td><td>September 14, 1987</td><td>8</td></tr><tr><td>10</td><td>December 19, 1987</td><td>December 26, 1987</td><td>8</td></tr><tr><td>11</td><td>March 25, 1988</td><td>April 2, 1988</td><td>9</td></tr><tr><td>12</td><td>June 6, 1990</td><td>June 12, 1990</td><td>7</td></tr><tr><td>13</td><td>April 3, 1992</td><td>April 11, 1992</td><td>9</td></tr><tr><td>14</td><td>September 16, 1994</td><td>September 23, 1994</td><td>8</td></tr><tr><td>15</td><td>July 6, 1997</td><td>August 9, 1997</td><td>35</td></tr><tr><td>16</td><td>August 12, 1997</td><td>August 30, 1997</td><td>19</td></tr><tr><td>17</td><td>September 9, 1997</td><td>September 21, 1997</td><td>13</td></tr><tr><td>18</td><td>April 20, 1998</td><td>April 29, 1998</td><td>10</td></tr><tr><td>19</td><td>April 8, 2005</td><td>April 18, 2005</td><td>11</td></tr><tr><td>20</td><td>October 5, 2006</td><td>October 11, 2006</td><td>7</td></tr><tr><td>21</td><td>September 26, 2009</td><td>October 6, 2009</td><td>11</td></tr><tr><td>22</td><td>May 14, 2010</td><td>May 20, 2010</td><td>7</td></tr><tr><td>23</td><td>July 25, 2012</td><td>August 1, 2012</td><td>8</td></tr><tr><td>24</td><td>June 2, 2014</td><td>June 9, 2014</td><td>8</td></tr><tr><td>25</td><td>June 11, 2014</td><td>June 17, 2014</td><td>7</td></tr><tr><td>26</td><td>July 5, 2014</td><td>July 12, 2014</td><td>8</td></tr><tr><td>27</td><td>July 25, 2014</td><td>July 31, 2014</td><td>7</td></tr><tr><td>28</td><td>May 4, 2015</td><td>May 12, 2015</td><td>9</td></tr><tr><td>29</td><td>May 31, 2015</td><td>June 6, 2015</td><td>7</td></tr><tr><td>30</td><td>June 14, 2015</td><td>July 6, 2015</td><td>23</td></tr><tr><td>31</td><td>July 22, 2015</td><td>July 30, 2015</td><td>9</td></tr><tr><td>32</td><td>August 15, 2015</td><td>August 21, 2015</td><td>7</td></tr><tr><td>33</td><td>August 23, 2015</td><td>September 13, 2015</td><td>22</td></tr><tr><td>34</td><td>September 15, 2015</td><td>September 30, 2015</td><td>16</td></tr><tr><td>35</td><td>December 10, 2015</td><td>December 17, 2015</td><td>8</td></tr></tbody></table>

<table><caption>**Table A2**
Dates and durations of extreme warm/dry events in the Santa River catchment (1981–2015)</caption><thead><tr><td>Event</td><td>Start Date</td><td>End Date</td><td>Duration</td></tr></thead><tbody><tr><td>1</td><td>May 5, 1983</td><td>May 12, 1983</td><td>8</td></tr><tr><td>2</td><td>August 8, 1983</td><td>August 21, 1983</td><td>14</td></tr></tbody></table>

(continued on next page)
<table><caption>Table A2 (continued)</caption><thead><tr><td>Event</td><td>Start Date</td><td>End Date</td><td>Duration</td></tr></thead><tbody><tr><td>3</td><td>July 17, 1997</td><td>July 27, 1997</td><td>11</td></tr><tr><td>4</td><td>July 30, 1997</td><td>August 9, 1997</td><td>11</td></tr><tr><td>5</td><td>August 19, 1997</td><td>August 29, 1997</td><td>11</td></tr><tr><td>6</td><td>September 9, 1997</td><td>September 20, 1997</td><td>12</td></tr><tr><td>7</td><td>April 20, 1998</td><td>April 29, 1998</td><td>10</td></tr><tr><td>8</td><td>April 12, 2005</td><td>April 18, 2005</td><td>7</td></tr><tr><td>9</td><td>May 31, 2015</td><td>June 6, 2015</td><td>7</td></tr><tr><td>10</td><td>June 14, 2015</td><td>June 20, 2015</td><td>7</td></tr><tr><td>11</td><td>July 22, 2015</td><td>July 28, 2015</td><td>7</td></tr><tr><td>12</td><td>August 28, 2015</td><td>September 9, 2015</td><td>13</td></tr><tr><td>13</td><td>September 15, 2015</td><td>September 29, 2015</td><td>15</td></tr></tbody></table>

## Data availability

The data produced for this study is publicly available through the Zenodo archive at doi:10.5281/zenodo.8028286.

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