A new process-based approach for evaluating gridded precipitation products in mountain watersheds: Test cases from the central Andes of Argentina

A new process-based approach for evaluating gridded precipitation products in mountain watersheds: Test cases from the central Andes of Argentina | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Hydrological Processes This is a preprint and has not been peer reviewed. Data may be preliminary. 11 June 2025 V1 Latest version Share on A new process-based approach for evaluating gridded precipitation products in mountain watersheds: Test cases from the central Andes of Argentina Authors : Ezequiel Toum 0000-0002-4482-0559 [email protected] , Juan A. Rivera , Jhan-Carlo Espinoza , and M. H. Masiokas Authors Info & Affiliations https://doi.org/10.22541/au.174966349.92613739/v1 Published Hydrological Processes Version of record Peer review timeline 360 views 198 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Adequate quantification of precipitation and its spatio-temporal variability is crucial for understanding the physical and biological processes within a watershed. Mountain watersheds pose particular challenges due to strong spatial heterogeneity in precipitation and typically sparse in-situ monitoring networks. The increasing availability of gridded precipitation products can help address these limitations, but their reliability at local or sub-regional scales remains difficult to assess. This study proposes a novel, process-based approach that incorporates daily streamflow data to evaluate the performance of four widely used gridded precipitation datasets (CHIRPS-v2, MSWEP-v2.8, GPCC-v2022, and TerraClimate). Five key watersheds in central-western Argentina (ca. 30–37°S) serve as case studies. The evaluation framework is based on five process-informed expectations derived from the region’s climate and topography: (a) most annual precipitation should fall as snow during winter (April–September); (b) a strong positive relationship should exist between winter precipitation and summer streamflow; (c) interannual variability of precipitation should exceed that of streamflow due to basin-scale damping effects; (d) runoff coefficients should be statistically lower than unity, reflecting mass conservation; and (e) winter precipitation should be concentrated at higher elevations. We apply simple non-parametric statistical tests to evaluate how well each dataset meets these expectations. A comparative assessment identifies the most reliable product for each watershed. Our findings show that MSWEP and TerraClimate perform best overall, particularly in capturing total precipitation and its seasonality. Other datasets fail to reproduce key hydrological signals, likely due to a lack of physically based inputs (e.g., reanalysis). Overall, this process-based, catchment-integrative evaluation offers a promising framework for assessing precipitation products in other snow-dominated mountain regions with limited ground observations, provided that the dominant hydroclimatic processes are well understood. Ezequiel Toum 1, 2* , Juan A. Rivera 1 , Jhan-Carlo Espinoza 3,4 and Mariano H. Masiokas 1 1 Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), CCT CONICET Mendoza, Av. Ruiz Leal s/n Parque General San Matín, Mendoza, Argentina. 2 Facultad Regional Mendoza - Universidad Tecnológica Nacional (FRM-UTN), Rodriguez 273 Ciudad de Mendoza, Argentina. 3 Université Grenoble Alpes, IRD, CNRS, Grenoble INP, Insitut des Géosciences de l’Environnement (IGE, UMR 5001), 38000, Grenoble, France. 4 Instituto de Investigación sobre la Enseñanza de las Matemáticas, Pontificia Universidad Católica del Perú (PUCP), Lima, Peru *Corresponding author: [email protected] Abstract Adequate quantification of precipitation and its spatio-temporal variability is crucial for understanding the physical and biological processes within a watershed. Mountain watersheds pose particular challenges due to strong spatial heterogeneity in precipitation and typically sparse in-situ monitoring networks. The increasing availability of gridded precipitation products can help address these limitations, but their reliability at local or sub-regional scales remains difficult to assess. This study proposes a novel, process-based approach that incorporates daily streamflow data to evaluate the performance of four widely used gridded precipitation datasets (CHIRPS-v2, MSWEP-v2.8, GPCC-v2022, and TerraClimate). Five key watersheds in central-western Argentina (ca. 30–37°S) serve as case studies. The evaluation framework is based on five process-informed expectations derived from the region’s climate and topography: (a) most annual precipitation should fall as snow during winter (April–September); (b) a strong positive relationship should exist between winter precipitation and summer streamflow; (c) interannual variability of precipitation should exceed that of streamflow due to basin-scale damping effects; (d) runoff coefficients should be statistically lower than unity, reflecting mass conservation; and (e) winter precipitation should be concentrated at higher elevations. We apply simple non-parametric statistical tests to evaluate how well each dataset meets these expectations. A comparative assessment identifies the most reliable product for each watershed. Our findings show that MSWEP and TerraClimate perform best overall, particularly in capturing total precipitation and its seasonality. Other datasets fail to reproduce key hydrological signals, likely due to a lack of physically based inputs (e.g., reanalysis). Overall, this process-based, catchment-integrative evaluation offers a promising framework for assessing precipitation products in other snow-dominated mountain regions with limited ground observations, provided that the dominant hydroclimatic processes are well understood. Keywords : gridded precipitation products; process-based rejection method; central-western Argentina; cold regions hydrology; Andes 1. INTRODUCTION Precipitation is a key variable in the hydrological cycle since it modulates water availability at basin scales (Dingman, 2014). An adequate quantification of the magnitude, intensity and spatio-temporal variability in precipitation is thus of vital importance to understand the different physical and biological processes that occur at different watersheds (Hobouchian et al., 2017, Wong et al., 2021; Zambrano-Bigiarini et al., 2017). Ideally, hydro-climatic studies involving precipitation data should base their estimates on realistic assumptions regarding their spatial and temporal distribution of these data (Maraun et al., 2010). These assumptions are usually complicated in mountain areas, where the understanding of the main precipitation patterns and variability is hampered by the complex topography and the usually scarce and/or incomplete in-situ precipitation data (Viale et al., 2019; Viviroli et al., 2011; Whiteman, 2000). In recent years, a large number of precipitation estimates derived from remote observations, numerical models, reanalysis, and blended products have become available (e.g.: Abatzoglou et al., 2018; Beck et al., 2019; Funk et al., 2015a,b; Schneider et al., 2022). Although the application of these precipitation products in flat homogeneous regions seems relatively straightforward, their reliability in mountain regions needs to be evaluated carefully. For these gridded products to be considered effective, they should at least capture the main regional hydroclimatic features of a given mountain watershed. The winter snow and the glaciers of the Central Andes of Argentina and Chile (~30°–37°S) are not only vital components of the regional hydrological system, but also essential water resources for numerous human activities in the adjacent lowlands (Ayala et al., 2020; Cornwell et al., 2016; Masiokas et al., 2020; Toum et al., 2025). In this Andean region most precipitation falls as snow during winter and represents the main source of water in the streamflow generation process during the subsequent warmer months (Crespo et al., 2020a, b; González-Reyes et al., 2017; Lauro et al., 2019; Masiokas et al., 2010; Viale and Nuñez, 2011). This temporal redistribution of water availability results in peak streamflows during the summer season (October–March), coinciding with the period of highest water demand for human consumption and agricultural and industrial uses on both sides of the mountain range (Rivera et al., 2021). Interestingly, despite the significant socio-economic importance of these Andean water resources and the pressing water crisis affecting the region (Garreaud et al., 2017, 2020; Rivera et al., 2021), assessments of total precipitation amounts and their spatio-temporal variability at the upper watersheds in Chile and Argentina remain scarce. Baez-Villanueva et al. (2021) used four precipitation products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8) in order to evaluate three parameter regionalization techniques in a hydrological model (TUWmodel) across 100 basins with different hydrological regimes in Chile. The researchers concluded that the product with the best performance in model calibration and validation does not necessarily turn out to be the best in terms of parametric regionalization. This result restricts the selection of the product to the chosen hydrological model and its subsequent calibration, thus undermining the selection of precipitation grids that correctly represent hydroclimatic features of the region (Kirchner et al., 2006). In an earlier work, Baez-Villanueva et al. (2018) evaluated six satellite precipitation products (TRMM 3B42v7, TRMM 3B42RT, CHIRPSv2, CMORPHv1, PERSIANN-CDR and MSWEPv2) and in-situ measurements in three South American watersheds and showed that the best product cannot be applied universally and that specific validation was always necessary. This conclusion restricts the applicability of the methodology to regions with reliable and well distributed field precipitation data. Hobouchian et al. (2017) evaluated four precipitation products—CMORPH, TRMM 3B42 RT and V7, and HYDRO—for the subtropical Andes of Argentina and Chile (32°S–42°S) during the 2004–2010 period using also in-situ precipitation measurements. Rivera et al. (2018, 2019) assessed the CHIRPS product using rainfall station data from the Central and north Patagonian Andes of Argentina, and found that it performs better in representing precipitation under warm conditions compared to cold conditions. However, as the available precipitation records were restricted to locations below 2500 masl, the assessment of high-elevation patterns associated with snow storms remains preliminary. Schumacher et al. (2020) used the WRF dynamic model with the ERA-Interim reanalysis to obtain 9 and 3 km nested precipitation grids and thus reproduce the variability of precipitation over the Central Andes of Argentina and Chile. To evaluate the simulations they used 62 rain gauges, which were mostly located below 3000 meters in elevation. Zambrano-Bigiarini et al. (2017) evaluated seven satellite precipitation products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1 and PGFv3) across various hydroclimates of Chile, relying heavily on sparse in-situ precipitation records as validation checks. In this Andean region, the lack of a well distributed station network precludes not only a careful assessment of the amounts of precipitation simulated by the climate models and products in different sectors, but also if these amounts are somehow coherent from a hydrological perspective. This represents a significant limitation that restricts the scientific understanding of the principal processes modulating the main regional hydroclimatic patterns and could also affect the local capacities to manage the scarce water resources in this region. This situation is particularly worrisome considering the climate projections that indicate, with high confidence, that this particular Andean sector will experience rising temperatures and sustained declines in precipitation throughout the current century (IPCC, 2021; Rivera et al., 2020). In this work we propose a new methodology that consists of evaluating the quality of different gridded precipitation products using streamflow measurements, remote sensing observations of snow cover, and an a priori knowledge about the hydroclimatic regime of this particular portion of the Andes. The approach avoids the use of hydrological models for testing the most suitable precipitation datasets, as these models may compensate for structural deficiencies in the precipitation products during the model parameter calibration process (Beven, 2016). The comparison with in-situ precipitation measurements are not used either because of the lack of a well distributed network of long, reliable precipitation records (Hobouchian et al., 2017; Baez-Villanueva et al., 2021; Mourre et al. 2016; Zambrano-Bigiarini et al., 2017). Our study focuses on the five most important river basins of the province of Mendoza, Argentina (Figure 1). These basins, which can be considered representative of the central Andes of Chile and Argentina, were selected because they contain the necessary set of streamflow measurements (in terms of quality and temporal extension) required for our tests (Masiokas et al., 2019). Although the methodology described below uses the CHIRPS-v2 (Funk et al. 2014, 2015 a, b), GPCC-v2022 (Schneider et al. 2022), MSWEP-v2.8 (Beck et al. 2019) and TerraClimate (Abatzoglou et al. 2018) precipitation products, the rationale behind our approach could be extended to other mountain watersheds that share similar characteristics and conditions as those described in the study area. 2. DATA AND METHODS 2.1 Study region The five basins under study (Mendoza, Tunuyán, Diamante, Atuel and Grande) are located in the western sector of the Mendoza province, Argentina (Figure 1). Due to the semi-arid conditions of this region, only 4% of the surface of this province (which totals ca. 150,000 km²) concentrates the vast majority of the human population, the industrial activities and the agricultural lands, which are almost exclusively irrigation-dependent. The total provincial population is the Andes, most human activities depend almost exclusively on the water generated in the Cordillera (Garreaud et al., 2017; Masiokas et al., 2020; Rojas et al., 2023). The Andes between 30 and 37°S reach mean maximum altitudes of region shows two well-differentiated seasons (Garreaud 2009). Winter extends roughly from April to September and is characterized by low temperatures and the arrival of frontal systems from the west that bring precipitation mostly in the form of snow (Viale and Nuñez, 2010; Garreaud, 2009; Masiokas et al., 2020). Mean maximum snow water equivalent values recorded at the few snow monitoring stations in this area (Figure 1) range between ~280 (Toscas) and year with a strong year-to-year variability and a clear west-east and south-north decreasing gradient (Viale et al., 2019; Garreaud 2009; Garreaud et al., 2017). The warm season - from October to March - is substantially drier and concentrates most of the snow melting accumulated during the colder months. The seasonal variations in temperature and precipitation determine a relatively simple unimodal pattern of the river discharges (Figure 1 - Masiokas et al., 2019). Glaciers in the study area are predominantly concentrated in the northern basins where they play an important hydrological role, contributing to river discharges throughout the year but becoming particularly significant in years with reduced winter snow accumulation (Ayala et al., 2020; Caro et al., 2024; Crespo et al., 2020a,b; McCarthy et al., 2022; Toum et al., 2025). Figure 1 . ( Left ) Map showing the basins under study. From north to south the catchments are Mendoza, Tunuyán, Diamante, Atuel and Grande, representing almost the entire Andean surface of the Argentinean Central Andes. Also shown are the gauging stations (yellow diamonds), the glaciers, and the snow stations (gray diamonds) recording snow water equivalent data in these basins. ( Right ). Pardé coefficients (Pardé, 1955) for the five rivers under study. The bar is the coefficient plus/minus one standard deviation, whereas the discharge value is the mean total annual specific discharge over the 1981/82-2019/20 period. 2.2 Streamflow records Daily streamflow records for the five watersheds under investigation were obtained from the National Water Information System of Argentina (https://snih.hidricosargentina.gob.ar/ - last access 2024-01-23). The series were first plotted and inspected visually to identify and remove possible outliers (Lauro et al., 2019 and Rivera et al., 2021 have previously evaluated these data, ensuring their good quality). The daily time series were subsequently aggregated at a monthly time scale allowing a maximum of five missing days per month. Then the monthly data were aggregated into mean summer (Nov-Feb) and annual (Jul-Jun) series without allowing any missing value (Table 1). All streamflow time series were divided by their corresponding watershed area to express their values as specific runoff and facilitate the comparison with the precipitation series. Table 1 . Basic details of the gauging stations used in this study. Mendoza Guido -32.9/-69.2 1408 1956-2020 93.8% - 100% Tunuyán Valle de Uco -33.8/-69.3 1199 1954-2020 98.5% - 100% Diamante La Jaula -34.7/-69.3 1457 1971-2020 98.0% - 100% Atuel La Angostura -35.1/-68.9 1302 1932-2020 91.0% - 96.6% Grande La Gotera -35.9/-69.9 1454 1973-2020 81.2% - 87.5% 2.3 Gridded precipitation products We used the CHIRPS-v2 (Funk et al 2014, 2015a,b), GPCC-v2022 (Schneider et al. 2022), MSWEP-v2.8 (Beck et al 2019) and TerraClimate (Abatzoglou et al., 2018) to test our approach. These products are available at various spatial resolutions (from 0.04° to 0.25°), were generated from different methodologies, and have already been used in the study area and elsewhere with different specific objectives (Condom et al. 2012; Espinoza et al. 2019; Rivera et al. 2018; Zambrano-Bigiarini et al. 2017 - Table 2). Briefly, CHIRPS-v2 is constructed using near-infrared satellite information from two NOAA sources, global precipitation climatologies, TRMM-3B42 estimates, CFSv2 model outputs, and in-situ precipitation observations. GPCC-v2022 uses only global in-situ precipitation measurements with a wind-undercatch correction factor. MSWEP-v2.8 is a global product that blends data from in-situ measurements, satellite information, and ERA-Interim reanalysis products. Finally, TerraClimate has global coverage and is built combining the CRU gridded dataset (Harris et al. 2020) and the Japanese reanalysis product JRA-55 (Kobayashi et al. 2015). Table 2 . Gridded precipitation products used in this work. 1 CHIRPS-v2 0.04° x 0.04° Daily 1981 - 2020 Blended Funk et al. (2014, 2015a,b) 2 GPCC-v2022 0.25° x 0.25° Monthly 1891 - 2020 Global gauge Schneider et al. (2022) 3 MSWEP-v2.8 0.1° x 0.1° Daily 1979 - 2020 Blended Beck et al. (2019) 4 TerraClimate 0.04° x 0.04° Monthly 1958 - 2020 Blended Abatzoglou et al. (2018) The common period 1981-2020 was used to facilitate the comparison between the four precipitation products (Table 2 and 1). Annual (cold season) total precipitation for each grid cell was computed aggregating their daily values from April to March (April-September). The spatial average was also calculated for each basin (Fig. 1). These calculations were performed using the spatial data library terra (Hijmans, 2023) and the tidyverse ecosystem (Wickham et al., 2019) freely available in the R programming language repository (R Core Team, 2023). 2.4 Snow data Updated series (1951-2022) of maximum annual snow water equivalent (hereafter maxSWE) from three stations -Toscas, Laguna del Diamante and Valle Hermoso- were used as indicators of maximum winter snow accumulation in the study area (Figure 1). These maxSWE series were initially developed by Masiokas et al. (2006, 2010), who demonstrated a strong positive association with mean summer and annual river discharges. We also used the daily L3 Global 500m cloud-free snow cover data set MOD10A1F product from the MODIS-Terra platform (Hall and Riggs, 2020). This product was selected because it maximizes the use of cloud free days in a region with prevalence of clear sky conditions (Cornwell et al., 2016). 2.5 Ancillary data Morphological and spatial characteristics of clean-ice glaciers were obtained from the National Glacier Inventory database (Zalazar et al. al., 2020; Table 3). The glacier volume for each basin and for the year 2020 was calculated using the global database developed by Millan et al. (2022). Table 3. Catchment area, englazed surfaces (Zalazar et al., 2020), glacier volume (Millan et al., 2022), and glacial water depth equivalent (glacier volume over the basin area). Mendoza 7137 232.21 (3.25%) 12.94 1813 Tunuyán 2447 172.54 (7.05%) 11.07 4524 Diamante 2798 30.89 (1.10%) 2.17 775 Atuel 3827 63.13 (1.65%) 3.72 972 Grande 5026 28.47 (0.57%) 1.49 296 The digital elevation model SRTM DEM v4 with previously filled gaps (Jarvis et al. 2008) combined with the R terra and whitebox packages (Hijmans et al., 2023; Wu and Brown, 2024) were used for watershed delimitation and to compute useful spatial information derived from the gridded data. 2.6 Process-based tests The rationale behind the methodology proposed in this article is based on the application of statistical tests to evaluate the degree to which a given precipitation product satisfies (or not) a series of well-known hydro-climatological processes that operate in the study region. These processes are: 1. Due to the dominant atmospheric circulation patterns and the topographical characteristics of the region, most of the annual precipitation in the upper watersheds should fall as snow during the winter months (April-September; Garreaud et al., 2017; Saavedra et al., 2017; Toum et al., 2025); 2. A strong and positive association between winter total precipitation (snow) and summer river discharges should be expected (Masiokas et al., 2006, 2010); 3. Due to the multiple damping effects occurring at the watershed scale, the interannual variability of total precipitation should be greater than the interannual variability of annual river discharges (Alvarez-Garretón et al. 2021; Dingman 2014); and 4. Due to the principle of mass conservation, the runoff coefficient should be statistically lower than unity (Dingman, 2014). Five nonparametric tests are used to evaluate how closely each precipitation product adheres to these conditions. The first three tests, classified here as “strict”, imply more stringent evaluation criteria and were used to accept (or reject) the precipitation products if they satisfied (or not) the hydro-climatic processes mentioned above. The last two tests, classified as “weak”, were used to identify the precipitation products that adhere partially to the processes or conditions mentioned above, but still would require some form of correction or adjustment to fully represent the precipitation variability in their corresponding watershed. 2.6.1 Binomial test on the ratio between winter to annual total precipitation The first test (hereafter referred to as “ winter vs. annual precipitation test ”) was selected to verify if the ratio between winter precipitation (Apr-Sept) and annual total precipitation (Apr-Mar) is significantly greater than a threshold value of 0.50 (i.e. if the winter accumulation accounts for, at least, 50% of the annual total precipitation in the basin). The probability of obtaining an “n” number of observed years with precipitation ratios below the threshold is calculated using a binomial distribution with a 0.05 probability of success. This test is a metric that represents the ability of the gridded precipitation products to reproduce the dominant precipitation seasonality in this region, which concentrates most of the accumulation during the cold season months (e.g. Garreaud et al., 2017; Masiokas et al., 2006; Viale et al., 2019). 2.6.2 Spearman rank correlation coefficient test between normalized total winter precipitation and summer river discharge This second test (hereafter “ winter precipitation vs. summer streamflow” ), with strong rejection criteria, evaluates the degree of association between the normalized (the time series is divided by its own 1981-2019 average value - Masiokas et al., 2006) total winter precipitation (April-Sept) and the normalized summer (Nov-Feb) river discharge. It is assumed that total winter precipitation falls mostly as snow (Garreaud et al., 2017) and hence is a reasonable proxy to the maximum snow water equivalent during winter. In two complementary works, Masiokas et al. (2006) and Masiokas et al. (2010) demonstrated the high degree of linear association (r = 0.945, alpha = 0.05) between the maximum winter snow water equivalent (data from snow courses and snow pillows) and the summer river discharge in the Central Andes of Argentina and Chile. In this work we used the non-parametric Spearman rank correlation coefficient (Walpole et al., 2016), whereas only values greater than 0.6 (winter precipitation - summer river discharge) were considered acceptable (at 0.05 significance level). Correlation values above the proposed threshold have been labeled as “strong” (0.6; 0.8) or “very strong” (0.8; 1.0) in other hydrometeorological studies (Gaál et al., 2015; Yan et al., 2019). To evaluate our assumption, we first calculate the Spearman’s rank correlation coefficient between the updated maxSWE series (Toscas, Laguna del Diamante and Valle Hermoso) from Masiokas et al. (2006) and the total winter precipitation time series (GPCC-v2022, CHIRPS-v2, TerraClimate and MSWEP-v2.8) in each basin (Mendoza, Tunuyán, Diamante, Atuel y Grande) during the 1981-2019 (39 years) period. All precipitation time series showed a significant and strong (rho > 0.6) Spearman correlation coefficient with the maxSWE series, supporting our hypothesis. As an example, a minimum value of 0.65 was reached between TerraClimate total winter precipitation time series for the Mendoza basin and Laguna del Diamante maxSWE series. Then, the proposed test was applied in all combinations between precipitation time series (4 per basin) and the summer river discharge at the closure point of each catchment (Fig. 1). 2.6.3 Flinger-Killen homogeneity of variances test between normalized total annual precipitation and annual river discharge This strong rejection criteria test (hereafter “ annual precipitation vs. annual discharge” ) evaluates homogeneity in variances between normalized total annual precipitation (Apr-Mar) and normalized annual (Jul-Jun) river discharge using the non-parametric Flinger-Killen test (Conover et al., 1981). We normalized both time series by dividing the annual values by their mean historical value (1981–2019) to mitigate variance magnitude issues caused by potential precipitation overestimation or underestimation errors in the original gridded products. The proposed test considers that total precipitation time series variance should be greater or equal than annual discharge variance (var[precip] >= var[discharge]). It is assumed that due to the catchment buffer (or damping) effect on total annual precipitation, its variability should be greater or equal than that of annual river discharge (Alvarez-Garretón et al. 2021; Dingman 2014). This test is considered to have a stringent rejection criterion, as it evaluates the temporal coherence of total precipitation variance from a hydrological perspective. It is important to note that the test is applied to headwater catchments, where groundwater imports from other basins are assumed to be negligible. 2.6.4 Binomial test on the annual runoff coefficient The fourth test focuses on the runoff coefficient (C), defined as the ratio between the annual river specific discharge and the annual total precipitation (C = Q / P) which due to the principle of mass conservation should be lower than 1 (Dingman, 2014). Then, the probability of obtaining an “n” number of years with C values greater than 1 is calculated using a binomial distribution with a 0.05 probability of success. Those precipitation products times series showing probabilities lower than 0.05 are labeled as precipitation deficient but are not rejected, since this issue could be adjusted using a multiplication factor or some geostatistical technique to correct the underestimation. 2.6.5 Spearman rank correlation coefficient test between winter total precipitation and the MODIS derived snow persistence index This fifth and last test (hereafter “ winter precip. vs. MODIS” ), with weak rejection criteria, evaluates the degree of association between the gridded winter total precipitation (April-Sept) and the MODIS derived snow persistence index defined in Wayand et al., (2018), by using the non-parametric Spearman rank correlation coefficient in each pixel. The underlying assumption is that, for snowmelt-dominated catchments like those from the Central Andes of Argentina and Chile, in snowy winters the snow accumulated in the ground should persist more during the summer months than in winters with shallow snowpacks (Garreaud et al., 2017; Musselman et al., 2017). First, the snow persistence index is calculated according to the method proposed by Wayand et al. (2018) but with dates adapted to the climatology of the studied zone (Saavedra et al., 2017). The cloud-free MODIS snow cover product MOD10A1F (Hall and Riggs, 2020) was used to calculate the number of days with snow during the summer (Oct-Mar) period for each hydrological year. Then, the number of days with snow was divided by the total number of days in the summer season. The result is a 500 m x 500 m spatial grid with the persistence snow index for each hydrological year for the period 2000/01-2019/20. As a second step, the winter total precipitation (Apr-Sept) for the gridded products CHIRPS, MSWEP and TerraClimate (2000/01-2019/20) was resampled to the MODIS grid size using the bilinear interpolation technique. This transformation allows the Spearman’s rank correlation to be calculated. It should be noted that the GPCC product was not considered here because of its coarse resolution (0.25°), and hence massive spatial-scale difference with MODIS. In the last step, the spatially distributed Spearman rank correlation coefficient between the snow persistence index and the winter total precipitation for each gridded product and basin combination during the hydrological years 2000/01-2019/20 (20 yrs) was calculated. Due to the strong expected relationship between winter total precipitation and the snow persistence index, basin median values greater than the statistically significant (alpha = .05 for 20 pair-values) 0.6 were considered as acceptable. It is also important to note that, due to the inherent scale differences between MODIS and the gridded precipitation products, as well as the potential influence of air temperature on the calculated snow persistence index (Garreaud et al., 2017; Musselman et al., 2017; Wayand et al., 2018), the rejection criterion for this test is considered weak. Table 4 summarizes the statistical tests applied as well as the rejection thresholds. Table 4 . Summary of the proposed methodology. The metric, variables under analysis, the rejection criteria and admissible thresholds are detailed. 1 Binomial probability Winter fraction of the annual total precipitation Strong Pwin / Pa >= 0.5 2 Spearman rank correlation Normalized total winter precipitation and summer river discharge Strong rho >= 0.6 3 Flinger-Killen homogeneity of variances Normalized total annual precipitation and annual river discharge Strong var(P) >= var(Q) 4 Binomial probability Annual runoff coefficient Weak C = Q / P = 0.6 3. RESULTS NOTE: It is recommended that your subsection titles of the results reflect the formulated research objectives 3.1 Binomial test on the ratio between winter to annual total precipitation Except for CHIRPS and GPCC in the Mendoza catchments, and CHIRPS in the Tunuyán basin, all precipitation-basin combinations successfully passed the seasonality test (Figure 2). Among the products, MSWEP and TerraClimate exhibited the highest overall seasonality, outperforming GPCC and CHIRPS. Notably, GPCC showed significant improvement in seasonality values for basins other than Mendoza, whereas CHIRPS consistently approached the lower limit of winter fraction (0.5), highlighting its weaker performance in capturing seasonal variation (Figure 2). Figure 2 . Box and whisker plots with jitters for the total winter precipitation fraction. The dotted horizontal line of ordinate 0.5 is the minimum acceptable seasonality, then the green (red) filled boxes represent the product-basin combination that (does not) pass the test. 3.2 Spearman rank correlation coefficient test between normalized total winter precipitation and summer river discharge All precipitation products successfully passed the second test across the entire domain. The weakest correlations were observed for GPCC and CHIRPS in the Mendoza River basin, while MSWEP showed the strongest performance in the Grande and Diamante River catchments (Figure 3). Notably, the correlation coefficient consistently increases toward the south, irrespective of the precipitation product analyzed. When comparing the performance of precipitation products, MSWEP emerges as the best overall, followed by TerraClimate (except in the Atuel basin) and CHIRPS (except in the Tunuyán and Atuel basins). These findings highlight a consistent limitation in capturing the relative magnitude of precipitation events in the northern basins, likely due to their higher elevations compared to the southern sectors. Additionally, the southern basins contain less glacier ice (Table 3), which may further influence the relationship between normalized summer river discharge and winter precipitation (Ayala et al., 2020; Crespo et al., 2020a, b; Toum et al., 2025). Figure 3 . Normalized summer streamflow discharges vs. total winter precipitation association in the five basins under analysis and for all precipitation products. The dotted line has a 1:1 slope, the values denote the Spearman rank correlation coefficient and the colors show that product-basin combination with values below (red) and above (green) the rejection threshold (rs= 0.6). 3.3 Flinger-Killen homogeneity of variances test between normalized total annual precipitation and annual river discharge The homogeneity of variance test was not passed by the CHIRPS product in the Mendoza, Tunuyán, and Grande basins, nor by GPCC in the Grande catchment (Figure 4). Notably, MSWEP and TerraClimate demonstrated the best performance, exhibiting greater variance across the entire domain. In contrast, GPCC and CHIRPS showed variance levels in the Diamante and Atuel basins that closely matched the normalized annual streamflow. Interestingly, the previous test revealed that despite strong correlation values in the Grande basin, CHIRPS and GPCC fail to capture the expected interannual precipitation variability. This underscores the importance of evaluating multiple, well-supported aspects of the hydrological cycle to gain a comprehensive understanding of precipitation product performance. Figure 4 . Box and whisker inside the violin plots for the normalized (ratio between the value and its historical average) of total annual precipitation and annual river specific discharge. The green (red) filled boxes represent the precipitation product-basin combination that (does not) pass the variability test when compared with annual streamflow (blue). 3.4 Binomial test on the annual runoff coefficient The test with soft (flexible) rejection criteria was applied exclusively to basin-product combinations that had passed the three prior tests under strict rejection criteria. Notably, all precipitation products passed the test for the Atuel basin, while MSWEP and TerraClimate succeeded in the Mendoza basin, and GPCC and MSWEP performed well in the Diamante catchment (Figure 5). Conversely, the three survival products failed in the Tunuyán basin. Additionally, CHIRPS and TerraClimate did not pass in the Diamante basin, and neither MSWEP nor TerraClimate met the criteria in the Grande basin (Figure 5). Overall, the results highlight MSWEP as the best-performing product, while CHIRPS consistently underperformed. Figure 5 . Box and whisker plots with jitters for the runoff coefficient (C) in the product-basin pairs that passed the three previous tests with strong rejection criteria. The black dashed horizontal line denotes the upper threshold used as a failure criteria for C. 3.5 Spearman rank correlation coefficient test between winter total precipitation and the MODIS derived snow persistence index The MSWEP gridded product consistently demonstrated correlation coefficients well above the 0.6 threshold across all basins. TerraClimate met this criterion in the Diamante, Atuel, and Grande basins, while CHIRPS only achieved it in the Atuel catchment (Figure 6-A). For all gridded precipitation products, the degree of association increased progressively toward the south. This pattern is further illustrated in the spatially distributed Spearman’s rank correlation coefficients for MSWEP (Figure 6-B). Non-significant values were observed in the glaciated areas of the Mendoza and Tunuyán basins, which represent the most glaciated catchments in the Argentinean Central Andes (Table 3, Fig. 1). In contrast, all other basins exhibited strong correlation coefficients, which were higher toward the west, coinciding with the location of the highest snowfall amounts (Garreaud et al., 2017; Cornwell et al., 2016; Cortés and Margulis 2017; Toum et al., 2025; Viale et al., 2019). Figure 6 . ( A ) Box and whisker plots inside the violins for the winter total precipitation and MODIS derived snow persistence index Spearman’s rank correlation coefficient. The dashed red line represents the statistically significant threshold, whereas precipitation gridded products with medians above it are considered acceptable. ( B ) Map with the spatially distributed correlation coefficient for the overall best precipitation gridded product MSWEP. Table 5 summarizes the results of all tests. When a precipitation product successfully passes the first three criteria, a failure in either the runoff coefficient or the winter precipitation vs. MODIS test may indicate issues related to the magnitude or spatial distribution of total precipitation. Table 5 . Tests summary for all basin-precip-products combinations. The X denotes that the test cannot be applied because of spatial-scale issues. The white box denotes that the precipitation product does not pass unless one of the first three tests (strong rejection criteria). CHIRPS GPCC MSWEP TerraClimate Mendoza winter vs. annual precipitation winter precip. vs. summer streamflow annual precip. vs. annual discharge runoff coefficient winter precip. vs. MODIS X Tunuyán winter vs. annual precipitation winter precip. vs. summer streamflow annual precip. vs. annual discharge runoff coefficient winter precip. vs. MODIS X Diamante winter vs. annual precipitation winter precip. vs. summer streamflow annual precip. vs. annual discharge runoff coefficient winter precip. vs. MODIS X Atuel winter vs. annual precipitation winter precip. vs. summer streamflow annual precip. vs. annual discharge runoff coefficient winter precip. vs. MODIS X Grande winter vs. annual precipitation winter precip. vs. summer streamflow annual precip. vs. annual discharge runoff coefficient winter precip. vs. MODIS X 4. DISCUSSION NOTE: The discussion should include a critical contextualisation of the main findings with the wider, international literature. If possible, it should also clearly state the wider implications of relevance for a wide, international readership. The binomial test on the ratio of winter to annual total precipitation (Test #1) assesses each product’s ability to capture the regional hydroclimatic seasonality, and is therefore considered to have strong discriminatory power. Most product–basin combinations passed this test, with the exceptions of CHIRPS in the Mendoza and Tunuyán basins, and GPCC in the Mendoza basin (Figure 2). These deficiencies may partially explain the weaker monotonic association with normalized summer streamflow observed in Fig. 3. For GPCC, its relatively coarse spatial resolution (Table 2) could be an additional limiting factor. CHIRPS, meanwhile, lies just above the lower threshold of the test, suggesting it does not fully represent the seasonal precipitation pattern of the Central Andes. It is well established that climate change has an amplified warming effect in mountain regions (Müller and Lovino, 2023; Pepin et al., 2015; Rivera et al., 2020), with notable consequences for the phase partitioning of precipitation (Jennings et al., 2018; Ombadi et al., 2023). Our test remains robust to such phase shifts, as it focuses on the ability of gridded products to reproduce the expected seasonal distribution of precipitation. However, climate change projections for the Central Andes of Argentina and Chile indicate a decline in total winter precipitation (Müller et al., 2023; Rivera et al., 2020), so caution is warranted when interpreting the winter-to-annual precipitation ratio in future scenarios. While we do not claim universal applicability for this test, the underlying concept of assessing seasonality may be relevant in other regions with similarly well-defined precipitation regimes. In the Spearman rank correlation coefficient test between normalized total winter precipitation and summer river discharge (Test #2), we assume a strong monotonic relationship between total winter precipitation (April-September) and summer river discharge. In this region, most of the annual total precipitation occurs during the austral winter (Garreaud et al., 2017), with snowfall being the dominant phase of precipitation (Cortés and Margulis, 2017; Saavedra et al., 2017). Seasonal variations in temperature and precipitation result in a relatively simple unimodal discharge pattern for these rivers, where substantial winter snow accumulation leads to higher river streamflow in summer, while minimal snow accumulation results in lower streamflow. This relationship has been studied at the regional scale by Masiokas et al. (2006, 2010) and is used by local stakeholders for water supply forecasting (pers. comm.). In this study, we assess the validity of our assumption by calculating the Spearman rank correlation coefficient between the updated maxSWE series from Masiokas et al. (2006) and the winter precipitation time series (GPCC-v2022, CHIRPS-v2, TerraClimate, and MSWEP-v2.8) for each basin under analysis during the 1981-2019 period (39 years). Our results support the hypothesis. The Fligner-Killeen test for homogeneity of variances, applied to normalized total annual precipitation and annual streamflow, also exhibits strong discriminatory capacity. It leverages the buffering effect of the catchment over long timescales (>30 years) within the normalized precipitation–discharge binomial framework (Alvarez-Garretón et al., 2021; Dingman, 2014). The test assumes negligible influence from groundwater inflows and human regulation, as these factors could distort the annual precipitation–streamflow relationship. We emphasize that this test should be applied over multidecadal periods (≥30 years) to minimize the influence of drought propagation within the hydrological cycle (Van Lanen et al., 2013; Van Loon, 2015). This is particularly relevant in the context of the Central Andes megadrought (2010–2020), during which streamflow anomalies exceeded precipitation deficits (Garreaud et al., 2017). Similar decoupling of precipitation–runoff relationships during drought periods has been reported in catchments with diverse hydroclimatic conditions worldwide (Saft et al., 2018; Tian et al., 2018; Espinoza et al., 2019). Despite been applied in the same hydroclimatic region (Masiokas et al., 2019), the study area encompass basins with little glacier melt influence on the total annual runoff (or more snowmelt dominated) like those of the Grande and Atuel, and others whereas glaciers could have more influence, with greater impact during years with snowfall deficits, like the Mendoza and Tunuyán (Table 3 - Ayala et al., 2020; Crespo et al., 2020a; Toum et al., 2025). This suggests that, in principle, this test could be applied in Mediterranean-like basins whose annual runoff is dominated by snowmelt (Fayad et al., 2016). Even though we cannot not claim the test to be universally valid, it has a strong process-based foundation and thus we speculate it could have application beyond the snow-dominated catchments of the Andes or other Mediterranean-like mountain basins. The binomial test on the annual runoff coefficient applies the mass conservation principle, a criterion that, like variability, could be used in other regions of the world. In general, MSWEP was the product with the fewest deficiencies in precipitation values, despite having the lowest spatial resolution (~10 km versus ~4 km for TerraClimate and CHIRPS - see Table 2). By far, the basin with the highest values for this coefficient was the Grande River, suggesting a systematic deficiency in the two best-performing products (MSWEP and TerraClimate) when quantifying precipitation amounts in the southern part of the region. The combined results from the three previous tests provide strong evidence of the quality of gridded precipitation products for hydrological studies at the catchment scale. However, those products that also pass this test may be preferred. The spatial non-parametric correlation values between total winter precipitation and the MODIS-derived snow persistence index provide a metric for the expected spatial pattern of winter total precipitation. Since snowfall is the main hydrological driver in these basins (González-Reyes et al., 2017; Masiokas et al., 2006, 2010), a good product is expected to accurately represent the decreasing west-east and south-north precipitation gradients (Cortés and Margulis, 2017; Garreaud et al., 2017; Viale et al., 2019). In this context, the calculated Spearman rank correlation coefficient improves towards the south and does not appear to have a direct relationship with the precipitation deficit quantified through the runoff coefficient (Fig. 5). For instance, the basin with the worst performance in the binomial test on the annual runoff coefficient (Grande River) shows the best spatial Spearman’s rank correlation coefficient values (Fig. 6). Significant advancements in remote sensing observations are driving research in hydrology and various other fields (Kalura et al., 2024), suggesting that tests beyond the one presented here could be used to constrain scientific hypotheses (e.g., Kalura et al., 2024; Kirchner et al., 2020). However, our Spearman rank correlation coefficient test, which evaluates the relationship between total winter precipitation and the MODIS-derived snow persistence index, appears to be particularly useful for mountainous Mediterranean-like regions. The hydrograph integrates a wide range of physical and biological processes, serving as a catchment-scale expression of these dynamics (Dingman, 2014; Kirchner et al., 2020; Krogh et al., 2022). This integrative characteristic contrasts sharply with the limited representativeness of snow precipitation measurements, which is particularly problematic in mountainous regions such as the Andes (Kochendorfer et al., 2017). In these settings, complex topography induces high spatial variability in precipitation, while in-situ measurements remain sparse (Viale et al., 2019; Viviroli et al., 2011; Whiteman, 2000). Moreover, streamflow records often represent the longest and most continuous hydrometeorological time series available, especially in the Andes and other remote mountain regions (Han et al., 2024; Masiokas et al., 2019), granting them significant scientific value (Araneo and Villalba, 2015; Lauro et al., 2019; Rivera et al., 2021). We argue that, in such contexts, the combination of streamflow records’ quality, length, and strong correlation with total winter precipitation should be prioritized over pixel-by-pixel comparisons between gridded precipitation products and point-based observations. This is particularly relevant for regions with Mediterranean-like climates (e.g., Central Andes of Argentina and Chile; Fayad et al., 2016), where spatial variability in precipitation is high and ground observations are scarce. Both theoretical and empirical evidence presented here suggest that pixel-wise comparisons (e.g., Hobouchian et al., 2017; Rivera et al., 2018; Zambrano-Bigiarini et al., 2017) offer limited insight into the performance of gridded precipitation products. In fact, in data-scarce regions like the Central Andes, most precipitation gauges are located at relatively low elevations (h < 3000 m a.s.l.), underrepresenting both the spatial distribution and average precipitation at the basin scale. By contrast, the proposed method, which exhibits strong discriminatory power, provides conclusive and catchment-integrated assessments of gridded precipitation product quality. Our proposed test results are promising, considering the long-term and integrative nature of streamflow records in the Andes (Masiokas et al., 2019) and worldwide (Han et al., 2024). They also suggest that methods relating regional hydroclimatic knowledge with well-established hydrological processes could be useful for evaluating gridded precipitation products globally. This approach could also help address one of the twenty-three unsolved problems in hydrology (UPH) related to modeling methods: how can we disentangle and reduce model structural/parameter/input uncertainty in hydrological prediction? (Blöschl et al., 2019). This can be achieved by selecting the best gridded precipitation products (input modeling data) through a methodology that is entirely independent of hydrological modeling efforts. In contrast, most published literature evaluates precipitation products against precipitation gauges, streamflow simulations from hydrological models, or both (e.g., Baez-Villanueva et al., 2018; Hobouchian et al., 2017; Schumacher et al., 2020; Zubieta et al., 2015, 2017; Zulkafi et al., 2014). To our knowledge, this is the first set of tests for evaluating gridded precipitation products that utilizes existing long-term, globally available streamflow records, combined with a well-established set of process-based hydrological relationships. Although the primary goal of this work is to present a novel process-based methodology for evaluating gridded precipitation products in the central-western Argentina, rather than to analyze each product in detail, it is important to note that both MSWEP and TerraClimate share the common feature of relying on reanalysis products (ERA-Interim and JRA-55 respectively). Similarly, GPCC has significant gaps in the availability of reference meteorological stations on the eastern slope of the Andes, leading to interpolated precipitation records that may not accurately reflect the true climatology. In contrast, CHIRPS utilizes an average of about 8 anchor meteorological stations in the region to correct infrared estimates of cold cloud duration (CDD), which results in poor regional performance, particularly in the upper portions of the basins. Therefore, in regions with limited in-situ measurements, a good precipitation product should ideally be based on solid physical principles. This issue is especially relevant in the northern basins, which are situated at higher altitudes, where the algorithms used to infer precipitation from radar data may require recalibration. 5. CONCLUSIONS This paper presents a process-based methodology to evaluate gridded precipitation products in the Mediterranean-like climate region of the Central Andes in Argentina and Chile. To the best of our knowledge, this is the first time a set of tests that utilizes long-term, globally available streamflow records in combination with a well-established set of process-based hydrological relationships are used for evaluating gridded precipitation products. The key findings of this approach are as follows: (a) climatic processes constrain most of the precipitation to the winter months (Apr-Sep); (b) in this region, streamflow generation is primarily driven by summer snowmelt, and thus a strong monotonic relationship between winter total precipitation (snow) and summer river discharge should be expected; (c) the streamflow generation process ensures that, due to the multiple damping effects at the basin scale, the interannual variability of total precipitation (or the source of water) should be greater than the interannual variability of annual river discharge (or the water’s exit from the system); and (d) the principle of mass conservation dictates that the runoff coefficient should statistically be lower than 1. The methodology relies on: (1) the binomial test on the ratio between winter to annual total precipitation; (2) the Spearman rank correlation coefficient test between normalized total winter precipitation and summer river discharge; (3) the Flinger-Killen homogeneity of variances test between normalized total annual precipitation and annual river discharge; (4) the binomial test on the annual runoff coefficient; and (5) the Spearman rank correlation coefficient test between winter total precipitation and the MODIS derived snow persistence index. We applied the method to the gridded-products CHIRPS-v2, MSWEP-v2.8, GPCC-v2022 and TerraClimate in the Argentinean basins of the Mendoza, Tunuyán, Diamante, Atuel and Grande rivers. The main conclusions of this study are summarized as follows: 1. The proposed set of tests can be effectively applied in Mediterranean-like mountain regions using only streamflow records. This approach enhances the value of long-term, catchment-integrative streamflow datasets. 2. In regions with sparse in-situ precipitation observations, existing validation approaches—such as comparisons against precipitation gauges or streamflow simulations from hydrological models—are necessary but not sufficient. In such contexts, process-based and catchment-integrative methods, like the one presented here, may offer more robust insights. 3. A clear articulation between regional hydroclimatic understanding and well-established hydrological processes can support the evaluation of gridded precipitation products worldwide. This approach contributes to addressing one of the 23 Unsolved Problems in Hydrology (UPH): how can we disentangle and reduce model structural, parameter, and input uncertainty in hydrological prediction? 4. Given the typically long-term availability of streamflow records, the proposed tests are applicable even under documented conditions of climatic change. 5. Among the gridded products evaluated, MSWEP and TerraClimate exhibited the best overall performance, suggesting they may be suitable for hydroclimatic analyses across the Central Andes of Argentina and Chile. Although the methodology proposed in this study has a strong regional focus—three of the five tests are specifically tailored to the hydroclimatic characteristics of the Central Andes—it also includes two tests based on general hydrological principles: the Fligner-Killeen homogeneity of variances test applied to normalized total annual precipitation and streamflow, and the binomial test of the annual runoff coefficient. These two tests may be applicable to other regions worldwide with appropriate hydrometeorological records. We further speculate that additional tests, grounded in region-specific process-based reasoning, could be developed to suit different hydroclimatic contexts. Moreover, the increasing availability of remote sensing data—such as soil moisture content—could facilitate the identification of events associated with high-intensity precipitation or snowmelt cycles. The exponential growth of remote observations is likely to enable the development of new tests for evaluating the plausibility of gridded precipitation products, which are critical inputs for hydrological analysis and modeling. ACKNOWLEDGEMENTS This work was partially funded by the project “Variablidad hidro-climática reciente y escenarios futuros para cuencas hídricas piloto en los Andes de Mendoza y San Juan” (PICT 2018-03211). ET acknowledges CONICET and its “Programa de Becas Doctorales y Posdoctorales” and the ECO-Sud project PA17A02 for support and funding. We thank Dr. Thomas Condom and Dr. Juan Pablo Sierra (Université Grenoble Alpes) for valuable comments and suggestions. We also appreciate the open source developer community behind the R language. 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Collection Hydrological Processes Keywords andes central-western argentina cold regions hydrology gridded precipitation products process-based rejection method Authors Affiliations Ezequiel Toum 0000-0002-4482-0559 [email protected] Instituto Argentino de Nivologia Glaciologia y Ciencias Ambientales View all articles by this author Juan A. Rivera Instituto Argentino de Nivologia Glaciologia y Ciencias Ambientales View all articles by this author Jhan-Carlo Espinoza Universite Grenoble Alpes Grenoble IAE View all articles by this author M. H. Masiokas Instituto Argentino de Nivologia Glaciologia y Ciencias Ambientales View all articles by this author Metrics & Citations Metrics Article Usage 360 views 198 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ezequiel Toum, Juan A. Rivera, Jhan-Carlo Espinoza, et al. A new process-based approach for evaluating gridded precipitation products in mountain watersheds: Test cases from the central Andes of Argentina. 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