dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, we further explore the long-term variability using the 10-year most likely location and intensity of El Niño and La Niña events. Continuous-wavelet analyses are used to estimate the maximum power spectrum over the full length of observational and simulated records, while accounting for temporal changes  87. Using continuous-wavelet analysis enables us to account for nonstationary significant patches of variability, which might not be significant over the full-length of the records, and would not be identified using fast Fourier transform. Significance of variability patches is tested at p = 0. 05, based on 1000 Monte-Carlo simulations of the red noise-background spectrum. Testing robustness in climate models, identifying large-scale patterns, and implications for future scenarios. We first examine whether historical and piControl runs, from CMIP5 and CMIP6 models, are able to reproduce a realistic range of locations and intensities for both El Niño and La Niña events, by comparing the simulated PDF to multiple observational datasets.
Statistical significance is then assessed by performing a two-sided Fisher's F-test at p=0. 05 between every 100-yr segment through the course of climate simulations, as compared with the period with significant decadal variance in the four longer-term observational SST datasets, from which the rate of success is quantified. Third, we compare the observed large-scale patterns associated with long-term variability in the location and intensity of El Niño and La Niña events to historical simulations. This consists of examining the differences in the patterns of pan-Pacific SSTa, wind stress, and thermocline depth at the Equator (5°S-5°N), which are computed using linear regression during composite El Niño and La Niña years, separately. Statistical significance of the regression patterns is calculated using 1000 permutations.
4 5 6. Finally, using an extended set of climate models, including three highest-and lowest-performing models, we examine future trajectories for ENSO diversity (i. e. , location and intensity) using RCP8. 5 and SSP5-8. 5 emission scenarios, using the same procedure as for the observational datasets. Specifically, we analyse how decadal variations can influence detection of future changes, and how the results differ in two contrasted simulations of the same models that only vary by their initial conditions, and depending on model performance. Once again, highestperforming models include models that represent similar mean and ranges of ENSO locations and intensity, as well as similar decadal variance and climatic background conditions, to observation during the historical period. Meanwhile, lowest performing models are models that present a tendency to favour either EPor CP events, as well as diverse biases in decadal variance.
Acknowledgements This work is part of the  I-SITE Bourgogne Franche-Comté Junior Fellowship IMVULA (Grant N° :  AAP2-JF-06 ), and the  Alliance Programme 2020  (Grant N°:  608081922 ), cofunded by the  British Council and Campus-France. AC was supported by the  NOAA Climate Program Office's Climate Variability and Predictability (CVP)  and Modelling, Analysis, Predictions and Projections (MAPP) programs. The authors would like to thank  Noel Keenlyside  and  Lander Crespo  ( Geophysical Institute, University of Bergen ) for their helpful discussions. The authors also thank three anonymous reviewers for their constructive comments and suggestions that have significantly improved the paper.
Data availability CMIP5 and CMIP6 data are publicly available at https://esgf-index1. ceda. ac. uk. Longterm observational SST datasets, i. e. , ERSST. v5, COBESST. v2, HadSST1, and OISST. v2, are available at https://climexp. knmi. nl. SODA. si3 and SODA. v2. 2. 4 are respectively available from https://psl. noaa. gov/data/gridded/data. 20thC_ReanV3. html and https:// iridl. ldeo. columbia. edu/SOURCES/. CARTON-GIESE/. SODA/. v2p2p4.
A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (Rain for Peru and Ecuador), at 0. 18 spatial resolution for the period 1981-2015 covering Peru and Ecuador. It is based on the application of 1) the random forest method to merge multisource precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and 2) observed and modeled streamflow data to first detect biases and second further adjust gridded precipitation by inversely applying the simulated results of the ecohydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gaugecorrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model.
For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Pacific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low, high, and peak flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.
SIGNIFICANCE STATEMENT: We developed a novel precipitation dataset RAIN4PE for Peru and Ecuador by merging multisource precipitation data (satellite, reanalysis, and ground-based precipitation) with terrain elevation using the random forest method. Furthermore, RAIN4PE was hydrologically corrected using streamflow data in watersheds with precipitation underestimation through reverse hydrology. The results of a comprehensive hydrological evaluation showed that RAIN4PE outperformed state-of-the-art precipitation datasets such as CHIRP, ERA5, CHIRPS, MSWEP, and PISCO in terms of daily and monthly streamflow simulations, including extremely low and high flows in almost all Peruvian and Ecuadorian catchments. This underlines the suitability of RAIN4PE for hydrometeorological applications in this region.
2020 ) was released, which has been shown to outperform previous reanalyses for precipitation estimation  (Beck et al. 2019a; Tall et al. 2019; Xu et al. 2019a; Fallah et al. 2020; Gleixner et al. 2020) , and has shown acceptable performance for hydrological modeling over North America  (Tarek et al. 2020) , the Amazon River basin  (Towner et al. 2019) , and at the global scale  (Alfieri et al. 2020). In recent years, global merged precipitation products that incorporate satellite and reanalysis information with gaugebased datasets such as CHIRPS  (Funk et al. 2015a ) and MSWEP  (Beck et al. 2017 (Beck et al. , 2019b) )  have been published and are available. Many studies worldwide have shown that these products have higher accuracy than precipitation estimates based on one source only (e.
2017 ) and in catchments draining into Titicaca Lake  (Satgé et al. 2019 (Satgé et al. , 2020)). To the best of our knowledge, there are no case studies in the literature on hydrological evaluation of CHIRPS and MSWEP in Peruvian and Ecuadorian watersheds, which is addressed in this study. At the regional scale, recently a high-resolution (0. 18) daily gridded precipitation dataset for Peru as part of PISCO datasets was developed (PISCO hereafter)  (Aybar et al. 2020). PISCO is based on the merging of satellite estimates (CHIRP) and ground-based observations. It is used by SENAMHI for operational purposes in Peru for droughts and floods monitoring at the national scale, and was applied for hydrological modeling of the Andean Vilcanota River catchment  (Fernandez-Palomino et al. 2020) , catchments draining into the Pacific Ocean (Asurza-Véliz and Lavado-Casimiro 2020), and Peruvian catchments  (Llauca et al.
2021). As the method used to generate PISCO mainly corrects the biases of CHIRP using in situ precipitation data, the higher accuracy of precipitation estimates is constrained to gauged regions such as the Pacific coast and the eastern and western slopes of the Andes of Peru  (Aybar et al. 2020; Llauca et al. 2021). Hence, the application of PISCO for Peruvian Amazon and transboundary river catchments is limited. This motivated us to generate a new rainfall dataset for hydrometeorological applications at the national scale of Peru and Ecuador, exploiting the lessons learned from precipitation estimates derived not only from gauges and satellites but also from the state-of-theart reanalysis ERA5. Indeed, ERA5 and CHIRP, which has long-term daily precipitation data available  (1981-present)  and hence appropriate for long-term hydrological applications, were used for the precipitation merging procedure in this study.
2017). To correct the underestimation of precipitation by gridded precipitation products, adjustment of precipitation data for regions covered by cloud forests has been proposed  (Strauch et al. 2017)  with reported increases of up to 50% of the precipitation values in the WFDEI dataset  (Weedon et al. 2014 ) required to improve streamflow simulation in the tropical montane watersheds. However, the cloud/fog water component is not represented in the aforementioned precipitation data sources. This lacuna, together with the dearth of precipitation gauges could explain some of the poor hydrologic model performances and problems with water budget closure reported in previous studies in páramo and/or montane catchments draining into the Amazon River  (Zulkafli et al. 2014; Zubieta et al. 2015; Manz et al.
2016; Zubieta et al. 2017; Strauch et al. 2017; Aybar et al. 2020). Thus, for reliable and accurate estimation of precipitation in regions such as the TMCF and páramos, it is important to consider the contribution of cloud/fog water to the terrestrial hydrological system. Correcting potential errors in gridded precipitation datasets for these areas requires application of other types of observations and estimates. Corrected estimates of precipitation using satellite soil moisture products have been derived in recent years  (Brocca et al. 2013; Román-Casc ón et al. 2017; Brocca et al. 2019). However, the utility of these products could be limited due to their low accuracy in regions with dense forests  (Brocca et al. 2020) , such as TMCF and rain forest areas. Streamflow observations, which are spatially integrative and could be another source of data supplementing information from sparse rain gauges, offer an additional method to infer precipitation patterns and evaluate precipitation datasets  (Le Moine et al.
This study is the first attempt to generate a precipitation dataset for Peru and Ecuador by merging different sources of precipitation and correcting precipitation estimates through reverse hydrology. Furthermore, we evaluate the applicability of the precipitation dataset generated in this study, uncorrected precipitation datasets used for merging procedure (CHIRP and ERA5), and current state-of-the-art local (PISCO) and global (CHIRPS and MSWEP) merged precipitation products for hydrological modeling of Peruvian and Ecuadorian river catchments. This will demonstrate the effectiveness of the new methods combined here, and will help illustrate the appropriateness of multiple precipitation datasets for the countrywide hydrometeorological applications both in Peru and Ecuador. The objectives of this study are 1) to generate a high-spatial-resolution and hydrologically adjusted precipitation dataset for Peru and Ecuador, and 2) to assess and compare the applicability of this precipitation data and the current state-of-the-art uncorrected and merged precipitation products for hydrological modeling.
a. Study area The study area covers Peru and Ecuador with elevation ranging from 0 to 6518 m MSL (Fig. 1 ). The new precipitation dataset [Rain for Peru and Ecuador (RAIN4PE)] is generated for the terrestrial land surface between 198S-28N and 828-678W. The study area has complex hydroclimatic conditions related to its variable climate zones and the Andes Cordillera, which acts as a topographic barrier between the cold and dry eastern Pacific and the warm and moist Amazon region. The Andes divides the study area into three natural drainage basins (Fig. 1 ): (i) the Pacific basin (watersheds located on the western side of the Andes that convey water to the Pacific Ocean); (ii) Amazon basin (watersheds located on the eastern side of the Andes that drain to Amazon River); and (iii) Titicaca Lake basin (catchments draining into Titicaca Lake).
1) GROUND-BASED PRECIPITATION DATA The precipitation data of a total of 804 precipitation gauges with record length greater than ten years for the 1981-2015 period were used for this study (Fig. 1 ), out of which 587 (217) gauges have daily (only monthly) precipitation data. The data were collected from different sources such as national hydrometeorological institutions and previous studies in the region. The data for Peru were obtained from the Peruvian ANA (Autoridad Nacional del Agua) and  Aybar et al. (2020) ; for Ecuador from  Morán-Tejeda et al. (2016) ,  Tamayo (2017) ,  Tobar and Wyseure (2018) ; for Brazil from  Xavier et al. (2016 Xavier et al. ( , 2017)) ; and for Colombia from IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales). We used 587 (804) precipitation gauges with daily (monthly) data for the merging of precipitation datasets at the daily (monthly) time step.
2) DISCHARGE DATA Discharge data of 72 streamflow stations (Fig. 1 ) with record lengths ranging from one to 33 years for 1983-2015 were obtained from different sources, such as the Peruvian ANA and SENAMHI for catchments draining into the Pacific Ocean and located in the Andes. For the Amazon lowland, data were obtained from the Critical Zone Observatory HYBAM (Hydrogéochimie du Bassin Amazonien, www. sohybam. org). This hydrological network has been operated by an international team from IRD (Institut de Recherche pour le Développement; France), SENAMHI (Peru), INAMHI (Instituto Nacional de Meteorología e Hidrología; Ecuador), and the Brazilian ANA (Agência Nacional de Águas; Brazil) since 2003  (Armijos et al. 2013; Santini et al. 2019). 3) GRIDDED PRECIPITATION DATA Table  1  presents the five precipitation datasets used in this study.
We used the non-gauge-corrected datasets (CHIRP and ERA5) for the merging procedure to generate RAIN4PE dataset. The satellite-based CHIRP precipitation dataset  (Funk et al. 2015a ) is obtained by considering infrared-based precipitation estimates and corresponding monthly precipitation climatology generated for  Funk et al. (2015b). We selected CHIRP since it has high spatial resolution and long-term (from 1981 onward) daily precipitation data, which is appropriate for long-term hydrometeorological applications. ERA5  (Hersbach et al. 2020)  is the latest climate reanalysis dataset produced by the European Centre for Medium Weather Forecasts (ECMWF). Compared with its predecessor ERA-Interim  (Dee et al. 2011 ) that became operational in 2006, ERA5 is based on the ECMWF's Integrated Forecasting System Cycle 41r2 which was operational in 2016.
ERA5 thus benefits from a decade worth of numerical weather prediction developments in model physics, core dynamics, and data assimilation relative to ERA-Interim. Moreover, ERA5 has a much higher temporal and spatial resolution than previous global reanalyses. The hourly ERA5 precipitation data were downloaded and aggregated to obtain daily time step records matching the local gauge observations (from 0700 to 0700 local time). To compare RAIN4PE against other gauge-corrected precipitation datasets besides the uncorrected ones (CHIRP and ERA5), we selected three merged products (CHIRPS, MSWEP, and PISCO) widely used in data evaluation and hydrometeorological applications in the region  (Wongchuig Correa et al. 2017; Paccini et al. 2018; Bhuiyan et al. 2019; FIG. 1. (left)  Study area and spatial distribution of precipitation gauges with record length greater than 10 years for the 1981-2015 period used for the merging procedure.
(right) Drainage systems, river networks, and streamflow stations used for hydrological model calibration based on the cascading calibration approach. Red polygons show the gauged catchments with water budget imbalance where gridded precipitation datasets are corrected using streamflow data through reverse hydrology. Nueva Loja station gauges the catchment "A", San Sebastian (B), Francisco De Orellana (C), Santiago (D), Borja (E), Shanao (F), Chazuta (G), Puerto Inca (H), and Lagarto (I). Boundaries of the páramo and tropical montane cloud forest (TMCF) ecosystems were obtained from  Helmer et al. (2019). Espinoza et al. 2019; Satgé et al. 2019 Satgé et al. , 2020;; Asurza-Véliz and Lavado-Casimiro 2020; Baez-Villanueva et al. 2020; da Motta Paca et al. 2020; Fernandez-Palomino et al. 2020; Llauca et al.
2021). CHIRPS  (Funk et al. 2015a ) and PISCO  (Aybar et al. 2020 ) are obtained by merging CHIRP and gauge estimates through deterministic and geostatistical interpolation methods. Finally, MSWEP is derived by optimally merging a range of gauge, satellite, and reanalysis precipitation estimates, where satellite and reanalysis datasets are merged using weights for each one based on the coefficient of determination between 3-day mean gauge-and grid-based precipitation time series  (Beck et al. 2017 (Beck et al. , 2019b)). The daily MSWEP precipitation data were provided for this study.
Methods The framework of this study involves three main steps (Fig. 2 ): (i) merging procedure through a machine learning technique at the daily and monthly scales; (ii) calibration of model parameters and hydrological adjustment through the reverse hydrology TABLE 1. List of gridded precipitation datasets used in this study. In uncorrected datasets, their temporal dynamics depend entirely on satellite (S) or reanalysis (R) data, while in gauge-corrected datasets, their temporal dynamics depend at least partly on gauge (G) data. In the spatial coverage column, "Global" means fully global coverage including oceans, while "Land" indicates that the coverage is limited to the terrestrial land surface. concept; and (iii) evaluation of all precipitation products through hydrological modeling.
2) RANDOM FOREST MODELING TO COMBINE DIFFERENT DATA SOURCES In this study, the RF method  (Breiman 2001 ) was applied to produce a gridded precipitation dataset by merging multiple precipitation sources (gauge, satellite, and reanalysis). RF has been used and proved recently to have similar or superior performance in the interpolation of environmental variables such as precipitation, temperature, and evapotranspiration compared to traditional spatial interpolation techniques, e. g. , regression kriging and inverse distance weighting  (Hengl et al. 2018; da Silva J únior et al. 2019; Sekulić et al. 2020). Last, RFbased methodologies  (Bhuiyan et al. 2019; Baez-Villanueva et al. 2020)  to merge precipitation products with groundbased measurements were developed and applied successfully in data-scarce and complex terrain regions such as the Peruvian and Colombian Andes  (Bhuiyan et al.
2019)  and Chilean territory  (Baez-Villanueva et al. 2020). RF is a multivariate and nonparametric machine learning algorithm, in which the prediction is generated as an ensemble estimate from a number of regression trees  (Breiman 2001)  as shown in Eq. (1): FIG. 2. Flowchart for (i) the generation of gridded precipitation dataset, (ii) hydrological model calibration and adjustment of precipitation datasets, and (iii) hydrological evaluation. Here d (m) indicates the daily (monthly) time step, BD (1), … ,(n) are buffer distances (distance from any point to all precipitation gauges), BCF is the bias correction factor, OFs are the objective functions for hydrological model calibration, and GOFs are the goodness of fit measures. BCF is optimized only over catchments with water budget imbalance.
Note that for hydrological evaluation (step iii), the model was rerun using the respective corrected precipitation data and optimum model parameters values with BCF set to 1. f x ( ) 5 1 N N b51 T b x ( ), (1) where f x ( ) is the final prediction, b is the individual bootstrap sample, N is the total number of trees, and T b is the individual regression tree. In RF, each tree is constructed from the random selection of covariates which ensures that trees are decorrelated with each other and a bootstrap sample of the observations  (Breiman 2001). The unsampled data, called out-of-bag, can be used to test the prediction accuracy and the importance of input variables, and thus no extra independent validation dataset is needed  (Breiman 2001). We implemented RF using the R package randomForest  (Liaw and Wiener 2002)  and the following RF parameters: 1) the number of trees (set at 1000); 2) the number of predictor variables randomly selected at each node (set at one-third of the number of variables, default value); 3) the minimum number of observation in a tree's terminal node (set at 5, default value); and 4) the out-of-bag portion to test the accuracy of the predictions (set at one-third of the total number of observations).
These parameter values were successfully used in other studies  (Baez-Villanueva et al. 2020; Fox et al. 2020; Sekulić et al. 2020 ). In the merging procedure (see Fig. 2 ), an RF model was trained using ground-based observations as the dependent variable and the selected covariates as predictor variables for each day and month in the 1981-2015 period. The trained RF models were then applied to covariates, yielding preliminary daily precipitation data (Pd0) and monthly precipitation data (Pm1). Finally, Pd0 was corrected to match Pm1. For that, the ratio of Pm1 over the monthly precipitation derived from Pd0 was computed on each grid cell for each month, and this ratio was then applied to multiply the Pd0 values on the grid for the month to generate the RF-based RAIN4PE dataset. This correction was because the interpolation of precipitation patterns at a monthly scale is more reliable and accurate than the daily interpolation  (Aybar et al.
b. Hydrological modeling and adjustment of precipitation datasets This section describes the approaches applied for hydrological model calibration and validation and hydrological adjustment of precipitation datasets using streamflow data through the reverse hydrology concept. The hydrological correction is applied only for nine catchments (Fig. 1 ) having a water budget imbalance due to underestimated streamflow in the simulations with uncorrected precipitation inputs, as reported in previous studies  (Zulkafli et al. 2014; Zubieta et al. 2015 Zubieta et al. , 2017;; Strauch et al. 2017). We applied the reverse hydrology using the Soil and Water Assessment Tool (SWAT;  Arnold et al. 1998 ) model in which both the bias correction factor (BCF) for precipitation fields and model parameters were calibrated jointly (Fig.
DATASETS The optimum BCFs obtained for each catchment with water budget imbalance (Fig. 1 ) in the calibration procedure were applied to the respective daily gridded precipitation data to obtain the hydrologically corrected daily precipitation dataset (Fig. 2 ). For that, a continuous BCF map at 0. 18 spatial resolution was produced where grid cells within the respective catchment retained the respective BCF, and for cells on the boundary, the area-weighted BCFs were estimated. It is noteworthy that applying the resulting BCF map to gridded precipitation data can result in spatial discontinuities of precipitation patterns at the border of the catchments. To reduce such discontinuities, we further applied a 5 3 5 mean filter to the BCF map. Finally, the corrected precipitation data were used as input to SWAT to run the model with the TABLE 3.