dataesr/scientific-paragraphs-categorization huggingface dataset:
1, 2] V X X X GW_DELAY Groundwater delay time (days) Q [1, 100] V X X X RCHRG_DP Deep aquifer percolation fraction Q [0, 1] V X X X GWQMN Threshold for return flow from shallow aquifer (mm) Q [500, 1000] V X X X ALPHA_BF Baseflow recession constant Q [0. 01, 1] V X X X CH_K2 Hydraulic conductivity of main channel (mm h 21 ) Q [0, 50] V X CHD Main channel depth (m) Q [20. 1, 0. 5] R X FP_W_F Ratio of floodplain width over bankfull width Q [1, 5] V X BCF Bias correction factor P, Q [0, 1] R X respective optimum parameters for the simulation period to compute the model performance measures for the hydrological evaluation of precipitation datasets.
2) HYDROLOGICAL EVALUATION We evaluated the accuracy of precipitation estimates through hydrological modeling for the three drainage systems in the study area. It is an adequate approach evaluating gauge-corrected precipitation datasets since streamflow observations are independent from ground precipitation observations that are TABLE 4. Mathematical formulation of the goodness of fit metrics and hydrological signatures. Here, O and S are observed and simulated flow (m 3 s 21 ), respectively; EP is exceedance probability; P, H, and L are the indices of the minimum flow of the peak flow, high flow, and low flow segments, respectively. In the optimization process for hydrological model calibration, lNSE was maximized, whereas FDC sign was minimized.
b. Hydrological correction of the gridded precipitation datasets The spatial variation of the obtained bias correction factors (BCFs) for six precipitation datasets is shown in Fig. 4. This differs from the method of  Strauch et al. (2017) , who applied a unique correction factor to WFDEI  (Weedon et al. 2014 ) dataset for all montane regions. The lower values of BCFs for ERA5 are related to significant precipitation overestimation along the Andes by ERA5 (Figs. 5  and 6 ). The results for the other datasets (Fig. 4 ) show that higher BCFs were the result for MSWEP (mean BCF 5 1. 66) and lower for RAIN4PE (mean BCF 5 1. 38). For a BCF of 1. 38, on average, 28% of total precipitation is the precipitation underpredicted in páramo and montane watersheds in the study area which falls in the range (0%-30%) of cloud/fog water contribution to total precipitation reported in previous studies of the region  (Gomez-Peralta et al.
2008; Cárdenas et al. 2017). Figure  4  also shows that significant benefits of precipitation correction made for RAIN4PE are obvious in a good representation of streamflow seasonality for all nine catchments. The correction of CHIRPS also works relatively well in most of the catchments in terms of seasonal streamflow prediction, although it fails over the southern Ecuadorian Amazon (at Santiago station). The hydrological correction of the other datasets (CHIRP, ERA5, MSWEP, and PISCO) performs well for southern catchments (from Borja to Lagarto station) but not in Ecuadorian catchments (from Nueva Loja to Santiago station) since the streamflow seasonality change is underestimated, indicating a serious drawback of these datasets.
c. Spatial patterns of precipitation In general, the spatial variability of the long-term average annual precipitation  portrayed by all precipitation datasets looks quite similar (Fig. 5 ), although PISCO shows distinct precipitation patterns and magnitudes in the rain forest regions. Figure  5  also shows the spatial patterns of the estimated precipitation underestimates for each precipitation dataset. As can be seen, these patterns look quite similar over the Peruvian Amazon for five datasets (CHIRP, CHIRPS, MSWEP, PISCO, and RAIN4PE) but vary over the northern Amazon basin in Ecuador. The substantial precipitation underestimation (ranging from 0 to 3369 mm, Fig. 5 ) found here suggests that precipitation correction was necessary to achieve the closure of the water budget and appropriate hydrological modeling of the páramo and montane watersheds.
In addition, a comparison of the unadjusted precipitation data with gauge observation was done (Fig. 6 ) to assess precipitation datasets' reliability or critical shortcomings. It shows that ERA5 overestimates precipitation significantly over the Andes. CHIRP, CHIRPS, MSWEP, and PISCO (CHIRP and CHIRPS) underestimate (overestimate) precipitation over the northern (arid southern) Pacific coastal areas. Furthermore, ERA5, CHIRP, CHIRPS, MSWEP, and PISCO have inconsistent temporal distribution of precipitation over the northern Amazon, which is confirmed by low values of correlation and determination coefficients that result from comparing these products with gauge observations at a monthly scale (Fig. 6 ) and SWAT-simulated seasonal streamflow using these datasets (Fig. 4 ). Therefore, these datasets are less suitable for characterizing spatiotemporal variability of precipitation over the Ecuadorian Amazon than RAIN4PE.
1) PERFORMANCE EVALUATION FOR DAILY STREAM-FLOW AND EXTREMES We investigated the spatial variability of hydrological model performance for streamflow simulation forced by six FIG. 6. Performance of the unadjusted precipitation datasets in comparison with gauge observations: MAP is the mean annual precipitation, ME is the mean error, r is the Pearson's correlation coefficient, and R 2 is the coefficient of determination. The comparison measures (ME, r, and R 2 ) were computed using monthly precipitation time series for 1981-2015. precipitation products in calibration (Fig. 7 , Table  S1 ) and validation (Fig. S2 , Table  S2 ) periods. These figures present the Kling-Gupta efficiency spatial distribution and show results in terms of seven criteria for all streamflow stations and catchments draining into the Titicaca Lake, the Pacific Ocean, and the Amazon River as boxplots.
Regarding two non-gauge-corrected datasets, CHIRP-SWAT has unsatisfactory performances for high-flow dynamics, and ERA5-SWAT significantly overestimates streamflow (Fig. S4 ). In the Pacific basin, CHIRPS-SWAT, MSWEP-SWAT, and even PISCO-SWAT have low KGE (#0. 5), high biases, and poor performance for high and peak flows for some stations. The outcome for MSWEP and PISCO aligns with the findings of previous studies  (Bhuiyan et al. 2019; Derin et al. 2019; Asurza-Véliz and Lavado-Casimiro 2020). CHIRP-SWAT has more skill than ERA5-SWAT, which shows a significant overestimation of streamflow; however, they both are outperformed by the gauge-corrected precipitation datasets. The overall good performance of RAIN4PE-SWAT (median KGE 5 0. 78) allowed us to conclude that RAIN4PE is the most suitable precipitation product for daily streamflow simulation (including all flow conditions and water budget closure) in the catchments draining into the Pacific Ocean.
a. Advantages of the merging methodology This study demonstrates a successful method for merging multiple precipitation sources (based on gauge, satellite, and reanalysis data) with surface elevation using the RF method to generate a spatially gridded precipitation dataset RAIN4PE. This is supported by the significant improvement of RAIN4PE for hydrological simulations compared to the non-gauge-corrected datasets (CHIRP and ERA5) used for the merging procedure. Furthermore, the superiority of RAIN4PE regarding the gauge-corrected datasets (CHIRPS, MSWEP, and PISCO) for hydrological simulations suggests that the methodology applied herein to generate RAIN4PE is much more robust than that of the other merged precipitation products. This means that the RF method is more effective in merging multiple precipitation data sources than deterministic and geostatistical interpolation methods  (Funk et al.
b. Hydrological correction of the gridded precipitation datasets The high BCF values (Fig. 4 ) obtained to correct gridded precipitation biases make evident that most of the datasets evaluated (CHIRP, CHIRPS, MSWEP, PISCO, and RAIN4PE) often have precipitation underestimation over the páramo and montane watersheds in the Amazon (Fig. 5 ). This underestimation, especially by gauge-corrected datasets, could be caused by the low number of precipitation gauges available (Fig. 1 ), which is further amplified by the fact that the gauges do not account for the important cloud/fog water input into the system  (Gomez-Peralta et al. 2008; Clark et al. 2014; Cárdenas et al. 2017). A substantial precipitation underestimation over the páramo and montane watersheds is critical since it might even lead to physically unrealistic runoff ratios above 1 in water budget estimates as reported in previous studies  (Zulkafli et al.
2014; Zubieta et al. 2015; Manz et al. 2016; Strauch et al. 2017; Builes-Jaramillo and Poveda 2018; Aybar et al. 2020 ). Furthermore, precipitation errors in the upstream catchments can negatively affect simulation results for downstream river catchments. For instance, the assignment of unrealistic model parameter values to counterbalance precipitation uncertainty can lead to misrepresentation of the basinwide water budget (see more details in appendix B). To overcome these deficiencies, we used streamflow data to adjust precipitation biases. Our results show that the hydrological correction of precipitation datasets was more efficient over the regions with the strongest rainfall seasonality such as the Peruvian catchments  (Espinoza Villar et al. 2009; Segura et al. 2019). This suggests that actual spatiotemporal precipitation fields over these regions are well depicted by the assessed datasets, whereas the correction efficiency over the Ecuadorian Amazon catchments, which experience precipitation throughout the year with high spatial regime variability  (Laraque et al.
In these catchments, other datasets such as gauge-based ORE HYBAM  (Guimberteau et al. 2012) , gauge-corrected WFDEI, reanalysis ERA-Interim, and satellite-based PERSIANN  (Hsu et al. 1997) , TMPA, CMORPH, and IMERG have also been reported to perform unsatisfactorily for streamflow simulation  (Zulkafli et al. 2014; Zubieta et al. 2015; Strauch et al. 2017; Zubieta et al. 2017; Towner et al. 2019). Overall, when comparing RAIN4PE to CHIRP, ERA5, MSWEP, PISCO, CHIRPS, and other datasets mentioned above, we can see that it shows satisfactory performance for monthly (Fig. 8 ) and seasonal (Fig. 4 ) streamflow simulations with SWAT over the Ecuadorian Amazon. However, its performance for daily simulation is still unsatisfactory (Fig. 7 ), which highlights that estimation of precipitation at a daily resolution over data-scarce regions such as the equatorial Amazon region is very challenging.
The exposed shortcomings of precipitation datasets suggest the urgent implementation and densification of precipitation and cloud/fog gauge networks over the Ecuadorian Amazon and Peruvian montane watersheds. These could help to improve the depiction of rainfall amounts and their spatiotemporal distribution and hence could be useful for improving streamflow simulations. It is important to keep in mind that the correction of the proposed precipitation product through the reverse hydrology concept was performed using the SWAT hydrological model, and therefore the performance of the RAIN4PE dataset may change if another hydrological model is used. Though, as SWAT is a widely used comprehensively verified model, we expect only minor deviation.
c. Implications for hydrological modeling The results of the hydrological evaluation clearly show the advantages and shortcomings of each evaluated precipitation dataset for streamflow simulation, including low, high, and peak flows. Moreover, we presented the comparison of SWAT-simulated seasonal streamflow using all evaluated datasets against observed seasonal streamflow for the three drainage systems (Titicaca Lake basin, Pacific basin, and Amazon basin) in Figs. S4-S6. These figures can assist practitioners in selecting the appropriate precipitation product for hydrological applications. In general, the hydrological evaluation highlighted RAIN4PE as the best precipitation dataset for hydrological modeling of the Peruvian and Ecuadorian watersheds. RAIN4PE is the only gridded precipitation product for Peru and Ecuador, which benefits from maximum available in situ observations, multiple precipitation sources, environmental variable (elevation data), and is supplemented by streamflow data to correct the precipitation underestimation over páramos and montane catchments.
The exploitation of all these variables using state-of-the-practice methods to generate RAIN4PE proved that RAIN4PE-SWAT was capable of closing the (hitherto) observed water budget imbalance over Peruvian and Ecuadorian catchments which, eventually, makes the RAIN4PE a good candidate for hydrological applications in the region. Despite this, we consider that RAIN4PE is still subject to uncertainties, especially in regions where precipitation was inferred from the observed streamflow data. For these regions, precipitation estimates should be viewed with some care due to uncertainties in streamflow data, inferred evapotranspiration, gridded precipitation data, and hydrological model structure. In this study, besides evaluating precipitation datasets for streamflow simulation, we show that uncertainties associated with precipitation estimates have implications in estimating hydrological model parameters (see appendix B) and water budget components (e.
It is also important to highlight that this study is the first applying SWAT at the country-level of Peru and performing a multiobjective calibration and validation using hydrograph goodness of fit and FDC signatures for large-domain modeling (1. 6 million km 2 ) in a region with complex hydroclimatic conditions. Our results show the robustness of signature-based calibration guiding the model to reproduce not only one common objective function (e. g. , high flows given by NSE) but all aspects of the hydrograph and FDC as supported by RAIN4PE-SWAT good performances reproducing all flow conditions. This is crucial for robust hydrometeorological applications including extremes such as droughts and floods as well as for the assessment of precipitation dataset reliability. Furthermore, our results reinforce previous study findings  (Shafii and Tolson 2015; Chilkoti et al.
d. Future development and application Based on the experiences we gained, our future investigations will focus on applying RAIN4PE-SWAT to analyze the water budget at the national scale of Peru, as well as climate change impacts on water resources using RAIN4PE as the basis for bias adjustment, and trends in frequency and intensity of meteorological and hydrological droughts. The current RAIN4PE data availability  is planned to be extended in the future. Moreover, the methodology presented in the paper will also be extended to the entire Amazon basin.
Summary and conclusions We developed a new hydrologically adjusted daily precipitation dataset (1981-2015, 0. 18 resolution) called RAIN4PE by merging three existing datasets for a domain covering Peru and Ecuador. This dataset takes advantages of ground-, satellite-, and reanalysis-based precipitation datasets, including CHIRP and ERA5, which are merged with terrain elevation using the random forest (RF) method to provide precipitation estimates. Furthermore, streamflow data was used to correct precipitation estimates over catchments with water budget closure problems (e. g. , the páramo and montane watersheds) through the reverse hydrology methods, for which the SWAT model was applied for the first time herein. Moreover, a comprehensive hydrological evaluation of RAIN4PE, CHIRP, ERA5, and the existing state-ofthe-art gauge-corrected precipitation datasets}CHIRPS, MSWEP, and PISCO}in the Peruvian and Ecuadorian river catchments using a range of performance metrics was performed.
For that, SWAT was calibrated and validated with each precipitation dataset in a number of catchments. We summarize our findings as follows. • The good RAIN4PE-SWAT performance for streamflow simulation suggests the effectiveness of the RF method to merge multisource precipitation estimates with terrain elevation to develop a reliable spatially gridded precipitation dataset. As all datasets (CHIRP, ERA5, and terrain elevation) used to develop RAIN4PE are freely available, this approach can be used in other data-scarce regions. • The utility of streamflow data to improve both precipitation and streamflow simulations over the páramo and montane watersheds with precipitation underestimation was demonstrated herein. This highlights that the reverse hydrology approach offers a new effective way of understanding the hydrological processes of the Andean-Amazon catchments, which have a key role in the hydrological variability of the entire Amazon basin.
• The hydrological evaluation results from uncorrected precipitation datasets forcing SWAT for streamflow simulation revealed that CHIRP outperformed ERA5, which significantly overestimate precipitation along the Andes. However, these products were outperformed by the gaugebased precipitation datasets. • Among the gauge-corrected precipitation datasets forcing SWAT for streamflow simulation, all products performed well in the catchments draining into the Titicaca Lake. For catchments draining into the Pacific Ocean and Amazon River, CHIRPS, MSWEP, and PISCO performed unsatisfactorily in several catchments, indicating the limitations of these products for hydrological modeling over these drainage systems. In contrast, RAIN4PE was the only product that provided consistently good performance for the daily and monthly streamflow simulations, including all discharge conditions (low, high, and peak flows) and water budget closure in almost all Peruvian and Ecuadorian river catchments.
• We found that CHIRP, CHIRPS, ERA5, MSWEP, and PISCO cannot represent the seasonal distribution of precipitation and hence the seasonal streamflow over the Ecuadorian Amazon. This is a critical drawback that can have implications in hydrometeorological applications in the Amazon basin. • We found that uncertainties in precipitation data in existing datasets affect the estimation of model parameters and water budget components, suggesting the importance of developing high-quality meteorological forcing datasets in mountainous regions. Our contribution is in line with this and marks progress in developing precipitation datasets in the region. The overall good performance of the RAIN4PE highlights its utility as an important new gridded precipitation dataset, which opens new possibilities for numerous hydrometeorological applications throughout Peru and Ecuador.
Examples are streamflow simulations, estimation of the water budget and its evolution, water resources management, understanding spatiotemporal variations of droughts and floods, and exploring spatial variations and regimes of precipitation. We consider that RAIN4PE and our RAIN4PE-SWAT model can be adopted as a benchmark to evaluate precipitation datasets in Peru and Ecuador. for detailed parameter definitions. Among the calibrated SWAT parameters, only two (SOL_AWC, GW_REVAP) can alter the water budget since they influence evapotranspiration and, subsequently, runoff estimation. The remaining parameters influence the surface runoff (SURLAG), groundwater (GW_DELAY, RCHRG_DP, GWQMN, ALPHA_BF), and flow routing (CH_K2, CHD, FP_W_F) not affecting water loss from the system. We illustrate in Figs.
B1-B3 the spatial patterns of the calibrated parameters related to six precipitation datasets. Figure  B1  shows that the SOL_AWC, which constrains the maximum amount of plant available water a soil can provide and was derived from the Harmonized World Soil Database (HWSD;  Abbaspour et al. 2019) , was adjusted mainly for the Andean catchments. This is a critical parameter since higher values can lead to higher evapotranspiration and vice versa. The results show that high SOL_AWC values were identified for compensating the ERA5 precipitation overestimation (see positive errors in Fig. 6 ). However, despite this trade-off, discharge overestimation by ERA5-SWAT remains (see PBIAS in Fig. 8 ), suggesting that ERA5 precipitation estimates must be bias-corrected for the Andean regions prior to hydrological applications.
Comparison of SWAT and Remotely Sensed Based Evapotranspiration Figure  C1  compares evapotranspiration (ET) estimates from the calibrated SWAT model driven by different precipitation datasets with the GLEAM and MOD16 estimates. All ET estimates show similar spatial patterns with increasing ET gradients from west to east. The differences in SWAT-simulated ET volumes can be attributed to inappropriate parameter estimation due to precipitation biases and uncertainties. The higher ET values for CHIRPS-SWAT are likely due to the prevalence of dry conditions in CHIRPS which affect the estimation of daily relative humidity and subsequently potential evapotranspiration, vapor stress on plant growth, and ET. We compared GLEAM and MOD16 against the simulated ET by RAIN4PE-SWAT since it represents the water budget well (see PBIAS in Fig.
and m2 are the lowest and highest flow EP, respectively, within the midsegment (20%-70%) Percent bias in FDC low segment volume (Yilmaz et al. 2008) … , L are flow indices located within the low flow segment (70%-100% flow EP) FDC signature (Chilkoti et al. 2018) FDC sign 5 1 4 S peak 1 S high 1 S mid | |1 S low | | FDC sign is the aggregated FDC signatureused in these datasets(Beck et al. 2020a; Brocca et al. 2020;  Satgé et al. 2020).
Acknowledgments. The authors thank the  EPICC  project that is part of the  International Climate Initiative (IKI). The  Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU)  supports this initiative on the basis of a decision adopted by the  German Bundestag. We also thank  SENAMHI, Peruvian ANA , observatory HYBAM,  Enrique Morán-Tejeda ,  Guido G. Tamayo ,  Juan J. Nieto ,  Vladimiro Tobar , and  Kevin J. Perez  for providing the hydrometeorological dataset. The authors are thankful to CHIRP, CHIRPS, ERA5, MSWEP, and PISCO data generation teams for providing the precipitation data at free of cost. We are thankful to  Dr. David Hadka  and  Dr. Patrick M. Reed  for making their software " BORG: Many-Objective Evolutionary Computing Framework " available for this study.
The African continent is probably the one with the lowest density of hydrometric stations currently measuring river discharge, despite the fact that the number of operating stations was quite important until the 70s. This new African Database of Hydrometric Indices (ADHI) is compiling data from different sources carefully checked for quality control. It includes about 1500 stations with at least 10 years of daily discharge data over the period 1950-2018. The average record length is 19 years and for over 100 stations complete records are available over 50 years. With this new dataset spanning most regions of the African continent, several hydrometric indices have been computed, representing mean flow characteristics and extremes (low flows and floods), and are made accessible to the scientific community.
, 2010; Mahe et al. , 2013; Stewart, 2015; Dixon et al. , 2020). African countries are largely under-represented in large-scale databases such as the Global Runoff Data Center (GRDC) or the recent GSIM initiative  (Do et al. , 2018; Gudmundsson et al. , 2018) , and/or the time series are mostly not updated. At the African scale, there is still a lack of coordination for hydrological data collection and dissemination, despite the launch in 1975 of the UNESCO Intergovernmental Hydrological Program (IHP) dedicated to water research, water resources management, as well as education and capacity building. This programme enhanced the set up and management of international rainfall and runoff databases at the regional scale of the FRIEND programs  (Van Lanen et al. , 2014) , but these are still largely not updated.