Evaluation of the Extreme Precipitation and Runoff Flow Characteristics in a Semiarid Sub-Basin Based on Three Satellite Precipitation Products

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1.     Line 134: The term "Datas" should be corrected to "Data."

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2.2. Data

(Page 5, Line: 158)

2.      Font inconsistency is observed in all figures. For example, Figure 1 contains both Arial and Times New Roman fonts (Line 128), while Figure 2 uses Arial only (Line 306), etc. Additionally, the text in the figures is unclear and difficult to read.

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Figure 1. Location of the El Saltito sub-basin, showing the distribution of the stations used in this study. (Page 4, Line: 133)

Figura 2. Assessing TRMM, CHIRPS, and CMORPH Rainfall Estimates for some estations, across Varied Topography Using Scatterplot Matrices. A) Mountain Stations B) Plane stations. (Page 12, Lines: 359).

We have also made the letters for the figures more legible.

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3.       Line 17 and Line 19: The abbreviation "PPS" is explained twice with different definitions. Please ensure consistency.

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“…platforms in monitoring precipitation extreme events, precipitation-runoff relation-ships, and seasonal/year-to-year variability on the Saltito semiarid subbasin in the Mexican state of Durango. Satellite precipitation products (PPS) …”

….

(Page 1, Lines:17-19).

4.      Line 566: The information regarding Supplementary Materials is incomplete. If there are no supplementary materials, please remove this section.

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5.      Satellite precipitation product errors vary across different terrains (plains and mountainous areas) in the study area. How do these errors change, and what are the possible causes?

According to Figure 2, satellite products overestimate precipitation in flat areas and underestimate it in mountainous regions. We confirm this tendency with a statistical comparison, which is included in Table 6. Satellite rainfall products (SSP) exhibit system-atic underestimation in mountainous stations and overestimation in plains, as evidenced by mean ratios (MR < 1 and MR > 1, respectively), and relative biases are average negative values for Mountain stations and positive to plane stations. In mountainous regions, un-derestimation likely stems from topographic obstruction of satellite sensors, leading to missed orographic precipitation [102,103]; as well as cold-cloud biases, where infra-red-based algorithms fail to detect shallow warm-rain processes common in highlands [104]; and gauge underrepresentation in complex terrain, reducing calibration accuracy [105,106]. Conversely, overestimation in plains may arise from anvil contamination in convective systems, where satellites misclassify non-precipitating ice clouds as rain [107,108]; surface emissivity errors over flat, homogeneous landscapes [109]; and (3) temporal sampling gaps, as infrequent satellite passes amplify errors during short-duration storms [106,110]. These highlighting the need for region-specific bias cor-rection and hybrid gauge-satellite approaches to mitigate spatial dependencies in error structure.

(Page 10, Lines:426-455).

    Line 118: The parentheses are incomplete.

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INEGI (https://www.inegi.org.mx/temas/hidrologia).

(Page 3, Line 127)

    Figure 1: The text is not clear. Also, please check whether the figure caption (Line 129) has an indentation at the beginning.

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Figure 1. Location of the El Saltito sub-basin, showing the distribution of the stations used in this study.

(Page 4, Line: 136)

    Line 186 and Line 495: There is no indentation. Please ensure consistency in formatting.

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       From 2014 to 2019, daily rainfall data were collected at 16 weather stations (Table 1) and Average Daily flow was obtained by The hydrometric station "El Saltito", located in….

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      With the observations measured by the runoff stations as input, we obtain a hy-drological simulation for the calibration (2015-2018) and validation (2018-2019) periods…

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    Hyperlinks in the text should be formatted properly: they should be blue without underlining. Please correct this issue in instances such as Line 190, Lines 193–194, Table 2, etc.

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“… (INEGI; https://www.inegi.org.mx/temas/edafologia/). The comparison between on-site measurements and satellite precipitation data was made point-by-point. processed with the help of free-to-use software ava (http://jdk.java.net/), RStudio (https://github.com/rstudio/rstudio), Python (https://www.python.org/)...”

(Page 7, Lines: 264-268)

https://data.chc.ucsb.edu/products/CHIRPS-2.0/

Index of /precip/global_CMORPH

https://gpm.nasa.gov/missions/trmm/mission-end

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    Table 4 and Section 2.3.3: There is an unreasonable gap between them. Please adjust the spacing.

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2.3.3. Hydrologic model calibration

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    Table content alignment is inconsistent—some content is aligned at the top while others are aligned at the bottom. Please ensure uniform formatting.

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    Lines 448–449 (Figure 4 caption): The figure caption should not be in bold.

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Figure 5. Comparison of extreme precipitation indices (RR95p, R25mm, Rx1d, Rx3d, Rx5d) across PPS showing (a) correlation coefficients, (b) RMSE, (c) mean error, and (d) bias"

(Page 16, Lines: 754).

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    Line 456 ("3.2"): The font should not be bold.

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3.2 The performance of SPPs in extreme flow capture

(Page 16, Lines 759).

    Reference formatting inconsistencies should be corrected. For instance, formatting differences are observed in Line 648, Line 669, etc. Please ensure uniform citation style.

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(Pages 21-28, Lines: 1316)

    Punctuation errors should be reviewed. For example, in Line 49, the punctuation is incorrectly written as .[7,8]..

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(Page 2, Lines:51-52)

    Line 122: "km2" should be formatted correctly as "km²."

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“…corresponds to an approximate drained area of 11,942 km². It is within the Mezquital River...” 

(Page 3, Line 132).

Comments and Suggestions

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Answers

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1.The study focuses only on CHIRPS, CMORPH, and TRMM but does not justify why other widely used products (e.g., IMERG, GSMaP) were not included. Given recent advancements in satellite precipitation datasets, IMERG (successor to TRMM) has superior spatiotemporal resolution and should be included for comparison. Also, look into Minor Comment 1. The references below would be helpful:

Kumar, S.; Amarnath, G.; Ghosh, S.; Park, E.; Baghel, T.; Wang, J.; Pramanik, M.; Belbase, D. Assessing the Performance of the Satellite-Based Precipitation Products (SPP) in the Data-Sparse Himalayan Terrain. Remote Sens. 2022, 14, 4810. https://doi.org/10.3390/rs14194810

Zhou, L.; Koike, T.; Takeuchi, K.; etc. A study on availability of ground observations and its impacts on bias correction of satellite precipitation products and hydrologic simulation efficiency. J Hydrol 2022, 610, 127595, doi:10.1016/j.jhydrol.2022.127595.

Thank you for your comment.

The selection of TRMM (instead of IMERG) was based on its greater historical availability (1997-2015) and its well-established validation in climate studies, particularly in tropical regions[43]. (Page 5, Lines: 166-168).

TRMM has extensively validated products (e.g., 3B42/3B43) that have been the reference in hydrology and climatology for more than 20 years. However, we acknowledge that newer satellite-based products such as IMERG (the successor to TRMM) and GSMaP offer superior spatiotemporal resolution (e.g., IMERG provides half-hourly 0.1°×0.1° data) and improved retrieval algorithms. Future work should include these datasets to evaluate potential advancements in precipitation estimation accuracy, particularly in regions with complex terrain or sparse gauge networks.

The authors acknowledge biases in satellite precipitation but do not apply a systematic correction technique. Without bias correction, the study provides only a raw evaluation rather than an improved application of satellite precipitation data for hydrological modeling.

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The approach in initial evaluation of uncorrected satellite estimates is critical to under-stand systematic errors before applying adjustment techniques [36]. High-quality gauge data required for reliable bias correction may be sparse or unavailable in a study region and correction methods assume stationarity, which may not hold for extreme events or long-term trends.

(Page 3, 109-113; ).

In a subsequent evaluation it would be of practical interest to apply corrections and re-evaluate the PPS and hydrological performance.

The SWMM model is used to evaluate precipitation-runoff relationships, but there is no discussion of parameter sensitivity or uncertainty analyses. In hydrological modeling, parameter uncertainty and sensitivity are critical for understanding model robustness, particularly in data-scarce basins.

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The model was deliberately calibrated with fixed parameters across all precipitation sources to enable direct inter-PPS comparison, following established protocols for in-put-focused hydrological evaluations [1,100] While this approach omits comprehensive parameter uncertainty analysis, it ensures any performance differences reflect precipitation product characteristics rather than parameter tuning variations.

(Page 9, Lines: 371-377)

While this approach does not characterize parametric uncertainty, it provides the most direct assessment of relative precipitation product performance - the primary objective of this study. Future work will incorporate comprehensive uncertainty analysis when optimizing the model for operational forecasting purposes.

The manuscript lacks a comparative discussion with previous evaluations of satellite precipitation products.

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We integrate a integrate a comparative discussion with previous evaluations of satellite precipitation products into your Results and Discussion section regarding to the main hightlights.

1.     Topographic Bias Analysis.

These findings align with previous evaluations of SPPs in topographically complex re-gions. For instance, studies in the Andean Altiplano [36] and Himalayan basins (Pra-kash et al., 2018)[114] similarly reported underestimations of orographic precipitation by TRMM (30–40% bias) and CMORPH (20–30% bias), attributed to cold-cloud algorithm limitations. However, our results contrast with evaluations in the Ethiopian Highlands [115], where CHIRPS showed minimal bias due to its gauge integration.

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2.     Daily/Monthly Performance of SPPs

The daily-scale superiority of CMORPH mirrors findings in the Mekong Basin (Thiemig et al., 2013), where its morphing technique reduced false alarms for light rain (<1 mm/day). However, our observed overestimation of dry days conflicts with studies in the Amazon (Rozante et al., 2018), where CMORPH underestimated drizzle events. This divergence may reflect regional differences in cloud microphysics or the threshold definitions of 'no precipitation' (e.g., <0.1 mm vs. <0.5 mm). Notably, the improved monthly performance of all SPPs echoes global assessments (Sun et al., 2021), where temporal aggregation to 30-day scales reduced RMSE by 50–60% across products.

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3.     Extreme Events Analysis

The difference in performance between these precipitation products (SPPs) can be at-tributed to the varying data sources and calibration algorithms used in their develop-ment[47,124]. For CMORPH, the probability distribution function (PDF) matching the uni-fied daily calibration analysis of the CPC MORPHing technique was applied to correct bi-ases [98,99]. This correction enhances CMORPH's performance for precipitation probabil-ity estimates, though it may still exhibit limitations in capturing extreme precipitation events, as evidenced by higher RMSE and bias values compared to CHIRPS. On the other hand, CHIRPS benefits significantly from its integration of IR-based precipitation esti-mates and a combination of NASA and NOAA satellite-based precipitation data. This multi-source approach, supported by robust calibration and validation techniques [125,126] contributes to its superior performance in estimating precipitation, particularly for extreme indices [127,128].

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4.     Extreme flow assessment of the three SPPs

CMORPH has Worst Performance with the highest Bias Volume (%), MSE, and Medium Bias among all sources. It was mentioned earlier that there is generally little or no precipitation in the area, and the SPPs tend to overestimate these measurements, similar to the performance of SPPs in modeling streamflow, in section above. These results are only observed for arid zones; a tendency to underestimate the observed averages has been reported in other studies whose study in arid zone[135,136]

(Page 18, Lines: 848-856).

    The abbreviation "PPS" is not used correctly and appears twice for two different items. It is recommended to use "Satellite Precipitation Products" (SPPs or SPP) instead.

Thank you so much for your observation. We addressed the writing error and we have deleted the abbreviation “PPS”.

“…platforms in monitoring precipitation extreme events, precipitation-runoff relation-ships, and seasonal/year-to-year variability on the Saltito semiarid subbasin in the Mexican state of Durango. Satellite precipitation products (SPP) …”

….

(Page 1, Lines:17-19)

    In Table 3, the unit contains non-English expressions. Besides, the format of Table 4 is incorrect. Similarly, improve the readability of Table 5.

(Page 7, Line: 312).

    Something wrong in Figure 2.

Thank you so much for your observation We have improved the resolution of Figure 2 (Now Figure 3)

(Page 12, Line: 530).

    It is recommend to use a chart to show result of Table 6 instead of a table.

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We change the Figure 4, for Figure 5. Comparison of extreme precipitation indices (RR95p, R25mm, Rx1d, Rx3d, Rx5d) across PPS showing (a) correlation coefficients, (b) RMSE, (c) mean error, and (d) bias"

(Page 16, Lines: 755).

Quite low quality of Figure 4

Thank you so much for your observation. We have changed and improved the resolution of Figure 4 (Now Figure 5).

(Page 16’, Line 755).

    Too many paragraphs in conclusion.

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Raw inter-comparison of Satellite products (SPP) allows us to identify intrinsic biases. Satellite products (SPP) tend to overestimate precipitation in flat areas, while in mountainous areas their performance is more variable, with cases of underestimation. Topographic complexity in mountainous areas affects the accuracy of satellite estimates.  The CMORPH and CHIRPS sensors generally perform better than the TRMM for the study area (semiarid zone).

Although these sensors tend to overestimate precipitation levels, given their spatial and temporal resolution, they represent a good alternative for analyzing spatial-temporal patterns of precipitation in the zone, with a moderate correlation agreement. However, for the analysis of climate change precipitation indices to evaluate the intensity of climate change in the region, their performance changes to weak agreement. This performance can be improved by creating a correction algorithm to increase their correspondence, which can be taken up in another future study.

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Precipitation index values show interannual variability, but no clear trend of increase or decrease during the period 2015-2019. Extreme precipitation events (Rx1d, Rx2d) and days with intense precipitation (Rx25mm) are relatively rare and do not follow a defined pattern.

We found a discrepancy between the good performance of Raw SPP rainfall estimations and in modeling runoff and its lower accuracy in estimating extreme precipitation indices.  It can be attributed to several factors related to the characteristics of the satellite data, the nature of extreme events, and the way hydrological models process information. SPP performed well in describing runoff (streamflow). Without precipitation and hydrometric information, which is usually very scarce in most semiarid areas, they can be used as a source of analysis for these hydrological processes.

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Comments and Suggestions

The changes requested are colored yellow

Answers

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Line 136: Why did the authors choose to evaluate satellite precipitation datasets using only five years of data? This is a relatively short period, which may limit the robustness and consistency of the analysis regarding the reliability of these datasets

Thank you so much for your comment.

In hydrological and climatic studies, robust validation of satellite data (such as CMORPH, TRMM and CHIRPS) requires extended periods to capture natural climate variability, systematic errors and extreme events. The literature suggests that at least 5 years of data are needed, according to the practical examples in [41-45].

(Page 5, Lines: 169-173)

Lines 141-142: The manuscript does not provide enough details on how satellite precipitation data were extracted and processed for comparison with ground station measurements. How were the satellite precipitation values retrieved for the exact locations of the gauge stations? Were interpolation techniques applied, or were the closest grid points used? If an averaging approach was used (e.g., taking the mean of neighboring pixels), the methodology should be clearly described.

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We get an estimation of precipitation daily values for CMORPH (Global, ~8km, daily) and TRMM (Tropics, ~25km, daily), and CHHIRPS in sixteen on-site rainfall stations. For CMORPH and TRMM, we used GEONETCast-Toolbox and for CHIRPS by the get command to connect and Download from the CHIRPS Website. The original format for these PPS layers is HDF4/NetCDF. After we converted the original format, NetCDF grids to GeoTIFF, using the Argis function. Finally, we extracted point by point by spatial clipping of spatial coordinates in sixteen rainfall site stations and GeoTIFF precipitation raster by ArcGIS tool raster to point.

(Page 5, Lines:173-181)

Lines 205-209: Here there are two phrases with the same meaning.

Thank you so much for your observation. We have removed the repeated phrase.

“…They demonstrate minimal noise and high significance, facilitating a thorough exami-nation of the intensity, frequency, and duration of extreme temperature and precipitation events…”

(Page 7, Lines: 284-286)

Line 242: The manuscript does not indicate whether a statistical significance test was applied to the correlation values. Which significance test was used to assess whether the correlations are statistically significant? If a significance test (e.g., t-test for correlation, p-value computation) was applied, the results should be reported. If no test was applied, consider adding a statistical test to assess the robustness of the correlation values.

Thank you so much for your observation. An apology for omitting this information in the first version of the manuscript. The significance test was used to evaluate whether the correlations were statistically significant (Pearson's correlation coefficient, p-value 0.05), for the correlation tests for 1) statistical comparison, between row rainfall data PPS and Gauge data 2) Error evaluation statistics of extreme climatic indexes (RR95p, R25mm, Rx1d and Rx5d), obtained from SPP in comparison of gauge data and.  This tests shown weak correlation, for this reason,  We replacing Pearson correlation coefficient, in Table 6 and able 7, with an alternative statistical measure, the Spearman's correlation coefficient (ρ).

This information can be found in the table captions.

(Page 7, Line 250; Page 8, Line 265; Page 11, Line 375; Page 14, Line 432; Page 19, Line 591)

Figure 2: The precipitation maps lack a legend or color scale, making it difficult to interpret precipitation values accurately. Additionally, it is unclear whether all maps use a same color bar, which is crucial for ensuring a fair and meaningful comparison between datasets.

Thank you so much for your observation. We have improved the Figure 2 (Now Figure 3).

(Page 13, Line: 566)

Figure 4: I recommend that the authors present the information in an alternative format, as the bars in each panel are difficult to distinguish, making interpretation challenging.

Thank you so much for your suggestion. We have addressed this inconsistence in the Figure 4. We have changed and improved the Figure 4 (Now Figure 5)

Figure 5. Comparison of extreme precipitation indices (RR95p, R25mm, Rx1d, Rx3d, Rx5d) across PPS showing (a) correlation coefficients, (b) RMSE, (c) mean error, and (d) bias"

(Page 16, Line: 755)

Tables 6 and 7: The manuscript reports Pearson correlation values between satellite precipitation data and gauge observations. However, in general, these values are low. Given the low number of analyzed years (2015–2019, only 5 years), it is difficult to obtain statistically significant correlations. Consider removing the Pearson correlation metric or replacing it with an alternative statistical measure that better captures the agreement between datasets in a small sample size scenario.

Thank you so much for your suggestion.

We replacing Pearson correlation coefficient, in Table 6 and able 7, with an alternative statistical measure, the Spearman's correlation coefficient (ρ). It better captures the agreement between in this case. This may be due to the nature of the data there are non-linear relationships between variables. With non-normal data or with extreme outliers (common in extreme precipitation events). In asymmetric distributions (such as extreme rainfall, which usually have positive skewness).

(Page 11, Line 461; Page 15, Line 667)

Line 464: What is NSE?

Thank you so much for your observation. We added the meaning of the acronym.

“….satellite-based data. All rainfall data sources achieve Nash-Sutcliffe Efficiency (NSE) [8]….”

(Page 17, Line: 807)

Line 494: Indicate that the results of the section are in Table 7.

Thank you so much for your suggestion. We have indicated that the results of the section are in Table 7.

“     To assess the ability of the four precipitation products (PPSs) to capture extreme flow events, three extreme flow indices—Qx1d, Qx3d, and Qx5d—were chosen for evalua-tion (See Table 8)…”

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Line 513: The authors conclude that satellite products tend to overestimate precipitation in flat areas and underestimate it in mountainous regions. However, this topic could be explored further with additional analysis. I recommend that the authors examine how these discrepancies vary with topographic factors, including statistical comparisons or spatial correlation analyses between precipitation bias and topographic characteristics.

Thank you so much your comment.

According to Figure 3, satellite products overestimate precipitation in flat areas and underestimate it in mountainous regions. We confirm this tendency with a statistical comparison, which is included in Table 6. Satellite rainfall products (SSP) exhibit system-atic underestimation in mountainous stations and overestimation in plains, as evidenced by mean ratios (MR < 1 and MR > 1, respectively), and relative biases are average negative values for Mountain stations and positive to plane stations. In mountainous regions, un-derestimation likely stems from topographic obstruction of satellite sensors, leading to missed orographic precipitation [102,103]; as well as cold-cloud biases, where infra-red-based algorithms fail to detect shallow warm-rain processes common in highlands [104]; and gauge underrepresentation in complex terrain, reducing calibration accuracy [105,106]. Conversely, overestimation in plains may arise from anvil contamination in convective systems, where satellites misclassify non-precipitating ice clouds as rain [107,108]; surface emissivity errors over flat, homogeneous landscapes [109]; and (3) temporal sampling gaps, as infrequent satellite passes amplify errors during short-duration storms [106,110]. These highlighting the need for region-specific bias correction and hybrid gauge-satellite approaches to mitigate spatial dependencies in error structure.

(Page 11 Lines: 433-456)

Line 17: Remove the acronym PPS, as it is only defined in Line 19.

Thank you so much for your observation. We addressed the writing error and we have deleted the abbreviation “PPS” (Line 17).

“…platforms in monitoring precipitation extreme events, precipitation-runoff relation-ships, and seasonal/year-to-year variability on the Saltito semiarid subbasin in the Mexican state of Durango. Satellite precipitation products (SPP) …”

(Page 1, Lines:17-19).

Lines 137-141: Revise this phrase;  CHIRPS, CMORPH, and TRMM appears twice.

Thank you so much for your observation. We have removed the repeated text.

    “We utilized the fusion data CHIRPS, CMORPH, and TRMM satellite precipitation products to analyze the suitability of using platforms SPPs in monitoring precipitation…”

(Page 5, Lines: 163-164)

Line 143: Isn’t it “three PPSs”, instead of “four”?

Thank you so much for your observation. We addressed this inconsistence.

     “Table 2 provides a brief description of these three SPPs. CHIRPS emerged from a…”

(Page 6, Line 256)