Dynamic Rainfall Erosivity Estimates Derived from GPM IMERG data
Abstract. Soil degradation is a critical threat to agriculture and food security around the world. Understanding the processes that drive soil erosion is necessary to support sustainable management practices and to reduce eutrophication of water systems from fertilizer runoff. The erosivity of precipitation is a primary control on the rate of soil erosion, but to calculate erosivity high frequency precipitation data is required. Prior global scale analysis has almost exclusively used ground-based rainfall gauges to calculate erosivity, but the advent of high frequency satellite rainfall data provides an opportunity to estimate erosivity using globally consistent gridded satellite rainfall. In this study, I have tested the use of GPM IMERG rainfall data to calculate global rainfall erosivity. I have tested three different approaches to assess whether simplification of IMERG data allows for robust calculation of erosivity, finding that the highest frequency 30-minute data is needed to best replicate gauge-based estimates. I also find that in areas where ground-based gauges are sparse, there is more disparity between the IMERG derived estimates and the ground-based results, suggesting that IMERG may allow for improved erosivity estimates in data-poor areas. The global extent and accessibility of IMERG data allows for regular calculation of erosivity on a month-to-month timeframe, permitting improved dynamic characterisation of rainfall erosivity across the world in near-real time. These results demonstrate the value of satellite data to assess the impact of rainfall on soil erosion and may benefit practitioners of sustainable land management planning.
Robert Alexander Emberson
Status: open (until 03 Apr 2023)
- RC1: 'Comment on egusphere-2022-1315', Anonymous Referee #1, 10 Feb 2023 reply
- RC2: 'Comment on egusphere-2022-1315', Anonymous Referee #2, 28 Feb 2023 reply
- RC3: 'Comment on egusphere-2022-1315', Anonymous Referee #3, 05 Mar 2023 reply
- CC1: 'Comment on egusphere-2022-1315', Panos Panagos, 06 Mar 2023 reply
Robert Alexander Emberson
Robert Alexander Emberson
Viewed (geographical distribution)
In the submitted paper author investigates the performance of the GPM IMERG rainfall data to calculate global rainfall erosivity. Three approaches are tested for the estimation of the global rainfall erosivity patterns. A detailed comparison with the GloREDa dataset and derived global erosivity map is conducted. The submitted paper is in the scope of the HESS journal. It is very well written and it is easy to follow the conducted methodological steps and discussion of the results. The presented results can be regarded as an important step towards determination of the so-called dynamic global rainfall erosivity maps that could be used as input data for the soil erosion models. Therefore, I only have a few moderate comments/suggestions:
Can author add some additional (besides what is written in sections 4.2 and 4.1) discussion about the uncertainty related to the satellite-based rainfall dataset and how does this transforms into the rainfall erosivity estimation and does uncertainty perhaps has some seasonal patterns. Would it be perhaps possible to compare calculated IMERG monthly rainfall erosivity to REDES dataset (subset of GloREDa data; monthly rainfall erosivity maps for Europe).
At the end of the manuscript, author wrote that more detailed comparison is needed with ground-based data in order to verify the IMERG dataset. Could perhaps some additional discussion be added regarding this possible further step (e.g., how this could be done). It should be also noted that also GloREDa dataset has its own limitations (e.g., different data periods were used for different stations, data temporal resolution was not uniform, etc.). Even if assume that GloREDa represents the “true” rainfall erosivity patterns it is question, how accurate this actually is. Is there any alternative way, without using GloREDa dataset. Perhaps satellite-based drop-size-distribution data and calculation of the rainfall erosivity directly from the DSD?
Figure 2: It would be perhaps good to include the 1:1 line to the first (A) figure as well.
Figure 4: Units should be added for x-axis. Is it meaningful to include MFI data? I see that you included Figure S1 but at least it should be noted in the Figure 4 caption that that MFI has different units. Perhaps the same could be done for Figure S7 and Figure S8.
Figure 5: Perhaps the y-axis caption is not the most clear, it should be total rainfall erosivity in specific month (units mo-1), right? Also it would be perhaps better to plot the absolute values (not log) since this can be easier to put in context (compared to annual erosivity values). Additionally, figure caption should specify what do black and red lines represent (mean and median?).
Figure 6: Similarly, as for Figure 5.
Figure 6: It is interesting why the monthly variations for S. America (and also for some other continents) are much larger as compared to Figure 5 (this is noted also in the text), any explanation?