the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the importance of phenology in the Miombo ecosystem: Evaluation of open-source satellite evaporation models
Henry Zimba
Miriam Coenders-Gerrits
Kawawa Banda
Petra Hulsman
Nick van de Giesen
Imasiku Nyambe
Hubert Savenije
Abstract. Accurate spatial-temporal information on evaporation is needed for use in many sectors including hydrology, agriculture and climate studies. This would require a dense observation network, which is practically impossible. Over the past decades, remotely sensed evaporation models to estimate spatially continuous evaporation have been developed. However, deciding which model to use is a challenge as these models vary in complexity and accuracy across the different global ecosystems. It is even more challenging for complex African ecosystems that have very few, or none at all, flux tower observations. In this study, we used the general water balance evaporation (Ewb) as reference to which we compared six models that determine evaporation, i.e., FLEX – TopoWB, TerraClimate (TMCWB), GLEAM, MOD16, SSEBop and WaPOR, in the Luangwa Basin, a semi-arid catchment in the Miombo ecosystem in southern Africa. FLEX – TopoWB and TMCWB models are calibrated on discharge, while GLEAM, MOD16, SSEBop and WaPOR have been validated on evaporation data from flux tower observations. Key focus is on inter-model performance comparison in the Miombo ecosystem across phenophases and land cover types. Results show that major spatial-temporal discrepancies in model performance occur in the forest and open water body land surfaces during the dormant and green-up phenophases in the dry season. Compared to Ewb, annually WaPOR consistently overestimated evaporation while GLEAM consistently underestimated evaporation. The rest of the models showed biases within the GLEAM and WaPOR boundaries. With reference to bias, SSEBop and WaPOR showed lowest aggregated 2009–2020 bias in terms of estimating long-term average annual evaporation. It appears that correct understanding of the Miombo vegetation phenology associated moisture feedbacks and incorporating these in model structure is likely to improve evaporation estimates in the Luangwa Basin and Miombo Woodland ecosystem as a whole.
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Henry Zimba et al.
Status: closed
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RC1: 'Comment on hess-2022-114', Anonymous Referee #1, 30 May 2022
I have read the manuscript with great interest. Most of my comments are embedded in the pdf cope of the manuscript. Although evaporation monitoring in Africa is of great ecological and hydrological significance, but this study is far from achieving publishable standard. No eddy covariance measurements were available. Authors used two different water balance models to compare the remote sensing evaporation products. The lead author should atleast read good papers before writing. Please check the paper of Trebs et al. (2021) (https://www.sciencedirect.com/science/article/pii/S0034425721003229) on how to do error analysis of evaporation models. A recent study of Thakur et al. (2022) also did global sensitivity analysis (https://www.nature.com/articles/s41598-022-12304-3). The paper just produced a huge list of figures and without any deep dive. Seeing the impact and prestige of HESS, the paper is inappropriate. My other comments are attached in the pdf.
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AC1: 'Reply on RC1', Henry Musonda Zimba, 31 May 2022
We appreciate your valuable insightful comments. This will definitely help improve the quality of our paper. We take note of your comments and hereby are our responses to the key issues raised.
- “No eddy covariance measurements were available”
What is also important to note is that there are currently no eddy covariance (EC) observations, at least known to us, focused on the Miombo forest/woodland region. There are extremely few, if any, flux observation sites in the Zambezi Basin. Furthermore, comparing remote sensing based evaporation with EC is also doubtful, although it's the common standard. However, one can question if this standard is a proper comparison due to non-closure of the energy balance, footprint issues and spatial heterogeneity (see e.g. Miriam Coenders-Gerrits, Bart Schilperoort, César Jiménez-Rodríguez “Evaporative processes on vegetation: an inside look” (2020). pp 35-48. Book chapter in “Precipitation Partitioning by Vegetation: A Global Synthesis“, editors John T. Van Stan, II; Ethan D. Gutmann; Jan Friesen; Springer.)
We, nonetheless, have made point based observations using the Distributed Temperature Sensing (DTS) system. The DTS based observations are now available. We are more than glad to incorporate these results to enhance the quality of the paper should it be deemed necessary. What is important is that at basin scale the use of the water balance for validation is an acceptable approach especially in sparsely gauged basins like the Luangwa and the Zambezi Basin in general. This is affirmed by the point field observations we have made that give similar results as the general water balance.
- “Authors used two different water balance models to compare the remote sensing evaporation products’.
In cases where spatially distributed measurements are not available as is the case with large basins and more importantly in the dominantly Miombo forest covered Zambezi Basin the use of the water balance approach is an acceptable approach (i.e., Weerasinghe et al., 2020; Liu et al., 2016). To this effect, we endeavoured to provide sufficient references in our paper. As explain in our manuscript we use two water balance approaches to understand two aspects, these being annual variability and monthly variability.
- At annual scale we used the general water balance at basin level, as explained in the manuscript relies on remote sensing (i.e., precipitation) and field observations (i.e. discharge/runoff). This is a generally accepted approach noting that spatially distributed rainfall observations at basin level is practically impossible. Before selecting the precipitation product used in the study we made effort to compare various products with point based field observations.
- At monthly scale, we used two water balance models calibrated on observed discharge. Our reasoning behind the use of this approach is that observed discharge takes into account the influence of vegetation. The focus of our study as the title suggests is on the importance of phenology on model performance. As can be seen from our study model performance appear to be influenced by the phenological stages. For instance, the most notable discrepancy in model performance is during the dormant phenological stage. This is the major issue our paper is highlighting. However, it will be a valuable addition if models sensitivity to some parameters like precipitation, soil moisture and land surface temperature is done. This, however, is not the focus of the study.
- The lead author should at least read good papers before writing. Please check the paper of Trebs et al. (2021) (https://www.sciencedirect.com/science/article/pii/S0034425721003229) on how to do error analysis of evaporation models.
The papers you have provided are indeed high quality though have very different objectives compared to our submission. Furthermore, we believe the methods we used for understanding model performance are adequate and some of them similar if not the same as the methods used in the papers you have provided. However, we will always strive to do our best to raise the quality of submission to the standard of the journal.
- “The paper just produced a huge list of figures and without any deep dive. Seeing the impact and prestige of HESS, the paper is inappropriate”
We understand your view point. The novelty in our view is in highlighting the unique Miombo ecosystem characteristics and the influence on evaporation model performance. This has never been done before. As we have indicated in our manuscript, Pelletier et al. (2018) and Tian et al. (2018) postulated that the functional traits of the Miombo Woodlands vegetation species is different from other ecosystems. What is even more challenging is that there are extremely few, if any, flux observation towers in the Miombo region none in the Luangwa Basin. This makes it difficult to know the moisture feedbacks of the ecosystem from field observations. This is where our study and the use of the general water balance becomes necessary. The general water balance method has been used before in other regions as has been cited in our study. To this effect our study makes available information on the performance of evaporation models with regard to the Miombo ecosystem that would otherwise be used without any form of validation. While we agree that field observations are needed, as we have indicated ourselves throughout the manuscript, the general water balance results at annual scale are still valid in our view. In our opinion the land cover specific field observations are needed though this is not likely to change the water balance results as our point observations using the Bowen ratio distributed temperature sensing system (BR-DTS) has shown for the Miombo forest.
Consequently, it would be helpful if the “without any deep dive’’ could be explained further so that we can adequately address the issues.
In summary
This study highlights difficulties in estimating evaporation by satellite-based evaporation models brought about by the unique physiological characteristics of the unexplored Miombo ecosystem in sub-Sahara Africa. We discuss the potential underlying factors to observed discrepancies in model performance across phenophases. We also suggest that understanding the Miombo woodland phenological characteristics and incorporating aspects that address these characteristics in the model structure and processes is likely to improve evaporation estimates. We believe our study, though not exhaustive, augments the scarcely available literature on evaporation in the Miombo ecosystem. Our study has potential to improve outcomes of climate, hydrological studies and water resources management which for example can benefit from improved estimates of evaporation in this vast unique ecosystem.
We have also responded to your valuable comments in the main document. We hope our responses will help clarify a number of issues.
- AC2: 'Reply on RC1', Henry Musonda Zimba, 02 Jun 2022
-
AC1: 'Reply on RC1', Henry Musonda Zimba, 31 May 2022
-
RC2: 'Comment on hess-2022-114', Anonymous Referee #2, 09 Oct 2022
Review of “On the importance of phenology in the Miombo ecosystem: Evaluation of open-source satellite evaporation models”
The paper presents a very interesting study on evaluating different remote sensing products for a specific ecosystem found across Africa. The paper contains a wealth of data and information but the presentation of the study and its results require significant improvement as currently it is not very clear to me how the methods used for the evaluation results in the overall conclusion. In addition to a critical review of the English language required, I have provided detailed annotated comments on the manuscript and below the general observations on the manuscript:
Title and abstract
The title in my opinion is not correct, shouldn’t open-source be open access?; and you are evaluating evaporation products, not the models itself (eg ETLook; SeBAL)? Also from the abstract it is not very clear that you are not only evaluating RS evaporation products, but also two (hydrological?) models. If you evaluated all 6 products, then the title is incorrect, but if you evaluated the 4 RS evaporation products, and used the other two models for validation, then the abstract needs to be re-written (line 21-25). Also I am not sure that the importance of phenology is reflected in the study and results for the evaluation of the evaporation products.
Methodology for evaluating RS evaporation products
Generally there is a bit of unclarity which products are being evaluated and against which data. For the annual comparison you compared the evaporation products (all 6) to an estimation of evaporation based on a water balance (Ewb), to which I have the following comments:
- Ewb was calculated for calendar years, not hydrological years which may influence the over year storage variations, better to reduce this additional source of error by comparing the hydrological year
- The selection of precipitation product used for the Ewb calculation seems to be based on 8 observations in three locations (Figure A1), this is a very limited validation and the data should therefore be considered with certain uncertainty and not seen as an absolute reference to which the other products need to adhere to (you already indicate that the absence of the over-year storage is reducing the value of Ewb as a validation product
- The explanation of the Ewb validation data (incl selection of the precipitation product) can be presented fully in the methodology (and not have two sentences at the start of the results sections, which seem a bit out of place/ duplication “sensitivity of the precipitation product”)
Regarding the trend analyses of the evaporation products:
- To me it is not clear what the purpose of the trend analyses and correlation analyses is, the reference data set (Ewb) has more variation than the evaporation products being evaluated, what does this mean? The fact that there is no significant trends (for 12 years of data, which is a too small data set) for the different products, what does this mean?
- Trend analyses at monthly timescales seems to be done for the entire dataset (144 months) instead of comparing similar data (jan alone), if you do take the full timeseries, the trend analyses is influenced by the seasonality of the data, how do you account for that?
Regarding the correlation analyses and inter-comparison of the different products:
- TopoFlex and TMC seem to be used as standard to compare the other products against (eg figure 6 & 10) however later in the paper it becomes clear that these products also have their limitation to model evaporation.
- For example figure 7 shows the mismatch between the NDVI and the spatial patterns of the TopoFlex and TMC models. The higher variability observed by SSEBop and WaPOR shows the basins is most likely due to the higher resolution of the data, including being able to identify water bodies with high evaporation
- Not clear to me how the NDVI spatial pattern is used to evaluate the spatial patterns of the different evaporation products? The text now seems to be descriptive.
Comparing to phenophases at basin level
The first analyses done are to compare the entire basin evaporation data against the Miombo phenophases, however we only find out in the discussion that only 60% of the basin is covered by Miombo woodlands (at least if we assume open forest classification is all Miombo woodlands). What is the justification for this assumption and would you assume that the entire basin would respond in a similar way as the Miombo woodlands?
Comparing the five selected locations (Figure 8)
The six selected locations vary in size as can be seen from the number of pixels used from the WaPOR data (256-2304), however for the low resolution data, each time only one pixel is considered in the analyses. It can be assumed that for the smaller areas, these pixels overlap with the surrounding areas, which could have different land cover types which may have influenced the temporal signature. How has this been taken care of. In table A5 you indicate the same number of observations for each of the locations, how did you aggregate the WaPOR data (average?) to compare it as one value against the other datasets?
Discussion
In the discussion section a lot of new data is presented, this is not normally a good place to present new data and analyses. For example figure 11 presents new data, but to me it is not very clear how this contributes to understanding how well the different evaporation data products are able to monitor the Miombo woodlands. Similarly the sections with the explanations on how SSEBop and WaPOR perform contain a lot of new information on how the Miombo woodlands work and which is used to confirm that the evaporation observed by SSEBop and WaPOR at the end of the dry season are not unrealistic. In my opinion it would have been helpful if this information was presented upfront, including figure 13 with the land cover classes observed in the Luangwa basin.
Specific comments:
You categorise the remote sensing evaporation products into energy balance models (EBM), however the WaPOR methodology is not a surface energy balance model, instead it uses Penman-Monteith (ETLook) for estimating evaporation.
Update graphs to remove the digits (eg 100.0 should be 100)
Figure 5 title of 5B monthly average (year 2009-2020) and not 2019-2020
-
AC3: 'Reply on RC2', Henry Musonda Zimba, 16 Oct 2022
We find this review to be highly objective and constructive. This will greatly help improve the quality of our submission.
Our response(s) to the issues raised are as per attached pdf document.
-
AC4: 'Reply on AC3', Henry Musonda Zimba, 16 Oct 2022
references
Asadullah, Anita, Neil McIntyre, and Max Kigobe. 2008. “Evaluation of Five Satellite Products for Estimation of Rainfall over Uganda.” Hydrological Sciences Journal 53 (6): 1137–50. https://doi.org/10.1623/hysj.53.6.1137.
Chidumayo, E. 2001. “Climate and Phenology of Savanna Vegetation in Southern Africa.” Journal of Vegetation Science 12 (3): 347. https://doi.org/10.2307/3236848.
Frost, P. 1996. The Ecology of Miombo Woodlands. The Miombo in Transition: Woodlands and Welfare in Africa. http://books.google.com/books?hl=nl&lr=&id=rpildJJVdU4C&pgis=1.
Fuller, Douglas O. 1999. “Canopy Phenology of Some Mopane and Miombo Woodlands in Eastern Zambia.” Global Ecology and Biogeography 8 (3–4): 199–209. https://doi.org/10.1046/j.1365-2699.1999.00130.x.
-
AC4: 'Reply on AC3', Henry Musonda Zimba, 16 Oct 2022
Status: closed
-
RC1: 'Comment on hess-2022-114', Anonymous Referee #1, 30 May 2022
I have read the manuscript with great interest. Most of my comments are embedded in the pdf cope of the manuscript. Although evaporation monitoring in Africa is of great ecological and hydrological significance, but this study is far from achieving publishable standard. No eddy covariance measurements were available. Authors used two different water balance models to compare the remote sensing evaporation products. The lead author should atleast read good papers before writing. Please check the paper of Trebs et al. (2021) (https://www.sciencedirect.com/science/article/pii/S0034425721003229) on how to do error analysis of evaporation models. A recent study of Thakur et al. (2022) also did global sensitivity analysis (https://www.nature.com/articles/s41598-022-12304-3). The paper just produced a huge list of figures and without any deep dive. Seeing the impact and prestige of HESS, the paper is inappropriate. My other comments are attached in the pdf.
-
AC1: 'Reply on RC1', Henry Musonda Zimba, 31 May 2022
We appreciate your valuable insightful comments. This will definitely help improve the quality of our paper. We take note of your comments and hereby are our responses to the key issues raised.
- “No eddy covariance measurements were available”
What is also important to note is that there are currently no eddy covariance (EC) observations, at least known to us, focused on the Miombo forest/woodland region. There are extremely few, if any, flux observation sites in the Zambezi Basin. Furthermore, comparing remote sensing based evaporation with EC is also doubtful, although it's the common standard. However, one can question if this standard is a proper comparison due to non-closure of the energy balance, footprint issues and spatial heterogeneity (see e.g. Miriam Coenders-Gerrits, Bart Schilperoort, César Jiménez-Rodríguez “Evaporative processes on vegetation: an inside look” (2020). pp 35-48. Book chapter in “Precipitation Partitioning by Vegetation: A Global Synthesis“, editors John T. Van Stan, II; Ethan D. Gutmann; Jan Friesen; Springer.)
We, nonetheless, have made point based observations using the Distributed Temperature Sensing (DTS) system. The DTS based observations are now available. We are more than glad to incorporate these results to enhance the quality of the paper should it be deemed necessary. What is important is that at basin scale the use of the water balance for validation is an acceptable approach especially in sparsely gauged basins like the Luangwa and the Zambezi Basin in general. This is affirmed by the point field observations we have made that give similar results as the general water balance.
- “Authors used two different water balance models to compare the remote sensing evaporation products’.
In cases where spatially distributed measurements are not available as is the case with large basins and more importantly in the dominantly Miombo forest covered Zambezi Basin the use of the water balance approach is an acceptable approach (i.e., Weerasinghe et al., 2020; Liu et al., 2016). To this effect, we endeavoured to provide sufficient references in our paper. As explain in our manuscript we use two water balance approaches to understand two aspects, these being annual variability and monthly variability.
- At annual scale we used the general water balance at basin level, as explained in the manuscript relies on remote sensing (i.e., precipitation) and field observations (i.e. discharge/runoff). This is a generally accepted approach noting that spatially distributed rainfall observations at basin level is practically impossible. Before selecting the precipitation product used in the study we made effort to compare various products with point based field observations.
- At monthly scale, we used two water balance models calibrated on observed discharge. Our reasoning behind the use of this approach is that observed discharge takes into account the influence of vegetation. The focus of our study as the title suggests is on the importance of phenology on model performance. As can be seen from our study model performance appear to be influenced by the phenological stages. For instance, the most notable discrepancy in model performance is during the dormant phenological stage. This is the major issue our paper is highlighting. However, it will be a valuable addition if models sensitivity to some parameters like precipitation, soil moisture and land surface temperature is done. This, however, is not the focus of the study.
- The lead author should at least read good papers before writing. Please check the paper of Trebs et al. (2021) (https://www.sciencedirect.com/science/article/pii/S0034425721003229) on how to do error analysis of evaporation models.
The papers you have provided are indeed high quality though have very different objectives compared to our submission. Furthermore, we believe the methods we used for understanding model performance are adequate and some of them similar if not the same as the methods used in the papers you have provided. However, we will always strive to do our best to raise the quality of submission to the standard of the journal.
- “The paper just produced a huge list of figures and without any deep dive. Seeing the impact and prestige of HESS, the paper is inappropriate”
We understand your view point. The novelty in our view is in highlighting the unique Miombo ecosystem characteristics and the influence on evaporation model performance. This has never been done before. As we have indicated in our manuscript, Pelletier et al. (2018) and Tian et al. (2018) postulated that the functional traits of the Miombo Woodlands vegetation species is different from other ecosystems. What is even more challenging is that there are extremely few, if any, flux observation towers in the Miombo region none in the Luangwa Basin. This makes it difficult to know the moisture feedbacks of the ecosystem from field observations. This is where our study and the use of the general water balance becomes necessary. The general water balance method has been used before in other regions as has been cited in our study. To this effect our study makes available information on the performance of evaporation models with regard to the Miombo ecosystem that would otherwise be used without any form of validation. While we agree that field observations are needed, as we have indicated ourselves throughout the manuscript, the general water balance results at annual scale are still valid in our view. In our opinion the land cover specific field observations are needed though this is not likely to change the water balance results as our point observations using the Bowen ratio distributed temperature sensing system (BR-DTS) has shown for the Miombo forest.
Consequently, it would be helpful if the “without any deep dive’’ could be explained further so that we can adequately address the issues.
In summary
This study highlights difficulties in estimating evaporation by satellite-based evaporation models brought about by the unique physiological characteristics of the unexplored Miombo ecosystem in sub-Sahara Africa. We discuss the potential underlying factors to observed discrepancies in model performance across phenophases. We also suggest that understanding the Miombo woodland phenological characteristics and incorporating aspects that address these characteristics in the model structure and processes is likely to improve evaporation estimates. We believe our study, though not exhaustive, augments the scarcely available literature on evaporation in the Miombo ecosystem. Our study has potential to improve outcomes of climate, hydrological studies and water resources management which for example can benefit from improved estimates of evaporation in this vast unique ecosystem.
We have also responded to your valuable comments in the main document. We hope our responses will help clarify a number of issues.
- AC2: 'Reply on RC1', Henry Musonda Zimba, 02 Jun 2022
-
AC1: 'Reply on RC1', Henry Musonda Zimba, 31 May 2022
-
RC2: 'Comment on hess-2022-114', Anonymous Referee #2, 09 Oct 2022
Review of “On the importance of phenology in the Miombo ecosystem: Evaluation of open-source satellite evaporation models”
The paper presents a very interesting study on evaluating different remote sensing products for a specific ecosystem found across Africa. The paper contains a wealth of data and information but the presentation of the study and its results require significant improvement as currently it is not very clear to me how the methods used for the evaluation results in the overall conclusion. In addition to a critical review of the English language required, I have provided detailed annotated comments on the manuscript and below the general observations on the manuscript:
Title and abstract
The title in my opinion is not correct, shouldn’t open-source be open access?; and you are evaluating evaporation products, not the models itself (eg ETLook; SeBAL)? Also from the abstract it is not very clear that you are not only evaluating RS evaporation products, but also two (hydrological?) models. If you evaluated all 6 products, then the title is incorrect, but if you evaluated the 4 RS evaporation products, and used the other two models for validation, then the abstract needs to be re-written (line 21-25). Also I am not sure that the importance of phenology is reflected in the study and results for the evaluation of the evaporation products.
Methodology for evaluating RS evaporation products
Generally there is a bit of unclarity which products are being evaluated and against which data. For the annual comparison you compared the evaporation products (all 6) to an estimation of evaporation based on a water balance (Ewb), to which I have the following comments:
- Ewb was calculated for calendar years, not hydrological years which may influence the over year storage variations, better to reduce this additional source of error by comparing the hydrological year
- The selection of precipitation product used for the Ewb calculation seems to be based on 8 observations in three locations (Figure A1), this is a very limited validation and the data should therefore be considered with certain uncertainty and not seen as an absolute reference to which the other products need to adhere to (you already indicate that the absence of the over-year storage is reducing the value of Ewb as a validation product
- The explanation of the Ewb validation data (incl selection of the precipitation product) can be presented fully in the methodology (and not have two sentences at the start of the results sections, which seem a bit out of place/ duplication “sensitivity of the precipitation product”)
Regarding the trend analyses of the evaporation products:
- To me it is not clear what the purpose of the trend analyses and correlation analyses is, the reference data set (Ewb) has more variation than the evaporation products being evaluated, what does this mean? The fact that there is no significant trends (for 12 years of data, which is a too small data set) for the different products, what does this mean?
- Trend analyses at monthly timescales seems to be done for the entire dataset (144 months) instead of comparing similar data (jan alone), if you do take the full timeseries, the trend analyses is influenced by the seasonality of the data, how do you account for that?
Regarding the correlation analyses and inter-comparison of the different products:
- TopoFlex and TMC seem to be used as standard to compare the other products against (eg figure 6 & 10) however later in the paper it becomes clear that these products also have their limitation to model evaporation.
- For example figure 7 shows the mismatch between the NDVI and the spatial patterns of the TopoFlex and TMC models. The higher variability observed by SSEBop and WaPOR shows the basins is most likely due to the higher resolution of the data, including being able to identify water bodies with high evaporation
- Not clear to me how the NDVI spatial pattern is used to evaluate the spatial patterns of the different evaporation products? The text now seems to be descriptive.
Comparing to phenophases at basin level
The first analyses done are to compare the entire basin evaporation data against the Miombo phenophases, however we only find out in the discussion that only 60% of the basin is covered by Miombo woodlands (at least if we assume open forest classification is all Miombo woodlands). What is the justification for this assumption and would you assume that the entire basin would respond in a similar way as the Miombo woodlands?
Comparing the five selected locations (Figure 8)
The six selected locations vary in size as can be seen from the number of pixels used from the WaPOR data (256-2304), however for the low resolution data, each time only one pixel is considered in the analyses. It can be assumed that for the smaller areas, these pixels overlap with the surrounding areas, which could have different land cover types which may have influenced the temporal signature. How has this been taken care of. In table A5 you indicate the same number of observations for each of the locations, how did you aggregate the WaPOR data (average?) to compare it as one value against the other datasets?
Discussion
In the discussion section a lot of new data is presented, this is not normally a good place to present new data and analyses. For example figure 11 presents new data, but to me it is not very clear how this contributes to understanding how well the different evaporation data products are able to monitor the Miombo woodlands. Similarly the sections with the explanations on how SSEBop and WaPOR perform contain a lot of new information on how the Miombo woodlands work and which is used to confirm that the evaporation observed by SSEBop and WaPOR at the end of the dry season are not unrealistic. In my opinion it would have been helpful if this information was presented upfront, including figure 13 with the land cover classes observed in the Luangwa basin.
Specific comments:
You categorise the remote sensing evaporation products into energy balance models (EBM), however the WaPOR methodology is not a surface energy balance model, instead it uses Penman-Monteith (ETLook) for estimating evaporation.
Update graphs to remove the digits (eg 100.0 should be 100)
Figure 5 title of 5B monthly average (year 2009-2020) and not 2019-2020
-
AC3: 'Reply on RC2', Henry Musonda Zimba, 16 Oct 2022
We find this review to be highly objective and constructive. This will greatly help improve the quality of our submission.
Our response(s) to the issues raised are as per attached pdf document.
-
AC4: 'Reply on AC3', Henry Musonda Zimba, 16 Oct 2022
references
Asadullah, Anita, Neil McIntyre, and Max Kigobe. 2008. “Evaluation of Five Satellite Products for Estimation of Rainfall over Uganda.” Hydrological Sciences Journal 53 (6): 1137–50. https://doi.org/10.1623/hysj.53.6.1137.
Chidumayo, E. 2001. “Climate and Phenology of Savanna Vegetation in Southern Africa.” Journal of Vegetation Science 12 (3): 347. https://doi.org/10.2307/3236848.
Frost, P. 1996. The Ecology of Miombo Woodlands. The Miombo in Transition: Woodlands and Welfare in Africa. http://books.google.com/books?hl=nl&lr=&id=rpildJJVdU4C&pgis=1.
Fuller, Douglas O. 1999. “Canopy Phenology of Some Mopane and Miombo Woodlands in Eastern Zambia.” Global Ecology and Biogeography 8 (3–4): 199–209. https://doi.org/10.1046/j.1365-2699.1999.00130.x.
-
AC4: 'Reply on AC3', Henry Musonda Zimba, 16 Oct 2022
Henry Zimba et al.
Data sets
ZAMSECUR Project Field Data Mpika, Zambia Henry Zimba, Miriam Coenders https://doi.org/10.4121/19372352.v2
Henry Zimba et al.
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