the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Sep 2024
The effects of upstream water abstraction for commercial export farming on drought risk and impact of agropastoral communities in the drylands of Kenya
Abstract. In the Horn of Africa Drylands (HAD) conflict over water and vegetation is prominent. Additionally, large-scale land acquisitions (LSLAs) are increasing the competition of water, putting local communities at greater risk. A key impact of increasing LSLA's is the decrease in water and land availability for vulnerable agropastoral communities. Despite recent studies, there is still a lack of research that includes the influence of upstream-downstream dynamics on drought risk and impacts of agropastoralists. Therefore, this study further develops an agent-based model (ADOPT-AP) to investigate how upstream large scale commercial farms influence downstream drought risk and impact of agropastoralists in the Upper Ewaso Ng’iro catchment in Kenya. After the model has been calibrated and validated, we assess how commercial exporting farms affect drought risk and impact of downstream communities by simulating different scenarios where the farms are replaced by agropastoral communities or forests. Our results show how both drought hazard characteristics and impacts differ among these scenarios. The analysis shows that in the scenarios where these farms are replaced by forests or communities, drought conditions are alleviated by increasing soil moisture, streamflow, and groundwater tables. These improvements are linked to reduced water abstraction and increased infiltration, benefiting downstream communities by decreasing the distance to household water, and increasing crop and milk production in times of dry periods. Policy interventions should prioritize equitable water distribution, regulation of water use, and promotion of sustainable agricultural practices to mitigate long-term impacts on water resources and community resilience.
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Status: open (until 25 Dec 2024)
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RC1: 'Comment on egusphere-2024-2382', Anonymous Referee #1, 31 Oct 2024
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General comments
Kenya is increasingly growing agricultural products for export. The water needed for commercial export farms competes directly with the water needed for agropastoral communities in Kenya. The authors did study this water competition. They developed a model that integrates a hydrological model with a decision model that simulates adaptation decisions by agropastoralists. After model calibration, they used the model to study the effect of upstream abstraction by commercial export farms on drought risk and its impact on agropastoral communities. The results show that these effects are relatively small compared to the effect of drought periods themselves.
The developed tool in this study is novel due to integrating a hydrological model with a human decision-making model. The upstream-downstream interactions and competition occur in many parts of the world and are therefore very relevant for the EGUsphere readers. The current increase in commercial export farms in different African countries makes the study very topical.
The setup of the study and the main results seem sound. However, the current manuscript raises a large number of questions and comments, which are listed below.
Major comments
Eq. 2: The water demand plays an important role in your study. I suppose that DRYP calculates the volumetric water content θ. If so, mention that here. It is not clear how extensive the irrigation is. Probably most agricultural fields are rainfed. Clarify how you determine the area that is irrigated.
Line 246: The sentence “Greenhouses are modelled as ‘closed systems’ and irrigation water is not added to the model while there is evaporation.” is unclear. Do you assume evaporation at the greenhouse locations?
Line 273: You mention that you list only the relative factors of the hydrology-related parameters in Table 2. Therefore all the factors are dimensionless. However, in this way the reader does not have any information on the actual values of the hydrology-related parameters. Therefore include in Table 2 a column in which you specify the reference values, such as Table S2 in the Supplement.
Line 295: In general de term “model validation” is reserved for the application of a model to different circumstances: a region or period for which the model was not calibrated. Am I right that the simulation results which you show in Figure 4 were derived after model calibration? In that case, you cannot call this “validation of the model”.
Figure 4: Milk production is one of the criteria you use to quantify the impact of water extraction. The simulated milk production does, despite the calibration, strongly deviate from the measured milk production. Especially in the dry periods, which are the focus of your study, the milk production is grossly underestimated by the model. Discuss how this may impact your results and conclusions.
Figure 6: What is the unit of soil moisture in the top graph? Earlier you used volumetric water content (-), which can never exceed 1.0.
Figure 6: This figure shows only the differences of scenarios 2 and 3 with the baseline scenario. In order to put these changes in perspective, I recommend to show also the time series in time, as depicted in Figure S6 of the Supplement.
Line 364: You discuss here where soil moisture is decreased. However, looking at the values in Figure 7, this decrease is always < 0.01. You should mention this here.
Lines 415-417: Here you discuss serious reductions (22 and 36%) of stream flows as a result of your study. However, you did not show these reductions in your paper. In Figure 7 you only mention absolute reductions (m3/d). You might add these % reductions in the Supplement as a function of time and refer to this information here.
Lines 482-483: “it should be noted that not all factors are included in this study and more factors may influence the adoption of drought measures”. Can you mention some of these factors?
Line 485: In line 59 you write: “The main goal of this paper is, therefore, to develop a coupled hydrological and agent-based model (ADOPT-AP) to investigate the influence of upstream large scale commercial export farms on downstream drought risk and adaptation by agropastoralists.” In this Conclusion section you discuss the influence of commercial export farms. However, how do you evaluate the performance of ADOPT-AP? Which parts of the model framework perform well and which parts need further development?
Lines 21-22 (abstract) + 489-490: You state: “The analysis shows that in the scenarios where these farms are replaced by forests or communities, drought conditions are alleviated by increasing soil moisture, streamflow, and groundwater tables.” Your results show that the simulated increases in soil moisture, stream flow and groundwater depth are very small and have a minor effect on crop production, milk production and distance to water. In my view, the current statements are too firm and should be put more in perspective.
Minor comments
Line 55: The phrase “minimum water availability” is unclear. Replace by “minimum river flows” (based on Lanari et al. (2018)).
Line 203: Equation 4 should be equation 2.
Line 206: Change “θ is the water content (–) and θfc is the water content at field capacity (–)” to “θ is the volumetric water content (–) and θfc is the volumetric water content at field capacity (–)”
Line 268: Table 3 should be Table 2.
Line 272: Do you mean Table 2 with “Table 5”?
Lines 366, 381 and 387: Check figure references.
Line 388: produciton should be production
Line 397: resuling should be resulting
Citation: https://doi.org/10.5194/egusphere-2024-2382-RC1 -
AC1: 'Reply on RC1', Ileen Streefkerk, 18 Nov 2024
reply
Response to referee #1
General comments
Kenya is increasingly growing agricultural products for export. The water needed for commercial export farms competes directly with the water needed for agropastoral communities in Kenya. The authors did study this water competition. They developed a model that integrates a hydrological model with a decision model that simulates adaptation decisions by agropastoralists. After model calibration, they used the model to study the effect of upstream abstraction by commercial export farms on drought risk and its impact on agropastoral communities. The results show that these effects are relatively small compared to the effect of drought periods themselves.
The developed tool in this study is novel due to integrating a hydrological model with a human decision-making model. The upstream-downstream interactions and competition occur in many parts of the world and are therefore very relevant for the EGUsphere readers. The current increase in commercial export farms in different African countries makes the study very topical.
The setup of the study and the main results seem sound. However, the current manuscript raises a large number of questions and comments, which are listed below.
>> Thank you for your review and your kind words on the relevance of the paper. Your suggestions and remarks are very much appreciated. We have addressed your comments in the table below.
Major commentsComment
Reply
Eq. 2: The water demand plays an important role in your study. I suppose that DRYP calculates the volumetric water content θ. If so, mention that here. It is not clear how extensive the irrigation is. Probably most agricultural fields are rainfed. Clarify how you determine the area that is irrigated.
Thank you for your remark. Yes, the volumetric content is calculated by DRYP. From the household surveys we obtained the distribution of agricultural field sizes and their irrigation status. This statistical input is used to generate farms across the model domain. We will add this explanation to the revised version of the paper. For your reference, around 8% of households in semi-arid zones, and 17% for semi-humid areas have irrigated lands.
Line 246: The sentence “Greenhouses are modelled as ‘closed systems’ and irrigation water is not added to the model while there is evaporation.” is unclear. Do you assume evaporation at the greenhouse locations?
We understand this is not clear and we will revise the text accordingly. We assume that the flowers evaporate/transpire the full water demand of 40 m3/hectare/day. This means that in the model irrigation and evaporation cancel each other out and we model the greenhouse as a closed system. So, irrigation is not added to the model and there is no evaporation at the greenhouses. We realise that this is not the case for the entire grid cell because the greenhouses do not occupy the whole grid cell, so we updated the evaporation (crop factor) of the model based on the percentage of greenhouses in a grid cell (assuming crop factor of 0 at the greenhouse and 1 at the rest of the cell). The sentence will be re-formulated as:
“Greenhouses are modelled as ‘closed systems’. We assume all water added as irrigation is transpired by the flowers, so irrigation and transpiration cancel each other out. However, as the greenhouses do not cover the entire grid cells, we take into account the percentage of coverage of a grid cell and calculate evaporation and transpiration for the part of the grid cell not occupied by greenhouses.”
Line 273: You mention that you list only the relative factors of the hydrology-related parameters in Table 2. Therefore all the factors are dimensionless. However, in this way the reader does not have any information on the actual values of the hydrology-related parameters. Therefore include in Table 2 a column in which you specify the reference values, such as Table S2 in the Supplement.
Thank you for your suggestion. We will incorporate the actual values as an extra column in the table in the revised version of the manuscript.
Line 295: In general de term “model validation” is reserved for the application of a model to different circumstances: a region or period for which the model was not calibrated. Am I right that the simulation results which you show in Figure 4 were derived after model calibration? In that case, you cannot call this “validation of the model”.
Yes the results were derived after model calibration. We agree with you that we should not call this section validation, but model performance instead.
Figure 4: Milk production is one of the criteria you use to quantify the impact of water extraction. The simulated milk production does, despite the calibration, strongly deviate from the measured milk production. Especially in the dry periods, which are the focus of your study, the milk production is grossly underestimated by the model. Discuss how this may impact your results and conclusions.
Thank you for these valid points. The model indeed underestimated the milk production compared to observed data. This might indicate that milk production is not just influenced by environmental stressors, but that responses of organisations have decreased the impact of drought on milk production (e.g. people receiving cash transfers through humanitarian aid). However, we should also note that the observed data might not be accurate. Milk production does not go down as much as one would expect from the severity of drought impact reported on food security (Reliefweb, 2023) (assuming high milk production is strongly correlated with low food insecurity; Jodlowski et al., 2016). We will add this to the discussion of the paper.
Jodlowski, M., Winter-Nelson, A., Baylis, K., & Goldsmith, P. D. (2016). Milk in the data: food security impacts from a livestock field experiment in Zambia. World Development, 77, 99-114.
ReliefWeb. (2023). Kenya 2022 Drought Response in Review—Kenya | ReliefWeb. https://reliefweb.int/report/kenya/kenya-2022-drought-response-review
Figure 6: What is the unit of soil moisture in the top graph? Earlier you used volumetric water content (-), which can never exceed 1.0.
Please note that 1e*-5 is on top of the x-axis. However, we understand that this might be hard to see and we will change the location.
Figure 6: This figure shows only the differences of scenarios 2 and 3 with the baseline scenario. In order to put these changes in perspective, I recommend to show also the time series in time, as depicted in Figure S6 of the Supplement.
We indeed thought about doing this, but as the changes are relatively small it is really hard to see the differences compared to the variation in time.
Line 364: You discuss here where soil moisture is decreased. However, looking at the values in Figure 7, this decrease is always < 0.01. You should mention this here.
Thank you for your suggestion, we will incorporate the values here.
Lines 415-417: Here you discuss serious reductions (22 and 36%) of stream flows as a result of your study. However, you did not show these reductions in your paper. In Figure 7 you only mention absolute reductions (m3/d). You might add these % reductions in the Supplement as a function of time and refer to this information here.
That is a good suggestion, we will incorporate the % reductions in the supplement and refer to it in the main text.
Lines 482-483: “it should be noted that not all factors are included in this study and more factors may influence the adoption of drought measures”. Can you mention some of these factors?
Other factors include for example the risk and time preferences, included in economic theories (Expected Utility Theory and Rank Depended Utility theory) (Schrieks et al., 2023). We will specify this a bit more in the revised version by adding ‘such as variables which are included in economic theories (e.g. risk and time preference).’
Line 485: In line 59 you write: “The main goal of this paper is, therefore, to develop a coupled hydrological and agent-based model (ADOPT-AP) to investigate the influence of upstream large scale commercial export farms on downstream drought risk and adaptation by agropastoralists.” In this Conclusion section you discuss the influence of commercial export farms. However, how do you evaluate the performance of ADOPT-AP? Which parts of the model framework perform well and which parts need further development?
Thank you for your reflection, we should indeed incorporate some results of the model performance in conclusion as well. We will mention that the model underestimates milk production during dry periods. The model framework as a whole is tested with performance metrics (BR, KGE). However, the human-decision part of the model needs more validation, but that is currently not possible due to lack of longitudinal data (as mentioned in the discussion).
Lines 21-22 (abstract) + 489-490: You state: “The analysis shows that in the scenarios where these farms are replaced by forests or communities, drought conditions are alleviated by increasing soil moisture, streamflow, and groundwater tables.” Your results show that the simulated increases in soil moisture, stream flow and groundwater depth are very small and have a minor effect on crop production, milk production and distance to water. In my view, the current statements are too firm and should be put more in perspective.
We agree the wording is too firm, and we will put this sentence in perspective. We will also add; “However, compared with the impact of drought hazard itself this change is very small.”
Minor comments
Line 55: The phrase “minimum water availability” is unclear. Replace by “minimum river flows” (based on Lanari et al. (2018)).
Line 203: Equation 4 should be equation 2.
Line 206: Change “θ is the water content (–) and θfc is the water content at field capacity (–)” to “θ is the volumetric water content (–) and θfc is the volumetric water content at field capacity (–)”
Line 268: Table 3 should be Table 2.
Line 272: Do you mean Table 2 with “Table 5”?
Lines 366, 381 and 387: Check figure references.
Line 388: produciton should be production
Line 397: resuling should be resulting
>> Thank for providing minor comments on spelling and wording, they will be incorporated in the revised version.Citation: https://doi.org/10.5194/egusphere-2024-2382-AC1
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AC1: 'Reply on RC1', Ileen Streefkerk, 18 Nov 2024
reply
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