the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Nov 2023
Mind the Gap: Misalignment Between Drought Monitoring and Community Realities
Abstract. Despite recent studies emphasising the dual human and physical nature of droughts, there is a lag in advancing this insight in drought monitoring and early warning systems (DEWS). These systems mainly depend on physical indices and often overlook the experiences of affected communities, resulting in a drought-monitoring gap. This study introduces the Monitoring Efficacy Matrix (MEM) to assess the alignment between officially monitored data, relevant to drought impacts, and the actual experiences of a rural community in Northeast Brazil, which we investigated through interviews. The MEM revealed 'drought-monitoring challenges', composed of mismatches and blindspots between the official data and local experiences. Mismatches stem from varying spatial and temporal levels; blindspots arise from the diversity of local resilience strategies, or vulnerabilities, influencing drought impacts. What we define as a ‘drought-monitoring gap’ results from the tendency to prioritise specific indices and pragmatic spatial and temporal levels over a comprehensive drought-monitoring approach. We posit that a first step to bridge this gap can draw inspiration from recent drought-impact-monitoring initiatives, which are focused on the continuous monitoring of non-extreme events by municipal technical extension officers. However, ultimately bridging the drought-monitoring gap remains conditional on the adaptation of DEWS frameworks to accommodate the integration of qualitative and local data representing the relevant drought-related local context.
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RC1: 'Comment on egusphere-2023-2726', Anonymous Referee #1, 05 Feb 2024
1. General comments
To address the drought-monitoring gap in existing drought early warning systems (DEWS), namely the focus on hydro-climatic drought hazard indicators without considering the human influence on drought risks, the authors present the Monitoring Efficacy Matrix (MEM) for identifying and analyzing data gaps in a systematic manner. For a case study region in Northeast Brazil, the authors use the MEM for comparing three types of data: Qualitative information on drought conditions and impacts as experienced by the rural population over several decades based on interviews, official agricultural data sets for quantifying drought impacts, and conventional drought hazard indicators. Based on this comparison, many monitoring gaps are identified that are classified into so-called mismatches and blindspots. To facilitate a more comprehensive and pro-active drought monitoring, the authors suggest the continuous monitoring of drought impacts of non-extreme events by municipal technical extension officers. They further state that the framework of current DEWS needs to be modified to enable the integration of this kind of qualitative impact data.
The paper provides several important findings regarding monitoring gaps in DEWS, which are informative for the drought research community and for practitioners. Especially the participatory approach (both in the applied methods and in the recommendations) seems valuable as it allows integration of the actual impacts as experienced by the risk groups that are (or should be) the focus of a DEWS. To enhance clarity of the presented methods and results, several minor changes are suggested below.
2. Specific comments
2.1 Writing style
The paper is generally well written. Especially the discussion and conclusions are interesting to read. However, some parts are a bit lengthy and repetitive (addressed in the list of suggestions below). During the revision of the manuscript, please make sure to use a concise language as well as precise definitions and descriptions.
2.2 IPCC risk concept
To understand the methods straightaway, it would be beneficial to apply the widely acknowledged IPCC risk concept throughout the manuscript (IPCC, 2014). According to this concept, drought risk (and impact as the manifested risk) is a function of drought hazard, exposure, and vulnerability. All these three components influence drought risk and impact. In a drought monitoring, it is crucial to first define the targeted risk system (the risk of what for whom) (which is basically your Table 4) and then to identify sector-specific indicators of drought hazard, exposure, and vulnerability that fit to the targeted risk (Hagenlocher et al, 2019; Meza et al, 2020 (with the latter reference already cited in the paper). By using these definitions, some redundant or unclear definitions can be avoided throughout the text.
Specifically, the conventional or physical indicators are drought hazard indicators, and the official data are impact data. Findings from the interviews represent qualitative impact data. These definitions should be used consistently in the manuscript.
The three different periods in Table 4 are distinguished based on the different levels of vulnerability due to the change from rainfed to irrigated agriculture, the construction of small reservoirs and other water storages, and the diversification of crops. I understand that this is addressed within the text, but it should be highlighted at the beginning when introducing Table 4. It should become clear that Table 4 presents the targeted risk systems (the risk of what for whom) and that the columns are characterized by different levels of vulnerability.
2.3 Drought monitoring in the case study area
Since the authors make suggestions on how to improve drought monitoring / management in the case study area, it would be informative to read how drought monitoring, management, and reporting is currently carried out in this region. For instance, which operational drought definition is in place? From the conclusions, I derive that drought response measures are carried out in the case of severe droughts (?). Also, in Table 5 you mention the threshold -1 for SPI. Is this the existing operational drought definition?
As per my understanding, the examined indicators are the only ones available for the study region and (partially?) used by the relevant institutions to trigger drought response measures? Some explanations in the text (e.g., “we chose to examine time series…”, L 75) could also mean that the authors made a selection from a set of available indicators. Then, it would be important to explain why other available indicators were not considered.
2.4 Specific comments in the text
L 13 and L 29: I suggest replacing the term “physical indices” by “drought hazard indices” or “hydro-climatic indices”. Physical indicators can also be indicators of drought exposure, for example.
L 40: Please add: drought hazard indices
L 40: Could you rephrase the last part of the sentence: “are unknown…” (unknown to drought managers?)
L 72: Please specify: “the available conventional drought hazard indices
L 73: Please delete this sentence, since it is too unspecific: “Conventional indices”…
L 74: To be more concise, I suggest starting this sentence with: “These include” instead of “To achieve this, we chose to examine time series characterizing”. Also, “we chose to examine” suggests that the indicators were selected from a larger number of available indicators. From my understanding, these are the only indicators that are available and applied for the study area?
Also, I would suggest to first list the examined hazard indicators (SPI, Brazilian Drought Monitor, reservoir volume and water surface area), and second the impact data (and describe them as hazard and impact indicators).
L 75: Please shortly describe the Brazilian Drought Monitor here. Is it a combination of indicators and expert information like the US Drought Monitor?
L 77: and are also related to the impacts (instead of “fit the impacts”). You show later that they do not always fit.
L 81: “We compare the data” instead of “findings”.
L 82: Please delete the sentence “This framework…” as it is redundant.
L 90: productive activities = irrigation? Please clarify here.
L 93: “smallholder farmers reliant on rainfed agriculture”. Table 4 lists only irrigated agriculture after 2003. If rainfed agriculture is still relevant after 2003, then it should also appear in Table 4 after 2003.
L 124: Please define “semi-structured” and “unstructured”.
L 142: Please shorten the first paragraph of Section 2.4 and use a more concise language to avoid redundancies throughout this section. I think the method would be easier to understand if you mention once at the beginning that it can be applied in the context of different phenomena, and then describe it for drought only (using only examples that are related to drought).
L 159: Please delete this sentence as it is redundant. Also, it suggests that the indicators examined are only a selection by the authors and do not represent the only indicators that are available and applied in the study area (see also comment L 74). Please avoid the term “fit” in this context as you show with the MEM that some indicators do not fit the targeted risk.
L 184-200: I would suggest deleting these two paragraphs as they present many findings that you describe in the results section. From my point of view, the general definition of mismatches and blindspots that you provide is sufficient at this point.
L 211: Table 4: Please mention in the table caption, why three different periods are distinguished (different levels of vulnerability).
L 257: I would suggest using a header that is more informative regarding what you assess or discuss in this section. Furthermore, I would use more specific terms for the indicators (monitored hazard indicators and official drought impact data). I would avoid the term “global data” as it has not been mentioned before and is too unspecific here.
L 260: Suggestion: “agricultural impact data” instead of “agricultural data”.
L 269: Please clarify if these are the only SPI variants that are applied by the relevant institutions. Otherwise, please explain why you chose the averaging periods 3, 6, and 12 for SPI.
L 282: Please describe in the caption of Figure 2 the severity classes according to the Brazilian Drought Monitor. Please also mention that the monitoring of the Sao José dam began in 2004.
L 331: Table 5:
Table 5A, header: “SPI values below -1“: Is this the operational drought definition triggering drought management responses? Please clarify at some point.
Table 5A: Please distinguish hazard indicators and impact indicators in the header.
Table 5A: Since the population did not depend on reservoir storage before 2003, I think it is incorrect to identify the missing reservoir data (water area and reservoir level) as a blindspot. I think this should be replaced by N/A.
Table 5A: The column for Crop yield/ha is left blank. Is this N/A?
Table 5A: M1 for „Quantity produced“ and „Area harvested“, while the description of M1 only refers to livestock. Is M1 correct here?
Table 5B: I cannot follow the explanation for the match for SPI and water security. Since people still used water from shallow and deeper wells and newly installed cisterns, I would suggest using the same explanation as before in Table 5A, which seems still valid here for shallow groundwater and cisterns.
Table 5D: Description „Blindspots“: Blindspots were also attributed to the SPI column (= drought hazard), so they are not only related to reslilience and vulnerability.
L 340, 341: As outlined for Table 5A, I do not think that this is a blindspot, since there was only rainfed agriculture in place before 2003.
L 345: If irrigation was relevant for cotton before 2003, it should have a separate row in Table 5A. Otherwise, please clarify that irrigation did not play a role before 2003 based on the interviews.
L 346: Mismatch 12 is not included in Table 5.
L 368: What type of capacities? “Eroded in the absence of…” is also not clear to me.
L 376: Mismatch 7 instead of 17. Why is this a case of wrong spatial or temporal scale?
L 395: Please add: conventional drought hazard indicators and official impact data
L 397: Please add: between spatial and temporal levels of monitoring
L 398: Suggestion: of all elements of drought hazard, vulnerability and exposure that are relevant for the focus system.
L 398: “that can be impacted by…”: It is not clear if this refers to the indicators or the system. I’d rather delete this sub-sentence.
L 405: Sentence is redundant (see L 403.)
L 406: Suggestion: “In the following periods, as the community diversified its livelihood, food, and water sources, the monitoring gap also increased. This indicates that as the systems became more complex and resilient, such that conventional indicators indices and data became […]”
L 471: Suggestion: two impact datasets
L 506: hydro-climatic rather than physical drivers
L 514: Suggestion: “how well official monitoring data comprising drought hazard and impact indicators relevant to drought impacts align with […]”
L 528: Suggestion: by continuously monitoring drought impacts of non-extreme events
3. Technical corrections
L 44: Meza et al., Carrao et al ,and Haile et al. not in the list of references.
L 269: “belowdepicts”: Please add blank space.
L 291: Given the low resolution of Figure 3, it would be better to replace the red dot by, e.g., a square to ensure that readers with colour vision deficiencies can distinguish the locations.
L 304: Figure 2a instead of 3a
L 353: Please change: […] the resilience […] has increased.
L 379: Figure 2a
L 384: “The same mismatch…”: Please check the grammar of this sentence.
4. References
Hagenlocher, M., Meza, I., Anderson, C. C., Min, A., Renaud, F. G., Walz, Y., et al. (2019). Drought vulnerability and risk assessments: State of the art, persistent gaps, and research agenda. Environmental Research Letters, 14(8), 083002. https://doi.org/10.1088/1748-9326/ab225d
IPCC 2014 Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change edVR Barros et al. (New York: Cambridge University Press).
Citation: https://doi.org/10.5194/egusphere-2023-2726-RC1 -
CC1: 'Reviewer comments on egusphere-2023-2726', Natasha Pauli, 25 Mar 2024
This paper presents an interesting mixed methods approach to understanding community needs for drought monitoring, using a case study approach combining interviews and data extraction from various portals. The method presented is novel and will likely be of interest to researchers and practitioners in the meteorology and agricultural communities. The authors assertions of the need to take into account physical and anthropogenic drivers of drought and water insecurity is well made – although, more clarity could be provided on what are the ‘human drivers’ of drought. The main areas suggested for refinement in the manuscript are as follows: 1) An expanded definition of what is meant by 'drought' should be present early on the manuscript. There are multiple, context-specific interpretations of what is meant by the word 'drought'. In some countries, it might be years without rain, and in other places, it might be an unusually low rainfall growing season. As the subject of this paper, drought deserves a more thorough, critical exploration. 2) The origin and development of the Monitoring Efficacy Matrix is not clear. Was this developed based on other similar frameworks, or is it developed as part of this research – no citations or other examples of the framework are provided in the paper. Further explanation is required. 3) The section reporting on the narrative and interview results presents one 'story' of the case study location. Some discussion of topics where there was disagreement, or vague memories, or that were not brought up by all respondents is warranted. 4) No detailed justification is provided as to why the very small case study community was chosen as the location for this research. Why not another community in Pique Carneiro? At some point in the manuscript, a wider research project with more interviewees is mentioned - is this the reason why Olho d'Agua was selected, as part of the other research? Some justification of the location is warranted. Specific comments follow. Line 34: ‘these have largely served only to increase the understanding of drought’ – something is missing in this sentence. It isn’t clear what areas are still deficient in understanding. Line 70 and onwards, methodological approach: it is suggested to write in paragraphs what the overall approach is, rather than bullet points and ‘step 1, step 2’, particularly because the bullet points are already quite long. Line 75: SPIs are not expanded the first time they are mentioned Line 80: No citation or information on the origin of the MEM is provided Line 88: Why Olho d’Agua? Is this a very typical town? Is it atypical? What does it represent? Section 2.2: Please explain whether the terms ‘monitored’ and ‘non-strategic’ are incompatible. Can there be a ‘monitored non-strategic’ reservoir? Or is it that all strategic reservoirs are monitored, and all non-strategic reservoirs are unmonitored? The repeated use of these terms and the addition of ‘officially’ and ‘informally’ and ‘formal’ is rather confusing, as is the phrase ‘officially unmonitored’. Suggest to first explain what is meant by ‘strategic’ and non-strategic reservoirs early in the section, and then describe the communities. Line 116-118: the rest of the fieldwork campaign is not mentioned again – is it relevant? If it is relevant please include a citation to the other work(s). Line 119: How long were the interviews (range in minutes). Were notes taken during the interview? Was ethics clearance required? Table 1: unclear what is meant by the column heading ‘query category’. Is this rather ‘question themes’? Table 1: Mentions ‘unstructured’ and ‘semi-structured’ interviews. Why the distinction? – and also, suggest to explain why the authors chose these forms of interviews, and explain in particular what is meant by ‘unstructured’. Unstructured can mean that it was a spontaneous conversation about whatever the interviewee wanted to converse about – but several ‘query categories’ are listed, which suggests that they may have been semi-structured, or unstructured but with a few broad topics in mind to start the discussion. In any case, it is suggested to provide some context/references on the methods that were chosen. Line 128: SPIs still not expanded Line 131: SPEI not expanded Line 142: CHIRPS not expanded Table 3: It is not clear what is supposed to go in the boxes of this matrix. Often such matrices (e.g. risk matrices) are filled with qualitative scores or categories such as high/medium/low risk etc. This looks less like a ‘matrix’ and more like a standard table or framework. Please provide some context as to how the matrix is actually supposed to be used/scored, for the benefit of others who may want to use or adapt this matrix. Unclear what is the difference between ‘livelihood system’ and ‘food security’ in a smallholder system (indeed in the later matrix items in these categories appear to overlap). Where did the three categories of ‘livelihood’, ‘food security’ and ‘water security’ come from? Suggest to provide a framework that serves as the basis for these categories (e.g. Line 161 – provide citations). Line 190: ‘flash drought’? The meaning of ‘flash flood’ is widely understood but I am not sure if the same is true for flash drought – is it worth providing a definition of flash drought (perhaps earlier on in the MS when elaborating on the different contextual meanings of drought). 195: ‘count’ should be something different like ‘number’. The phrasing of this whole sentence is a little awkward and could be simplified. Table 4: uses ‘livelihood system’ and ‘food system’ and ‘water system’ whereas previous tables used ‘food security’ and ‘water security’ Line 222 – is it possible to define the policies that changed in 2003? Line 247 – are there any census data available on population demographics of Pique Carneiro which would help illustrate these changes? Figure 2: Provide an overall title as well as the subtitles. It isn’t completely clear why all this information needs to be in the same Figure. Could it be more than one figure? Legends are difficult to read on all subgraphs. Banana yield/area colours are impossible to distinguish – please use more contrast. The tiny graphs at the bottom of SPI1s and drought colours are very small and consequently difficult to interpret. Are they part of Figure d and if so why? Also, the subgraphics are not labelled (which one is 2A, for example?). Figure 3: Very difficult to interpret at the scale provided. Please enlarge. Please label the locations – impossible to make out which is the red circle and which is the black circle. Presumably also this figure presents ‘surface area’ rather than just area. It is the surface area of water? Table 5: This is the main piece of results for the paper. It is unfortunately quite difficult to read and interpret and in some ways resembles a ‘defragmentation diagram’ on a computer. Suggestions to improve legibility include: 1) Use a consistent colour framing for blindspots, mismatches, and matches and provide a legend 2) distinguish different grades of blindspots and mismatches perhaps with different colours 3) provide three separate tables rather than three sub-tables, and give each one more space 4) provide a code that is more descriptive than ‘M1’ but less wordy than the descriptions at the bottom of the table. Unclear why the ‘B’s start with B8. Why not B1? In any event, the letter/number codes are difficult to interpret. The table overall is important and very interesting, but it is just extremely difficult to read given that some cells have text, others have colours, there are two different colours for blindspots, and no discernible pattern to the use of codes and numbers. If the goal of this ‘matrix’ is for it to be used by others, it needs to be easy to read and use. Line 343 – really interesting results here and elsewhere that describe differences in perceptions of local people and the ‘official’ records Line 349 – elsewhere the paper refers to ‘2003-2012’ but here it appears as 2004-2011 – please be consistent. Line 405 and surrounding paragraph – very interesting interpretation and important highlighting of the impact of this approach. Line 468: It is a little disappointing that the paper does not provide alternative indicators. Why not? A couple could be suggested to help overcome the most important blindspots? It seems a real shame not to have included here some suggestions for improving monitoring. Line 480: Humans are not machines and so yes, all interviews will be different and present a version of reality. Question whether ‘biases are negligible’ can be proven based on the fact that other interviewees said similar things – as nothing else has changed (questions and research set up are the same) – this is more about triangulation and corroboration rather than addressing individual people’s ‘bias’. Line 484: question whether a paper ‘under consideration’ can be used as a citation, update with actual citation if now accepted Line 506: Drivers are not qualitative. The way drivers are assessed might be done using qualitative methods. Some slight change in language suggested. Line 521: Excellent and relevant conclusion Line 528: Is this practical? Were technical officers interviewed? Is this something that they have capacity and funding to do?
Citation: https://doi.org/10.5194/egusphere-2023-2726-CC1
Data sets
Data underlyling the publication: Mind the Gap: Misalignment Between Drought Monitoring and Community Realities Sarra Kchouk, Louise Cavalcante, Lieke Melsen, David Walker, Germano Ribeiro Neto, Rubens Gondim, Wouter Smolenaars, and Pieter van Oel https://doi.org/10.4121/6edb96df-569e-41e8-9e6c-ba0a324c4729.v1
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