Round 1
Reviewer 1 Report
Dear authors,
the manuscript presents an interesting research. Although I suggest its revision to make it more interesting to readers as well as more cited. I suggest the following.
Introduction - to add more research studies with the similar topic.
Methodology - use and point out the equations separately, not as a part of the text. The make the methodology of the research clear to present the flowchart of the used methodology.
Conclusion - to point out the novelty of the presented research.
I suggest to make all figures clearer as the text in the figures is too small.
Author Response
We appreciate your time and valuable comments.
Thank you for your comment. We reviewed new related studies and have complemented our previous review presented in the Introduction (Page 2).
The review includes the following.
1. Adedeji et al. [19] assessed the spatiotemporal variation of droughts in Nigeria using remote sensing and geographic information system techniques to find regions prone to droughts risk. Jincy Rose and Chitra [20] employed SPI and the Standardized Precipitation
Evapotranspiration Index (SPEI) drought indices to evaluate the temporal variation of drought in an Indian River basin. They
demonstrated that the number of drought events increased based on SPEI compared to SPI. Lin et al. [21] used SPI for drought
characterization and introduced a drought risk assessment framework based on drought hazard, vulnerability and exposure. Liu et al. [22] used the SPI and SPEI to investigate drought spatiotemporal patterns in China's Sichuan Province. They also detected drought hotspots and carried out a drought characterization.- Thanks for this recommendation. We placed Eq. 5 separately (Page 9). Eqs. 1 to 9, which are used in this research, are also presented separately. To make our method more replicable, we have placed a diagram in which we
present the methodology steps (Figure 2, page 7). The outline of “Sect. 2.3 Drought risk assessment” also follows such a methodological structure. Regarding the rest of the formulas and definitions presented throughout the paper, we believe that it is better to leave them since they are not used to carry out the methodology but to introduce the state of the art, definitions, and previous formulations. We believe that if we add them as equations, we could confuse the reader, thinking that they are part of our methodology.
- Thanks. Our first manuscript already pointed out the novelty of the investigation. We have improved the wording in the Conclusions to be more precise. The Conclusions now presents the following. We propose a novel comprehensive method for drought risk assessment that integrates three components (1) drought hazard (DHI), (2) vulnerability (DVI) and (3) resilience (DREI). This method is applied in eight sub-basins of the Lempa River basin, the longest river in Central America. The drought index SEDI is used to calculate drought. SEDI is found to be capable of capturing agricultural and hydrological drought. SEDI-derived drought characteristics are used to compute DHI. This research introduces an improvement of the DVI calculation by considering different weights for the seven socioeconomic and physical/ infrastructural factors that are evaluated. These weights are based on the AHP experts’ opinions approach. Our formulation of drought risk index (DRI) considers the DREI that is calculated based on four weighted factors. The three indices DHI, DVI, and DREI, are combined through the geometric mean to calculate the drought risk index (DRI). The DHI obtained from the SEDI takes actual evapotranspiration into account and pictures useful results, especially for agricultural drought, which is one of the most important practices over the basin. This research also contributes to developing a drought risk assessment methodology and the drought study in Central America. Our study focuses on the hydrological instead of the sub-basins' political boundaries, which has rarely been taken into account before. The paper is helpful for the decision-makers in the area to have a broader vision of the basin. It can also come in handy to allocate resources more smartly or interfere immediately with Drought Risk Reduction (DRR). Results of this research are also useful for those interested in socioeconomic drought.
- Thank you very much for this comment. We update the following Figures 1, 5, 6, 7, 9, 10, 11, 12, and 13.
Note: We include a new Figure (Fig. 2, page 7) that shows the methodology’s flowchart. Therefore, Figures from 3 to 15 correspond to 2 to 14 of our previous draft.
Best regards,
Vitali
Reviewer 2 Report
I read the article entitled “Spatiotemporal drought risk assessment considering resilience and heterogeneous vulnerability factors: Lempa transboundary river basin in the Central American dry corridor”. The article is interesting, but in my opinion it needs improvement.
- in part 2.1: the hydrographic network of individual sub-catchments and the location of meteorological and hydrological stations used for the study should be shown. It would be useful to know about average annual precipitation totals, air temperature and unit runoff. People from outside must be familiar with the geography of the catchment area
- in part 2.2: There is no information on how the individual drought indicators were calculated (averaging the values ​​for different posts, or selecting 1 meteorological post that was representative for a given sub-catchment).
- I miss an explanation in the form of a methodological diagram/scheme, method of data selection and verification, correlations between indicators
- I believe that the drought values ​​should be presented for the period 1981-2010, while the data for the calculation of drought indicators come from 1980-2010. The later adoption of 5-year periods, e.g. 1981-85 against the background of the multi-year period 1981-2010, will then be comparable. There is no information whether the droughts were calculated on the basis of 1 or more meteorological or hydrological stations
Technical notes - selected examples:
- correct figure 14
- p. 26 line 48 is “Yevjevich, V.M. and n. 23, Objective approach to definitions and investigations of continental hydrologic droughts, An. Hydrology papers, 1967. " and should be “Yevjevich, V.M. An objective approach to definitions and investigations of continental hydrologic droughts. Hydrology Papers 1967, 23. "
- p.24 line 2 is "Journal of climate" and should be "Journal of Climate"
- p. 25 line 20 is “Science of the total environment” and should be “Science of the total environment
- p. 21 line 21 Marasco, S., et al., please list all authors, similarly in other items
And much more ....
- go through the literature list carefully
Author Response
We appreciate your time and valuable comments.
- Thanks, we updated Figure 1 (Page 4) following your comment. The following text has been included in Sect. 2.2. Data (Page 5) to address your comment. The basin has a daily average temperature of 23.5℃, a total annual rainfall average of 1,698 mm, and a total annual runoff average of 10,780 million m3
- Thanks, now, we have explicitly written that the drought index SEDI was calculated with the catchmentwide actual (AET) and potential evapotranspiration (PET) derived from WEAP model (Page 5, last paragraph). The following text has been included.
In each of the eight sub-basins, the catchmentwide variables considered in the water balance were calculated. We used the catchment-wide actual (AET) and potential evapotranspiration (PET) for calculating the drought index. The drought index
calculation was carried out in each of the eight subbasins following the procedure presented in Sect. 2.3.1.2.
- Thanks, we have included Figure 2 (page 7) that shows a scheme of the methodology to carry out the drought risk index calculation. The hydro-meteorological data used as input for the WEAP model was provided already verified for the Salvadoran meteorological services. Moreover, instead of verifying the actual (AET) and potential evapotranspiration (PET) calculated with WEAP, we focused on comparing the calculated droughts and drought-related historical information of cereal production and the ENSO indices (El Nino/La Nina years). The Discussion section presents our comparison (Pages 21 and 22). Although it is part of this research (in its whole), in this paper, we do not compare drought indices, we only use SEDI. We have improved the wording of our manuscript to clarify this. In particular, we modified the following statement presented in Sect. “3. Results” by
removing some parts.
- Thanks. We decided to present the drought values for the period 1980-2010 because although we have only six months with values (the second half of the year) due to the 6-month aggregation period, the percentages of drought area show the month of September 1980 in drought in the Lempa River basin (Figure 8), and also because we compared with data of cereal production and ENSO index for the period 1980-2010. For the drought risk assessment, we consider from 1981 because, in the case of 1980, we do not have the entire year, and we believe that this would not allow us to compare between periods correctly. For the calculation of drought, please see comment #2. We have also improved the following section “2.3.1.2. Standardized Evapotranspiration Deficit Index (SEDI)”.
- Thanks for the noted typos. We corrected the figure, now Figure 15 (page 22). We have updated the reference list using the MDPI style with EndNote and then manually check them carefully.
Best regards,
Vitali.
Round 2
Reviewer 2 Report
The authors only partially took into account the comments.
My comments:
- change the annual runnoff average to unit runoff (dm3.s-1.km-2) or discharge (m3.s-1)
- Fig 1 requires correction, the Lempa river should be marked with a thicker blue line, and its tributaries with thinner blue lines, the direction of the main river should be marked and a linear scale should be inserted on the map
- correct the writing of the literature, eg Yevjevich
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Review of the manuscript (jmse-1043694) entitled “Spatiotemporal drought risk assessment considering resilience and heterogeneous vulnerability factors: Lempa transboundary river basin in the Central American dry corridor”. I have read the revised manuscript and specific observations are detailed below:
Introduction
Line 43: reference 27 is it correct?
From line 47 to 72: the Sendai framework of DRR by UNDRR adopt a different glossary and approach to the disaster risk formula, why in this paper the authors choose a different approach? Moreover, why Exposure component is inserted in the Vulnerability component by the authors? It seems to me quite difficult to interact with decision makers using a non common definition of risk.
Line 66-67: could the authors provide some reference for the statement?
Line 84-85: could you provide more explanation about choosing SEDI for the calculation of drought?
Line 87: Who are the experts? Do the authors conduct a survey within the local administration?
Line 92: The vulnerability of the basin to drought is only function of population density and agricultural occupation? Could you clarify this concept?
Materials and methods
As general comment, it seems to me that the paper is more addressed to the hydrological drought, then the outcomes are used to assess agricultural drought and socio-economic drought. Different agricultural production systems have different responses to drought stress. But, for instance, in the proposed methodology there is no distinction between irrigated lands and rainfed agriculture. Cash crops, such as coffee, cotton, sugar cane have different water needs in respect to cereals. Not only by their physiological needs, but also in terms of the organization of the production and the use of irrigation. The percentage of irrigated land is considered by the authors directly related to drought (line 287) while normally the presence of irrigation avoid water stress in the crop fields. In this case it means that lack of water availability (hydrological drought) could trigger drought stress in the crop fields because of insufficent water for irrigation (please note: phenomenon which is delayed in time). Last but not least the monthly resolution is not sufficient to intercept short dry spells that could hit crops in their most vulnerable stages (i.e. flowering stage).
Line 117-121: the source of data are not well described. The reference 27 is incomplete. The PET and AET are calculated with WEAP using which dataset?
Line 126-127: Could the authors provide a more detailed description of the basin sub-division? Which soil dataset has been used in this paper?
Figure 1: It seems that the national boundaries are not completely represented in the map (El Salvador - Honduras)
Line 137-141: Do the authors use a specific parametrization of the formula for the Lempa basin?
Line 227: Table 2 or Figure 2?
Line 278-279: Could you provide some references?
Line 280-281: Could you provide some references?
Line 283-284: The percentage of people is calculated using statistics in different years or an unique value? The population value is the aggregation of the sub-basin population? Which is the data source?
Line 287-288: Could you provide more explanation about this assumption with some references?
Line 289-291: could you specify the source of soil dataset?
Line 292-294: Do the authors means the agricultural production? Which dataset and which aggregation do they use? Does any crop system in Lempa basin equally vulnerable to drought? Does the income of cash crops is taken in account in the vulnerability index?
Line 335-339: Could the authors provide a more detailed description of the factors in the DREI formula, the source of data, the assumptions (as for the vulnerability component) and the choice method of the coefficients in the formula (8)
Results
Line 346-348: the assumption of the low flows in the basin, trough the SEDI06, are the best proxy of the drought condition in the basin is more oriented to the assessment of the hydrological drought while the paper seems to manage all the three droughts (meteorological, agricultural and hydrological) in one single index.
Discussion
Line 489-492 I strongly recommend to plot the drought condition in the basin with the Nino/La Nina index values in a same figure allowing an overall vision of the evolution of the two phenomena and their correlation.
Figure 13: The cereal production is increasing in the last years while the DRI is higher. Could you explain this two conflicting trends? Why in this graph you do not plot the cash crops production too?
Conclusion
Line 563-565: In my opinion, this statement is not supported by evidence in your paper.
Line 576: The study is conducted at sub-basin scale (that could be more than 1000 Km²). The definition of hot-spot and more sensitive and resistant zone is quite coarse. In my experience this scale is not useful for decision makers at national and sub-national level.
About the paper: why the authors use the 1980-2010 period considering that we are in 2020? And, for me, it is quite strange to compare the 1980-1985 (6 years) with the following 5-years aggregations, it could be more consistent to use the 5-years aggregation in the 1981-2010 period.
Author Response
We appreciate your time and valuable comments.
1. Thank you very much. We appreciate your review and valuable comments that helped us improve our paper.
2. Thanks, the reference has been updated following the style guide for MDPI Journals. The new reference number in the revised manuscript is 34.
Lines 141-143
3. a) Thank you for this observation. The text has been updated, indicating your comment (regarding the Sendai framework) as follows.
Despite its importance, drought risk assessment, especially in the presence of resilience, is still a new and open debate area [20, 23, 24]. The framework for studying drought risk, including the terminology used, is not a fixed matter since it is updated as new studies are available. The terminology presented in this research corresponds to that commonly used in drought studies. Globally, there are efforts to unify terminologies and frameworks for disaster risk reduction. One of these efforts is the Sendai Framework for Disaster Risk Reduction 2015-2030 (hereafter Sendai Framework), where the United Nations Office for Disaster Risk (UNDRR) is tasked to support its implementation [25]. One of the Sendai Framework's objectives is to point out who is responsible for estimating, managing, and reducing risk: the state; however, it also indicates that local governments and the private sector, among other stakeholders, should share such tasks. Although elaborated with the time's scientific and technical knowledge, Sendai Framework was mainly focused on decision-makers; however, its consultation is recommended to the interested reader. This research's study framework is mainly based on scientific publications and is relatively similar to Pacific Disaster Center's (PDC) approach [26]. The terminology, the approach, and the target audience may differ from the Sendai Framework. The way this research and previous ones relate to the Sendai Framework is beyond the research scope.
Lines 73-88
b) As indicated in the text (lines 291-315), we have taken the existing vulnerability components from the literature. We have made its calculation more robust by employing AHP to obtain the weights of heterogeneous components.
4. The references [12, 13, 22] have been provided.
Lines 67
5. The following explanation has been added to the text. The Standardized Evapotranspiration Deficit Index (SEDI) is based on the Evapotranspiration Deficit (ED) anomalies, where PET and AET are used. By taking into account AET, which is the volume of the water evaporated directly from the soil, SEDI captures a more accurate image of the ground's drought condition [35, 44, 45]. As SEDI is more related to the water anomalies in the soil, it can be used to assess agricultural drought due to the use of AET.
Lines 246-250
6. The experts include both practitioners and scientists at the local and international levels. More precisely, this has been indicated in the text as follows.
We implemented AHP as follows. The assignment of weights is based on experts' opinions. Weights range from 0 to 1, and the sum of all equal to the unity. Based on experts' opinions, including the MARN staffs as practitioners as well as local and international scientists of the scope, we ranked the seven factors considered (population density, female to male ratio, poverty level, agricultural occupation, irrigated land, soil water holding capacity, and food production) from 1 (the most vulnerable factor) to 7. Then, we employed AHP to find the weight of each factor (wi).
Lines 347-352.
7. Thank you for pointing this out. We mention population and agricultural occupation as two factors that make the selected basin vulnerable to drought. In the introduction, we have clarified that we considered seven factors to calculate the drought vulnerability index (DVI) (Lines 101-103). These factors are population density, female to male ratio, poverty level, agricultural occupation, irrigated land, soil water holding capacity, and food production. A detailed overview of these factors is provided in the Methodology section within lines 291-315.
In order to prevent ambiguity, our statement is revised as follows.
We assess drought risk spatiotemporally considering eight sub-basins. The Lempa River basin located in Central America is selected to illustrate the methodology because it is vulnerable to drought mainly due to its high population density and agricultural occupation [27, 28].
Lines 105-108.
8. a) The data used come from the WEAP model carried out in the basin. The approach for calculating drought indices using hydrological model data is widely used (e.g., Maskey and Trambauer, 2014). In this approach, the use of data from hydrological modeling does not necessarily imply a preference for hydrological drought. Given the Standardized Evapotranspiration Deficit Index (SEDI) formulation, the mainly addressed drought is agricultural. However, SEDI has also been used to calculate meteorological and hydrological drought in previous studies. Moreover, the runoff data is not used for calculating the drought index but the potential and actual evapotranspiration data.
We have included the following explanation in the document. Although SEDI has been used to calculate meteorological and hydrological drought in previous studies [10, 49, 50], due to data input it uses, i.e., actual and potential evapotranspiration, SEDI is also used for agricultural drought monitoring. SEDI with the following calculation method was employed to calculate the agricultural drought.
Lines 251-254.
b) To take the comment into account, further explanations have been added to the text as follows.
Irrigated Land (IL): the percentage of irrigated land to total land. As the supplied water for irrigated land depends on the surface and groundwater resources, the IL factor is directly related to meteorological drought [2, 11, 55]. IL involves cash crops that usually are dependent on irrigation. Usually, the presence of irrigation avoids water stress in the crop fields in comparison to rainfed. At the same time, IL still represents the system's vulnerability to lack of supplied water due to droughts [56]. We employed a systematic approach that considers all parts of the system, not the separate ones.
Lines 303-309.
In calculating DVI, seven factors were considered, including Irrigated Land (IL) and Soil Water holding Capacity (SWC). These two factors were included to make a general assessment of the different agricultural production systems. The irrigated agricultural area is directly related to IL, while rainfed agriculture is linked with SWC. Currently, DVI is limited to considering an overall assessment of the agricultural condition. Therefore, the different water needs of the various crops may not be fully represented. However, factors considered in DVI calculation make it robust for assessments at the basin scale.
Lines 645-651.
c) Thank you for indicating this important remark. This limitation is mentioned in The Conclusions as a future research direction as follows. Finally, in this paper, drought is calculated on a monthly scale, so the effects of short dry spells that could hit crops in their most vulnerable stages (i.e., flowering stage) may not be captured in the DHI calculation. Therefore, applying the proposed approach in shorter time resolutions are also recommended.
Lines 659-662.
9. The source of data is indicated as follows.
Precipitation, temperatures, and soil data used in the WEAP model were obtained from El Salvador's Ministry of Environment and Natural Resources (MARN) [34]. Streamflow data were also obtained from the same source.
Lines 141-143
Reference 27 has been updated, now it is Ref. 34 (Line 142).
PET and AET were calculated with precipitation, temperatures, and soil data obtained from MARN.
The methodology for calculating PET and AET is described in Lines 144-159.
10. The division of the sub-basins was carried out within the framework of the "El Salvador Rapid Assessment Mission" project in which an evaluation of two aspects about water resources was carried out, its availability and quality. After the interaction between local authorities and scientists in charge of carrying out the study, the division mentioned above was obtained. Runoff modeling was carried out within the framework of the mentioned project.
Lines 129-134
11. Figure 1 was updated to show the boundaries of the countries.
Line 125
12. We have used the standard procedure in WEAP for calculating runoff based on MARN input data.
13. It is Table 2.
14. The references have been provided.
Lines 294-295
15. The references have been provided.
Lines 296-298
16. The percentage of people in the agricultural segment was separately obtained for each year. The comment is considered in the text as follows.
FM, PL, AO, IL, and FP time series of 31 years have been obtained from World Bank data [60].
Line 320
17. Further explanations have been added as follows. Irrigated Land (IL): the percentage of irrigated land to total land. As the supplied water for irrigated land depends on the surface and groundwater resources, the IL factor is directly related to meteorological drought [2, 11, 55]. IL involves cash crops that usually are dependent on irrigation. Usually, the presence of irrigation avoids water stress in the crop fields in comparison to rainfed. At the same time, IL still represents the system's vulnerability to lack of supplied water due to droughts [56]. We employed a systematic approach that considers all parts of the system, not the separate ones.
Lines 303-309
18. This is indicated in the text as follows. FM, PL, AO, IL, and FP time series of 31 years have been obtained from World Bank data [60]. We also have used the "Global Assessment of Water Holding Capacity of Soils" dataset [61] to calculate SWC in each sub-basin.
Lines 320-322
19. Food production includes food crops that are considered edible and contain nutrients. This is mentioned in lines 313-315.
FP time series of 31 years have been obtained from World Bank data [60] (Line 320).
We understand that food product leads to income. However, as the amount of food income depends on various dynamic factors, including price, it cannot reflect a precise vision of vulnerability in our paper.
20. Thank you for your comment. The text has been modified as follows.
In this study, we consider governance, infrastructure, economic capacity, and environmental capacity as the factors to assess DREI. The high values of DREI show better conditions. Based on the weighted average to address differences in data quality and availability [26, 38], DREI is calculated with Eq. 8.
Lines 367-370
The factors to calculate DREI are proposed based on the previous studies of the Pacific Disaster Center (PDS) [63]. A description of each factor, based on the PDS, is provided as follows.
(1) Governance (Go): The stability and effectiveness of institutional structures to provide equitable public services, freedom in selecting government, and enforcement of laws to prevent and control crime and violence. The total number of voter participation, violent crimes, extortion, and threats per 10,000 populations, as well as the percentage of householders that receive trash collection, are five components that create the Go factor [26, 26].
(2) Infrastructure (In): The ability to exchange information and physically distribute goods and services (Transportation and Health Care). Three healthcare infrastructure components including the number of physicians, nurses, midwives, and hospital beds per 10,000 population, and two transportation infrastructure components including the length of road and rail lines by total land area and the number of ports and airports per 10,000 km2 land area [23], and two communicational infrastructure components including the percentage of householders with a fixed phone line and percentage of householders with at least one cellular phone [64], are the seven components creating the In factor [26].
(3) Economic capacity (EC): A region's ability to absorb economic losses and mobilize financial assets to provide the required assistance. Total monthly income per capita, census value added per capita, and percentage of households that receive remittances are the three components of EC [26, 64].
(4) Environmental capacity (EnC): The environment's ability to recover and maintain species health, biodiversity, and critical ecosystem services after impact. The percentage of total land area that is protected represents this factor [26]. Components that create each DREI factor are normalized based on the max-min approach (with a value between 0 to 1) and equally contribute to creating the factor (i.e., the average of components). Each component has been calculated for the eight sub-basins. This value is obtained from the weighted average of the calculated values for departments that the sub-basin embraces. Each department's weight has been gained from the percentage of the sub-basin area, which is covered by the department.
Lines 373-401
21. Thank you for pointing this out. In order to prevent this ambiguity, we have modified the text as follows:
Mercado et al. [50] showed that their SEDI is highly correlated with SPI and SPEI (as meteorological drought indices) in different time steps, especially in lower than nine months. These results are identical to our findings. On the other hand, SPI with 3 or 6 months is considered as an agricultural drought index [3, 65]. Additionally, we compared the river streamflow and SEDI for 3, 6, 9, and 12 months. Lines 408-412
As mentioned in the methodology, SEDI is more oriented to calculating agricultural drought for its formulation. However, it has also been used to calculate meteorological and hydrological drought as a proxy. The procedure indicated where low flows were compared with different aggregation periods of SEDI (1, 3, 6, 9, and 12), allowing us to identify the aggregation period that better correlates with the low flows time series. In this sense, SEDI06 allows the identification of more than one type of drought.
A drought indicator to identify more than one type of drought is a common practice in drought monitoring [50] and (Maskey and Trambauer 2015).
22. Thank you. To take the comment into account, we have added the El Niño/La Niña plot in figure 13 (line 539). This figure 13 is compared with figure 7.
Since the drivers of El Niño/La Niña and its correlation with the drought were not the main objective of this study, we limited our research to carry out a general analysis of drought occurrence and the presence of El Niño/La Niña years.
23. a) This is justified in the text as follows:
These observations indicate that the results could be used for the assessment of agricultural drought. Generally, a growing pattern in cereal and crop production is observed during our study horizon, which may seem odd at first glance as the drought risk has increased. This pattern is because cereal and crop productions are influenced by different factors, including agricultural land and technology. As an instance, El Salvador's agricultural land has grown from 14100 km2 (or 68.05 % of land area) in 1980 to 15350 (or 74.08 % of land area) in 2010 [70].
Lines 565-570
b) We have also added crop production index in figure 14, and the following text is added: Drought is a significant driver that leads to cereal loss both in yield and quality worldwide [67]. If ED and thereby SEDI depict the drought, there should exist a relation between SEDI and cereal production when droughts are severe both spatially and temporally [68]. Accordingly, we also compared the patterns of drought areas (figure 7) with cereal production of El Salvador [69]. Figure 14 shows the country's cereal and crop production for the period 1980-2010.
Lines 550-554
24. Thanks, we have removed our statement.
25. Thanks for this comment. The statement is removed, and this is indicated in conclusion as a future direction. Our case study is investigated at a sub-basin scale. Accordingly, applying our proposed method within higher resolution cases and fully distributed manner is useful for specifying hotspot, more sensitive, and resistant zones when performing evaluations at national and sub-national levels.
Lines 656-662
26. Our study horizon is within 1980-2010 due to the availability of input data for WEAP simulations, specifically PET and AET.
The study horizon does not affect our paper's purpose to design a new structure for drought risk assessment. The period allows the calculation of the drought index and the implementation of the methodology introduced in the paper.
As figures 3 and 7 show, there is no considerable event during the first year (1980). Accordingly, selected periods did not affect the DHI and DRI maps when we omitted the results of 1980. We corrected the first period to 1981-1985 (5 years), based on your precise comment.
Reference
Maskey, S. and P. Trambauer (2015). Hydrological modeling for drought assessment. Hydro-Meteorological Hazards, Risks and Disasters, Elsevier: 263-282.
