View of Assessment of Drought Disasters (EDI) Based on ENSO and NOAA Climate Data Using ANN in Bondowoso Regency

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Jurnal Teknik Pengairan: Journal of Water Resources Engineering, 2023, 14(1) pp. 25-37 https://jurnalpengairan.ub.ac.id/ | p-ISSN : 2086-1761 | e-ISSN : 2477-6068

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Assessment of Drought Disasters (EDI) Based on ENSO and NOAA Climate Data Using ANN in Bondowoso Regency

Evid Zulhaqi¹, Gusfan Halik^2*), Retno Utami Agung Wiyono²

1Student, Master’s Program of Civil Engineering, Faculty of Engineering, University of Jember

2Lecturer, Master’s Program of Civil Engineering, Faculty of Engineering, University of Jember

Article info: Research Article

DOI :

10.21776/ub.pengairan.2023.014.01.03

Keywords:

ANN; drought; drought assessment;

EDI; ENSO; NOAA

Article history:

Received: 24-09-2022 Accepted: 10-02-2023

*)Correspondence e-mail:

[email protected]

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Bondowoso Regency is declared to be at high risk for the threat of drought based on the IRBI map of the National Disaster Management Agency in 2020. This study aims to assess drought disasters based on ENSO and NOAA data. The proposed method for rainfall modeling was Statistical Downscaling (SD) using the Backpropagation Neural Network (BPNN), for which the output models were used for drought assessment using the EDI. The reliability test of the rainfall model is to compare the rainfall model with the observed rainfall. The reliability test of the EDI is to compare the results of the EDI analysis from the input rain model data with the observed rainfall data. ANN modeling results showed that monthly rainfall predictions are better. This is indicated by the R² monthly, and 10-day base values of 0.97 and 0.83, respectively, with RMSE values of 0.05 and 0.07, and the best modeling in EDI analysis was R² 0.88 and 0.63 with RMSE 0.35 and 0.65. Based on the results of this study, it is shown that drought disaster assessment based on ENSO and NOAA climate data can be used as an alternative to support the decision-making system for drought mitigation.

Cite this as: Zulhaqi, E., Halik, G., Wiyono, R, U, A. (2023). Assessment of Drought Disasters (EDI) Based on ENSO and NOAA Climate Data Using ANN in Bondowoso Regency. Jurnal Teknik Pengairan: Journal of Water Resources Engineering, 14(1), page.25-37. https://doi.org/10.21776/ub.pengairan.2023.014.01.03.

1. Introduction

In 2007, agricultural land of 403.9 Ha in Bondowoso Regency experienced crop failure caused by a drought. Droughts have significant environmental and socio-economic impacts [1]. A drought is a condition where a region experiences a deficit of rainfall for a sufficiently long period, which occurs temporarily and recurrently [2].

Rainfall is dynamic; the extreme decrease and increase in rainfall are affected by the El Nino- Southern Oscillation (ENSO) [3]. This phenomenon occurs due to an anomaly in the sea surface temperature in the Pacific Ocean. ENSO has La Nina and El Nino phases, where the La Nina phase impacts longer periods of rainy seasons. In comparison, El Nino gives the impact of comparatively longer periods of the dry season to the rainy season. This condition becomes the cause of occurring drought disasters.

Many studies have examined drought as part of the efforts to mitigate disasters. However, these studies are often impeded by observed rainfall data that is not complete. To overcome this hurdle, this research involved modeling rainfall by utilizing data from ENSO and climate data from the National Oceanic and Atmospheric Administration (NOAA), which comprise real-time global climate data in the atmosphere. This dataset has been used in several prior studies for the evaluation of drought [4],[5],[6]. Unfortunately, ENSO data and NOAA climate data are data with a low resolution; to be able to be used to model rainfall at the regency scale, a downscaling technique is required.

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26 Zulhaqi, Halik, Wiyono: Prediction of Drought Disasters Based on ENSO and NOAA Climate Data

Downscaling techniques are composed of Dynamic Downscaling (DD) and Statistical Downscaling (SD). The Statistical Downscaling (SD) technique has the advantages of a simple approach and does not require a large amount of computational resources.

Statistical Downscaling (SD) uses the Backpropagation learning algorithm, which is a development of the Artificial Neural Network (ANN) and has the advantage of being able to result in smart models based on data from past occurrences [7]. In this research, the Statistical Downscaling (SD) modeling technique takes advantage of the Backpropagation learning algorithm to be used to model rainfall.

The downscaled rainfall model was then used as a variable to measure the severity, intensity, and duration of droughts. The evaluation of droughts requires an index [8]; one of these utilized indices is the meteorological drought index. This drought index is comprised of several methods, among which are Percent Normal Index (PNI), Reconnaissance Drought Index (RDI), Effective Drought Index (EDI), Standardized Precipitation Index (SPI), Z-score index (ZSI), and China Z Index (CZI).

In prior studies, these methods have been used to evaluate the drought disaster in Lumajang, East Java [9], and tested in Iran and India [10],[11]. The EDI method was the best method to evaluate droughts because of the correspondence of analysis results toward drought occurrences on the field.

In addition, this method has the advantage of being able to illustrate the durations of drought occurrences [12]. This research aims to assess drought in Bondowoso Regency, use the EDI drought index based on rainfall modeling by utilizing ENSO and NOAA climate data, and evaluate the relationship between drought occurrences and the El Nino phenomenon.

2. Materials and Methods 2.1 Research Location

The location for this research is Bondowoso Regency, Province of East Java, Indonesia. Its geographical location is situated at the coordinates of 113° 48’ 10” - 113° 48’ 26” East Longitude and 7° 50’ 10” - 7° 56’ 41” South Latitude. The research location is presented in Figure 1.

Figure 1. Map of the Research Location

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2.2 El Nino-Southern Oscillation (ENSO)

El Nino-Southern Oscillation (ENSO) is a phenomenon that occurs due to the interaction between the atmosphere and the sea surface temperature (SST) in the Pacific Ocean. ENSO has phases of El Nino, neutral, and La Nina [13]. El Nino and La Nina are the factors that cause the extreme decrease and increase in rainfall in Indonesia [3].

2.2.1. Sea Surface Temperature (SST)

The phases of ENSO are measured based on the anomaly of the sea surface temperature (SST) that occurs in the Pacific Ocean. The locations NOAA uses to monitor the sea surface temperature (SST) are presented in Figure 2 [14].

Figure 2. Regions of Measurement for Sea Surface Temperature (SST)

In the figure, the box with a green border represents regions 1 and 2 (SST 1 and 2), the purple border represents region 3 (SST 3), the yellow border represents region 4 (SST 4), and finally, the red border is region 3.4 (SST 3.4), which is a splice of regions 3 and 4. A previous study used the sea surface temperature (SST) to model rainfall in Ethiopia [15]. In this research, SST from 1990 to 2021 became one of the variables used to model rainfall.

2.2.2. Oscillation Nino Index (ONI)

Evaluation of the level and duration of occurrences for the phases of ENSO requires an index;

one of the indices the NOAA uses is the ONI (Oscillation Nino Index) [14]. This index was used to measure the influence of the decrease and increase in rainfall intensity in West Java [3]. This research used ONI to evaluate the correlation between the ENSO phenomenon and drought occurrences.

2.3 Rainfall Modelling

At the rainfall modeling stage, selecting the appropriate input variables is crucial to obtain the best model. For example, in previous research in West Java, ENSO data in the form of SST 3, SST 3.4, and SST 4 indicated a relationship with rainfall intensity [3], while in Bondowoso Regency, East Java, Indonesia, there was a relationship of rainfall intensity with NOAA climate data for Prec_Wtr, Rhum, Shum, Uwind, and Vwind at the levels of the surface as well as 500 and 850 millibars [16]

ENSO data are data that possess the characteristics of high complexity and time-series property [17].

In contrast, NOAA climate data possess the characteristic of being consistent toward occurrences of rain.

In this research, ANN modeling was performed with the input variables of ENSO data and NOAA climate data from 1990 to 2021. ANN modeling with its input variables is shown in Table 1.

Table 1 shows nine trial scenarios for different combinations of selected input variables. One is Sea Surface Temperature (SST) data for regions 3, 3.4, and 4. Next, Prec_Wtr is the weight of raindrops in the air. Then, there are the variables of humidity at levels of 500 and 850 millibars, composed of relative humidity (Rhum), as the comparison of the amount of water vapour in the air and the maximum amount of water vapour that may be contained in the air, and specific humidity

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(Shum), as the mass of water vapour contained in the air. Finally, the last variables involve wind speed at the surface levels as well as 500 and 850 millibars, composed of Uwind as the speed of the wind from east to west and Vwind as the speed of the wind from north to south.

Table 1. Input Variables for ANN Modelling

Variable Name Input Variable

ANN 1 ANN 2 ANN 3 ANN 4 ANN 5 ANN 6 ANN 7 ANN 8 ANN 9

SST 3 √ √ √ √ √ √ √ √ √

SST 3.4 √ √ √ √ √ √ √ √ √

SST 4 √ √ √ √ √ √ √ √ √

Prec_Wtr √ √ √ √ √ √ √ √ √

Rhum 500 √ √ √ √ √ √ √ √

Rhum 850 √ √ √ √ √ √ √

Shum 500 √ √ √ √ √ √

Shum 850 √ √ √ √ √

Uwind √ √

Uwind 500 √ √

Uwind 850 √ √ √

Vwind √ √

Vwind 850 √ √ √

2.4 Downscaling

ENSO data and NOAA climate data are global data; a downscaling process is first required to model rainfall at the local scale. Downscaling is an effort to relate climatic circulation at the global and local scales [18]. This method was used based on the assumption that climate at the global scale influences local climate [19]. The downscaling technique is composed of two approaches. The first is downscaling based on a continuously executed process, Dynamic Downscaling (DD); this approach requires many computational resources. The second is Statistical Downscaling (SD), which is the determination of a small-scale grid based on an empirical equation in a statistical manner on a large-scale grid in a certain period; the success of this approach is determined by the grid domain selection [20]. This research used the method of Statistical Downscaling (SD) to transform data on a global scale to become data on a local scale.

2.5 Artificial Neural Network (ANN)

Artificial Neural Network (ANN) is a system that is developed to overcome weaknesses in conventional data analysis. This system can store data based on previous occurrences, thus resulting in a smart model [21].

Each ANN architecture covers an Input Layer where data is introduced to the network, a Hidden Layer where data is processed, and an Output Layer as the place of data processing results [22].

In this research, the Input Layer was given the input variable as the best schema for the combined ENSO and NOAA climate data (Table 1) from 1990 to 2021. Meanwhile, the target variable was given the input data of observed rainfall from 1990 to 2021, and the Output Layer would result in the model rainfall variable from 1990 to 2021. The ANN modeling results are determined by the inter-neuron relationship pattern, the training method, and the activation function [18].

2.6 Effective Drought Index (EDI)

The Effective Drought Index (EDI) method is a method to monitor meteorological drought, which was discovered by Byun in 1999. In Iran, this method was tested to evaluate the drought level [11];

it was also used to evaluate the drought in the Ngrowo watershed, Trenggalek, East Java [23]. In this research, drought analysis was performed on the observed rainfall and model rainfall from 1990 to 2021. Drought analysis using the EDI method in this research involved two kinds of drought analysis in two stages. The first was EDI drought analysis using the input variable of observed rainfall, called the observed EDI and the second was EDI drought analysis using the input variable of model rainfall, called the predicted EDI.

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The first stage in analyzing drought by the EDI method is the calculation of Effective Precipitation (EP) using the formula below [24]:

𝐸𝑃𝑖 = ∑^𝑖_𝑛=1[(∑^𝑛_𝑚=1𝑃𝑚)/𝑛] (1) Where:

i = Length of Summation

Pm = Rainfall m of the Previous Period n = Number of Data

The second stage is the calculation of mean Effective Precipitation (MEP) for n years and the difference between EP and MEP as the Difference of Effective Precipitation (DEP), with the following formula:

𝐷𝐸𝑃 = 𝐸𝑃 − 𝑀𝐸𝑃 (2) The final stage calculates the EDI value by dividing the DEP value by the DEP standard deviation

value.

𝐸𝐷𝐼 = ^DEP

SD (DEP) (3) The classification of drought levels for the EDI method is presented in Table 2.

Table 2. Effective Drought Index (EDI) Values Drought Level EDI Value Approximately Normal -1.0 < EDI ≤ 1.0

Moderate Drought -1.5 < EDI ≤ -1.0 Severe Drought -2.0 < EDI ≤ -1.5 Extreme Drought EDI ≤ -2.0 Source: [25]

2.7 Model Reliability

A modeling activity requires an analysis stage for model reliability. This analysis is intended to measure the accuracy and magnitude of modeling results toward observations. For this research, analysis was carried out in three stages: training, validation, and testing. Analysis of the modeling results used observed rainfall data from different periods: the training stage used data from 1990 to 2011, the validation stage used data from 2012 to 2016, and the testing stage used data from 2017 to 2021.

This analysis used statistical indicators as coefficients of determination R-squared (R²) and Root- Mean-Square Error (RMSE). The model is good if the R² value approaches 1 and the RMSE value approaches 0. The following are the equations [26]:

𝑅²= [ ^∑^𝑁^𝑖=1^(𝑂^𝑖^−𝑂̅^𝑖^)(𝑀^𝑖^−𝑀^̅^𝑖⁾

√∑^𝑁_𝑖=1(𝑂_𝑖−𝑂̅_𝑖)²√∑^𝑁_𝑖=1(𝑀_𝑖−𝑀̅_𝑖)²

] (4)

𝑅𝑀𝑆𝐸 = √¹

𝑁∑^𝑁_𝑖=1(𝑂_𝑖− 𝑀_𝑖)² (5) Where:

Oi = Observed Data Mi = Model Data

N = Number of Tested Data 3. Results and Discussion

3.1 Rainfall Modelling

Rainfall modeling was based on nine variable input scenarios (Table 1). The performance results of each rainfall modeling scenario for Wonosroyo Station are presented in Figure 3.

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0,00 0,02 0,04 0,06 0,08 0,10 0,12

0,80 0,82 0,84 0,86 0,88 0,90 0,92 0,94 0,96 0,98

JST1 JST2 JST3 JST4 JST5 JST6 JST7 JST8 JST9

= R_squared

= RMSE

RMSE

R_squar ed

0,00 0,02 0,04 0,06 0,08 0,10 0,12 0,14

0,70 0,75 0,80 0,85 0,90 0,95 1,00

= R_squared

= RMSE

RMSE

R_squared

0,00 0,02 0,04 0,06 0,08 0,10 0,12

0,82 0,84 0,86 0,88 0,90 0,92 0,94 0,96 0,98

= R_squared

= RMSE

RMSE

R_squared

R² = 0.9172

0,00 100,00 200,00 300,00 400,00 500,00 600,00 700,00 800,00 900,00 1.000,00

0 200 400 600 800 1000

Model Rainfall (mm)

Observed Rainfall (mm)

R² = 0.8031

0 50 100 150 200 250 300 350 400 450

0 50 100 150 200 250

Model Rainfall (mm)

Observed Rainfall (mm)

Figure 3. Rainfall Modeling Performance for Wonosroyo Station

Figure 3 shows that scenario 8 (ANN 8) had the best performance for the rainfall model, with R² values for the training, validation, and testing stages that ranged from 0.92 to 0.97 and RMSE values that ranged between 0.054 and 0.041. This scenario used the input variables of SST3, SST3.4, SST4, Prec_wtr, Rhum 500, Rhum 850, Shum 500, Shum 850, Uwind surface, Uwind 850, Vwind surface, and Vwind 850. Monthly rainfall modeling for Wonosroyo Station STA Wonosroyo using scenario 8 (ANN 8) is presented in Figures 4 and 5.

Figure 4. Monthly Rainfall Chart, Wonosroyo Figure 5. Monthly Rainfall R²Value, Wonosroyo Figures 4 and 5 showed that the model performance has an R-squared (R²) value of 0.92. Thus, patterns were correlated between the monthly rainfall from the modeling results and the observed rainfall at Wonosroyo Station. This situation is similar to the results of research conducted in the Sampean watershed. According to [7], rainfall modeling based on NOAA data resulted in a model resembling observed rainfall.

Figure 6. 10-Day Rainfall Chart, Wonosroyo Figure 7. 10-Day Rainfall R²Value, Wonosroyo

Training Stage (1990-2011)

Validation Stage (2012-2016)

Testing Stage (2017-2021)

0 100 200 300 400 500 600 700 800

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

= OBSERVED RAINFALL

= MODEL RAINFALL

Rainfall (mm)

Year

0 50 100 150 200 250 300 350 400 450

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Rainfall (mm)

= OBSERVED RAINFALL Year

= MODEL RAINFALL

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Rainfall modeling was also performed for the 10-day rainfall; the results are presented in Figures 6 and 7. They show the comparison between the observed and model rainfall and that the model performance value by R-squared (R²) for Wonosroyo Station was found to be 0.80. R² and RMSE values for all stations are presented in Table 3.

Table 3. Performance of Rainfall Modelling No

Rainfall Station

Monthly Rainfall

10-Day Rainfall

R² RMSE R² RMSE

1 Grujugan Lor 0.96 0.06 0.80 0.11 2 Maesan 0.95 0.06 0.75 0.14 3 Sukokerto 0.96 0.07 0.78 0.08 4 Pakisan 0.96 0.08 0.77 0.23 5 Tlogosari 0.95 0.07 0.81 0.12 6 Pinang Pait 0.95 0.08 0.71 0.14 7 Wonosroyo 0.96 0.05 0.80 0.08 8 Kejayan 0.94 0.08 0.80 0.11 9 Kesemek 0.94 0.08 0.83 0.10 10 Sbr. Gading 0.96 0.06 0.75 0.14 11 Jeru 0.92 0.08 0.70 0.14 12 K. Pengairan 0.96 0.05 0.79 0.08 13 Ancar 0.95 0.06 0.78 0.07 14 Klabang 0.95 0.07 0.75 0.11 15 Selolembu 0.97 0.05 0.78 0.17 16 Wringin 0.94 0.06 0.74 0.07 17 Blimbing 0.94 0.06 0.80 0.10 18 Wonosari 0.96 0.06 0.81 0.10 19 Prajekan 0.94 0.06 0.67 0.08 20 Glendengan 0.94 0.06 0.78 0.06 21 Tallep 0.94 0.06 0.78 0.07 22 Pringduri 0.95 0.06 0.80 0.08 23 Kolpoh 0.94 0.06 0.76 0.07 24 Cermee 0.92 0.05 0.69 0.07

Table 3 shows that the best performance for monthly rainfall modeling had values of 0.97 (R²) and 0.05 (RMSE), and for 10-day rainfall modeling had values of 0.83 (R²) and 0.07 (RMSE). The results of monthly rainfall modeling had more accurate results compared to 10-day rainfall modeling.

3.2 Analysis of Observed Drought

Analysis of observed drought involved the analysis of drought with the input data using the data of observed rainfall on the field. This analysis was used as a reference to measure the performance of the predicted EDI and to compose the spatial map of drought distribution. The analysis results using observed rainfall at Wonosroyo Station from 1991 to 2021 are presented in Figures 8 and 9.

Figures 8 and 9 show that the most extreme drought (EDI ≤ -2.0) at Wonosroyo Station during the past 30 years occurred in 2007 with values of -2.53 (monthly rainfall) and -2.32 (10-day rainfall).

Extreme drought also occurred in 2003 and 1992. Drought index values of all stations for the year of the most extreme drought in the last 30 years, which occurred in 2007, are presented in Table 4.

Table 4 shows the drought index values for the year 2007. The above table indicates that extreme drought occurred in 13 rain stations for the analysis results using monthly rainfall data and 14 rain stations for the analysis results using data. The analysis required evaluating its correspondence toward the actual situation on the field by comparing the results with the data on the agricultural lands affected by drought from 2004 to 2009. The correspondence evaluation is presented in Figure 10.

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-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

EDI

Year

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

EDI

Year

Figure 8. EDI, Monthly Rainfall, Wonosroyo Figure 9. EDI, 10-Day Rainfall, Wonosroyo Table 4. Drought Index Values for 2007 (Extreme Drought)

No

Rainfall Station Monthly Rainfall 10-Day Rainfall

EDI Category EDI Category

1 Grujugan Lor -2.33 Extreme Drought -2.38 Extreme Drought 2 Maesan -2.49 Extreme Drought -2.58 Extreme Drought 3 Sukokerto -1.87 Severe Drought -2.32 Extreme Drought 4 Pakisan -1.76 Severe Drought -1.65 Severe Drought 5 Tlogosari -1.80 Severe Drought -2.00 Extreme Drought 6 Pinang Pait -1.96 Severe Drought -2.11 Extreme Drought 7 Wonosroyo -2.53 Extreme Drought -2.32 Extreme Drought 8 Kejayan -1.94 Severe Drought -2.00 Extreme Drought 9 Kesemek -1.91 Severe Drought -2.28 Extreme Drought 10 Sbr. Gading -1.71 Severe Drought -2.23 Extreme Drought 11 Jeru -1.70 Severe Drought -1.22 Moderate Drought 12 K. Pengairan -2.30 Extreme Drought -2.61 Extreme Drought 13 Ancar -2.17 Extreme Drought -1.56 Severe Drought 14 Klabang -2.17 Extreme Drought -1.57 Severe Drought 15 Selolembu -2.34 Extreme Drought -2.20 Extreme Drought 16 Wringin -2.14 Extreme Drought -1.42 Moderate Drought 17 Blimbing -1.93 Severe Drought -1.27 Moderate Drought 18 Wonosari -2.57 Extreme Drought -2.12 Extreme Drought 19 Prajekan -2.55 Extreme Drought -2.10 Extreme Drought 20 Glendengan -2.18 Extreme Drought -2.11 Extreme Drought 21 Tallep -2.24 Extreme Drought -1.79 Severe Drought 22 Pringduri -2.02 Extreme Drought -1.97 Severe Drought 23 Kolpoh -1.96 Severe Drought -1.37 Moderate Drought 24 Cermee -1.50 Severe Drought -1.16 Moderate Drought

Figure 10 shows that the highest level of drought severity, which occurred in 2007, from the EDI analysis results with both monthly and 10-day rainfall, corresponded with the data on the greatest area of agricultural lands affected by drought, 309.4 Ha.

3.3 Analysis of Predicted Drought

Analysis of the model drought used the input variable of rainfall modeling (Figures 5 and 7) based on ENSO data and NOAA climate data. The analysis results for predicted EDI for Wonosroyo Station from 1991 to 2021 are presented in Figures 11 and 12.

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-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00 4,00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

EDI

YEAR

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00 4,00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

EDI

YEAR

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00 4,00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

EDI

YEAR

= EDI Observation

= EDI Prediction

Figure 10. Correspondence Testing of Analysis Results for the Drought Index

Figure 11. Predicted EDI Values, Monthly Rainfall, Figure 12. Predicted EDI Values, 10-Day Rainfall, Wonosroyo Wonosroyo

Figures 11 and 12 show that based on the analysis of predicted EDI with the modeling input of monthly rainfall, extreme drought (EDI ≤ -2.0) occurred in 2007 with a value of -2.33; meanwhile, with 10-day rainfall, the value was -2.72.

Next, the two analysis results for the predicted EDI above had to be tested regarding their modeling performance; the test was performed by comparing the analysis results of predicted EDI with observed EDI. The comparison of predicted EDI and observed EDI is presented in Figures 13 and 14.

Figure 13. Predicted EDI and Observed EDI Figure 14. Predicted EDI and Observed EDI (Monthly Rainfall, Wonosroyo) (10-Day Rainfall, Wonosroyo)

Figures 13 and 14 illustrate a correspondence of patterns between the analysis results of predicted EDI and the observed EDI with monthly rainfall, shown by the most extreme drought occurring in

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00 4,00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

EDI

YEAR

= EDI Observation

= EDI Prediction

0 50 100 150 200 250 300 350

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00

Jan 2004 Mrt 2004 Mei 2004 Juli 2004 Spt 2004 Nov 2004 Jan 2005 Mrt 2005 Mei 2005 Juli 2005 Spt 2005 Nov 2005 Jan 2006 Mrt 2006 Mei 2006 Juli 2006 Spt 2006 Nov 2006 Jan 2007 Mrt 2007 Mei 2007 Juli 2007 Spt 2007 Nov 2007 Jan 2008 Mrt 2008 Mei 2008 Juli 2008 Spt 2008 Nov 2008 Jan 2009 Mrt 2009 Mei 2009 Juli 2009 Spt 2009 Nov 2009 AFFECTED AREA (HA)

EDI

YEAR

= EDI (Monthly Rainfall)

= Area of Land Affected by Drought

= EDI (10-Day Rainfall)

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R² = 0.8728

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00

EDI Model

EDI Observation

R² = 0.6337

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00 4,00

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00

EDI Model

EDI Observation

2007. The model performance using the R-squared (R²) statistical indicator for Wonosroyo Station is presented in Figures 15 and 16.

Figure 15. R²Values, Predicted EDI, Wonosroyo Figure 16. R²Values, Predicted EDI, Wonosroyo (Based on Monthly Rainfall) (Based on 10-Day rainfall)

Figures 15 and 16 show the performance values of the analysis results for predicted EDI based on monthly rainfall data and 10-day rainfall data, each with R²values of 0.87 and 0.63. R² and RMSE values for all stations are presented in Table 5.

Table 5. Correspondence Testing of Predicted EDI toward Observed EDI

No Rainfall Station

EDI (Based on Monthly Rainfall)

EDI (Based on 10-Day Rainfall)

R² RMSE R² RMSE

1 Grujugan Lor 0.84 0.40 0.57 0.74

2 Maesan 0.83 0.41 0.60 0.72

3 Sukokerto 0.76 0.49 0.62 0.69

4 Pakisan 0.83 0.41 0.40 0.85

5 Tlogosari 0.84 0.41 0.47 0.79 6 Pinang Pait 0.84 0.40 0.54 0.73 7 Wonosroyo 0.87 0.36 0.63 0.65

8 Kejayan 0.86 0.37 0.56 0.73

9 Kesemek 0.81 0.44 0.54 0.72

10 Sbr. Gading 0.80 0.45 0.56 0.74

11 Jeru 0.86 0.37 0.62 0.70

12 K. Pengairan 0.83 0.41 0.50 0.80

13 Ancar 0.84 0.40 0.48 0.81

14 Klabang 0.84 0.40 0.56 0.71

15 Selolembu 0.81 0.44 0.58 0.73

16 Wringin 0.83 0.42 0.55 0.71

17 Blimbing 0.86 0.38 0.49 0.77 18 Wonosari 0.78 0.47 0.40 0.89 19 Prajekan 0.85 0.39 0.58 0.69 20 Glendengan 0.82 0.42 0.48 0.81

21 Tallep 0.86 0.37 0.60 0.69

22 Pringduri 0.82 0.43 0.56 0.76

23 Kolpoh 0.88 0.35 0.59 0.70

24 Cermee 0.86 0.38 0.57 0.71

Table 5 shows the performance of EDI based on monthly rainfall and 10-day rainfall, each with the best values for R²of 0.88 and 0.63 as well as RMSE of 0.35 and 0.65.

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3.4 Relationship of Drought and ENSO

Figure 17. Relationship of the ENSO Phenomenon and Drought by EDI

Figure 17 illustrates occurrences of El Nino and drought from 1991 to 2021; of the nine occurrences of the El Nino phenomenon, eight coincided with droughts. The analysis results indicated a relationship between the El Nino phenomenon and occurrences of droughts in Bondowoso Regency. As with countries that have four seasons, droughts are affected by the El Nino phenomenon, as indicated by research conducted in Yunnan, China [27].

3.5 Spatial Map of Drought

The spatial map of drought distribution used the distribution analysis by the IDW method with the input data of the observed EDI (based on monthly rainfall) in 2007, the year with the most extreme occurrence of drought within the period of the last 30 years. The drought distribution map is presented in Figure 18.

Figure 18. Distribution Map of Drought by EDI, 2007

-3,00 -2,00 -1,00 0,00 1,00 2,00 3,00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

= EL NINO

= NORMAL

= LA NINA

= EDI (Monthly)

= EDI (10-Day)

YEAR

EDI

(12)

Figure 18 illustrates the distribution of drought levels in Bondowoso Regency, with the red- colored areas representing regions with extreme drought conditions in several sub-districts, among them Maesan, Bondowoso, Wonosari, and Wringin, as well as parts of Prajekan and Sukosari. The areas that are colored yellow illustrate further decreasing drought levels.

4. Conclusion

The modeling of monthly rainfall using ENSO data and global climate data from the NOAA satellite can provide very satisfactory results. It is shown by the very good reliability criteria of the model, with R² values from 0.86 to 0.92 and RMSE values from 0.08 to 0.05. Rainfall modeling considering the integration of input data of local climate (ENSO) and global climate (NOAA), can result in dynamics of rainfall changes that approximate field conditions.

Drought analysis by the EDI method for the past 30 years shows that the most extreme drought occurred in 2007. The analysis results correlated with the data for the areas of agricultural land affected by drought. As such, it can be stated that the EDI method for analyzing drought can illustrate drought conditions in the past 30 years. The drought that occurred in 2007 coincided with the El Nino phenomenon.

Based on the results of this research, it can be stated that drought disasters are not only affected by the global climate but are also affected by the local climate phenomenon of El Nino. Therefore, EDI drought assessment based on ENSO and NOAA climate data may be used to make mitigation decisions for drought disasters.

This meteorological study of droughts needs to be developed in further research, particularly in assessing the impact of climate change on drought disasters.

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