Hydro-meteorological Disasters Research Articles

Navigating Samarinda's Climate: A Comparative Analysis of Rainfall Forecasting Models

Modeling rainfall data is critical as one of the steps to mitigate natural disasters due to weather changes. This research compares the goodness of traditional and machine learning models for predicting rainfall in Samarinda City. Monthly rainfall data was recapitulated by the Meteorology, Climatology, and Geophysics Agency from 2000 to 2020. The traditional models used are Exponential Smoothing and ARIMA, while the machine learning model is a Neural Network. Data is divided into training and testing with a proportion of 90:10. Evaluation of goodness-of-fit using Root Mean Squared Error Prediction (RMSEP). The research results show that the Neural Network has better accuracy in predicting rainfall in Samarinda. Forecasting results indicate that monthly rainfall trends suggest that the months with the highest rainfall occur around November to March. This research provides important implications for developing a warning system for hydrometeorological disasters in Samarinda. The superior points in this research are:• Modeling rainfall data in Samarinda City using several forecasting methods: Exponential Smoothing, ARIMA, and Neural Network.• The Neural-Network algorithm used is Backpropagation with data standardization.• Information about predicted high rainfall can be used to issue early warnings of floods or landslides. Disaster mitigation through policies to regulate water discharge based on rainfall predictions to prevent floods and drought.

The Effect of Applying A Problem-Based Learning Model Using Google Earth on Understanding Hydrometeorological Disaster Mitigation at SMAN 1 Tanjung Palas Tengah

This study aims to reveal whether there is a positive influence in the application of the problem-based learning model using google earth media on the understanding of hydrometeorological disaster mitigation in class XI of SMA N1 Tanjung Palas Tengah. The research design used in this study is a nonequivalent control group design with a quasi-experimental research method (Quasi Experiment). The sampling technique used in this research is purposive sample. The research instruments used are test instruments in the form of multiple-choice questions, as well as non-test instruments in the form of observation and documentation. The results of the study showed that there was a positive influence in the application of the problem-based learning model using google earth media on the understanding of hydrometeorological disaster mitigation in class XI at SMAN 1 Tanjung Palas Tengah in the form of a significant increase in the average class score. This is based on the results of N-Gain obtained a value of 66.96% and the t-test obtained a value of Sig. (2-tailed) of 0.001. This is also supported by the results of observational studies that show a positive response from the research subjects.

Spatial evaluation rainfall estimation on weather radar using Marshall–Palmer reflectivity–rainfall rate in Banten

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Progress and Challenges Toward Coherence Among Disaster Risk Reduction, Climate Change Adaptation, and Sustainable Development

In 2023, which marked the midpoint of the post-2015 global agendas, the findings of midterm review process of the Sendai Framework (MTR SF) were presented. Various data demonstrate that hydro-meteorological disasters, presumably exacerbated by climate change, are increasing in frequency and intensity and causing widespread adverse impacts and associated losses, which calls for coherence among disaster risk reduction (DRR), climate change adaptation, and sustainable development. This article provides findings and recommendations through an analysis of publicly available data and reports, including monitoring for the Sendai Framework and Sustainable Development Goals as well as the MTR SF. The main findings in the MTR SF are that countries focus primarily on short- to mid-term adjustments rather than on long-term structural transformation and that policies and measures to reduce exposure are less well described. Recommendations include (i) the development of a national disaster loss database to collect data on both large- and small-scale and slow-onset disasters; (ii) the implementation of “custom indicators” to supplement the Sendai Framework Monitoring (SFM) by utilizing available loss data collection and exceptional governance measures in the case of high-impact disasters; (iii) the employment of common metrics with the SFM at the national level for climate change statistics and indicators to promote integrated data collection and coherence; (iv) the introduction of new indicators to measure and verify implementation; and (v) formulation and elaboration of a long-term comprehensive national strategy that goes beyond national DRR strategies to integrate DRR-related sectoral policies in support of relevant stakeholders, underpinned by secured resources and adaptive governance with monitoring and evaluation to enable a transformation toward more resilient and sustainable future.

Comparison of Climatology of Precipitation Diurnal Cycle in Kalimantan Deduced from Rain Gauge and IMERG Data

Abstract The diurnal cycle is repetitive pattern of changes in weather factors on land and oceans due to the Earth rotation. Kalimantan Island as the largest island in the Maritime Islands region has a complex rainfall pattern that is influenced by local factors and global circulation. The research was conducted to find out the climatological comparison of the diurnal cycle of rain in Kalimantan based on rain gauge data and Integrated Multi-SatellitE Retrievals for GPM Version 07 (IMERG V07) data for the period 2016 - 2022. Data validation using rain gauge data to validate and calibrate IMERG satellite data, improving the accuracy and reliability of satellite rainfall estimates. The diurnal cycle is evaluated through a Precipitation Amount (PA), Precipitation Frequency (PF), and Precipitation Intensity (PI) every hour. The results show that the maximum rainfall occurs in the afternoon and evening along the coast, and in the middle of the night and morning on the Kalimantan plains. Kalimantan has high rainfall intensity values at short rainfall durations, i.e. rain lasting less than 3 hours. This is expected to improve the quality of hydrometeorological disaster mitigation in Kalimantan.

Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes

The analysis of rainfall patterns in the Indonesian region utilized the Empirical Orthogonal Function (EOF) method to identify spatial and temporal variations. The study evaluated the dynamic influence of the Tropical Indian Ocean (TIO) and the Tropical Pacific Ocean (TPO) on Indonesian rainfall using monthly data from the Southeast Asian Climate Assessment and Dataset (SACA&D) spanning from January 1981 to December 2016 and encompassing three extreme El Niño events in 1982/1983, 1997/1998 and 2015/2016. Using combined reanalysis and gridded-observation data, this study evaluates the potential impact of the two primary modes in the tropical Indo-Pacific region, namely the Indian Ocean Dipole (IOD) and the El Niño-Southern Oscillation (ENSO) on Indonesian rainfall. The analysis using the EOF method revealed two main modes with variances of 35.23% and 13.07%, respectively. Moreover, the results indicated that rainfall in Indonesia is highly sensitive to sea surface temperatures (SST) in the southeastern tropical Indian Ocean and the central Pacific Ocean (Niño3.4 and Niño3 areas), suggesting that changes in SST could significantly alter rainfall patterns in the region. This research is useful for informing government policies related to anticipating changes in rainfall variability as part of Indonesia’s preparedness for hydrometeorological disasters.

Annual and Interannual Rainfall Variability in Indonesia Using Empirical Orthogonal Function (EOF) Analysis and Its Response to Ocean-Atmosphere Dynamics

We investigate rainfall variability in Indonesia using the Empirical Orthogonal Function (EOF) method. The analysis starts by taking three main modes of EOF results, namely EOF1, EOF2, and EOF3. The EOF1 region is southern Indonesia, from southern Sumatra to Timor Island, parts of Kalimantan, parts of Sulawesi, and parts of Irian Jaya. The EOF2 region is located in northwestern Indonesia and includes the northern part of Sumatra and the northwestern part of Kalimantan. The EOF3 region covers Maluku. This study aims to analyze the annual and inter-annual variability of rainfall in anticipation of the threat of hydrometeorological disasters. Based on the correlation value of the principal component (PC) with the dipole mode index (DMI) and Niño3.4 index, it has a period similar to El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Rainfall in Indonesia is very sensitive to sea surface temperature (SST) in the southeastern Indian Ocean and the central Pacific Ocean, which means that rainfall patterns in Indonesia can change significantly if SST in the region changes.

Hybrid Model for Multistep-Ahead Rainfall Forecast in Northeast India: A Comparative Study

Abstract Among all hydrometeorological parameters, rainfall strongly correlates with hydrometeorological disasters. The rainfall forecast process remains challenging due to the nonlinear, nonstationary nature and multiscale variability of rainfall. Moreover, the unique microclimate in different regions further complicates the forecasting process. This study proposes a hybrid model employing multivariate singular spectrum analysis (MSSA) and long short-term memory (LSTM) for multistep-ahead hourly rainfall forecasting in urban areas of northeast India. The model is trained and evaluated using high-resolution (12 km) hourly meteorological data from the Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset for Guwahati (plain) and Aizawl (hilly) regions from 2015 to 2019. The hybrid model outperforms the single LSTM model in both plain and hilly regions, with an average percentage gain of 47.99% and 43.88% for symmetric mean absolute percentage error (SMAPE) and root-mean-square error (RMSE) in the case of the Guwahati dataset and 84.59% and 82.27% in the case of the Aizawl dataset, respectively. The performance of the LSTM model significantly improves as the zero values in the observed data are eliminated after reconstruction by MSSA. This enables the model to discern essential patterns and relationships in the data, which leads to more accurate forecasts. However, the hybrid model underestimates the rainfall, which can be tackled by hypertuning the parameters. The study highlights the importance of considering the interplay between rainfall and meteorological parameters for accurate rainfall forecasting in urban areas. The proposed MSSA–LSTM model can be used as a decision support tool for urban planning and disaster management. Significance Statement This study addresses a critical need in multistep-ahead hourly rainfall forecasting in urban areas, with a focus on the unique conditions of northeast India. Our research has identified the presence of red noise in the rainfall data, shedding light on the complexities of the underlying rainfall patterns. Furthermore, we delve into the intricate interplay between rainfall and meteorological parameters, providing valuable insights into the factors influencing rainfall dynamics. Notably, our study underscores the region-specific challenges in rainfall forecasting. While the hybrid model demonstrates reasonable accuracy in the plains, its performance in the hilly region falls short of expectations. This highlights the nuanced nature of rainfall prediction in areas with varying topography and emphasizes the need for tailored forecasting approaches.

Socio-psychological factors explaining public engagement and support for drought disaster risk management

Droughts have become a critical high-impact hydro-meteorological disaster and are projected to become more extreme in the near future. The Iberian Peninsula is unarguably considered a pronounced hot spot for drought. Worrisome projections for drought risk come to emphasize the urgency of building drought-resilient societies, through integrated risk management processes that bring together all societal spheres, including lay citizens. However, citizens remain disengaged from climate risk and adaptation processes. Therefore, it is critical to understand how societies respond to climate-related hazardous processes, such as drought, and to determine the predictors of citizens' engagement in disaster risk management. To respond to this research need, this study sought to explore citizens' preferences for drought management policies and to ascertain the psychosocial predictors of citizens' willingness to take action towards drought and support for drought risk management. A cross-sectional study was implemented, in which 401 Portuguese participants enrolled in a survey questionnaire measuring predictor variables of citizens' willingness to take action and support for drought risk management. A robust set of statistical analysis was implemented, mainly based on structural equation modelling. Results demonstrate that citizens tend to prefer low-cost measures and policies that due not imply personal heightened burden, at a financial and behavioral level. Moreover, personal experience with drought, risk perception, perceived urgency and emotional states play a critical role in citizens’ engagement and support for disaster risk management. Findings are discussed in light of their theoretical and practical implications, particularly for risk communication and for risk management policymaking.

Flood Disaster Risk Management In Manado City

Geological and hydrometeorological disasters are disasters that are often experienced by every region in Indonesia. Hydrometeorological disasters such as floods are influenced by strong westerly winds and world climate change. Apart from that, inappropriate land conversion is also one of the factors causing this disaster. One of the regions in Indonesia that is highly vulnerable to flood disasters is the city of Manado. Geographically, Manado City is surrounded by mountains and 5 large rivers that pass-through Manado City, namely the Tikala river and the Tondano river which merge in the Paal 2 area, the Malalayang river, the Sario River and the Bailang River. To overcome this disaster, it is necessary to carry out appropriate mitigation efforts to minimize the impact caused by the flood disaster in Manado City. The aim of this research is to analyze the risk level of flood disasters in Manado City and provide appropriate recommendations related to flood disaster mitigation in Manado City. This research uses analysis with quantitative descriptive research methods. The results of this research are that there are 3 classes of flood disaster risk levels in Manado City. In the high class there are 53 subdistricts, medium 2 subdistricts and low 32 subdistricts. Policy recommendations are made based on the level of risk by taking into account the main causal factors. Furthermore, it is recommended in every sub-district.

Community Resilience to Global Climate Change-Related Disasters

Global climate change-related disasters have dramatically escalated throughout the world. Among a range of unexpected calamities, hydrometeorological disasters have come to the fore. This is because, torrential rains, mass flooding events, cyclones, storms, landslides (wet), droughts, heat waves and cold waves are very predominant. In addition to that, global warming, wild/forest fires, and melting down of glacier/ice covers of polar and nearby regions are very serious. This is because community resilience and ecological resilience are dramatically eroding and challenging. The existing situation is very serious due to the inherent nexus between community resilience and ecological resilience. In this context, the study aims to conduct a systematic literature review on the extant literature of resilience and to identify the cardinal variables that are appropriate to measure community resilience to the ongoing global climate change scenarios. Methodologically, the study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model to filter the appropriate literature (followed by the identifying, screening, and including process). The study considered the manuscripts and reports published in English. Accordingly, inappropriate literature works were excluded. Finally, the study considered 33 literature works on resilience out of 117. According to the analysis and results revealed, six cardinal resiliency aspects were identified, namely, Socioeconomic resilience, Demographic resilience, Health resilience, Sociopolitical resilience, Social capital resilience, and Ecological resilience. These key resiliency aspects will extremely be important for securing the lives and livelihoods of the human population from the ongoing climate change impasses/crisis.

Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast performance from NMME-2 over Indonesia

Abstract Global Subseasonal-to-Seasonal (S2S) precipitation forecast was known to bridge the gap between weather and seasonal forecast, can be utilized as supporting information for decision-making related to hydrometeorological disaster mitigation activities. However, uncertainty in global models caused forecast performance can be different across regions and time periods. Therefore, it is important to evaluate forecast performance before utilizing the prediction result in any region. In this study, performance of probabilistic precipitation forecast from Multi-model Ensemble (MME) of three models in The North American Multi-Model Ensemble phase 2 (NMME-2) project was evaluated in Indonesia region based on two evaluation metrics: continuous ranked probability score (CRPS) and reliability diagram. The evaluation was conducted during the boreal summer (May–October) and boreal winter (November–April) periods, as well as during the active period of subseasonal climate variability phenomenon Madden-Julian Oscillation (MJO). Our result shows that S2S precipitation forecast from MME of CFSv2, CanCM4 and GEOS5 models are sufficiently accurate and reliable during the boreal summer period for Central Sumatra, Southern Sumatra, Southern Kalimantan, Java, Southern Sulawesi, and Southern Papua regions, with a range of CRPS values between 4-16 mm/7 days and a ‘perfect’ reliability category. There is no notable distinction in the performance of S2S precipitation forecast between the active and inactive events of the Madden-Julian Oscillation (MJO). The difference in CRPS values between these two periods is only around 0.8-1.2 mm/7 days, and there is no difference in reliability categories across Indonesia as a whole, nor significant spatial pattern differences.

HYDRA: A Board Game for Disaster Risk Knowledge, Awareness and Preparedness on Hydrometeorological Hazards

The inclusion of DRR education in the curriculum has been part of the response of the government to the increasing threats of disasters in the Philippines. Yet it was found out that students are still mostly uncertain when a disaster occurs, more specifically on evacuation plans, hazards and disaster-prone areas . This study aimed to develop an instructional material to increase the knowledge, preparedness, and awareness towards hydrometeorological hazards of Grade 11 students in Caloocan City Science High School. The Hydrometeorological Disaster Risk Awareness board game (HYDRA), was first evaluated by seven DRRR experts and Science teachers using the DepEd LRMDS evaluation tool, before it was implemented to the Grade 11 students. Data were collected from 20 participants using a 20-item test questionnaire and a 16-item survey form. The results of the pretests and posttests have shown significant differences with large effect sizes, which implies that the game has significantly increased the students’ disaster risk knowledge (p 0.001; d = 1.5), preparedness (p 0.001; d = 1.92), and awareness (p 0.001; d = 1.96). The game also received an overall evaluation rating of 3.99 out of 4, which means that it met the criteria for an appropriate instructional material. These findings suggest that HYDRA can be used as an alternative educational tool in teaching DRRR.

Hydrometeorological Disaster Mitigation Through Rainfall Intensity Mapping Using IDF in Sumatera Island, Indonesia

Hydrometeorological Disaster Mitigation Through Rainfall Intensity Mapping Using IDF in Sumatera Island, Indonesia

Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters

Hydrometeorological disasters are one of the disasters that often occur in big cities like Semarang. Hydrometeorological disasters that often occur are floods caused by high-intensity rainfall in the area. Early mitigation needs to be done by knowing about future rain. Rainfall data in Semarang City fluctuates, so the Adaptive Neuro-Fuzzy Inference System (ANFIS) method approach is very appropriate. This research will use the Grid Partitioning (GP) approach to produce more accurate forecasting. The data used in this research is daily rainfall observation data from the Meteorology Climatology Geophysics Agency (BMKG). The membership functions used are Gaussian and Generalized Bell. The two membership functions will be compared based on the RMSE and MAPE values to get the best one. The data used in this research is daily rainfall data. Rainfall in Semarang City every month experiences anomalies, which can result in flood disasters. The ANFIS-GP method with a Gaussian membership function is the best, with an RMSE value of 0.0898 and a MAPE of 5.2911. Based on the forecast results for the next thirty days, a rainfall anomaly of 102.53 mm on the thirtieth day could cause a flood disaster.

On The Interannual Variability of Indonesian Monsoon Rainfall (IMR): A Literature Review of The Role of its External Forcing

The IMR variability is notorious for its hydrometeorological disasters. This paper examines recent studies on IMR and the main factors controlling its variability. The focus of this study is to investigate the impact of the atmosphere-ocean interaction that acts as the external forcing of IMR in the tropical Indian and Pacific Oceans. Specifically, the study will examine the influence of two climate phenomena, namely the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) and their interdecadal changes associated Pacific Decadal Oscillation (PDO), on the IMR. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. Furthermore, data sets (such as rainfall, wind field, and SST) spanning 1990-2020 were used to verify the key findings. In general, this study concludes that the majority of the authors coincided with the following conclusion: ENSO and IOD events impact IMR by changing its amplitude, duration, intensity, and frequency of mean and extreme rainfall. Additionally, it has been shown that their impacts on IMR are most substantial during the dry seasons, specifically in June, July, and August (JJA), and not as strong as during the wet seasons, specifically in December, January, and February (DJF). Spatially, the effects of ENSO and IOD on IMR variability are clearly found more eastward and westward of the region, respectively. The expansions towards the east and west directions were facilitated by the displacement of the ascending and descending of Walker circulation patterns in the Indonesian region, respectively. Given the interannual fluctuations in IMR, caused mainly by ocean-atmosphere interactions, the knowledge gap of atmospheric factors like the Quasi-Biennial Oscillation (QBO) must be investigated in the future, as suggested by previous research and our preliminary study.

Investigation of the discharge performance in infiltration wells based on several soil subgrade models

Heavy precipitation would result in hydrometeorological disasters such as landslides and floods. In general, disasters cause hazardous situations and property loss. Infiltration wells are water and soil conservation techniques that can reduce surface runoff and increase infiltration to mitigate the effects of hydrometeorological disasters. However, evaluations of geotechnical properties have not yet jointly or actively considered these environmental developments. This paper aims to analyse the wide range of soil types against several diameters and depths of the holes. The variation in soil varied from type 1 to 6, containing different properties and consistencies. The holes diameter used are 0.4m, 0.5m, 0.6m, 0.7m, and 0.8m, associated with several depths; 0.5m, 0.75m, 1m, 1.25, and 1.5m. The analysis was conducted with Geostudio SEEP-W in different model variations to obtain the well’s discharge value. The result shows that all the depth depicts similar behaviour of the discharge well. The sand model has the highest value of all categories, with the second place being the sand-clay layer model. In contrast, the clay simulation generates the lowest value of this research. This output offers a strong recommendation between infiltration well structure and soil parameters, which show sand is the most required property to increase the performance of hole infiltration.

Preface

Disaster Research Center Universitas Sebelas Maret successfully held the 2nd International Conference on Disaster Management and Climate Change 2023 (ICoDMC 2023) via the online Zoom system on August 12 2023. ICoDMC 2023 aims to disseminate research results and various ideas regarding controlling environmental damage and disaster management for a better life and provide input to the government and other policy-making institutions in the environment and disaster management field. The theme for 2023 is Climate Changes and Hydrometeorological Disaster Mitigation.ICoDMC 2023 invites great speakers who are highly competent in natural disasters and disaster environments, and they are Prof. Ir. Dwikorita Karnawati, M.Sc. PhD (Head of the Meteorology, Climatology and Geophysics Agency), Prof. Christopher Andre Gomez (Department of Maritime Sciences, Kobe University, Japan), Ahmad Marzuki, S.Sc. Ph.D (Sebelas Maret University, Indonesia), Prof. Dr. Martin Coy (Institut Für Geographie, Universität Innsbruck, Austria), and Dr. Annisa Triyanti (Utrecht University, Netherlands).The topic of this seminar is Climate Changes and Ecology, Disaster Mitigation, Disaster Risk Reduction, Environmental Risk Assessment Infrastructure for Disaster, Natural Resources, Natural and Non-Natural Disaster, Oceanography and Coastal Problems, Water and Hydrology, and Spatial Planning for Disaster. There were 198 articles submitted according to the topic above, and after selection, selected 135 articles were presented and submitted to the IOP-EES Proceedings. The article’s authors include lecturers, practitioners, teachers, and students. Two hundred twenty-six participants joined the seminar.Thank you to all speakers, authors and participants. See you at the third ICoDMC.RegardsProf. Dr. Chatarina Muryani, M.SiChairman of the committee Committee 2nd International Conference on Disaster Management and Climate Changes (ICoDMC 2023) List of Steering Committee, Conference Chair, Conference Vice Chair, Secretary, Editor are available in this pdf.

Flood mitigation with the support of demographic bonuses in Indonesia

Floods during the rainy season are still a polemic for Indonesia because disaster create various losses and fatalities. Although, drainage development, green environment and outreach have been implemented. However, public awareness of waste that block the flow of water needs to be evaluated. Therefore, the bibliometric analysis then using Vos Viewer software to discuss relevant research topics. The aim is to provide the management of hydrometeorological disaster mitigation. Therefore, the literature review in this article then discusses various flood threats, efforts by several regions to overcome them, human resources and character of education. The results of the discussion show that flood control requires with collaboration. For this purpose, the demographic bonus in Indonesia needs to be used to optimize public participation. Moreover, recommendation from the research is that any schools to hold group teens go clean activities as mutual cooperation learning. Policy alternatives to build some intrinsic motivation and extrinsic motivation for each individual in creating a flood free area in their environment.

Design of the Bali Province Food Security Action Plan towards Food Independence

The problem of hunger is one of the problems that still occur in Indonesia. The problem of hunger is closely related to the problem of staple foods, such as the unmet security of staple food, rice as the community's main food, and the increasing demand for staple foods. This research aims to analyze the challenges faced by Bali Province in maintaining sustainable food security and to design food security action designs in Bali Province towards food independence. The data used in this research are primary data and secondary data with a descriptive analysis method using ISM analysis. The results of the research show that food challenges that occur in Bali Province include reduced agricultural land, unbalanced supply and demand for food commodities, weak use and mastery of technology, lack of farming management and product marketing, the proportion of crop loss and food waste is still quite high, food loss due to inaccuracy in handling food, the area of agricultural land per capita is very limited, the number of young people interested in working in the agricultural sector is still problematic, the age of farmers over 45 years reaches 64.2%, Indonesia is also still facing three times the burden of nutrition, climate change, and hydrometeorological disasters. The Bali Province's food security design towards food independence consists of farmers, government, companies, financial institutions, and agricultural cooperatives who are interconnected with each other.