ISU for Sustainability

Embracing SDGs towards Quality Education and Academic Experience in the Countryside

Research | SDG 13 – Climate Action

Technical Research Category

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Orlando F. Balderama, Christine B. Mata, Lanie A. Alejo, Jeoffrey Lloyd R. Bareng, Elmer Rosete, Czarimah L. Singson, Alvin John B. Felipe, Jeremy T. Balderama, Anthony Jordan Justo, Genesis Querubin


Philippines is one of the most flood-affected countries in the world. While floods have become more damaging throughout time, it requires risk assessment quantifying flood risk damages as accurately as possible. An up-to-date database containing information on hazard-prone regions is critical for supporting hazard preparedness and response operations, particularly in the case of recurring floods. To deal with flood damage assessment due to lack of visualization and estimation, a satellite-imagery based method for flood inundation map using Google Earth Engine was performed. The study reported the recent flood inundation (November 2020) during typhoon Ulysses in the Cagayan Valley in CRB that led to the inundation of an extensive area 661.73 km2. The flood affected an enormous population (approximately 225,634 people), with worst in Cagayan (53.96% of inundation; 50.36% of the total affected population) followed by Isabela (45.93% of inundation; 49.62% of the total affected population) the flood severely affected approximately 5.04% (477.44 km2) of the total cropland and 16.14% (4.36 km2) of wetlands in the region. The overall accuracy of datasets used is 97.78% while flood extent is 95%. The data and methodology of the study can be replicated for other flood events in the Philippines.

Keywords: socio-economic impact, flood, synthetic aperture radar, flood risk assessment, Google Earth Engine, damage

Orlando F. Balderama, Christine B. Mata, Lanie A. Alejo, Jeoffrey Lloyd R. Bareng, Elmer Rosete, Czarimah L. Singson, Alvin John B. Felipe, Jeremy T. Balderama, Anthony Jordan Justo, Genesis Querubin, Jayzelle Ventura


This study was conducted to assess the impacts of climate change on the inflow of Magat Dam using Soil and Water Assessment tool (SWAT) Model. The knowledge of intake or inflow parameters is essential in planning and scheduling dam discharges, measuring and anticipating current and future power production, and preventing floods. Climate projections from CLIRAM tool from PAG-ASA was used as climate change scenarios. The SWAT was then used to simulate, calibrate and validate the model. Based on the calibration and validation results, the SWAT Model can adequately predict the inflow of water in the Magat Reservoir. The results showed an NSE of 0.73, R2 of 0.745, RSR of 0.52 and a PBIAS of 8.24, which were statically acceptable. The model showed that there would be at most 20.42% increase and 27.08% decrease in the inflow for dry and wet years, respectively. Furthermore, peak inflows were likely to occur during the months of September and October with at most 317.12 m3/s. The results of the model should be used as a basis for long-term plans of NIA-DRD, RBC and LGUs to prepare and respond to future climate risks in water resources especially in the reservoir.

Keywords: Climate Change, Climate Projection, Inflow, Peak, SWAT

Laila M. Gallego, Isagani P. Angeles, Jr., Yew-Hu Chien


This study investigated the effects of light source [LED White (LW), fluorescent white (T5) and LED Blue (LB)] and photoperiod (12:12, 16:08, 24:00 light: dark) on growth of duckweed Landoltia punctata and the resulting effects on its water quality for 16 days. The average daily relative growth rate (RGR) reached about 0.519 g d-1. Both light source and photoperiod, had no significant difference on the mean RGR, however, their interaction had significant effects on duckweed’s growth (p≤0.05). Moreover, except T5 (24:00) > [T5 (12:12) ≥ T5 (16:08) ≥ LB (24:00)], LW (12:12) > [T5 (16:08) ≥ LB (24:00)], and LB (12:12) > LB (24:00), there were no differences in RGR in all pair-comparisons of treatment (p≤0.05). Nitrate (NO3-) influenced the most in weight increment (WI), 70 %. For light source and photoperiod effects on water quality, no total ammonia nitrogen (TAN) was detected in all treatments after 16 days while NO3- increased gradually. In addition, results show that most of the total nitrogen (TN) was contributed from NO3- (R2= 0.9999). Overall, our findings could contribute on producing duckweed in a controlled and programmed condition for maximum production and quality. Constructed models and practical application contribute in predicting nutrients sensitivity and proven useful in water management or water quality assessments.

Technological Research Category

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Betchie E. Aguinaldo, Marvee Cheska B. Natividad


The major causes of disasters in our country are typhoons and floods and Cagayan Valley Region particularly in the provinces of Cagayan and Isabela are vulnerable to these calamities amidst the pandemic due to Covid-19. With this, preemptive evacuation measures are important. In this paper, past trends of flood disasters are investigated to predict the pre-emptive plan. Based on the result, the predictive model using classification techniques which is kNN has the best accuracy rate and served as best model.

Orlando F. Balderama, Christine Gay P. Liberato, Lanie A. Alejo, Jeoffrey Lloyd R. Bareng, Elmer Rosete, Jeremy T. Balderama, Alvin John B. Felipe, Jayzelle Ventura, Genesis Querubin


During the rainy season, flash floods have occurred in the Cagayan River Basin (CRB) downstream. The Cagayan Valley is a 27,000-square kilometer elongated watershed. The lack of hydrological monitoring stations in CRB's tributaries and subwatersheds makes flood forecasting difficult. Flood forecasting is critical for avoiding and minimizing flood damage. During extreme weather events, accurate, simple, and user-friendly rainfall-runoff models for estimating input from the upstream region of the Cagayan River Basin are required. The inflow in Magat Dam and the water level in Buntun Bridge in Tuguegarao City were simulated in this study utilizing Typhoon Ulysses and the successfully calibrated Rainfall-Runoff-Inundation (RRI) Model. With RSR, NSE, PBIAS, and R2 equal to 0.36, 0.87, 6.90, and 0.88, the model accurately predicted the influx in Magat. Also, at 0.50, 0.75, -0.39, and 0.75, respectively, the RSR, NSE, PBIAS, and R2 exhibited good agreement with the measured river water level data. Typhoon Tisoy in December 2019 and Monsoon Rains in December 2020 were used to test and validate the RRI calibrated parameters. For both Magat Inflow and Buntun water levels, the results yielded a suitable statistical index. During extreme weather events, the calibrated RRI parameter in this study could be used to forecast Magat Dam inflow and flood inundation in the Cagayan River Basin for effective protective planning, decision making, and flood early warnings.

Keywords: Cagayan River Basin, Magat Dam, Rainfall-Runoff-Inundation, forecasting, simulated, inflow

Reonel Ferreria


Changes in rainfall and temperature patterns jeopardize agricultural production and intensify the risk and vulnerability whose livelihoods are dependent on agriculture, which includes the majority of the world's poor. Climate change disrupts food markets, putting the food supply at risk for the entire population. Threats can be mitigated by enhancing farmers' adaptive capacity, as well as food production systems' resilience and resource efficiency. Climate-smart agriculture is a strategy for developing agricultural methodologies that utilize digital technologies to modernize agricultural systems with the goal of achieving sustainable agriculture and ensuring food security in the context of climate change. This paper discusses the application of cloud-based IoT in agriculture. Smart farming is a concept that emphasizes the importance of providing the appropriate amount of resources at the appropriate time. The proposed paper makes use of IOT to implement precision agriculture. The fundamental concept is to sense all of the required parameters from the agricultural field and to make the appropriate decision regarding irrigation control. These agricultural parameters are Soil Moisture, Temperature, and Relative Humidity in the immediate vicinity of the plant.

Keywords: climate-smart agriculture; climate change; smart agriculture; Internet of Things; microcontroller

Social Research Category

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Daren T. Baui, Jessica R. Balite


The Asia-Pacific region generally, and the Philippines in particular, are highly susceptible to natural disasters. Undeniably, the Philippines is situated along a highly seismic area lying along the PACIFIC RING OF FIRE where is highly-prone to earthquakes and volcanic eruptions. In order to be well prepared, educational institutions should implement the integration of the Disaster Risk Reduction and Management strategies in their curriculum to raise awareness among students and the community, therefore it will yield to a well-prepared society. The descriptive design was used in the study where 45 teachers in the different colleges and 50 students per college were randomly selected as the source of the study. Students and teachers are fully aware on disaster risk reduction management strategies. The level of preparedness and prevention was rated strongly agree and the level of resiliency as perceived by the students and teachers are both resilient. The level of integration among the teachers is described as oftentimes as reflected in their weighted mean which is 3.24. This is in consonance with the result that there were few subjects were Disaster Risk Reduction Management were integrated. There is no significant difference between the level of awareness, preparedness and prevention, and resiliency of the students and teachers. With the result of the study, every teacher must capitalize on implementing specific strategies and action plans for DRRM education and training focusing on specific target groups. Teachers should also create opportunities for young people to learn more about this.

Keywords: DRRM strategies, awareness, preparedness, resiliency, integration

Higher Education Research Category

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Yzzel T. Agorilla, Marris R. Reyes


The purpose of this study is to develop and validate a contextualized instructional learning material for the institutional course Climate Change and Disaster Risk Reduction and Management (CCDRM) of Isabela State University. The study was divided into two parts: the identification of the level of student awareness to Climate Change and Disaster Risk Management which aims to determine their actual concepts on CCDRM and the actual development of the contextualized learning material based from the student responses. The study employed a descriptive survey design involving a five – point Likert scale adapted and modified from Agboola and Emmanuel (2016) the modification requires the opinion of the students using an interview guide to collect student responses which were later analyzed using thematic content coding technique adopting both a priori and in-vivo codes for coding frames. The student’s awareness on Climate Change and Disaster Risk Reduction and Management enables educators to design and create contextualized instructional learning materials that can help maximize learning of the subject matter. This study provided an actual account of the students views on the CCRDM issues and concerns in their locality which can be used to guide educators and local policy makers in crafting learning materials as well as adaptation and mitigation activities offered in the community. The contextualization of learning materials provides an immersive approach in teaching issues regarding CCDRM and management. The awareness and the students understanding on CCDRM also provide a springboard for curriculum crafters and policy makers to develop programs/ activities that suits the students’ views and understanding.

Keywords: Climate Change, Disaster Risk Management, Contextualization, Learning Material