Reducing the Risk of Flood Disasters in Lamongan Regency Using the Geographic Information System (GIS)

Flood disasters cause negative impacts, such as damage to facilities to the onset of fatalities. Reducing the risk of flooding needs to be done to reduce the impact caused by this disaster. Lamongan Regency is one of the regencies in East Java affected by floods every year in most of its areas. This study aims to reduce the risk caused by flooding by using GIS (Geographic Information System). Mitigation is done by determining areas with a high potential risk of being affected by flooding. The study used spatial analysis functions in ArcGIS. Supporting variables used rainfall, land cover, slope, soil texture, and watershed area, and it becomes important in determining flood-prone areas. From the results, the largest soil classification is the Kpl soil type. Litosol Gray Grumosol, The wide distribution of rainfall from 1500-1750 mm has the widest distribution is 66,67 ha. The slope of 0-8% has the widest distribution of 92,257 ha, making Lamongan a very vulnerable high flood area. Laren District is the District with the greatest flood potential, and Irrigated Field is the dominant land cover type affected by the flood. With the flood disaster map generated from this research, local governments can seek prevention in areas with high flood potential. They can carry out socialization based on disaster mitigation, especially for districts with potential flooding

Modelling causality between agricultural and meteorological drought indices in the Corong River basin, East Java Indonesia

The Lamongan Regency is an area in East Java, Indonesia, which often experiences drought, especially in the south. The Corong River basin is located in the southern part of Lamongan, which supplies the irrigation area of the Gondang Reservoir. Drought monitoring in the Corong River basin is very important to ensure the sustainability of the agricultural regions. This study aims to analyse the causal relationship between meteorological and agricultural drought indices represented by standardised precipitation evapotranspiration index (SPEI) and standard normalisation difference vegetation index (NDVI), using time series regression. The correlation between NDVI and SPEI lag 4 has the largest correlation test results between NDVI and SPEI lag, which is 0.41. This suggests that the previous four months of meteorological drought impacted the current agricultural drought. A time series regression model strengthens the results, which show a causal relationship between NDVI and SPEI lag. According to the NDVI-SPEI-1 lag 4 time series model, NDVI was influenced by NDVI in the previous 12 periods, and SPEI-1 in the last four periods had a determinant coefficient value of 0.4. This shows that the causal model between SPEI-1 and NDVI shows a fairly strong relationship for drought management in agricultural areas (irrigated areas) and is considered a reliable and effective tool in determining the severity and duration of drought in the study area