Climate change has had a significant impact on regional climate in many countries, including Indonesia. The coastal sector is one of the most vulnerable to climate change. Pekalongan City is one of the most vulnerable coastal areas in Indonesia to the effects of climate change. Tidal flooding is a disaster that occurs in coastal areas due to rising sea levels, which average 6-10 mm per year on Java’s northern coast. Tidal flood hazard analysis was performed using indicators of sea-level rise, distance to the sea, distance to the river, slope, topography, land cover, and land subsidence. The results of the field validation were consistent with those of the tidal flood hazard analysis. Areas covered by the tidal flood hazard modeling include Pasirkratonkramat, Bandengan, Kandang Panjang, Panjang Baru, Panjang Wetan, Krapyak, and Degayu. Pekalongan City experienced an increase in hazard area of 6.18% of the total area of Pekalongan City based on the results of the CSIRO modeling RCP 4.5 scenario. In comparison, the other three models show a decrease of up to 3.13% in the total area of Pekalongan City. However, when viewed at the site scale (per urban village), Podosugih Village experienced the highest increase in hazard area, up to 20.5% of the total area for the CSIRO-modeled RCP 4.5. The highest decrease in the area occurred in Klego Village, at 8.94% of the area for the RCP 4.5 scenario of the MIROC model. Future changes in rainfall, sea level, and land subsidence will cause these changes.

Flooding is the most common disaster in Indonesia, it is classified as a disaster if it affects humans causing physical and financial losses. Flood damage depends on the type of flood, flow velocity, and duration. The increase in population will cause an increase in infrastructure that will affect the environment, including the carrying capacity of rivers and catchment areas, while flooding in urban areas will also have an impact on infrastructure and assets, increasing flood damage. This study discusses the factors that cause flooding (rainfall, topography, soil type, land slope, distance from rivers, river waste, population density, etc.), as well as various types of floods that occur. The method used in this research was based on the qualitative analysis of the information from the government and literature over the last five years obtained from online databases and search engines. The results of this study can provide a reference for the theory regarding disaster risk assessment and flood hazard prediction in watersheds. This research was conducted in the Ciliwung Watershed (DAS), where the factors and the dominant type of flooding were determined. Knowing the contributing factors can be beneficial for flood risk management. This research focuses on identifying factors that contribute to fluvial flood events and understanding their influence so that a more integrated flood risk management that takes into account the upstream, middle, and downstream parts of the watershed can be arranged in other areas based on the example of measures implemented by the local government in the Ciliwung watershed. This conceptual effort provides a much-needed foundation for developing better mitigation efforts in watersheds.

Daily solar radiation is a critical input for estimating plant growth and development, yet this variable is infrequently measured compared to other climate variables. This study evaluates the sensitivity of simulated maize and soybean production from the CERES-Maize and CROPGRO-Soybean modules of the Decision Support System for Agrotechnology Transfer (DSSAT) to daily solar radiation estimates obtained from traditional (stochastic, empirical, and mechanistic models) and non-traditional (satellite estimation, reanalysis datasets, and regional climate model simulations) approaches, using as an example radiation estimates for Hancock, Wisconsin, USA. When compared to observations, radiation estimates obtained from empirical and mechanistic models and a satellite-based dataset generally had smaller biases than other approaches. Daily solar radiation estimates from a reanalysis dataset and regional climate model simulations overestimate incoming daily solar radiation. When the radiation estimates were used as an input to CERES-Maize, no significant differences were found for maize yield obtained from the different radiation estimates compared to yield from observed radiation, even though differences were found in the daily values of leaf area index, crop evapotranspiration, and crop dry weight (biomass). In contrast, significant differences were found in simulated soybean yield from CROPGRO-Soybean for the majority of the radiation estimates.

In recent years, use of the stochastic model has been growing due to the high complexity and dynamics of the atmosphere, especially the rainfall process. Various concepts have been applied to rainfall modeling, ranging from simplistic approaches to more complex models. It is important to understand different stochastic rainfall modeling approaches, as well as their advantages and limitations. This paper determines the development of the latest stochastic rainfall models in the Asia region, where different concepts of stochastic rainfall models were highlighted. It reviews the different methodologies used, including rainfall forecasting, spatio-temporal analysis, and extreme event. We select 30 articles from 1571 literature published between 2013–2022 from the Scopus database. The results show that the stochastic models often used in the literature consist of Markov chain, weather generator, probability distribution, ARIMA, and the Bayesian model. In the recent development in Asia, stochastic models in rainfall modeling research are widely used to generate the occurrence and amount of rainfall data, statistical downscaling, future rainfall trends, and estimation of extreme values. The difference in spatio-temporal, climate conditions, and the parameters model can cause the performance of each model to be different.

West Java has excellent potential in hydropower energy development, partly because it has high-intensity rainfall. However, some things could be improved with the water supply to produce electricity, mainly due to rainfall and temperature variability. Therefore, it is necessary to analyze the magnitude of the contribution of climate factors to the availability of hydropower energy, as well as the sensitivity of the total potential loss of water supply due to variability and changes in rainfall and temperature. Data analysis was carried out by modeling the impact of rainfall and temperature on the availability of power supply for hydropower operations. The results show that rainfall greatly contributes to the loss of hydropower supply with an R2 value of 0.9. However, the temperature does not contribute to the Cirata and Saguling hydropower plants, seen from the R2 value of only 0.03. In contrast, for the Kracak hydropower plant, the temperature contributes to the loss of power supply with an R2 value of 0.7. Loss of power supply in 2021 to 2040 will tend to be higher and occur more frequently than the observation year, with the highest potential loss of 1 MW (5%) at the Kracak, 300 MW (29%) at the Cirata, and 300 MW (42%) ) at the Saguling. The total potential loss of power supply for the Kracak, Cirata, and Saguling hydropower plants is sensitive to changes in rainfall and temperature in these areas.