Indonesia is one of the tropical area which its climate is generally experienced of two distinct seasons, rainy season and dry season. However, the beginning and end of each season is not always occurred at the same month on every year. Characteristics of hydrology variables, especially rainfall events, is strongly influenced by local and global climatic conditions. Thus it is important to identify how far the changes of global climate would effect the characteristic of the hydrology variables in a certain watershed. These informations will be valuable to determine the correct measures for the watershed's development and management in the future. Kapuas River, one of rivers in Borneo, Kalimantan-Indonesia, has streamflow which is empirically changed during the last three decades. The changes of hydrological characteristics are analysed statistically based on rainfall data recorded at Supadio Rainfall and Meteorological Station-Pontianak. The monthly rainfall data were available from 1968-2013, while the daily rainfall data were available during 1981-2013. The 1968-2013 data were then used to analyze the trend of annual rainfall, while the 1981-2013 were used to analyze the changes of hydrological characteristics. The results showed that the numbers of average annual rainfall is 3,206mm, while the average monthly rainfall is 267mm. During the 45-year period (1968-2013) the annual rainfall tends to decrease, but for the past 30 years it tends to increase. The number of rainy days during the past 30 years tends to increase. Whereas the rainfall intensity on short duration tends to decrease. Even though the availability of streamflow in the river during the past 30 years tends to decrease. It was proven based on observed streamflow in the tributary of Kapuas, Sekayam River. It was found that during 35 years (1978-2012) the availability of streamflow tends to decrease. It is, therefore can be concluded that hydrological characteristics of lowlands at Kapuas River has changed

The main problem in the management of the floodplain of Lake Tempe is the weak involvement of affected communities in disaster management. The purpose of this research is to comprehensively analyze and map stakeholders in managing floodplain. The penta helix concept is used in stakeholder mapping. This research applied mixed methods, qualitative and quantitative. Data collection was done through in-depth interviews with various parties who understand the problem of research and direct observation "pre", "whilst", and "post" a disaster occured. Affected communities are found to be an important part of the disaster management stage. Affected communities, who have been the object of disaster, should be the subject of disaster management. The recommendation for the results of this research is that the concept of five stakeholders in penta helix was ​​not sufficient in disaster management. Affected communities deserve to be placed as one of the stakeholders, in addition to the government, business world, universities, NGOs, mass media. This six-stakeholder concept is called "hexa helix". Key words: Stakeholders Mapping, Penta Helix, Flood Disaster Management, Lake Tempe.

The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R² = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface.

Disaster management to mitigate or avoid impacts of hazards by reducing vulnerability has been conducted in Mount Merapi since 1969. Vulnerability introduced since 1980s has two main characteristics, such as physical vulnerability (i.e., impacts of hazards) and social vulnerability (i.e., composite characteristics including social, economic and environmental factors). As regulations in Indonesia, i.e. Law of Republic Indonesia No. 24 Year 2007, emphasizes the community involvement in disaster management, individuals or groups of individuals have significant roles in reducing social vulnerability. To promote the community involvement effectively in disaster management, this research hypothesized nine relationships between disaster management programs to assess effects of disaster management programs on the community involvement, such as individuals’ preparedness. Since disaster management programs are not measurable quantitatively, variance-based partial least squares structural equation modeling (PLS-SEM) was applied to test hypothesized causal relationships between the programs. As a result, all nine hypotheses were substantiated. The model revealed that individuals’ preparedness is significantly influenced by emergency logistics and financial aid through self-efficacy, and contingency plans affect reconstruction significantly and successively reconstruction affects rehabilitation.

Kapuas River is located in the province of West Kalimantan. Kapuas river length is 1,086 km and river basin areas about 100,000 Km². The availability of river flow data in the Long River and very wide catchments are difficult to obtain, while river flow data are essential for planning waterworks. To predict the water flow in the catchment area requires a lot of hydrology coefficient, so it is very difficult to predict and obtain results that closer to the real conditions. This paper demonstrates that artificial neural network (ANN) could be used to predict the water flow. The ANN technique can be used to predict the incidence of water discharge that occurs in the Kapuas River based on rainfall and evaporation data. With the data available to do training on the artificial neural network model is obtained mean square error (MSE) 0.00007. The river flow predictions could be carried out after the training. The results showed differences in water discharge measurement and prediction of about 4%.