Hydropower development priority using MCDM method

Hydropower is recognized as a renewable and clean energy sources and its potential should be realized in an environmentally sustainable and socially equitable manner. Traditionally, the decision criteria when analyzing hydropower projects, have been mostly a technical and economical analysis which focused on the production of electricity. However, environmental awareness and sensitivity to locally affected people should also be considered. Multi-criteria decision analysis has been applied to study the potential to develop hydropower projects with electric power greater than 100kW in the Ping River Basin, Thailand, and to determine the advantages and disadvantages of the projects in five main criteria: electricity generation, engineering and economics, socio-economics, environment, and stakeholder involvement. There are 64 potential sites in the study area. Criteria weights have been discussed and assigned by expert groups for each main criteria and subcriteria. As a consequence of weight assignment, the environmental aspect is the most important aspect in the view of the experts. Two scenarios using expert weight and fair weight have been studied to determine the priority for development of each project. This study has been done to assist policy making for hydropower development in the Ping River Basin.Multi-criteria Hydropower Renewable energy

Root Zone Soil Moisture Assessment at the Farm Scale Using Remote Sensing and Water Balance Models

Water resource planning and management necessitates understanding soil moisture changes with depth in the root zone at the farm scale. For measuring soil moisture, remote sensing methods have been relatively successful. Soil moisture is estimated from image data, using in situ moisture and an empirical scattering model via regression fit analysis. However, in situ sensor data are prone to misinterpretations, requiring verification. Herein, we aimed at investigating the application of soil moisture from the water balance model towards verification of in situ soil moisture sensor data before in situ data was assessed for its relationship with remote sensing data. In situ soil moisture sensor data was obtained at 10 and 30 cm, and CROPWAT8.0 furnished root zone soil moisture data. The correlation between the in situ soil moisture at 10 and 30 cm was 0.78; the correlation between the soil moisture from CROPWAT8.0 and the in situ soil moisture were 0.64 and 0.62 at 10 and 30 cm, respectively. The R2 between Sentinel-1 backscatter coefficients and in situ moisture were 0.74 and 0.68 at each depth, respectively. Therefore, the water balance model could verify sensor results before assessing in situ soil moisture data for relationship with remote sensing data

Root Zone Soil Moisture Assessment at the Farm Scale Using Remote Sensing and Water Balance Models

Water resource planning and management necessitates understanding soil moisture changes with depth in the root zone at the farm scale. For measuring soil moisture, remote sensing methods have been relatively successful. Soil moisture is estimated from image data, using in situ moisture and an empirical scattering model via regression fit analysis. However, in situ sensor data are prone to misinterpretations, requiring verification. Herein, we aimed at investigating the application of soil moisture from the water balance model towards verification of in situ soil moisture sensor data before in situ data was assessed for its relationship with remote sensing data. In situ soil moisture sensor data was obtained at 10 and 30 cm, and CROPWAT8.0 furnished root zone soil moisture data. The correlation between the in situ soil moisture at 10 and 30 cm was 0.78; the correlation between the soil moisture from CROPWAT8.0 and the in situ soil moisture were 0.64 and 0.62 at 10 and 30 cm, respectively. The R2 between Sentinel-1 backscatter coefficients and in situ moisture were 0.74 and 0.68 at each depth, respectively. Therefore, the water balance model could verify sensor results before assessing in situ soil moisture data for relationship with remote sensing data