Many calibration techniques have been developed for the Soil and Water Assessment Tool (SWAT). Among them, the SWAT calibration and uncertainty program (SWAT-CUP) with sequential uncertainty fitting 2 (SUFI-2) algorithm is widely used and several objective functions have been implemented in its calibration process. In this study, eight different objective functions were used in a calibration of stream flow of the Pursat River Basin of Cambodia, a tropical monsoon and forested watershed, to examine their influences on the calibration results, parameter optimizations, and water resources estimations. As results, many objective functions performed better than satisfactory in calibrating the SWAT model. However, different objective functions defined different fitted values and sensitivity rank of the calibrated parameters, except Nash–Sutcliffe efficiency (NSE) and ratio of standard deviation of observations to root mean square error (RSR) which are equivalent and produced quite identical simulation results including parameter sensitivity and fitted parameter values, leading to the same water balance components and water yields estimations. As they generated reasonable fitted parameter values, either NSE or RSR gave better estimation results of annual average water yield and other water balance components such as annual average evapotranspiration, groundwater flow, surface runoff, and lateral flow according to the characteristics of the river basin and the results and data of previous studies. Moreover, either of them was also better in calibrating base flow, falling limb, and overall the entire flow phases of the hydrograph in this area.

Soil salinization of irrigated lands is a global problem in providing the necessary food and feed to meet the needs of a growing world population. Salinization in arid and semiarid areas can occur when the water table is three and more meters above the soil surface. Nowadays, innovative technologies are widely implemented in agriculture to increase yields and monitor changes in any area timely. Advanced technologies such as remote sensing (R.S.) data have become an economically efficient tool for assessing, detecting, mapping, and monitoring saline areas. This study aims to develop a spatial database for evaluating salinization using R.S. and GIS. This research employs various soil salinity indices based on Landsat 8 OLI images and other related geospatial datasets of the study areas. It aims to predict soil salinity using four machine learning methods (Gaussian Mixture Model (GMM), Random Forest (R.F.), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)). Results showed that R.F. is the most suitable for predicting the soil salinity in the study area with 93 percent overall accuracy. This research contributes to improving the quality of monitoring and improvement of the state of irrigated lands. Also, it develops a preliminary step toward decision-making tools for agricultural policies, such as managing saline areas related to crop production.

Food security is often threatened by droughts during rice production. Although most of the rice is produced in lowland or irrigated “wet” rice fields, terraced paddy fields are important in the rice production system in island or mountainous countries. With the intensifying frequency of El Niño periods in recent decades, there has been a risk of droughts in terraced paddy areas. To mitigate drought, remote sensing data analysis could be an efficient and reliable tool for obtaining scarce ground monitoring data. In this study, crop water stress index (CWSI) and temperature vegetation dryness index (TVDI) were applied to evaluate the drought intensity, and hydrological monitoring data was provided as a support for the evaluation. The results indicated that droughts normally occurred during the dry season, and intensified during El Niño periods. CWSI and TVDI were visible to predict drought occurrences in the watershed area. TVDI overestimated the drought inside Keduang watershed compared to CWSI because of the complex condition of the terraced paddy area, including the hydrology in this area. The complex topography, high groundwater table, and continuous plot-to-plot irrigation helped to maintain the water availability and mitigated the drought impact for rice production in the studied terraced paddy field.