Tasuku Kato

Rice cultivation commonly employs the continuous flooding (CF) method, which depends heavily on water availability creating anaerobic conditions for methane (CH₄) emissions. Rainfed rice areas rely on precipitation for irrigation, making the system sensitive to climatic variability. This study examines associations between ENSO phases and satellite-observed atmospheric XCH₄ variability over Thailand using GOSAT as the primary long-term dataset from 2012 to 2022, with Sentinel-5P/TROPOMI used as a supporting dataset for recent spatial patterns. The analysis conducted covers three cropping seasons: (1) January–April, (2) May–August, and (3) September–December. The results indicate comparable average atmospheric methane concentrations of 1787.94 ± 11.50 XCH₄ (ppb) during El Niño, 1788.8 ± 11.22 XCH₄ (ppb) in neutral conditions, and 1793.45 ± 10.93 XCH₄ (ppb) during La Niña. The obtained data indicate a seasonal variability, with the highest satellite-observed XCH₄ values found during September–December, corresponding to the main growing period of wet-season rice. The results suggest that climate change amplifies these anomalies through altered precipitation patterns and water availability. Current rice cultivation practices warrant reconsideration, in particular the alternate wetting and drying (AWD) method, offering reduced CH₄ emissions while conserving water resources. This underscores the importance of water management strategies for sustainable rice production and resilience to climate variability.

The majority of irrigated land in the Sirdarya area in Uzbekistan is susceptible to salinization. Yet, due to the benefits of its geographical and temporal data, satellite imaging has the ability to efficiently monitor territorial resources. The objectives of this paper are (1) to find how strong surface water mineralization used for irrigation correlated with soil salinity, and (2) to introduce a simplified approach for the soil salinity assessment using SEM based on satellite imagery analysis. The focus of this research is to explore the potential of using SEM methodology for soil salinity assessment in the study area by streamlining spatial image analysis processes. Throughout data collection of 261 surface water samples and 206 soil samples in Sirdaria irrigation district, while satellite images and other official datasets were our secondary material. We used the Inverse Distance Weighting interpolation method in ArcGIS to map soil salinity and statistical packages in R software served as analyst tools to validate our findings. Our findings imply that while MODIS satellite imagery has a lesser connection with EC values, however, the EC values in terms of soil salinization in Sirdarya Province can be predicted using several Landsat bands without using remote sensing indices. We proved that the wavelength ranges of 0.64-0.67 μm and 2.11-2.29 μm are acceptable for mapping soil salinity. Nevertheless, continuous research is needed to understand the mechanisms behind this relationship and the potential applications of these findings in other drylands, land management, and environmental monitoring.

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.

Water productivity of rice is imperative for global water security. Currently, water saving management techniques have been proposed and applied to rice systems. The crop water stress index (CWSI) is a major index for evaluating crop water use. The utility of the CWSI in rice in a humid temperate climate has been given little attention. Previous studies have focused on upland crops and readily available constant reference baselines, primarily the water stressed baseline (WSB), which does not inherently reflect transpiration flux. This study examined the performance of the estimated non-water stressed baseline (NWSB) and WSB for rice in a humid climate and the CWSI sensitivity under variable reference baseline scenarios in a 2-year pot trial under phytotron and field environment conditions with two rice genotypes (IRAT109 and Takanari) in a flooded (FL) and aerobic (AR) water regime. We observed that the dynamics of CWSI is dependent not only on the water regimes but could be strongly influenced by genotype sensitivity to vapor pressure deficit (VPD). A higher slope (pooled data) in the field environment (−5.68 °C kPa⁻¹) compared to the phytotron (−3.04 °C kPa⁻¹) reflected transpiration water loss sensitivity to VPD thresholds. Further studies with diverse rice germplasms to explore generalizability to field conditions and reformulation of reference baselines considering the VPD threshold sensitivity could prove to be significant.

Simulation of rice paddy systems in SWAT: A review of previous applications and proposed SWAT+ rice paddy module

Artificial capillary barriers (CBs) are used to improve root zone conditions as they can keep water and nutrients in the root zone by limiting downward percolation. Numerical analysis is one of the promising tools for evaluating CB systems’ performance during the cultivation of leafy vegetables. This study aims to investigate the effects of the CB system on soil water dynamics during spinach cultivation in a soil column under different irrigation scenarios using HYDRUS (2D/3D) by comparing uniform (UNI), line-source (LSI), and plant-targeted (PTI) irrigations combined with alternative irrigation schedules. Simulation results of volumetric soil water contents were generally corresponding to measured data. Simulation results with various hypothetical irrigation scenarios exhibited that the CB was an effective system to diminish percolation losses and improve the root zone’s soil water storage capacity. On the other hand, evaporation loss can be increased as more water is maintained near the surface. While this loss can be significantly minimized by reducing the water application area, the irrigation amount must be carefully defined because applying water in a smaller area may accelerate downward water movement so that the water content at the CB interface can reach close to saturation. In addition to the malfunction of the CB layer, it may also cause a reduction of plant root water uptake (RWU) because the root zone is too wet. Among evaluated irrigation scenarios, irrigating every two days with PTI was the best scenario for the spinach as water use efficiency was greatly improved.

The soil and water assessment tool (SWAT) is a well-known hydrological modeling tool that has been applied in various hydrologic and environmental simulations. A total of 206 studies over a 15-year period (2005–2019) were identified from various peer-reviewed scientific journals listed on the SWAT website database, which is supported by the Centre for Agricultural and Rural Development (CARD). These studies were categorized into five areas, namely applications considering: water resources and streamflow, erosion and sedimentation, land-use management and agricultural-related contexts, climate-change contexts, and model parameterization and dataset inputs. Water resources studies were applied to understand hydrological processes and responses in various river basins. Land-use and agriculture-related context studies mainly analyzed impacts and mitigation measures on the environment and provided insights into better environmental management. Erosion and sedimentation studies using the SWAT model were done to quantify sediment yield and evaluate soil conservation measures. Climate-change context studies mainly demonstrated streamflow sensitivity to weather changes. The model parameterization studies highlighted parameter selection in streamflow analysis, model improvements, and basin scale calibrations. Dataset inputs mainly compared simulations with rain-gauge and global rainfall data sources. The challenges and advantages of the SWAT model’s applications, which range from data availability and prediction uncertainties to the model’s capability in various applications, are highlighted. Discussions on considerations for future simulations such as data sharing, and potential for better future analysis are also highlighted. Increased efforts in local data availability and a multidimensional approach in future simulations are recommended.

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.