The current global water body maps have a resolution of approximately 30 m depending on the available remote sensing data. A water body map with higher spatial resolution is required to distinguish smaller rivers for advanced applications involving global carbon cycle and real-time flood predictions. Conventional water extraction methods use water indices that combine visible and infrared spectra. State-of-the-art remote sensing data including aerial photography, offers a few-meter spatial resolution but only contains the visible spectrum. Here, we established a water extraction method at 60-cm resolution via Bayesian inference, using aerial photography’s visible spectrum combining the Landsat-based Global 1-second Water Body Map and the Open Street Map (OSM). The Japan Flow Direction Map (J-FlwDir) was used to link the water bodies. Our method detected the main streams of the Tsurumi and Tama rivers and their tributaries, which were not resolved by the Landsat-based dataset. With our approach, rivers with widths >10 m were detected and water extent was obtained for 37% of small rivers, represented as lines on the OSM. These findings indicate that our method, solely based on visible spectra with hydrography data and existing water body maps, improves the spatial resolution of water mapping without requiring infrared.
In global hydrological models, river discharge is accumulated by following the river network, which presumes that there is one downstream destination for each grid. Implementing “diversions” where there are multiple downstream destinations, such as bifurcations and inter-basin water transfers, requires an extension of river routing algorithms. Previous global water resources models that implemented diversions typically used a semi-implicit numerical scheme where river discharge should be calculated in an upstream-to-downstream order. The major obstacle to flexible application to any river network with diversions was that ad-hoc modification of the model’s code was required because the river sequence map to specify calculation order was developed without considering diversions. To overcome this limitation, we developed two new river routing algorithms that can generally represent diversions. One is automatically updating river sequence considering diversions and the other is introducing the Forward Time Centered Space scheme. The former has the advantage of stability under longer time steps, while the latter’s strength lies in its easy implementation and applicability where backwater happens. We confirmed that both algorithms efficiently handle the complex canal network in the Indus River. These approaches allow us to flexibly implement diversions in river routing algorithms.
The tertiary irrigation system (TIS) was designed for the Muda Irrigation Scheme (MIS) to distribute irrigation water to farmers’ fields to ensure the reliability of water supply for cultivating rice paddies twice a year. Variability in farming practices, influenced by farmer autonomy along the tertiary canal adds complexity and uncertainty to adherence monitoring. Traditional on-site data collection methods are limited in scope and efficiency, whereas Earth observation (EO) enables continuous monitoring. In this study, we introduced a methodology that uses EO datasets to monitor individual field adherence to rice-planting schedules under TIS. These tools improve the monitoring of rice-planting schedule adherence by identifying non-adherent fields for further countermeasures. This study highlights the potential use of EO datasets and advanced data processing techniques for efficient agricultural monitoring.