Airborne laser scanning technology has become widely used and has made it possible to measure the height of a wide range of ground surfaces. In this study, we conducted a basic analysis of the relationship between snow depth and vegetation/topographic features using airborne laser scanning in the northernmost island of Japan, Hokkaido. The vegetation was differentiated between forest areas and non-forest areas, and topographic features such as elevation, overground-openness, slope angle, and slope aspect were selected for analysis. As a result, trends in spatial variability of snow depth differed significantly between forest and non-forest areas, clearly confirming the influence of vegetation. Spatial variability was relatively smaller inside the forest than outside the forest. In terms of topographic features, a clear relationship with overground-openness and slope aspect (non-forest only) was found. We also confirmed that our findings are generally consistent in quantitative terms with those of previous studies, showing that the spatial heterogeneity of snow depth is smaller in forest areas and greater in non-forest areas.

The primary cause of conflicts over water allocation is growing demand and limited supply, which has become an increasingly serious issue in many watersheds. To alleviate water disputes, effective management strategies can be employed, particularly in the context of intensifying agricultural production and unpredictable changes in weather. In this study, two models, SWAT and WEAP, and the modified surface water supply index (MSWSI) were utilized to evaluate water allocation in the Srepok River Watershed (SRW), considering the prioritization of demand and various irrigation methods, during both wet and dry years. The crop irrigation was chosen to be the main focus in relation to the unmet water demand (UWD). The results indicated that coffee was the primary cause of UWD in the middle of the watershed during the second half of the dry season, and annual crops (AC) were the secondary cause. This research further elucidated that while prioritizing demand had an insignificant impact, transitioning from hose irrigation to sprinkler irrigation could be remarkably effective in mitigating the issues of UWD in coffee crops during both wet and dry years.

Climate change will contribute to sea level rise (SLR), impacting coastal land use, groundwater salinity, and coastal flooding. Previous studies have analyzed the direct impact of SLR on flooding by considering coastal subsidence and the enhancement of high tide events; however, it also impacts river hydrodynamics, further worsening fluvial flooding. Here, we analyzed how SLR could enhance fluvial flooding via backwater effects and impact coastal megacities under various climate and SLR scenarios using a global hydrodynamic model. We found that the future mean inundation area for these cities is projected to increase by up to 11.2 ± 9.0% under the warmest scenario. Similarly, the projected increase in mean flood exposure is by up to 11.1 ± 9.1% at the end of the century. Cities with high increases in flood exposure are located less than 100 km from the coast, while some cities located far inland could be impacted. Climate tipping scenarios show an even greater impact. More than three-fourths of examined cities were affected due to the backwater effect of SLR in the warmest scenario, with a minimum of 141% larger inundated area compared to simple coastal subsidence, suggesting consideration of SLR’s effect on fluvial flooding is indispensable for future flood risk assessment.

The Advanced Himawari Imager (AHI) accurately measures the surface temperatures of a large lake, except during cloudy conditions, which cause data gaps and often prevent the detection of their daily cycle. Random forest regression (RFR), a machine learning tool, was employed and tested for various possible predictors to gap-fill the AHI-derived lake surface temperatures (LSTs). The results show that the near-surface lake water temperature (LWT) was the best predictor of LSTs when auxiliary meteorological data such as downward long-wave radiation and wind speed (WS) were used together. The gap-fill operation produced reasonable LSTs with the root mean square error of the LST estimates of 0.1–0.2°C against measured LSTs.

Stable water isotope ratios (WIRs) are sensitive to phase changes and are crucial for understanding water cycle processes. However, limited observational data and model uncertainties in the southern hemisphere pose challenges for accurately reproducing these ratios. This study compared isotope-enabled climate models (IsoGSM and MIROC5-iso) with ship-based observations over the Indian Ocean sector of the Southern Ocean in January 2006. The models accurately simulated temperature, pressure, and humidity but faced challenges with vapor δ¹⁸O and δD. By categorizing atmospheric conditions into southerly flows from Antarctica, northerly flows with atmospheric rivers (ARs), and the other cases, we found varying model performance. In the other cases, models showed agreement with observations. In contrast, in southerly cases, the models failed to capture substantial decreases in δD, likely because of inadequate representation of Antarctic land-surface processes. In northerly cases, both models showed model-dependent discrepancies in δD. A newly developed simple WIR model indicated these discrepancies stem from inadequate precipitation processes. These findings reveal that synoptic-scale processes influence WIR variations in ways not fully captured by current climate models. As ARs deliver precipitation to Antarctica that forms ice cores, these limitations impact paleoclimate interpretations, highlighting the need to improve synoptic-scale processes.

It is of paramount importance for improved water resource management to understand long-term changes in human-flood interactions. Although spatiotemporally homogenous data of long-term changes in human and flood are necessary to deepen our understanding of complex human-flood interactions, such data are unavailable in regional and global scales. Here, we present the prototype of a “reanalysis” dataset of human-flood interactions as an analogy of atmospheric and oceanic reanalysis datasets in Earth science. Following practices in earth science, we develop a socio-hydrological data assimilation system which integrate observations in both human and hydrology domains into a socio-hydrological flood risk model. Then, we generate long-term socio-hydrological data in counties in the United States. By assimilating levee height and population data into a flood risk model, we successfully constrain the trajectory of human-flood interactions. Unobservable variables, like social collective memory, create persistent uncertainties. The output of the data assimilation system (i.e. reanalysis data) clearly indicates that many communities have been getting vulnerable to flood as the flood protection level rises since the social collective preparedness to flood has been declined. Overall, we propose the data assimilation approach and the concept of reanalysis of human-flood interactions as promising ways to realize effective flood management strategies.

Neural networks (NNs) have recently gained attention for establishing Rainfall-runoff Predictive Models (RPMs) to receive accurate upcoming hydrographs. This study established Bidirectional Long-Short-Term Memory Model-based RPMs (BiLSTM-RPMs) for a small-to-medium-scale urban watershed, the Upper Kanda basin, occupying nearly 11 km². The average rainfall of six stations and streamflow of a water level gauge were considered to gather a hundred events with minute-to-minute intervals for 12 years from 1999. The influence of batch-wise shuffling was investigated by developing BiLSTM-RPMs with a sliding window generator (SWG) technique for target lengths (TLs) such as 10 minutes (TL10), TL20, TL30, and TL60. Batch-wise shuffling was supported to predict seamless hydrographs, where all TLs achieved Nash–Sutcliffe model efficiency (NSE) above 0.8, Root Mean Square Error (RMSE) below 0.01 mm/min, and coefficient of determination (R²) for peak alignment above 0.95. Long-Short-Term Memory Model-based RPMs (LSTM-RPMs) derived appreciable forecasted hydrographs for TL10 and TL20; however, they performed poorly for TL30 and TL60, where the R² was 0.88 and 0.62, respectively.

In lacustrine ecosystems, aquatic plants are key components that significantly influence water quality, making their monitoring essential for environmental management. Traditional diver-based surveys are costly and labor-intensive, highlighting the need for simpler methods. This study compares a method using an Acoustic Doppler Current Profiler (ADCP) to estimate aquatic plant height with direct measurements by divers at six sites in the southern basin of Lake Biwa. Results indicate a correlation between ADCP estimates and diver measurements and this study also roughly estimated PVI (Percent Volume Infestation) and biomass. Using past survey data, this study estimated the water depth, average aquatic plant height, PVI, and biomass in the southern basin of Lake Biwa for 2020, confirming the method’s accuracy compared to other approaches. The ADCP method significantly improves the cost and efficiency of aquatic plant monitoring, offering wide applicability in future water quality surveys and management.