Identifying the complex patterns of human-flood interactions over longer periods of time is very important in floodplain management activities. The recently introduced socio-hydrology (SH) model contributes to capture these long-term behaviors of human-flood systems. This model can be utilized to explain the long-term dynamics of human-water interaction in floodplains. The current SH model exclusively illustrates the impact of river floods on floodplain communities. However, in some river basins, urban floods (due to high intensity rainfall) are dominant, whereas in other river basins, both river floods and urban floods influence the dynamics of the system. It is often difficult to distinguish the type of flood from actual local disaster data sets. In this study, we proposed an improvement to the existing SH model to capture the dynamics of both river floods and urban floods based on a case study from the Lower Kelani Basin, Sri Lanka, using simulated historical flood damages. The improved model was applied to capture flood damages in the target watershed, and the results further emphasize the importance of flood risk perception in flood damage reduction.

Our previous study showed a close relationship between spot-flow rate (Q) and periodical (30, 14 and 8 days) mean Q by correlation regression analysis for 64 spot Q readings in 2011–2016. The results suggested that spot Q could be used instead of periodical mean Q in discharge load (L)–flow rate (Q) equations with exponential coefficients that are close to unity. This finding supports the estimation of periodical mean L, even when low frequency samplings to know the concentrations (C) of Chemical Oxygen Demand, Total Organic Carbon, Total Nitrogen, Total Phosphorus and D-SiO₂ are conducted. Moreover, changes in the correlation factor and the slope of the regression could be used to estimate the most appropriate sampling frequency. Here, the validity of using the spot Q instead of the periodical (30, 14, 8 and 4 days) mean Q is examined using datasets obtained for the Maekawa River (2011–2015) and the Kurosano River (2015–2017) in addition to the pre-reported Okura River (2011–2016). All three rivers are forestry rivers in Miyagi Prefecture, Japan. Despite the marked variations in differences in their catchment and river characteristics, regression analyses suggested that spot Q is well suited for estimating the periodical mean Q.

Although rice is a major agricultural product in northeast Thailand, its productivity is low and unstable because rice is cultivated under rainfed conditions and there are issues with soil salinization. In this region, some of the paddy fields have been abandoned because of severe soil salinization. Therefore, effective agricultural management strategies are needed to manage soil salinization for achieving stable agricultural production. To realize effective management strategies, it is necessary to understand the spatiotemporal distribution of soil salinization. To evaluate and update the spatiotemporal soil salinization using remote sensing data, we first evaluate the relationship between soil electrical conductivity (ECe) and soil moisture content (SMC) during dry seasons. Subsequently, the effect of changes in this relationship on the normalized difference salinity index (NDSI) derived from remote sensing data for estimating soil ECe is evaluated. The relationship between ECe and NDSI, depending on the soil moisture conditions in the study area, is clarified. The findings of this study indicate that for estimating ECe using the NDSI, it is necessary to consider not only soil moisture conditions but also its quality at the time of satellite observation.

Rainfall-runoff models associated with optimal parameters are essential for the effective planning and management of water resources. However, there may be difficulties in parameter optimization, particularly in a basin with incomplete hydrological data. Therefore, we investigated whether a parameter optimization tool called hydroPSO could outperform the existing manual tuning approach and whether a larger number of tuning parameters would yield better model results in the Upper Chao Phraya River Basin of Thailand. We applied the particle swarm optimization (PSO) algorithm to H08, a grid-based land surface model, to systematically search for two setups containing four and 12 parameters at hydrological gauges with both adequate and inadequate observational data. The overall H08–PSO simulations with 12 parameters associated with land use outperformed those of both the H08–Plain and the H08–PSO with four parameters at most hydrological gauges. However, the simulations with 12 parameters produced an unsatisfactory performance at the stations with inadequate observations. This finding suggests that the parameter optimization tool could replace the laborious manual tuning approach because it improves model performance; however, more observations and monitoring are needed in regions with poor simulation performance.