The expansion of Moso bamboo forests in Japan might change transpiration and therefore reduce the availability of water resources. Moso bamboo stands are often composed of culms with various ages and older culms may have lower sap flux density (F_d), which may in turn affect individual culm transpiration (Q_t), probably because vascular bundles do not regenerate after sprouting. Information related to the differences of F_d and Q_t between younger and older culms would be important for (i) understanding the effects of culm age structure changes on stand-scale transpiration (E_C), and (ii) developing sampling strategies for E_C estimates in Moso bamboo forests. We conducted sap flux measurements for 15 individuals from four culm age classes in a managed Moso bamboo forest in Kameoka, Kyoto, Japan. Differences in F_d were not significant among the four culm age classes with almost the same stem diameter at breast height (DBH). Q_t was related to DBH across four age classes, indicating that culm age had no apparent effect on Q_t in the forest. Our results suggest the effects of culm age structure changes on E_C are small, and contribute to development of sampling strategy without considering culm age structure for E_C estimates at this site.

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The objective of this study is to develop an open access web-based conceptual Xinanjiang model for supporting calibration process with user-friendly interfaces. This makes it possible to calibrate the model for any user irrespective of the location, resource availability or technical capability. This interface not only allows the user to run the model in a user-friendly environment, but also provides useful support for better calibration by suggesting parameter settings based on observed hydro-climatic data, calculating Nash-Sutcliffe efficiency at daily, monthly and annual scales using time series of observed and simulated discharge data for every successful model run and hydrograph visualization. Moreover, the user can further verify the agreement between the observed and simulated discharge by visually inspecting the hydrograph. The interface allows rendering hydrographs using simulated and observed discharge for a respective model run or group of model runs. The user can perform repeated model runs with different parameter sets until it achieves a satisfactory accuracy. The user can download the result and parameter files for all or any specific model run.

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Modified soil hydrological schemes were implemented in process-based ecosystem model Biome-BGC to improve water cycle simulation. The new schemes employed a 2-layer soil model and introduced new outflow components. Surface flow is input water exceeding soil infiltration capacity, and a scheme to predict it in the shorter duration than 1 day was proposed. Drainage from bottom soil layer and soil water redistribution between the layers driven by matric potential gradient and gravity were also modeled. The modified model was tested using the meteorological data and site parameters distributed with the original version. In comparison with the original, the modified model calculated 14% larger mean annual outflow. It also calculated a rapid outflow under dry antecedent soil condition, and reduced outflow peaks with slow drainage recession after soil wetted up, which were not simulated by the original model. The ecosystem carbon cycle responded to the change in soil water budget and net primary production (NPP) decreased by the modified model. Newly introduced parameters were determined automatically, so that users can use the same input data and parameter set for the original version. These simple schemes can be applied in other process-based ecosystem models.

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This study develops a tide propagation model in order to forecast water levels and velocities at a given time and location for the largest city in the Mekong Delta, Can Tho City. The simulation model is applied to a complex waterway system that is characterised by a number of small canals and tributaries, which connect with the main stream. The model, which is verified by comparison with observed water levels during a typical dry season, enables examination of the mechanisms of tidal propagation, which have an impact on floods, inundation and saline water intrusion. The model analysis indicates that the difference in tidal amplitude between a connecting tributary and the main stream is small, whereas the flow velocity largely varies depending on the location. The flow velocity in the tributary, which exceeded 1 m/s, is almost three times that of the main river. This kind of local amplification in flow velocity is important when evaluating flood/inundation risks in urban areas of the Mekong Delta, as small ships are likely to encounter difficulties in handling or risk being overturned due to unexpectedly rapid flows that occur during these abnormal high tides or typhoon storm surges.

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Rainwater harvesting is increasingly recognized as an important source of water supply. However, the technique is practiced for very different purposes depending on region and rainfall conditions. In this study, the performance of rainwater harvesting was evaluated in accordance with local user practice to determine its suitability as a primary water supply. A water balance model using a long-term time series was applied to simulate system behavior under various scenarios of tank size and monsoon patterns in the Asian tropical monsoon region, investigated at 111 sites in Vietnam. The results of the study show that a limited range of 20–110 L/d can meet basic demand with 95% reliability. However, an additional water of 50–400 L/d is available for extra supply during rainy season. The diversity of monsoon patterns leads to considerable variation of additional available water (AAW) despite uniform amounts of annual precipitation. Tank size is recognized as playing a crucial role in improvement of supply capacity for basic demand while roof area and precipitation exerts a higher influence on AAW.

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Shallow riverbank groundwater along permanent rivers represents a good water resource. The particle distribution in a soil determines its hydraulic conductivity, which is a critical criterion in the selection of riverbank sites for filtration. Over time, particle size distributions (PSD) may change because of clogging and the weathering of local soil. In this study, the effect of PSD on the removal of color and Escherichia coli (E. coli) from groundwater was investigated. A laboratory scale model was constructed to determine the horizontal hydraulic conductivity of local alluvial soil with different PSD. The results were analyzed on the basis of two factors: (a) different alluvium soils and (b) soil uniformity coefficient (C_u). The alluvial soils (Sands A, B, C, and D) showed hydraulic conductivities ranging from 6.87 × 10^–4 m/s to 8.96 × 10^–4 m/s. Results indicated that an increase in C_u can improve the removal of color and E. coli. The Sand A, which had a well-graded PSD and the highest C_u value, achieved color and E. coli removal rates of up to 70% and 100%, respectively.

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Inundation depth (or level) is the most basic information for flood risk assessment; however, its mapping suffers from lack of in situ data in many cases. The aim of this study is to propose a new method for estimating inundation depth and spatially distributed water level for a local-scale pluvial flood using a combination of flood extent information derived from remote sensing imagery and hydrodynamic simulations. The study assumes the location of the inundation area given by the remote sensing imagery is mostly, but not completely, reliable. The estimation error of ground surface area wetted by inundation water body (wetted area) is used as an index to determine the most likely distribution of inundation depth. The proposed method was applied to two study areas to examine the performance of the method for different topographic characteristics. It showed promising results with an estimation precision of 0.02 to 0.17 m. An additional experiment suggested that water level could not be correctly estimated without flood extent information, complementing errors of the ground elevation data, and furthermore, using different topographic datasets revealed that the performance was highly influenced by the ground elevation data.

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Collecting and analysing bathymetric information is essential for lake management. This is particularly true regarding Lake Nasser/Nubia in Egypt, where accumulated sediment in the lake must be examined. This is typically accomplished through field measurements, which are time consuming and costly. However, remotely sensed imagery provides wide coverage, low cost, and time-saving solutions for bathymetric measurements, especially in shallow areas with high erosion or sediment accumulation, such as at the entrance of Lake Nasser/Nubia. In this study, bagging (Bag) and least square boosting (LSB) fitting algorithms that use reflectance of green and red band logarithms, green/red band logarithms ratio, and blue/red band logarithms ratio are proposed for bathymetry detection. For validation, the proposed approaches were compared with the ratio method (RM) and neural network (NN) conventional methods. Bathymetric data obtained from all methods using SPOT-6 imagery were evaluated by means of global positioning system (GPS) and echo sounder data field measurements. The Bag ensemble outperformed all methods with 0.85 m RMSE, whereas RM, LSB, and NN yielded 1.03, 0.99, and 0.97 m respectively. The results showed that the proposed approaches outperform and are more accurate than RM conventional method and the Bag approach is more accurate than the NN model when applied over shallow water depths of up to 6.5 m.

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