For the monitoring of water and energy cycles, satellite-based cloud water estimations provide valuable information. Nevertheless, most existing methods estimate cloud water with emphasis on one category of cloud particle type. Because differences in cloud particle types affect cloud characteristics, water contents must be estimated separately. Moreover, applying those methods to estimations over land is a widely recognized challenge. This study newly developed a 36.5 GHz estimation method based on 89.0 GHz cloud and land simultaneous estimation (Seto et al., 2018), and compared the features of estimation results of the two methods and satellite cloud radar products. Moreover, based on the revealed features, this study assessed the possibility of cloud-rain-ice partitioned estimation. Results show that liquid water contents over land can be estimated reasonably well using 36.5 GHz. Furthermore, liquid-ice partitioning using the difference between 36.5 GHz and 89.0 GHz measurements and cloud-rain partitioning using the difference between horizontal and vertical polarizations at 36.5 GHz appear to be possible.
For close monitoring of nitrate concentrations in groundwater, a method is proposed for interpolating nitrate concentrations at locations where they are not measured using spatiotemporal analysis. Conventionally, the interpolated nitrate concentrations are calculated using measured nitrate concentrations based on three factors: spatial distance from the unmeasured location to the measured location, time interval from the unmeasured period to the measured period, and correlation coefficients between the nitrate concentrations in measured and unmeasured periods. As described herein, we propose improvement of the interpolation accuracy by adding the difference between the land use around the measured and unmeasured locations at each period to the three factors of the conventional method. To verify the effectiveness of the proposed method, experiments were conducted using nitrate concentrations in February and August during 2007 - 2016, as measured at 808 shallow wells in the Miyakonojo Basin. The experimentally obtained results show that the proposed method can interpolate the nitrate concentrations with higher accuracy than the conventional method.
To improve the prediction accuracy of numerical simulation models of water and sediment transport phenomena in mountainous catchments, we discuss extraction methods of the governing processes, and important site conditions and external forces affecting the governing processes. Results show a relationship between catchment area and water and sediment discharges, as derived from observations conducted at multiple locations within a catchment, can be typed according to differences in the spatial distribution of governing processes and controlling conditions. Elucidating relations between the catchment area and water and sediment discharges at multiple locations within the catchment might reveal the governing processes and controlling conditions of water and sediment transport phenomena. Based on these analyses, we discussed issues and approaches reflecting the observed information related to the spatial distribution of water and sediment discharge in the numerical prediction models. Results revealed (1) the importance of longitudinal observations at multiple streams in a catchment for ascertaining characteristics of the spatial distribution of water and sediment discharges. This study also clarified (2) the importance of cataloging spatial distribution of site conditions and, (3) processes that are and are not be expressible solely by topography, Furthermore, we argued (4) a method of modelling processes that cannot be determined solely by topography.
Doing a research is by no means a solitary venture. It is indeed a collaborative effort of all the stakeholders involved. An onus lies with the principal investigator to generate the innovative research idea, for which one needs to be aware of the knowledge gap, known and unknowns, resources available ? the time, budget and physical resources. To understand the knowledge gap, one needs to understand the scientific implication, social needs, policy aspects, and fundamentals of implementation. In this essay, I am discussing my experiences on changing the research field, which in fact is both a challenge and opportunity, and my experiences of transferring scientific knowledge to policy synthesis.