Katsunori Tamagawa

Improving land surface soil moisture and energy flux simulations over the Tibetan plateau by the assimilation of the microwave remote sensing data and the GCM output into a land surface model

The land surface soil moisture is a crucial variable in weather and climate models. This study presents a land data assimilation system (LDAS) that aims to improve the simulation of the land surface soil moisture and energy fluxes by merging the microwave remote sensing data and the general circulation model (GCM) output into a land surface model (LSM). This system was applied over the Tibetan Plateau, using the National Centers for Environmental Prediction (NCEP) reanalysis data as forcing data and the Advanced Microwave Scanning Radiometers for EOS (AMSR-E) brightness temperatures as an observation. The performance of our four data sources, which were NCEP, AMSR-E, LDAS and simulations of Simple Biosphere Model 2 (SiB2), was assessed against 5 months of in situ measurements that were performed at two stations: Gaize and Naqu. For the surface soil moisture, the LDAS simulations were superior to both NCEP and SiB2, and there was more than a one-third reduction in the root mean squared errors (RMSE) for both of the stations. Compared with the AMSR-E soil moisture retrievals, the LDAS simulations were comparable at the Gaize station, and they were superior at the Naqu station. For the whole domain intercomparison, the results showed that the LDAS simulation of the soil moisture field was more realistic than the NCEP and SiB2 simulations and that the LDAS could estimate land surface states properly even in the regions where AMSR-E failed to cover and/or during the periods that the satellite did not overpass. For the surface energy fluxes, the LDAS estimated the latent heat flux with an acceptable accuracy (RMSE less than 35 W/m 2 ), with a one-third reduction in the RMSE from the SiB2. For the 5-month whole plateau simulation, the LDAS produced a much more reasonable Bowen Ratio than the NCEP, and it also generated a clear contrast of the land surface status over the plateau, which was wet in the southeast and dry in the northwest, during the monsoon and post-monsoon seasons. Because the LDAS only uses globally available data sets, this study reveals the potential of the LDAS to improving the land surface energy and water flux simulations in ungauged and/or poorly gauged regions.

A radiative transfer model and an algorithm for soil moisture including very dry conditions

Many of microwave radiometer algorithms for soil moisture tend to provide overestimation in very dry cases, partly due to the effects of the volume scattering effects. This study investigates the feasibility of an AMSR-E soil moisture algorithm, which includes the volume scattering effect in soil, by applying it to the dry/wet transition conditions. The algorithm is based on the radiative transfer in the soil medium by the discrete ordinate method (4 streams) and the Henyey-Greenstein phase function. The scattering effects of soil particles are calculated using the Mie theory. The effects of surface roughness are incorporated by using the polarization mixing parameter and the roughness parameter. The vegetation water content and albedo are used to estimate the effects of vegetation. The algorithm was applied to the observed data by the Soil Moisture Temperature Measurement System (SMTMS) and Automatic Weather Stations (AWS) in Mongolia from July to August, 2003. Results show that the proposed algorithm can improve accuracy of soil moisture estimation even in very dry cases. Keywords-soil moisture; AMSR-E; radiative transfe model; volume scattering; CEOP; SMTMS

River management system development in Asia based on Data Integration and Analysis System (DIAS) under GEOSS

This paper introduces the process of development and practical use implementation of an advanced river management system for supporting integrated water resources management practices in Asian river basins under the framework of GEOSS Asia water cycle initiative (AWCI). The system is based on integration of data from earth observation satellites and in-situ networks with other types of data, including numerical weather prediction model outputs, climate model outputs, geographical information, and socio-economic data. The system builds on the water and energy budget distributed hydrological model (WEB-DHM) that was adapted for specific conditions of studied basins, in particular snow and glacier phenomena and equipped with other functions such as dam operation optimization scheme and a set of tools for climate change impact assessment to be able to generate relevant information for policy and decision makers. In situ data were archived for 18 selected basins at the data integration and analysis system of Japan (DIAS) and demonstration projects were carried out showing potential of the new system. It included climate change impact assessment on hydrological regimes, which is presently a critical step for sound management decisions. Results of such three case studies in Pakistan, Philippines, and Vietnam are provided here.

Optimizing Multidam Releases in Large River Basins by Combining Distributed Hydrological Inflow Predictions with Rolling-Horizon Decision Making

AbstractFor the purpose of real-time dam operation in large river basins, an integrated simulation and optimization system (ISOS) has been constructed by combining distributed hydrological inflow predictions with rolling horizon decision making. First, with operational quantitative precipitation forecasts (QPFs) over a forecast horizon (FH), the ISOS is applied to obtain an optimal rule for dam releases; second, with the corrected satellite precipitation, the prescribed rule is employed for the reservoir operation over a decision horizon (DH). The ISOS is applied to the Red River Basin (169,000  km2), to test its performance for optimizing multidam releases in the large river basin. Results show that by using the ISOS, two things were simultaneously achieved, reducing the water level at Hanoi to avoid flooding while raising water storage in Hoa Binh reservoir at the end of flood season for better hydropower generation. Through comparing reservoir performances with different FHs, the effective forecast hor...

Climate Change Assessment Due to Long Term Soil Moisture Change and Its Applicability Using Satellite Observations

© 2013 Lu et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Climate Change Assessment Due to Long Term Soil Moisture Change and Its Applicability Using Satellite Observations

Improving land surface energy and water fluxes simulation over the Tibetan Plateau with using a land data assimilation system

The land-atmosphere interaction in the Tibetan Plateau plays an important role in the Asian summer monsoon and the global energy and water cycle. This study presents a method to improve the land surface water and energy fluxes simulation by using a land data assimilation system (LDAS), which merging microwave remote sensing data and GCM output into a land surface model. NCEP reanalysis data is used as the background field and also as the meteorological forcing for the land surface model. Two experiments were designed as by driving LDAS-UT with two sets of atmospheric forcing data, (1) with in situ observed forcing data and (2) with NCEP reanalysis data at Gaize and Naqu sites. Results show that LDAS is able to estimate land surface soil moisture and energy fluxes accurately. The RMSE of soil moisture simulation is around 0.03–0.05 and RMSE of net radiation simulation is around 30W/M2. This study reveals the potential for using satellite remote sensing data to improve land surface fluxes estimation.

Estimating land surface energy and water fluxes by using the Land Data Assimilation System developed at the university of Tokyo (LDASUT)

This paper reports an application of an Land Data Assimilation System developed in the University of Tokyo (LDASUT) on the Gaize PBL site at the northwest of Tibet Plateau, for the period from July to August, 2007. The objectives of this study are: (1) to validate LDASUT in bare soil field using in-situ observation, (2) to check the feasibility to estimate areal land surface variables reliably with using LDASUT driven by GCM output data. For the system validation, LDASUT was first driven by in-situ observed micrometeorological data, and simulated energy fluxes were compared to hourly direct measurements; simulated soil moisture content was compared to the in-situ soil moisture observation at the depth of 4 cm. The results show that LDAS can generally simulate those variables well and thus the capability of LDAS is validated. In order to check the possibility of applying LDAS globally and simulating surface energy and water budget worldwide, Japan Meteorology Agency (JMA) Model Output Local Time Series (MOLTS) data were used as the driven data of LDAS. Performance of LDAS was not so good when it was driven by the JMA MOLTS data. This result demonstrated that there were systemic biases lied in JMA MOLTS data in the study region and thus it can not directly apply to LSM or LDAS.

Monitoring soil moisture change in North Africa with using satellite remote sensing and land data assimilaiton system

In this study, we generated and compared two sets of soil moisture data in the North Africa region, by using the land data assimilation system developed at the University of Tokyo (LDAS-UT) which is driven by the NECP reanalysis data and UKMO forcing data, separately. GPCP precipitation data was used as reference data for the indirect validation, after its accuracy was confirmed by comparing it with the in-situ observation in the Medjerdah Basin. The soil moisture generated by LDAS-UT with NECP forcing is in a good relationship to the GPCP rainfall data, while that generated by LDAS-UT with UKMO forcing is mismatching with the GPCP precipitation patterns.