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Abstract Determining the distribution and dynamics of water on land at any given moment poses a significant challenge due to the constraints of observation. Consequently, as advancements in land surface models (LSMs) have been made, numerical simulation has emerged as an increasingly accurate and effective method for hydrological research. Nonetheless, systems that represent multiple land surface parameters in a near‐real‐time manner are scarce. In this study, we present an innovative land surface and river simulation system, termed Today's Earth (TE), which generates state and flux values for the near‐surface environment with multiple outputs in near‐real‐time. There are currently three versions of TE, distinguished by the forcing data utilized: JRA‐55 version, employing the Japanese 55‐year Reanalysis (JRA‐55, from 1958 to the present); GSMaP version, utilizing, the Global Satellite Mapping of Precipitation (GSMaP, from 2001 to the present), and MODIS version, utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS, from 2003 to the present). These long‐term forcing data set allow for outputs of the JRA‐55 version from 1958, the GSMaP version from 2001, and the MODIS version from 2003. Aiming to provide water and energy values on a global scale in real‐time, the TE system utilizes the LSM Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) (Takata et al., 2003, https://doi.org/10.1016/s0921‐8181(03)00030‐4; Yamazaki et al., 2011, https://doi.org/10.1029/2010wr009726) at a horizontal resolution of 0.5°, along with the river routing model CaMa‐Flood (Yamazaki et al., 2011, https://doi.org/10.1029/2010wr009726) at a horizontal resolution of 0.25°. Both land surface and river products are available in 3‐hourly, daily, and monthly intervals across all three versions. A notable feature of TE is its ability to release both state and flux parameters in near‐real‐time, offering convenience for various aspects of hydrological research. In addition to presenting the general features of TE‐Global, this study examines the performance of snow depth, soil moisture, and river discharge data in daily intervals from 2003 to 2021, with validation spanning 2003 to 2016. When comparing snow depth results, the correlation coefficient ranged between 0.644 and 0.658, while for soil moisture it ranged between 0.471 and 0.494. These findings suggest that the LSM yields comparable results when utilizing JRA‐55, MODIS, or GSMaP. Interestingly, river output from the three products exhibited distinct characteristics varying from GSMaP to JRA‐55 and MODIS. For river discharge, the correlation coefficient ranged from 0.494 to 0.519, the root mean square error ranged from 3,730 m3/s to 6,330 m3/s, and the mean absolute error ranged from 3,000 m3/s to 5,160 m3/s among the different forcing versions. The overall bias in river discharge from GSMaP was 1,570 m3/s, in contrast to −589 m3/s for JRA‐55 and −200 m3/s for MODIS. These metrics demonstrate that the TE system is capable of generating practical land surface and river products, highlighting differences arising from the use of various types of forcing data. This comprehensive system would be valuable for monitoring water‐related movements, predicting disasters, and contributing to sophisticated water resource management. Regarding its application, the TE system has been included in the World Meteorological Organization as a Global Hydrological Modelling System. All TE‐Global products can be freely accessed through File Transfer Protocol.

Abstract Ensemble simulations of climate models are used to assess the impact of climate change on precipitation, and require downscaling at the local scale. Statistical downscaling methods have been used to estimate daily and monthly precipitation from observed and simulated data. Downscaling of short-term precipitation data is necessary for more accurate prediction of extreme precipitation events and related disasters at the regional level. In this study, we developed and investigated the performance of a downscaling method for climate model simulations of hourly precipitation. Our method was designed to recognize time-varying precipitation systems that can be represented at the same resolution as the numerical model. Downscaling improved the estimation of the spatial distribution of hourly precipitation frequency, monthly average, and 99th percentile values. The climate change in precipitation amount and frequency were shown in almost all areas by using the 50 ensemble averages of estimated precipitation, although the natural variability was too large to compare with observations. The changes in precipitation were consistent with simulations. Therefore, our downscaling method improved the evaluation of the climatic characteristics of extreme precipitation events and more comprehensively represented the influence of local factors, such as topography, which have been difficult to evaluate using previous methods.

Measurement(s) Precipitation Technology Type(s) Weather station Sample Characteristic - Environment Climate system Sample Characteristic - Location Japan

Significant regional disparities exist in the time left to prepare for unprecedented drought and how much we can buy time depending on climate scenarios. Specific regions pass this timing by the middle of 21st century even with stringent mitigation.

Abstract Stable hydrogen isotopes in monsoonal precipitation (δDp) at three sites (Port Blair, Barisal and Darjeeling) reveal the factors governing δDp variations over a south‐north gradient across the Bay of Bengal. We found that the δDp at each site continuously decreases from May to September and these trends become more pronounced from south to north. The decreasing trends of downstream δDp closely follow the decreasing trends of upstream stable hydrogen isotopes in water vapor (δDv), which indicates that upstream δDv properties shape initial spatiotemporal patterns of the downstream δDp (“shaping effect”). Additionally, our results demonstrate that, during moisture transport, upstream vertical air motions (convection and downward motion) and topographic relief magnify the amplitude of the decreasing trends of downstream δDp (“magnifying effect”). Our findings imply that upstream δDv properties and relevant atmospheric and topographical conditions along the moisture transport pathway need to be considered collectively to better interpret paleoclimate records.

Abstract Stable water isotopes, which depend on meteorology and terrain, are important indicators of global water circulation. During the past 10 years, major advances have been made in general circulation models that include water isotopes, and the understanding of water isotopes has greatly progressed as a result of innovative, improved observation techniques. However, no previous studies have combined modeled and observed isotopes using data assimilation, nor have they investigated the impacts of real observations of isotopes. This is the first study to assimilate real satellite observations of isotopes using a general circulation model, then investigate the impacts on global dynamics and local phenomena. The results showed that assimilating isotope data improved not only the water isotope field but also meteorological variables such as air temperature and wind speed. Furthermore, the forecast skills of these variables were improved by a few percent, compared with a model that did not assimilate isotope observations.

Abstract Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding.

Abstract Accurate simulations of land processes are crucial for many purposes, such as climate simulation, weather, flood, and drought prediction, and climate change impact assessment studies. In this paper, we present a new land simulator called the Integrated Land Simulator (ILS). The ILS consists of multiple models that represent processes related to land (hereafter, referred to as “land models”). They are coupled by a general-purpose coupler, Jcup, and executed using the Multiple Program Multiple Data approach. Currently, ILS includes a physical land surface model, the Minimal Advanced Treatments of Surface Interaction and Runoff model, and a hydrodynamic model, the Catchment-based Macro-scale Floodplain model, and the inclusion of additional land models is planned. We conducted several test simulations to evaluate the computational speed and scalability and the basic physical performance of the ILS. The results will become a benchmark for further development.

Abstract Sediment dynamics play an important role in various aspects of earth system modeling. In this study, we developed a global sediment dynamics model that considers suspended sediment and bedload at short timescales. We validated suspended sediment from four observation stations in the Amazon River basin and over 60 observation stations from around the world based on a variable criteria such as availability of data samples. Our model was able to effectively reproduce seasonality and spatial distribution of suspended sediment flow. However, our global estimate of approximately 4 Bt/a was significantly lower than previous estimates; therefore, we discuss potential causes of this discrepancy, including target time period and discrepancies with previous extrapolated methods. Our newly developed sediment dynamics model could provide a better understanding of global sediment transfer and contributes to various related research fields such as coastal modeling and natural disasters.

Abstract This study presents the results of the ERTDF S-12 project for searching an optimum reduction scenario of the short-lived climate pollutants (SLCPs) to simultaneously mitigate the global warming and environmental problems. The study utilized REAS emission inventory, Asia-Pacific Integrated Model-Enduse (AIM/Enduse), MIROC6 climate model, NICAM non-hydrostatic atmospheric model, and models for estimating environmental damages to health, agriculture, and flood risks. Results of various scenario search indicate that it is difficult to attain simultaneous reduction of global warming and environmental damages, unless a significant reduction of CO2 is combined with carefully designed SLCP reductions for CH4, SO2, black carbon (BC), NO x , CO, and VOCs. In this scenario design, it is important to take into account the impact of small BC reduction to the surface air temperature and complex atmospheric chemical interactions such as negative feedback between CH4 and NO x reduction. We identified two scenarios, i.e., B2a and B1c scenarios which combine the 2D-scenario with SLCP mitigation measures using End-of-Pipe (EoP) and new mitigation technologies, as promising to simultaneously mitigate the temperature rise by about 0.33 °C by 2050 and air pollution in most of the globe for reducing damages in health, agriculture, and flood risk. In Asia and other heavy air pollution areas, health-care measures have to be enhanced in order to suppress the mortality increase due to high temperature in hot spot areas caused by a significant cut of particulate matter. For this situation, the B1b scenario is better to reduce hot spot areas and high-temperature damage to the public health.

Abstract Integrating reservoir flood control operations in global flood forecasting systems is important for accurately estimating discharge and other variables. Because existing modeling operational rules and parameters do not reflect the actual variability due to a lack of associated data, globally applicable modeling of flood regulation needs to be studied further. In this study, we developed a flood control operation scheme with refined parameters and algorithms to tackle this problem. We used recently developed objective data sets of reservoir dynamics to more realistically estimate different reservoir parameters, such as flood storage capacity. Furthermore, the algorithm for the operational rule was modified by introducing a new release coefficient such that the peak attenuation depended on the reservoir's ability to regulate floods. The operation scheme for 2,169 dams was introduced into the CaMa‐Flood global hydrodynamic model. Compared to the simulation without reservoirs, the Nash‐Sutcliffe Efficiency and peak discharge error in the daily river discharge improved at 62.2% and 49.9% of the gauges, respectively, after the integration. Compared to the parameter estimated by a method in previous studies, the newly estimated flood storage capacity parameters reduced the peak discharge error at 47% of the gauges downstream of the reservoirs where the flood storage capacity ratio is small (≤50%). Furthermore, the modification of operational rules had the effect of mitigating people exposed to flooding by ∼10%. This study informs the integration and the refinements of the reservoir operation scheme to improve global flood simulation and forecasting.

Abstract Global water resource assessment has been conducted primarily for surface water and shallow groundwater (unconfined aquifers). Groundwater is a major water resource worldwide. Nevertheless, future water resource assessment integrating surface water and groundwater has not been carried out at the global scale. Large grid sizes are used in global‐scale models due to computer resource constraints. Underground, the unsaturated zone that connects surface water and groundwater is expressed using a vertically one‐dimensional grid of fixed size. However, the thickness of the unsaturated zone varies greatly between dry and humid areas, and future unsaturated zone conditions may differ from those at present due to climate change. In addition, for mountainous areas with steep slopes, representing the unsaturated zone in one vertical dimension is a major limitation. We believe that such a representation causes difficulty in adequately expressing underground water flow and thus introduces uncertainties into future prediction results. In this study, we developed a code to represent variably saturated flow with groundwater storativity in three dimensions based on physical equations, which can be used as references for parameterization in the future. To confirm the accuracy of this code, we verified it using vertical one‐ and two‐dimensional infiltration problems, a three‐dimensional groundwater pumping problem, hillslope problem, and water balance. The code achieved reliable results for each problem.

Abstract Water isotopic composition (δ18O and δD) in terrestrial proxies of past precipitation enable us to better understand and interpret variation in the Indian Summer Monsoon (ISM). Previous studies have suggested that the origin of precipitation is an important factor controlling the isotopic composition of precipitation around the Indian subcontinent; however, it is difficult to quantify using the Lagrangian approach because the approach does not satisfy the assumption of an adiabatic process over a convective area. We investigated the isotopic composition of precipitation at three sites over Bangladesh in 2010 and estimated the origins of precipitation by the Eulerian approach using an isotope-incorporating Atmospheric General Circulation Model. Our observations showed similar seasonal and intraseasonal variations in the δ18O values of precipitation among the sites, whereas the temporal characteristics of the precipitation amount differed among the sites. The isotopic composition was linked to the migration of organized convective activity around the region. The model showed that the pre-monsoon season (from mid-March to early June) was characterized by high δ18O values of precipitation originating from the Bay of Bengal and the Arabian Sea. In the ISM season (from mid-June to early October), the contribution of these sources to precipitation gradually decreased, while the contribution from the Indian Ocean increased, resulting in decreasing δ18O values of precipitation due to the enhanced rainout process during the transportation. These moisture contributed less to precipitation over Bangladesh in the post-monsoon season (from mid-October to November), whereas moisture originating from the Pacific Ocean and land surface (i.e., recycling of water) contributed to precipitation in the season. Because the recycling of water originated from past precipitation with low δ18O values in the ISM season, its contribution to precipitation reduced the δ18O values of precipitation in the ISM and post-monsoon seasons. These results suggest that the origins of precipitation and the migration of organized convective activity are the dominant factors controlling the isotopic composition of precipitation in the region. These characteristics can be used to identify monsoon onset and withdrawal based on water isotopic composition.

Abstract The radioactive materials are generally concentrated downwind of their origins when the prevailing winds blow continuously in one direction. If this principle determined the pattern of dispersion in all cases, dispersion directions could be estimated by wind patterns. However, this hypothesis has not been sufficiently verified because of the complexity of dispersion processes and weather systems. Here, we show that dispersion directions, which are divided into four ranges, can be estimated by wind patterns using a machine learning approach. The five-year average hit rates of the directions of dispersion estimated using near-surface winds exceed 0.85 in all months. The dispersion directions can be estimated up to 33 hours in advance using forecast winds. In particular, high hit rates exceeding 0.95 are achieved in January and March, when large-scale weather systems dominate. These results indicate that the dispersion directions are determined by the wind patterns that correspond to large-scale weather systems and diurnal circulation patterns in most cases. Our findings also provide more reliable information on dispersion patterns with reduced uncertainties, given that reasonable skill is achieved at a sufficient lead time for evacuation.

Rapid and frequent mapping of flood areas are essential for monitoring and mitigating flood disasters. The Advanced Land Observing Satellite-2 (ALOS-2) carries an L-band synthetic aperture radar (SAR) capable of rapid and frequent disaster observations. In this study, we developed a fully automatic, fast computation, and robust method for detecting flood areas using ALOS-2 and hydrodynamic flood simulation data. This study is the first attempt to combine flood simulation data from the Today’s Earth system (TE) with SAR-based disaster mapping. We used Bayesian inference to combine the amplitude/coherence data by ALOS-2 and the flood fraction data by TE. Our experimental results used 12 flood observation sets of data from Japan and had high accuracy and robustness for use under various ALOS-2 observation conditions. Flood simulation contributed to improving the accuracy of flood detection and reducing computation time. Based on these findings, we also assessed the operability of our method and found that the combination of ALOS-2 and TE data with our analysis method was capable of daily flood monitoring.

This paper aims to improve thermal efficiency of spark ignition engine by numerical calculation with detailed chemistry. Experimental results from a four-stroke-single-cylinder engine are compared with that of simulations. It is experimentally found that peak efficiency is achieved at lean-limit combustion under excess air ratio λ=1.6. Due to engine output power loss, further investigations are conducted under lean-boost operations. The best condition of the lean-boost mode is at λ=1.3 and 150 kPa boosted pressure (abs). To further improve the efficiency without power loss, simulations are conducted under lean-boost combustion with dilution rate, high engine swirl, and high knock resistant fuel.

Intra-annual variability of tree-ring oxygen stable isotopes (δ ^18 O) can record seasonal climate variability and a tree’s ecophysiological response to it. Variability of sub-annual tree-ring δ ^18 O maxima and minima, which usually occur in different parts of the growing season, may exhibit different climatic signals and can help in understanding past seasonal moisture conditions, especially in Asian monsoon areas. We developed minimum and maximum tree-ring δ ^18 O series based on sub-annual tree-ring δ ^18 O measurements of Pinus massoniana at a humid site in southeastern China. We found that interannual variability in minimum tree-ring δ ^18 O is primarily controlled by the July–September soil water supply and source water δ ^18 O, whereas the maximum latewood tree-ring δ ^18 O is primarily controlled by the relative humidity (RH) in October. The maximum of variability of earlywood tree-ring δ ^18 O records the RH of October of the previous year. We used minimum and maximum tree-ring δ ^18 O to develop two reconstructions (1900–2014) of seasonal moisture availability. The summer soil water supply (July–September self-calibrated Palmer drought severity index) and the RH in fall show contrasting trends, which may be related to late-growing seasonal warming leading to a high vapor capacity and high atmospheric moisture. Our findings are valuable for research that aims to explore seasonal moisture changes under anthropogenic climate change and the ecological implications of such contrasting trends.

Sensitivity experiments testing two scale-selective bias correction (SSBC) methods have been carried out to identify an optimal spectral nudging scheme for historical dynamically downscaled simulations of South Asia, using the coordinated regional climate downscaling experiment (CORDEX) protocol and the regional spectral model (RSM). Two time periods were selected under the category of short-term extreme summer and long-term decadal analysis. The new SSBC version applied nudging to full wind components, with an increased relaxation time in the lower model layers, incorporating a vertical weighted damping coefficient. An evaluation of the extraordinary weather conditions experienced in South Asia in the summer of 2005 confirmed the advantages of the new SSBC when modeling monsoon precipitation. Furthermore, the new SSBC scheme was found to predict precipitation and wind patterns more accurately than the older version in decadal analysis, which applies nudging only to the rotational wind field, with a constant strength at all heights.

This study assesses future change of surface runoff due to climate change over Korea using a regional climate model (RCM), namely, the Global/Regional Integrated Model System (GRIMs), Regional Model Program (RMP). The RMP is forced by future climate scenario, namely, A1B of Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The RMP satisfactorily reproduces the observed seasonal mean and variation of surface runoff for the current climate simulation. The distribution of monsoonal precipitation-related runoff is adequately captured by the RMP. In the future (2040–2070) simulation, it is shown that the increasing trend of temperature has significant impacts on the intra-annual runoff variation. The variability of runoff is increased in summer; moreover, the strengthened possibility of extreme occurrence is detected in the future climate. This study indicates that future climate projection, including surface runoff and its variability over Korea, can be adequately addressed on the RMP testbed. Furthermore, this study reflects that global warming affects local hydrological cycle by changing major water budget components. This study adduces that the importance of runoff should not be overlooked in regional climate studies, and more elaborate presentation of fresh-water cycle is needed to close hydrological circulation in RCMs.

Satellite-based observations of free tropospheric water vapour isotopologue ratios (δD) with good global and temporal coverage have become recently available. We investigate the potential of these observations for constraining the uncertainties of the atmospheric analyses fields of specific humidity (q), temperature (T), and δD and of variables that capture important properties of the atmospheric water cycle, namely the vertical velocity (!), the latent heating rate (Q2), and the precipitation rate (Prcp). Our focus is on the impact of 5 the δD observations if used in addition to the observation of q and T, which are much easier to be observed by satellites and routinely in use for atmospheric analyses. For our investigations we use an Observing System Simulation Experiment, i.e. simulate the satellite observations of q, T, and δD with known uncertainties, then use them within a Kalman filter based assimilation framework in order to evaluate their potential for improving the quality of atmospheric analyses. The study is made for low latitudes (30°S to 30°N) and for 40 days between mid-July and end of August 10 2016. We find that the assimilation of q and T observations alone well constrains the atmospheric q and T fields (analyses skills in the free troposphere of up to 60%), and moderately constrains the fields of δD, !, Q2, and Prcp (analyses skills of 20%-40%). The additional assimilation of δD observations further improves the quality of the analyses of all variables. We use Q2 as proxy for the presence of condensation and evaporation processes, and we show that the additional improvement is rather weak when evaporation or condensation are negligible (additional analyses skills of generally below 5%), and strongest for high condensation rates (additional skills of about 15% and above). The very high condensation rates (identified by large positive Q2 values) are rare, but related to extreme events (very high ! and Prcp) that are not well captured in the analyses (for these extreme events also the analyses uncertainties of !, Q2, and Prcp are very large), i.e. the additional assimilation of δD observations significantly improves the analyses of the water cycle related variables for the events when an improvement is most important. In real world satellite datasets δD observations affected by such strong latent heating events are frequently available, suggesting that the here demonstrated additional δD impact for the simulated world is also a realistic scenario for a real world data assimilation. [ABSTRACT FROM AUTHOR]