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Shinjiro Kanae, Taikan Oki, and Katumi Musiake

Abstract

It is now widely recognized that tropical deforestation can change the regional climate significantly. The increasing population and the spreading deforestation in the Indochina Peninsula, especially in Thailand, make it urgent to assess the effects of deforestation on the regional climate. Most of the previous numerical experiments generally have shown that decreases in precipitation occur as a result of deforestation. However, in most cases, these hydrometeorological changes have not been detected in observations. In this study, the nonparametric Mann–Kendall rank test and linear regression analysis were applied to analyze precipitation data obtained over a period of more than 40 yr, for each month, at each meteorological station in Thailand. Significant decreases in precipitation over Thailand were detected only in the time series of monthly precipitation in September. Amounts of precipitation recorded at many meteorological stations in September have decreased by approximately 100 mm month−1 (approximately 30% relative change) over the past three or four decades. Numerical experiments with a regional climate model based on the Regional Atmospheric Modeling System with a simple land surface scheme were carried out for the Indochina Peninsula. In these experiments, the type of vegetation in the northeastern part of Thailand was specified as either short vegetation (the current vegetation type) or forest (the former vegetation type). The experiments were carried out using the initial and boundary meteorological conditions of August and September in 1992–94. The initial and boundary conditions were interpolated from the data of the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis. In these numerical experiments, a decrease in precipitation over the deforested area was obtained for September, but not for August. The magnitude of the mean decrease in precipitation over the whole deforested area in these experiments was 26 mm month−1 (7% relative change), and the local maximum decrease was 88 mm month−1 (29% relative change). Precipitation in the wet season over the Indochina Peninsula basically occurs under the influence of the Southeast Asian summer monsoon system. The strong summer monsoon westerlies bring abundant moisture to the Indochina Peninsula as a source of precipitation. The monsoon westerlies are the predominant external force influencing the regional climate. However, the strong westerlies over the Indochina Peninsula disappear in September, although that is typically the month of maximum precipitation. Accordingly, it is inferred that the effect of local deforestation appears significantly only in September because of the absence of this strong external force.

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Tomohito J. Yamada, Randal D. Koster, Shinjiro Kanae, and Taikan Oki

Abstract

This study reveals the mathematical structure of a statistical index, Ω, that quantifies similarity among ensemble members in a weather forecast. Previous approaches for quantifying predictability estimate separately the phase and shape characteristics of a forecast ensemble. The diagnostic Ω, on the other hand, characterizes the similarity (across ensemble members) of both aspects together with a simple expression. The diagnostic Ω is thus more mathematically versatile than previous indices.

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Yukiko Imada, Shinjiro Kanae, Masahide Kimoto, Masahiro Watanabe, and Masayoshi Ishii

Abstract

Predictability of above-normal rainfall over Thailand during the rainy season of 2011 was investigated with a one-tier seasonal prediction system based on an atmosphere–ocean coupled general circulation model (CGCM) combined with a statistical downscaling method. The statistical relationship was derived using singular value decomposition analysis (SVDA) between observed regional rainfall and the hindcast of tropical sea surface temperature (SST) from the seasonal prediction system, which has an ability to forecast oceanic variability for lead times up to several months. The downscaled product of 2011 local rainfall was obtained by combining rainfall patterns derived from significant modes of SVDA. This method has the advantage in terms of flexibility that phenomenon-based statistical relationships, such as teleconnections associated with El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), or the newly recognized central Pacific El Niño, are considered separately in each SVDA mode. The downscaled prediction initialized from 1 August 2011 reproduced the anomalously intense precipitation pattern over Indochina including northern Thailand during the latter half of the rainy season, even though the direct hindcast from the CGCM failed to predict the local rainfall distribution and intensity. Further analysis revealed that this method is applicable to the other recent events such as heavy rainfall during the rainy seasons of 2002 and 2008 in Indochina.

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Yukiko Hirabayashi, Taikan Oki, Shinjiro Kanae, and Katumi Musiake

Abstract

A simple algorithm is presented for transferring root-zone soil moisture from surface soil moisture data on a global scale. Analysis of offline soil moisture data shows that the climatological relationship between surface and root-zone soil moisture becomes linear when appropriate time lags are applied. The climatological relationship of root-zone soil moisture among different land surface models (LSMs) is also linear; therefore, the root-zone and surface soil moisture obtained from one LSM can be applied to another. The algorithm is then applied to the surface soil moisture observations made by the precipitation radar on board the Tropical Rainfall Measuring Mission precipitation radar (TRMM/PR), and the transferred root-zone soil moisture is input to a general circulation model (GCM) summer—June, July, August—precipitation simulation as the boundary condition. The approach is computationally efficient, and the simulation using the root-zone soil moisture by the transfer method is much better than a simulation using root-zone soil moisture without the transfer method, assuming that the volumetric percentage of TRMM/PR is representative of the root zone. The result indicates that the simple transfer process will increase the utility of surface soil moisture data for a GCM.

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Qiuhong Tang, Taikan Oki, Shinjiro Kanae, and Heping Hu

Abstract

The effects of natural and anthropogenic heterogeneity on a hydrological simulation are evaluated using a distributed biosphere hydrological model (DBHM) system. The DBHM embeds a biosphere model into a distributed hydrological scheme, representing both topography and vegetation in a mesoscale hydrological simulation, and the model system includes an irrigation scheme. The authors investigated the effects of two kinds of variability, precipitation variability and the variability of irrigation redistributing runoff, representing natural and anthropogenic heterogeneity, respectively, on hydrological processes. Runoff was underestimated if rainfall was placed spatially uniformly over large grid cells. Accounting for precipitation heterogeneity improved the runoff simulation. However, the negative runoff contribution, namely, the situation that mean annual precipitation is less than evapotranspiration, cannot be simulated by only considering the natural heterogeneity. This constructive model shortcoming can be eliminated by accounting for anthropogenic heterogeneity caused by irrigation water withdrawals. Irrigation leads to increased evapotranspiration and decreased runoff, and surface soil moisture in irrigated areas increases because of irrigation. Simulations performed for the Yellow River basin of China indicated streamflow decreases of 41% due to irrigation effects. The latent heat flux in the peak irrigation season [June–August (JJA)] increased 3.3 W m−2 with a decrease in the ground surface temperature of 0.1 K for the river basin. The maximum simulated increase in the latent heat flux was 43 W m−2, and the ground temperature decrease was 1.6 K in the peak irrigation season.

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Shinjiro Kanae, Yukiko Hirabayashi, Tomohito Yamada, and Taikan Oki

Abstract

Outputs from two ensembles of atmospheric model simulations for 1951–98 define the influence of “realistic” land surface wetness on seasonal precipitation predictability in boreal summer. The ensembles consist of one forced with observed sea surface temperatures (SSTs) and the other forced with realistic land surface wetness as well as SSTs. Predictability was determined from correlations between the time series of simulated and observed precipitation. The ratio of forced variance to total variance determined potential predictability. Predictability occurred over some land areas adjacent to tropical oceans without land wetness forcing. On the other hand, because of the chaotic nature of the atmosphere, considerable parts of the land areas of the globe did not even show potential predictability with both land wetness and SST forcings.

The use of land wetness forcing enhanced predictability over semiarid regions. Such semiarid regions are generally characterized by a negative correlation between fluxes of latent heat and sensible heat from the land surface, and are “water-regulating” areas where soil moisture plays a governing role in land–atmosphere interactions. Actual seasonal prediction may be possible in these regions if slowly varying surface conditions can be estimated in advance. In contrast, some land regions (e.g., south of the Sahel, the Amazon, and Indochina) showed little predictability despite high potential predictability. These regions are mostly characterized by a positive correlation between the surface fluxes, and are “radiation-regulating” areas where the atmosphere plays a leading role. Improvements in predictability for these regions may require further improvements in model physics.

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Qiuhong Tang, Taikan Oki, Shinjiro Kanae, and Heping Hu

Abstract

A distributed biosphere hydrological (DBH) model system was used to explore the internal relations among the climate system, human society, and the hydrological system in the Yellow River basin, and to interpret possible mechanisms for observed changes in Yellow River streamflow from 1960 to 2000. Several scenarios were evaluated to elucidate the hydrological response to climate system, land cover, and irrigation. The results show that climate change is the dominant cause of annual streamflow changes in the upper and middle reaches, but human activities dominate annual streamflow changes in the lower reaches of the Yellow River basin. The annual river discharge at the mouth is affected by climate change and by human activities in nearly equal proportion. The linear component of climate change contributes to the observed annual streamflow decrease, but changes in the climate temporal pattern have a larger impact on annual river discharge than does the linear component of climate change. Low flow is more significantly affected by irrigation withdrawals than by climate change. Reservoirs induce more diversions for irrigation, while at the same time the results demonstrate that the reservoirs may help to maintain environmental flows and counter what otherwise would be more serious reductions in low flows.

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Masahiro Ryo, Oliver C. Saavedra Valeriano, Shinjiro Kanae, and Tinh Dang Ngoc

Abstract

The study demonstrates that the temporal downscaling of rain gauge–measured precipitation with satellite-based precipitation estimates enhances the accuracy of hydrological simulations, especially for flood duration. Multiple regression analysis was examined to predict which hydrometeorological parameters have a significant influence on accuracy. The approach was examined at the Hương River basin in Vietnam (1520 km2), which is a mountainous region subject to heavy rainfall. The multisensor algorithm Global Satellite Mapping of Precipitation, version Moving Vector with Kalman (MVK; GSMaP_MVK; P sat), was employed to downscale the daily gauge precipitation measurements into 6-h time steps. Discharge in the rainy season in 2006–09 was simulated by a distributed hydrological model with 6-h time steps with four precipitation datasets: 6-h gauge (P control), daily uniform gauge (P uni), P sat, and the downscaled satellite product (P ds). Flood simulation with P ds performed better than that with P uni in 14 out of the 18 flood events, being close to the results with P control (median Nash–Sutcliffe efficiencies of 0.776, 0.261, and 0.710, respectively). Multiple regression analysis showed that the effectiveness of the downscaling method was significantly related to the bias and random errors of the satellite product. In conclusion, satellite-based precipitation measurements have potential for temporal downscaling of discharge simulations from daily to subdaily resolutions in moderate-sized watersheds that lack subdaily rainfall records, and the degree of simulation improvement can be estimated by statistical analysis. The suggested method can be broadly applied to watersheds where the daily precipitation is measured and when satellite-based precipitation measurements are available.

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Yadu Pokhrel, Naota Hanasaki, Sujan Koirala, Jaeil Cho, Pat J.-F. Yeh, Hyungjun Kim, Shinjiro Kanae, and Taikan Oki

Abstract

Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km3 yr−1) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ~50 W m−2 in latent heat flux averaged over June–August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ~450 km3 yr−1, which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ~40% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology.

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Randal D. Koster, Y. C. Sud, Zhichang Guo, Paul A. Dirmeyer, Gordon Bonan, Keith W. Oleson, Edmond Chan, Diana Verseghy, Peter Cox, Harvey Davies, Eva Kowalczyk, C. T. Gordon, Shinjiro Kanae, David Lawrence, Ping Liu, David Mocko, Cheng-Hsuan Lu, Ken Mitchell, Sergey Malyshev, Bryant McAvaney, Taikan Oki, Tomohito Yamada, Andrew Pitman, Christopher M. Taylor, Ratko Vasic, and Yongkang Xue

Abstract

The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.

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