研究等業績 - その他 - 河合 隆行
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Evaluation of satellite rainfall estimates over the Lake Tana basin at the source region of the Blue Nile River
Ayele Almaw Fenta, Hiroshi Yasuda, Katsuyuki Shimizu, Yasuomi Ibaraki, Nigussie Haregeweyn, Takayuki Kawai, Ashebir Sewale Belay, Dagnenet Sultan, Kindiye Ebabu
Atmospheric Research ( Elsevier Ltd ) 212 43 - 53 2018年11月
Satellite rainfall estimates (SREs) have become alternative sources of rainfall data for several applications. However, the accuracy of the SREs is likely to vary from region to region and must be evaluated on a local basis. This study evaluated the accuracy of three SREs for the Lake Tana basin in northwestern Ethiopia. This basin is characterized by complex topography comprising both lowlands and highlands. The three SREs were the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), the Climate Hazard Infrared Precipitation with Stations (CHIRPS), and the Africa Rainfall Climatology (ARC). The SREs were compared with gauge measurements in lowland and highland regions during the period 1995–2010 on a point-to-pixel basis at daily, dekadal (10 days), monthly, and seasonal periodicities. The results show that the three SREs underestimated rainy events, but TAMSAT captured rainfall occurrence relatively well in both regions. ARC better estimated light rain rates (1–5 mm d−1) than did TAMSAT and CHIRPS
however, all the SREs markedly underestimated moderate and heavier rain rates (≥10 mm d−1). TAMSAT and CHIRPS estimated the amount of rainfall reasonably well (high efficiency, low random errors, and bias <
10%) at daily, dekadal, and monthly time scales, whereas ARC did not perform satisfactorily (high random errors, low efficiency, and bias >
20%) at any time scale. On a seasonal scale, CHIRPS estimated the secondary rainy season (March–May) rainfall better than did ARC and TAMSAT, whereas TAMSAT outperformed both CHIRPS and ARC during the primary rainy season (June–September). Overall, the rainfall detection capabilities and rainfall amount estimates of the SREs were better over the lowlands, and the cumulative rainfall estimates tended to improve with increasing integration time (i.e., from daily to seasonal totals). -
Teleconnection of rainfall time series in the central Nile Basin with sea surface temperature
Yasuda H, S. N, Pandaa, Mohamed, A. M. Abd Elbasitc, T. Kawai, T. Elgamrie, A. A. Fenta, H. Nawata
Paddy and Water Environment 16 805 - 821 2018年08月
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都市部の人を森林へ誘うための試み
前田雄一, 矢部 浩, 小山 敢, 中村徳和, 河合隆行, 土屋竜太
樹木医学研究 22 ( 2 ) 113 - 114 2018年04月
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Spatial distribution and temporal trends of rainfall and erosivity in the Eastern Africa region
Ayele Almaw Fenta, Hiroshi Yasuda, Katsuyuki Shimizu, Nigussie Haregeweyn, Takayuki Kawai, Dagnenet Sultan, Kindiye Ebabu, Ashebir Sewale Belay
HYDROLOGICAL PROCESSES ( WILEY ) 31 ( 25 ) 4555 - 4567 2017年12月
Soil erosion by water is one of the main environmental concerns in the drought-prone Eastern Africa region. Understanding factors such as rainfall and erosivity is therefore of utmost importance for soil erosion risk assessment and soil and water conservation planning. In this study, we evaluated the spatial distribution and temporal trends of rainfall and erosivity for the Eastern Africa region during the period 1981-2016. The precipitation concentration index, seasonality index, and modified Fournier index have been analysed using 5x5-km resolution multisource rainfall product (Climate Hazards Group InfraRed Precipitation with Stations). The mean annual rainfall of the region was 810mm ranging from less than 300mm in the lowland areas to over 1,200mm in the highlands being influenced by orography of the Eastern Africa region. The precipitation concentration index and seasonality index revealed a spatial pattern of rainfall seasonality dependent on latitude, with a more pronounced seasonality as we go far from the equator. The modified Fournier index showed high spatial variability with about 55% of the region subject to high to very high rainfall erosivity. The mean annual R-factor in the study region was calculated at 3,246 +/- 1,895MJ mm ha(-1)h(-1)yr(-1), implying a potentially high water erosion risk in the region. Moreover, both increasing and decreasing trends of annual rainfall and erosivity were observed but spatial variability of these trends was high. This study offers useful information for better soil erosion prediction as well as can support policy development to achieve sustainable regional environmental planning and management of soil and water resources.
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強度の枝打ち後に発生したヒノキ林の立ち枯れ被害_速報
前田雄一, 河合隆行, 土屋竜太, 中村徳和, 矢部 浩, 小山 敢
樹木医学研究 21 ( 1 ) 20 - 21 2017年04月
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広葉樹緑化木に発生していた日焼け被害
前田雄一, 河合隆行, 土屋竜太, 小山 敢, 矢部 浩
樹木医学研究 20 ( 3 ) 138 2017年01月
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The effects of ocean SST dipole on Mongolian summer rainfall
Hiroshi Yasuda, Banzragch Nandintsetseg, Ronny Berndtsson, Ganbat Amgalan, Masato Shinoda, Takayuki Kawai
GEOFIZIKA ( UNIV ZAGREB , ANDRIJA MOHOROVICIC GEOPHYS INST ) 34 ( 1 ) 199 - 218 2017年
Cross-correlations between inter-annual summer rainfall time series (June to August: JJA) for arid Mongolia and global sea surface temperatures (GSST) were calculated for prediction purposes. Prediction of summer rainfall for four vegetation zones, Desert Steppe (DS), Steppe (ST), Forest Steppe (FS), and High Mountain (HM) using GSSTs for time lags of 5, 6, and 7 months prior to JJA rainfall was evaluated. Mongolian summer rainfall is correlated with global SSTs. In particular, the summer rainfall of FS and HM displayed high and statistically significant correlations with SST in specific parts of the oceans. SST dipoles (pairs of positively and negatively correlated areas) were identified, and correlation for time series of the SST differences between SST dipoles (positive - negative) with the summer rainfall time series was larger than the original correlations. To predict the summer rainfall from SST, an artificial neural network (ANN) model was used. Time series of the SST difference that represents the strength of the dipole were used as input to the ANN model, and Mongolian summer rainfall was predicted 5, 6, and 7 months ahead in time. The predicted summer rainfall compared reasonably well with the observed rainfall in the four different vegetation zones. This implies that the model can be used to predict summer rainfall for the four main Mongolian vegetation zones with good accuracy.