Village Baseline Study: Site Analysis Report for Ekxang village Phonghong district, Vientiane province, Lao PDR

Data collection for the Village Baseline Study for the Ekxang Climate-Smart Village located in Phonhong District, Vientiane Province, Lao PDR, took place on November 19th - 21st, 2014. Three days of focus group discussions were conducted separately for men and women. Participatory methods were used to gather information on community resources, organizational landscapes, information networks, and the community’s vision for the future. Men and women in Ekxang village had different point of views on their community’s resources. Women were focused more on the conservation and increased forest land as they are responsible for collecting the Non-Timber Forest Product. Men were more interested in the development of agro-forestry. Regarding agriculture, men were focused on rice paddies while the women were more focused on the smaller household vegetable gardens. There were several changes in community resources. Forest and pasture areas were significantly degraded due to urban development, increasing people demands, and expansion of agricultural lands since 1980s. Infrastructures for irrigation were improved 30 years ago to expand the irrigated area but only few households in Ekxang could benefit from it. Villagers experience that soil fertility has declined compared to 25 years ago. There were a number of organizations operating at the village, half of them related to food security, food crisis and natural resource management. However, linkages is not strong the organizations. Farmer-to-farmer, mobile phone and television are main sources of information that support farmers in their decision making. There is a high potential to develop ICT-based technologies in order to support climate-smart farming practices to farmers. From the farmers’ perspective, their Climate-Smart Village should be an agroforestry landscape with smart groundwater use, smart pest management and crop diversification, and smart information services

メソモデルによるインドシナ域でのダウンスケール数値天気予報実験

全球数値天気予報モデルによって「完璧な天気予報」が為されたという仮定の下で，高解像メソスケール領域モデルを用いてインドシナ地域においてダウンスケールハインドキャスト実験を行った。実験は雨季である6月縲・月について2003年縲・006年の4年間に対して行い，実験結果の検証にはラオスの地表観測点17点の気温を用いた。ここでは散布図を用いて観測との相関係数やバイアスの改善を評価する方法を提案する。観測とモデル結果の相関係数は7月と9月に高く6月と8月に低かった。バイアスは各地点の地形表現の誤差に伴うバイアスに加えて領域モデル結果においては全地点において約1 Kのバイアスが見られた。We perform a downscaling hindcast experiment in Indochina region with a fine-mesh mesoscale regional model under the assumption of the "perfect forecast" produced by a global numerical weather prediction model. The experiment is done for June-to-September of the years 2003-to-2006 in the rainy season. Validations of the downscaling hindcast are made with temperature data obtained at 17 surface stations in Laos. We propose a new method to diagnose the improvement of correlation or bias by the downscaling using a scatter diagram. The correlation between the model results and observations is higher in July and September than that in June or August. We find a rather common bias for all the stations of about 1 K in the model in addition to the bias due to the elevation error of each station.全球数値天気予報モデルによって「完璧な天気予報」が為されたという仮定の下で，高解像メソスケール領域モデルを用いてインドシナ地域においてダウンスケールハインドキャスト実験を行った。実験は雨季である6月縲・月について2003年縲・006年の4年間に対して行い，実験結果の検証にはラオスの地表観測点17点の気温を用いた。ここでは散布図を用いて観測との相関係数やバイアスの改善を評価する方法を提案する。観測とモデル結果の相関係数は7月と9月に高く6月と8月に低かった。バイアスは各地点の地形表現の誤差に伴うバイアスに加えて領域モデル結果においては全地点において約1 Kのバイアスが見られた。We perform a downscaling hindcast experiment in Indochina region with a fine-mesh mesoscale regional model under the assumption of the "perfect forecast" produced by a global numerical weather prediction model. The experiment is done for June-to-September of the years 2003-to-2006 in the rainy season. Validations of the downscaling hindcast are made with temperature data obtained at 17 surface stations in Laos. We propose a new method to diagnose the improvement of correlation or bias by the downscaling using a scatter diagram. The correlation between the model results and observations is higher in July and September than that in June or August. We find a rather common bias for all the stations of about 1 K in the model in addition to the bias due to the elevation error of each station

An Experimental Numerical Weather Prediction in Indochina Region with a Meso-Scale Model

We perform a downscaling hindcast experiment in Indochina region with a fine-mesh meso-scale regional model under the assumption of the "perfect forecast" produced by a global numerical weather prediction model. The experiment is done for July and August in the wet Southwest Monsoon period. Validations of the downscaling hindcasts are done with surface station data of temperature and accumulated rainfall in Lao PDR. Some improvements in the downscaling hindcast are attained as a result of the better resolution of the surface topography. Further application of this kind of downscaling forecasts is discussed.We perform a downscaling hindcast experiment in Indochina region with a fine-mesh meso-scale regional model under the assumption of the "perfect forecast" produced by a global numerical weather prediction model. The experiment is done for July and August in the wet Southwest Monsoon period. Validations of the downscaling hindcasts are done with surface station data of temperature and accumulated rainfall in Lao PDR. Some improvements in the downscaling hindcast are attained as a result of the better resolution of the surface topography. Further application of this kind of downscaling forecasts is discussed