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| 1 | An integrated methodology for soil moisture analysis using multispectral data in Mongolia显示文摘Soil moisture(SM)content is one of the most important environmental variables in relation to land surface climatology,hydrology,and ecology.Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions.The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data.The study area is Tuv(48°40′30″N and 106°15′55″E)province in the forest steppe zones in Mongolia.In addition to this,land surface temperature(LST)and normalized difference vegetation index(NDVI)from Landsat satellite images were integrated for the assessment.Furthermore,we used a digital elevation model(DEM)from ASTER satellite image with 30-m resolution.Aspect and slope maps were derived from this DEM.The soil moisture index(SMI)was obtained using spectral information from Landsat satellite data.We used regression analysis to develop the model.The model shows how SMI from satellite depends on LST,NDVI,DEM,Slope,and Aspect in the agricultural area.The results of the model were correlated with the ground SM data in Tuv province.The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area.Further research is focused on moisture mapping for different natural zones in Mongolia.The innovative part of this research is to estimate SM using drivers which are vegetation,land surface temperature,elevation,aspect,and slope in the forested steppe area.This integrative methodology can be applied for different regions with forest and desert steppe zones. | Enkhjargal Natsagdorj Tsolmon Renchin Martin Kappas Batchuluun Tseveen Chimgee Dari Oyunbileg Tsend Ulam-Orgikh Duger | 2017 | Geo-Spatial Information Science2017，20，1： | 2 |
| 2 | Uncertainty analysis of hydrological modeling in a tropicalarea using different algorithms显示文摘Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model para-meters), making quantification of uncertainty in hydro- logical modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Like-lihood Uncertainty Estimation (GLUE), Sequential Uncer-tainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and R- factor, coefficient of determination (R^2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor > 0.83, R-factor < 0.56 and R^2>0.91, NSE > 0.89, and 0.18 < PBIAS < 0.32. Hence, we would suggest to use SUFI-2 initially to set the parameter ranges, and further use PSO for final analysis. Indeed, the uncertainty analysis must be accounted when the outcomes of the model use for policy or management decisions. | Ammar RAFIEI EMAM Martin KAPPAS Steven FASSNACHT Nguyen Hoang Khanh LINH | 2018 | Frontiers of Earth Science2018，12，4： | 1 |
| 3 | Variability and change of climate extremes from indigenous herder knowledge and at meteorological stations across central Mongolia显示文摘In semi-arid regions,air temperatures have increased in the last decades more than in many other parts of the world.Mongolia has an arid/semi-arid climate and much of the population are herders whose livelihoods depend upon limited water resources that fluctuate with a variable climate.Herders were surveyed to identify their observations of changes in climate extremes for two soums of central Mongolia,Ikh-Tamir in the forest steppe north of the Khangai Mountains and Jinst in the desert steppe south of the mountains.The herders’indigenous knowledge of changes in climate extremes mostly aligned with the station-based analyses of change.Temperatures were warming with more warm days and nights at all stations.There were fewer cool days and nights observed at the mountain stations both in the summer and winter,yet more cool days and nights were observed in the winter at the desert steppe station.The number of summer days is increasing while the number of frost days is decreasing at all stations.The results of this study support further use of local knowledge and meteorological observations to provide more holistic analysis of climate change in different regions of the world. | Sukh TUMENJARGAL Steven RFASSNACHT Niah BHVENABLE Alison PKINGSTON Maria EFERNANDEZ-GIMENEZ Batjav BATBUYAN Melinda JLAITURI Martin KAPPAS GADYABADAM | 2020 | Frontiers of Earth Science2020，14，2： | 0 |
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