## 4 Discussion  
The increasing availability of multi-sensor satellite remote sensing data provides new opportunities for integrating satellite data with ecosystem models to explain the responses of a complex ecosystem to factors influencing its dynamics [25; 26]. The present study tested the applicability of multi-sensor satellite data for reproducing the observed change in livestock numbers in Mongolian nomadic pastures. In this study, a process-based schematic model for producer-herbivore trophic interactions and their dependence on seasonal climate conditions was suggested for Mongolian nomadic landscapes. The model was used to select major potential drivers important for changes in livestock numbers that can be produced from multi-sensor satellite data. Then, the data were used as explanatory variables in a regression model and a machine learning model, making it possible to reproduce the livestock changes and identify key variables for the changes. In the RF modeling results, summer variables were identified as the key factors more than winter variables to explain the annual livestock changes from 2003 to 2020 (61% vs. 39%), while the proportions were almost equal for the summer and winter variables in the SMR model (51% vs. 49%) (Figure 6). Both the RF and SMR models produced certain regional patterns of primary natural factors: e.g., dryness (ET/P\({}_{s}\)) in the south and coldness (T\({}_{w}\)) in the north (Figure 7). The regional patterns seem to correspond with endemic climate regions, such as hot and dry climate in the south and cold and humid climate in the north, which limits regional vegetation growth and livestock loss [21; 45]. Meanwhile, grassland production (NDVI\({}_{s}\)) was a common key determinant across the middle of Mongolia, especially in the great valley region of western Mongolia with cold and dry climate but relatively abundant water resources. Our findings indicate that regional endemic conditions in weather and foraging resource availability should be primary concerns for enhancing the adaptive capacity of pastoral livelihoods in Mongolia. On the other hand, the importance of winter snow (P\({}_{w}\)) was confirmed throughout Mongolia, likely reflecting the complexity of snowfall mechanisms. Our PCA and modeling analyses using the satellite-driven variables provided useful insights into characterizing the impacts of the serious 2009-2010 _Dzud_. The countrywide _Dzud_ was well-characterized by PCA as cold with a snowy winter and low summer foraging biomass (Figure 5). This demonstrates a strong coupling between the _Dzud_ and critical  
Figure 9: Percentage (%) of each key variable by livestock change classes in 2010: \(>\)0, greater than zero; \(<\)0, from zero to \(-15\)%; \(<\)\(-15\), from \(-15\) to \(-30\)%; \(<\)\(-30\), from \(-30\) to \(-45\)%; \(<\)\(-45\), below \(-45\)%. natural conditions bringing temporary failure in livestock management [1; 8]. In more detail, though _Dzud_ was known as a winter disaster, this study reconfirmed findings from previous studies on the importance of summer factors to exert a great influence on _Dzud_ frequency and severity [1; 13; 16]. In the analysis of _Dzud_ severity (Figure 9), it was found that the damage during the 2009-2010 _Dzud_ was severe in areas where \(\mathrm{NDVI_{s}}\) and \(\mathrm{ET}/\mathrm{T_{s}}\) were the main factors. This point was more noticeable in the SMR model. Dryness and \(\mathrm{NDVI_{s}}\) explained as much as 41% of _soums_, showing that the severe livestock losses were worse than \(-\)30% in 2010. The percentage was comparable with the contributions of the winter variables, \(\mathrm{T_{w}}\) (33%) and \(\mathrm{P_{w}}\) (20%). The results indicate that the 2009-2010 _Dzud_ gave severe impacts to _soums_ where summer aridity and forage availability have primarily regulated the annual change of livestock numbers.