The dzud, a specific type of climate disaster in Mongolia, is responsible for serious environmental and economic damage. It is characterized by heavy snowfall and severe winter conditions, causing mass livestock deaths that occur through the following spring. These events substantially limit socioeconomic development in Mongolia. In this research, we conducted an analysis of several dzud events (2000, 2001, 2002, and 2010) to understand the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. The present paper also establishes how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (_aAI_Z), time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and livestock data. We also correlated _aAI_Z, NDVI, and seasonal precipitation in the varied ecosystems of the study area. The results illustrate that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in humid areas. Another result is that snowy winters can cause a 10 to 20-day early peak in NDVI and a following increase in vegetation growth. During a drought year with dry winter conditions, the vegetation growth phase begins later due to water deficiency, which leads to weaker vegetation growth. Livestock loss and the reduction of grazing pressure play a crucial role in vegetation recovery after extreme climatic events in Mongolia.

Monitoring of soil moisture dynamics provides valuable information about grassland degradation, since soil moisture directly affects vegetation cover. While the Mongolian soil moisture monitoring network is limited to the urban and protected natural areas, remote sensing data can be used to determine the soil moisture status elsewhere. In this paper, we determine whether in situ and remotely sensed data in the unaccounted areas of Southwestern Mongolia are consistent with each other, by comparing Soil Moisture and Ocean Salinity (SMOS) first passive L-band satellite data with in situ measurements. To evaluate the soil moisture products, we calculated the temporal, seasonal, and monthly average soil moisture content. We corrected the bias of SMOS soil moisture (SM) data using the in situ measured soil moisture with both the simple ratio and gamma methods. We verified the bias-corrected SMOS data with Nash–Sutcliffe method. The comparison results suggest that bias correction (of the simple ratio and gamma methods) enhances the reliability of the SMOS data, resulting in a higher correlation coefficient. We then examined the correlation between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems. Analysis of the SMOS and in situ measured soil moisture data revealed that spatial soil moisture distribution matches the rainfall events in Southwestern Mongolia for the period 2010 to 2015. The results illustrate that the bias-corrected, monthly-averaged SMOS data has a high correlation with the monthly-averaged NDVI (R² > 0.81). Both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia. During 2015, we detected decreasing soil moisture in approximately 30% of the forest-steppe and steppe areas. We assume that the current ecosystem of land is changing rapidly from forest to steppe and also from steppe to desert. The rainfall rate is the most critical factor influencing the soil moisture storage capacity in this region. The collected SMOS data reflects in situ conditions, making it an option for grassland studies.

A Dzud is a climate event in the Mongolian that causes serious environmental and economic damage. Although a natural phenomenon, its effects can be exacerbated by human activities such as livestock overgrazing and inadequate fodder, resulting in mass deaths of the livestock in the spring following a severe winter. This article is based on the analysis of various Dzud events (2000, 2001, 2002, and 2010) and their specific effect on the vegetation condition by analyzing Normalized Difference Vegetation Index NDVI in the Gobi regions of Mongolia. Our evaluation methods utilize the seasonal aridity index, time series of MODIS NDVI and data from livestock statistics. Heavy snowfall is one of the limiting factors for animal productivity and socioeconomic development in Mongolia. Based on the findings, steppe areas have the highest degree of vulnerability of climate, with the potential decline of growth grassland being stronger for humid areas. When there are high snowy winters, there is a 10 to a 20-day earlier peak of NDVI values as well as an increase in vegetation growth. Additionally, grazing pressure (caused by high livestock loss) played a minor role in plant growth. We found that during the dry winter conditions of a black Dzud, low soil moisture, and high evapotranspiration, the vegetation growth phase begins later due to water deficiency, leading to a lower peak in growth. During the year 2009/2010, a white Dzud occurred in the presence of a thick snow layer, which acted as a water reservoir. The effect of livestock loss and the reduction of grazing pressure played a minor part in vegetation recovery after different types of Dzud events in Mongolia. 

A new soil moisture index for monitoring drought occurrence and intensity is presented. The index is based on the integration of different remote sensing products and in situ observations. Due to a shortage of precipitation, droughts reduce vegetation productivity, and thus, aggravate the impact of moisture stress on pastureland. The spatial distribution of soil moisture index with high-resolution images in Mongolia is still being one of the essential goals in the remote sensing and rangeland community. Specifically, we examined a new composite Gobi soil moisture index (GI) based on the combination of Ocean Salinity (SMOS) Soil Moisture, several products from the MODIS satellite, and in situ Soil Moisture (SM) observations. A multiple linear regression method was used for the estimation of GI soil moisture index. The former includes the surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission, the Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST), normalized difference vegetation index (NDVI), potential evapotranspiration (PET). The latter includes a standardized precipitation index (SPI) from in-situ data. The validation of the approach is based on the relationship between SPI and in-situ soil moisture (SM) observations, and their comparison to remote sensing (RS) – derived indices. The results show that the correlation was statistically significant between GI and in-situ SM observations from the meteorological stations at 10 – 15 cm depths (p < 0.0001). The correlation between GI and SPI, as represented by the correlation coefficient (r) was 0.64. The GI empirical equations that utilize at least three key atmospheric variables are (a) NDVI, (b) land surface temperature, and (c) potential evapotranspiration. The established new GI soil moisture index was retrieved at the 1 km spatial resolution for Southwest Mongolia from 2000 to 2018, and their two summer months (July, August) were used for monitoring drought and vegetation response to the varying soil/climatic conditions. Now, based on the assessment of drought severity, the new soil moisture index allowed us to assess a large-scale spatial coherence of droughts across the Southwestern part of Mongolia.