Quantifying Impact of Anthropogenic Disturbances on Water Availability
and Water Stress in Mongolian Urban and Mining Hubs by Using
Process-Based Eco-Hydrology Model
Abstract
In Mongolia, overuse and degradation of groundwater is a serious issue,
mainly in the urban and economic hub, Ulaanbaatar, and the Southern Gobi
mining hub. In order to explicitly quantify spatio-temporal variations
in water availability, a process-based eco-hydrology model, NICE
(National Integrated Catchment-based Eco-hydrology) (Nakayama and
Watanabe, 2004), was applied to two contrasting river basins including
these hubs. The authors built a high-resolution grid data representing
water use for livestock, urban populations, and mining by combining a
global dataset, statistical data, GIS data, observation data, and field
surveys. The model simulated the effects of climatic change and
human-induced disturbances on water resources during 1980-2018 (Nakayama
et al., 2021). Although drinking by herders’ livestock had some impact
on the hydrologic change, the groundwater level in the Tuul River was
shown to have been extremely degraded by water use in Ulaanbaatar over
the last few decades whereas that in the Galba River has declined
markedly as a result of Oyu Tolgoi mining since 2010. Analysis of the
relative contribution of environmental factors also helped us to
separate the effects of climatic change and human activities on
spatio-temporal change in the groundwater level. Further, they extended
NICE to couple with inverse method for sensitivity analysis and
parameter estimation of anthropogenic water uses (NICE-INVERSE). This
new model quantified the spatio-temporal variations of livestock water
use in these river basins (Nakayama, et al., in press). The livestock
water use was generally small for each soum (district), and could also
be heavily returned back to the ecosystems. The result also showed a
temporal decreasing trend of unit water use in some typical livestock
(cattle, sheep, and goats), suggesting a substantial increase in water
stress due to local-regional eco-hydrological degradation by
urbanization and mining. Sensitivity analysis and inverse estimation of
model parameters helped to improve the accuracy of hydrologic budgets in
basins. This methodology is powerful for evaluating spatio-temporal
variations of water availability and supporting water management in
regions with fewer inventory data.