Grazing removal rate of grasses needs to be determined for various climate conditions to address eco-environmental concerns (e.g., desertification) related to steppe grassland degradation. The conventional approach, which requires survey data on animal species and heads as well as grass consumption per individual animal, is too costly and time-consuming to be applied at a watershed scale. The objective of this study was to present a new approach that can be used to estimate grazing removal rate with no requirement of animal-related data. The application of this new approach was demonstrated in a Eurasian semiarid typical-steppe watershed for an analysis period of 2000 to 2010. The results indicate that the removal rate tended to become larger, but its temporal variation tended to become smaller, from the upstream to downstream. Averaged across the watershed, the removal rate ranged from 63.9 to 401.0 g DM m´2 (or 22.4 to 60.9%) during the analysis period. As expected, the removal rate in an atmospherically wetter year was higher than that in an atmospherically drier year. Nevertheless, none of the eleven analysis years had a removal rate higher than the threshold value of 65%, above which the risk of grassland degradation would become much greater.
Estimating infiltration losses is very important for calculating runoff and recharge. However, the accuracy of contemporary infiltration models for disturbed urban soils may not be adequate, potentially compromising calculations based upon them. The objective of this study was to assess the performance of the two most prevalent infiltration models, Horton and Green–Ampt, for applications in urban soils. The data were measured by the US Environmental Protection Agency in a large city for soils with various characteristics of texture, structure, age, compactness, and dryness/wetness. The results indicate both models performed better in predicting infiltration rates for clayey rather than sandy soils, for new rather than old soils, and for wet rather than dry soils. For the clayey soils, both models performed better for the noncompact than compact soils. The opposite was true for sandy soils. Overall, neither infiltration model performed well for most soils, with the sole exception of the new clayey, wet, noncompact soils. The generally poor performance of the models in disturbed soils will likely increase uncertainty in model predictions. This study demonstrates the need to develop improved, more robust infiltration algorithms applicable to urban soils and various kinds of urban development that are based on carefully measured field experimental data.
Understanding dynamics of soil water content (SWC) and pore air relative humidity (RHpa), as influenced by wetting-drying cycles, is crucial for sustaining fragile ecosystems of desert lands across the world and needed for improving the prediction accuracy of global climate change. However, to date, such an understanding is still incomplete. The objective of this dissertation was to examine such dynamics at a typical desert site within the Horqin Sandy Land, located in Mongolian Plateau of north China. The examination was done by using a HYDRUS-1D computer simulation model and the continuous sensor-based soil water data for two calendar years. HYDRUS-1D was selected because it can well mimic the verticallydominant two-phase (i.e., liquid-vapor) processes of water movement within soils of semiarid sandy ecosystem. The results indicated that vaporization primarily occurred at a depth of around 10 cm below the ground surface. The diurnal variations of the SWC and RHpa in the top 10 cm soils were much larger than those in the soils at a deeper depth. For a non-rainy day, the SWC and RHpa were mainly determined by the relative magnitude of atmospheric temperature over soil temperature, whereas, for a rainy day, the SWC and RHpa were primarily controlled by the rainfall pattern and amount. The retardation role of the top dry soil layer, which is about 10 cm thick and exists most time at the study site, can prevent the beneath moist soils from being further dried up, and thus is beneficial for sustaining the desert ecosystem.
Sea level rise (SLR) can negatively affect the hydrology of coastal watersheds. However, the relevant information is incomplete and insufficient in existing literature. The objective of this study is to present a modeling approach to predict long-term effects of SLR on changes of flood peak, flood stage, and groundwater table with an assumption that the historical climate would reoccur in the future. The study was conducted for a typical coastal watershed in southeast USA. The results indicate that sea level had been rising at a rate of 4.21 mm yr−1 from 1948 to 1982 but at a faster rate of 5.16 mm yr−1 from 1983 to 2013. At such SLR rates and by 2113, the groundwater table beneath the eastern part of the watershed would be raised by 0.10 to 0.29 m, while the annual mean peak discharge and flood stage at the watershed outlet would be increased by 13.84 m3 s−1 (from 3.63 to 17.47 m3 s−1) and 0.92 m (from zero to 0.92 m), respectively. The other parts of the watershed would be relatively less affected by SLR. For coastal watersheds, SLR will probably raise the groundwater table, and increase the magnitude and occurrence of peak discharge and flood stage.
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