Lili Yao

Bio: Lili Yao is an academic researcher from University of Central Florida. The author has contributed to research in topics: Aquifer & Geology. The author has an hindex of 4, co-authored 7 publications receiving 54 citations. Previous affiliations of Lili Yao include Beijing Normal University.

Numerical studies on the influences of the South-to-North Water Transfer Project on groundwater level changes in the Beijing Plain, China

Abstract: College of Water Sciences, Beijing Normal University, Beijing 100875, China Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China Engineering Research Center of Groundwater Pollution Control and Remediation of Ministry of Education, Beijing 100875, China Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA Correspondence Litang Hu, College of Water Sciences, Beijing Normal University, Beijing 100875, China. Email: litanghu@bnu.edu.cn

Diagnosis toward predicting mean annual runoff in ungauged basins

Abstract: . Prediction of mean annual runoff is of great interest but still poses a challenge in ungauged basins. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual mean annual runoff on average across the study watersheds, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of mean annual runoff is mainly caused by the underestimation of the area percentage of low soil water storage capacity due to neglecting the effect of land surface and bedrock topography. Higher spatial variability of soil water storage capacity estimated through the height above the nearest drainage (HAND) and topographic wetness index (TWI) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography, and bedrock. It leads to better diagnosis of the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model finally.

Surrogate Models for Sub-Region Groundwater Management in the Beijing Plain, China

Abstract: Overexploitation of groundwater resources has caused groundwater-related problems all over the world. Effective groundwater governance is a favorable guarantee for its protection and sustainable utilization. Accurate prediction of groundwater level (GWL) or depth to groundwater (GWD) plays an important role in groundwater resource management. Due to the limitations and complexity of numerical models, this study aims to develop surrogate models that can dually control the GWL (or GWD) and groundwater quantity (GWQ) in each district of the Beijing Plain, China, using the methods of multiple linear regression (MLR) and back propagation artificial neural network (BP-ANN). This study used 180 monthly GWD data records, including the first 168 data records for model development (training) and the remaining 12 data records for model verification. The results indicate that the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (R) for both the MLR and BP-ANN models are high in most districts and that the MLR models are more appropriate in this study. Fifteen scenarios under different conditions of groundwater use and precipitation are designed to demonstrate the applicability of the developed model in groundwater management. The surrogate models are effective tools that can be used by decision-makers for groundwater management.

Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground-based measurements (EI)

Abstract: [1] Changes in regional groundwater storage in North China were estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites data and ground-based measurements collected from 2003 to 2010. The study area (∼370,000 km2) included the Beijing and Tianjin municipality, the Hebei and Shanxi province, which is one of the largest irrigation areas in the world and is subjected to intensive groundwater-based irrigation. Groundwater depletion in North China was estimated by removing the simulated soil moisture changes from the GRACE-derived terrestrial water storage changes. The rate of groundwater depletion in North China based on GRACE was 2.2 ± 0.3 cm/yr from 2003 to 2010, which is equivalent to a volume of 8.3 ± 1.1 km3/yr. The groundwater depletion rate estimated from monitoring well stations during the same time period was between 2.0 and 2.8 cm/yr, which is consistent with the GRACE-based result. However, the estimated groundwater depletion rate in shallow plain aquifers according to the Groundwater Bulletin of China Northern Plains (GBCNP) for the same time period was only approximately 2.5 km3/yr. The difference in groundwater depletion rates estimated from GRACE and GBCNP data indicates the important contribution of groundwater depletion from deep aquifers in the plain and piedmont regions of North China.

Vegetation control on water and energy balance within the Budyko framework

Abstract: [1] Budyko's framework has been widely used to study basin-scale water and energy balances and one of the formulations of the Budyko curve is Fu's equation. The curve shape parameter ϖ in Fu's equation controls how much of the available water will be evaporated given the available energy. Previous studies have found that land surface characteristics significantly affect variations in the parameter ϖ. In this study, we focus on the vegetation impact and examine the conditions under which vegetation plays a major role in controlling the variability of ϖ. Using data from 26 major global river basins that are larger than 300,000 km2, the basin-specific ϖ parameter is found to be linearly correlated with the long-term averaged annual vegetation coverage. A simple parameterization for the ϖ parameter based solely on remotely sensed vegetation information is proposed, which improves predictions of annual actual evapotranspiration by reducing the root mean square error (RMSE) from 76 mm to 47 mm as compared to the default ϖ value used in the Budyko curve method. The controlling impact of vegetation on the basin-specific ϖ parameter is diminished in small catchments with areas less than 50,000 km2, which suggests a scale-dependence of the role of vegetation in affecting water and energy balances. In small catchments, other key ecohydrological processes need to be taken into account in order to fully capture the variability of the ϖ parameter in Fu's equation.

South-to-North Water Diversion stabilizing Beijing’s groundwater levels

Abstract: Groundwater (GW) overexploitation is a critical issue in North China with large GW level declines resulting in urban water scarcity, unsustainable agricultural production, and adverse ecological impacts. One approach to addressing GW depletion was to transport water from the humid south. However, impacts of water diversion on GW remained largely unknown. Here, we show impacts of the central South-to-North Water Diversion on GW storage recovery in Beijing within the context of climate variability and other policies. Water diverted to Beijing reduces cumulative GW depletion by ~3.6 km3, accounting for 40% of total GW storage recovery during 2006–2018. Increased precipitation contributes similar volumes to GW storage recovery of ~2.7 km3 (30%) along with policies on reduced irrigation (~2.8 km3, 30%). This recovery is projected to continue in the coming decade. Engineering approaches, such as water diversions, will increasingly be required to move towards sustainable water management. The authors here address water sustainability in the greater area of Beijing, China. Specifically, the positive effects towards Beijing groundwater levels via water diversion from the Yangtze River to the North are shown.

Land subsidence due to groundwater withdrawal in the northern Beijing

Abstract: Abstract Beijing is an international metropolis, where over-exploration of water resource makes land subsidence becoming more and more serious. The related problems cannot be avoided in the coming years because of the giant increase of population. The aims of this study are to quantify land subsidence over the period 2003 to 2010, grasp the evolution of the process, and investigate the relation with the triggering factors in the northern area of the Beijing plain. Various data, including deep compaction from vertical multiple borehole extensometers, land subsidence from Persistent Scatterer Interferometry and leveling surveys, groundwater levels, hydrogeological setting from wellbores, and Landsat TM image were collected and effectively used to detect the spatial and temporal features of land subsidence and its possible relation with groundwater level changes, compressible layer thickness, and urban development. Results show that land subsidence is unevenly distributed and continuously increased from 2003 to 2010. The average loss of elevation over the monitoring period amounted to 92.5 mm, with rates up to 52 mm/y. The distribution of the subsidence bowl is only partially consistent with that of the groundwater depression cone because of the variable thickness of the most compressible fine deposits. In fact, extensometers reveal that silty-clay layers account for the larger contribution to land subsidence, with the 15 m thick silty-clay layer between 102 and 117 m depth accounting for about 25% of the total subsidence. Finally, no clear correlation has been observed between the subsidence rates and the increase of the load on the land surface connected to the impressive urban development. This study represents a first step toward the development of a physically-based model of the subsidence occurrence to be used for planning remediation strategies in the northern Beijing plain.

Modeling Inter-annual Variability of Seasonal Evaporation and Storage Change Based on the Extended Budyko Framework

Abstract: [1] Long-term climate is the first-order control on mean annual water balance, and vegetation and the interactions between climate seasonality and soil water storage change have also been found to play important roles. The purpose of this paper is to extend the Budyko hypothesis to the seasonal scale and to develop a model for interannual variability of seasonal evaporation and storage change. A seasonal aridity index is defined as the ratio of potential evaporation to effective precipitation, where effective precipitation is the difference between rainfall and storage change. Correspondingly, evaporation ratio is defined as the ratio of evaporation to effective precipitation. A modified Turc-Pike equation with a horizontal shift is proposed to model interannual variability of seasonal evaporation ratio as a function of seasonal aridity index, which includes rainfall seasonality and soil water change. The performance of the seasonal water balance model is evaluated for 277 watersheds in the United States. The 99% of wet seasons and 90% of dry seasons have Nash-Sutcliffe efficiency coefficients larger than 0.5. The developed seasonal model can be applied for constructing long-term evaporation and storage change data when rainfall, potential evaporation, and runoff observations are available. On the other hand, vegetation affects seasonal water balance by controlling both evaporation and soil moisture dynamics. The correlation between NDVI and evaporation is strong particularly in wet seasons. However, the correlation between NDVI and the seasonal model parameters is only strong in dry seasons.