Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin

Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model was trained with the GRACE data from January 2003 to December of 2013 and validated with GRACE data from January 2014 to December of 2016. Groundwater anomaly from January 2017 to December of 2019 was forecasted with the tested model. Autocorrelation and partial autocorrelation plots were drawn to identify and construct the seasonal ARIMA models. ARIMA order for each grid was evaluated based on Akaike’s and Bayesian information criterion. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. The proposed models can be used to forecast groundwater variability for sustainable groundwater planning and management

Vertical Displacements Driven by Groundwater Storage Changes in the North China Plain Detected by GPS Observations

The North China Plain (NCP) has been experiencing the most severe groundwater depletion in China, leading to a broad region of vertical motions of the Earth’s surface. This paper explores the seasonal and linear trend variations of surface vertical displacements caused by the groundwater changes in NCP from 2009 to 2013 using Global Positioning System (GPS) and Gravity Recovery and Climate Experiment (GRACE) techniques. Results show that the peak-to-peak amplitude of GPS-derived annual variation is about 3.7~6.0 mm and is highly correlated (R > 0.6 for most selected GPS stations) with results from GRACE, which would confirm that the vertical displacements of continuous GPS (CGPS) stations are mainly caused by groundwater storage (GWS) changes in NCP, since GWS is the dominant component of total water storage (TWS) anomalies in this area. The linear trends of selected bedrock-located IGS CGPS stations reveal the distinct GWS changes in period of 2009–2010 (decrease) and 2011–2013 (rebound), which are consistent with results from GRACE-derived GWS anomalies and in situ GWS observations. This result implies that the rate of groundwater depletion in NCP has slowed in recent years. The impacts of geological condition (bedrock or sediment) of CGPS stations to their results are also investigated in this study. Contrasted with the slight linear rates (−0.69~1.5 mm/a) of bedrock-located CGPS stations, the linear rates of sediment-located CGPS stations are between −44 mm/a and −17 mm/a. It is due to the opposite vertical displacements induced by the Earth surface’s porous and elastic response to groundwater depletion. Besides, the distinct renewal characteristics of shallow and deep groundwater in NCP are discussed. The GPS-based vertical displacement time series, to some extent, can reflect the quicker recovery of shallow unconfined groundwater than the deep confined groundwater in NCP; through one month earlier to attain the maximum height for CGPS stations nearby shallow groundwater depression cones than those nearby deep groundwater depression cones

Vertical Displacements Driven by Groundwater Storage Changes in the North China Plain Detected by GPS Observations

The North China Plain (NCP) has been experiencing the most severe groundwater depletion in China, leading to a broad region of vertical motions of the Earth’s surface. This paper explores the seasonal and linear trend variations of surface vertical displacements caused by the groundwater changes in NCP from 2009 to 2013 using Global Positioning System (GPS) and Gravity Recovery and Climate Experiment (GRACE) techniques. Results show that the peak-to-peak amplitude of GPS-derived annual variation is about 3.7~6.0 mm and is highly correlated (R > 0.6 for most selected GPS stations) with results from GRACE, which would confirm that the vertical displacements of continuous GPS (CGPS) stations are mainly caused by groundwater storage (GWS) changes in NCP, since GWS is the dominant component of total water storage (TWS) anomalies in this area. The linear trends of selected bedrock-located IGS CGPS stations reveal the distinct GWS changes in period of 2009–2010 (decrease) and 2011–2013 (rebound), which are consistent with results from GRACE-derived GWS anomalies and in situ GWS observations. This result implies that the rate of groundwater depletion in NCP has slowed in recent years. The impacts of geological condition (bedrock or sediment) of CGPS stations to their results are also investigated in this study. Contrasted with the slight linear rates (−0.69~1.5 mm/a) of bedrock-located CGPS stations, the linear rates of sediment-located CGPS stations are between −44 mm/a and −17 mm/a. It is due to the opposite vertical displacements induced by the Earth surface’s porous and elastic response to groundwater depletion. Besides, the distinct renewal characteristics of shallow and deep groundwater in NCP are discussed. The GPS-based vertical displacement time series, to some extent, can reflect the quicker recovery of shallow unconfined groundwater than the deep confined groundwater in NCP; through one month earlier to attain the maximum height for CGPS stations nearby shallow groundwater depression cones than those nearby deep groundwater depression cones

Estimating The Spatial Distribution of Snow Water Equivalent Using in situ and Remote Sensing Observations

Mountain snowpack is one of the primary surface water sources for about one-sixth of the global population. More than 75% of the total runoff originates from mountain snowpacks in the Western U.S. Snowmelt water recharges reservoirs and aquifers gradually in the melting season, providing vital water supplies for urban and agricultural areas. Therefore, accurately monitoring the spatial and temporal distribution of mountain snowpack &ndash; often measured as snow water equivalent (SWE) &ndash; is crucial for effective water management. While existing SWE estimation approaches remain highly uncertain, particularly when applied over large mountainous regions, the remotely-sensed snow data provide new opportunities to better characterize the spatial distributions of mountain snowpack. This dissertation investigates the approaches that optimally blend satellite, airborne, and ground snow observations to improve (near) real-time SWE estimation over mountainous terrain. The second chapter of this dissertation evaluates the accuracy of existing SWE estimation models in Sierra Nevada California. Five large-scale SWE datasets at fine spatial resolutions (&lt;= 1000 m) are comprehensively validated and compared with the Airborne Snow Observatory (ASO) SWE data in the Tuolumne River Basin (2013-2017), and ground snow pillow and snow course SWE observations across the Sierra Nevada (2004-2014). These SWE datasets include REC-INT, REC-ParBal, a Sierra Nevada SWE reanalysis (REC-DA), and two operational SWE datasets from the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE), respectively. The results show that the REC-DA overall provides the most accurate SWE estimates across the Sierra Nevada (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%), followed by the REC-ParBal (R2 = 0.73, MAE = 83 mm, PBIAS = -6.4%), which is the least biased SWE estimates. Generally, SNODAS (R2 = 0.66, MAE = 106 mm, PBIAS = 9.3%) and REC-INT (R2 = 0.61, MAE = 131 mm, PBIAS = -28.3%) exhibit comparable but lower accuracy than the earlier mentioned two datasets, while NWM-SWE (R2 = 0.49, MAE = 142 mm, PBIAS = -25.2%) shows the least accuracy among the five SWE datasets. Given that REC-DA is not applicable in real-time, in the third chapter, a SWE data-fusion framework is developed, which integrates the historical SWE patterns derived from REC-DA into a statistically-based linear regression model (LRM) to estimate SWE in real-time. To investigate the influence of satellite-observed daily mean fractional snow-covered area (DMFSCA) on SWE estimation accuracy, two LRMs are compared: a baseline regression model (LRM-baseline) in which physiographic data and historical SWE patterns are used as independent variables, and an FSCA-informed regression model (LRM-FSCA) in which the DMFSCA from MODIS satellite imagery is included as an additional independent variable. By incorporating DMFSCA, LRM-FSCA outperforms LRM-baseline with improved R2 from 0.54 to 0.60, and reduced PBIAS from 2.6% to 2.2% in snow pillow cross-validation. The improvement in LRM-FSCA&rsquo;s performance is more significant during snow accumulation periods than during the snowmelt seasons. Compared to the ASO SWE, the LRM-FSCA explains 85% of the variance on average, which is at least 21% higher than the operational SNODAS (R2 = 0.64) and NWM-SWE (R2 = 0.33) in comparison. In chapter 4, a SWE bias correction framework (SWE-BCF) is developed that incorporates the ASO SWE and machine learning (ML) algorithms to further improve LRM SWE estimates in real-time. The performance of a wide range of commonly used machine learning algorithms is examined in the SWE-BCF including Gaussian Process Regression (GPR), Support Vector Machine (SVM), Bayesian Regularized Neural Networks (BRNN), Random Forest (RF), and Gradient Boosting Machine (GBM). The results indicate that all ML algorithms are capable of improving LRM-SWE accuracy substantially. While no single model performs significantly better than others, GPR, overall, shows the best performance with a 20% (0.14) increase in mean R2 value, a 31% (51 mm) reduction in mean RMSE, and a 61% (18.0%) reduction in absolute PBIAS compared with the original LRM using ASO SWE data for model validation. RF shows the most robust and stable performance in SWE bias correction with a 10% (0.08) increase in median R2 and a 41% (50 mm) reduction in median RMSE compared with the original LRM.</p