Spatial-temporal analysis of subsurface water content and applications in Oklahoma: wastewater injection induced earthquakes and a multi sensor soil moisture product

Subsurface water is liquid water found below the ground surface, including soil water above the water table and ground water below the water table, but does not include water chemically bound to minerals or organic matter. Two important contents of subsurface water in Oklahoma have aroused the interest of more and more scientists: the wastewater injected into the ground during the oil and gas production and the surface soil moisture. This dissertation aims to develop contributions to two important topics for the sustainability of Oklahoma that are related to earthquakes and water resources: (1) the effects of deep underground waste-water injection on triggering regional seismicity and (2) the quantification of state-wide shallow-soil water content as a new tool for multiple applications in reservoir management, water resources, agriculture, natural hazards, and water management. The results of this study could help in setting sustainable limits for the oil and gas extraction industry in order to minimize the expected number and magnitude of induced quakes, thus avoiding future human and property losses. The results of this study also provide a new perspective for comparatively assessing multi-source soil moisture products, as well as a basis for objective data merging to capitalize on the strengths of multi-sensor multiplatform soil moisture products. Moreover, the new merged soil moisture product will be beneficial for multiple applications in water resources management, agriculture, and natural hazards

Calibration of Cosmic-Ray Soil Neutron Sensors (CRNS) in Different Land Use-Land Covers in Lower Brazos River Basin: A Modeling Approach

The cosmic-ray neutron sensors (CRNS) are a proximal sensor that can be used to estimate spatially averaged soil moisture at hectometer scale. The sensor measures the number of thermalized neutrons created by the collision between cosmic rays and atmosphere that interact with hydrogen atoms present in the environment and can be used to estimate soil moisture. However, extensive in-situ soil moisture measurements are needed to separate the signal of soil moisture from all other hydrogen pools such as aboveground biomass and atmospheric water content to calibrate the sensor. The objective of this study is to introduce a new technique of calibrating the sensor by evaluating water budget closures using CRNS and a calibrated sub-surface model Hydrus with minimal ground measurements. We installed CRNS at three sites in the Brazos river basin representing different land covers and management practices: i) traditional agriculture, ii) native prairie, and iii) managed prairie. The model was parameterized by inverting profile soil moisture information from just three locations in each land cover using the Shuffled Complex Evolution Algorithm in Hydrus-1D. The hydraulic parameters for the entire field were estimated by interpolating between the three locations to populate a Hydrus 2D model domain which was used to simulate the soil moisture distribution in the field. The CRNS was calibrated against the area average of modeled soil moisture distribution in the field. The calibrated dataset was able to capture the soil water budget at all the three sites with a water budget closure error of 0.01 m^3m^-3 -0.07 m^3m^-3 . The first part of validation was done by evaluating the calibrated output against intensively measured gravimetric soil moisture. We achieved acceptable values of RMSE (0.03m^3m^-3 - 0.06 m^3m^-3 ).For second part of validation we compare the evapotranspiration (ET) derived from Landsat thermal sensors and calibrated CRNS output. The ET from Landsat 8 was derived using METRIC algorithm which solves energy balance equation to provide the estimates. The values are calibrated against the reference ET acquired using Penman-Monteith equation. ET from CRNS is calculated using piecewise linear regression model. CRNS performed better than the Landsat-ET and has higher temporal resolution. The method reduces the labor in the regions where conducting field campaigns is difficult. Additionally, CRNS presents itself as a viable alternative to in-situ electromagnetic sensors in the clayey soil where the performance of these sensors is poor due to signal distortion

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R2 and MAE of 0.0518 m3/m3, 0.7312 and 0.0374 m3/m3, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary

Upscaling In Situ Soil Moisture Observations To Pixel Averages With Spatio-Temporal Geostatistics

Validation of satellite-based soil moisture products is necessary to provide users with an assessment of their accuracy and reliability and to ensure quality of information. A key step in the validation process is to upscale point-scale, ground-based soil moisture observations to satellite-scale pixel averages. When soil moisture shows high spatial heterogeneity within pixels, a strategy which captures the spatial characteristics is essential for the upscaling process. In addition, temporal variation in soil moisture must be taken into account when measurement times of ground-based and satellite-based observations are not the same. We applied spatio-temporal regression block kriging (STRBK) to upscale in situ soil moisture observations collected as time series at multiple locations to pixel averages. STRBK incorporates auxiliary information such as maps of vegetation and land surface temperature to improve predictions and exploits the spatio-temporal correlation structure of the point-scale soil moisture observations. In addition, STRBK also quantifies the uncertainty associated with the upscaled soil moisture which allows bias detection and significance testing of satellite-based soil moisture products. The approach is illustrated with a real-world application for upscaling in situ soil moisture observations for validating the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture product in the Heihe Water Allied Telemetry Experimental Research experiment (HiWATER). The results show that STRBK yields upscaled soil moisture predictions that are sufficiently accurate for validation purposes. Comparison of the upscaled predictions with PLMR soil moisture observations shows that the root-mean-squared error of the PLMR soil moisture product is about 0.03 m3 · m-3 and can be used as a high-resolution soil moisture product for watershed-scale soil moisture monitoring.</p

Upscaling In Situ Soil Moisture Observations to Pixel Averages with Spatio-Temporal Geostatistics

Validation of satellite-based soil moisture products is necessary to provide users with an assessment of their accuracy and reliability and to ensure quality of information. A key step in the validation process is to upscale point-scale, ground-based soil moisture observations to satellite-scale pixel averages. When soil moisture shows high spatial heterogeneity within pixels, a strategy which captures the spatial characteristics is essential for the upscaling process. In addition, temporal variation in soil moisture must be taken into account when measurement times of ground-based and satellite-based observations are not the same. We applied spatio-temporal regression block kriging (STRBK) to upscale in situ soil moisture observations collected as time series at multiple locations to pixel averages. STRBK incorporates auxiliary information such as maps of vegetation and land surface temperature to improve predictions and exploits the spatio-temporal correlation structure of the point-scale soil moisture observations. In addition, STRBK also quantifies the uncertainty associated with the upscaled soil moisture which allows bias detection and significance testing of satellite-based soil moisture products. The approach is illustrated with a real-world application for upscaling in situ soil moisture observations for validating the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture product in the Heihe Water Allied Telemetry Experimental Research experiment (HiWATER). The results show that STRBK yields upscaled soil moisture predictions that are sufficiently accurate for validation purposes. Comparison of the upscaled predictions with PLMR soil moisture observations shows that the root-mean-squared error of the PLMR soil moisture product is about 0.03 m3·m−3 and can be used as a high-resolution soil moisture product for watershed-scale soil moisture monitoring

Principles and methods of scaling geospatial Earth science data

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V

Mapping Soil Moisture from Remotely Sensed and In-situ Data with Statistical Methods

Soil moisture is an important factor for accurate prediction of agricultural productivity and rainfall runoff with hydrological models. Remote sensing satellites such as Soil Moisture Active Passive (SMAP) offer synoptic views of soil moisture distribution at a regional-to-global scale. To use the soil moisture product from these satellites, however, requires a downscaling of the data from an usually large instantaneous field of view (i.e. 36 km) to the watershed analysis scales ranging from 30 m to 1 km. In addition, validation of the soil moisture products using the ground station observations without an upscaling treatment would lead to cross-level fallacy. In the literature of geographical analysis, scale is one of the top research concens because of the needs for multi-source geospatial data fusion. This dissertation research introduced a multi-level soil moisture data assimilation and processing methodology framework based on spatial information theories. The research contains three sections: downscaling using machine learning and geographically weighted regression, upscaling ground network observation to calibrate satellite data, and spatial and temporal multi-scale data assimilation using spatio-temporal interpolation. (1) Soil moisture downscaling In the first section, a downscaling method is designed using 1-km geospatial data to obtain subpixel soil moisture from the 9-km soil moisture product of the SMAP satellite. The geospatial data includes normalized difference vegetation index (NDVI), land surface temperature (LST), gross primary productivity (GPP), topographical moisture index (TMI), with all resampled to 1-km resolution. The machine learning algorithm – random forest was used to create a prediction model of the soil moisture at a 1-km resolution. The 1-km soil moisture product was compared with the ground samples from the West Texas Mesonet (WTM) station data. The residual was then interpolated to compensate the unpredicted variability of the model. The entire process was based on the concept of regression kriging- where the regression was done by the random forest model. Results show that the downscaling approach was able to achieve better accuracy than the current statistical downscaling methods. (2) Station network data upscaling The Texas Soil Observation Network (TxSON) network was designed to test the feasibility of upscaling the in-situ data to match the scale of the SMAP data. I advanced the upscaling method by using the Voronoi polygons and block kriging with a Gaussian kernel aggregation. The upscaling algorithm was calibrated using different spatial aggregation parameters, such as the fishnet cell size and Gaussian kernel standard deviation. The use of the kriging can significantly reduce the spatial autocorrelation among the TxSON stations because of its declustering ability. The result proved the new upscaling method was better than the traditional ones. (3) Multi-scale data fusion in a spatio-temporal framework None of the current works for soil moisture statistical downscaling honors time and space equally. It is important, however, that the soil moisture products are consistent in both domains. In this section, the space-time kriging model for soil moisture downscaling and upscaling computation framework designed in the last two sections is implemented to create a spatio-temporal integrated solution to soil moisture multi-scale mapping. The present work has its novelty in using spatial statistics to reconcile the scale difference from satellite data and ground observations, and therefore proposes new theories and solutions for dealing with the modifiable areal unit problem (MAUP) incurred in soil moisture mapping from satellite and ground stations

The Stability of Temperate Lakes Under the Changing Climate

There is a collective prediction among ecologists that climate change will enhance phytoplankton biomass in temperate lakes. Yet there is noteworthy variation in the structure and regulating functions of lakes to make this statement challengeable and, perhaps, inaccurate. To generate a common understanding on the trophic transition of lakes, I examined the interactive effects of climate change and landscape properties on phytoplankton biomass in 12,644 lakes located in relatively intact forested landscapes. Chlorophyll-a (Chl-a) concentration was used as a proxy for phytoplankton biomass. Chl-a concentration was obtained via analyzing Landsat satellite imagery data over a 28-year period (1984-2011) and using regression modelling. The most common lake trophic state was oligotrophic (median Chl-a \u3c 2.6 μg L-1), while the least common was hyper-eutrophic (median Chl-a \u3e 56 μg L-1). Lake volume was the most important factor in determining the present trophic state of the lakes. The majority of the lakes (91.6%) did not show a change in trophic state over an almost 3-decade long sampling period; only 4.0% of the lakes became more eutrophic, and 4.4% of the lakes became more oligotrophic. Lakes with smaller volumes were further responsive to temperature (warmer lakes were more eutrophic), while lakes with larger volumes were more responsive to precipitation (wetter lakes were more oligotrophic). Early warning indicators of change in trophic state were examined in the patterns of the residuals of the time series of Chl-a once non-stationary and stationary trends were removed. Remarkably, the majority (56.5%) of the lakes showed patterns in the residuals that were not defined by a single trophic metric but fluctuated among different trophic states. There was an unexpected instability among some lakes as they switched between oligotrophic and eutrophic states (12.5%) or were transitioning from eutrophic towards oligotrophic states (23.4%), or from oligotrophic towards eutrophic states (20.6%). The complex responses of phytoplankton biomass to climate change suggests that our ability to predict the future trophic state of lakes will be limited but enhanced if we recognize that lakes and their catchments will be both impacted by climate change