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

Using Wavelet to Analyze Periodicities in Hydrologic Variables

The trend and shift in the seasonal temperature, precipitation and streamflow time series across the Midwest have been analyzed, for the period 1960-2013, using the statistical analyses (Mann- Kendall test with and without considering short term persistence (MK2 and MK1, respectively) and Pettitt test). The paper also utilizes a relatively new approach, wavelet analysis, for testing the existence of trend and shift in the time series. The method has the ability to decompose a time series in to lower (trend) and higher frequency components (noise). Discrete wavelet transform (DWT) has been employed in the present study with an aim to find which periodicities are mainly responsible for trend in the original data. The combination of MK1, MK2, and DWT along with Pettitt test hasn’t been extensively used up to this time, especially in detecting trend and shift in the Midwest. The analysis of climate division temperature and precipitation data and USGS naturalized streamflow data revealed the presence of periodicity in the time series data. All the incorporated time series data were seasonal to analyze the trends and shifts for four seasons-winter, spring, summer and fall independently. D3 component of DWT were observed to be influential in detecting real trend in temperature, precipitation and streamflow data, however unlike temperature, precipitation and streamflow showed decreasing trend as well. Shift was relatively observed more than trend in the region with dominance of D3 component in the data. The result indicate the significant warming trend which agrees with the “increasing temperature” observations in the past two decades, however a clear explanation for precipitation and streamflow is not obvious

Bringing Statistical Learning Machines Together for Hydro-Climatological Predictions - Case Study for Sacramento San Joaquin River Basin, California

Study region: Sacramento San Joaquin River Basin, California Study focus: The study forecasts the streamflow at a regional scale within SSJ river basin with largescale climate variables. The proposed approach eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962–2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). New hydrological insights: The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the proposed approach showed better forecasting skills with preprocessed large scale climate variables rather than using the predefined indices. The proposed study is simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds

Coupling HEC-RAS and HEC-HMS in Precipitation Runoff Modelling and Evaluating Flood Plain Inundation Map

The climate change and land use change have raised the challenges associated with increased runoff and flood management. The risks associated with flooding have been increasing with development in flood plain and changing climate resulting in increase in inundation of flood plain. The current study will help to evaluate the extent of flood plain in the study area – copper slough watershed (CSW) in Champaign, Illinois; utilizing the known precipitation and land use. The study of CSW is taken into account, as this is the largest watershed of Champaign City and had undergone major land use change increasing the flooding issues in the region. The conducted research utilizes the hydrologic engineering center - hydrologic modelling system (HEC-HMS) and Hydrologic Engineering Center – River Analysis System (HEC-RAS) as the modelling tool to develop runoff and floodplain inundation evaluation model for known precipitation. The model also incorporates Aeronautical Reconnaissance Coverage Geographic Information System (ARCGIS) extensions- HEC-GeoRAS and HEC-GeoHMS for the spatial analysis of the watershed. The hydrologic analysis is performed using HEC-HMS while the hydraulic modeling is done using HEC-RAS. Forcing the model with forecasted precipitation can also help with flood warning system by generating pre-flood inundation maps

HYDROLOGIC VARIABILITY WITHIN THE CLIMATE REGIONS OF CONTINENTAL UNITED STATES AND ITS TELECONNECTION WITH CLIMATE VARIABLES

The entropy of all systems is supposed to increase with time, this is also observed in the hydroclimatic records as increased variability. The current dissertation is primarily focused on the hydrologic variability of the hydrologic records in the climate regions across Continental United States. The study evaluated the effects of serial correlation in the historical streamflow records on both gradual trend and abrupt shift in streamflow. The study also evaluated the trend before and after the shift occurrence to validate whether the observed changes in streamflow is a result of long-term variability or climate regime shift. Secondly, the current dissertation evaluated the variability within western US hydrology which is highly driven by the oscillation of Pacific Ocean such as El Niño – Southern Oscillation (ENSO). The dissertation evaluated the variability in snow water equivalent (SWE) of western US as the winter snow accumulation of the region drives the spring-summer streamflow in the region which contributes to the major portion of yearly streamflow. The SWE variability during the individual phases of ENSO were analyzed to reveal the detailed influence of ENSO on historic snow accumulations. The study is not solely limited to the hydrologic variability evaluation rather; it also delves into obtaining the time lagged spatiotemporal teleconnections between large scale climate variables and streamflow and forecast the later based on the obtained teleconnections. To accomplish the research goals the current dissertation was subdivided into three research tasks. First task dealt with the streamflow records of 419 unimpaired streamflow records which were grouped into seven climate regions based on National Climate Assessment, to evaluate the regional changes in both seasonal streamflow and yearly streamflow percentiles. Non-parametric Mann-Kendall test and Pettitt’s test were utilized to evaluate the streamflow variability as gradual trend and abrupt shift, respectively. Walker test was performed to test the global significance of the streamflow variability within each climate regions based on local trend and shift significance of each streamflow stations. The task also evaluated the presence of serial correlation in the streamflow records and its effects on both trend and shift within the climate regions of continental United States for the first time. Maximum variability in terms of both trend and shift were observed for summer as compared to other seasons. Similarly, greater number of stations showed streamflow variability for 5th and 50th percentile streamflow as compared to 95th and 100th percentile streamflow. It was also observed that serial correlation affected both trend and step while, accounting for the lag-1 autocorrelation improved shift results. The results indicated that the streamflow variability has more likely occurred as shift as compared to the gradual trend. The outcomes of the current result detailing historic variability may help to envision future changes in streamflow. The second task evaluated the spatiotemporal variability of western US SWE over 58 years (1961–2018) as a trend and a shift. The task tested whether the SWE is consistent during ENSO phases utilizing the Kolmogorov – Smirnov (KS) test. Trend analysis was performed on the SWE data of each ENSO phase. Shift analysis was performed in the entire time series of 58 years. Additionally, the trend in the SWE data was evaluated before and after shift years. Mann- Kendal and Pettit\u27s tests were utilized for the detection of trend and shift, respectively. The serial correlation was considered during the trend evaluation, while Thiel-Sen approach was used for the evaluation of the trend magnitude. The serial correlation in time series which is the potential cause of overestimation and underestimation of the trend evaluation was found to be absent in the SWE data. The results suggested a negative trend and a shift during the study period. The negative trend was absent during neutral years and present during El Niño and La Niña years. The trend magnitudes were maximum during La Niña years followed by those during El Niño years and the entire length of the data. It was also observed that if the presence of negative shift in the SWE was considered, then most of the stations did not show a significant trend before and after the occurrence of a shift. The third task forecasted the streamflow at a regional scale within Sacramento San Joaquin (SSJ) River Basin with largescale climate variables. SSJ is an agricultural watershed located in the drought sensitive region of California. The forecast techniques involved a hybrid statistical framework that eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962 to 2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the forecasting approach showed better forecasting skills with preprocessed large-scale climate variables rather than using the predefined indices. The techniques involved in this task was simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds

Evaluating Future Flood Scenarios using CMIP5 Climate Projections

Frequent flooding events in recent years have been linked with the changing climate. Comprehending flooding events and their risks is the first step in flood defense and can help to mitigate flood risk. Floodplain mapping is the first step towards flood risk analysis and management. Additionally, understanding the changing pattern of flooding events would help us to develop flood mitigation strategies for the future. This study analyzes the change in streamflow under different future carbon emission scenarios and evaluates the spatial extent of floodplain for future streamflow. The study will help facility managers, design engineers, and stakeholders to mitigate future flood risks. Variable Infiltration Capacity (VIC) forcing-generated Coupled Model Intercomparison Project phase 5 (CMIP5) streamflow data were utilized for the future streamflow analysis. The study was done on the Carson River near Carson City, an agricultural area in the desert of Nevada. Kolmogorov–Smirnov and Pearson Chi-square tests were utilized to obtain the best statistical distribution that represents the routed streamflow of the Carson River near Carson City. Altogether, 97 projections from 31 models with four emission scenarios were used to predict the future flood flow over 100 years using a best fit distribution. A delta change factor was used to predict future flows, and the flow routing was done with the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) model to obtain a flood inundation map. A majority of the climate projections indicated an increase in the flood level 100 years into the future. The developed floodplain map for the future streamflow indicated a larger inundation area compared with the current Federal Emergency Management Agency’s flood inundation map, highlighting the importance of climate data in floodplain management studies

Analyzing the Impacts of Serial Correlation and Shift on the Streamflow Variability within the Climate Regions of Contiguous United States

The spatiotemporal hydrologic variability over different regions of the contiguous United States poses the risk of droughts and floods. Understanding the historic variations in streamflow can help in accessing future hydrologic conditions. The current study investigates the historic changes in the streamflow within the climate regions of the continental United States. The streamflow records of 419 unimpaired streamflow stations were grouped into seven climate regions based on the National Climate Assessment, to evaluate the regional changes in both seasonal streamflow and yearly streamflow percentiles. The non-parametric Mann–Kendall test and Pettitt’s test were utilized to evaluate the streamflow variability as a gradual trend and abrupt shift, respectively. The Walker test was performed to test the global significance of the streamflow variability within each climate region based on local trend and shift significance of each streamflow station. The study also evaluated the presence of serial correlation in the streamflow records and its effects on both trend and shift within the climate regions of the contiguous United States for the first time. Maximum variability in terms of both trend and shift was observed for summer as compared to other seasons. Similarly, a greater number of stations showed streamflow variability for 5th and 50th percentile streamflow as compared to 95th and 100th percentile streamflow. It was also observed that serial correlation affected both trends and steps, while accounting for the lag-1 autocorrelation improved shift results. The results indicated that the streamflow variability has more likely occurred as shift as compared to the gradual trend. The outcomes of the current result detailing historic variability may help to envision future changes in streamflow. The current study may favor the water managers in developing future decisions to resolve the issues related to the streamflow variability in flood and drought-prone regions

Analyzing the Effects of Short-Term Persistence and Shift in Sea Level Records along the US Coast

Is sea level change affected by the presence of autocorrelation and abrupt shift? This question reflects the importance of trend and shift detection analysis in sea level. The primary factor driving the global sea level rise is often related to climate change. The current study investigates the changes in sea level along the US coast. The sea level records of 59 tide gauge data were used to evaluate the trend, shift, and persistence using non-parametric statistical tests. Mann-Kendall and Pettitt’s tests were utilized to estimate gradual trends and abrupt shifts, respectively. The study also assessed the presence of autocorrelation in sea level records and its effect on both trend and shift was examined along the US coast. The presence of short-term persistence was found in 57 stations and the trend significance of most stations was not changed at a 95% confidence level. Total of 25 stations showed increasing shift between 1990–2000 that was evaluated from annual sea level records. Results from the current study may contribute to understanding sea level variability across the contiguous US. This study extends an elaborative understanding of sea level trends and shifts which might be useful for water managers

Evaluating Soil Moisture–Precipitation Interactions Using Remote Sensing: A Sensitivity Analysis

The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture–precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: theAdvancedMicrowave Scanning Radiometer for EarthObserving System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture–precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results

Evaluating Soil Moisture–Precipitation Interactions Using Remote Sensing: A Sensitivity Analysis

The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture–precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: theAdvancedMicrowave Scanning Radiometer for EarthObserving System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture–precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results