Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model.

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer-CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA's land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.

Understanding global groundwater-climate interactions.

Global warming is emerging as an important predictor of water availability and future water supplies across the world through inducing the frequency and severity in hydrological extremes. These extremes (e.g., drought) have potential impacts on groundwater, environmental flows, as well as increase social inequalities (limited access to water by the poor), among a range of other issues. Understanding the influence of global climate on groundwater systems is thus critical to help reshape global water markets through policies underpinned by the knowledge of climatic processes driving the water cycle and freshwater supply. The main aim of this study is to improve understanding of the influence of climate variability on global groundwater using statistical methods (e.g., multi-linear regression and wavelet analyses). The response of groundwater to climate variability are assessed and the feasibility of identifying climatic hotspots of groundwater-climate interactions are explored (2003-2017). Generally, climate variability plays a major role in the distribution of groundwater recharge, evidenced in the groundwater-rainfall relationship (r ranging from 0.6 to 0.8 with lags of 1-5 months) in several regions (Amazon and Congo basins, West Africa, and south Asia). Some of the areas where no relationship exists coincide with major regional aquifer systems (e.g., Nubian sand stone in north Africa) in arid domains with fossil groundwater. Our results also show that groundwater fluxes across the world are driven by global climate teleconnections. Notable among these climate teleconnections are PDO, ENSO, CAR, and Nino 4 with PDO showing the strongest relationship (r= 0.80) with groundwater in some hotspots (e.g. in South America). The explicit role of the Pacific ocean in regulating groundwater fluxes provides an opportunity to improve the prediction of climate change impact on global freshwater systems. As opposed to remarkably large productive hydrological systems (Amazon and Congo basins), in typically arid domains, groundwater could be restricted during prolonged drought, constraining the persistence of surface water in the maintenance of a healthy surface-groundwater interactions.

Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators.

The poor investments in gauge measurements for hydro-climatic research in Africa has necessitated the need to investigate how decision makers can leverage on sophisticated space-borne measurements to improve knowledge on surface water hydrology that can feed directly into water accounting processes, and risk assessment from extreme droughts and its impacts. To demonstrate such potential, a suite of satellite earth observations (Sentinel-2, altimetry, Landsat, GRACE, and TRMM) and model data are combined with the standardized precipitation evapotranspiration index to assess the impacts of global climate on freshwater dynamics over the LCB (Lake Chad basin), Africa's largest endorheic basin. As shown in the results of this study, the significant relationship of climate modes (AMO; r=0.68 and 0.59; and AMM; r=0.42 and 0.47) with drought patterns in the LCB highlights the evidence of global climate influence in the region. The significant declines in drought extents and their intensities (2004 - 2015) over LCB coincide with the rise in surface water extent of the Lake Chad during the same period. Change detection analysis of open water features in the southern pool of Lake Chad during the 2015 - 2019 period shows that on the average, only 28.4% of inundated areas within the vicinity of the Lake persisted during the period. While the association of terrestrial water storage (TWS) with model-derived surface water storage (SWS) is strongest (r=0.89) in the catchments that provide the most nourishment to the Lake Chad, the relationship of rainfall (2002 - 2017) with TWS (r=0.85), model TWS (r=0.87) and SWS (r=0.88) confirm that the LCB's hydrology is predominantly climate-driven. This notion is further reinforced as the predicted SWS over the LCB using a support vector machine regression scheme was found to be strongly correlated (r=0.95 at α=0.05) with observed SWS.

Modelling the impacts of global multi-scale climatic drivers on hydro-climatic extremes (1901-2014) over the Congo basin.

The knowledge of interactions between oceanic and atmospheric processes and associated influence on drought episodes is a key step toward designing robust measure that could support government and institutional measures for drought preparedness to promote region-specific drought risk-management policy solutions. This has become necessary for the Congo basin where the preponderance of evidence from few case studies shows long-term drying and hydro-climatic extremes attributed to perturbations of the nearby oceans. In this study, statistical relationships are developed between observed standardised precipitation index (SPI) and global sea surface temperature using principal component analysis as a regularization tool prior to the implementation of a canonical scheme. The connectivity between SPI patterns and global ocean-atmosphere phenomena was thereafter examined using the output from this scheme in a predictive framework based on non-linear autoregressive standard neural network. The Congo basin is shown to have been characterized by persistent and severe multi-year droughts during the earlier (1901-1930) and latter (1991-2014) decades of the last century. The impacts of these droughts were extensive affecting more than 50% of the basin between 1901 and 1930 and about 40% during the 1994-2006 period. Analysis of the latest decades (1994-2014) shows that relative to the two climatological periods between 1931 and 1990, the Congo basin has somewhat become drier. This likely contributed to the observed change in the hydrological regimes of the Congo river (after 1994) as indicated by the relationship between SPI and runoff index (r = 0.69 and 0.64 for 1931-1990 and 1961-1990 periods, respectively as opposed to r = 0.38 for 1991-2010 period). Pacific ENSO influences large departures in precipitation (r = 0.89) but prediction skill metrics demonstrate that multi-scale ocean-atmosphere phenomena (R2 = 84%, 78%, and 77% for QBO, AMO, and ENSO, respectively) significantly impact on hydro-climatic extremes, especially droughts over the Congo basin.

Exploring evapotranspiration dynamics over Sub-Sahara Africa (2000-2014).

Monitoring changes in evapotranspiration (ET) is useful in the management of water resources in irrigated agricultural landscapes and in the assessment of crop stress and vegetation conditions of drought-vulnerable regions. Information on the impacts of climate variability on ET dynamics is profitable in developing water management adaptation strategies. Such impacts, however, are generally unreported and not conclusively determined in some regions. In this study, changes in MODIS (Moderate Resolution Imaging Spectroradiometer)-derived ET (2000-2014) over large proportions of Sub-Sahara Africa (SSA) are explored. The multivariate analyses of ET over SSA showed that four leading modes of observed dynamics in ET, accounting for about 90% of the total variability, emanated mostly from some sections of the Sudano-Sahel and Congo basin. Based on Man-Kendall's statistics, significant positive trends (α = 0.05) in ET over the Central African Republic and most parts of the Sahel region were observed. Over much of the Congo basin nonetheless, ET showed significant (α = 0.05) distributions of widespread negative trends. These trends in ET were rather found to be consistent with observed changes in model soil moisture but not in all locations, perhaps due to inconsistent trends in maximum rainfall and land surface temperature. However, the results of spatio-temporal drought analysis confirm that the extensive ET losses in the Congo basin were somewhat induced by soil moisture deficits. Amidst other prominent drivers of ET, the dynamics of ET over the terrestrial ecosystems of SSA appear to be a more complex phenomenon that may transcend natural climate variations.

Ocean Wave Separation Using CEEMD-Wavelet in GPS Wave Measurement.

Monitoring ocean waves plays a crucial role in, for example, coastal environmental and protection studies. Traditional methods for measuring ocean waves are based on ultrasonic sensors and accelerometers. However, the Global Positioning System (GPS) has been introduced recently and has the advantage of being smaller, less expensive, and not requiring calibration in comparison with the traditional methods. Therefore, for accurately measuring ocean waves using GPS, further research on the separation of the wave signals from the vertical GPS-mounted carrier displacements is still necessary. In order to contribute to this topic, we present a novel method that combines complementary ensemble empirical mode decomposition (CEEMD) with a wavelet threshold denoising model (i.e., CEEMD-Wavelet). This method seeks to extract wave signals with less residual noise and without losing useful information. Compared with the wave parameters derived from the moving average skill, high pass filter and wave gauge, the results show that the accuracy of the wave parameters for the proposed method was improved with errors of about 2 cm and 0.2 s for mean wave height and mean period, respectively, verifying the validity of the proposed method.