Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context.

With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of the state of the hydrological processes via data assimilation. Observations can be assimilated into numerical models using dynamics and data-driven approaches. The present study aims to assess these assimilation frameworks for integrating different sets of satellite measurements in a hydrological context. To this end, we implement a traditional data assimilation system based on the Square Root Analysis (SQRA) filtering scheme and the newly developed data-driven Kalman-Takens technique to update the water components of a hydrological model with the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS), and soil moisture products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) in a 5-day temporal scale. While SQRA relies on a physical model for forecasting, the Kalman-Takens only requires a trajectory of the system based on past data. We are particularly interested in testing both methods for assimilating different combination of the satellite data. In most of the cases, simultaneous assimilation of the satellite data by either standard SQRA or Kalman-Takens achieves the largest improvements in the hydrological state, in terms of the agreement with independent in-situ measurements. Furthermore, the Kalman-Takens approach performs comparably well to dynamical method at a fraction of the computational cost.

Understanding the global hydrological droughts of 2003-2016 and their relationships with teleconnections.

Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003-2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts.

Understanding the association between climate variability and the Nile's water level fluctuations and water storage changes during 1992-2016.

With the construction of the largest dam in Africa, the Grand Ethiopian Renaissance Dam (GERD) along the Blue Nile, the Nile is back in the news. This, combined with Bujagali Dam on the White Nile are expected to bring ramification to the downstream countries. A comprehensive analysis of the Nile's waters (surface, soil moisture and groundwater) is, therefore, essential to inform its management. Owing to its shear size, however, obtaining in-situ data from "boots on the ground" is practically impossible, paving way to the use of satellite remotely sensed and models' products. The present study employs multi-mission satellites and surface models' products to provide, for the first time, a comprehensive analysis of the changes in Nile's stored waters' compartments; surface, soil moisture and groundwater, and their association to climate variability (El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) over the period 1992-2016. In this regard, remotely sensed altimetry data from TOPEX/Poseidon (T/P), Jason-1, and Jason-2 satellites along with the Gravity Recovery And Climate Experiment (GRACE) mission, and the Tropical Rainfall Measuring Mission Project (TRMM) rainfall products are applied to analyze the compartmental changes over the Nile River Basin (NRB). This is achieved through the creation of 62 virtual gauge stations distributed throughout the Nile River that generate water levels, which are used to compute surface water storage changes. Using GRACE total water storage (TWS), soil moisture data from multi-models based on the Triple Collocation Analysis (TCA) method, and altimetry derived surface water storage, Nile basin's groundwater variations are estimated. The impacts of climate variability on the compartmental changes are examined using TRMM precipitation and large-scale ocean-atmosphere ENSO and IOD indices. The results indicate a strong correlation between the river level variations and precipitation changes in the central part of the basin (0.77 on average) in comparison to the northern (0.64 on average) and southern parts (0.72 on average). Larger water storages and rainfall variations are observed in the Upper Nile in contrast to the Lower Nile. A negative groundwater trend is also found over the Lower Nile, which could be attributed to a significantly lower amount of rainfall in the last decade and extensive irrigation over the region.

Understanding linkages between global climate indices and terrestrial water storage changes over Africa using GRACE products.

Africa, a continent endowed with huge water resources that sustain its agricultural activities is increasingly coming under threat from impacts of climate extremes (droughts and floods), which puts the very precious water resource into jeopardy. Understanding the relationship between climate variability and water storage over the continent, therefore, is paramount in order to inform future water management strategies. This study employs Gravity Recovery And Climate Experiment (GRACE) satellite data and the higher order (fourth order cumulant) statistical independent component analysis (ICA) method to study the relationship between terrestrial water storage (TWS) changes and five global climate-teleconnection indices; El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Madden-Julian Oscillation (MJO), Quasi-Biennial Oscillation (QBO) and the Indian Ocean Dipole (IOD) over Africa for the period 2003-2014. Pearson correlation analysis is applied to extract the connections between these climate indices (CIs) and TWS, from which some known strong CI-rainfall relationships (e.g., over equatorial eastern Africa) are found. Results indicate unique linear-relationships and regions that exhibit strong linkages between CIs and TWS. Moreover, unique regions having strong CI-TWS connections that are completely different from the typical ENSO-rainfall connections over eastern and southern Africa are also identified. Furthermore, the results indicate that the first dominant independent components (IC) of the CIs are linked to NAO, and are characterized by significant reductions of TWS over southern Africa. The second dominant ICs are associated with IOD and are characterized by significant increases in TWS over equatorial eastern Africa, while the combined ENSO and MJO are apparently linked to the third ICs, which are also associated with significant increase in TWS changes over both southern Africa, as well as equatorial eastern Africa.

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme.

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003-2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year.