Using Satellite-Based Terrestrial Water Storage Data: A Review

Review on sea water quality (SWQ) monitoring using satellite remote sensing techniques (SRST)

Due to extensive anthropogenic activities in coastal areas and rivers connected to the seas, effective and timely monitoring the sea water quality (SWQ) is crucial for maintaining ecosystem health. SWQ monitoring involves examining the chemical, physical, and biological parameters of sea water. Satellite remote sensing techniques (SRST) make us able to measure many of the SWQ parameters effectively. Compared to traditional SWQ monitoring techniques, SRST offers significant advantages due to its global coverage, long-term observation capabilities, flexibility, cost-effectiveness, and efficiency. This paper reviews the literature from the past three decades on space-based SWQ monitoring using a semi-systematic review approach. It outlines the definition and characteristics of SWQ parameters estimated by SRST. In addition to exploring the evolution of satellite sensors, this study also focuses on the methodological aspects of SWQ monitoring using SRST. It finds that semi-empirical algorithms combined with multivariate statistical approaches outperform other methods, recent studies indicate that machine learning models often achieve superior accuracy, with R2 values frequently exceeding 0.90. The SWQ parameters reviewed some important physical, biological and chemical parameters, include sea surface temperature (SST), chlorophyll-a (Chl-a), sea surface salinity (SSS), coloured dissolved organic matter (CDOM), particulate organic carbon (POC), total suspended solids/matter (TSS/TSM), and Secchi disk depth (SDD). The review concludes that data from optical and passive microwave-based satellite sensors are widely and effectively used for SWQ monitoring. The most frequently monitored SWQ parameters are Chl-a, SST, CDOM, and more recently, POC, with indirect studies also addressing non-optically active variables like total nitrogen (TN) and total phosphorus (TP) through empirical correlations.

A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins

The coarse spatial resolution of about 300 km in Total Water Storage Anomalies (TWSA) data from the Gravity Recovery And Climate Experiment (GRACE) and its follow-on (GRACE-FO, hereafter GRACE) missions presents significant challenges for local water resource management. Previous approaches to addressing this issue through statistical downscaling have been limited by the reliance on the scale-invariance assumption, residual correction, hydrological models, and a lack of consideration for spatial correlations among the TWSA grids. This study introduces the DownGAN generative adversarial network, which downscales GRACE TWSA to 25 km, as exemplified in the Yangtze River Basin (YRB) and the Nile River Basin (NRB). Additionally, we propose a novel downscaling scheme to address the above limitations. DownGAN receives static and dynamic variables as inputs while considering their potential time-delay effects. The downscaled TWSA is validated using a synthetic example, in-situ runoff, groundwater levels, and two hydrological models. The potential benefits of the downscaled TWSA in closing the water balance budget and monitoring hydrological droughts in the YRB and NRB are explored. The synthetic example indicates that DownGAN trained using the proposed downscaling scheme can downscale the YRB and NRB's TWSA from 1° to 0.5° and 0.25°, respectively. DownGAN outperforms RecNet, a fully convolutional neural network, producing continuous, consistent, and realistic downscaled TWSA. The downscaled TWSA exhibits high correlations with the runoff and groundwater levels in the YRB and NRB, respectively. In addition, DownGAN demonstrates better performance in closing the water balance budget and monitoring drought events in both the YRB and NRB than HR GRACE mascon products, as evidenced by its higher correlations with the total water storage changes derived from the water balance equation and two drought indices, respectively. DownGAN is adaptable to other downscaling tasks and regions, offering a flexible downscaling factor, minimal assumptions, cost-effectiveness, and realistic predictions.

An Abrupt Decline in Global Terrestrial Water Storage and Its Relationship with Sea Level Change

As observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions, global terrestrial water storage (TWS), excluding ice sheets and glaciers, declined rapidly between May 2014 and March 2016. By 2023, it had not yet recovered, with the upper end of its range remaining 1 cm equivalent height of water below the upper end of the earlier range. Beginning with a record-setting drought in northeastern South America, a series of droughts on five continents helped to prevent global TWS from rebounding. While back-to-back El Niño events are largely responsible for the South American drought and others in the 2014-2016 timeframe, the possibility exists that global warming has contributed to a net drying of the land since then, through enhanced evapotranspiration and increasing frequency and intensity of drought. Corollary to the decline in global TWS since 2015 has been a rise in barystatic sea level (i.e., global mean ocean mass). However, we find no evidence that it is anything other than a coincidence that, also in 2015, two estimates of barystatic sea level change, one from GRACE/FO and the other from a combination of satellite altimetry and Argo float ocean temperature measurements, began to diverge. Herein, we discuss both the mechanisms that account for the abrupt decline in terrestrial water storage and the possible explanations for the divergence of the barystatic sea level change estimates.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10712-024-09860-w.

Multi-model hydrological reference dataset over continental Europe and an African basin

Although Essential Climate Variables (ECVs) have been widely adopted as important metrics for guiding scientific and policy decisions, the Earth Observation (EO) and Land Surface and Hydrologic Model (LSM/HM) communities have yet to treat terrestrial ECVs in an integrated manner. To develop consistent terrestrial ECVs at regional and continental scales, greater collaboration between EO and LSM/HM communities is needed. An essential first step is assessing the LSM/HM simulation uncertainty. To that end, we introduce a new hydrological reference dataset that comprises a range of 19 existing LSM/HM simulations that represent the current state-of-the-art of our LSM/HMs. Simulations are provided on a daily time step, covering Europe, notably the Rhine and Po river basins, alongside the Tugela river basin in Africa, and are uniformly formatted to allow comparisons across simulations. Furthermore, simulations are comprehensively validated with discharge, evapotranspiration, soil moisture and total water storage anomaly observations. Our dataset provides valuable information to support policy development and serves as a benchmark for generating consistent terrestrial ECVs through the integration of EO products.

The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain

Groundwater storage and depletion fluctuations in response to groundwater availability for irrigation require understanding on a local scale to ensure a reliable groundwater supply. However, the coarser spatial resolution and intermittent data gaps to estimate the regional groundwater storage anomalies (GWSA) prevent the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GARCE-FO) mission from being applied at the local scale. To enhance the resolution of GWSA measurements using machine learning approaches, numerous recent efforts have been made. With a focus on the development of a new algorithm, this study enhanced the GWSA resolution estimates to 0.05° by extensively investigating the continuous spatiotemporal variations of GWSA based on the regional downscaling approach using a regression algorithm known as the geographically weighted regression model (GWR). First, the modified seasonal decomposition LOESS method (STL) was used to estimate the continuous terrestrial water storage anomaly (TWSA). Secondly, to separate GWSA from TWSA, a water balance equation was used. Third, the continuous GWSA was downscaled to 0.05° based on the GWR model. Finally, spatio-temporal properties of downscaled GWSA were investigated in the North China Plain (NCP), China's fastest-urbanizing area, from 2003 to 2022. The results of the downscaled GWSA were spatially compatible with GRACE-derived GWSA. The downscaled GWSA results are validated (R = 0.83) using in-situ groundwater level data. The total loss of GWSA in cities of the NCP fluctuated between 2003 and 2022, with the largest loss seen in Handan (-15.21 ± 7.25 mm/yr), Xingtai (-14.98 ± 7.25 mm/yr), and Shijiazhuang (-14.58 ± 7.25 mm/yr). The irrigated winter-wheat farming strategy is linked to greater groundwater depletion in several cities of NCP (e.g., Xingtai, Handan, Anyang, Hebi, Puyang, and Xinxiang). The study's high-resolution findings can help with understanding local groundwater depletion that takes agricultural water utilization and provide quantitative data for water management.

Remote sensing-based monitoring and evaluation of the basin-wise dynamics of terrestrial water and groundwater storage fluctuations

The recent dynamics of terrestrial water storage (TWS) and groundwater storage (GWS) fluctuations were investigated based on the Gravity Recovery And Climate Experiment (GRACE) observations over 25 basins of Türkiye. Coarse-resolution GRACE estimates were downscaled based on the Random Forest algorithm. The impacts of precipitation (P) and evapotranspiration (ET) on the variations of water storage were also assessed. The findings demonstrated good performance for the RF model in simulating finer resolution estimates of TWS. The results indicated a diminishing trend of TWS and its hydrologic components over all the basins from 2003 to 2020. The Doğu Akdeniz Basin with the annually decreasing TWS and GWS of [Formula: see text] and [Formula: see text] was the most critical basin of Türkiye. The least storage loss was observed in the Batı Karadeniz Basin with the annual TWS and GWS loss of [Formula: see text] and [Formula: see text], respectively. Based on the results, Türkiye has lost, on average, an estimated [Formula: see text] and [Formula: see text] of its TWS and GWS, respectively, which are equivalent to the total storage loss of [Formula: see text] and [Formula: see text] of TWS and GWS during the last 18 years. The results also indicated that P and ET interact differently with the variations of TWS and GWS. The net water flux was revealed to be partially correlated with the total water storage fluctuations, suggesting the governing role of other deriving forces particularly the anthropogenic factors in the spatiotemporal variations of Türkiye's water storage; therefore, a sector-specific analysis of the water storage variations is crucial for the country, particularly by concentrating more on the dynamics of GWS.