Omid Elmi

Abstract

In the 30 years of its availability, satellite altimetry has established itself as an important tool for understanding the Earth system. Originally developed for oceanography and geodesy, it has also proven valuable for monitoring water level variation of lakes and rivers. However, when using altimetry for inland waters, there is always a critical issue: retracking i.e. the procedure in which the range from the satellite to the water surface is (re)estimated. The current retracking methods heavily rely on single waveforms, which results in a high sensitivity to every individual peak in the waveform and in a strong dependency on the waveform's shape. Here, we propose the Bin-Space-Time (BiST) retracking method that moves beyond finding a single point in a 1D waveform and instead seeks a retracking line within a 2D radargram, for which the temporal information over different cycles is also considered. The retracking line divides the radargram into two segments: the left (Front) and right-hand side (Back) of the retracking line. Such a segmentation approach can be interpreted as a binary image segmentation problem, for which spatiotemporal information can be incorporated. We follow a Bayesian approach, exploiting a probabilistic graphical model known as a Markov Random Field (MRF). There, the problem is arranged as a Maximum A Posteriori estimation of an MRF (MAP-MRF), which means finding a retracking line that maximizes a posterior probability density or minimizes a posterior energy function. Our posterior energy function is obtained by a prior energy function and a likelihood energy function, both of them depending on signal intensity and bin: 1) The prior: the bin-space energy function defined between first-order neighbouring pixels of a radargram modeling the spatial dependency between their labels for given intensities and bins and 2) The likelihood: the temporal energy function of a pixel for labeling Front or Back given its overall temporal evolution. The realization of the field with the minimum sum of the bin-space and the temporal energy functions is then found through the maxflow algorithm. Consequently, the retracking line, the boundary between the Back and Front region is obtained. We apply our method to both pulse-limited and SAR altimetry data over nine lakes and reservoirs in the USA with different sizes and different altimetry characteristics. The resulting water level time series are validated against in situ data. Across the selected case studies, on average, the BiST retracker improves the RMSE by approximately 0.5m compared to the best existing retracker. The main benefit of the proposed retracker, which operates in bin, space, and time domains, is its robustness against unexpected waveform variations, making it suitable for diverse inland water surfaces.

Abstract

An accurate estimate of river discharge is vital to quantifying the global hydrological cycle and managing water resources. In view of the steadily deteriorating data provision from gauge networks, hydrological monitoring through spaceborne sensors becomes a necessity. The SWOT mission is the first satellite to conduct a global survey of the Earth's surface waters, measuring water surface elevation, river width, and surface slope for estimating discharge. Since SWOT can only sample mid-latitude locations approximately twice per its 21-day cycle, we develop a linear dynamic system for daily discharge estimation over a continuous single-branch river network. The linear dynamic system includes a process model based on a physically-based spatiotemporal correlation and observation equations utilizing SWOT discharge products. We solve this dynamic system through a Kalman filter, which is simultaneously executed in the time and space domain to obtain daily discharge. Since SWOT discharge products are currently inaccessible, we use a perturbed version of synthetic SWOT datasets obtained by Monte Carlo simulation to test the feasibility of our approach. The validation of the estimated discharge against true discharge in the synthetic datasets over all rivers leads to a median correlation as high as 0.95, a median NSE for residuals as high as 0.82, and a median relative bias as high as 5.22%, respectively. Our method delivers promising results and the hope of obtaining daily discharge once the required SWOT data is available.

Abstract

The availability and variations of continental water storage are of great importance for society, as they influence agricultural, industrial, and domestic water use. Among the water storage components, terrestrial surface water specifically lakes and reservoirs are essential for wildlife and human habitats as they store freshwater in the most accessible way, control seasonal floods and generate hydropower. Despite the importance, the estimation of surface water storage variation at a global scale is usually obtained from simplified models due to the absence of necessary gauge and remote sensing measurements. In this study, we produce monthly water volume anomaly time series of 182260 global lakes and reservoirs larger than 1 km² for 1985-2018. To do so, water area time series of lakes and reservoirs are obtained from the Joint Research Center Global Surface Water data set. We gather all publicly available in situ water level time series and generate water level time series using satellite altimetry data from various missions and data sets. For the remaining lakes and reservoirs, water height information is extracted from the TerraSAR-X digital elevation model. After collecting the required data, first, the empirical water area-level model is developed for each object and then the water volume variation time series are estimated. With this data set, we can investigate the temporal and spatial variations of surface water stored in lakes and reservoirs from 1985-2018 on a global scale. This study aims to answer these fundamental questions: 1) What are the temporal behaviors of surface water volume variations in different river basins? 2) Does water volume variation trend agree with other hydrological parameters’ temporal variation, And 3) what are the major natural and anthropogenic 45 factors that explain the long-term water volume variation?

Abstract

he quantification of river discharge is essential for understanding a broad range of scientific questions focused on hydrology, hydraulics and water resource management. However, the Global Runoff Data Center (GRDC) data set has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. For extending the discharge estimates of inactive GRDC stations, we develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. First, we evaluated the feasibility of extending discharge estimates of gauges in the GRDC dataset benefiting from river water height time series obtained from satellite altimetry missions (2000--2020) and river width estimates obtained from Landsat 4-8 mission images (1984–2020). Then we employ a stochastic nonparametric mapping algorithm to extend the discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. Finally, we conduct a rigorous quality assessment on our discharge estimates, involving statistical validation, tests and visual inspection, resulting in the salvation of discharge records for 3377 out of 6015 GRDC stations with an average discharge exceeding 10 m^3/s. The quality of discharge estimates for the rivers with a large or medium mean discharge is quite satisfactory (average KGE value > 0.5) however for river reaches with a low mean discharge the average KGE value drops to 0.33. The RSEG data set regains monitoring capability for 83% of total river discharge measured by GRDC stations, equivalent to 7895 km^3/month, providing valuable insight into Earth’s river systems with comprehensive and up-to-date information.

Abstract

The Congo Basin is of global significance for biodiversity and the water and carbon cycles. However, its freshwater availability and distribution remain relatively unknown. Using satellite data, here we show that currently the Congo Basin's Total Drainable Water Storage lies within a range of 476þinspacekm3 to 502þinspacekm3, unevenly distributed throughout the region, with 63\% being stored in the southernmost sub-basins, Kasaï (220--228þinspacekm3) and Lualaba (109--169þinspacekm3), while the northern sub-basins contribute only 173þinspace±þinspace8þinspacekm3. We further estimate the hydraulic time constant for draining its entire water storage to be 4.3þinspace±þinspace0.1 months, but, regionally, permanent wetlands and large lakes act as resistors resulting in greater time constants of up to 105þinspace±þinspace3 months. Our estimate provides a robust basis to address the challenges of water demand for 120 million inhabitants, a population expected to double in a few decades.

Abstract

The accurate monitoring of the surface water storage as an essential component of the global water cycle requires a realistic representation of river networks and channel characteristics. Since such a representation has not been available for many rivers and is becoming less available even for many gauged rivers, crucial questions about the spatio-temporal dynamics of freshwater in river networks cannot be answered properly. The global coverage and fine temporal resolution of satellite imagery provide the opportunity to obtain time series of surface water extent at the global scale for almost all rivers. However, despite recent advances in satellite imaging sensors, water extraction algorithms, and big data processing capabilities, none of the available global water extent data sets can meet the necessary requirements in terms of accuracy and spatio-temporal resolutions. Due to the inherent complexity of monitoring the river surface extent, efforts have been limited to the development of global river extent data sets with a limited number of temporal layers usually obtained from long-term averaged satellite imagery. In this study, we propose a region-based image restoration algorithm to obtain the river surface extent from a pre-existing global inland water data set by incorporating temporal and spatial constraints between pixel labels. We employ our algorithm on the Monthly Water History maps of the Global Surface Water data set (Pekel et al., 2016). We validate the proposed method on 98 river reaches that their average width ranges from approximately 36m to 3400m with in situ discharge measurements in the Mississippi, Amazon, Niger and Po river basins. The obtained river width time series exhibit a strong monotonic relationship with discharge measurements as the Spearman correlation coefficients are predominantly larger than 0.70 (on average 0.74 for Mississippi, 0.84 for Amazon, 0.86 for Niger and 0.77 for Po rivers). Such a performance confirms that the proposed method can facilitate the acquisition of a global dynamic river extent data set, which plays a key role in better understanding the distribution and availability of freshwater across continents.

Abstract

Abstract The number of active gauges with open-data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques for estimating river discharge from space, developing a rating curve between the ground-based discharge and spaceborne river water level or width is the most straightforward one. However, this does not always lead to successful results, since the river section morphology often cannot simply be modeled by a limited number of parameters. Moreover, such methods do not deliver a proper estimation of the discharge’s uncertainty as a result of the mismodeling and also the coarse assumptions made for the uncertainty of inputs. Here we propose a nonparametric model for estimating river discharge and its uncertainty from spaceborne river width measurements. The model employs a stochastic quantile mapping scheme by, iteratively: 1) generating realizations of river discharge and width time series using Monte Carlo simulation, 2) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, 3) adjusting the measurement uncertainties according to the point cloud scatter. We validate our method over 14 different river reaches along the Niger, Congo, Po rivers and several river reaches in the Mississippi river basin. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodelling. Moreover, the proposed algorithm delivers a meaningful uncertainty for the estimated discharge and allows us to calibrate the error bars of in situ discharge measurements.