Omid Elmi

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.