The GRACE satellites have proved hugely effective at tracking water storage change at large scales where changes in water storage like groundwater movement manifest in earth's gravity field (e.g., Lee et al., 2011 Syed et al., 2009), but GRACE cannot provide river discharge at the small watershed scale. Scientists have recently turned to satellites to search for this needed flow data. This map also highlights issues of data latency, as very few gauges are provided to the GRDC in near real time. Others, like those in Russia and in the Nile, have only a limited record. The gauges displayed here have varying degrees of temporal coverage: some (like those provided by the USGS) have decades of history that continue to the present. The map shows every gauge in the GRDC database as of December 2016, made by querying their gauge catalog. This map illustrates the rivers with at least some historical data of the kind needed to implement the methods here. This loss of data means that the crucially needed future assessments of water resources that result from well-calibrated and validated models used in conjunction with future climate scenarios suffer from greater uncertainty.
We often have historical records from a less-politically sensitive and better-funded era, and yet many of these records are more than a decade old and reflect watersheds that are changed from those we know today. In addition, calibrated models provide detailed flow information in spatially distributed reaches, essentially extending point gauge measurements in space.
OSWAN FAST FORWARD DRIVER
This decline of gauge information has broader impacts beyond the loss of publically available measurements of flow: gauges are also a prime driver of hydrologic modeling, where they are used as calibration and tuning data to assure that models represent particular watersheds as accurately as possible. These river gauges have been a mainstay of providing flow information to the global community, but are in sharp decline due to expense of maintenance (Hannah et al., 2011 Vörösmarty et al., 2010) or are withheld from the public knowledge for political reasons (Gleason & Hamdan, 2015) (Figure 1). The prime reason for this difficulty is a decline in monitoring infrastructure (i.e., gauging stations). Approximately two fifths of total global rainfall eventually winds its way through rivers before reaching the sea (Oki & Kanae, 2006 Vörösmarty et al., 2010), yet despite this vast importance we have a surprisingly poor grasp on how much water is flowing through our rivers globally. Rivers in particular occupy pride of place in providing freshwater resources (despite almost complete dependence on groundwater in certain areas), and humans have settled in river valleys since the dawn of civilization in the Fertile Crescent.
Key Pointsįreshwater is arguably the most important resource required by civilizations, and it drives industry, agriculture, and ecosystem function the world over. Finally, we purposefully do not offer prescriptive solutions for Nile management, and rather hope that the methods demonstrated herein can prove useful to river stakeholders in managing their own water. Thus, our results coupling physical models and remote sensing is a promising first step and proof of concept toward future modeling of ungauged flows, especially as developments in cloud computing for remote sensing make our method easily applicable to any basin. Accounting for this 2 month lag yields a hydrograph RMSE of 270 m 3/s (25.7%). This improvement is substantial but not perfect: tuned flows have a 1–2 month wet season lag and a negative base flow bias. By contrast, the original simulation had an order-of-magnitude flow error. The resulting tuned modeled hydrograph shows a large improvement in flow magnitude: validation of the tuned monthly hydrograph against a historical gauge (1978–1984) yields an RMSE of 439 m 3/s (40.8%). Specifically, we first estimate initial discharges from 87 Landsat images and AMHG (1984–2015), and then use these flow estimates to tune the model, all without using gauge data. A different approach is therefore needed, and we here combine remotely sensed discharge estimates made via at-many-stations hydraulic geometry (AMHG) and the PCR-GLOBWB hydrological model to estimate discharge over the Lower Nile. Remote sensing and water balance modeling are frequently cited as potential solutions, but these techniques largely rely on these same in-decline gauge data to make accurate discharge estimates.
Declining gauging infrastructure and fractious water politics have decreased available information about river flows globally.
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