Development of hydraulic relationships for estimating in-bank river discharge using remotely sensed data

An evaluation of river hydraulic data currently or potentially available from satellite and other remote platforms was completed, and a set of discharge estimation models proposed that can use the remotely sensed information to estimate discharge with reasonable accuracy. Reasonable accuracy is defined as within +/-20% of the observed on average for a large number of estimates. The proposed estimation models are based on the Manning and Chezy flow resistance equations, and utilize combinations of potentially observable variables including water-surface width, maximum-channel (or bankfull) width, mean water depth, mean maximum-channel depth, mean water velocity, and channel slope. Both statistically and rationally derived prediction models are presented, developed and calibrated on a data base of river discharge measurements and a quasi-theoretical data base of synthetic data. It was found that the channel slope can be used in lieu of a measured water surface slope with very little reduction in prediction accuracy when considering many estimates. Notably absent from this list is a resistance variable, which is included in both the Manning and Chezy equations, because this variable cannot be observed or directly measured. One of the key outcomes of the research is that an exponent of 0.33 on the slope explains much of the variability in the resistance variable, and provides better predictive qualities than the traditional value of 0.5. A dimensionally homogeneous form of the Manning equation was developed which derives the slope exponent of 0.33 based on stable-bed grain size considerations. The prediction models were tested on two data sets of remotely sensed hydraulic information that included width, maximum channel width, and channel slope. Predictions were also made from a single radar image that also included remotely sensed surface velocity, demonstrating the potential for greatly improved accuracy with this additional information. Additionally, the prediction models were tested with channel slope information derived from a digital elevation model, and used to define river channel geometry for a continental scale runoff model

Assessing SWOT discharge algorithms performance across a range of river types

Scheduled for launch in 2020, the Surface Water and Ocean Topography (SWOT) satellite mission will measure river height, width, and slope, globally, as well as characterizing storage change in lakes, and ocean surface dynamics. Four discharge algorithms have been formulated to solve the inverse problem of river discharge from SWOT observations. Three of these approaches are based on Manning’s equation, while the fourth utilizes at-many-stations hydraulic geometry relating width and discharge. In all cases, SWOT will provide some but not all of the information required to estimate discharge. The focus of the inverse approaches is estimation of the unknown parameters. The algorithms use a range of a priori information. This paper will generate synthetic measurements of height, width, and slope for a number of rivers, including reaches of the Sacramento, Ohio, Mississippi, Platte, Amazon, Garonne, Po, Severn, St. Lawrence, and Tanana. These rivers have a wide range of flows, geometries, hydraulic regimes, floodplain interactions, and planforms. One-year synthetic datasets will be generated in each case. We will add white noise to the simulated quantities and generate scenarios with different repeat time. The focus will be on retrievability of the hydraulic parameters across a range of space-time sampling, rather than on ability to retrieve under the specific SWOT orbit. We will focus on several specific research questions affecting algorithm performance, including river characteristics, temporal sampling, and algorithm accuracy. The overall goal is to be able to predict which algorithms will work better for different kinds of rivers, and potentially to combine the outputs of the various algorithms to obtain more robust estimates. Preliminary results on the Sacramento River indicate that all algorithms perform well for this single-channel river, with diffusive hydraulics, with relative RMSE values ranging from 9% to 26% for the various algorithms. Preliminary sensitivity tests indicate that Manning-based approaches are more sensitive to slope error on the Sacramento River than on the Garonne River, but more sensitive to width error for the Garonne River. Fully understanding these tradeoffs is critical for reliable deployment of these algorithms for global discharge estimation from SWOT observations

A Hybrid of Optical Remote Sensing and Hydrological Modelling Improves Water Balance Estimation

Declining gauging infrastructure and fractious water politics have decreased available information about river flows globally. Remote sensing and water balance modelling are frequently cited as potential solutions, but these techniques largely rely on these same in-decline gauge data to make accurate discharge estimates. 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. 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. The resulting tuned modelled 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 m3/s (40.8%). By contrast, the original simulation had an order-of-magnitude flow error. This improvement is substantial but not perfect: tuned flows have a one-to two-month wet season lag and a negative baseflow bias. Accounting for this two-month lag yields a hydrograph RMSE of 270 m3/s (25.7%). Thus, our results coupling physical models and remote sensing is a promising first step and proof of concept toward future modelling of ungauged flows, especially as developments in cloud computing for remote sensing make our method easily applicable to any basin. 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

An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results

Using air photos to parameterize landscape predictors of channel wetted width

We investigated which landscape and climate-related data (including information on hydrological source of flow) were statistically significant predictors of channel wetted width (WW) across a sizeable (2200 km2) region of the UK. This was conducted specifically when flow was less than mean daily flow (MDF) and where channels are in a near natural state. Orthorectified air photos at 25 cm spatial resolution were used to measure WW, with the magnitude of the errors in these measurements quantified. We used flow information from local gauging stations to ensure that channels were below MDF for the days on which the air photos were captured. The root mean squared difference between the field and air photo measurements of WW (n = 28 sites) was small (0.14 m) in comparison to median WW (3.07 m). We created points along sections of channels visible in air photos and used a terrain model to create drainage catchments for these points and computed their catchment area (CA). We selected a subset of points (n = 472) and measured their WW from air photos, and computed landscape-related data for each of their catchments (mean slope, mean annual rainfall, land cover type, elevation) and also mean BFIHOST, a quantitative index relating to hydrological source of flow. We used a linear mixed model to predict WW by including the landscape data (including CA 0.5) as fixed effects, plus a spatial covariance function estimated by residual maximum likelihood to determine unbiased estimates of the predictors. There was no evidence for retaining the spatial covariance function. With the exception of land cover, all the predictors were statistically significant and accounted for 76% of the variance of WW. When CA 0.5 alone was used as a predictor it captured 54% of the variance. The vast majority of this difference was due to inclusion of an interaction between CA and hydrological source of flow (BFIHOST). As catchment area increases, those channels with larger mean catchment BFIHOST values (greater proportion of baseflow contribution) have narrower WW in comparison with those having smaller mean BFIHOST for the same CA. Improved predictions of channel WW (based on our findings) could be used in channel restoratio

Improvement in symptoms and pulmonary function of asthmatic patients due to their treatment according to the Global Strategy for Asthma Management (GINA)

<p>Abstract</p> <p>Background</p> <p>Global Initiative Strategy for Asthma Management (GINA) is poorly applied in undeveloped and developing countries. The current study examined the effects of applying GINA guidelines on treatment efficacy in asthmatic patients in Iran.</p> <p>Methods</p> <p>Twenty four asthmatic patients (usual care group) were treated as usual and 26 patients (intervention group) according to the GINA for 2 months. Asthma symptom score, asthma severity, frequency of symptoms/week and wheezing were recorded at the beginning (first visit), one month after treatment (second visit), and at the end of the study (third visit). Pulmonary function tests (PFTs) were performed by spirometry, and the patients' use of asthma drugs and their symptoms were evaluated, at each visit.</p> <p>Results</p> <p>Asthma symptoms, frequency of symptoms/week, chest wheezing, and PFT values were significantly improved in the intervention group at the second and third visits compared to first visit (p < 0.001 for all measures). In addition, exercise induced cough and wheeze were significant improved in the third visit compared to the second visit in this group (p < 0.01 for both measures). In the second and third visits all symptoms were significantly lower, and PFT values higher, in the intervention group compared to the usual care group (p < 0.005 to p < 0.001). In the usual care group, there were only small improvements in some parameters in just the second visit (p < 0.01 for all measures). The use of asthma drugs was unchanged in the usual care group and significantly reduced in the intervention group (p < 0.01) by the end of the study.</p> <p>Conclusion</p> <p>Adoption of GINA guidelines improves asthma symptoms and pulmonary function in asthmatic patients in Iran.</p

Exploring the Factors Controlling the Error Characteristics of the Surface Water and Ocean Topography Mission Discharge Estimates

The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50–100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning-Strickler equation, complementing in situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g., friction coefficient and bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT-like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst-case SWOT discharge accuracy

Engaging the user community for advancing societal applications of the surface water ocean topography mission

Scheduled for launch in 2021, the Surface Water and Ocean Topography (SWOT) mission will be a truly unique mission that will provide high-temporal-frequency maps of surface water extents and elevation variations of global water bodies (lakes/reservoirs, rivers, estuaries, oceans, and sea ice) at higher spatial resolution than is available with current technologies (Biancamaria et al. 2016; Alsdorf et al. 2007). The primary instrument on SWOT is based on a Ka-band radar interferometer (KaRIN), which uses radar interferometery technology. The satellite will fly two radar antennas at either end of a 10-m (33 ft) mast, allowing it to measure the elevation of the surface along a 120-km (75 mi)-wide swath below. The availability of high-frequency and high-resolution maps of elevations and extents for surface water bodies and oceans will present unique opportunities to address numerous societally relevant challenges around the globe (Srinivasan et al. 2015). These opportunities may include such diverse and far-ranging applications as fisheries management, flood inundation mapping/risk mitigation/forecasting, wildlife conservation, global data assimilation for improving forecast of ocean tides and weather, reservoir management, climate change impacts and adaptation, and river discharge estimation, among others