The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

The Surface Water and Ocean Topography (SWOT) satellite mission, expected to launch in 2022, will enable near global river discharge estimation from surface water extents and elevations. However, SWOT’s orbit specifications provide non-uniform space–time sampling. Previous studies have demonstrated that SWOT’s unique spatiotemporal sampling has a minimal impact on derived discharge frequency distributions, baseflow magnitudes, and annual discharge characteristics. In this study, we aim to extend the analysis of SWOT’s added value in the context of hydrologic model calibration. We calibrate a hydrologic model using previously derived synthetic SWOT discharges across 39 gauges in the Ohio River Basin. Three discharge timeseries are used for calibration: daily observations, SWOT temporally sampled, and SWOT temporally sampled including estimated uncertainty. Using 10,000 model iterations to explore predefined parameter ranges, each discharge timeseries results in similar optimal model parameters. We find that the annual mean and peak flow values at each gauge location from the optimal parameter sets derived from each discharge timeseries differ by less than 10% percent on average. Our findings suggest that hydrologic models calibrated using discharges derived from SWOT’s non-uniform space–time sampling are likely to achieve results similar to those based on calibrating with in situ daily observations

Assessing the potential for the Surface Water and Ocean Topography (SWOT) mission for constituent flux estimations

The recently launched Surface Water and Ocean Topography (SWOT) satellite will simultaneously measure river surface water widths, elevations, and slopes. These novel observations combined with assumptions for unobserved bathymetry and roughness enable the derivation of river discharge. Derived discharge data will not be available until the fall of 2023, despite the satellite having completed approximately 6 months of observations for validation and calibration and transitioning into the nominal orbit phase. SWOT has an irregular flyover frequency, ranging from roughly 1 to 10 times per 21 days. Here, we present how best to use SWOT data when it becomes live, including consideration of how best to accommodate or utilize the irregular flyover frequency of SWOT as it intersects with river reaches. We investigate the predicted capabilities of SWOT for several major rivers using synthetic/theoretical SWOT time series data and evaluate how the characteristics of river discharge dynamics and SWOT sampling frequency impact discharge estimates. This analysis indicates the irregular frequency of SWOT best captures the hydrology of larger, more stable, rivers but presents challenges in smaller, flashier rivers, particularly when sampling frequency decreases (i.e., falls to once per 21 days). Further, the use of SWOT discharge for quantifying constituent fluxes is considered. We provide recommendations concerning how to best use SWOT data for applications related to hydrology and biogeochemistry, including how to design studies to accommodate its irregular orbit cycle

Presentation, evaluation and sensitivity of a discharge algorithm for remotely sensed river measurements : Test cases on Sacramento and Garonne Rivers

We present an improvement to a previously presented algorithm that used a Bayesian Markov Chain Monte Carlo method for estimating river discharge from remotely sensed observations of river height, width, and slope. We also present an error budget for discharge calculations from the algorithm. The algorithm may be utilized by the upcoming Surface Water and Ocean Topography (SWOT) mission. We present a detailed evaluation of the method using synthetic SWOT-like observations (i.e., SWOT and AirSWOT, an airborne version of SWOT). The algorithm is evaluated using simulated AirSWOT observations over the Sacramento and Garonne Rivers that have differing hydraulic characteristics. The algorithm is also explored using SWOT observations over the Sacramento River. SWOT and AirSWOT height, width, and slope observations are simulated by corrupting the ‘‘true’’ hydraulic modeling results with instrument error. Algorithm discharge root mean square error (RMSE) was 9% for the Sacramento River and 15% for the Garonne River for the AirSWOT case using expected observation error. The discharge uncertainty calculated from Manning’s equation was 16.2% and 17.1%, respectively. For the SWOT scenario, the RMSE and uncertainty of the discharge estimate for the Sacramento River were 15% and 16.2%, respectively. A method based on the Kalman filter to correct errors of discharge estimates was shown to improve algorithm performance. From the error budget, the primary source of uncertainty was the a priori uncertainty of bathymetry and roughness parameters. Sensitivity to measurement errors was found to be a function of river characteristics. For example, Steeper Garonne River is less sensitive to slope errors than the flatter Sacramento River

Recursive classification of satellite imaging time-series: An application to water mapping, land cover classification and deforestation detection

A wide variety of applications of fundamental importance for security, environmental protection and urban development need access to accurate land cover monitoring and water mapping, for which the analysis of optical remote sensing imagery is key. Classification of time-series images, particularly with recursive methods, is of increasing interest in the current literature. Nevertheless, existing recursive approaches typically require large amounts of training data. This paper introduces a recursive classification framework that improves the decision-making process in multitemporal and multispectral land cover classification algorithms while requiring low computational cost and minimal supervision. The proposed approach allows the conversion of an instantaneous classifier into a recursive Bayesian classifier by using a probabilistic framework that is robust to non-informative image variations. Three experiments are conducted using Sentinel-2 data. The first one consists in the water mapping of an embankment dam in California (United States), the second one is a land cover classification experiment of the Charles river area in Boston (United States) and the last experiment addresses deforestation detection in the Amazon rainforest (Brazil). A classifier based on the Gaussian mixture model (GMM), a logistic regression (LR) classifier, and a spectral index classifier (SIC) are compared to their recursive counterparts. SICs are introduced to convert the NDWI, MNDWI and NDVI spectral indices into predictive probabilities. Two state-of-the-art deep learning-based models are also used as a benchmark for the water mapping experiment. Results show that the proposed method significantly increases the robustness of existing instantaneous classifiers in multitemporal settings. Our method also improves the performance of deep learning-based classifiers without the need for additional training data.Comment: Without supplementary results: 30 pages, 11 figures. With supplementary results: 40 pages, 21 figure

Characterization of Terrestrial Water Dynamics in the Congo Basin Using GRACE and Satellite Radar Altimetry

The Congo Basin is the world's third largest in size (approx.3.7 million sq km), and second only to the Amazon River in discharge (approx.40,200 cu m/s annual average). However, the hydrological dynamics of seasonally flooded wetlands and floodplains remains poorly quantified. Here, we separate the Congo wetland into four 3deg 3deg regions, and use remote sensing measurements (i.e., GRACE, satellite radar altimeter, GPCP, JERS-1, SRTM, and MODIS) to estimate the amounts of water filling and draining from the Congo wetland, and to determine the source of the water. We find that the amount of water annually filling and draining the Congo wetlands is 111 cu km, which is about one-third the size of the water volumes found on the mainstem Amazon floodplain. Based on amplitude comparisons among the water volume changes and timing comparisons among their fluxes, we conclude that the local upland runoff is the main source of the Congo wetland water, not the fluvial process of river-floodplain water exchange as in the Amazon. Our hydraulic analysis using altimeter measurements also supports our conclusion by demonstrating that water surface elevations in the wetlands are consistently higher than the adjacent river water levels. Our research highlights differences in the hydrology and hydrodynamics between the Congo wetland and the mainstem Amazon floodplain

What do experienced water managers think of water resources of our nation and its management infrastructure?

This article represents the second report by an ASCE Task Committee Infrastructure Impacts of Landscape-driven Weather Change under the ASCE Watershed Management Technical Committee and the ASCE Hydroclimate Technical Committee. Herein, the \u27infrastructure impacts are referred to as infrastructure-sensitive changes in weather and climate patterns (extremes and non-extremes) that are modulated, among other factors, by changes in landscape, land use and land cover change. In this first report, the article argued for explicitly considering the well-established feedbacks triggered by infrastructure systems to the land-atmosphere system via landscape change. In this report by the ASCE Task Committee (TC), we present the results of this ASCE TC\u27s survey of a cross section of experienced water managers using a set of carefully crafted questions. These questions covered water resources management, infrastructure resiliency and recommendations for inclusion in education and curriculum. We describe here the specifics of the survey and the results obtained in the form of statistical averages on the \u27perception\u27 of these managers. Finally, we discuss what these \u27perception\u27 averages may indicate to the ASCE TC and community as a whole for stewardship of the civil engineering profession. The survey and the responses gathered are not exhaustive nor do they represent the ASCE-endorsed viewpoint. However, the survey provides a critical first step to developing the framework of a research and education plan for ASCE. Given the Water Resources Reform and Development Act passed in 2014, we must now take into account the perceived concerns of the water management community. © 2015 Hossain et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Effect of historical changes in land use and climate on the water budget of an urbanizing watershed

Author Posting. © American Geophysical Union, 2006. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Water Resources Research 42 (2006): W03426, doi:10.1029/2005WR004131.We assessed the effects of historical (1931-1998) changes in both land-use and climate on the water budget of a rapidly urbanizing watershed, Ipswich River basin (IRB), in northeastern Massachusetts. Water diversions and extremely low flow during summer are major issues in the IRB. Our study centers on a detailed analysis of diversions and a combined empirical/modeling treatment of evapotranspiration (ET) response to changes in climate and land-use. A detailed accounting of diversions showed that net diversions increased due to increases in water withdrawals (primarily ground water pumping) and export of sewage. Net diversions constitute a major component of runoff (20% of streamflow). Using a combination of empirical analysis and physically based modeling we related an increase in precipitation (2.7 mm/yr) and changes in other climate variables to an increase in ET (1.7 mm/yr). Simulations with a physically based water-balance model showed that the increase in ET could be attributed entirely to a change in climate, while the effect of land-use change was negligible. The land-use change effect was different from ET and runoff trends commonly associated with urbanization. We generalized these and other findings to predict future streamflow using climate change scenarios. Our study could serve as a framework for studying suburban watersheds, being the first study of a suburban watershed that addresses long-term effects of changes in both land-use and climate, and accounts for diversions and other unique aspects of suburban hydrology.This research was partially supported by NSF grants (DEB-9726862, OCE-9726921 and OCE-0423565)