Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.
- Award ID(s):
- 1652293
- NSF-PAR ID:
- 10183699
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Earth System Science Data
- Volume:
- 11
- Issue:
- 4
- ISSN:
- 1866-3516
- Page Range / eLocation ID:
- 1567 to 1581
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract Channel bifurcations control the distribution of water and sediment in deltas, and the routing of these materials facilitates land building in coastal regions. Yet few practical methods exist to provide accurate predictions of flow partitioning at multiple bifurcations within a distributary channel network. Herein, multiple nodal relations that predict flow partitioning at individual bifurcations, utilizing various hydraulic and channel planform parameters, are tested against field data collected from the Selenga River delta, Russia. The data set includes 2.5 months of time‐continuous, synoptic measurements of water and sediment discharge partitioning covering a flood hydrograph. Results show that width, sinuosity, and bifurcation angle are the best remotely sensed, while cross‐sectional area and flow depth are the best field measured nodal relation variables to predict flow partitioning. These nodal relations are incorporated into a graph model, thus developing a generalized framework that predicts partitioning of water discharge and total, suspended, and bedload sediment discharge in deltas. Results from the model tested well against field data produced for the Wax Lake, Selenga, and Lena River deltas. When solely using remotely sensed variables, the generalized framework is especially suitable for modeling applications in large‐scale delta systems, where data and field accessibility are limited.
-
We develop a set of highly efficient and effective computational algorithms and simulation tools for fluid simulations on a network. The mathematical models are a set of hyperbolic conservation laws on edges of a network, as well as coupling conditions on junctions of a network. For example, the shallow water system, together with flux balance and continuity conditions at river intersections, model water flows on a river network. The computationally accurate and robust discontinuous Galerkin methods, coupled with explicit strong stability preserving Runge-Kutta methods, are implemented for simulations on network edges. Meanwhile, linear and nonlinear scalable Riemann solvers are being developed and implemented at network vertices. These network simulations result in tools that are added to the existing PETSc and DMNetwork software libraries for the scientific community in general. Simulation results of a shallow water system on a Mississippi river network with over one billion network variables are performed on an extreme-scale computer using up to 8,192 processor with an optimal parallel efficiency. Further potential applications include traffic flow simulations on a highway network and blood flow simulations on a arterial network, among many others.more » « less
-
more » « less
Abstract This study reports on a blending approach using snowpack measurements from a wireless‐sensor network, gauge precipitation, and atmospheric‐moisture data to estimate mountain precipitation amount and phase. We applied the approach in California's American River basin, using dense measurements from a network consisting of over 130 sensor nodes distributed across the upper, more snow‐dominated part of the basin (≥1,500 m elevation). Analysis of 60 precipitation events in water years 2014–2017 showed that the approach provides estimates of precipitation and orographic enhancement that reduce uncertainty from apparent snow undercatch by limited gauges. This approach also infers total precipitation based on snow measurements during rain‐on‐snow events. The sensor network and blending approach yielded median upper‐basin orographic precipitation gradients (OPGs) of 0.57 km−1, smaller than the also‐positive lower‐basin (<1,500 m) medians OPGs from precipitation gauges and a gauge‐based gridded data set of 1.23 and 1.00 km−1, respectively. However, during 73% of the events, both gauges and the gridded product showed negative OPGs in the upper basin, inconsistent with typically positive values from the distributed sensor network. Upper‐basin OPGs from gauges and the gridded product were more negative (
p ‐values < 0.03) during heavy events related to atmospheric rivers and Sierra barrier jets than during milder events, revealing the challenges for gauges to reliably measure precipitation from large moisture transport by strong winds. In snow‐dominated headwater areas, precipitation from the blending approach is recommended as being more accurate for decision support, providing critical rain‐versus‐snow amounts and complementing precipitation‐gauge data. -
more » « less
Abstract Groundwater is the primary source of water in the Bengal Delta but contamination threatens this vital resource. In deltaic environments, heterogeneous sedimentary architecture controls groundwater flow; therefore, characterizing subsurface structure is a critical step in predicting groundwater contamination. Here, we show that surface information can improve the characterization of the nature and geometry of subsurface features, thus improving the predictions of groundwater flow. We selected three locations in the Bengal Delta with distinct surface river network characteristics—the lower delta with straighter tidal channels, the mid‐delta with meandering and braided channels, and the inactive delta with transitional sinuous channels. We used surface information, including channel widths, depths, and sinuosity, to create models of the subsurface with object‐based geostatistical simulations. We collected an extensive set of lithologic data and filled in gaps with newly drilled boreholes. Our results show that densely distributed lithologic data from active lower and mid‐delta are consistent with the object‐based models generated from surface information. In the inactive delta, metrics from object‐based models derived from surface geometries are not consistent with subsurface data. We further simulated groundwater flow and solute transport through the object‐based models and compared these with simulated flow through lithologic models based only on variograms. Substantial differences in flow and transport through the different geologic models show that geometric structure derived from surface information strongly influences groundwater flow and solute transport. Land surface features in active deltas are therefore a valuable source of information for improving the evaluation of groundwater vulnerability to contamination.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/essd-11-1567-2019
