Julie E. Kiang

Accelerating advances in continental domain hydrologic modeling

In the past, hydrologic modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity among the catchment, global water security, and land surface modeling communities such that these communities are converging toward continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community toward this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine-independent formats for model replication or reanalysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process-based approach to model parameter estimation, and appropriate parameterizations to represent large-scale fluxes and scaling behavior; (3) maintaining a balance between model complexity and data availability as well as uncertainties; and (4) quantifying and communicating significant advancements toward these modeling goals.

Estimating Discharge Measurement Uncertainty Using the Interpolated Variance Estimator

AbstractMethods for quantifying the uncertainty in discharge measurements typically identify various sources of uncertainty and then estimate the uncertainty from each of these sources by applying the results of empirical or laboratory studies. If actual measurement conditions are not consistent with those encountered in the empirical or laboratory studies, these methods may give poor estimates of discharge uncertainty. This paper presents an alternative method for estimating discharge measurement uncertainty that uses statistical techniques and on-site observations. This interpolated variance estimator (IVE) estimates uncertainty based on the data collected during the streamflow measurement and therefore reflects the conditions encountered at the site. The IVE has the additional advantage of capturing all sources of random uncertainty in the velocity and depth measurements. It can be applied to velocity-area discharge measurements that use a velocity meter to measure point velocities at multiple vertical...

Improving predictions of hydrological low-flow indices in ungaged basins using machine learning

Abstract We compare the ability of eight machine-learning models (elastic net, gradient boosting, kernel-k-nearest neighbors, two variants of support vector machines, M5-cubist, random forest, and a meta-learning ensemble M5-cubist model) and four baseline models (ordinary kriging, a unit area discharge model, and two variants of censored regression) to generate estimates of the annual minimum 7-day mean streamflow with an annual exceedance probability of 90% (7Q10) at 224 unregulated sites in South Carolina, Georgia, and Alabama, USA. The machine-learning models produced substantially lower cross validation errors compared to the baseline models. The meta-learning M5-cubist model had the lowest root-mean-squared-error of 26.72 cubic feet per second. Partial dependence plots show that 7Q10s are likely moderated by late summer and early fall precipitation and the infiltration capacity of basin soils.

A Comparison of Methods for Streamflow Uncertainty Estimation

Streamflow time series are commonly derived from stage-discharge rating curves, but theuncertainty of the rating curve and resulting streamflow series are poorly understood. While differentmethods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding ofhow uncertainty estimates differ between methods due to different assumptions and methodologicalchoices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at threeriver locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a widerange of estimates, particularly for high and low flows. At the simplest site on the Isere River (France), fullwidth 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast,uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of therating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (UnitedKingdom), where the hydraulic control is unstable at low flows. Differences between methods result fromdifferences in the sources of uncertainty considered, differences in the handling of the time-varying nature ofrating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptionswhen extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of anuncertainty method requires a match between user requirements and the assumptions made by theuncertainty method. Given the signi ficant differences in uncertainty estimates between methods, we suggestthat a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.