Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70014835

Quantifying peak discharges for historical floods

J.L. Cook
View original source ↗Export metadata ↓

Abstract

It is usually advantageous to use information regarding historical floods, if available, to define the flood-frequency relation for a stream. Peak stages can sometimes be determined for outstanding floods that occurred many years ago before systematic gaging of streams began. In the United States, this information is usually not available for more than 100-200 years, but in countries with long cultural histories, such as China, historical flood data are available at some sites as far back as 2,000 years or more. It is important in flood studies to be able to assign a maximum discharge rate and an associated error range to the historical flood. This paper describes the significant characteristics and uncertainties of four commonly used methods for estimating the peak discharge of a flood. These methods are: (1) rating curve (stage-discharge relation) extension; (2) slope conveyance; (3) slope area; and (4) step backwater. Logarithmic extensions of rating curves are based on theoretical plotting techniques that results in straight line extensions provided that channel shape and roughness do not change significantly. The slope-conveyance and slope-area methods are based on the Manning equation, which requires specific data on channel size, shape and roughness, as well as the water-surface slope for one or more cross-sections in a relatively straight reach of channel. The slope-conveyance method is used primarily for shaping and extending rating curves, whereas the slope-area method is used for specific floods. The step-backwater method, also based on the Manning equation, requires more cross-section data than the slope-area ethod, but has a water-surface profile convergence characteristic that negates the need for known or estimated water-surface slope. Uncertainties in calculating peak discharge for historical floods may be quite large. Various investigations have shown that errors in calculating peak discharges by the slope-area method under ideal conditions for recent floods (i.e., when flood elevations, slope and channel characteristics are reasonably certain), may be on the order of 10-25%. Under less than ideal conditions, where streams are hydraulically steep and rough, errors may be much larger. The additional uncertainties for historical floods created by the passage of time may result in even larger errors of peak discharge.

Explore related subjects

Journal of HydrologyArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J.L. Cook. 1987. Quantifying peak discharges for historical floods. https://doi.org/10.1016/0022-1694(87)90141-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Inverse modeling for post-wildfire hydrologic process understanding over snow-dominated watersheds

Wildfire-induced changes to vegetation and soil properties can alter snowpack dynamics and hydrologic processes in ways that complicate predictions of post-wildfire water availability. To better understand these effects, we coupled the Parameter ESTimation in C++ (PEST++) software with the Weather Research & Forecasting Hydrologic model (WRF-Hydro) across three burned, snow-dominated watersheds in the western United States. We incorporated three burn-severity-based land cover categories into WRF-Hydro and used an iterative Ensemble Smoother (iES) within PEST++ to estimate model parameters controlling snow accumulation and melt, infiltration, and streamflow generation. This framework supported three complementary objectives: (1) assessing parameter sensitivities under pre- and post-wildfire conditions, (2) comparing pre- and post-wildfire parameter ensembles to identify wildfire-driven shifts in hydrologic processes, and (3) using counterfactual simulations to isolate the hydrologic response to wildfire from interannual climate variability. Our findings indicate that model parameter sensitivity varied by domain and shifted following wildfire. Maximum snow albedo and snow albedo decay were most influential in the higher-elevation East Troublesome domain, while the rain-snow partitioning temperature threshold dominated in the lower-elevation Chippy Creek and Las Conchas domains. Following wildfire, streamflow sensitivity shifted away from snowpack parameters toward soil hydraulic parameters, consistent with wildfire-driven reductions in evapotranspiration and increases in soil moisture. Incorporating wildfire effects led to complex changes in simulated snowpack accumulation and streamflow timing, generally leading to earlier snowmelt freshet initiation and cessation and reduced late season flows. Simulations were more sensitive to interannual climate variations than wildfire land cover changes. These results highlight both the potential and challenges of ensemble-based inverse modeling for understanding post-wildfire hydrologic change in snow-dominated watersheds.

Colorado, Montana, New Mexico↗
USGS2026

Reservoir releases and land cover interact to drive event-scale nitrate export in a large agricultural basin

Understanding the drivers of nitrate export in rivers is critical for developing effective nutrient management strategies. However, few studies have explored event-scale drivers of export in large river basins with human modifications like reservoirs. Here, we analyzed nitrate concentration-discharge (C-Q) relationships from 215 events at the outlet of the Kansas River Basin, USA (155,690 km 2 ) from 2014 to 2022 to (1) characterize event-scale nitrate export behaviors in a large agricultural basin, and (2) determine how different event characteristics are linked to these behaviors. We found that C-Q behaviors varied greatly, with 60% of events producing nitrate enrichment (n = 130) and 40% of events producing nitrate dilution (n = 85). These behaviors were correlated with complex spatial interlinkages between climate and land cover: across most of the basin, nitrate enrichment was correlated with drier antecedent conditions, but in wetter areas with higher proportions of urban and forested land cover, enrichment was more strongly correlated with precipitation magnitude/intensity. This difference in hydroclimatic controls on nitrate export might be related to differential distributions of nitrate sources within these land covers. Upstream of major reservoirs, however, neither variable was strongly correlated with C-Q behavior, suggesting that nitrate attenuation within reservoirs decouples event-scale concentration signals in upstream waters from those downstream. Reservoir outflows had variable impacts on C-Q behavior, reflecting reservoir-specific variations in nitrate attenuation efficiency. Together, these results identify specific complex interactions between hydroclimate, land cover, reservoir positioning, and individual reservoir properties that control event-scale nitrate export from large basins.

Colorado, Kansas, Nebraska↗
USGS2026

Deep learning error post-processing improves stochastic watershed modeling

Hydrologic extremes, including floods and droughts, pose substantial societal risks that are expected to intensify with climate change. Deterministic watershed models (DWMs) remain a mainstay for modeling these extremes, but lack explicit representation of uncertainty, limiting their utility for risk-informed planning. Stochastic watershed models (SWMs) address this limitation by generating ensembles of streamflow via models of observed DWM residuals. However, most SWMs struggle with the complex dependence between DWM residuals and the underlying hydrologic state, which can complicate stochastic simulations under nonstationary climates. Deep learning (DL) models, whether used as standalone models or post-processors for process-based DWMs, offer a pathway to address this challenge by reducing conditional dependence. In this study, we evaluate SWMs applied to seven models: three process-based models (PRMS, Hymod, and HBV), their hybrid process-DL counterparts, and a pure DL DWM, focusing on daily simulations and extremes under both historical conditions and synthetic climate change scenarios. Results for a case study watershed in Massachusetts show that SWMs applied to hybrid or pure DL DWMs consistently outperform those applied to process-based DWMs. However, an SWM applied to the pure DL model exhibits weaknesses at low flows for this study basin, underscoring the value of hybrid approaches. Extending the analysis across 73 additional basins demonstrates that these improvements are robust and generalizable statewide. This work highlights the potential of a DL-enhanced stochastic watershed modeling framework to advance hydrologic risk prediction under changing climate conditions, offering a scalable methodology for integrating uncertainty into watershed modeling for long-term planning.

Journal of Hydrology↗