Using Environmental Tracers to Reduce Uncertainty in Natural Flood Management Modeling

TY - JOUR

T1 - Using Environmental Tracers to Reduce Uncertainty in Natural Flood Management Modeling

AU - Collins, Sarah L.

AU - Peskett, Leo

AU - Black, Andrew R.

AU - Jackson, Christopher R.

AU - Young, Andy

AU - MacDonald, Alan M.

N1 - Publisher Copyright: © 2026. The Author(s).

PY - 2026/5

Y1 - 2026/5

N2 - Natural flood management (NFM) is a nature-based solution that has grown in importance within flood risk policy and management over the last two decades. There is limited evidence on nature-based solutions' effectiveness, and no accepted best practice on forecasting their performance. To explore NFM effectiveness, we built a hydrological model of a catchment in the UK uplands typical of areas targeted globally for NFM interventions. The model was calibrated on streamflow and groundwater contribution to streamflow, estimated from alkalinity data (ANC). We demonstrated this simple tracer can be a useful tool in model calibration, highlighting significant differences in performance between model runs that were hidden when analysing streamflow alone. In particular the use of the tracer helped identify models that better represented partitioning of flow between surface and sub-surface. The tracer reduced predictive uncertainty in peak flows when applied to a woodland planting scenario by upto 39% and showed even greater potential for reducing uncertainty (∼50%) at low flows (below Q60). Further, by exploring three common representations of woodland we showed that the dominant remaining source of uncertainty (>50%) within the scenario modeling was the choice of how to represent the woodland planting on model parameters. This work underlines the value of using additional calibration data sets to improve process representation and prediction; the importance of long-term monitoring for improving the evidence for NFM effectiveness; and the need to further develop the representation of woodland planting in catchment models to improve forecasts of their impact on flow.

AB - Natural flood management (NFM) is a nature-based solution that has grown in importance within flood risk policy and management over the last two decades. There is limited evidence on nature-based solutions' effectiveness, and no accepted best practice on forecasting their performance. To explore NFM effectiveness, we built a hydrological model of a catchment in the UK uplands typical of areas targeted globally for NFM interventions. The model was calibrated on streamflow and groundwater contribution to streamflow, estimated from alkalinity data (ANC). We demonstrated this simple tracer can be a useful tool in model calibration, highlighting significant differences in performance between model runs that were hidden when analysing streamflow alone. In particular the use of the tracer helped identify models that better represented partitioning of flow between surface and sub-surface. The tracer reduced predictive uncertainty in peak flows when applied to a woodland planting scenario by upto 39% and showed even greater potential for reducing uncertainty (∼50%) at low flows (below Q60). Further, by exploring three common representations of woodland we showed that the dominant remaining source of uncertainty (>50%) within the scenario modeling was the choice of how to represent the woodland planting on model parameters. This work underlines the value of using additional calibration data sets to improve process representation and prediction; the importance of long-term monitoring for improving the evidence for NFM effectiveness; and the need to further develop the representation of woodland planting in catchment models to improve forecasts of their impact on flow.

KW - environmental tracers

KW - flooding

KW - nature-based solutions

UR - https://www.scopus.com/pages/publications/105037582692

U2 - 10.1029/2025WR041145

DO - 10.1029/2025WR041145

M3 - Article

AN - SCOPUS:105037582692

SN - 0043-1397

VL - 62

JO - Water Resources Research

JF - Water Resources Research

IS - 5

M1 - e2025WR041145

ER -