Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions

Abstract

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.

Does deadwood moisture vary jointly with surface soil water content?

“…Wind direction was monitored using an analog wind vane. Precipitation was measured with a NOAH IV rain gauge that is part of the Hubbard Brook Experimental Forest long‐term monitoring in a clearing 240 m from the log (Green et al., 2018). All data except precipitation were logged with the Arduino‐based logger (Mayfly, Stroud Water Research Center) on a 15‐min basis.…”

A catchment water balance assessment of an abrupt shift in evapotranspiration at the Hubbard Brook Experimental Forest, New Hampshire, USA

“…This increase was substantially less than the observed WBR increase, which averaged 667 mm/year over that time, and therefore cannot explain the difference. In June 2016, the P network was also reduced from 23 to 9 P gauges and the spatial interpolation method was changed from the Thiessen polygon to the inverse distance weighting method (Green et al, 2018;USDA Forest Service, 2020a, 2020b. This transition caused a subtle decrease in P estimates for W6 and no systematic change in P estimates for W3, W7 and W8 (Supplemental results).…”

“…Precipitation and stream discharge have been measured at the HBEF reference catchments considered in the present study starting in the mid‐1950s to late‐1960s (Campbell et al, 2021). Precipitation (P) was estimated at the catchment‐scale by spatial interpolation of measurements from multiple rain gauges (Green et al, 2018; USDA Forest Service, 2020a). Gauge sites were initially instrumented with standard cumulative rain gauges that were shielded (Alter type), located in maintained forest openings and measured weekly (Leonard & Reinhart, 1963).…”

“…Precipitation and stream discharge have been measured at the HBEF reference catchments considered in the present study starting in the mid-1950s to late-1960s . Precipitation (P) was estimated at the catchment-scale by spatial interpolation of measurements from multiple rain gauges (Green et al 2018, USDA Forest Service 2019.…”

Quantifying uncertainty in annual runoff due to missing data

“…This work represents significant progress towards describing the uncertainty associated with infilling stream flow gaps in hydrologic datasets. Our estimates of uncertainty in runoff will contribute to uncertainty in estimates of other variables that rely on discharge, including stream solute loads (Campbell et al, 2016) and evapotranspiration (Green et al, 2018). Quantifying uncertainty provides the basis to prioritize improvements to streamflow monitoring strategies.…”