Rapid surface-water volume estimations in beaver ponds

Abstract

Abstract. Beaver ponds are surface-water features that are transient through space and time. Such qualities complicate the inclusion of beaver ponds in local and regional water balances, and in hydrological models, as reliable estimates of surface-water storage are difficult to acquire without time-and labour-intensive topographic surveys. A simpler approach to overcome this challenge is needed, given the abundance of the beaver ponds in North America, Eurasia, and southern South America. We investigated whether simple morphometric characteristics derived from readily available aerial imagery or quickly measured field attributes of beaver ponds can be used to approximate surface-water storage among the range of environmental settings in which beaver ponds are found. Studied were a total of 40 beaver ponds from four different sites in North and South America. The simplified volume-area-depth (V-A-h) approach, originally developed for prairie potholes, was tested. With only two measurements of pond depth and corresponding surface area, this method estimated surface-water storage in beaver ponds within 5 % on average. Beaver pond morphometry was characterized by a median basin coefficient of 0.91, and dam length and pond surface area were strongly correlated with beaver pond storage capacity, regardless of geographic setting. These attributes provide a means for coarsely estimating surface-water storage capacity in beaver ponds. Overall, this research demonstrates that reliable estimates of surfacewater storage in beaver ponds only requires simple measurements derived from aerial imagery and/or brief visits to the field. Future research efforts should be directed at incorporating these simple methods into both broader beaver-related tools and catchment-scale hydrological models.

Outsized effect of predation: Wolves alter wetland creation and recolonization by killing ecosystem engineers

“…In total, we estimate that wolves altered the establishment of ~88 ponds per year (95 and 99% nonparametric bootstrap CI = 36 to 162 ponds and 24 to 194 ponds, respectively) and the storage of ~194,000 m 3 of water per year [95% CI = 79,100 to 355,900 m 3 ; this assumes 2197 m 3 of water stored per pond (44)]. Notably, CIs generated via the nonparametric and parametric bootstrap approaches were nearly identical.…”

“…We estimated the volume of surface water wolves displaced by preventing beaver pond creation and recolonization by multiplying the number of ponds wolves altered by the average volume of water stored in beaver ponds in the GVE [2197 m 3 per pond; (44)]. We used this singular estimate of surface water storage from Karran et al (44)-who only measured a small sample of ponds-to coarsely estimate the overall magnitude of the effect wolves have on water storage; a more intensive study would be needed to provide a more precise estimate of the surface water volume wolves displace each year by killing dispersing beavers.…”

Addressing the Water–Energy Nexus by Coupling the Hydrological Model with a New Energy LISENGY Model: A Case Study in the Iberian Peninsula

“…The average values of parameter b are derived from [25] for each reservoir category. Reservoir morphometry can be assessed by computing many different variables [23]. Among them, in our study, we used the bathymetric capacity Bwc and the morphometry coefficient p.…”

“…As the storage of the reservoir is often highly correlated with surface area [17][18][19] or depth [20][21][22], empirical volume-area-depth relationships can be estimated by using remotely sensed maximum water areas and reservoir characteristics. These relationships are responsible for flood routing, hydropower generation and reservoir classification [23]. The more complex the morphometry of a reservoir is, the more difficult it is to establish such a relationship [24].…”

Beaver‐mediated water table dynamics in a Rocky Mountain fen

“…Studies in mineral soil environments have shown that, above the ground surface, beaver dams increase surface water area and storage (Hood & Bayley, 2008;Karran, Westbrook, Wheaton, Johnston, & Bedard-Haughn, 2017), thereby enhancing groundwater-surface water interactions (Majerova, Neilson, Schmadel, Wheaton, & Snow, 2015;Puttock, Graham, Cunliffe, Elliott, & Brazier, 2017). This can induce changes below the surface by raising water tables such that groundwater flowpaths readjust and more recharge to underlying aquifers is possible (Lowry, 1993;Westbrook, Cooper, & Baker, 2006).…”