Using Geomatic Techniques to Estimate Volume-Area Relationships of Watering Ponds

Watering ponds represent an important part of the hydrological resources in some water-limited environments. Knowledge about their storage capacity and geometrical characteristics is crucial for a better understanding and management of water resources in the context of climate change. In this study, the suitability of different geomatic approaches to model watering pond geometry and estimate pond-specific and generalized volume-area-height (V-A-h) relationships was tested. Terrestrial structure-from-motion and multi-view-stereo photogrammetry (SfM-MVS), terrestrial laser scanner (TLS), laser-imaging detection and ranging (LIDAR), and aerial SfM-MVS were tested for the emerged terrain, while the global navigation satellite system (GNSS) was used to survey the submerged terrain and to test the resulting digital elevation models (DEMs). The combined use of terrestrial SfM-MVS and GNSS produced accurate DEMs of the ponds that resulted in an average error of 1.19% in the maximum volume estimation, comparable to that obtained by the TLS+GNSS approach (3.27%). From these DEMs, power and quadratic functions were used to express pond-specific and generalized V-A-h relationships and checked for accuracy. The results revealed that quadratic functions fit the data particularly well (R-2 >= 0.995 and NRMSE < 2.25%) and can therefore be reliably used as simple geometric models of watering ponds in hydrological simulation studies. Finally, a generalized V-A power relationship was obtained. This relationship may be a valuable tool to estimate the storage capacity of other watering ponds in comparable areas in a context of data scarcity.

Automated summary

The study tested different geomatic approaches to model watering pond geometry and estimate volume-area-height relationships, with a focus on accuracy and reliability for hydrological simulation studies. The results showed that quadratic functions fit the data particularly well, providing a valuable tool for estimating storage capacity in areas with limited data.