Signatures of Hydrologic Function Across the Critical Zone Observatory Network

Abstract

Despite a multitude of small catchment studies, we lack a deep understanding of how variations in critical zone architecture lead to variations in hydrologic states and fluxes. This study characterizes hydrologic dynamics of 15 catchments of the U.S. Critical Zone Observatory (CZO) network where we hypothesized that our understanding of subsurface structure would illuminate patterns of hydrologic partitioning. The CZOs collect data sets that characterize the physical, chemical, and biological architecture of the subsurface, while also monitoring hydrologic fluxes such as streamflow, precipitation, and evapotranspiration. For the first time, we collate time series of hydrologic variables across the CZO network and begin the process of examining hydrologic signatures across sites. We find that catchments with low baseflow indices and high runoff sensitivity to storage receive most of their precipitation as rain and contain clay‐rich regolith profiles, prominent argillic horizons, and/or anthropogenic modifications. In contrast, sites with high baseflow indices and low runoff sensitivity to storage receive the majority of precipitation as snow and have more permeable regolith profiles. The seasonal variability of water balance components is a key control on the dynamic range of hydraulically connected water in the critical zone. These findings lead us to posit that water balance partitioning and streamflow hydraulics are linked through the coevolution of critical zone architecture but that much work remains to parse these controls out quantitatively.

Extreme rainstorms drive exceptional organic carbon export from forested humid-tropical rivers in Puerto Rico

“…1 ), and are both small catchments (3.26 km 2 and 17.8 km 2 , respectively) with granodioritic (Icacos) and volcaniclastic (Mameyes) bedrocks that do not contain substantial petrogenic organic carbon, reduced sulfur, or carbonates 9 , 15 . These catchments have very high rainfall (average of 5000 and 3720 mm yr −1 in the Icacos and Mameyes, respectively) 16 , 17 and runoff (3906 and 2770 mm yr −1 , respectively) (Table 1 ). This study includes >4000 river samples collected over 25 years, distributed across four orders of magnitude of SS and POC concentrations and three orders of magnitude of runoff rates (Fig.…”

“…The mean-maximum-annual temperature was 26.0 °C and the mean-minimum-annual temperature was 20.8 °C at the El Verde Field station located in El Yunque Forest 51 . Average annual rainfall (from 1991 to 2015) was 5000 mm yr −1 in Icacos and 3720 mm yr −1 in Mameyes 16 , 17 (Table 1 and See Supplementary Data 2 ). The two catchments are described in detail in Murphy and Stallard 25 .…”

Table_2.DOCX

“…Instead of evapoconcentration or dilution, our findings point to deeper groundwater sources driving increased concentrations as the streams dried. Our findings are consistent work by Wlostowski et al (2020), whose findings describe loamy and sand-rich soils and relatively shallow bedrock in unburned JD resulting in more baseflow-dominated streamflow with less subsurface water storage compared to CZOs with clay-rich soils. Distinct from other baseflow dominated streams, groundwater contributed to low flow in our streams, not high flow as observed in the Jemez River Basin Critical Zone observatory in New Mexico (McIntosh et al, 2017).…”

Climate-Catchment-Soil Control on Hydrological Droughts in Peninsular India

“…Previous studies 15,16,58,59 conducted on catchment-scale droughts provide important yet incomplete insights into the role of potential drivers in hydrological drought propagation. Based on an earlier study 60 that establishes structural control on catchment sensitivity, our approach further expanded on geomorphological features by exploring additional covariates, a range of terrain, and soil characteristics influencing various drought characteristics, which have not been investigated so far -neither in observational assessments nor in land surface model-based simulation 10,61 . The sources of uncertainty in the analyses stem from the quality of available records.…”