The Keeling plot approach, a general method to identify the isotopic composition of source atmospheric CO₂ and water vapor (i.e., evapotranspiration), has been widely used in terrestrial ecosystems. The isotopic composition of ambient water vapor (δ_a), an important source of atmospheric water vapor, is not able to be estimated to date using the Keeling plot approach. Here we proposed two new methods to estimate δ_a using the Keeling plots: one using an intersection point method and another relying on the intermediate value theorem. As the actual δ_a value was difficult to measure directly, we used two indirect approaches to validate our new methods. First, we performed external vapor tracking using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to facilitate explaining the variations of δ_a. The trajectory vapor origin results were consistent with the expectations of the δ_a values estimated by these two methods. Second, regression analysis was used to evaluate the relationship between δ_a values estimated from these two independent methods, and they are in strong agreement. This study provides an analytical framework to estimate δ_a using existing facilities and provides important insights into the traditional Keeling plot approach by showing (a) a possibility to calculate the proportion of evapotranspiration fluxes to total atmospheric vapor using the same instrumental setup for the traditional Keeling plot investigations and (b) perspectives on the estimation of isotope composition of ambient CO₂ (δ_a¹³C).

Intermittent river‐groundwater connectivity can be a dynamic, transient process, but quantifying transition from disconnection to connection has focused mainly on steady‐state behavior. To quantitatively evaluate the transient connection behavior, HYDRUS‐2D was used to simulate the water flow beneath the lower Rio Grande riverbed in southern New Mexico during the beginning of dam water release. The river wave initially arrived in the channel center and later spread across the entire channel. The infiltration was monitored by shallow buried water content and pressure sensors. Transient and steady‐state simulations were used to quantify the relationship between hydraulic head difference and the infiltration rate during transition to connection. Transient and steady state disconnection‐connection transitions were both impacted by heterogeneity when evaluated with infiltration versus head difference. During surface water arrival, steady‐state infiltration behavior only increased nonlinearly with head difference, whereas transient infiltration behavior increased and then decreased with increasing head difference. This revealed three piecewise linear trends oriented in a triangular shape including both increases and decreases of infiltration rates. Infiltration tends to be significantly underestimated at the beginning of surface water arrival, when using steady‐state conditions rather than transient conditions. These findings have implications for informing management of ephemeral stream systems, which impacts water resources prediction and sustainable water management, especially in arid and semi‐arid regions where intermittent streams and rivers are more common.

The isotopic composition of evapotranspiration δ_ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculate δ_ET, though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested the δ_ET uncertainty using the mean point [σ_ET (mean)] and median point [σ_ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. The σ_ET (mean) would be greater than σ_ET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter of r² > 0.8, around 70% of σ_ET (mean) was greater than σ_ET (median). This phenomenon might be due to the normality of the vapor concentration C_υ producing the asymmetric distribution of 1/C_υ. The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentration C_υ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.

Crop coefficient (K_c), the ratio of crop evapotranspiration (ET_c) to reference evapotranspiration (ET_o), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations in ET_c and growth-stage-specific K_c in flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of two temperature-based ET_o estimation methods of Hargreaves–Samani and Blaney–Criddle with the widely used Penman–Monteith method. The results indicated that the total ET_c over the whole growth stage for flood-irrigated winter wheat was 556.4 mm on a two-year average, while the average deep percolation (DP) was 2.93 cm and 2.77 cm, accounting for 28.8% and 27.2% of applied irrigation water in the 2021–2022 and 2022–2023 growing seasons, respectively. The ET_o over the growing season, computed using Penman–Monteith, Hargreaves–Samani, and Blaney–Criddle equations, were 867.0 mm, 1015.0 mm, and 856.2 mm in 2021–2022, and 785.6 mm, 947.0 mm, and 800.1 mm in 2022–2023, respectively. The result of global sensitivity analysis showed that the mean temperature is the main driving factor for estimated ET_o based on Blaney–Criddle and Hargreaves–Samani methods, but the sensitivity percentage for Blaney–Criddle was 76.9%, which was much higher than that of 48.9% for Hargreaves–Samani, given that Blaney–Criddle method is less accurate in ET_o estimation for this area, especially during the hottest season from May to August. In contrast, wind speed and maximum temperature were the main driving factors for the Penman–Monteith method, with sensitivity percentages of 70.9% and 21.9%, respectively. The two-year average crop coefficient (K_c) values at the initial, mid, and late growth stage were 0.54, 1.1, and 0.54 based on Penman–Monteith, 0.51, 1.0 and 0.46 based on Blaney–Criddle, and 0.52, 1.2 and 0.56 based on Hargreaves–Samani. The results showed that the Hargreaves–Samani equation serves as an alternative tool to predict ET_o when fewer meteorological variables are available. The calculated local growth-stage-specific K_c can help improve irrigation water management in this region.