Recent theoretical advances related to time-variant water age in hydrologic systems have opened the door to a new method that probes water mixing and selection behaviors using StorAge Selection (SAS) functions. In this study, SAS functions were applied to investigate storage, water mixing behaviors, and nitrate (NO3-) export regimes in a tile-drained corn-soybean rotation field in the Midwestern United States. The natural abundance stable nitrogen and oxygen isotopes of tile drainage NO3- were also measured to provide constraints on biogeochemical NO3- transformations. The SAS functions calibrated using chloride measurements at tile drain outlets revealed a strong young water preference during tile discharge generation. The use of a time-variant SAS function for tile discharge generated unique water age dynamics that reveals an inverse storage effect driven by activation of preferential flow paths and mechanically explains the observed variations in NO3- isotopes. Combining the water age estimates with NO3- isotope fingerprinting delineated NO3- export dynamics at the tile-drain scale, where a lack of strong contrast in NO3- concentration across the soil profile results in chemostatic NO3- export regimes. For the first time, NO3- isotopes were embedded into a water age-based transport model to model reactive NO3- transport under transient conditions. Results from this modeling study provided a proof-of-concept for the potential of coupled water age modeling and NO3- isotope analysis in elucidating complex mechanisms that control the coupled water and NO3- transport. Further integration of water age theory and NO3- isotope biogeochemistry is expected to significantly improve reactive NO3- transport modeling.

Irrigation parameterizations in land surface models have been advanced over the past decade, but the newly available data from the Soil Moisture Active Passive (SMAP) satellite has seldom been used to improve irrigation modeling. Here, we investigate the potential of assimilating SMAP soil moisture (SM) data into the Community Land Model (CLM) to improve irrigation representation. Simulations are conducted at 3 arc-minute resolution over the highly irrigated region in the central US, fully enclosing the upstream areas of the river basins draining over the High Plains Aquifer (i.e., the Missouri and Arkansas), and Colorado River basins. We test the original CLM4.5 irrigation scheme and two new irrigation parameterizations using SMAP data assimilation by: (1) directly integrating raw SMAP data, and (2) integrating SMAP data using 1-D Kalman Filter (KF) smoother. An a priori scaling approach is also used to account for bias correction of the shortly-recorded SMAP data based on the ground observations, enabling us to use SMAP for out-of-sample tests (i.e., assessment of the new parameterizations during a non-SMAP period). The ground-based SM observations from three monitoring networks, namely Soil Climate Analysis Network (SCAN), US Climate Reference Network (USCRN), and SNOwpack TELemetry (SNOTEL) are employed for bias correcting SMAP data and validating SM simulations. Results show that SMAP data assimilation using 1-D KF significantly improves irrigation simulations. Bias correction of SMAP data further improves results from KF assimilation in some regions. However, the improvements are small compared to those achieved from 1-D KF application alone, indicating the robustness of using SMAP data and KF globally even for the regions where ground-based data are not available for bias correction. The data assimilation also improves the accuracy of the temporal dynamics and vertical profile of simulated SM. These results are expected to provide a basis for improved modeling of irrigation water use and land-atmosphere interactions.