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In many agricultural regions, the human use of water for irrigation is often ignored or poorly represented in land surface models (LSMs) and operational forecasts. Because irrigation increases soil moisture, feedback on the surface energy balance, rainfall recycling, and atmospheric dynamics is not represented and may lead to reduced model skill. In this work, we describe four plausible and relatively simple irrigation routines that can be coupled to the next generation of hyper-resolution LSMs operating at scales of 1 km or less. The irrigation output from the four routines (crop model, precipitation delayed, evapotranspiration replacement, and vadose zone model) is compared against a historical field-scale irrigation database (2008-2014) from a 35 km2 study area under maize production and center pivot irrigation in western Nebraska (USA). We find that the most yield-conservative irrigation routine (crop model) produces seasonal totals of irrigation that compare well against the observed irrigation amounts across a range of wet and dry years but with a low bias of 80 mm yr-1. The most aggressive irrigation saving routine (vadose zone model) indicates a potential irrigation savings of 120 mm yr-1 and yield losses of less than 3% against the crop model benchmark and historical averages. The results of the various irrigation routines and associated yield penalties will be valuable for future consideration by local water managers to be informed about the potential value of irrigation saving technologies and irrigation practices. Moreover, the routines offer the hyper-resolution LSM community a range of irrigation routines to better constrain irrigation decision-making at critical temporal (daily) and spatial scales (<1 km). [ABSTRACT FROM AUTHOR]

We investigate the observed positive trends in annual runoff in several basins in central Nebraska using the Budyko hypothesis as a diagnostic tool. In basins where runoff is dominated by base flow we found that the estimated annual evapotranspiration (ETa) to precipitation (P) ratio (ETa/P) from data is negatively related to the aridity index (ETp/P, where ETp is potential annual evapotranspiration). This observation is inconsistent with the Budyko hypothesis. We hypothesized that the observed negative trend results from significant interannual changes in basin water storage. This hypothesis is tested using data from groundwater monitoring wells in the Sand Hills region of Nebraska. Plots of the yearly changes in groundwater storage versus the annual aridity index revealed the mean annual aridity index as a critical climatological variable that controls basin storage gain-loss dynamics. For the same absolute deviation from the mean climate, we found that a wetter year leads to a larger gain in groundwater storage than the net loss in a drier year. We argue that this storage gain-loss behavior builds a climate memory in the hydrologic system, causing persistence and statistically significant trends in annual runoff. A parsimonious model was developed that couples the Budyko hypothesis with a linear reservoir equation for base flow and was used to examine the possible causes of observed positive trends of annual runoff. We found that subtle, statistically insignificant, increases in annual P have led to positive and statistically insignificant trends in annual ETa and P - ETa. Annual runoff, on the other hand, was predicted to have high persistence and statistical significance, consistent with observations. Further model sensitivity analyses showed that increasing the size of the groundwater reservoir is associated with increased long-term (multidecadal) persistence in annual runoff and translates high-frequency, high-amplitude variation in climate to low-frequency, low-amplitude runoff response. Our results underscore the importance of evaluating apparent trends in any system variable in a complete water budget context. [ABSTRACT FROM AUTHOR]