Ray G. Anderson

Analytical steady‐state solutions for water‐limited cropping systems using saline irrigation water

Due to the diminishing availability of good quality water for irrigation, it is increasingly important that irrigation and salinity management tools be able to target submaximal crop yields and support the use of marginal quality waters. In this work, we present a steady-state irrigated systems modeling framework that accounts for reduced plant water uptake due to root zone salinity. Two explicit, closed-form analytical solutions for the root zone solute concentration profile are obtained, corresponding to two alternative functional forms of the uptake reduction function. The solutions express a general relationship between irrigation water salinity, irrigation rate, crop salt tolerance, crop transpiration, and (using standard approximations) crop yield. Example applications are illustrated, including the calculation of irrigation requirements for obtaining targeted submaximal yields, and the generation of crop-water production functions for varying irrigation waters, irrigation rates, and crops. Model predictions are shown to be mostly consistent with existing models and available experimental data. Yet the new solutions possess advantages over available alternatives, including: (i) the solutions were derived from a complete physical-mathematical description of the system, rather than based on an ad hoc formulation; (ii) the analytical solutions are explicit and can be evaluated without iterative techniques; (iii) the solutions permit consideration of two common functional forms of salinity induced reductions in crop water uptake, rather than being tied to one particular representation; and (iv) the utilized modeling framework is compatible with leading transient-state numerical models.

Spatial interpolation quality assessment for soil sensor transect datasets

Near-ground soil sensing improves interpolation of soil properties.Parallel (clustered) transect sampling is generally used on soil-sensor surveys.Unbiased interpolation quality assessment can be achieved using h-block resampling. Near-ground geophysical soil sensors provide valuable information for precision agriculture applications. Indeed, their readings can be used as proxy for many soil parameters. On-the-go soil sensor surveys are, typically, carried out intensively (e.g., every 2m) over many parallel transects. Two types of soil sensors measurements are considered in this paper: apparent electrical conductivity (4 fields in California, USA) and reflectance (1 field in Italy). Two types of spatial interpolations are carried out, universal kriging (model-based) and inverse distance weighting (deterministic). Interpolation quality assessment is usually carried out using leave-one-out (loo) resampling. We show that loo resampling on transect sampling datasets returns overly-optimistic, low interpolation errors, because the left-out data point has values very close to that of its neighbors in the training dataset. This bias in the map quality assessment can be reduced by removing the closest neighbors of the validation observation from the training dataset, in a (spatial) h-block (SHB) fashion. The results indicate that, for soil sensor data acquired along parallel transects: (i) the SHB resampling is a useful tool to test the performance of interpolation techniques and (ii) the optimal (i.e., rendering the same errors of un-sampled locations between transects) SHB threshold distance (h.dist) for neighbor-exclusion is proportional to the semi-variogram range and partial sill. This procedure provides research scientists with an improved means of understanding the error of soil maps made by interpolating soil sensor measurements.

21st century California drought risk linked to model fidelity of the El Niño teleconnection

Greenhouse gas induced climate change is expected to lead to negative hydrological impacts for southwestern North America, including California (CA). This includes a decrease in the amount and frequency of precipitation, reductions in Sierra snow pack, and an increase in evapotranspiration, all of which imply a decline in surface water availability, and an increase in drought and stress on water resources. However, a recent study showed the importance of tropical Pacific sea surface temperature (SST) warming and an El Niño Southern Oscillation (ENSO)-like teleconnection in driving an increase in CA precipitation through the 21st century, particularly during winter (DJF). Here, we extend this prior work and show wetter (drier) CA conditions, based on several drought metrics, are associated with an El Niño (La Niña)-like SST pattern. Models that better simulate the observed ENSO-CA precipitation teleconnection also better simulate the ENSO-CA drought relationships, and yield negligible change in the risk of 21st century CA drought, primarily due to wetting during winter. Seasonally, however, CA drought risk is projected to increase during the non-winter months, particularly in the models that poorly simulate the observed teleconnection. Thus, future projections of CA drought are dependent on model fidelity of the El Niño teleconnection. As opposed to focusing on adapting to less water, models that better simulate the teleconnection imply adaptation measures focused on smoothing seasonal differences for affected agricultural, terrestrial, and aquatic systems, as well as effectively capturing enhanced winter runoff.HYDROCLIMATE: future winter rain reduces California drought riskCalifornia, drought-ridden between 2012 and 2016, may be less drought-prone in the future than previously thought. Robert Allen from the University of California Riverside, and co-author Ray Anderson, use a suite of climate models to show that those able to better capture relationships with El Niño—changes in the temperature of the tropical Pacific Ocean—provide more reliable estimates of future drought conditions in the US southwest. During winter, the ‘good’ models predict more rainfall, higher river levels and increasing soil moisture. This wintertime rain offsets drying trends predicted for the rest of the year, leading to small changes in overall drought risk. However, such increasing seasonality—reduced drought risk in winter, but enhanced drought risk during the dry seasons—poses new challenges to ensure year-round water security in California.

Incorporating field wind data to improve crop evapotranspiration parameterization in heterogeneous regions

Accurate parameterization of reference evapotranspiration (ET0) is necessary for optimizing irrigation scheduling and avoiding costs associated with over-irrigation (water expense, loss of water productivity, energy costs, and pollution) or with under-irrigation (crop stress and suboptimal yields or quality). ET0 is often estimated using the FAO-56 method with meteorological data gathered over a reference surface, usually short grass. However, the density of suitable ET0 stations is often low relative to the microclimatic variability of many arid and semi-arid regions, leading to a potentially inaccurate ET0 for irrigation scheduling. In this study, we investigated multiple ET0 products from six meteorological stations, a satellite ET0 product, and integration (merger) of two stations’ data in Southern California, USA. We evaluated ET0 against lysimetric ET observations from two lysimeter systems (weighing and volumetric) and two crops (wine grapes and Jerusalem artichoke) by calculating crop ET (ETc) using crop coefficients for the lysimetric crops with the different ET0. ETc calculated with ET0 products that incorporated field-specific wind speed had closer agreement with lysimetric ET, with RMSE reduced by 36 and 45% for grape and Jerusalem artichoke, respectively, with on-field anemometer data compared to wind data from the nearest station. The results indicate the potential importance of on-site meteorological sensors for ET0 parameterization; particularly where microclimates are highly variable and/or irrigation water is expensive or scarce.