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Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

 

Crop growth depends on the root-zone soil moisture (RZSM) (~top 1m). Accurate estimation of RZSM is vital to optimize irrigation management for saving water and energy while sustaining crop yield. The High-Resolution Land Assimilation System (HRLDAS) from NCAR can generate RZSM at field scales for irrigation management. The soil moisture data from various agriculture sites in the AmeriFlux network, U.S. Climate Reference Network (USCRN), and Soil Climate Analysis Network (SCAN) are used to verify the soil moisture products generated by HRLDAS. Although the HRLDAS products is not location specific and could be applied nationwide, this study will focus on Nebraska for evaluation, validation, and further calibration. We also compared NASA’s SMAP surface soil moisture products to HRLDAS surface layer soil moisture. Since the accuracy of the SMAP product is known, this comparison directly validates the HRLDAS surface soil moisture product and indirectly validate its RZSM products. Results from these two validation methods show a good accuracy of HRLDAS soil moisture products. The conspicuous differences between HRLDAS and SMAP products indicate that HRLDAS omits the irrigation activities as its simulation is based on weather variables and energy balance. It’s hard for HRLDAS to consider and include the irrigation actions in its results, while as SMAP products remotely sense the soil moisture as it is, the changes caused by irrigation are clearly reflected. Therefore, a simple calibration is applied to the HRLDAS products by including irrigation amount as its variables. 

Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, to stakeholders. The disseminated datasets include both numerical model results of reanalysis and forecasting from HRLDAS (High-Resolution Land Data Assimilation System), and the remote sensing datasets from NASA SMAP (Soil Moisture Active Passive) and MODIS (Moderate-Resolution Imaging Spectroradiometer). The system aims to quickly and easily create a smart, customized irrigation scheduler for individual fields to relieve the burden on farmers and to significantly reduce wasted water, energy, and equipment due to excessive irrigation. The system is prototyped here with an application in Nebraska, demonstrating its ability to collect and deliver information to end-users via the web application, which provides online analytic functionality such as point-based query, spatial statistics, and timeseries query. Systems such as this will play a critical role in the next few decades to sustain agriculture, which faces great challenges from climate change and increased natural disasters.