Agriculture Dynamics in the Lower Colorado River Basin
Climate and Socioeconomic Factors Drive Irrigated Agriculture Dynamics
The Colorado River Basin (CRB) includes seven states and provides municipal and industrial water to millions of people across all major southwestern cities both inside and outside the basin. Agriculture is the largest part of the CRB economy and crop production depends on irrigation, which accounts for about 74% of the total water demand cross the region. A better understanding of irrigation water demands is critically needed as temperatures continue to rise and drought intensifies, potentially leading to water shortages across the region.
We specifically focused on diverse CRB agricultural regions: the lower Colorado River planning (LCRP) area and the Pinal and Phoenix active management areas (PPAMA).
We produced annual estimates of fallow and active cropland extent at high spatial resolution (30 m) from 2001 to 2017 by applying the fallow-land algorithm based on neighborhood and temporal anomalies (FANTA).
Our primary goal in developing these improved cropland extent and productivity data was to better understand their interannual variability and driving factors at a scale consistent with that of individual fields. Additionally, we also explore the influence of key biophysical (e.g., temperature, precipitation, vapor pressure deficit, and aridity) and socioeconomic factors (e.g., water rights and crop market value) on annual cropland extent and productivity.
The fallow-land algorithm based on neighborhood and temporal anomalies (FANTA) was adapted to be driven by 30-m, Landsat-based NDVI estimates (Wallace et al., 2017).
The NLCD products were used as a mask of cultivated lands for the year they were released and the next four consecutive years until an updated NLCD product was released (e.g., 2001 NLCD was used as a cropland mask from 2001 to 2005).
The FANTA algorithm uses monthly NDVI temporal and spatial anomalies to classify a given cropland pixel as either active or fallow, comparing each pixel to its historical values and to its neighboring cropland pixel values based on a series of logical statements
Based on ground observations collected in 2014 and 2017, we found overall classification accuracies of 88.9% and 87.2% for the LCRP and PPAMA,
Total number of years croplands remained in active production for the full LCRP and PPAMA study areas (A); an example region within LCRP (B); and an example region within PPAMA (C).
The relationship between key climate factors and active cropland extent and productivity (iNDVI) from 2001 to 2017. LCRP active cropland extent (A) and iNDVI (C) were most sensitive to cool-season vapor pressure deficit, whereas PPAMA active cropland extent was most sensitive to mean cool-season aridity (B) and PPAMA cropland iNDVI was most sensitive to mean warm-season aridity (D). Each point indicates an annual mean estimate from 2001 to 2017. Dashed lines show the linear regression fit of all significant (p < 0.05) relationships. Blue signifies a relationship with cool-season climate variable while red represents a relationship with warm-season climate variable.
The role of crop market value in the coupling of active cropland extent and active cropland productivity (iNDVI) to climate. PPAMA active cropland extent (A) and iNDVI (B) were most sensitive to mean cool season aridity during years of low market value (blue diamonds). Dashed lines show the linear regression fit of the low-market value relationships, which are significant (p < 0.05); Correlations and slopes are reported in the plot.
