Global Crop Water Productivity and Savings through waterSMART (GCWP)

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The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals  of waterSMART are to 1) establish water availability and its use  based on an understanding of the past and present water users and to 2) project water availability and use scenarios into the future taking into consideration climate variability and change. Given that worldwide about 90% of all human water use goes toward producing food through agriculture, the focus of this component of the waterSMART project will be to establish crop water productivity (“crop per drop”) of the world’s leading agricultural crops (e.g., wheat, rice, barley, corn, soybeans, cotton, potatoes, pulses, alfalfa) and to determine how much water can be saved by improving crop water productivity. Initially, our primary focus study area is the United States of America (USA). Subsequently, we plan to expand this globally once the methods and approaches mature. The approach and methods involve multi-sensor remote sensing data utilization using machine learning algorithms and coding.

                                           Statement of Problem

waterSMART intends to place technical information and tools in the hands of stakeholders, allowing them to answer two primary questions about water availability:

  • Does the nation have enough freshwater to meet both human and ecological needs? 
  • Will this water be present to meet needs in the future?

It is important to note the connectivity between the waterSMART and the National Water Census. WaterSMART is a Department of the Interior (DOI) initiative on water conservation. It includes activities in: Bureau of Reclamation, United States Geological Survey, and the Office of the Assistant Secretary for Water and Science. The National Water Census is an integral part of the U.S. Geological Survey’s Science Strategy to conduct an ongoing assessment of the nation’s water resources. The project envisions expanding to other countries of the world, especially to areas of the world where agriculture is a major activity and tied to water and food security of the country, region, and the world. This expansion will depend on opportunities and resource availability.

Since nearly 80-90% of all human water use globally is consumed by agricultural croplands to produce food, it is of great importance to understand, model, map, and monitor agricultural croplands and their water use. In California, 80% of all water used by humans is for irrigated croplands; about 250 different crops are grown on about 10 million acres, using 41,922 million cubic meters of water of the 53,019 million cubic meters diverted from surface waters or pumped from groundwater (California Department of Water Resources). There are three significant advances envisaged in this project:

  1. Mapping cropland fallows is very crucial to overcome the uncertainty that currently exists in crop water use assessments. The proportion of croplands left fallow will have a significant role in determining the quantum of water used. Hence there is a critical need in developing accurate map of cropland fallows.
  2. The need to explore new methods to produce cropland mapping (croplands as well as cropland fallows) by automated machine learning algorithms and cloud computing to bring in both speed and accuracy.
  3. Understanding, modeling, mapping, and monitoring agricultural crop water productivity (or “crop per drop”) (Figure 1). There is increasing pressure to reduce agricultural water use by producing more food from existing or even reduced:

(a) areas of croplands (more crop per unit area); and

(b) quantities of water (more crop per unit of water).

This increased food production per unit of water or increasing water productivity (or “more crop per drop”; kg/m3) is expected to lead to a “blue revolution” in agriculture making a major contribution to global food security in the twenty-first century. In this project we will study the current and the past water consuming patterns of the crops by modeling and mapping crop water productivity in California’s Central Valley and other places in the United States. This will allow us to quantify historical and current water use by crops in study areas. Water savings in the study areas can then be determined by understanding and establishing:

1. Percentage of cropland areas under medium or low crop water productivity.

2. Quantum of water used by each crop for each level of water productivity.

 

Subsequently, the water that could be saved will be modeled for different scenarios, including:

1. Increasing crop water productivity,

2. Growing less water consuming crops (e.g., wheat in place of rice) in some areas.

3. Growing second season crops with low water consuming, short season crops that still provide rich nutrition and economic value (e.g., lentil, chickpea, fava bean, in place of certain proportion of rice fields).

waterSMART Overview Image 1

Figure1. Meta-analysis study benchmark areas used in crop water productivity studies of wheat, rice, and corn crops (Source: Foley et al., 2019)

Such approaches can potentially save massive quantities of water that could be used to replenish existing surface and ground water reservoirs and/or help create new “water banks”. We will study these separately for rainfed and irrigated croplands. Growing fewer water consuming crops and/or increasing water productivity in rainfed croplands will help recharge groundwater and/or fill existing or new small ponds or reservoirs throughout the cropland areas. These are “new green water banks” (water saved from rainfed croplands). It may also help retain water in the reservoirs which can then be used for alternative uses. These are termed “new blue water banks” (water saved from irrigated croplands).

 

                                            Novel Objectives

Given the above context, there are several novel objectives that will be achieved by this waterSMART project for 2001-2025 time-period using multisensory remote sensing high resolution imagery (30 m or better), machine learning, and cloud computing. These objectives are:

  1. Establish automated cropland algorithm's (ACA's) for Conterminous United States (CONUS).
  2. Develop automated cropland fallow algorithm (ACFA) for CONUS.
  3. Develop cropland versus cropland fallow products for the CONUS using Landsat, Sentinel, and MODIS satellite sensor data, cloud computing, and machine learning. This will be a unique contribution hitherto not produced by others;
  4. Conduct crop water productivity (“crop per drop”) research by taking major world crops (e.g., wheat, rice, corn, soybeans, cotton) in irrigated and rainfed cropland areas of the US using multi-sensor remote sensing, cloud computing, and machine learning.

These models and products by this research team are intended to compliment the CONUS products from USDA and other USGS groups. These studies will be conducted considering the factors such as the global irrigated and rainfed croplands (Figure 2), and Koppen-Geiger climate classification (Figure 3) and taking major world crops such as wheat, rice, barley, corn, soybeans, cotton, potatoes, sugarcane, pulses, alfalfa, etc. into consideration.

 

                         Outputs, Web Access, and Dissemination

We will perform a comprehensive assessment of the state-of-art of crop water productivity (CWP; “crop per drop”) research worldwide using remote sensing and non-remote sensing methods and approaches based on existing research and meta-analysis. A peer-reviewed journal article will be published based on this work. Then, the study will develop and publish three unique models for CONUS:

  1. Automated cropland fallow algorithm (ACFA)
  2. Automated cropland algorithm (ACA)
  3. Crop water productivity models (CWPM’s)

Nominal 30 m products for 2001-2025 will then be generated for CONUS using ACFA, ACA, and CWPM’s. Next, the study will “pin-point” cropland areas with low and high CWP. These maps and models will provide precise locations from where we can save water and by how much. Spatial representations will pinpoint “hotspots” across the regions where water availability will be severely limited in the future. Scenarios will then be developed that determine how much water (and where) can be saved via improved CWP, and/or the planting of water saving, short duration crops. The scenario outcomes would identify where "new water" would be generated and/or the opportunities for replenishment of existing water resources- both surface and ground water. These scenario analyses will be conducted separately for rainfed and irrigated crops to establish “green water” savings (from rainfed croplands), and “blue water” savings (from rainfed croplands). We will nominate several new or existing reservoirs for this new water as well as establish where and how much of this will be below ground. The research will analyze potential reductions in applied water, which allow farmers and water agencies to remove less water from streams, improving stream quality and ecosystem health, while reducing pumping, delivery, and treatment costs. We will show, through improved CWP in irrigated croplands, various quanta of “new water” that becomes available for alternative uses like urban, industrial, riparian restoration, and re-forestation. Once CWP maps are produced at different resolutions for the representative areas and extrapolated to larger areas using the best models, we will build spatial models for each of the 3 WP study river basins in GEE cloud that will simulate “new water” saved through various scenarios such as improved WP and re-allocation of crops (e.g., growing wheat instead of rice). Users will be able to view and query, compare scenario maps, and generate customized maps from the website. All data and products will be made available through USGS global croplands data portal (e.g., www.croplands.org; LP DAAC). Finally, scenario analysis will allow us to compare existing water use based on existing normal water productivity of various existing crops. This will allow us to suggest alternative pathways.

   

                                        Impact and Outcomes

This project is targeted to demonstrate: (a) scientific advances in understanding, modeling, and mapping cropland fallows and crop water productivity (“crop per drop”) through advanced remote sensing data; (b) methodological advances in crop-by-crop water use/ET modeling and automated machine learning algorithms for cropland fallows, crop water use, and crop water productivity in cloud computing; (c) societal benefits made by clear demonstration of opportunities for water savings by modeling, mapping, and pin-pointing areas of low and high water productivity and thus contributing to food security. It should help in “understanding variability of agricultural water use and characterizing short and long-term imbalances between agricultural water supplies and agricultural water requirements.”

 

 

Figure 1

Figure1.  Spatial variability of mean crop water productivity (CWP) for irrigated wheat by country analyzed in Foley et al. (2019). Broadly, CWP, can be grouped into countries with three levels: Low CWP (<0.75 kg/m³), medium CWP (>0.75 to <1.10 kg/m³), and high CWP (>1.10 kg/m³). 

(Source: Foley et al., 2019)

Figure 3

Figure2.  Spatial variability of mean crop water productivity (CWP) for irrigated rice by country analyzed in this study in Foley et al. (2019). Broadly, CWP, can be grouped into countries with three levels: Low CWP (<0.70 kg/m³), medium CWP (>0.70 to <1.25 kg/m³), and high CWP (>1.25 kg/m³).

(Source: Foley et al., 2019)

Figure 2

Figure3.  Spatial variability of mean crop water productivity (CWP) for irrigated corn by country analyzed in Foley et al. (2019). Broadly, CWP, can be grouped into countries with three levels: Low CWP (<1.25 kg/m³), medium CWP (>1.25 to <1.75 kg/m³), and high CWP (>1.75 kg/m³).

(Source: Foley et al., 2019)