Search NASA⌕ Search

SEARCH · Search NASA

Results for “irrigation modernization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

IrrigationViz: A Geospatial Visualization Application to Facilitate Irrigation Modernization

Irrigation water delivery infrastructure, such as canals and pipelines, are essential to agriculture in the Western U.S., yet many of these systems are reaching the end of their useful lives. Reinvestment can achieve a wide variety of benefits, from water conservation to energy savings or renewable energy generation. Modernization requires significant planning, design, and implementation funding which can be a challenge for many irrigation districts. Here, this paper introduces IrrigationViz, a web-based mapping application designed to help irrigation districts visualize and generate high-level cost and benefit estimates for infrastructure modernization projects. These estimates can help water managers identify projects that benefit from additional engineering resources and ultimately obtain funding. IrrigationViz includes map interactions, graphs, and visualization components to facilitate planning and communication to stakeholders and funders. IrrigationViz combines user-provided information about a water-delivery system with public datasets and basic engineering formulas to generate estimates of the benefits of reinvestment. Estimates include the amount of water seepage in earthen canals, potential hydropower generation associated with replacing a canal with a pressurized pipe, and pipe size recommendations. We found that hydropower generation estimates compared favorably with real world projects, but seepage loss estimates showed high variability relative to on-the-ground measurements

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Creating a Training Dataset for Semantic Segmentation of Canal Networks for Irrigation Modernization

Canal infrastructure has provided critical irrigation water to the western United States for over a century. To continue providing vital water resources to the semi-arid West, irrigation systems must undergo maintenance and modernization. Many canal companies are resource-constrained, and because funding opportunities often require detailed knowledge of existing infrastructure, they can struggle to secure financial capital. We address this problem by creating training data for a semantic segmentation deep learning model to map canal networks throughout the western United States. To create a diverse and robust training dataset, we labelled 1-m NAIP imagery with the locations of no canals, wet canals, and dry/vegetated canals. Since creating these datasets is time consuming, we first developed a preprocessing methodology to identify canals within our four study areas. We used NAIP imagery and provided canal centerline data to buffer, standardize, and cluster the imagery, automating the labeling process as much as possible. However, this still required manual cleaning and manual classification of canal type. Challenges arose when canals were interrupted (e.g., road culverts or piped sections) or when nearby features shared similar characteristics (e.g., irrigated fields, trees, and shadows). Combining automated preprocessing with manual refinement produced four detailed canal masks to be used in the semantic segmentation model developed by Richard Tapia.

13 - HYDRO ENERGY↗
DOE OSTI2026

Irrigation Infrastructure and Modernization – Setting a Baseline: Estimates of Irrigation Water Conveyance Infrastructure Extents and Composition and the Potential Water, Energy, and Economic Benefits of Modernization in the Western U.S.

In the Western United States (U.S.), water delivery for irrigation is still largely managed using century-old equipment and designs. Modernization of this vital water conveyance infrastructure, such as piping of earthen canals, is known to improve water availability and water quality for farmers, while saving energy and enabling new hydropower. However, there is sparse information about the extent of irrigation water delivery infrastructure, which makes it challenging to estimate the cost of upgrades at scale and the potential benefits of accelerating modernization work. This report estimates a variety of previously unquantified data points related to irrigation water delivery infrastructure in the Western U.S. to support stakeholders interested in nationwide modernization planning. The findings should be considered approximations, useful for understanding the scale, range, or variability of these indicators. Taken together, the findings of this report illustrate some of the challenges and opportunities involved in modernizing the agricultural water delivery infrastructure in the Western U.S. Accelerating the pace of modernization could strengthen the long-term resilience of U.S. food systems while providing significant economic, energy, water, and environmental benefits.

13 HYDRO ENERGY↗
NASA NTRS2010

Irrigation Induced Surface Cooling in the Context of Modern and Increased Greenhouse Gas Forcing

There is evidence that expected warming trends from increased greenhouse gas (GHG) forcing have been locally masked by irrigation induced cooling, and it is uncertain how the magnitude of this irrigation masking effect will change in the future. Using an irrigation dataset integrated into a global general circulation model, we investigate the equilibrium magnitude of irrigation induced cooling under modern (Year 2000) and increased (A1B Scenario, Year 2050) GHG forcing, using modern irrigation rates in both scenarios. For the modern scenario, the cooling is largest over North America, India, the Middle East, and East Asia. Under increased GHG forcing, this cooling effect largely disappears over North America, remains relatively unchanged over India, and intensifies over parts of China and the Middle East. For North America, irrigation significantly increases precipitation under modern GHG forcing; this precipitation enhancement largely disappears under A1B forcing, reducing total latent heat fluxes and the overall irrigation cooling effect. Over India, irrigation rates are high enough to keep pace with increased evaporative demand from the increased GHG forcing and the magnitude of the cooling is maintained. Over China, GHG forcing reduces precipitation and shifts the region to a drier evaporative regime, leading to a relatively increased impact of additional water from irrigation on the surface energy balance. Irrigation enhances precipitation in the Middle East under increased GHG forcing, increasing total latent heat fluxes and enhancing the irrigation cooling effect. Ultimately, the extent to which irrigation will continue to compensate for the warming from increased GHG forcing will primarily depend on changes in the background evaporative regime, secondary irrigation effects (e.g. clouds, precipitation), and the ability of societies to maintain (or increase) current irrigation rates.

Cook, Benjamin I.↗
NASA NTRS2010

Effects of Irrigation on Global Climate During the 20th Century

Various studies have documented the effects of modern ]day irrigation on regional and global climate, but none, to date, have considered the time ]varying impact of steadily increasing irrigation rates on climate during the 20th century. We investigate the impacts of observed irrigation changes over this century with two ensemble simulations using an atmosphere general circulation model. Both ensembles are forced with transient climate forcings and observed sea surface temperatures from 1902 to 2000; one ensemble includes irrigation specified by a time ]varying data set of irrigation water withdrawals. Early in the century, irrigation is primarily localized over southern and eastern Asia, leading to significant cooling in boreal summer (June.August) over these regions. This cooling spreads and intensifies by century fs end, following the rapid expansion of irrigation over North America, Europe, and Asia. Irrigation also leads to boreal winter (December.February) warming over parts of North America and Asia in the latter part of the century, due to enhanced downward longwave fluxes from increased near ]surface humidity. Precipitation increases occur primarily downwind of the major irrigation areas, although precipitation in parts of India decreases due to a weaker summer monsoon. Irrigation begins to significantly reduce temperatures and temperature trends during boreal summer over the Northern Hemisphere midlatitudes and tropics beginning around 1950; significant increases in precipitation occur in these same latitude bands. These trends reveal the varying importance of irrigation ]climate interactions and suggest that future climate studies should account for irrigation, especially in regions with unsustainable irrigation resources.

Puma, M. J.↗
DOE OSTI2024

INTEGRATION OF CONDUIT HYDROPOWER AND BATTERIES INTO IRRIGATION INFRASTRUCTURE

Irrigation districts, ditch companies, and other agricultural water providers across the West operate and maintain canals, ditches, and reservoirs that store and deliver water for agricultural production, municipal needs, and other purposes. Co-locating energy generation and storage with this infrastructure provides opportunities to improve resilience and reduce energy costs. This memo discusses two case studies of co-located infrastructure and their contexts. The first case study discusses the development of an integrated microgrid, hydropower, solar, and battery storage project in North Unit Irrigation District (NUID) in Oregon. The second case study discusses the development of a battery storage project in Tulelake Irrigation District (TID) in northern California. Together, these two projects demonstrate the potential for co-located energy and water infrastructure.

13 - HYDRO ENERGY↗
NASA NTRS2013

Where Does the Irrigation Water Go? An Estimate of the Contribution of Irrigation to Precipitation Using MERRA

Irrigation is an important human activity that may impact local and regional climate, but current climate model simulations and data assimilation systems generally do not explicitly include it. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) shows more irrigation signal in surface evapotranspiration (ET) than the Modern-Era Retrospective Analysis for Research and Applications (MERRA) because ERA-Interim adjusts soil moisture according to the observed surface temperature and humidity while MERRA has no explicit consideration of irrigation at the surface. But, when compared with the results from a hydrological model with detailed considerations of agriculture, the ET from both reanalyses show large deficiencies in capturing the impact of irrigation. Here, a back-trajectory method is used to estimate the contribution of irrigation to precipitation over local and surrounding regions, using MERRA with observation-based corrections and added irrigation-caused ET increase from the hydrological model. Results show substantial contributions of irrigation to precipitation over heavily irrigated regions in Asia, but the precipitation increase is much less than the ET increase over most areas, indicating that irrigation could lead to water deficits over these regions. For the same increase in ET, precipitation increases are larger over wetter areas where convection is more easily triggered, but the percentage increase in precipitation is similar for different areas. There are substantial regional differences in the patterns of irrigation impact, but, for all the studied regions, the highest percentage contribution to precipitation is over local land.

ERA-Interim↗
DOE OSTI2025

Machine Learning Prediction of Tritium‐Helium Groundwater Ages in the Central Valley, California, USA

Abstract Groundwater ages provides insight into recharge rates, flow velocities, and vulnerability to contaminants. The ability to predict groundwater ages based on more accessible parameters via Machine Learning (ML) would advance our ability to guide sustainable management of groundwater resources. In this study, ML models were trained and tested on a large data set of tritium concentrations and tritium‐helium groundwater ages from the California Central Valley, a large groundwater basin with complex land use, irrigation, and water management practices. The ML models were trained on 63 features, including location, well construction information, landscape characteristics, and climate variables, water chemistry, and stable isotopes. The Bagging regressor method can accurately classify (F1‐score = 0.91) groundwater samples as either modern or pre‐modern whereas the accuracy of the ML prediction of continuous tritium‐helium groundwater ages is limited and explains only of the variability in this data set. In general, ML groundwater age prediction relies mostly on features related to (a) the source of groundwater recharge, (b) contaminant history, (c) aquifer materials, (d) well construction, and (e) geochemical reactions along flow paths.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Soil Moisture Estimation in South Asia via Assimilation of SMAP Retrievals

A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional soil moisture estimation as well as to provide a consistent regional soil moisture dataset. This study aims to improve the spatiotemporal variability of soil moisture estimates by assimilating Soil Moisture Active Passive (SMAP) near-surface soil moisture retrievals into a land surface model. The Noah-MP (v4.0.1) land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA2) and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) provide the meteorological boundary conditions to the land surface model. Assimilation is carried out using both cumulative distribution function (CDF)-corrected (DA-CDF) and uncorrected SMAP retrievals (DA-NoCDF). CDF matching is applied to correct the statistical moments of the SMAP soil moisture retrieval relative to the land surface model. Comparison of assimilated and model-only soil moisture estimates with publicly available in situ measurements highlights the relative improvement in soil moisture estimates by assimilating SMAP retrievals. Across the Tibetan Plateau, DA-NoCDF reduced the mean bias and RMSE by 8.4 % and 9.4 %, even though assimilation only occurred during less than 10 % of the study period due to frozen (or partially frozen) soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. The general lack of publicly available in situ measurements across irrigated areas limited a domain-wide direct model validation. However, comparison with regional irrigation patterns suggested correction of biases associated with an unmodeled hydrologic phenomenon (i.e., anthropogenic influence via irrigation) as a result of SMAP soil moisture retrieval assimilation. The greatest sensitivity to assimilation was observed in cropland areas. Improvements in soil moisture potentially translate into improved spatiotemporal patterns of modeled evapotranspiration, although limited influence from soil moisture assimilation was observed on modeled processes within the carbon cycle such as gross primary production. Improvement in fine-scale modeled estimates by assimilating coarse-scale retrievals highlights the potential of this approach for soil moisture estimation over data-scarce regions.

Jawairia Ahmad↗