Estimates Of Water Use Research Articles

Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.

How might climate change affect river flows across West Africa?

West Africa and its semi-arid Sahelian region are one of the world’s most vulnerable regions to climate change with a history of extreme climate variability. There is still considerable uncertainty as to how projected climate change will affect precipitation at local and regional scales and the consequent impact on river flows and water resources across West Africa. Here, we aim to address this uncertainty by configuring a regional-scale hydrological model to West Africa. The model (hydrological modelling framework for West Africa—HMF-WA) simulates spatially consistent river flows on a 0.1° × 0.1° grid (approximately 10 km × 10 km) continuously across the whole domain and includes estimates of anthropogenic water use, wetland inundation, and local hydrological features such as endorheic regions. Regional-scale hydrological simulations driven by observed weather data are assessed against observed flows before undertaking an analysis of the impact of projected future climate scenarios from the CMIP5 on river flows up to the end of the twenty-first century. The results indicate that projected future changes in river flows are highly spatially variable across West Africa, particularly across the Sahelian region where the predicted changes are more pronounced. The study shows that median peak flows are projected to decrease by 23% in the west (e.g. Senegal) and increase by 80% in the eastern region (e.g. Chad) by the 2050s. The projected reductions in river flows in western Sahel lead to future droughts and water shortages more likely, while in the eastern Sahel, projected increases lead to future frequent floods.

Corn yield response to irrigation level, crop rotation, and irrigation system

ABSTRACT Economic sustainability is a result of efficient crop management, irrigation system selection, proper crop rotation, and crop yield. A long-term corn (Zea mays L.) yield research project was conducted in Southwest Georgia, USA (84°36”W, 30°44”N) during the 2001 to 2018 crop years, which compared four corn crop rotations, three irrigation systems, and three irrigation levels. Drip and overhead irrigation systems, along with a dryland control, were implemented. Drip systems included shallow subsurface drip (S3DI) and subsurface drip irrigation (SSDI). Irrigation levels included dryland, 33%, 50%, 62/66%, and 100% of estimated water use. Crop rotations were 1) peanut (Arachis hypogaea L.)-corn-corn, 2) peanut-corn, 3) peanut-corn-cotton, (Gossypium hirsutum L.), and 4) peanut-corn-corn-cotton. There were yield differences due to crop rotation; however, the differences were not consistent across years, irrigation levels, or irrigation systems. The SSDI system had consistently lower yield (9659 kg/ha) than both the S3DI (11,791 kg/ha) and sprinkler (12,230 kg/ha) at 100% irrigation level when averaged across all crop rotations and years. In wet years, irrigation level had no effect on yield due to total rainfall amount and timing. However, in dry years, corn yield for sprinkler and SSDI were influenced by irrigation level but not always for S3DI. When selecting an irrigation system, long-term yield should not be the only criterion but should consider crop rotation, system installation cost, field size, irrigation system controls, and irrigation and crop system management.

Interannual Variability and Seasonal Dynamics of Evapotranspiration of Arundo donax L. and Populations of its Biological Control Agent (Tetramesa romana)

Giant reed (Arundo donax L.), a woody grass native to the Mediterranean, has become a cause of concern for national water security in its invaded range of the arid southwestern United States, Australia, New Zealand, and South Africa. The main objective of this study was to provide the first, landscape-level estimates of water use by giant reed. The study utilized the eddy covariance method to quantify evapotranspiration (ET) throughout the 2014 and 2015 growing seasons along the Rio Grande River in Eagle Pass, Texas. We monitored ET concurrently with the implementation of a biological control program targeting giant reed. Daily ET rates were strongly associated with the phenology of giant reed, as estimated by degree day accumulation. Monthly ET was higher in each month of the active growing season (May–October) of giant reed in 2014 than in 2015. Consequently, seasonal (DOY 121-304) ET was substantially higher in 2014 (842 mm) than in 2015 (625 mm) even though the 2015 growing season was wetter. Cumulative rainfall from May to October was 188 and 264 mm in 2014 and 2015, respectively. Our study indicated that the reduction in ET could be attributed to the presence of specialist insect biological control agent, the stem-galling arundo wasp (Tetramesa romana). In 2015, we observed a 28.6% (May), 53.5% (June), and 44% (July) increase in the number of arundo wasps captured in sticky traps from the previous year. Additional studies are needed to quantify conservation and economic benefits of the biological control program in the Rio Grande Basin.

Crop coefficient of pomegranate in the Comarca Lagunera

For agricultural development, water is the most important thing, so today farmers are looking for crops that have some degree of resistance to drought and high economic value such as pomegranate, however, there is poor literature on its production. The Crop Coefficient (Kc) helps us determine the water requirement during plant development, which is critical for reducing production costs and saving water. The objective of this study was to know the Kc during the phenological development of the pomegranate, in an orchard located in the municipality of Gómez Palacio, Durango, Mexico, using 8 Landsat satellite images and geographic information systems. The estimation of Kc based on the Normalized Difference Vegetation Index (NDVI), was performed as proposed by Calera (2016). The KC values obtained range from 0.33 to 0.65. Its evolution with satellite images is consistent according to the development stages of the crop. The relationship between the NDVI and KC may be a promising tool for farmers to estimate water use of pomegranate trees on a regional scale based on satellite imagery.

Prediksi Pendistribusian Air di Perusahaan Daerah Air Minum (PDAM) dengan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

Water is a very important requirement for the life of living things. Clean water is a major component of human activities, both for drinking water, household needs and other supporting activities. To help make the distribution of water in the Tirta Kepri Tanjungpinang PDAM even better, the author wants to predict the distribution of water per month, so that the PDAM Tirta Kepri Tanjungpinang can estimate water use requirements based on water distribution inhibiting factors. Prediction is done using the Adaptive Neuro Fuzzy Inference System (ANFIS) method with the k-means cluster. In this study the data used for prediction of water distribution began from January 2014 - October 2018. The results of the tests carried out with 17 data using the learning rate 0.6 resulted in the RMSE value of 0.07991.

Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multi-component metrics

Crop water use can be a useful indicator of in-season crop conditions including health and greenness on sodic soils. However, in-field manual measurements of crop water use can be tedious, time and labour-intensive and may also exhibit large spatial variability, and inefficient for surveying large areas. Here we propose a novel approach to estimate in-field crop water use of wheat using spatial interpolation techniques at critical crop development stages (tillering and close to flowering) on a moderately sodic and a highly sodic site in southern Queensland, Australia. The six spatial interpolation techniques; ordinary kriging, empirical Bayes kriging, inverse distance weighting, spline, local polynomial interpolation, and radial basis function were employed on ground-measured crop water use data and compared to accurately estimate the spatial distribution of crop water use. Initially, the comparison was made using root mean square error, mean absolute error, and coefficient of determination values using cross-validation. For a more clear comparison and to account for complexities of site-specific crop water use distribution, the standardized, multi-component model performance efficiency metrics, i.e. Kling-Gupta efficiency and spatial efficiency were adopted and tested over cross-validation techniques. Results showed that Kling-Gupta efficiency was slightly better at selecting the optimum interpolation models for crop water use estimation at a field scale than the spatial efficiency metric. Spline and local polynomial interpolation were relatively better estimators of crop water use for both the sites at tillering, while the radial basis function and spline were superior close to flowering. The estimated crop water use was positively and closely associated with seasonal GreenSeeker® normalized difference vegetation index data (R2 = 0.49 and 0.39 at tillering; and R2 = 0.71 and 0.62 at close to flowering, for the moderately sodic and highly sodic sites, respectively). The research improves our understanding of selecting appropriate spatial interpolation methods for in-season crop water use estimation that could help detect changes in crop health due to in-field and seasonal variations of crop water use in a sodic soil environment.

ERA5-based global assessment of irrigation requirement and validation.

While only 20% of harvested lands are actually irrigated, 40% of global agricultural production originates from irrigated areas. Therefore, assessing irrigation requirements is essential for the development of effective water-related policies for an efficient management of water resources. Moreover, global-scale analyses are becoming increasingly relevant, motivated by globalized production and international trade of food as well as by the need of common strategies to address climate change.In this study, a comprehensive model to estimate crop growth and irrigation requirements of 26 main crops at global scale is presented. The model computes a soil water balance using daily precipitation and reference evapotranspiration based on a high-resolution ERA5 reanalysis dataset from the European Copernicus Program. The irrigation requirement, defined as the minimum water volume to avoid water stress, is computed for year 2000 at the resolution of 5 arc-min (or 0.0833°) and aggregated at different spatial and temporal scales for relevant analyses.The estimated global irrigation requirements for 962 km3 is described in detail, also in relation to the spatial variability and to the monthly variation of the requirements. A focus on different areas of the world (California, Northern Italy and India) highlights the wealth of information provided by the model in different climatic conditions.National data of irrigation withdrawals have been used for an extensive comparison with model results. A crop-specific validation has also been made for the State of California, comparing model results with local data of irrigation volume and independent estimates of crop water use. In both cases, we found a good agreement between model results and real data.

RETRACTED: Economic value of agricultural water use for Arizona

This paper aims to estimate the economic value of agricultural water use in Arizona based on an improved economic value estimation model (EVIW model). In this research, EVIW method is optimized for the economic value estimation of agricultural water use from the perspective of crops. Crop Price per unit for crop production, crop yield per acre and irrigation area are used to calculate gross profit. Crop variable costs per acre (labor costs, chemical costs, machinery fuel and repair costs, etc.) and corresponding irrigation areas are used to calculate variable costs. Gross revenues and variable costs are used to calculate the net returns generated by irrigation water. Arizona agricultural water use for crops are estimated based on meteorological data and Cropland Data Layers. Finally, economic values of agricultural water are determined by the net returns of irrigated crops and agricultural water use. The results show that the economic values of agricultural water are positive and negative with great variations. The models applied in the research bring forward practical conclusions for further water allocations, transfer, and policies.

Estimation of state-wide and monthly domestic water use in India from 1975 to 2015

ABSTRACT Domestic water use is one of India's primary water uses that also includes irrigation, industrial, and environmental water uses. However, there is a lack of reliable data that hinders the estimation of domestic water use in India. Previous large-scale assessments often estimated domestic water use using population alone as a predictor. Economic and technological development and the improvement in the country's living standards may increase per-capita domestic water use. The present study's objective was to develop a large-scale domestic water use model in India, which considered the temporal variation of per-capita domestic water use. The economic indicator per-capita Gross Domestic Product (GDP) was used to model the time-variant per-capita domestic water use. The model results showed acceptable performance (NSE>0.7) at both the national and state levels. Maharashtra exhibited the highest per-capita domestic water use in the country because of the rapid economic and technological development relative to other States.

Reanalysis of Water Withdrawal for Irrigation, Electric Power, and Public Supply Sectors in the Conterminous United States, 1950–2016

AbstractAccurately measuring water use by the economy is essential for developing reliable models of water resource availability. Indeed, these models rely on retrospective analyses that provide insights into shifting human population demands and adaptions to water shortages. However, accurate, methodologically consistent, empirically authentic, and spatiotemporally comprehensive historical datasets for water withdrawals are scarce. Herein, we present a reanalysis of annual resolution (1950–2016) historical data set on irrigation, electric power, and public supply water withdrawal within the conterminous United States (US) at the county‐level, and, for power plants, at the site‐level. To estimate electric power water use, we synthesized a historically comprehensive list of generators and historic patterns in generation across fuels, prime movers, and cooling technologies. Irrigation water use estimation required building a crop‐demand model that utilized historical information on irrigated acreage for crops and golf courses, stage‐specific crop water demand, and climate information. To estimate public water supply use, we developed a random forest model constructed from information on population, infrastructure, climate, and land cover. These estimates generally agree with total county and state water use information provided by the US Geological Survey (USGS) water use circular and estimates generated from independent studies for specific years. However, we also observed discrepancies between our estimates and USGS data that appear to be caused by inconsistencies in the methods used by the USGS's primary data sources at the state level over decades of data collection, highlighting the importance of reanalysis to yield spatiotemporally consistent and intercomparable estimates of water use.

Keragaan Lima Varietas Melon (Cucumis melo L.) dengan Perlakuan Irigasi Cincin di Rumah Kaca

Perakitan varietas melon unggul baru memerlukan materi genetik yang beragam. Ekspresi keragaman genetik memerlukan lingkungan yang mendukung, salah satunya adalah ketersediaan air bagi tanaman. Penelitian ini bertujuan: (1) mempelajari pengaruh varietas (V), perlakuan irigasi (I), interaksi VxI terhadap karakter melon, (2) mengestimasi komponen ragam dan heritabilitas arti luas karakter melon, (3) mengestimasi produktivitas air dan penggunaan air tiap tanaman melon. Percobaan dilakukan di rumah kaca Kebun Percobaan Cikabayan, Departemen Agronomi dan Hortikultura, Fakultas Pertanian IPB, Darmaga, Bogor, pada bulan September-November 2017 menggunakan rancangan tersarang dengan dua faktor dan lima ulangan. Lima varietas melon (‘Honey Dew’, ‘Honey Blonde’, ‘Brilliant’, ‘Athena’, dan ‘Serenade’) ditempatkan secara acak pada tiap ulangan di dalam tiap taraf perlakuan irigasi (784 L, 1,127 L, dan 1,407 L). Varietas berpengaruh nyata terhadap karakter diameter batang, panjang daun, lebar daun, umur berbunga jantan, umur berbunga hermafrodit, panjang buah dan padatan terlarut total. Perlakuan irigasi tidak berpengaruh nyata terhadap semua karakter tanaman, namun interaksi VxI berpengaruh nyata pada karakter umur panen. Karakter padatan terlarut total dan panjang daun memiliki nilai heritabilitas arti luas >50%. Produktivitas air irigasi adalah 153.7 kg ha-1 mm-1 dan total penggunaan air untuk tiap tanaman pada perlakuan irigasi P1, P2, dan P3 masing-masing adalah 38.58 L, 50.78 L, dan 73.19 L.
 Kata kunci: heritabilitas, pemuliaan melon, produktivitas air

Stable carbon isotope used to estimate water use efficiency can effectively indicate seasonal variation in leaf stoichiometry

Estimates of seasonal variation in plant stoichiometry and water use efficiency (WUE) are critical for predicting the time courses of carbon and water fluxes. However, the relationship between seasonal stoichiometry and WUE, and their relationship with climatic factors remains unclear. The carbon isotope composition has been widely used to evaluate the WUE. We hypothesized that WUE is closely related to seasonal variation in plant stoichiometry, and then stable carbon isotope can be used to indicate the variation in future models. For this study, we investigated seasonal changes in WUE and 14 elements (C, N, P, S, K, Na, Ca, Mg, Al, Fe, Mn, Zn, Cu, and Ba) of Quercus variabilis in a warm temperate forest, Central China. The WUE gradually reduced from late spring until leaf senescence in fall. Leaf C and N initially increased and then decreased. Leaf P, S, and K generally decreased, whereas Ca, Ba, Al and Fe gradually accumulated throughout the growing season. Leaf C:N, C:P, and N:P were lowest in early season, and then typically increased. As expected, WUE was positively correlated with N, P, S, K, and Mg, while negatively with C, Ca, Al, and Fe as well as C:N, C:P, and N:P. WUE increased with higher temperatures but had no relationship with precipitation. Leaf N, P, S, and K increased, while C:N, C:P, and N:P decreased with higher temperatures. Our results suggested that seasonal stoichiometry and WUE was closely coupled with plant growth, and temperature may be the main dynamic driver of water and nutrients in forest ecosystems. As WUE was estimated by carbon isotope composition, our findings provide new insights toward integrating carbon isotope with Earth system models.

Where is the Planetary Boundary for freshwater being exceeded because of livestock farming?

Livestock production has significant impacts on the environment, including due to the use of water. In this study, we provide a spatially explicit estimation of livestock blue water use, by analyzing feed crop water use and livestock drinking water. For the past four decades, livestock water use has increased from 145 km3/year in 1971 to 270 km3/year in 2012 with an increasing trend of 1.36%/year. The proportion of livestock drinking water use has remained relatively stable at around 10% of total water use attributable to livestock production. Several hotspots of water use, including eastern China, northern India, US high plains, are identified in terms of the long-term averages, while South America and Central Africa show the most rapidly increasing trends. In USA, climate change is found to contribute most to the changes in water use attributable to livestock, while feed cropping intensity and land use change are the dominant driver in China and India, respectively. Though, in total, livestock water use makes a relatively modest contribution to the Planetary Boundary (PB) that has been proposed for anthropogenic water use (4000 km3/year), we argue that this aggregate number is not particularly meaningful, so we identify places where livestock is a major contributor to the unsustainable use of water, in northern India, part of the Middle East, Northern China and Central US. 7% of rivers where excessive water withdrawals mean that there is insufficient residual flow to sustain the aquatic environment (which we take to be the local manifestation of a PB) have been tipped over that boundary because of livestock farming, whilst in a further 34% of rivers, livestock farming on its own exceeds the water PB. Our results provide new and more geographically specific evidence about the impact that the meat industry makes on the PB for water.

Remote Sensing and Modelling Based Framework for Valuing Irrigation System Efficiency and Steering Indicators of Consumptive Water Use in an Irrigated Region

Water crises are becoming severe in recent times, further fueled by population increase and climate change. They result in complex and unsustainable water management. Spatial estimation of consumptive water use is vital for performance assessment of the irrigation system using Remote Sensing (RS). For this study, its estimation is done using the Soil Energy Balance Algorithm for Land (SEBAL) approach. Performance indicators including equity, adequacy, and reliability were worked out at various spatiotemporal scales. Moreover, optimization and sustainable use of water resources are not possible without knowing the factors mainly influencing consumptive water use of major crops. For that purpose, random forest regression modelling was employed using various sets of factors for site-specific, proximity, and cropping system. The results show that the system is underperforming both for Kharif (i.e., summer) and Rabi (i.e., winter) seasons. Performance indicators highlight poor water distribution in the system, a shortage of water supply, and unreliability. The results are relatively good for Rabi as compared to Kharif, with an overall poor situation for both seasons. Factors importance varies for different crops. Overall, distance from canal, road density, canal density, and farm approachability are the most important factors for explaining consumptive water use. Auditing of consumptive water use shows the potential for resource optimization through on-farm water management by the targeted approach. The results are based on the present situation without considering future changes in canal water supply and consumptive water use under climate change.

GNSS characterization of hydrological loading in South and Southeast Asia

SUMMARY The elastic response of the lithosphere to surface mass redistributions produces geodetically measurable deformation of the Earth. This deformation is especially pronounced in South and Southeast Asia, where the annual monsoon produces large-amplitude hydrological loads. The Myanmar–India–Bangladesh–Bhutan (MIBB) network of about 20 continuously operating Global Navigation Satellite Systems (GNSS) stations, established in 2011, provides an opportunity to study the Earth's response to these loads. In this study, we use GRACE temporal gravity products as an estimate of long-wavelength surface water distribution and use this estimate in an elastic loading calculation. We compare the predicted vertical deformation from GRACE with that observed with GNSS. We find that elastic loading inferred from the GRACE gravity model is able to explain the phase and much of the peak-to-peak amplitude (typically 2–3 cm) of the vertical GNSS oscillations, especially in northeast India and central Myanmar. GRACE-based corrections reduce the RMS scatter of the GNSS data by 30–45% in these regions. However, this approach does not capture all of the seasonal deformation in central Bangladesh and southern Myanmar. We show by a synthetic test that local hydrological effects may explain discrepancies between the GNSS and GRACE signals in these places. Two independent hydrological loading models of water stored in soil, vegetation, snow, lakes and streams display phase lags compared to the GRACE and GNSS observations, perhaps indicating that groundwater contributes to the observed loading in addition to near-surface hydrology. The results of our calculations have implications for survey-mode GNSS measurements, which make up the majority of geodetic measurements in this region. By using the GNSS data together with estimates of hydrological loading from independent observations and models, we may be able to more accurately determine crustal motions caused by tectonic processes in South and Southeast Asia, while also improving our ability to monitor the annual monsoon and resulting water storage changes in the region.

Carbon footprint and water footprint of rice and wheat production in Punjab, India

Carbon footprint and water footprint assessments can be powerful tools to guide sustainable food production systems. The present study simultaneously quantified the carbon footprint (CF) and water footprint (WF) of rice and wheat production in the five agro-climatic zones of Punjab, India using farm survey data. Further, the variability in CF among the five agro-climatic zones and farm sizes was analysed. The carbon footprint per unit area of rice and wheat was found to be 8.80 ± 5.71 and 4.18 ± 1.13 t CO2eq/ha respectively. The CF per tonne of rice and wheat was 1.20 ± 0.70 and 0.83 ± 0.23 t CO2eq/t respectively. Large farms had 39% lower CF per tonne of rice compared to small farms. Residue burning, direct methane emissions and fertilizer use were the most important factors that contributed to the CF of rice and wheat production in Punjab. Nitrogen fertilizer use was identified as the major hotspot for mitigation. The average WF of rice and wheat was found to be 1097 and 871 m3/t respectively. A disparity between CROPWAT estimates of blue WF and actual blue water use was established indicating the need for actual blue WF accounting, particularly for flood irrigated crop production. Additionally, policy measures based on ground situation are discussed and the major role of local government policies in mitigating carbon and water footprint is highlighted.

Water balance as an indicator of natural resource condition: Case studies from Great Sand Dunes National Park and Preserve

Managing climate impacts to natural resources in protected areas can be hampered by lack of monitoring data, poor understanding of natural resource responses to climate, or lack of timely condition assessments that can inform management actions. Here we demonstrate the utility of water balance as a tool for understanding natural resource responses to climate by developing case studies focused on stream flow, vegetation production, and wildfire ignition at Great Sand Dunes National Park and Preserve (GSDNP), U.S.A. The efficacy of water balance to predict these responses stems from the explicit integration of climate with site conditions that modify the effects of climate. This in turn results in estimates of water availability, water use, and water need that are proximal drivers of aquatic and terrestrial natural resource conditions. The water balance model successfully forecasted stream flow (r2 = 0.69, P < 0.001); determined the critical water needs for maintaining annual vegetation production in different vegetation types spanning a large environmental gradient (r2 = 0.18–0.71); and predicted proportion of historic wildfire ignitions in forest (r2 = 0.96–0.99) and non-forest (r2 = 0.96–0.97) vegetation types. Collectively, these case studies demonstrate practical approaches to translate climate data into assessments of natural resource condition that inform long-term planning and near-term strategic actions needed for conservation of protected areas.

A Data‐Driven Framework to Characterize State‐Level Water Use in the United States

AbstractAccess to credible estimates of water use is critical for making optimal operational decisions and investment plans to ensure reliable and affordable provisioning of water. Furthermore, identifying the key predictors of water use is important for regulators to promote sustainable development policies to reduce water use. In this paper, we propose a data‐driven framework, grounded in statistical learning theory, to develop a rigorously evaluated predictive model of state‐level, per capita water use in the United States as a function of various geographic, climatic, and socioeconomic variables. Specifically, we compare the accuracy of various statistical methods in predicting the state‐level, per capita water use and find that the model based on the random forest algorithm outperforms all other models. We then leverage the random forest model to identify key factors associated with high water‐usage intensity among different sectors in the United States. More specifically, irrigated farming, thermoelectric energy generation, and urbanization were identified as the most water‐intensive anthropogenic activities, on a per capita basis. Among the climate factors, precipitation was found to be a key predictor of per capita water use, with drier conditions associated with higher water usage. Overall, our study highlights the utility of leveraging data‐driven modeling to gain valuable insights related to the water use patterns across expansive geographical areas.

Development of a dynamic model to estimate canopy par interception

Models of the shadows cast by orchard trees can provide useful information to assist in canopy and nutrient management, estimation of potential yield and water use. In this study, an analytical model was developed that calculates the shape, size, position and orientation of the shadows cast by the tree canopy and described the diurnal and seasonal changes in those shadows. The objective of this study was to derive analytical expressions to describe the positions and potential effects of shadows in time and space based on shape and size of the canopy, row and tree spacing, row orientation, and the latitude and the longitude of the orchard to compute canopy PAR interception considering the shading from neighbouring trees. The model was based on the assumption that canopies have spherical or ellipsoidal shape. Simulations were performed at different times of the day to study the shading patterns of trees and evaluated in terms of solar radiation received, transmitted and intercepted. The model was calibrated using field data to assist orchard design and canopy management based on lightbar scans obtained at different times of the day. The calibration procedure was tested on an almond orchard by comparing the shadows scanned and estimated by the model. The maximum average values between precision and recall were 87.50% and 87.75%, respectively.