Understanding and predicting deep percolation under surface irrigation

Bethune, M. G.; Selle, Benny; Wang, QJ

doi:10.1029/2007wr006380

Cited by 63 publications

(55 citation statements)

References 22 publications

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“…This simple model was readily interpretable. It supported that steady-state percolation during irrigation, as represented in the conceptual model developed by Bethune et al (2008), was the dominant process contributing to DP in the lysimeter experiment. However, non-steady-state percolation, which was also represented in the conceptual model, was likely to be a minor process contributing to DP and may even be not identifiable from the experimental data set.…”

Section: Discussionmentioning

confidence: 53%

“…SELLE AND MUTTIL: TESTING THE STRUCTURE OF HYDROLOGICAL MODELS Bethune et al (2008) found, using the conceptual model, that steady-state percolation during irrigation was the dominant process contributing to deep percolation on most of the studied soils. Non-steady-state percolation (redistribution) was also important for some soil types.…”

Section: Conceptual Model Of Deep Percolationmentioning

confidence: 99%

“…For a more detailed description of the lysimeter experiment, the reader is referred to Bethune et al (2008).…”

Section: Lysimeter Data Setmentioning

confidence: 99%

“…For the remainder of the irrigation season, rainfall was small compared to irrigation and thus did not have much impact on DP. Bethune et al (2008) developed a conceptual model of deep percolation based on the data from the lysimeter experiment. The conceptual model of DP is given by:…”

Section: Lysimeter Data Setmentioning

confidence: 99%

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Testing the structure of a hydrological model using Genetic Programming

Selle¹,

Muttil

2011

Journal of Hydrology

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Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, water table depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

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Section: Discussionmentioning

confidence: 53%

Section: Conceptual Model Of Deep Percolationmentioning

confidence: 99%

“…For a more detailed description of the lysimeter experiment, the reader is referred to Bethune et al (2008).…”

Section: Lysimeter Data Setmentioning

confidence: 99%

Section: Lysimeter Data Setmentioning

confidence: 99%

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Testing the structure of a hydrological model using Genetic Programming

Selle¹,

Muttil

2011

Journal of Hydrology

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“…Reference crop evapotranspiration (ETo) was calculated using the method described by Hargreaves and Samani [37]. Estimation of actual evapotranspiration (AET) was calculated assuming an average crop coefficient (Kc) equal to one, as recommended for surface irrigated grazing pastures [38][39][40]. AET was calculated by converting daily ETo estimates to hourly ETo and multiplying it by time to field capacity tfc which was defined as the total number of hours from the onset of irrigation to field capacity.…”

Section: Water Balance Methodsmentioning

confidence: 99%

Surface Water and Groundwater Interactions in Traditionally Irrigated Fields in Northern New Mexico, U.S.A.

Gutiérrez-Jurado

Fernald

Guldan

et al. 2017

Water

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Better understanding of surface water (SW) and groundwater (GW) interactions and water balances has become indispensable for water management decisions. This study sought to characterize SW-GW interactions in three crop fields located in three different irrigated valleys in northern New Mexico by (1) estimating deep percolation (DP) below the root zone in flood-irrigated crop fields; and (2) characterizing shallow aquifer response to inputs from DP associated with irrigation. Detailed measurements of irrigation water application, soil water content fluctuations, crop field runoff, and weather data were used in the water budget calculations for each field. Shallow wells were used to monitor groundwater level response to DP inputs. The amount of DP was positively and significantly related to the total amount of irrigation water applied for the Rio Hondo and Alcalde sites, but not for the El Rito site. The average irrigation event DP using data for the complete irrigation season at each of the three sites was 77.0 mm at El Rito, 54.5 mm at Alcalde and 53.1 mm at Rio Hondo. Groundwater level rise compared to pre-irrigation event water levels ranged from 3 to 1870 mm, and was influenced by differences in irrigation practices between sites. Crop evapotranspiration estimates averaged across irrigation events were highest in Rio Hondo (22.9 mm), followed by El Rito (14.4 mm) and Alcalde (10.4 mm). Results from this study indicate there are strong surface water-groundwater connections in traditionally irrigated systems of northern New Mexico, connections that may be employed to better manage groundwater recharge and river flow.Keywords: irrigation systems; hydrology; surface water; shallow groundwater; deep percolation; water balance method; groundwater recharge IntroductionThe agriculture sector faces increasing challenges as expected climate change conditions create uncertainty in water delivery, timing, and availability [1][2][3]. In snowmelt-runoff dominated areas such as in the southwestern United States (USA), climate variability trends indicate that decreases in snowpack and warmer winters may result in stream flows coming earlier in the season before water is needed for agriculture [1][2][3][4]. Consequently, farmers will face pressure to develop new water saving and management strategies to cope with water scarcity [5][6][7][8].Water 2017, 9, 102; doi:10.3390/w9020102www.mdpi.com/journal/waterWater 2017, 9, 102 2 of 14In New Mexico (NM) in the southwestern USA, more than 50% of irrigated acreage uses flood irrigation methods [9]. Approximately 18% of surface water withdrawals of the Rio Grande river basin in NM (10% of the total surface water withdrawals of the state) are used by acequia irrigation systems [9,10]. Acequia refers to both earthen ditch systems that enable small-scale farming and shared water governance institutions [10][11][12].Agricultural practices such as irrigation can directly or indirectly affect the interactions of surface water and groundwater [13]. Several studies have shown tha...

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Transient potential groundwater recharge under surface irrigation in semiarid environment: An experimental and numerical study

Dadgar

Nakhaei

Porhemmat

et al. 2018

Hydrological Processes

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Groundwater recharge, a key component of the global water cycle, is critical in understanding water dynamics in managed and natural ecosystems. In this study, an experimental and numerical method was used to investigate potential groundwater recharge (PGR) in a semiarid region in Iran. A large soil column with four distinct layers was constructed using soil from an agricultural farm in the area. A 140‐day experiment with 10 applications of irrigation was carried out, and soil water was measured at different depths. Deep drainage started around 40 days after the first irrigation and increased with more water applications. The soil water dynamics for different irrigation tests were modelled using the HYDRUS‐1D software package, and the model was calibrated using laboratory collected data. Good agreement was achieved between the HYDRUS‐1D simulated and laboratory measurement data. The calibrated model was used to simulate PGR using meteorological station data between November 2008 and 2012. Three scenarios were applied, including fully cropped land with irrigation and precipitation water (R1), irrigation water (R2), and bare soil with precipitation water (R3) only. Results showed that the average annual PGR rates were 32.42%, 10.8%, and 4.26% for the three scenarios, respectively. Temporal variability of recharge for the R1 showed that recharge increased with a 2‐month delay corresponding to the increased water infiltration during harvest time. Recharge flux started after high precipitation values for R3. A clear relation between monthly precipitation and recharge rate was not found even after a 2‐month shifted recharge, but a relatively high correlation was observed between monthly 2‐month shifted recharge and precipitation after a threshold value cut‐off. Additionally, the results showed that the traditional recharge estimation in semiarid regions with episodic natural precipitation may not be as simplified as generally assumed.

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Understanding and predicting deep percolation under surface irrigation

Cited by 63 publications

References 22 publications

Testing the structure of a hydrological model using Genetic Programming

Testing the structure of a hydrological model using Genetic Programming

Surface Water and Groundwater Interactions in Traditionally Irrigated Fields in Northern New Mexico, U.S.A.

Transient potential groundwater recharge under surface irrigation in semiarid environment: An experimental and numerical study

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