Using short-term ensemble weather forecast to evaluate outcomes of irrigation

2021 ◽  
Author(s):  
Danlu Guo ◽  
Andrew Western ◽  
Quan Wang ◽  
Dongryeol Ryu ◽  
Peter Moller ◽  
...  
Keyword(s):  
Soil Water ◽  
Root Zone ◽  
Water Status ◽  
Water Productivity ◽  
Limited Resource ◽  
Weather Forecast ◽  
Irrigation Scheduling ◽  
Irrigation Event ◽  
Optimal Timing ◽  
Short Term

<p>Irrigation water is an expensive and limited resource. Previous studies show that irrigation scheduling can boost efficiency by 20-60%, while improving water productivity by at least 10%. In practice, scheduling decisions are often needed several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs such as soil water, weather and crop conditions. Since each input can be subject to its own uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions.</p><p>This study aims to evaluate the outcomes of alternative irrigation scheduling decisions under uncertainty, with a focus on the uncertainties arising from short-term weather forecast. To achieve this, we performed a model-based study to simulate crop root-zone soil water content, in which we comprehensively explored different combinations of ensemble short-term rainfall forecast and alternative decisions of irrigation scheduling. This modelling produced an ensemble of soil water contents to enable quantification of risks of over- and under-irrigation; these ensemble estimates were summarized to inform optimal timing of next irrigation event to minimize both the risks of stressing crop and wasting water. With inclusion of other sources of uncertainty (e.g. soil water observation, crop factor), this approach shows good potential to be extended to a comprehensive framework to support practical irrigation decision-making for farmers.</p>

scholarly journals THE WATER RELATIONS AND IRRIGATION REQUIREMENTS OF SUGAR CANE (SACCHARUM OFFICINARUM): A REVIEW

2011 ◽  
Vol 47 (1) ◽  
pp. 1-25 ◽  
Author(s):  
M. K. V. CARR ◽  
J. W. KNOX
Keyword(s):  
Water Stress ◽  
Water Relations ◽  
Sugar Cane ◽  
Root Zone ◽  
Water Productivity ◽  
Crop Coefficient ◽  
Saccharum Officinarum ◽  
Irrigation Scheduling ◽  
Background Information ◽  
Technology Choice

SUMMARYThe results of research on the water relations and irrigation needs of sugar cane are collated and summarized in an attempt to link fundamental studies on crop physiology to irrigation practices. Background information on the centres of production of sugar cane is followed by reviews of (1) crop development, including roots; (2) plant water relations; (3) crop water requirements; (4) water productivity; (5) irrigation systems and (6) irrigation scheduling. The majority of the recent research published in the international literature has been conducted in Australia and southern Africa. Leaf/stem extension is a more sensitive indicator of the onset of water stress than stomatal conductance or photosynthesis. Possible mechanisms by which cultivars differ in their responses to drought have been described. Roots extend in depth at rates of 5–18 mm d−1 reaching maximum depths of > 4 m in ca. 300 d providing there are no physical restrictions. The Penman-Monteith equation and the USWB Class A pan both give good estimates of reference crop evapotranspiration (ETo). The corresponding values for the crop coefficient (Kc) are 0.4 (initial stage), 1.25 (peak season) and 0.75 (drying off phase). On an annual basis, the total water-use (ETc) is in the range 1100–1800 mm, with peak daily rates of 6–15 mm d−1. There is a linear relationship between cane/sucrose yields and actual evapotranspiration (ETc) over the season, with slopes of about 100 (cane) and 13 (sugar) kg (ha mm)−1 (but variable). Water stress during tillering need not result in a loss in yield because of compensatory growth on re-watering. Water can be withheld prior to harvest for periods of time up to the equivalent of twice the depth of available water in the root zone. As alternatives to traditional furrow irrigation, drag-line sprinklers and centre pivots have several advantages, such as allowing the application of small quantities of water at frequent intervals. Drip irrigation should only be contemplated when there are well-organized management systems in place. Methods for scheduling irrigation are summarized and the reasons for their limited uptake considered. In conclusion, the ‘drivers for change’, including the need for improved environmental protection, influencing technology choice if irrigated sugar cane production is to be sustainable are summarized.

Irrigation scheduling of paddy rice using short-term weather forecast data

2019 ◽  
Vol 213 ◽  
pp. 714-723 ◽  
Author(s):  
Jingjing Cao ◽  
Junwei Tan ◽  
Yuanlai Cui ◽  
Yufeng Luo
Keyword(s):  
Weather Forecast ◽  
Irrigation Scheduling ◽  
Paddy Rice ◽  
Short Term ◽  
Forecast Data

scholarly journals Understanding the Role of Shallow Groundwater in Improving Field Water Productivity in Arid Areas

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3519
Author(s):  
Xiaoyu Gao ◽  
Zhongyi Qu ◽  
Zailin Huo ◽  
Pengcheng Tang ◽  
Shuaishuai Qiao
Keyword(s):  
Soil Water ◽  
Soil Salinity ◽  
Water Productivity ◽  
Shallow Groundwater ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Groundwater Depth ◽  
Groundwater Salinity ◽  
Arid Areas ◽  
Soil Water And Salt

Soil water and salt transport in soil profiles and capillary rise from shallow groundwater are significant seasonal responses that help determine irrigation schedules and agricultural development in arid areas. In this study the Agricultural Water Productivity Model for Shallow Groundwater (AWPM-SG) was modified by adding a soil salinity simulation to precisely describe the soil water and salt cycle, calculating capillary fluxes from shallow groundwater using readily available data, and simulating the effect of soil salinity on crop growth. The model combines an analytical solution of upward flux from groundwater using the Environmental Policy Integrated Climate (EPIC) crop growth model. The modified AWPM-SG was calibrated and validated with a maize field experiment run in 2016 in which predicted soil moisture, soil salinity, groundwater depth, and leaf area index were in agreement with the observations. To investigate the response of the model, various scenarios with varying groundwater depth and groundwater salinity were run. The inhibition of groundwater salinity on crop yield was slightly less than that on crop water use, while the water consumption of maize with a groundwater depth of 1 m is 3% less than that of 2 m, and the yield of maize with groundwater depth of 1 m is only 1% less than that of 2 m, under the groundwater salinity of 2.0 g/L. At the same groundwater depth, the higher the salinity, the greater the corn water productivity, and the smaller the corn irrigation water productivity. Consequently, using modified AWPM-SG in irrigation scheduling will be beneficial to save more water in areas with shallow groundwater.

Root-weighted soil water status for plant water deficit index based irrigation scheduling

2017 ◽  
Vol 189 ◽  
pp. 137-147 ◽  
Author(s):  
Xun Wu ◽  
Wenjing Zhang ◽  
Wen Liu ◽  
Qiang Zuo ◽  
Jianchu Shi ◽  
...  
Keyword(s):  
Soil Water ◽  
Water Deficit ◽  
Water Status ◽  
Irrigation Scheduling ◽  
Plant Water ◽  
Soil Water Status ◽  
Plant Water Deficit

Detecting changes in root zone soil moisture from radar vegetation backscatter

2020 ◽  
Author(s):  
Coleen Carranza ◽  
Tim van Emmerik ◽  
Martine van der Ploeg
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Hydrological Cycle ◽  
Root Zone ◽  
Water Status ◽  
Roughness Parameter ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Vegetation Water Content ◽  
Vegetation Water

<p>Root zone soil moisture (θ<sub>rz</sub>) is a crucial component of the hydrological cycle and provides information for drought monitoring, irrigation scheduling, and carbon cycle modeling. During vegetation conditions, estimation of θ<sub>rz</sub> thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (σ<sub>soil</sub>), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (σ<sub>veg</sub>) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, σ<sub>veg </sub>provides information about vegetation water content. Furthermore, it has been known in agronomy that pre-dawn leaf water potential is in equilibrium with that of the soil. Therefore soil water status can be inferred by examining  the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (Δθ<sub>rz</sub>) shows corresponding changes in vegetation backscatter (Δσ<sub>veg</sub>) at pre-dawn. We utilized Sentinel-1 (S1) descending pass and in situ soil moisture measurements from 2016-2018 at two soil moisture networks (Raam and Twente) in the Netherlands. We focused on corn and grass which are the most dominant crops at the sites and considered the depth-averaged θ<sub>rz</sub> up to 40 cm to capture the rooting depths for both crops. Dubois’ model formulation for VV-polarization was applied to estimate the surface roughness parameter (H<sub>rms</sub>) and σ<sub>soil </sub>during vegetated periods. Afterwards, the Water Cloud Model was used to derive σ<sub>veg</sub> by subtracting σ<sub>soil</sub> from S1 backscatter (σ<sub>tot</sub>). To ensure that S1 only measures vegetation water content, rainy days were excluded to remove the influence of intercepted rainfall on the backscatter. The slope of regression lines (β) fitted over plots of Δσ<sub>veg</sub> against Δθ<sub>rz</sub> were used investigate the dynamics over a growing season. Our main result indicates that Δσ<sub>veg </sub>- Δθ<sub>rz</sub> relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in β’s over a growing season follow the trend in a crop coefficient (K<sub>c</sub>) curve, which is a measure of crop water requirements. Grasses, which are perennial crops, show trends corresponding to the mature crop stage. The correlation between soil moisture (Δθ) at specific soil depths (5, 10, 20, and 40 cm) and Δσ<sub>veg </sub> matches root growth for corn and known rooting depths for both corn and grass. Dry spells (e.g. July 2018) and a large increase in root zone water content in between two dry-day S1 overpass (e.g. from rainfall) result in a lower β, which indicates that Δσ<sub>veg</sub> does not match well with Δθ<sub>rz</sub>. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer Δσ<sub>veg</sub> - Δθ<sub>rz</sub> relation compared to grass. The sensitivity of Δσ<sub>veg</sub> to Δθ<sub>rz</sub> in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring  periods of water stress.</p>

Assessing canopy temperature-based water stress indices for soybeans under subhumid conditions

2020 ◽  
Author(s):  
Angela Morales Santos ◽  
Reinhard Nolz
Keyword(s):  
Water Stress ◽  
Soil Water ◽  
Water Status ◽  
Canopy Temperature ◽  
Irrigation Scheduling ◽  
Stress Index ◽  
Crop Water ◽  
Stress Indices ◽  
Soil Water Status ◽  
Crop Water Stress

<p>Sustainable irrigation water management is expected to accurately meet crop water requirements in order to avoid stress and, consequently, yield reduction, and at the same time avoid losses of water and nutrients due to deep percolation and leaching. Sensors to monitor soil water status and plant water status (in terms of canopy temperature) can help planning irrigation with respect to time and amounts accordingly. The presented study aimed at quantifying and comparing crop water stress of soybeans irrigated by means of different irrigation systems under subhumid conditions.</p><p>The study site was located in Obersiebenbrunn, Lower Austria, about 30 km east of Vienna. The region is characterized by a mean temperature of 10.5°C with increasing trend due to climate change and mean annual precipitation of 550 mm. The investigations covered the vegetation period of soybean in 2018, from planting in April to harvest in September. Measurement data included precipitation, air temperature, relative humidity and wind velocity. The experimental field of 120x120 m<sup>2</sup> has been divided into four sub-areas: a plot of 14x120 m<sup>2</sup> with drip irrigation (DI), 14x120 m<sup>2</sup> without irrigation (NI), 36x120 m<sup>2</sup> with sprinkler irrigation (SI), and 56x120 m<sup>2</sup> irrigated with a hose reel boom with nozzles (BI). A total of 128, 187 and 114 mm of water were applied in three irrigation events in the plots DI, SI and BI, respectively. Soil water content was monitored in 10 cm depth (HydraProbe, Stevens Water) and matric potential was monitored in 20, 40 and 60 cm depth (Watermark, Irrometer). Canopy temperature was measured every 15 minutes using infrared thermometers (IRT; SI-411, Apogee Instruments). The IRTs were installed with an inclination of 45° at 1.8 m height above ground. Canopy temperature-based water stress indices for irrigation scheduling have been successfully applied in arid environments, but their use is limited in humid areas due to low vapor pressure deficit (VPD). To quantify stress in our study, the Crop Water Stress Index (CWSI) was calculated for each plot and compared to the index resulting from the Degrees Above Canopy Threshold (DACT) method. Unlike the CWSI, the DACT method does not consider VPD to provide a stress index nor requires clear sky conditions. The purpose of the comparison was to revise an alternative method to the CWSI that can be applied in a humid environment.</p><p>CWSI behaved similar for the four sub-areas. As expected, CWSI ≥ 1 during dry periods (representing severe stress) and it decreased considerably after precipitation or irrigation (representing no stress). The plot with overall lower stress was BI, producing the highest yield of the four plots. Results show that DACT may be a more suitable index since all it requires is canopy temperature values and has strong relationship with soil water measurements. Nevertheless, attention must be paid when defining canopy temperature thresholds. Further investigations include the development and test of a decision support system for irrigation scheduling combining both, plant-based and soil water status indicators for water use efficiency analysis.</p>

Area-representative validation of remotely sensed high resolution soil moisture using a cosmic-ray neutron sensor

2020 ◽  
Author(s):  
Dragana Panic ◽  
Isabella Pfeil ◽  
Andreas Salentinig ◽  
Mariette Vreugdenhil ◽  
Wolfgang Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Frequency ◽  
Cosmic Ray ◽  
Irrigation Scheduling ◽  
Water Index ◽  
The Impact

<p>Reliable measurements of soil moisture (SM) are required for many applications worldwide, e.g., for flood and drought forecasting, and for improving the agricultural water use efficiency (e.g., irrigation scheduling). For the retrieval of large-scale SM datasets with a high temporal frequency, remote sensing methods have proven to be a valuable data source. (Sub-)daily SM is derived, for example, from observations of the Advanced Scatterometer (ASCAT) since 2007. These measurements are available on spatial scales of several square kilometers and are in particular useful for applications that do not require fine spatial resolutions but long and continuous time series. Since the launch of the first Sentinel-1 satellite in 2015, the derivation of SM at a spatial scale of 1 km has become possible for every 1.5-4 days over Europe (SSM1km) [1]. Recently, efforts have been made to combine ASCAT and Sentinel-1 to a Soil Water Index (SWI) product, in order to obtain a SM dataset with daily 1 km resolution (SWI1km) [2]. Both datasets are available over Europe from the Copernicus Global Land Service (CGLS, https://land.copernicus.eu/global/). As the quality of such a dataset is typically best over grassland and agricultural areas, and degrades with increasing vegetation density, validation is of high importance for the further development of the dataset and for its subsequent use by stakeholders.</p><p>Traditionally, validation studies have been carried out using in situ SM sensors from ground networks. Those are however often not representative of the area-wide satellite footprints. In this context, cosmic-ray neutron sensors (CRNS) have been found to be valuable, as they provide integrated SM estimates over a much larger area (about 20 hectares), which comes close to the spatial support area of the satellite SM product. In a previous study, we used CRNS measurements to validate ASCAT and S1 SM over an agricultural catchment, the Hydrological Open Air Laboratory (HOAL), in Petzenkirchen, Austria. The datasets were found to agree, but uncertainties regarding the impact of vegetation were identified.</p><p>In this study, we validated the SSM1km, SWI1km and a new S1-ASCAT SM product, which is currently developed at TU Wien, using CRNS. The new S1-ASCAT-combined dataset includes an improved vegetation parameterization, trend correction and snow masking. The validation has been carried out in the HOAL and on a second site in Marchfeld, Austria’s main crop producing area. As microwaves only penetrate the upper few centimeters of the soil, we applied the soil water index concept [3] to obtain soil moisture estimates of the root zone (approximately 0-40 cm) and thus roughly corresponding to the depth of the CRNS measurements. In the HOAL, we also incorporated in-situ SM from a network of point-scale time-domain-transmissivity sensors distributed within the CRNS footprint. The datasets were compared to each other by calculating correlation metrics. Furthermore, we investigated the effect of vegetation on both the satellite and the CRNS data by analyzing detailed information on crop type distribution and crop water content.</p><p>[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004<br>[2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030<br>[3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X</p>

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