HS5.3
Orals: Tue, 25 Apr | Room 2.15
Climate extremes bring both challenges and opportunities for increasing resilience in agriculture and communities. Drought impact assessments are useful to identify systemwide vulnerabilities and downstream effects from water shortages to agriculture, and aid governments, irrigation user organizations and farmers in both short-term response and planning. The recent climate extremes in California, USA over the past 2012-2022 decade provide a useful case study as one of the largest irrigated agricultural systems which are applicable to other semi-arid areas in the world. We present a framework to gather water supply availability for irrigation in California’s large and complex water supply system, estimate idle land, potential cropping patterns response and economic costs to irrigated agriculture, downstream food processing sectors and regional economies. Recent groundwater regulation forcing sustainable pumping rates at a local level bring additional challenges to cope with water scarcity. We employ regional water balances which consider diverse water supply portfolios for agriculture, remote sensing, and economic models which estimate profit-maximizing crop response and economic costs of water shortages to agriculture and related sectors. We also discuss data challenges in quantifying ultimate impacts of low precipitation, surface water reserves and groundwater restrictions, in a highly engineered and diversified water supply system. Estimated impacts on agriculture and regionwide income and employment from the 2012-2016 and the more recent 2019-2022 drought in California are discussed along with insights for short-term response, and longer-term water management, planning and policy.
How to cite: Medellin-Azuara, J., Escriva-Bou, A., Rodriguez-Flores, J., Cole, S., Abatzoglou, J., Viers, J., and Sumner, D.: On assesssing water supply availability, land idling and economic impacts of agricultural droughts: Cases studies from recent California climate extremes, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17022, https://doi.org/10.5194/egusphere-egu23-17022, 2023.
An effective, efficient, and sustainable allocation of resources has gained prominence on a global scale in recent years, taking into account far more than in the past the environmental needs of the ecosystems linked to them. Proper resource management and planning, as well as the detection of specific sustainable indicators, are required to respond to increasing demand while keeping in mind that its increase corresponds to a gradual reduction in the availability and usability of resources, which is also a result of climate change. This becomes even more important when the resource under consideration is water, which is essential for human survival and well-being as, after all, agricultural production. In comparison to other European countries, Italy has an abundance of meteoric inflows, albeit unevenly distributed across its entire territory. Because of this, the Italian country is particularly vulnerable to water crises, which are becoming more common, particularly in South Italy. The management of water resources, which is already complex, becomes even more so when dealing with scarcity situations, an area in which it is critical, to begin with the cognitive assumption of hydrological balance. Remote Sensing (RS) approaches are essential for investigating and assessing water bodies, meteoric inflows, and water balance parameters, allowing for effective surface water management support. RS is widely used for the aforementioned purposes due to the increasing availability of novel medium-high-resolution remote sensing big data, as well as Copernicus services and data related to water management (https://climate.copernicus.eu/water-management). Thus, the goal of this study is to take a "snapshot" of the current state of natural water resource availability in the Apulian region by extracting and estimating the main hydrological balance components introduced by the BIGBANG model (Braca et al., 2021) by exploiting Copernicus services and freely available medium-high resolution satellite data. Following the collection of all necessary input data, such as high-resolution Digital Elevation Model (DEM), Corine Land Cover maps, remote sensing-based soil sealing maps, mean monthly air temperature and rainfall, Google Earth Engine (GEE) environment, a free cloud platform recently released by Google to manage big geospatial data, were used to handle and estimate the main hydrological balance components. The proposed approach, based on the integration of Copernicus services and the BIGBANG model, appears as a useful and operational tool for supporting sustainable and adaptive resource management activities, particularly in water crisis situations. In fact, it allows extracting trustworthiness water balance components quickly.
How to cite: Capolupo, A., Barletta, C., and Tarantino, E.: Copernicus services and data to support a sustainable and adaptive water resource management , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2449, https://doi.org/10.5194/egusphere-egu23-2449, 2023.
With the ongoing economic development and population growth, the shortage of water resources has become a severe problem which involves conflicts and tradeoffs among society, economy, environment, and ecology. Although previous researches proposed multi-objective optimization models, human-water coupled models, and hydro-economic models to deal with these conflicts and tradeoffs, they still did not address water demands’ integration and lacked future vision of water resource allocation. This paper proposed a Water Resource Allocation Model based on coupled Socio-economic-Environment-Ecology-Resources System (WRAM-SEERS) which considered integrally optimization objectives of socio-economic, environmental, and ecological subsystems under the constraints of water and land resources. The proposed model has the following advantages: (a) It could reflect all the closely related elements of the evolution of human society including urban and ecological space planning, cultivated structure and scale, population structure and size, industrial structure and scale, and so on, (b) It could generate the Pareto frontier surface, which maximized the socio-economic interest while minimizing the adverse externalities reacting in environment and ecology, and (c) It could forecast the future development range of each subsystem under hydrologic uncertainties. We applied WRAM-SEERS to allocate water resources of Yinchuan City in China in 2021-2035 and explained issues related the future perspective of Yinchuan: (a) “what is the lower and upper limits of subsystems’ development targets”, (b) “how to set targets consistent with sustainable development”, and (c) “how to achieve the settled targets”. The above explanations provided a scientific basis and decision-making reference for improving the water safety guarantee ability of Yinchuan's economic and social development and promoting a green, sustainable and high-quality development.
How to cite: Shi, Y., Wang, Z., Chen, J., and Chen, J.: A Water Resource Allocation Model Based on Coupled Socio-economic-Environment-Ecology-Resources System, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4674, https://doi.org/10.5194/egusphere-egu23-4674, 2023.
Indicators on the sustainability of productive human sectors help boosting societal awareness and provide remarkable information for political decision-making and resources management. Prominent examples of relevant environmental indicators currently available are those that form the footprint family. In agriculture, the water footprint approach provides indicators that integrate direct and indirect freshwater usage. While a considerable number of studies developed so far used tabulated values for crop parametrization, the less explored application of dense remote sensing time series provides huge benefits.
This paper aims to present the spatiotemporal estimation of the green and blue Remote Sensing-based Agriculture Water Footprint (RS-AWAF) at the Pinios River Basin (11,000 km2) in Greece (year 2017), combining two globally accepted and operational methodologies: the Soil Water Balance published by the Food and Agriculture Organization in its irrigation and drainage paper 56 for water accounting purposes, and the standardized methodology for Agricultural Water Footprint estimation of growing a crop or tree published by the Water Footprint Network. Initially, the RS-AWAF applies dense temporal series of the Normalized Difference Vegetation Index produced by Sentinel-2 data at 10m spatial resolution to monitor the crops provided by local authorities through the Land Parcel Information System and derive the biophysical parameters along its development, such as the basal crop coefficient and the fraction of soil surface covered by vegetation. Those are then integrated into a validated and operational Remote Sensing-based Soil Water Balance that day after day and within a pixel spatial scale, estimates among other components of the balance, the adjusted crop evapotranspiration (ETcadj) and the net irrigation requirements (NIR). In a second step, both previous components are combined to estimate the blue crop water use (CWUblue), related to the NIR, and the green crop water use (CWUgreen), related to the fraction of the ETcadj that comes from other freshwater sources different than irrigation, the precipitation. Finally, crop yield values collected from official statistics per crop or crop group are used to estimate the blue water footprint (WFblue) and the green water footprint (WFgreen).
Once the green and blue RS-AWAF is estimated, a collection of thematic maps over the Pinios River Basin is ready for use by local stakeholders at their desired working scale. In that sense, monthly and annual thematic maps of ETcadj, NIR, CWUgreen and CWUblue are available, as well as annual thematic maps of WFblue and WFgreen. In parallel, tabulated values are created from these parameters using zonal statistics through GIS at the spatial scale appropriate to the final user (i.e. water user associations).
These results are part of the EU Horizon 2020 project REXUS (Managing Resilient Nexus Systems Through Participatory Systems Dynamics Modelling), in which stakeholders from water user associations to river basin water managers are evaluating the information. At this stage, our final goal is to provide spatiotemporal distributed accounting of agricultural freshwater resources over large areas that enhance regional knowledge and increases efficiency in water management and subsequently contributing to energy-saving, since the major agricultural water volume is abstracted from deep groundwater wells.
How to cite: Garrido-Rubio, J., González-Piqueras, J., Calera, A., Babakos, K., Pisinaras, V., Panagopoulos, A., and Osann, A.: Spatial and temporal estimation of the green and blue Remote Sensing-based Agriculture Water Accounting and Footprint at the Pinios River Basin, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14825, https://doi.org/10.5194/egusphere-egu23-14825, 2023.
Water distribution networks (WDNs) need restructuring/sectorization into District Metered Areas (DMAs) depicting smaller communities for ease in ensuring equitable water distribution and pressure management. DMA demarcation also helps in systems operation and management, apart from leak and contaminant localization. Many different strategies for DMA demarcation are in use, as none is established to be universally superior. Hence, there is ambiguity in the choice of a strategy for DMA demarcation. A WDN can be viewed as a complex network due to strong interconnections among its components and imposed limitations (being a 2D network). Against this backdrop, the recent decade witnessed the use of community detection approaches from complex network theory (CNT) for DMA demarcation. Community detection is a very basic yet pivotal task in the field of CNT. Modularity maximization is the most widely used approach for community detection. The modularity index defines the quality of the subgraphs or communities delineated from a network. In the case of a WDN, some nodes may be shared by more than one DMA, in which case the conventional and existing variants of the modularity index cannot be used for assessing the quality of the delineated DMAs. A more comprehensive community (DMA) detection procedure must incorporate such nodes with multiple associations among communities. Facilitating this, the present study proposes a comprehensive approach for DMA demarcation in large and complex WDNs considering a weighted modularity index. Edge weights are assigned to incorporate the hydraulic behaviour of a network and the association of nodes among various communities. The efficacy of the proposed approach vis-à-vis existing methods is demonstrated through a case study on a benchmark WDN. Effective demarcation of DMAs helps in their prioritization (based on the existing network level measures) to devise mitigation strategies for improving their performance.
How to cite: Pandey, J. and Srinivas, V. V.: A New Weighted Modularity-Based Approach for DMA Demarcation in Large and Complex Water Distribution Networks, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-609, https://doi.org/10.5194/egusphere-egu23-609, 2023.
In California’s Central Valley, the forecast from climate models is that future precipitation events will be less frequent, more extreme, and deliver a higher proportion of the precipitation as rain. The combination of these effects will challenge the state’s ability to capture and store this critical freshwater resource and will also threaten downstream communities with flooding. A water management strategy gaining popularity in California is managed aquifer recharge (MAR), where excess surface water is captured and directed to selected sites to recharge the underlying groundwater systems. AgMAR is a form of MAR where water is spread over agricultural land to recharge the underlying aquifer over short periods during the wet winter months. One concern with this strategy is the decades of intensive agriculture in the Central Valley. The intense usage of fertilizers such as nitrogen threatens to contaminate the groundwater systems on which municipalities and homeowners rely. Research into nitrate mobilization has shown that the stratigraphic characterization of a site is the dominant factor in determining the infiltration rate and mobilization of contaminants. Therefore, in order to develop a model of nitrate mobilization, detailed information is needed about surface stratigraphy. In this study, a towed-transient electromagnetic geophysical method (tTEM) was used to image the subsurface, in combination with sediment type logs, to characterize the subsurface sediments. tTEM data were acquired on a 56-ha commercial almond farm in the early spring of 2022. The tTEM data were inverted to recover a resistivity model of the site, exhibiting a high degree of spatial variability. Regions of high resistivity typically suggest coarser grained material that is more permeable and hydraulically conductive, whereas regions of lower resistivity tend to be composed of finer grained material and are less hydraulically conductive. We created a site-specific resistivity-to-sediment-type transform to extract sediment-type data from the tTEM data using data from the 1D resistivity models along with twenty co-located sediment type logs and water table measurements. Using the maximum likelihood model, we transformed the recovered resistivity model into a sediment-type model. The integration of tTEM data and well-derived information about sediment types to construct a sediment-type model can provide information about connected pathways for recharge and help inform nitrate mobilization models. This study is allowing us to develop a methodology that can be applied elsewhere for the assessment of a site for agMAR when there are concerns about nitrate mobilization. This work is in support of a larger project on groundwater sustainability in agricultural systems in the southwestern United States.
How to cite: Peralta, J., Knight, R., Goebel, M., and Kang, S.: Geophysical site characterization, with the tTEM system, for studies of nitrate mobilization during recharge in the Central Valley of California, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10821, https://doi.org/10.5194/egusphere-egu23-10821, 2023.
Climate change scenarios have projected that more frequent and severe droughts are likely to occur, especially in arid and semi-arid regions such as a gran part of the Mediterranean basin, where the effects of climate change on irrigated agriculture are more accentuated. These regions are generally characterized by an agroecosystem based on perennial crops that are sensitive to water scarcity. Citrus is among perennial crops in the Mediterranean area currently facing climate change effects and water scarcity. In the Mediterranean region, the changes in rainfall patterns and the increase in temperature caused by climate change will lead to higher evapotranspiration and consequently, increased irrigation needs for this crop that already has high water requirements.
Thus, it is mandatory to develop climate change adaptation measures to reduce their impacts on water resources in arid and semi-arid regions with intensive use of water for irrigation to increase capacity and efficiency for irrigation and ease the projected water stress. One approach to ensure efficient water management is the development of decision-support tools to improve water management efficiency in irrigation. Crop simulation models are a key tool in extrapolating the impacts of climate change on irrigation water management.
In this context, our study aims at developing an agronomic model for woody crops, especially for citrus, since agronomic models for woody crops are practically absent, in contrast to the wide range of alternatives for annual crops, to define water resource management strategies. The model, named AquaCitrus, is a new functional soil water balance for citrus that simulates on daily time step water fluxes in the soil-plant-atmosphere complex. In short, AquaCitrus is composed of a set of sub-models computing the fluxes of effective precipitation, infiltration, runoff, soil evaporation, drainage, and crop transpiration. The model includes the routine of rainfall interception by the canopy, and computes the soil evaporation and crop transpiration separately. Soil evaporation is calculated using the model of Ritchie and citrus transpiration is calculated by the transpiration coefficient method. AquaCitrus considers the heterogeneity of the soil, given that localized irrigation keeps a small fraction of the soil frequently wet while the remaining area remains dry, unless it rains. Therefore, the model is divided into two compartments that solve the water balance separately for each soil zone.
To assess its predictive power, AquaCitrus was evaluated using a 2-year period of soil moisture data from an experimental field conducted in a citrus orchard in Valencia, Spain. The results pointed out a good agreement between simulated and measured soil water contents at different soil depths; the model predicts the water balance of the system satisfactorily. We concluded that AquaCitrus is a useful tool to simulate strategies for improving irrigation water use efficiency in citrus crops, highlighting that there is additional room for improving its robustness.
Acknowledgements:
This research has been supported by the ADAPTAMED project (RTI2018-101483-B-I00), funded by the Ministerio de Economía y Competitividad (MINECO) of Spain including EU FEDER funds; and by the GoNEXUS project (GA. 101003722), funded by the European Union Horizon Programme call H2020-LC-CLA-2018-2019-2020.
How to cite: Boubakri, N., Garcia-Prats, A., and Pulido-Velázquez, M.: AquaCitrus: Soil water balance model for irrigation management in citrus orchards, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13408, https://doi.org/10.5194/egusphere-egu23-13408, 2023.
Innovative soil moisture (SM) monitoring and modelling methods are key to reduce irrigation water use in the face of expected water scarcity and increase of droughts related to climate change. A promising irrigation monitoring method is Cosmic-Ray Neutron Sensing (CRNS), which is based on the negative correlation between fast neutrons originating from Cosmic-Ray neutron intensities and SM content. The CRNS key advantage lies in its relatively large sensing volume of several hectares, which allows to use a single CRNS instead of a network of point-scale sensors. Additionally, land surface models such as the Community Land Model (CLM5) that simulate the exchange of water, energy, carbon and nitrogen at the land–atmosphere interface can be a valuable tool to study the efficiency of irrigation and effects on crop growth. In this study, novel CRNS and the newly developed CLM5-FruitTree were tested in two small (~1.2 ha) irrigated apple orchards located in the Pinios Hydrologic Observatory (Greece). In 2020, a climate station (Atmos21) and a network of 12 SoilNet nodes, each with two SM sensors at 5, 20 and 50 cm depth, were installed in each field, as well as water meters to measure irrigation timing and amounts. In addition, a CRNS was installed in each field to test the possibility of monitoring irrigation and informing irrigation models. We found that the CRNS was very sensitive to the weekly irrigation events. However, the magnitude of the SM fluctuations caused by the irrigation was underestimated by the CRNS resulting in an RMSE of up to 0.058 cm3 cm-3. This can be attributed to the fact that the CRNS has a large footprint, and the neutron counts were therefore also influenced by the surroundings of the irrigated field. Therefore, to compensate for this influence, an additional SoilNet node was installed outside one of the two irrigated fields in 2022. By combining these data with neutron transport simulations of the study area, a correction of CRNS-derived SM was developed to better capture both timing and magnitude of SM changes (RMSE reduced to 0.031 cm3 cm-3). In parallel, CLM5-FruitTree was able to reproduce the observed SM response to irrigation when the local irrigation schedule was considered (i.e., defining starting date, timing, and target soil moisture for irrigation). Interestingly, the simulated irrigation in 2021 and 2022 used 10 to 60 % less water than the amount applied by the farmer. This suggests a great water saving potential through a reduction in irrigation amounts or through improvements in irrigation efficiency by reducing losses through evaporation or deep percolation. However, existing model weaknesses in the representation of soil properties and water fluxes need to be further addressed for this modelling approach. Nevertheless, the results of this study are a further step towards the use of novel CRNS and modelling tools as a decision support system in irrigation for more efficient use of water resources.
How to cite: Brogi, C., Dombrowski, O., Bogena, H. R., Pisinaras, V., Köhli, M., Hendricks-Franssen, H.-J., Panagopoulos, A., Babakos, K., and Chatzi, A.: Smart irrigation using novel cosmic ray neutron sensors and land-surface modelling approaches , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8570, https://doi.org/10.5194/egusphere-egu23-8570, 2023.
The search for innovative approaches to agricultural production is fundamental to face the challenge of providing sustainable food production without depleting natural resources for growing population. Therefore, an accurate assessment of the green and blue water needs of cultivated land under different agricultural strategies is essential for systematic water management in agriculture. Among the limits to global food production, water availability, and soil and water salinity play key roles, especially in semi-arid and arid regions. In those areas, a possible solution for a sustainable increase in crop production while preserving natural resources is shifting small vegetable crop productions in a controlled environment, as greenhouses, where temperature, humidity, light, and other factors can be adjusted to meet the plant's needs. Here, we propose a method to estimate the water needed to grow small vegetables under different crop production techniques, from the more traditional approach in the field to innovative soilless cultivation techniques in greenhouses, with a case study in Egypt. To do so, we use the spatially distributed agro-hydrological model WATNEEDS to simulate the plant growth, including the effect of greenhouse production on crop water demand. Moreover, we simulate the possible use of brackish water for irrigation. Results show that by shifting to protected cultivation, the reduction in crop water requirement is 60%,65%, and 30% for tomato, watermelon, and pepper, respectively. This model could be used for irrigation planning and resource management policies. Besides, it can be helpful on multiple scales, from farm to global scale.
How to cite: Chiarelli, D. D., Karimzadeh, S., d'Odorico, P., and Rulli, M. C.: Modeling innovative approaches for agricultural production with a case study for small vegetable production in Egypt, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13258, https://doi.org/10.5194/egusphere-egu23-13258, 2023.
Climate change is increasingly affecting agriculture water resources. This adverse situation can be addressed by developing approaches focusing on optimization for agricultural water management. Groundwater depletion is a serious issue for the sustainability of irrigated agriculture in the southern and central parts of the High Plains Aquifer (HPA), USA. Crops that require more water to grow (e.g., maize) may not receive sufficient irrigation due to decline in pumping capacities, and growers can experience yield loss, jeopardizing the farm profits. Geospatial crop modeling can be seen as a tool to simulate different scenarios of water availability for crops in regions like Texas and Oklahoma Panhandle. Open-source version of AquaCrop (AC-OSPy) was run under a gridded environment on the maize pixels of crop frequency layer developed by National Agricultural Statistics Service. Long-term simulations for past 30-year period (1991-2020) were run using the historical weather data for multiple irrigation application rates. Also, deficit irrigation was tested to assess the impact of skipping irrigation in different crop stages. The simulations were able to capture the variation of weather and soil patterns in the region. Mean irrigation requirement ranged between 78 mm and 314 mm under 50% available water capacity irrigation threshold, and mean yield varied from 8.8 to 14.3 Mg-ha-1. Deficit irrigation showed a potential of water saving during initial and vegetative stages (up to 113 mm), whereas a significant decline in yields was noted for skipping irrigation during flowering. Overall, the results of the study showed great potential of using geospatial crop modeling approach for regional agricultural water planning and drought mitigation efforts.
How to cite: Saddique, Q., Ajaz, A., and Jain, S.: Using spatial crop modeling to improve the regional agricultural water planning. , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4511, https://doi.org/10.5194/egusphere-egu23-4511, 2023.
Posters on site: Tue, 25 Apr, 16:15–18:00 | Hall A
With the increasing impact of global climate change, the effective utilization of water resources become more and more important. Among different water use sectors, agricultural irrigation accounts for about 70% of global water use. Different from other water uses which can be utilized at any time, irrigation is not only a matter of quantity, it is more important to accurately schedule according to the water demand of crops. However, in many studies in the field of water resources or agriculture, the non-rejuvenation of crop growth is usually ignored when calculating crop yield. These studies usually simply assume that irrigation water is effective in each period, and finally add up the yield of each period as the total benefit of irrigation. To solve this problem, the study aims to explore the dynamic decision-making of irrigation schedules with the consideration of uncertainty under water scarcity. Considering the characteristics of crop growth, we propose an analytical model in the form of a max-inf problem to investigate a two-stage stochastic optimal allocation of water to maximize the yield expectations of the two stages. We first assume that the rainfall in the first stage is known, and the rainfall is described by a given probability distribution. With FAO 33, an empirical production function assessing the yield response to water, we need to apply the concept of max-inf to determine the expected yield. Accordingly, we found the optimal condition which maximizes the yield, satisfying a linear relationship between the probability of the first stage dominance and the water demand and yield response factor of the two stages. With this optimal condition, we can use the known crop water demand and yield response factor to estimate the probability of the first stage dominance and adjust the irrigation water to achieve the condition of maximum yield expectation, to achieve the goal of maximum yield. Following, this study proposes four scenarios to examine the optimal decision with numerical experiments, not only to verify the analytic solution but also to examine the decision-making under different conditions. So far the decision is still discussed within the two-stage framework, assuming that the rainfall in the first and second stages is known, and adding the rainfall uncertainty in the third stage to analyze irrigation water. It will extend to the multi-stage framework which could more reasonable presentation of crop yield decisions. In this way, this study can better help us to understand irrigation decision-making among different water supply stages under uncertainty.
How to cite: Cai, Y.-S. and You, G. J.-Y.: Optimal Irrigation Water Allocation among Different Growth Stages, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4269, https://doi.org/10.5194/egusphere-egu23-4269, 2023.
Pinios River Basin in central Greece (PRB) is region of highly productive agriculture where irrigation intensification and climate change have caused a significant depletion of groundwater resources. In the framework of the EU-Horizon 2020 project ATLAS, a precision irrigation scheduling service has been developed that aims at improving irrigation water management at the field scale. Such service is intended to protect crops from water stress by keeping the soil moisture (SM) in the root zone above the maximum allowable deficit (MAD). The presented approach is developed in two highly instrumented apple orchard pilot fields (~1.2 ha extent each) located at the Pinios Hydrologic Observatory ILTER site in PRB. In each pilot field and for two consecutive cultivation periods (2021 and 2022), intensive monitoring of meteorological parameters plus SM in 12 locations and at three depths (5, 20, 50 cm) was performed. To determine the time and volume of the next irrigation event, the forecast of meteorological variables for the next six days provided by the Global Forecast Model (GFS) was included in the service. The irrigation service performance was evaluated via comparison of the model estimated crop evapotranspiration (ETc) values against the SM content distribution monitored by the cluster of the installed SM sensors. The potential service contribution to reduce irrigation water consumption was assessed via comparison of the modelled irrigation water demands against the actual water consumption monitored at the irrigation blocks that divide each field. Statistical metrics demonstrate a good agreement between modeled crop evapotranspiration (ETc) and the monitored SM dynamics as captured by the SM sensors. Comparisons between the calculated irrigation demands and the actual water consumption monitored at the irrigation blocks of the pilot fields show that irrigation water applied in the fields may be reduced from 15% up to 50% or more in some instances, without considerably impacting crop health and yield. On the contrary, significant gains may be achieved on water saving and consequently on energy consumption to abstract irrigation water, thus contributing considerably to the region’s water-energy-food nexus sustainability.
How to cite: Tsakmakis, I., Babakos, K., Chatzi, A., Pisinaras, V., Brogi, C., Bogena, H., Dombrowski, O., and Panagopoulos, A.: Precision Irrigation Scheduling through High Frequency Data Monitoring. Implementation in Apple Orchard Cultivations - central Greece., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5186, https://doi.org/10.5194/egusphere-egu23-5186, 2023.
Most of the climate scenarios predict increased water scarcity in arid areas, such as Hungary. However, the irrigated area in Hungary covers 2% of agricultural land, mostly with outdated irrigation technology. The aim of the research was to develop the basis of a variable rate irrigation for water-saving precision sprinkler irrigation system on an arable area (85 ha) which is located in the reference area of the Tisza Riven Basin. There is limited available water resources at the site, therefore alternative water sources utilization system was set up for irrigation to adapt to climate change and reduce fertilizers. The basis of the alternative water resources are excess water, treated wastewater, biogas fermentation sludge which is collected in a water reservoir with 114000 m3 capacity. For proper irrigation scheduling, heterogeneity of topography, hydrological, soil and crop conditions has to be explored and monitored. Therefore physically-based modelling of the water balance and remote sensing-based surplus water and vegetation status surveying are tested to use for accurate irrigation scheduling.
Shallow groundwater and/or soil compaction can also contribute to excess inland water. This may occur even if there are drought periods in a year (e.g. in the Pannonian region), resulting in spots with a low crop yield. A LiDAR-based digital elevation model was found to provide appropriate data to identify sites affected by excess inland water. The spots identified can be used as spatial input data to compile a variable rate irrigation prescription map for imposing reduced (or zero) irrigation at areas more vulnerable to the occurrence of excess inland water. The water balance was also assessed for sites with physically-based models. Hydrus was used to model soil moisture changes at the Hungarian case study site.
A model concept for crop evapotranspiration estimation was also developed based on vegetation indices calculated from satellite imagery. Several combinations of sensors and remote sensing products were tested to use in ETc modelling potentially. This approach was tested both at the Hungarian case study sites. Remote sensing-based analysis of crop evapotranspiration, combined with physically-based modelling, appears to be a promising method in water balance modelling of maize fields, especially if these fields are in summer when the crop is fully developed. However, the remotely sensed information verification is essential for the proper utilization of the remote sensing data in ETc modelling and predicting the spatio-temporal dynamics of crop yield, evapotranspiration, and irrigation demands.
There is a need further benchmark scenarios to improve both physically-based models and satellite-based crop evapotranspiration models to achieve more accurate and valid simulations.
The abstract was funded by European Union’s Horizon 2020 “WATERAGRI Water retention and nutrient recycling in soils and steams for improved agricultural production” research and innovation programme under Grant Agreement No. 858375. This research was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
How to cite: Nagy, A., Fehér, Z. Z., Szabó, A., Buday-Bódi, E., Magyar, T., and Tamás, J.: Management of alternative water resources for variable rate irrigation - a Hungarian case study, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5592, https://doi.org/10.5194/egusphere-egu23-5592, 2023.
In many production areas, water is the main limiting factor for agricultural development. The consequences of global climate change, population growth, changes in land use and overexploitation due to economic growth have caused water resources to be subject to severe degradation. As the main consumer of water resources, water use in this sector is of great importance. However, the wide variety of stakeholders and the need for food supply make the management of agricultural systems very complex. In this context, it is necessary to incorporate water management practices and technologies that contribute to the sustainability of agricultural activity. To ensure the success of these practices, their choice and adoption must take into account the interests of different stakeholders. Therefore, the first objective of this work is to identify the sustainable water management practices that are best suited to the context of the study area, as well as the main barriers and facilitators to their incorporation. For this purpose, several qualitative research tools are used in consecutive phases (literature review, in-depth interviews, Delphi method and workshop). The results show that the most suitable practice for mainstreaming is rainwater harvesting (RWH). Facilitators for the adoption of this practice include the existence of farmer networks and access to the necessary technology, while installation costs and certain characteristics of the study area and farms act as main barriers. The second objective of this work was to identify the factors affecting farmers' decision to adopt RWH systems to use harvested water for agricultural irrigation. For this purpose, farmers in the study area were surveyed and a binary logistic regression model was carried out. The variables found to be significant in explaining farmers' behaviour were age, educational level, farm size, pond capacity and volume, income and level of environmental awareness.
How to cite: Velasco Muñoz, J. F., López Felices, B., Balacco, G., and Aznar Sánchez, J. A.: Adoption of rainwater harvesting systems for agricultural irrigation to improve water management, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-133, https://doi.org/10.5194/egusphere-egu23-133, 2023.
Climate change has led to the redistribution of water resources in many regions due to changes in global and regional water cycles. Sustainable water management is essential to ensure further socioeconomic development in the fastest-growing megalopolitan region, such as the Greater Bay Area (GBA) in China. Although optimal water allocation policies for various jurisdictions, provinces, cities, and areas in the GBA have been widely explored, most studies have focused on solutions within the context of existing infrastructure. Optimizing allocation on such a regional scale is a significant challenge due to differences in objectives, decision-makers, long-term contracts, trade deals, and other factors that are difficult to model or that make obtaining reliable data difficult. The presented study is part of a 3-year collaborative efforts among experts in climate change, water resources and operation research funded by the Hong Kong government (CRF Ref. No. C6032-21GF). And instead of focusing on determining optimal allocation, we intend to investigate the sustainability of the current scheme in line with the area’s rapid development under intensified climate variability and provide supportive information on the alleviation of system stress and bottlenecks over time.
We start with developing an aggregate rain-flow allocation model over a relevant time horizon to minimize shortage and overage costs. The model is infrastructure cost-agnostic and focuses on the marginal value of added storage capacity and network connectivity. We use the dual variables of the optimization problem, aggregated over different demand and supply scenarios, to identify infrastructure projects that can best improve the performance of the system based on projected but uncertain demand growth. Specifically, we first obtain the allowable range that can be solved by a linear programming based on the dual problem and subsequent problem reformulation for a single project. Then we introduce the approximated allowable range by aggregating over multiple scenarios to improve accuracy and computational efficiency. Combined with the aggregated marginal value, these features are used to create a list of the most promising projects in terms of their ability to improve the matching of supply and demand. The model can use feedback from decision makers to eliminate from consideration projects that are too expensive to build. The analysis can be used recurrently to obtain further improvements leading to a feedback loop with a finite number of rounds. This feedback loop can save significant time and effort compared to cost-based models that require obtaining cost data for many projects that will never be built. Based on our current results, we find that this process is quite efficient, and the feedback loop will basically end in a few rounds. These results can be extended in several directions including the discounting of cash flows. Moreover, we identify pairs of projects that have positive synergies making one more effective in the presence of the other.
* The author list is in alphabetical order
How to cite: Gallego, G., He, Y., Lu, M., and Qi, J.: A Cost-Agnostic Model to Identify Infrastructure Projects to Improve Rain-flow Allocations in a Growing Demand Environment, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4652, https://doi.org/10.5194/egusphere-egu23-4652, 2023.
This paper addresses the doubts regarding the spatial characteristics of the commonly used rules for parallel reservoir system operation. The rules based on aggregation-decomposition determine the system total release first and then assign this release to individual reservoirs, without considering the water demand distribution in the river network. In this paper, a conceptual model for parallel reservoir systems with distributed water demands is proposed. Three specific optimality conditions are derived for determining the optimal analytical solution. A rigorous proof shows that the aggregation-decomposition-based rules are a special case of the derived rules. An efficient algorithm is then developed based on the optimality conditions and shortage allocation index (SAI), in which a larger SAI indicates taking a higher percentage of the system water shortage, as release or storage. Unlike traditional algorithms that modify the violated variables empirically, we propose a criterion in terms of relative deviation indicators to determine the crucial priority of variable modification. This criterion can effectively address constraint violations. The optimal rules along with the solution algorithm are then demonstrated by the operation of a parallel reservoir system in the Shiyang River Basin, China. The results show that the proposed rules and algorithm are more efficient and effective than traditional algorithms and aggregation-decomposition-based rules, especially in dry seasons with more binding constraints.
How to cite: Meng, W., Wan, W., Zhao, J., and Wang, Z.: Optimal Operation Rules for Parallel Reservoir Systems with Distributed Water Demands, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10741, https://doi.org/10.5194/egusphere-egu23-10741, 2023.
Irrigation in northern Italy takes advantage of the Maggiore, Como, Iseo, Idro and Garda pre-alpine lakes, whose management rules and structures allow to stock rain and snowmelt outside the irrigation season and share it among the downstream users during late spring and summer. Consorzio Irrigazioni Cremonesi founded in 1883 (CIC in the following) is the most important irrigation consortia in the province of Cremona (northern Italy) in terms of amount of discharged water for irrigation purposes and manages a channel network that dates back to the 16th century. The maximum discharge derived from the Oglio river and the Adda river by CIC is 57.8 m3/s transported to the different withdrawal points (271) by an open channel network with a length of approximately 261 km. The water distribution provided by CIC is regulated by a complex and rigid timetable of the water turn, which defines the amount of water delivered to each user and the time duration. The intakes of the channel network are provided by the regulation of pre-alpine Lake Iseo and Lake Como, whose level regulation dates back to 1930 and was defined by law considering a set of conflicting constraints as well as the water demand of the irrigated areas. The water distributed by CIC provides a set of ecological services that go beyond simple irrigation. Although the management of these Lakes is expected to change under the effects of the climate change, on the other hand the management of the irrigation water system is very stiff, being based on pure historical custom and relying on the practical experience of a small group of people. Accordingly, it is likely that this traditional management will become unsuitable in the future and practical experience could be of little use in search of new optimized water distribution frameworks. To manage this transition, CIC is building a mathematical model of the channel network that will be used to test different management options, following the reduction of available discharge caused by different conditions of the lake. The mathematical model, based on the one-dimensional formulation of the Saint-Venant equations, should be able to perform long time simulations for a set of complex interconnected channels in order to capture the different regulations of the gates in correspondence of the withdrawal points along the channel and to take into account the large number of structures which affect the flow along the channel network.
How to cite: Farina, G., Milanesi, L., and Pilotti, M.: Modelling a complex lowland irrigation channel network to optimize management operation under future scenarios of climate change, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12723, https://doi.org/10.5194/egusphere-egu23-12723, 2023.
Events-based hydrology phenomena are affected by extreme spatio-temporal variability. Precipitation is the first source of variability. Storms can start at different times across a catchment and can evolve differently over time thus creating a complex frame for events-based hydrological modelling. Both are affected by a random character. On many occasions a scarce number of weather stations are available within the catchments so researchers and hydrologists are forced to use interpolation methods for estimating precipitation. Classical interpolation methods base on deterministic algorithms not properly accounting for the random character of storm. In this work we analyse the spatial variability of the recorded storms in a set of urban weather stations in Madrid delivering some criteria for dealing with spatio-temporal variability of storms for modelling events-based hydrological processes.
How to cite: Rodriguez-Sinobas, L., Zubelzu, S., Bernal, C., Gómez, M. T., López Santiago, J., Zanella, A., Bennis, M., Capuzzo, M., Matendo, S. E., Ghalkha, A., and Ben Issaid, C.: Analysing random spatio-temporal variability of storm events for hydrological modelling, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13966, https://doi.org/10.5194/egusphere-egu23-13966, 2023.
Multiple cropping is an effective measure to increase the intensity of land use. The North China Plain is one of China's most important grain production areas, with 70 % of the arable land under double rotation of winter wheat and summer corn. Soil water flow affects the distribution of irrigation water between the two cropping seasons, as the final soil moisture condition of one season is the initial soil moisture condition of the next season. Therefore, yield response and deficit irrigation scheduling are sensitive to soil hydraulic characteristics. The study analyzes this sensitivity depending on factors such as initial soil moisture condition, soil texture, and irrigation scheduling under different levels of limitation in irrigation water. Simulation results indicate that under most scenarios, the impact of initial soil moisture conditions on yield was much more significant than that of soil hydraulic characteristics. However, the impact can also vary depending on the selected irrigation strategy and water limitations. Therefore, for optimal full and deficit irrigation in a crop rotation system, the intra-annual irrigation water allocation should consider the soil water flow between two cropping seasons. In addition, an optimal irrigation strategy can largely mitigate the adverse effects of unfavorable soil hydraulic characteristics. Furthermore, the optimal irrigation strategy improves crop water productivity and food security at the same time.
How to cite: Fan, X. and Schütze, N.: Response of yield for a deficit irrigated crop rotation system: sensitivity of soil hydraulic parameters, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14625, https://doi.org/10.5194/egusphere-egu23-14625, 2023.
Irrigated agriculture is the world’s largest water consumer, while at the same time water resources are under increasing pressure from rapidly growing demands and climate change. In Greece, about 45% of the total cultivated area is being irrigated and groundwater is the main source of irrigated water supply. Due to the scarcity of fresh water, the islands of Greece face serious problems by saltwater intrusion in coastal aquifers. In these areas, it is a common practice to utilize saline groundwater in irrigated olive orchards. Thus, estimation of all water fluxes temporally and spatially within and out of the crop root zone, and evaluation of issues like salinity are necessary to fully assess the efficiency of irrigation systems and methods. Simulation models can be used to investigate these issues over several seasons and scenarios. In this study, HYDRUS 2D/3D was used to evaluate data measured during one season (2022) in an olive (Olea europaea) orchard in Crete, Greece. The model efficiency was assessed by comparing model simulations against the observations of θ and EC obtained by an IoT-based monitoring system installed in the frame of HORIZON 2020-Agricapture project in irrigated fields of the Merabello area (Eastern Crete). The system includes the monitoring of soil moisture and atmospheric sensors, providing information on irrigation scheduling to farmers. Three IoT-devices were established in the study field, connected with an advanced soil moisture, temperature, and electrical conductivity sensor Teros12 (METER group, Inc. USA) installed at 0.3 m depth. Meteorological data collection was possible through a weather station (Davis Vantage Pro2™) installed in the area. Comparison of simulated against observed θ and EC showed a good precision of HYDRUS 2D/3D model for olive trees irrigation, with the Nash-Sutcliffe and the Root Mean Square Error (RMSE) being within the acceptable ranges. Model results can be used to improve the decision-making IoT system and advise farmers on various aspects of irrigation under saline environment, as, for example, in scheduling irrigation events for leaching salts, to avoid crop damage.
The authors acknowledge contribution of the AgriCapture CO2 - Horizon 2020 project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004282.
How to cite: Morianou, G., Tzerakis, K., Psarras, G., and Kourgialas, N.: Assessing soil moisture and salinity dynamics in an irrigated olive orchard using the HYDRUS 2D/3D model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15080, https://doi.org/10.5194/egusphere-egu23-15080, 2023.
In the Mediterranean area, Greece is considered as one of the most important olive producing countries. Specifically, olive (Olea europaea) is the main crop covering more than 75% of the total tree cultivation area, while about 45% of the total cultivated area is being irrigated. Groundwater is the main source of irrigated water in Greece, but the growing demand and the climate change are putting this resource in risk. The design of an optimal irrigation plan, based on detailed measurements and modeling tools, can effectively contribute towards water saving with no loss on crop yield, in the area. In this study, an IoT-based decision-making system for the management of irrigation water resources of olive orchards in a small sub-basin of Lasithi, Crete, Greece is presented. The system integrates monitoring of soil moisture and atmospheric parameters in four fields within the study area and modeling approaches, using the modules of MIKE-SHE model, to simulate water flow in the unsaturated zone at the sub-basin level. Additionally, 45 soil samples (from 3 different soil depths) have been collected and analyzed from the study area for soil texture, bulk density, rock percentage, pH, organic matter, and mineral nutrients. After the successful calibration of the model (comparison of simulated against observed soil moisture values) the spatio-temporal representation of soil moisture is used as guidance for developing optimal irrigation scheduling, considering olive tree water requirements, for the entire study area (sub-basin), even for olive orchards with lack of monitoring equipment installation.
ACKNOWLEDGEMENTS
The authors acknowledge contribution of the AgriCapture CO2 - Horizon 2020 project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004282
How to cite: Kourgialas, N., Morianou, G., Tzerakis, K., Malandraki, A., and Psarras, G.: Decision-making irrigation system based on MIKE SHE model for addressing water scarcity in Mediterranean olive orchards, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16060, https://doi.org/10.5194/egusphere-egu23-16060, 2023.
Posters virtual: Tue, 25 Apr, 16:15–18:00 | vHall HS
Viticulture in recent decades is particularly affected by reduction of water availability due to rising temperatures, drought and heat waves. Climate change projections towards global warming and drought set grape production at risk. Vineyards are largely located in semi-arid areas, such as Mediterranean regions, where the intensity of the drought season largely affect the final yield and quality of production. In order to complete its vegetative cycle, the vine needs large quantities of water. However, an optimal use of irrigation water is imperative for performing a sustainable cultivation, and this can only be achieved through a number of cultural practices, including regulated deficit irrigation and soil management. Moreover, robust techniques to accurately detect plant water stress are necessary.
The development of new technologies related to proximal sensing are assuming great importance in vineyard management. Among them, are non-invasive methodologies based on infrared thermography for assessing plant water status, and supporting tools for irrigation scheduling. Moreover, proximal sensing techniques based on digital images are becoming a valuable tool for detecting crop physiological status.
In this study, thermal and visible images of three varieties of grapevine, under two deficit irrigation regimes, were analysed and evaluated as a tool for supporting crop irrigation management. The experiment was conducted in two vineyards located in Sardinia, Italy, and consisted of two regulated deficit irrigation (RDI-1 moderate and RDI-2 severe) treatments and two reference treatments maintained under stress and well-watered conditions. Digital images were acquired daily, during the entire growing season, using Campbell CC5MPx digital cameras. Thermal images were acquired using the InfRec R500Pro thermal camera (Nippon Avionics Co., Ltd.). Artificial surfaces were used as target reference for wet and dry temperature. Vegetation indices from thermal and digital images, i.e. Crop water stress index (CWSI) and green and red chromatic indices (ExG, GRVI, REI), were then calculated for each observation day.
The analysis of thermal images gave an accurate estimation of the differences in the water status of the vineyard over the RDI treatments. This technique proves to be able to well-differentiate different regimes in water management, confirming its good performance. The differences in CWSI values between moderate or severe water deficit treatments (RDI-1 and RDI-2) were in almost all cases (sites and varieties) statistically significant. These results were also confirmed by the seasonal pattern of both green and red chromatic indices (RGBs indices: ExG, GRVI, REI).
The development of non-destructive, cost-effective and easy-to-use methods for continuous monitoring of grapevine water status is a challenge to be faced in the future. In this context, proximal sensing techniques tested in this study, can provide useful information to develop tools and models for irrigation management.
How to cite: Piga, A., Cesaraccio, C., Ventura, A., Arca, A., and Duce, P.: Thermal and digital images as a tool for detecting water status in Grapevine , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12411, https://doi.org/10.5194/egusphere-egu23-12411, 2023.
