Irrigation has been applied by humans for as long as they have been cultivating plants. Nowadays, the amount of water used for agricultural purposes is rising because of an increasing food demand. However, this human influence on the water distribution on land is typically not, or poorly, parameterized in regional and larger-scale Land Surface Models (LSM). Satellite-based microwave observations indirectly observe irrigation, when they sense the entire integrated soil-vegetation system. The optimal integration of fine-scale modeling and satellite observations using data assimilation (DA) is promising to detect irrigation and possibly improve the estimation of irrigation amounts.

This work was realized in the framework of the European Space Agency (ESA) Irrigation+ project. The main aim of this study was to test potential improvements in irrigation simulation due to the assimilation of 1-km Sentinel-1 backscatter data into a system composed by the Noah-MP LSM, equipped with a sprinkler irrigation scheme, and a backscatter operator represented by a Water Cloud Model (WCM), as part of the NASA Land Information System (LIS).  The calibrated WCM was used as an observation operator in the DA system to map model surface soil moisture and Leaf Area Index (LAI) into backscatter predictions and, conversely, map observation-minus-forecast residuals in backscatter back to updates in soil moisture and LAI through an Ensemble Kalman Filter (EnKF). Two separate DA experiments were realized using backscatter data at VV and VH polarizations. The system was tested  at two irrigated sites, located in the Po Valley (Italy) and in northern Germany.

Results confirm a stronger link between the backscatter VV with soil moisture and larger updates in the vegetation state variables when using the VH polarization. The backscatter DA introduced both improvements and degradations in soil moisture, evapotranspiration and irrigation estimates. The spatial and temporal scale had a large impact on the outcomes, with more contradicting results for a detailed analysis at the plot scale. Above all, this study sheds light on the limitations resulting from a poorly-parameterized sprinkler irrigation scheme which prevents large improvements in the irrigation simulation due to the ingestion of Sentinel-1 data and points out to future developments needed to improve the system.

How to cite: Modanesi, S., Massari, C., Bechtold, M., Tarpanelli, A., Brocca, L., Lievens, H., Dorigo, W., Zappa, L., and De Lannoy, G.: Benefits of Sentinel-1 backscatter assimilation to improve land surface model irrigation estimates in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3564, https://doi.org/10.5194/egusphere-egu22-3564, 2022.

Irrigated rice agriculture, if traditionally conducted applying continuous flooding, requires much more irrigation water than non-ponded crops. This is usually a constraint in areas facing water scarcity issues that might directly affect rice production and the competition for water. Climate change might furthermore amplify current difficulties, depending on the hydrological regime, the availability of irrigation infrastructures, rice variety and rice agronomic management practices, among other factors.

Whereas different water availability conditions determine differences in rice growth and yields, the response of this crop is not well established for the rice producing area of the Lower Mondego region (Portugal), which is identified as an area vulnerable to climate change, in particular with respect to increasing precipitation and temperature variability. In coastal areas’ lowlands, the groundwater table (e.g., depth and quality) can also play an important role, namely under the influence of sea level rise. For this region, in the proximity of the Atlantic Ocean, we report on using remote sensing tools to assess irrigated rice growth, in areas i) served by a full gravity irrigation system, and ii) fed directly from a small, non-regulated, river. The data used in our study include land surface images of rice cultivated areas obtained from satellite Sentinel-2A during several years (including a particularly dry year). Although the remote sensing data available from satellite multispectral imagery present some practical constraints (e.g. cloud cover, resolution), results from this study show that remote sensing tools, including the Normalized Difference Vegetation Index (NDVI), are able to differentiate between established rice growth phases, which highlights their usefulness as rice monitoring tools and potential role in assessing the impact of applying different irrigation and agriculture management practices on rice cultivation.

This work was conducted under the umbrella of the international project MEDWATERICE (www.medwaterice.org) that focuses on improving the sustainable use of water in the Mediterranean rice agro-ecosystem and aims at exploring the opportunity to apply water-saving, alternative, rice irrigation methods.

How to cite: Gerardo, R. and de Lima, I.: Rice monitoring in Lower Mondego (Portugal) using multi-temporal Sentinel-2 satellite images: comparison between different irrigation conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7942, https://doi.org/10.5194/egusphere-egu22-7942, 2022.

The agricultural sector is the biggest and least efficient water user, accounting for around 80% of total water use in South Europe, which will be further impacted by climate change in the incoming years. Precision agriculture tools are then needed to increase water use efficiency.

Here, the proposed system couples together remotely sensed land surface temperature (LST), leaf area index (LAI) and ground soil moisture data (SM) with a pixel wise crop-water-energy balances model, for improving irrigation management. The SAFY (Simple Algorithm for Yield) crop model has been fully coupled with the energy water balance FEST-EWB model, exchanging in a double direction the LAI evolution in time from SAFY, which is used by FEST-EWB for evapotranspiration computation, while FEST-EWB provides soil moisture (SM) and LST to SAFY model for constraining crop growth.

A data assimilation framework, based on the Ensemble Kalman filter approach, is implemented to reduce the requirements for parameters calibration, either for soil assimilating satellite LST and for crop growth using LAI. This framework allows overcoming the issues related to crop exposure to shocks due extreme events non-reproducible by the model alone, as well as nutrient lack, crops hybrids or precise amount of irrigation water.

The FEST-EWB-SAFY model has been applied in two Irrigation Consortia in the North and South of Italy which differ for climate and agricultural practices, using data from Sentinel2, Landsat 7 and 8 satellites. The model has then been validated in specific fields where ground measurements of evapotranspiration, soil moisture and crop yields are available.

Overall, the results suggested that the under-calibrated model estimates of LST, LAI, SM and yield are enhanced through the assimilation of satellite data, suggesting the potential for improving irrigation management at both field and Irrigation Consortium scales.

How to cite: Corbari, C., Al Bitar, A., Skokovic, D., sobrino, J., and mancini, M.: Irrigation management though the assimilation of multiple remote sensing data into an energy-water-crop model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10127, https://doi.org/10.5194/egusphere-egu22-10127, 2022.

The impact of climate forcings on the agricultural water demand is a key issue for a globalized food secure world. Most of the withdrawn freshwater is globally consumed by agriculture and assessing how climate variability affect the crop irrigation requirements is essential for effective irrigation policies and large-scale water management. Moreover, given that rainfed agriculture provides 60% of total food production and it is highly dependent on meteorological factors, the assessment of climate-driven changes of crop water requirements and water stress periods is very important to highlight potential impacts on the global food security.

This study deals with the spatio-temporal changes of crop water requirements over 50 years, considering 26 main agricultural products. A comprehensive model for the assessment of daily crop water requirement has been used, based on a soil water balance and considering both rainfed and irrigated scenarios. The analysis exploits the potential of the ERA5 reanalysis dataset from the Climate Change Service of the Copernicus Programme, providing hydro-climatic variables over a multi-decade period. The study analyses the variability of water requirement induced by climate variability and the consequent periods of water stress and irrigation volumes per unit harvested areas.

Results show the evolution of water requirement from 1970 to 2019, enabling the analysis of trends in stressed periods over rainfed areas and of changes in irrigation requirements over lands equipped for irrigation. Significant increases of water stress have been found in almost 40% of global rainfed areas, and 62% of irrigated lands require more irrigation comparing the 1970s and 2010s decades. The irrigation requirement has been estimated per crop, pointing out significant increases through the years and comparing the length of dry periods with the precipitation availability during the growing seasons. A global assessment of crop requirement changes can support policies of water management in different areas of the world, considering also the effects of climate change in the densely harvested areas of the world.

How to cite: Tamea, S., Rolle, M., and Claps, P.: Trends of crop daily water requirements driven by 50-years global hydro-climatic data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11841, https://doi.org/10.5194/egusphere-egu22-11841, 2022.

This research aims at introducing a new methodology to create maps of irrigation types at very high resolution, with yearly updates. While different studies were already performed on simply mapping irrigated areas, there is still no research on classifying irrigation types based on remotely sensed data. This information has a critical scientific value since detailed information on irrigation greatly improves the understanding of human activities on the water cycle. In particular, precise knowledge of different irrigation types is needed in order to correctly model the anthropogenic impact in various land surface models (Ozdogan et al., 2010; Evans and Zaitchik, 2008). Additionally, these maps are also useful for administrative purposes, to estimate the percentage of different irrigation types, monitor changes in irrigation practices and consequently encourage more sustainable use of the freshwater resources. In this research, we produce maps of irrigation types combining state-of-the-art supervised AI classification algorithms for time series classification together with a selection of hydrological variables. In order to train and test the AI models, a field campaign to collect ground truth data was performed in November 2020 around the intensely cultivated region of Catalunya, Spain. From this campaign, important information about crop types and irrigation types (sprinkler, flood, drip/subsurface and non-irrigated) were retrieved for a large number of fields, ensuring to collect a representative sample of the different cultivation and irrigation types employed in the area. Three different models were tested using as inputs a large variety of hydrological variables both alone and combined in multivariate models. Two machine learning models, Time-Series Forest and Rocket, and one Deep Neural Network model, ResNET, were selected for this classification task. The classification was performed using time-series from three different years in order to train the models with a more general and robust dataset, independent from specific meteorological conditions of a single year. The main finding of the research was that Soil Moisture (SM) and Actual Evapotranspiration (ETa) at very high spatial resolution (20 m) consistently showed the highest accuracy, when combined together, with respect to the other variables considered, regardless of the AI model used. Additionally, ResNET showed consistently better performance than the other two AI models over all the metrics used for the comparison (accuracy, precision, recall and kappa). The final classification accuracy retrieved from ResNET using SM and ETa as inputs was 86.59 +/- 2.79, obtained from 10 different runs of the model trained each time with different ground truth data subsamples. As a result of these findings, yearly maps of irrigation types can be created for large areas at field level, delivering detailed information on the status and evolution of irrigation practices.  

REFERENCE:

Ozdogan, M.; Rodell, M.; Beaudoing, H.K.; Toll, D.L. Simulating the effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data. J. Hydrometeorol 2010, 11, 171–184.

Evans, J.P.; Zaitchik, B.F. Modeling the large-scale water balance impact of different irrigation systems. Water Res. 2008, 44, W08448.

How to cite: Paolini, G., Escorihuela, M. J., Bellvert, J., Merlin, O., Villar, J. M., and Cester, I.: A novel methodology for mapping irrigation types from very high resolution remotely sensed data , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12307, https://doi.org/10.5194/egusphere-egu22-12307, 2022.

The use of numerical weather prediction (NWP) outputs in hydrological modeling combined with remote sensing data for forecasting irrigation water demand in the short-medium term, has becoming one of the key actions adopted in precision farming for decreasing water and energy consumptions in the long-term perspective of sustainability.

In the last decades, ensemble prediction systems (EPS) have been developed to support operational decision-making processes in many environmental fields. Unlike traditional deterministic forecasts where the numerical weather prediction model is run only once, in EPS the NWP model is run several times from very slightly different initial conditions and perturbed model parameters, to produce an ensemble of forecasts that are used to account for uncertainty in initial atmospheric conditions and NWP model errors. Moreover, in recent years, limited area ensemble prediction systems (LEPS) have been developed as dynamic regional downscaling of global ensemble prediction systems, opening new opportunities for the application of weather forecasts in agriculture and water resource management. Indeed, high resolution probabilistic forecasting may allow water irrigation managers to set-up agrometeorological advisory services based on a more reliable risk analysis.

This study exploits the potential economic benefit (i.e., economic value) related to the use of an ensemble numerical weather prediction model, such as COSMO-LEPS (20 members, 7 km of spatial horizontal resolution) for irrigation scheduling at farm scale in Southern Italy, by combining its outputs with high resolution satellite images in the visible and near infrared wavelengths for crop parameter estimations. An adaptive ensemble Kalman filter is employed for bias correcting weather forecasts by assimilating ground based meteorological variables. Then, a bucket model for soil-vegetation-atmosphere modeling is implemented for providing spatial and temporal estimates of crop water requirements and irrigation schedules along with their predictive uncertainty.

How to cite: Pelosi, A., Battista Chirico, G., Falanga Bolognesi, S., De Michele, C., and D'Urso, G.: The economic value of ensemble numerical weather forecasts combined with remote sensing data and hydrological modeling for irrigation scheduling: an application to Southern Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13316, https://doi.org/10.5194/egusphere-egu22-13316, 2022.