HS6.5

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.

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.

Frequent water table measurements are crucial for sustainable groundwater management in arid regions. These locations have developed a problem with excessive withdrawal throughout time. However, continuous readings are not available for the majority of these locations. Therefore, an approximate estimate of the rate of increase/decrease in water consumption over time may serve as a temporary substitute for the missing database. The goal is achieved by tracking the increase/decrease in vegetated areas that will generally correlate with changes in the rate of water use. The technique is based on two remote sensing data sets: Landsat7&8 from 2001 to 2021 and Sentinel2A from 2015 to 2021, as well as five vegetation indices: Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Transformed Vegetation Index (TVI). The datasets chosen provided the best performance for small-scale land farms at the research location. (Landsat7) data with a resolution of 30m revealed a substantial increase in land farms from 2.9km² in 2001 to 23.3km² in 2021. The use of the five indices with (Sentinel2A) allowed the classification of vegetated regions as heavy, moderate, or light, as well as the tracking of each class's increase from 2015 through 2021. Additionally, preliminary scenarios were built to measure the pace of growth in water use at the research site by evaluating the rise in vegetated areas and obtaining general information about crop types from farmers. Finally, the NDVI index was modified to better suit the arid areas. The new index is named Arid Vegetation Index or (AVI).

How to cite: Morsy, M., Michaelides, S., Scholten, T., and Dietrich, P.: Monitoring and integrating the expansion of vegetated areas with the rate of groundwater use in arid regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11902, https://doi.org/10.5194/egusphere-egu22-11902, 2022.

Falling water tables in several parts of India, especially in the southern part, experiencing semi-arid climatic conditions with hard rock aquifer systems, possess a threat to food, water and economic security to millions of citizens. Understanding of the water budget in such an area is paramount to take necessary steps towards planning of water usage and its management. Land use information at 1km is recognized as sufficient in hydrological modeling. But what is the best resolution of land use forcing variables for agro-hydrological modeling to simulate the water budget by taking agricultural water withdrawal into account. This study focuses on the use of medium (500m) and high resolution (10m) land cover maps derived from satellite products to map the seasonal rice inundated area extent in the Telangana state to estimate the Irrigation Water Demand (IWD) and withdrawal. We employed Soil and Water Assessment Tool (SWAT), a process based ecohydrological river basin or watershed model, to assess how resolution of land use maps may affect the water budget representation of Telangana. The model is calibrated and validated for a period from 2015 to 2020 (6 years) using monthly river runoff data, groundwater and terrestrial water fluctuation derived from respectively governmental piezometric observations (TSGWD) and GRACE. An uncertainty analysis was performed using the Sequential Uncertainty Fitting (SUFI-2) algorithm. Preliminary results suggest that though trends in runoff are influenced by climate drivers, as southwest monsoon contributes appx. 80% of annual rainfall. However, the farmers seasonal land cover adaptation to surface and groundwater availability have a strong impact on water balance over the study area. Precise land cover information of such temporal variations based on appropriate spatial resolution satellite observations contributes to accurate estimate of IWD especially in groundwater fed areas where rice areas are spread in small aggregates. This study also highlights the adaptation and importance of temporal and spatial resolution of datasets in strategic planning and water management practices in water stressed regions. 

How to cite: Paswan, A. K., Ferrant, S., Selles, A., Tiwari, V. M., and Ahmed, S.: Importance of land-cover data-set spatio-temporal resolution on water budget modeling in highly irrigated areas, Telangana, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12078, https://doi.org/10.5194/egusphere-egu22-12078, 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.