Zenaida Chitu

The agricultural sector heavily relies on weather forecasts for informed decision-making. However, existing forecasting methods often lack localized precision, leading to significant uncertainties in predicting extreme weather events crucial for agricultural planning and management. The MAGDA H2020 project aims to address these challenges by developing a modular system deployed to farms, integrating observations from European space-based and ground-based assets to enhance tailored weather forecasts. This project represents a significant advancement by synergizing spaceborne, airborne, and ground-based measurement technologies, including GNSS and meteodrones observations, with meteorological models to benefit agriculture and water management operations. The project targets improvements in weather forecast accuracy, particularly concerning severe weather events like heavy rain, hail, windstorms. Inaccurate predictions of these events can lead to substantial crop damage, over-irrigation, or water shortages. Key challenges in Numerical Weather Models (NWM) stem from uncertainties in initial atmospheric conditions at small scales, necessitating enhanced observational data for improved model performance. Recent advancements in forecasting heavy rainfall events through data assimilation techniques show promising results. Studies have demonstrated the positive impact of integrating reflectivity data and in situ observations into meteorological models for predicting severe weather phenomena in various regions. Additionally, experiments incorporating Sentinel-derived and GNSS-derived products into high-resolution NWM have shown positive outcomes, particularly in predicting convective processes. The MAGDA project employs a cloud resolving modeling approach with a grid spacing of 2-3 km, coupled with rapid update cycles every 1-3 hours, to address uncertainties in weather prediction. The assimilation process integrates GNSS data to monitor integrated water vapor content, weather radar reflectivity to reconstruct the 3D cloud field, in situ weather stations for capturing near-surface atmospheric conditions, and meteodrones observations to collect information about the vertical profiles. By leveraging a combination of advanced technologies and data assimilation techniques, the project aims to enhance the accuracy and usefulness of weather forecasts tailored for agriculture and water management applications. The weather forecasts will be used as an input for the irrigation advisory, next to being used for generating warnings for extreme weather events. The warnings and irrigation advisories will ultimately be channeled through a Farm Management System to ensure the capability to effectively reach farmers and agricultural operators.

Irrigated agriculture will be impacted by climate change as average temperatures and rainfall variability increase. This trend will continue in the future, according to numerical experiments with climate models, but how it develops will be strongly influenced by the anthropogenic emission levels of greenhouse gases. However, the effects of climate change have not been, and will not be, uniform across regions or over time because human-induced warming is superimposed on natural climate variability (IPCC, 2021).Recent studies (Caian et al., 2023) focused on the projected changes in extreme agro-climatic indicators reveal that Southern Romania appears as a regional hot-spot of climate change because the projected changes are higher and more accelerated than other regions of the country. In this context farmers will need to improve crop water allocation for sustainable irrigation as a measure of climate change adaptation.Irrigation is the largest consumer in the agriculture sector and the efficient use of water is crucial in the next decades. Monitoring soil moisture will improve water allocation in space and time in irrigated agriculture. Braila County has the largest irrigated areas in Romania and efficient water allocation will mitigate the environmental issues related to water scarcity and soil degradation by salinization and erosion. According to the Koeppen-Geiger classification, the climate of this area is warm temperate humid with hot summers (Cfa) (Cheval et al., 2023). The mean annual precipitation is 450 mm, while the mean annual potential evapotranspiration exceeds 800 mm. The agro-climatic conditions require the use of irrigation in order to avoid crop losses and to ensure high crop productivity.In this study, we focus on investigating the feasibility of satellite soil moisture products (AMSR-2, ASCAT, SMOS and SMAP) to derive amount of water applied for irrigation and the applicability of this approach to climatic and irrigation conditions specific to Braila County, Romania. This study has received funding from the European Union Agency for the Space Programme under the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101082189 (MAGDA project).

This paper presents a methodology for evaluating the uncertainties caused by the misalignment between two digital elevation models in estimating landslide kinematics. The study focuses on the earthflow near the town of Chirlești, located in the Bend Subcarpathians, Buzău County, Romania, which poses a high risk of blocking the DN10 national road. Four flights were conducted between 2018 and 2022 using a DJI Phantom 4 UAV using the same flight plan. Monte Carlo simulations were used to model uncertainty propagation of the DEM misalignments in the landslide kinematics analysis. The simulations were applied to the accuracy values of the structure from a motion process used to generate the digital elevation models. The degree of uncertainty was assessed using the displaced material’s total amount in conjunction with the spatial correlation of the displaced material between two consecutive flights. The results revealed that the increase in the RMS values did not determine an increase in the displaced earth between two UAV flights. Instead, combining the RMS values and the correlation coefficient clearly indicated the correspondence between the spatial distribution of the displaced earth material and the overall changes reported between the two UAV flights. An RMS value of up to 1 unit associated with a correlation coefficient of 0.95 could be considered the maximum allowable error for estimating landslide kinematics across space and time. The current methodology is reliable when studying slow-movement landslides and when using short intervals between UAV flights. For rapid movements or significant terrain changes, such as translational and rotational landslides, careful analysis of the correlation coefficient in conjunction with the RMS values is recommended.

The paper presents a methodology to rapidly assess and map the landslide kinematics in areas with dense vegetation cover. The method uses aerial imagery collected with UAVs (Unmanned Aerial Vehicles) and their derived products obtained from the structure from motion technique. The landslide analysed in the current paper occurred in the spring of 2021 and is located in Livadea village from Curvature Subcarpathians, Romania. This landslide affected the houses in the vicinity, and people were relocated because of the risk of landslide reactivation. To mitigate the landslide consequences, a preliminary investigation based on UAV imagery and geological-geomorphological field surveys was carried out to map the active parts of the landslide and establish evacuation measures. Three UAV flights were performed between 6 May and 10 June using DJI Phantom 4 and Phantom 4 RTK UAVs (Real-Time Kinematic Unmanned Aerial Vehicles). Because it is a densely forested area, semi-automated analyses of the landslide kinematics and change detection analysis were not possible. Instead, the landslide displacement rates and the changes in terrain morphology were assessed by manually interpolating the landmarks, mostly tilted trees, collected from all three UAV flights. The results showed an average displacement of approximately 20 m across the landslides, with maximum values reaching 45 m in the transport area and minimum values below 1 m in the toe area. This approach proved quick and efficient for rapid landslide investigations in a densely forested area when fast response and measures are necessary to reduce the landslide consequences.

Curvature Subcarpathians is one of Romania's most complex geological and geomorphic areas, frequently affected by landslides. The juxtaposition of snowmelt and spring rainfalls triggers significant damages to roads and buildings every few years (2018, 2021). In this context, accurately delineating the most affected areas becomes critical for evaluating landslides exposure. Aerial images have begun to be used more and more for different risk assessment phases to detect natural phenomena spread and damaged infrastructure elements. In this study, we use fully automatic detection of the landslide body and infrastructure elements (intact or collapsed buildings and roads) to support Regional Civil Protection Agencies in disaster intervention decision support. Our methodology is based on deep learning techniques for automatic detection, mapping and classification of landslide and infrastructure elements. A U-Net model was trained to detect the landslide body, and several Mask RCNN models were trained to detect the landslide features and infrastructure elements. The training accuracy for the U-Net model used for landslide body mapping is 0.86, and the validation accuracy is 0.80. The training accuracy of the Mask RCNN models is 0.76 for landslide cracks, 0.82 for roads and 0.92 for buildings. Some confusions between landslide cracks and local roads without asphalt are often seen in rural areas. The models are run on high-resolution aerial imagery collected with Unammend Aerial Vehicles after a landslide event. The data obtained from the deep learning models are further integrated with information from various sources such as aerial/satellite imagery, online GIS resources, weather forecasts, and spatial analysis techniques for providing a helpful tool to emergency management specialists. The tools have been integrated into a GIS platform that acts as a decision support system, and it can be used from a graphical user interface without the need to have programming skills.AcknowledgementThis work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-5152, within PNCDI III (project coordinator Ionuț Șandric, https://slidemap.gmrsg.ro) and by the project PN19450103 / Core Program (project coordinator Viorel Ilinca).

In recent decades, water scarcity has become a frequent and widespread phenomenon. Intensification of water scarcity will have economic impact on the main water-using sectors. The highest pressure on the water resources is exerted by agriculture. Irrigation is the largest consumer of the agriculture sector and the efficient use of water is of utmost importance. The aim of this study is to explore the capability of an innovative platform that combines Earth Observation data, weather forecasts and numerical simulations to plan more precisely water allocation in space and time in the irrigated agriculture. This platform, created in the framework of MOSES, provides in Romania short-term irrigation forecasts adapted to the hydrological behavior of alluvial soils specific to the Lower Danube Floodplain. The short-term irrigation forecasts have been tested with applied water volumes and in situ water resources measurements in order to assess the water allocation in the irrigated agriculture. Although irrigation forecast was run operationally only one crop year (2017–2018), in the framework of MOSES Project, the comparison revealed that the irrigation scheduling in this area is more influenced by the infrastructure characteristics and less by the spatial distribution of crop water needs and availability of water resources. Our results show that short-term irrigation forecasts accompanied by real time monitoring of water resources could be successfully used in the irrigation scheduling activity for improving water allocation in space and time in the irrigated agriculture.