Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use efficiency and reduce crop environmental impact. Yet, SMP monitoring presents challenges and sometimes leads to gaps in the collected data. This research sought to address these data gaps in the SMP time series. Using meteorological and field measurements, we developed a filtering and imputation algorithm by implementing three prominent predictive models in the algorithm to estimate missing values. Over 2 months, we gathered hourly SMP values from a field north of the Péribonka River in Lac-Saint-Jean, Québec, Canada. Our study evaluated various data input combinations, including only meteorological data, SMP measurements, or a mix of both. The Extreme Learning Machine (ELM) model proved the most effective among the tested models. It outperformed the k-Nearest Neighbors (kNN) model and the Evolutionary Optimized Inverse Distance Method (gaIDW). The ELM model, with five inputs comprising SMP measurements, achieved a correlation coefficient of 0.992, a root-mean-square error of 0.164 cm, a mean absolute error of 0.122 cm, and a Nash-Sutcliffe efficiency of 0.983. The ELM model requires at least five inputs to achieve the best results in the study context. These can be meteorological inputs like relative humidity, dew temperature, land inputs, or a combination of both. The results were within 5% of the best-performing input combination we identified earlier. To mitigate the computational demands of these models, a quicker baseline model can be used for initial input filtering. With this method, we expect the output from simpler models such as gaIDW and kNN to vary by no more than 20%. Nevertheless, this discrepancy can be efficiently managed by leveraging more sophisticated models

Modeling of the Effect of Rain Exclusion on Water Dynamics in the Soil of the National Forest of Tapajós, Amazonia.

Desequilíbrios ambientais provocados pela combinação de queimadas, desmatamentos e os fenômenos de ENSO (Oscilação Sul de El Nino) podem ser os responsáveis pelo aumento de períodos de seca na região Amazônica. Com o propósito de compreender as conseqüências que longos períodos de seca podem causar na Floresta Nacional de Tapajós, foi desenvolvido um modelo numérico que simula a dinâmica da água no solo e o Balanço Hídrico para um latossolo (Haplustox, na taxonomia Americana), muito comum na região Amazônica. As simulações foram realizadas para o período de 1999 a 2003, utilizando dados de precipitação, evapotranspiração, umidade do solo, curvas de retenção, propriedades físicas do solo coletadas no local de estudo. Este estudo integra o Projeto Seca-Floresta do grupo de pesquisa do LBA (Experimento de Grande Escala da Biosfera-Atmosfera na Amazônia) para o experimento de exclusão parcial da chuva no projeto na Floresta Nacional Tapajós no que diz respeito a componente de modelagem hidrológica. Os resultados mostraram que, mesmo com a diminuição da quantidade de água disponível para a Floresta, não houve mudanças significativas em relação ao balanço hídrico da floresta, mostrando que a floresta provavelmente se adaptou, para sobreviver a longos períodos de secaEnvironmental instability caused by the combination of fire, deforestation and the ENSO phenomena (El Nino South Oscillation) can be the responsible for increases of dry periods in the Amazonian region. With the purpose of understanding the consequences that long dry periods can cause on the National Forest of Tapajós, a mathematical model that determines the water dynamics in soil and the hydrological balance was developed for a typical soil of the Amazonian region the Amazonianlatossol (Haplustox). The simulation were performed for the period from 1999 to 2003, using precipitation, evapotranspiration, soil moisture, retention curves and soil physical properties data obtained in the study area. The present study integrates the Dry-Forest Project of the LBA (Large Scale Biosphere-Atmosphere experiment in the Amazon) research group for the experiment of rainfall exclusion in the National Forest of Tapajos and concerns the hydrological modeling component of the project. The results showed that even with a decrease in the amount of water available to the Forest, significant changes in the hydrological balance of the forest did not occur, showing that the forest had probably adapted itself to survive to longer periods of drough

Poster Study of the effect of Compaction on Soil Hydrodynamic Properties

   STUDY OF THE EFFECT OF COMPACTION ON THE SOIL HYDRODYNAMIC  PROPERTIES </p

Multi-Tempo Forecasting of Soil Temperature Data; Application over Quebec, Canada

The profound impact of soil temperature (TS) on crucial environmental processes, including water infiltration, subsurface movement, plant growth, and its influence on land–atmosphere dynamics, cannot be undermined. While satellite and land surface model-based data are valuable in data-sparse areas, they necessitate innovative solutions to bridge gaps and overcome temporal delays arising from their dependence on atmospheric and hydro–meteorological factors. This research introduces a viable technique to address the lag in the Famine Early Warning Network Land Data Assimilation System (FLDAS). Notably, this approach exhibits versatility, proving highly effective in analyzing datasets characterized by significant seasonal trends, and its application holds immense value in watershed-scaled hydrological research. Leveraging the enhanced state-space (SS) method for forecasting in the FLDAS, this technique harnesses TS datasets collected over time at various depths (0–10 cm, 10–40 cm, and 40–100 cm), employing a multiplicative SS model for modeling purposes. By employing the 1-step, 6-step, and 12-step-ahead models at different depths and 2 locations in Quebec, Canada, the outcomes showcased a performance with an average coefficient of determination (R2) of 0.88 and root mean squared error (RMSE) of 2.073 °C for the dynamic model, R2 of 0.834 and RMSE of 2.979 °C for the 6-step-ahead model, and R2 of 0.921 and RMSE of 1.865 °C for the 12-step-ahead model. The results revealed that as the prediction horizon expands and the length of the input data increases, the accuracy of predictions progressively improves, indicating that this model becomes increasingly accurate over time