Benchmark-aware Ensemble Kalman Filter assimilation of soil moisture and nitrate for water conditioned leaching risk diagnosis in semi-arid maize

Ebenezer Aquisman Asare , Dickson Abdul-Wahab

Agricultural Environment and Sustainability ›› 2026, Vol. 1 ›› Issue (3) : 100029

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Agricultural Environment and Sustainability ›› 2026, Vol. 1 ›› Issue (3) :100029 DOI: 10.1016/j.ages.2026.100029
Original Research Article
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Benchmark-aware Ensemble Kalman Filter assimilation of soil moisture and nitrate for water conditioned leaching risk diagnosis in semi-arid maize
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Abstract

Root zone water redistribution and nitrate mobility are difficult to diagnose in semi-arid agricultural profiles because point observations are noisy and temporally uneven, while open-loop process models may drift after rainfall, irrigation and fertilizer events. This study presents a benchmark-aware, observation-role-audited Ensemble Kalman Filter (EnKF) workflow for plot-depth soil moisture and nitrate state estimation in a maize field at Lawra, Upper West Region, Ghana, from 1 June to 13 September 2025. Calibrated observations at 10, 30 and 60 cm were integrated with weather forcing, irrigation and fertilizer event logs, management records and laboratory nitrate support within a layered soil-water-nutrient model. The contribution is not a new Kalman-filter algorithm, but a reproducible field-scale protocol combining asynchronous moisture-nitrate updating, calibration-update-validation role separation, benchmark-aware interpretation and water-conditioned nitrate-risk diagnosis. Relative to the open-loop model, EnKF assimilation reduced full-period soil moisture RMSE from 0.0902 to 0.0336 m3 m−3 and MAE from 0.0801 to 0.0297 m3 m−3; validation-period RMSE declined from 0.1038 to 0.0386 m3 m−3. Sensitivity testing showed that soil moisture RMSE remained below the open-loop value across tested ensemble size, covariance, inflation and update-frequency settings, with greatest deterioration under daily rather than hourly updating. Forecast error increased with lead time: moisture RMSE rose from 0.0176 m3 m−3 at 1 day to 0.0687 m3 m−3 at 7 days, while nitrate RMSE rose from 2.27 to 4.37 mg N kg−1. Because aggregate NSE values remained negative, the workflow is presented as drift correction and short-horizon diagnostic state estimation, not proof of reduced nitrate loss or agronomic improvement.

Keywords

Benchmark-aware data assimilation / Observation role audit / Root zone hydrology / Forecast uncertainty / Water conditioned nitrate diagnosis

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Ebenezer Aquisman Asare, Dickson Abdul-Wahab. Benchmark-aware Ensemble Kalman Filter assimilation of soil moisture and nitrate for water conditioned leaching risk diagnosis in semi-arid maize. Agricultural Environment and Sustainability, 2026, 1 (3) : 100029 DOI:10.1016/j.ages.2026.100029

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