Accurate riverine flood forecasting is crucial for effective river management. This paper utilized the Time‐Series Dense Encoder (TiDE), Neural Hierarchical Interpolation for Time Series Forecasting (N‐HiTS), and Patch Time Series Transformer (PatchTST) to forecast riverine flood and benchmarked their results against Long Short‐Term Memory (LSTM). Each model was implemented with varying forecast lead times for the Proctor Creek–Chattahoochee watershed, Georgia, USA. Additionally, the sensitivity of each model was evaluated by excluding meteorological forcing features one by one to determine how the performance varied across different variables. The time of concentration () was incorporated as a physical parameter in the algorithm's lookback window. The trained models were evaluated separately on event‐based simulations. The Diebold–Mariano statistical test was utilized for a thorough analysis of performance. Analysis revealed that PatchTST outperformed the other models during moderate‐flow regimes while falling behind during extreme flooding events. An 18‐ to 24‐h lookback window was found to be statistically optimal for the models. The sensitivity analysis results indicated that PatchTST was slightly more sensitive to the selected training data features. Incorporating into the lookback window revealed that TiDE and LSTM showed better performance when using a sequence length of 18 h while PatchTST achieved the best results with a 24‐h lookback window.
Krishna Panthi, Mostafa Saberian, Vidya S. Samadi· JAWRA Journal of the America...· 0 citations
Crop modelling is essential for agricultural water management but often relies on simplified water balance routines that limit representation of soil moisture dynamics. To address this limitation, we developed a coupled model that integrates the 1‐D Richards equation, solved using a finite difference method into the FAO AquaCrop. The coupled model was calibrated and validated using soil moisture, canopy cover, above‐ground biomass and seed cotton yield data from field experiments in the southeastern United States. Compared with hourly field measurements of soil moisture, AquaCrop–Richards achieved an average root mean square error (RMSE) of 0.023 m
3
m
−3
across three soil depths over the growing season. Model performance for canopy cover, biomass and yield resulted in RMSE values of 12.18%, 1.77 t ha
−1
and 0.96 t ha
−1
, respectively, against observations. Under fully irrigated conditions, both models produced statistically indistinguishable yield estimates. However, under rainfed conditions, AquaCrop simulated 15.5% higher yields than AquaCrop–Richards. Analysis showed that AquaCrop produced rapid stepwise drainage, resulting in root‐zone water content 33%–37% lower than the coupled model. This reduced soil moisture triggered earlier water stress which led to yield overestimation. These results indicate that AquaCrop‐Richards improves soil moisture representation and is robust under water‐limited conditions.
Krishna Panthi, Vidya Samadi, Carlos Toxtli· Irrigation and Drainage· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
