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Accurate aboveground vegetation biomass forecasting is essential for livestock management, climate impact assessments, and ecosystem health. While artificial intelligence (AI) techniques have advanced time series forecasting, a research gap in predicting aboveground biomass time series beyond single values persists. This study introduces RECMO and DirRecMO, two multi-output methods for forecasting aboveground vegetation biomass. Using convolutional neural networks, their efficacy is evaluated across short-, medium-, and long-term horizons on six Kenyan grassland biomass datasets, and compared with that of existing single-output methods (Recursive, Direct, and DirRec) and multi-output methods (MIMO and DIRMO). The results indicate that single-output methods are superior for short-term predictions, while both single-output and multi-output methods exhibit a comparable effectiveness in long-term forecasts. RECMO and DirRecMO outperform established multi-output methods, demonstrating a promising potential for biomass forecasting. This study underscores the significant impact of multi-output size on forecast accuracy, highlighting the need for optimal size adjustments and showcasing the proposed methods' flexibility in long-term forecasts. Short-term predictions show less significant differences among methods, complicating the identification of the best performer. However, clear distinctions emerge in medium- and long-term forecasts, underscoring the greater importance of method choice for long-term predictions. Moreover, as the forecast horizon extends, errors escalate across all methods, reflecting the challenges of predicting distant future periods. This study suggests advancing hybrid models (e.g., RECMO and DirRecMO) to improve extended horizon forecasting. Future research should enhance adaptability, investigate multi-output impacts, and conduct comparative studies across diverse domains, datasets, and AI algorithms for robust insights. [ABSTRACT FROM AUTHOR]

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates the effectiveness of deep learning (DL) algorithms in predicting aboveground vegetation biomass with limited-size data. It employs an iterative forecasting procedure for four target horizons, comparing the performance of DL models—multi-layer perceptron (MLP), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM—against the traditional seasonal autoregressive integrated moving average (SARIMA) model, serving as a benchmark. Five limited-size vegetation biomass time series from Kenyan grasslands with values at 15-day intervals over a 20-year period were chosen for this purpose. Comparing the outcomes of these models revealed significant differences (p < 0.05); however, none of the models proved superior among the five time series and the four horizons evaluated. The SARIMA, CNN, and CNN-LSTM models performed best, with the statistical model slightly outperforming the other two. Additionally, the accuracy of all five models varied significantly according to the prediction horizon (p < 0.05). As expected, the accuracy of the models decreased as the prediction horizon increased, although this relationship was not strictly monotonic. Finally, this study indicated that, in limited-size aboveground vegetation biomass time series, there is no guarantee that deep learning methods will outperform traditional statistical methods. [ABSTRACT FROM AUTHOR]

Stream temperatures have been increasing worldwide, in some cases reaching unsustainable levels for aquatic life. Riparian revegetation has been identified as a strategy for managing stream temperatures by blocking direct solar radiation. In this study, the effects of riparian vegetation on stream temperatures were included within the Soil & Water Assessment Tool (SWAT) model through a shade factor parameter. An equilibrium temperature approach was used to integrate the shade factor in an energy balance context. The stream temperature sub-model was improved using the new energy balance equation and integrated into SWAT. Unlike existing models, the modified SWAT model enables improved representation of two processes – mass and heat transfer – that influence stream temperature change and enables simulation of shading and its effects on stream temperatures at sub-basin scales. The updated SWAT model was tested in Dairy McKay Watershed, OR, USA, for four scenarios: current conditions of riparian vegetation, full restoration, efficient restoration, and no vegetation. The model calibration under current riparian vegetation showed good performance (Nash–Sutcliffe efficiency NSE > 0.74). Stream temperature reduction and number of days with stream temperatures above survival limits (NDSTASL) for aquatic species were also evaluated as measures of riparian shade performance. Findings showed average temperature reductions of 0.91 ∘ C (SD = 0.69 ∘ C) and reductions in NDSTASL of 17.1 d over a year for full riparian restoration and average reductions of 0.86 ∘ C (SD = 0.67 ∘ C) and 16.2 d for efficient restoration. Notwithstanding the similar benefits, efficient restoration was 14.4 % cheaper than full riparian vegetation restoration. [ABSTRACT FROM AUTHOR]

Stream temperatures have been increasing worldwide, in some cases, reaching unsustainable levels for aquatic life. Riparian re-vegetation has been identified as a strategy for managing stream temperatures by blocking direct solar radiation. In this study, the effects of riparian vegetation on stream temperatures were included within the Soil Water Assessment Tool (SWAT) model through a shade factor parameter. An equilibrium temperature approach was used to integrate the shade factor in an energy balance context. The stream temperature sub-model was improved using the new energy balance equation and integrated into SWAT. Unlike existing models, the modified SWAT model developed enables improved representation of two processes - mass and heat transfer - that influence stream temperature change and enables simulation of shading and its effects on stream temperatures at sub-basin scales. The updated SWAT model was tested in Dairy McKay Watershed, OR, USA, for four scenarios: current conditions of riparian vegetation, full restoration, efficient restoration, and no vegetation. The model calibration under current riparian vegetation showed good performance (NSE>0.74). Stream temperature reduction and number of days with stream temperatures above survival limits (NDSTASL) for aquatic species were also evaluated as measures of riparian shade performance. Findings showed average temperature reductions of 0.91 °C (SD = 0.69 °C) and reductions in NDSTASL of 17.1 days over a year for full riparian restoration, and average reductions of 0.86 °C (SD = 0.67 °C) and 16.2 days for efficient restoration. Notwithstanding the similar benefits, efficient restoration was 14.4% cheaper than full riparian vegetation restoration. [ABSTRACT FROM AUTHOR]