| 20 May 2026
Soil water forecasting for Australian dryland agriculture
Abstract. Soil water availability is a critical constraint to agricultural productivity. While soil water forecasting has previously been conducted in the literature for irrigated fields and surface soil (0–10 cm), there is a limited understanding of subsurface soil water in dryland paddocks, particularly in Australia. Due to rooting depth typically extending below 10 cm, subsurface soil water forecasts in cropping systems, for example, enable decision-making relating to sowing, fertiliser use, and seasonal yield potential. This study aimed to identify the best performing methods, and the underlying variables that affect accuracy when forecasting subsurface soil water (30–100 cm). The methods appraised in this study are Random Forest, XGBoost, Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and an Encoder-decoder LSTM (ENCDEC). Various in situ and remotely sensed meteorological data were used to forecast soil water up to multiple months ahead, at 54 probe sites across Australia.
We find all models can perform with relatively similar accuracy for sub-monthly forecasts. For longer lead times, MLP and XGBoost performed better for sites with uniform rainfall throughout the year, and the memory-based models, LSTM and ENCDEC, better at sites with seasonally-dominant rainfall. Furthermore, we determine an upper limit to model utility, as accuracy can become poor many months ahead, and can be replaced by a historic average as an estimate. We find our models to be 'useful' up to 4 months ahead on average, after which accuracy is too low, or a historic average outperforms a model. We find rainfall intensity and seasonality, and model choice, to be key drivers of forecast accuracy at a new site. This study highlights the capability of elementary forms of each ensemble and deep learning model to provide sufficiently accurate soil water forecasts, particularly in the absence of a rainfall forecast feature.
Amir et al. evaluate several ML models for soil moisture forecasting. Their results indicate that (a) DL models generally outperform ML methods such as MLP and XGBoost, and (b) how limited the models are compared to a "standard" threshold. While the application is relevant and the Australia-wide aggregation of probe records is potentially valuable, I do not see the manuscript as fit for publication. Several major concerns, in my opinion, should be addressed:
1) The methodological contribution appears limited. Benchmarking established models such as MLP and XGBoost, without introducing a novel architecture, theoretically grounded formulation, or substantive methodological advance, is insufficiently innovative for a 2026 study. In addition, the site-specific and geographically imbalanced dataset limits the generalizability of the findings and their broader contribution to data-driven soil-water forecasting. The authors should also justify the exclusion of meteorological forecasts and reanalysis products, such as ERA5 soil moisture. Incorporating these sources or systematically evaluating their added value could support more transferable models and provide a stronger benchmark for assessing the proposed approach.
2) The methodology is not clear. The authors should define one training example explicitly: forecast issue date `t`, target date `t+h`, every feature timestamp available at `t`, sequence/lookback window, target, and lead time `h`. This is essential because the paper calls the task forecasting but uses retrospective gridded and remotely sensed products. It is not clear what the forecast step is, what the issue date is, and what inputs are used. Does the LSTM use past observed or past series of meteorological forcing? If so, what is the lookback, and is this the case with other methods? If not, what is the usage of LSTM?
3) Validation, test use, and hyperparameter selection are ambiguous and may contaminate the test set. The text says tuning and training occur on a training set and the models are validated on a test set. Then it says high-performing combinations are "tested across all lead times" and selected for low KGE variance. What are the train, validation, and test sets? The final one- or two-year test period must remain untouched until all preprocessing, architecture choices, and hyperparameters are fixed.
4) How was the architecture of the encoder-decoder selected?
5) If my guess is correct, for each lead time, a model has been developed. This point should be very clear in the text, but unfortunately, it is not. If this is the case, the autoregressive property is being discarded.
6) Model implementations are not reproducible. Code and data should be provided. Based on EGU policies, this should have been flagged before archiving.
7) Search spaces differ greatly in capacity and completeness. Random Forest does not tune `max_features` or depth; XGBoost does not tune the regularization parameters discussed at lines 181–183; MLP width is restricted to 5–12 units and its depth is undisclosed; the recurrent networks use small, different grids. Consequently, conclusions about algorithm classes may instead reflect unequal tuning budgets or constrained implementations.
8) A fixed seed improves reproducibility but does not quantify variability. MLP/LSTM/ENCDEC results can vary with initialization and minibatch order. For fair comparison, an ensemble of seeds needs to be evaluated.
9) Models are trained separately at each site. Testing later dates at those same sites measures temporal evaluation, not evaluation at a new site. Zone summaries likewise do not constitute out-of-site validation. Therefore, please rephrase all "new site" claims or add leave-one-site-out/grouped-site validation in which the held-out site contributes no training, preprocessing, climatology, or tuning information.
10) Hyperparameters are optimized using scaled KGE′ but models are ranked and interpreted primarily by RMSE. Explain why this objective/evaluation mismatch is appropriate and report both metrics on untouched test data. RMSE alone does not reveal bias, event timing, extremes, or uncertainty; add MAE, bias, correlation/KGE components, and performance during wetting/drying events.
11) Without timeseries plots and more metrics, an evaluation cannot be completed.
12) The referencing needs an overhaul. For example, see the LSTM section. No reference for the properties is provided.
13) Many abbreviations are not defined or are defined late. e.g., ML, PCA, ConvLSTM, StemGNN, ....
14) Many abbreviations have been defined several times. e.g., MLP, LSTM, RNN, ...