The partitioning of available energy into dissipative fluxes over land surfaces is dependent on the state variable of the surface energy balance (land surface temperature) and the state variable of the surface water balance (soil moisture). The direct measurement of the turbulent fluxes is achieved with in situ instruments at tower sites. These point‐scale measurements are sparsely distributed. Broader scale mapping of the turbulent fluxes is mostly dependent on land surface temperature (LST) and optical/infrared vegetation that can be sensed remotely. There are several data assimilation approaches currently in use that intake sequences of daytime LST that attain different diurnal amplitudes depending on available energy and the relative efficiency of surface energy balance to infer the magnitude of surface flux components such as latent and sensible heat flux. In this study we perform stability analysis on the evolution of LST in order to provide insights into the physical bases for why LST variations can be used to diagnose surface energy balance (SEB) components. The derived relative efficiencies of SEB components in dissipating available energy at the land surface are tested using two field experiment measurements. The results show that the theoretically derived relative efficiencies of SEB components agree well with field observations. The study provides insight into how LST sequences implicitly contain the signature of partitioning of available energy among SEB components and can be used to infer their magnitudes.

The accurate forecast of air temperature plays an important role in water resources management, land–atmosphere interaction, and agriculture. However, it is difficult to accurately predict air temperature due to its non-linear and chaotic nature. Several deep learning techniques have been proposed over the last few decades to forecast air temperature. This study provides a comprehensive review of artificial neural network (ANN)-based approaches (such as recurrent neural network (RNN), long short-term memory (LSTM), etc.), which were used to forecast air temperature. The focus is on the works during 2005–2020. The review shows that the neural network models can be employed as promising tools to forecast air temperature. Although the ANN-based approaches have been utilized widely to predict air temperature due to their fast computing speed and ability to deal with complex problems, no consensus yet exists on the best existing method. Additionally, it is found that the ANN methods are mainly viable for short-term air temperature forecasting. Finally, some future directions and recommendations are presented.

Applying data mining techniques for rainfall modeling because of a lack of sufficient memory components may increase uncertainty in rainfall forecasting. To solve this issue, in this research, a deep-learning-based long short-term memory (LSTM) model is developed for the first time for forecasting monthly rainfall data, and its capability is compared with a random forest (RF) data-driven model. To this end, monthly rainfall data for a period of 41 years (1980–2020) from two meteorological stations in Turkey, namely Rize and Konya, with different climatic conditions, are used. The analysis is carried out using optimum window sizes for determining the optimum lag times of rainfall time series. The performance of the models is evaluated using five statistical measures, namely root mean square error (RMSE), RMSE-observations standard deviation ratio (RSR), Legate and McCabe’s index (LMI), correlation coefficient (R) and Nash–Sutcliffe efficiency (NSE), and also using two visual means, namely Taylor and violin diagrams. The results reveal that the LSTM model, as a more efficient tool, outperforms the RF model in forecasting rainfall at both stations, with improved RMSE of 12.2–14.9%, RSR of 12.3–14.8%, R of 9.4–13.5% and NSE of 32.9–33.2%. The LSTM-based approach proposed herein could be adopted over any global climatic conditions to forecast the monthly rainfall with reasonable accuracy. GRAPHICAL ABSTRACT

This paper belongs to our Special Issue “Application of Climate Data in Hydrologic Models”