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July is the rainy peak month of central China, with a large interannual variation of local precipitation often causing serious droughts and floods. The seasonal prediction of the central China July precipitation (CCJP) is an important but still challenging task. Here, we suggest several robust seasonal predictors for the CCJP, including the preceding winter intensity of El Niño‐South Oscillation (ENSO), the winter‐to‐spring decaying rate of ENSO signals in the central Pacific, as well as the spring tropical and subpolar North Atlantic sea surface temperature anomalies. A physics‐based empirical model is then developed to predict the CCJP by using the principal component regression of the aforementioned seasonal predictors. In our statistical model, the seasonal prediction skill of the CCJP is high, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This suggests a skillful seasonal prediction of the CCJP, with potentially enormous benefits for the local society and economy. Plain Language Summary July contributes about 20% of annual precipitation for the densely populated central China, which could exert tremendous socio‐economic impacts over the region, including agriculture, water resources, food security, ecosystems, disaster mitigation, infrastructure construction, human health, and so on. Thus, how to skillfully predict the interannual variation of the central China July precipitation (CCJP) is a widespread scientific and socio‐economic concern. This work establishes a statistical model for the seasonal prediction of the CCJP by combining the physical precursor factors that drive the interannual variation of the CCJP. Our physics‐based empirical model can well predict the CCJP at one‐season lead, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This provides a substantial skill of the seasonal prediction of the CCJP and is of potentially great importance to the regional agrarian‐based livelihood of hundreds of millions of people. Key Points The Pacific and North Atlantic sea surface temperature precursors are suggested as robust seasonal predictors of the central China July precipitation (CCJP) A physics‐based empirical prediction model of the CCJP is developed based on the principal component regression of the seasonal predictors The prediction skill of the CCJP in our statistical model is high, with a cross‐validated reforecast skill at 0.81 during 1993–2021

Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data.

Hansen Solubility Parameters (HSPs) are widely used as a tool in solubility studies. Given the variety of existent approaches to predict these parameters, this investigation focused on estimating the HSPs of a set of Natural Deep Eutectic Systems (NADES), using empirical (EM) and semi-empirical models (SEM), and then understanding their differences/similarities. Although these theoretical models are designed and recommended mostly for simple molecules or simple solutions, they are still being used in eutectic systems studies, mainly empirical ones. Thus, a preliminary test was conducted with a set of conventional solvents, in which their experimental values of HSPs are known. Besides the confirmation of the EM as the most suitable for these kinds of regular solvents, the results found also showed a very similar behaviour to what was observed in NADES, i.e., in terms of suggesting the EM and SEM with the highest/lowest similarity. Furthermore, it was concluded that although there is a large discrepancy between the estimated values of the hydrogen bond parameter, especially for systems with a higher polar character, there is still a good similarity for the other parameters. In fact, it was observed that, when combining the semi-empirical models, it was possible to obtain a value of the hydrogen bond parameter more similar to the empirical ones.

Extreme precipitation events have severe impacts on human activity and natural environment, but prediction of extreme precipitation events remains a considerable challenge. The present study aims to explore the sources of predictability and to estimate the predictability of the summer extreme precipitation days (EPDs) over eastern China. Based on the region- and season-dependent variability of EPDs, all stations over eastern China are divided into two domains: South China (SC) and northern China (NC). Two domain-averaged EPDs indices during their local high EPDs seasons (May–June for SC and July–August for NC) are therefore defined. The simultaneous lower boundary anomalies associated with each EPDs index are examined, and we find: (a) the increased EPDs over SC are related to a rapid decaying El Nino and controlled by Philippine Sea anticyclone anomalies in May–June; (b) the increased EPDs over NC are accompanied by a developing La Nina and anomalous zonal sea level pressure contrast between the western North Pacific subtropical high and East Asian low in July–August. Tracking back the origins of these boundary anomalies, one or two physically meaningful predictors are detected for each regional EPDs index. The causative relationships between the predictors and the corresponding EPDs over each region are discussed using lead-lag correlation analyses. Using these selected predictors, a set of Physics-based Empirical models is derived. The 13-year (2001–2013) independent forecast shows significant temporal correlation skills of 0.60 and 0.74 for the EPDs index of SC and NC, respectively, providing an estimation of the predictability for summer EPDs over eastern China.

Summer surface air temperature (SAT) variability over Northern Hemisphere (NH) continents can profoundly impact human society, yet its seasonal prediction remains challenging, partly due to the limited prediction skill of dynamical models, especially over extratropical and high-latitude areas. Previous research has defined five indices associated with different atmospheric circulation patterns, which have important contributions to variations of summer SAT. This study further establishes a physically based empirical model (P–E model) using the Bayesian dynamic linear model method for the prediction of the indices, and uses the predicted indices to reconstruct the summer SAT anomaly field. Results show that the P–E model can reasonably well predict the five indices during 1950–2021. Combining this with the linear trend, the total summer SAT anomaly is also reconstructed. The high cross-validated hindcast skill for the period of 1950–2021 and independent forecast skill of 2022 indicate that the summer SAT over NH continents can be reasonably predicted by the P–E model.

Hydro-pedotransfer functions (PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties (SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than 4 decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterise models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards–Richardson equation are mostly limited to predicting parameters of the van Genuchten–Mualem soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while often being applied at the field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium (ISMC) Pedotransfer Functions Working Group with the aim of systematising PTF research and providing a roadmap guiding both PTF development and use. We close with a 10-point catalogue for funders and researchers to guide review processes and research.

Bedload flux estimation from reach‐averaged hydraulic conditions, while tractable and generally reliable over long integration periods, struggles to capture the intrinsic variability of transport in turbulent flow and the strong influence of local sediment size and morphological heterogeneity. Here we suggest an empirical equation to simplify the relationship between seismic power spectral density (PSD) and bedload flux relative to a full physics‐based seismic model. We posit that adding seismic PSD as a predictor to hydraulics‐based bedload equations improves bedload flux predictions by accounting for short‐ and medium‐term flux variations independent of flow conditions. We present a new calibrated empirical equation combining seismic PSD and excess shear stress to predict bedload flux at high temporal resolution (minute‐scale). The calibrated coefficients for the shear contribution are consistent with existing hydraulics‐based equations (e.g., Meyer‐Peter and Müller) that have been calibrated across a broad range of channels, suggesting that the values for the seismic parameter might also be broadly applicable across channels. In comparison to field data from a sandy‐gravel‐bed alluvial river in New Mexico, USA, the locally‐trained equation reduces scatter in bedload flux predictions relative to methods solely using either seismic PSD or shear stress. We further validate the equation with independent flow events from the same river and from a separate gravel bed channel, the Nahal Eshtemoa in Israel. Notably, the seismic‐hydraulic equation was able to be calibrated on low‐transport data and reliably predict high‐transport data. Likewise, the seismic‐hydraulic equation reduced apparent overestimations of bedload flux by the hydraulics‐based equation during high‐shear conditions.

The international reference ionosphere (IRI) is the internationally recognized and recommended standard for the specification of plasma parameters in Earth’s ionosphere. It describes monthly averages of electron density, electron temperature, ion temperature, ion composition, and several additional parameters in the altitude range from 60 to 1,500 km. A joint working group of the Committee on Space Research (COSPAR) and the International Union of Radio Science (URSI) is in charge of developing and improving the IRI model. As requested by COSPAR and URSI, IRI is an empirical model being based on most of the available and reliable data sources for the ionospheric plasma. The paper describes the latest version of the model and reviews efforts towards future improvements, including the development of new global models for the F2 peak density and height, and a new approach to describe the electron density in the topside and plasmasphere. Our emphasis will be on the electron density because it is the IRI parameter most relevant to geodetic techniques and studies. Annual IRI meetings are the main venue for the discussion of IRI activities, future improvements, and additions to the model. A new special IRI task force activity is focusing on the development of a real-time IRI (RT-IRI) by combining data assimilation techniques with the IRI model. A first RT-IRI task force meeting was held in 2009 in Colorado Springs. We will review the outcome of this meeting and the plans for the future. The IRI homepage is at http://www.IRI.gsfc.nasa.gov .

The precise description of the topside ionosphere using an ionospheric empirical model has always been a work in progress. The NeQuick topside model is greatly enhanced by adopting radio occultation data from the FORMOSAT-7/COSMIC-2 constellation. The topside scale height H formulation in the NeQuick model is simplified into a linear combination of an empirically deduced parameter H 0 and a gradient parameter g . The two-dimensional grid maps for the H 0 and g parameters are generated as a function of the foF 2 and hmF 2 parameters. Corrected H 0 and g values can be interpolated easily from two grid maps, allowing a more accurate description of the topside ionosphere than the original NeQuick model. The improved NeQuick model (namely NeQuick_GRID model) is statistically validated by comparing it to Total Electron Content (TEC) integrated from COSMIC-2 electron density profiles and space-borne TEC derived from onboard Global Navigation Satellite System observations, respectively. The results show that the NeQuick_GRID model can reduce relative errors by 38% approximately when compared to the integrated TEC from COSMIC profiles and by 15% approximately when compared to the space-borne TEC. Furthermore, a long-term statistical analysis during years of both high and low solar activities reveals that grid maps of the scale factor H 0 and the gradient parameter g have very similar features, allowing rapid and efficient acquisition of high-precision electron density during different solar activity.

Wind erosion is one of the most severe environmental problems in arid, semiarid, and dry sub-humid regions of the planet. This paper aimed to identify areas sensitive to wind erosion in Northeastern Algeria (Wilaya of Tebessa) based on empirical model using analytic hierarchy process, fuzzy analytic hierarchy process approaches, and geomatics-based techniques. Sixteen causative factors were used incorporating meteorological, soil erodibility, physical environment, and anthropogenic impacts as main available inputs in this approach. Weighted linear combination algorithm was adopted to combine all standardized raster layers. Area under curve value equal to 0.96 indicates an excellent accuracy for the proposed approach. Globally, wind erosion risk increases gradually from the North to South of the whole area. Besides, it was found that areas with slight, moderate, high, and very high risk covered 9.65%, 25.83%, 24.30%, and 40.22% of the total area, respectively. Our results highlighted the potential of additive linear model and free available medium resolution multi-source remote sensing data in studying natural hazards and disasters mainly under data-scarce or areas of difficult access in developing countries. In addition, restoration and re-vegetation activities of sensitive areas at high risk of wind erosion represent a challenge for researchers and decision-makers.