Yunquan Wang

and 2 more

Jieliang Zhou

and 6 more

Pedotransfer functions (PTFs) are widely used to estimate soil hydraulic properties (SHPs) from easily measurable characteristics. However, most existing PTFs rely on unimodal hydraulic models, which fail to accurately represent the bimodal SHPs caused by soil structure common in field conditions. In this study, we developed new PTFs using two bimodal soil hydraulic models and introduced soil physics-informed neural networks (SPINN) to embed the models into PTF training. The results showed that the new PTFs effectively captured bimodality in hydraulic conductivity curves, achieving an RMSE of 0.578 in the test set, compared to 0.709 for unimodal models. The PTFs also improved soil water retention curve (SWRC) predictions but struggled with bimodal SWRCs for some samples, likely due to the limited number of bimodal SWRCs in the dataset. An independent dataset evaluation revealed that the RMSE for hydraulic conductivity predicted by the new PTFs was approximately one-third of that of classic PTFs. This underscores the significant role of soil structure in SHPs, which classic PTFs fail to capture. Additionally, PTFs developed using the SPINN method outperformed those optimized fitted hydraulic parameters via machine learning, a common approach in the literature. We also found that separate versus simultaneous optimization of water retention and hydraulic conductivity greatly affects PTF performance. Finally, we provided global 1 km-resolution maps of soil hydraulic parameters for the bimodal model.

Rui Ma

and 3 more

The role of groundwater in maintaining streamflow in the alpine area with distribution of permafrost and seasonal frost is a poorly studied topic of considerable interest. The stream and groundwater interactions and groundwater contributions to the Heihe River were investigated during this study in a representative subcatchment in the headwater region of the Heihe River Basin, the northeastern Qinghai-Tibet Plateau of China. The hydraulic, chemical and isotopic data as well as Bayesian mixing model results show that groundwater-stream water interactions were both spatially and temporally variable. The tributaries were primarily recharged by springs within the permafrost zone during the frozen period when the water source and sediments were frozen and the groundwater discharged to the mainstream within the seasonal frost zone to maintain the streamflow. The groundwater contribution to mainstream discharge decreased from 95% during the frozen period to 80-90% in the thawing period due to the inflow from tributaries. However, the stream and groundwater interactions vary several times along altitude during the thawed period from June to early September, due to the increased glacial/snow meltwater volume, deepened active layer, and melted seasonal frost. Groundwater contribution decreased to ~40-60% of the mainstream discharge during the thawed period because tributary streams contribution largely increased. As shown by ~70-90% contribution from groundwater to the mainstream discharge, the mainstream flow mainly sourced from the release of groundwater in aquifers in the freeze-back period. These data indicate that the variations in groundwater-surface water interactions were largely influenced by the distribution and freeze-thaw cycle of permafrost and seasonal frost. The importance of groundwater storage in maintaining streamflow in the Heihe headwater region was highlighted by this study.

Yunquan Wang

and 4 more

The commonly applied pedotransfer functions (PTFs), which predict soil hydraulic properties (SHPs) from easily measured soil properties such as texture information, often account only for capillary forces. Recent advances in soil hydraulic modeling suggest that, to improve the prediction of SHPs under dry conditions, the impact of adsorption forces has to be taken into account. However, the lack of observations in particularly dry conditions, due to the difficult and time-consuming measurement, hinders the development of PTFs that predict SHPs from saturation to oven dryness. In this paper, we first present a simple method for predicting complete SHPs with limited measurements that cover only a relatively high matric potential range. With this method, we extended a public dataset to cover dry conditions, and then applied it to develop PTFs that can predict SHPs from saturation to oven dryness. This was achieved by applying the complete soil hydraulic model proposed by Wang et al. (2021), which accounts for both capillary and adsorptions forces and overcomes the unrealistic decrease near saturation for fine-textured soils. The impact of vapor diffusion was also considered. We further applied this method in extending an existing capillary-based PTF to dry conditions. The results showed that: 1) the proposed method performs very well in describing SHPs over the entire moisture range; 2) the PTFs developed with the extended observations and the complete model show a superior prediction performance, especially for the hydraulic conductivity; and 3) the extended capillary-based PTF improves the performance in describing SHPs under dry conditions.