In the Heihe River basin, China, increased salinity and water shortages present serious threats to the sustainability of arid wetlands. It is critical to understand the interactions between soil water and salts (from saline shallow groundwater and the river) and their effects on plant growth under the influence of shallow groundwater and irrigation. In this study, the Hydrus-1D model was used in an arid wetland of the Middle Heihe River to investigate the effects of the dynamics of soil water, soil salinization, and depth to water table (DWT) as well as groundwater salinity on Chinese tamarisk root water uptake. The modeled soil water and electrical conductivity of soil solution (EC_sw) are in good agreement with the observations, as indicated by RMSE values (0.031 and 0.046 cm³·cm⁻³ for soil water content, 0.037 and 0.035 dS·m⁻¹ for EC_sw, during the model calibration and validation periods, respectively). The calibrated model was used in scenario analyses considering different DWTs, salinity levels and the introduction of preseason irrigation. The results showed that (I) Chinese tamarisk root distribution was greatly affected by soil water and salt distribution in the soil profile, with about 73.8% of the roots being distributed in the 20–60 cm layer; (II) root water uptake accounted for 91.0% of the potential maximal value when water stress was considered, and for 41.6% when both water and salt stress were considered; (III) root water uptake was very sensitive to fluctuations of the water table, and was greatly reduced when the DWT was either dropped or raised 60% of the 2012 reference depth; (IV) arid wetland vegetation exhibited a high level of groundwater dependence even though shallow groundwater resulted in increased soil salinization and (V) preseason irrigation could effectively increase root water uptake by leaching salts from the root zone. We concluded that a suitable water table and groundwater salinity coupled with proper irrigation are key factors to sustainable development of arid wetlands.

Modeling and prediction of soil hydrologic processes require identifying soil moisture spatial-temporal patterns and effective methods allowing the data observations to be used across different spatial and temporal scales. This work presents a methodology for combining spatially- and temporally-extensive soil moisture datasets obtained in the Shale Hills Critical Zone Observatory (CZO) from 2004 to 2010. The soil moisture was investigated based on Empirical Orthogonal Function (EOF) analysis. The dominant soil moisture patterns were derived and further correlated with the soil-terrain attributes in the study area. The EOF analyses indicated that one or two EOFs of soil moisture could explain 76–89% of data variation. The primary EOF pattern had high values clustered in the valley region and, conversely, low values located in the sloping hills, with a depth-dependent correlation to which curvature, depth to bedrock, and topographic wetness index at the intermediate depths (0.4 m) exhibited the highest contributions. We suggest a novel approach to integrating the spatially-extensive manually measured datasets with the temporally-extensive automatically monitored datasets. Given the data accessibility, the current data merging framework has provided the methodology for the coupling of the mapped and monitored soil moisture datasets, as well as the conceptual coupling of slow and fast pedologic and hydrologic functions. This successful coupling implies that a combination of diverse and extensive moisture data has provided a solution of data use efficiency and, thus, exciting insights into the understanding of hydrological processes at multiple scales.

Using “smart” tracers such as Resazurin (Raz) allows assessment of sediment-water interactions and associated biological activity in streams. We compared two approaches to simulate the effects of transient storage (TS) on the transport of conservative and reactive tracers. The first approach considered TS as composed of metabolically active and metabolically inactive compartments, while the second model approach accounted for the surface transient storage (STS) and hyporheic transient storage (HTS). Experimental data were collected at a perennial first-order creek in Maryland, MD, USA, by injecting the conservative tracer bromide (Br) and the reactive (Raz) tracer and sampling water at two weir stations. The STS–HTS approach led to a more accurate simulation of Br transport and tails of the Raz and its product Rezorufin (Rru) breakthrough curves. Sediments support large microbial communities, and the STS–HTS model in creeks provides additional parameters to characterize the habitats of microbial water-quality indicator organisms.

Building highway and its biological protection system in a drought-affected shifting-sand desert is a great challenge. This challenge was completed by the construction of the Taklimakan Desert Highway Shelterbelt (TDHS)—the longest of its kind in the world (436 km). The TDHS can serve as a model for highway construction and desertification control using eco-friendly and cost-effective approaches in other desert regions. Notably, we proved that local saline groundwater irrigation offers potential advantages and opportunities for the growth of halophytes and sandy soil development in hyper-arid desert environments. Here, we systematically (1) summarize the project, its results, and vital technical issues of saline water irrigation; (2) address soil hydrological processes that play a crucial role in maintaining those systems; and (3) highlight useful insights for soil development, plant survival, and soil–plant–water–biota synergy mechanisms. Indeed, the TDHS project has provided a proof of concept for restoration and desert greening initiatives.

Cyanotoxins in agricultural waters pose a human and animal health risk. These toxins can be transported to nearby crops and soil during irrigation practices; they can remain in the soil for extended periods and be adsorbed by root systems. Additionally, in livestock watering ponds, cyanotoxins pose a direct ingestion risk. This work evaluated the performance of the random forest algorithm in estimating microcystin concentrations using eight in situ water quality measurements at one active livestock water pond and two working irrigation ponds in Georgia and Maryland, USA. Measurements of microcystin along with eight in situ-sensed water quality parameters were used to train and test the machine learning model. The models performed better at the Georgia ponds compared to the Maryland pond, and interior models performed better than nearshore or whole-pond models. The most important variables for microcystin prediction were water temperature and phytoplankton pigments. Overall, the random forest algorithm(RF), augmented with a ‘trainControl’ function to perform repeated cross validations, was able to explain 40% to 70% of the microcystin concentration variation in the three agricultural ponds. Water quality measurements showed potential to aid water monitoring/sampling design by predicting the microcystin concentrations in the studied ponds by using readily available and easy to collect in situ data.