High-resolution simulation and validation of soil moisture in the arid region of Northwest China

High-resolution European daily soil moisture derived with machine learning (2003-2020)

Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1°) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003-2020. Compared to its previous version with a lower spatial resolution (0.25°), it shows a closer agreement with independent in-situ data in terms of temporal variation, demonstrating the enhanced usefulness of in-situ observations when processed jointly with high-resolution meteorological data. Regional comparison with other gridded datasets also demonstrates the ability of SoMo.ml-EU in describing the variability of soil moisture, including drought conditions. As a result, our new dataset will benefit regional studies requiring high-resolution observation-based soil moisture, such as hydrological and agricultural analyses.

New Framework for Dynamic Water Environmental Capacity Estimation Integrating the Hydro-Environmental Model and Load-Duration Curve Method-A Case Study in Data-Scarce Luanhe River Basin

A better understanding of river capacity for contaminants (i.e., water environmental capacity, WEC) is essential for the reasonable utilization of water resources, providing government’s with guidance about sewage discharge management, and allocating investments for pollutant reduction. This paper applied a new framework integrating a modified hydro-environmental model, Soil and Water Assessment Tool (SWAT) model, and load–duration curve (LDC) method for the dynamic estimation of the NH₃-N WEC of the data-scarce Luanhe River basin in China. The impact mechanisms of hydrological and temperature conditions on WEC are discussed. We found that 77% of the WEC was concentrated in 40% hydrological guarantee flow rates. While the increasing flow velocity promoted the pollutant decay rate, it shortened its traveling time in streams, eventually reducing the river WEC. The results suggest that the integrated framework combined the merits of the traditional LDC method and the mechanism model. Thus, the integrated framework dynamically presents the WEC’s spatiotemporal distribution under different hydrological regimes with fewer data. It can also be applied in multi-segment rivers to help managers identify hot spots for fragile water environmental regions and periods at the basin scale.

Stoichiometry of C:N:P in the Roots of Alhagi sparsifolia Is More Sensitive to Soil Nutrients Than Aboveground Organs

The stoichiometry of carbon, nitrogen, and phosphorus (C:N:P) among leaves, stems, and roots reflects trade-offs in plants for acquiring resources and their growth strategy. The widely distributed plant Alhagi sparsifolia is an ideal species to study the ecological stoichiometry in different organs in response to the availability of nutrients and water in the desert ecosystem. However, which response of organs is most sensitive to environmental conditions is still unclear. To answer this question, we collected samples of plants and soils including not only aboveground leaves and stems, but also underground roots and soils from a wide range of arid areas during the growing season. The C, N, P, C:N, C:P, and N:P ratios in leaves, thorns, stems, and roots were derived to explore their relationship as well as their response mechanisms to nutrients and water spanning 1 m deep in the soil. The results showed that the order of N concentration was leaves > thorns > stems > roots, that the concentration of P in the leaves, thorns, and stems was similar, and that their values were higher than those in the roots. First, the C:N ratios in the leaves and stems were significantly positively correlated with the ratio in roots. The C:N ratios in each organ showed a significant relationship with the soil alkali hydrolyzable nitrogen (SAN) above a depth of 60 cm. In addition to SAN, soil available phosphorus (SAP) and soil organic carbon (SOC) affect the C:N ratio in the roots. Second, the C:P and N:P ratios in aboveground organs showed no correlations with the ratios in roots. The C:P and N:P ratios in the leaves and thorns have no relationship with soil nutrients, while the C:P ratio in roots was influenced by SAN and SOC in all soil layers. Finally, the N:P ratios in roots were also affected by nutrients in different soil depths at 0-20 and 60-80 cm. These results illustrate that the roots were more sensitive to soil nutrients than the aboveground parts. Our study of ecological stoichiometry also suggests a novel systematic approach for analyzing the sensitivity of responses of an organ to environmental conditions.

Characteristics of land-atmosphere interaction parameters in hinterland of the Taklimakan Desert

The importance of the energy exchange between the land surface and the atmosphere can be characterized by bulk transfer coefficients for momentum, C_d, and heat, C_h. The diurnal and monthly variations of both bulk transfer coefficients and lengths of surface roughness are analyzed. Based on observed data from January to December 2009 in hinterland of the Taklimakan Desert, the characteristics of aerodynamic roughness length, z_0m, and thermal roughness length, z_0h, are discussed. It should be noted that the diurnal and monthly variations of the parameters are fundamentally different from those reported in vegetated areas. Specifically, four unique features can be identified in the surface layer. First, in Taklimakan Desert, z_0m does not vary with seasons; however, it significantly depends on wind speed. Second, z_0h is higher in the daytime and lower at night, showing obvious diurnal characteristics. The high values appear at sunrise and sunset. Third, both C_d and C_h have two peaks, one peak at sunrise, and another one at noon. Fourth, both C_d and C_h have larger values in winter season and smaller values in summer season.