Jinzhong Yang’s research while affiliated with Wuhan University and other places

Accurate estimation of seepage losses in large-scale canal systems and identification of their impact factors are important for improving water conveyance efficiency in agricultural districts. However, seepage losses can vary widely across different regions and periods, making it difficult to obtain a complete understanding of the variation process based solely on local scale studies. In addition, although there are currently some complex numerical models available for large-canal systems in agricultural districts, they are rarely used in practice due to their complexity. This study evaluated the regional-scale spatio-temporal seepage processes of the Zaohuo canal, a 55 km's sub-main earthen canal located in the Hetao Irrigation District, China, under current and future water-saving conditions using MODFLOW-SWR. In addition, a pre-processing tool was developed to process spatial geographic data and spatial topology between different canals. Furthermore, the sensitivity of different influencing factors, such as the permeability of canal bed sediments, surface and groundwater level, and local lining, was also investigated. The optimal relationship between lining areas when partial lining is used and seepage losses was also investigated. The calculated water conveyance efficiency coefficient is 0.7871, which fits well with the reported results and proves the reliability of the simulation. In addition, it was found that seepage losses are most sensitive to the surface water level of the canal, followed by the permeability of canal bed sediments and then the groundwater level. Moreover, new hybrid lining can reduce the seepage losses by about 92.02%, but ongoing maintenance is vital. When lining the key portion of the canal, the seepage losses will be significantly reduced with the increase of lining area. The seepage losses reduction factor increases by 5.8% for every 1 × 10 5 m 2 increase in lining area when the lining area is below 1 × 10 6 m 2 , while the effect is not significant when that limitation is exceeded. This study can support decision-making for water-saving projects in large water conveyance canals in regional-scale agriculture districts.

Many algorithms have been proposed to cope with groundwater numerical simulation associated with local grid refinements typically for subsurface flow driven by sources acting on diverse scales. In this context, we focus on establishing an efficient local grid refinement method with nonmatching grids for groundwater flow modelling. The new model is based on the vertex-centred finite-volume method (VCFVM). The core idea of the algorithm resting on set all unknowns on vertices, and the flux between two vertices (the numerical flux) is expressed as a function of the hydraulic heads at the vertices of the element containing the lateral surface. The total outflow of the control volume of a given vertex is expressed as the sum of numerical fluxes. Since the algorithm sets all unknowns on vertices and a control volume can be defined for each vertex, our scheme readily embeds treatment of arbitrary polygonal grids, including nonmatching grids, in the presence of local grid refinement, without additional treatment at the nonmatching nodes. Six test cases, including five assumed ones and a real-world case, were adopted to evaluate the accuracy and efficiency of 3 the new algorithm. The hydraulic heads calculated by the new algorithm were compared with those by a widely used and tested numerical groundwater model, called MODFLOW, and the analytic solutions. The mass balance error and the CPU time were compared with those of the MODFLOW 6 model, which owns ability to cope with nonmatching grids. The results show that the new algorithm yields high accuracy and efficiency in simulating groundwater flows with local grid refinements.

Balancing water allocations in river basins between upstream irrigated agriculture and downstream cities, industry and environments is a global challenge. The effects of changing allocations are exemplified in the arid Hetao Irrigation District on the Yellow River, one of China's three largest irrigation districts. Amongst the many challenges there, the impact of changing climate on future irrigation water demand is an underlying concern. In this paper we analyse trends in local climate data from the late 1950s and consider the implications for irrigation in the Basin. Since 1958, daily minimum temperatures, Tmin in the Basin have increased at three times the rate of daily maximum temperatures, Tmax. Despite this, there has been no significant increases in annual precipitation, P or pan evaporation, Epan. The difference between the increasing trends in Tmax and Tmin means that the average annual diurnal temperature range, DTR, has decreased very significantly, part of a global phenomenon. Hargreaves empirical approach is used to estimate changes in both incoming solar radiation, Rs, and potential evaporation, ET0. Changes in estimated ET0 correlated well with changes in measured pan evaporation, Epan. Paradoxically, the estimated decreasing trend in Rs does not correspond to a significant decreasing trend in Epan. Implications of changing climate on water use and soil salinity in the Basin are discussed.

Soil salinization is a major eco-environmental problem in irrigated agro-ecosystems. Understanding regional soil salinity spatial patterns and seasonal dynamics and their driving factors under changing environments is beneficial to managing soil salinity to maintain agricultural production in arid agricultural areas. To better investigate this topic, soil salinity was measured, ranging from topsoil to the depth of 1.8 m in an irrigation district with 68 sampling sites before and after the crop growing seasons of the dry year of 2017 and wet year of 2018. Soil texture, groundwater table depth, groundwater salinity, and crop type were monitored. The results indicated that an increase in soil salinity in the root zone (0–0.6 m) was accompanied by a decrease in soil salinity in the deep soil (0.6–1.8 m) through the crop growing season due to water movement from the deep layer to shallow layer, whereas the opposite trend was observed during the fallow seasons. During the dry year, the area with soil desalted was measured to be 19.89%, 14.42%, and 2.78% lower at depths of 0–0.6 m, 0.6–1.2 m, and 1.2–1.8 m than that during the wet year. The groundwater table depth in the crop growing season had the least impact on the change in root zone soil salinity (p > 0.05). Interactions between crop types and groundwater table depth had a significant effect on the change of soil salinity in the root zone during the growing season of the dry year, but were insignificant during the wet year. Crop types, groundwater table depth, and climate conditions determined the contribution of shallow groundwater to crop water consumption and, to a greater extent, soil salinity. Regression tree analysis showed that groundwater salinity and soil texture had a greater influence on soil salinity than groundwater table depth and land elevation. The effect of groundwater on soil salinity is strongly related to soil texture, and the salinity of fine-textured soil was 36–54% greater than that of coarse-textured soil due to large capillary action. Therefore, we suggest strengthening groundwater management in areas with fine-textured soil to relieve soil salinization, particularly during dry years.

Machine learning methods provide new perspective for more convenient and efficient prediction of groundwater flow. In this study, a deep learning method “GW-PINN” without labeled data for solving groundwater flow equations with wells was proposed. GW-PINN takes the physics inform neural network (PINN) as the backbone and uses either the hard or soft constraint in the loss function for training. A locally refined sampling strategy (LRS) is adopted to generate the consistent spatial sampling points for problems with strong hydraulic head change, and then combined with an appropriate temporal sampling scheme to obtain the final spatial-temporal sampling points. A snowball-style two-stage training strategy by dividing the temporal domain into two subdomains is designed to decrease the sampling points. Five cases were designed to test the training performance of GW-PINN under different sampling strategies and two constraints. The predicted results of GW-PINN were compared with MODFLOW and the analytical solution. The results demonstrate that GW-PINN possesses strong ability in capturing the hydraulic head change for both confined and un-confined aquifers. The hard constraint owns more robust learning ability than the soft constraint. The LRS strategy can generate more accurate results with much fewer sampling points than traditional sampling strategies, and the snowball-style two-stage training strategy is significantly efficient for problems with the drastic change of hydraulic head. Furthermore, the application of GW-PINN as a surrogate model for parameterized groundwater flow equations is illustrated. This study provides an option tool for efficient groundwater flow simulation, especially for those with local refinements are needed.

Accurate estimation of the spatial-temporal distribution of phreatic evapotranspiration (ET) is critical for managing water resources and preventing soil salinization in arid and semiarid agricultural areas where substantial water-saving efforts are needed. Traditional phreatic ET estimation approaches either are for small scales or cannot consider spatial and/or temporal variations in phreatic ET. This study developed a new approach for estimating the spatial-temporal phreatic ET based on the normalized difference vegetation index (NDVI) and measured water table depths. The NDVI was used to calculate the actual evapotranspiration (ETc act) by scaling it with the reference crop evapotranspiration (ETo). The water table depths measured during the periods of no other factors (i.e., precipitation, irrigation and groundwater extraction) were used to establish an equation used to estimate the phreatic ET contribution coefficient (defined as the ratio of phreatic ET to the corresponding ETc act). To improve estimation accuracy, a time-related correction factor was considered in the equation for estimating the phreatic ET. The new approach was used to estimate monthly phreatic ET with a spatial resolution of 250 m in the Hetao irrigation district, located in arid Northwest China. The estimated phreatic ET at different spatial and temporal scales matched well with the groundwater balance model results. The results show that the spatial distribution of phreatic ET is affected by both natural factors (e.g., land cover types) and human activities (e.g., groundwater extraction, planted crops). In the Hetao irrigation district, phreatic ET contributes an average of 24.4% to ETc act during the non-freezing-thawing period (from June to November), demonstrating the essential role of phreatic ET in supporting crop growth and the general ecological environment in arid areas with shallow water table depths. The new approach of estimating spatial-temporal phreatic ET may be used for designing effective and efficient water resource management policies at the regional scale.

Irrigated agriculture in arid and semi-arid regions is seriously threatened by water shortage and soil salinization. The well-canal conjunctive irrigation scheme provides a stable groundwater resource for irrigation and can reduce surface salt accumulation by decreasing the groundwater levels, which makes it more suitable to alleviate the problems of irrigated agriculture in arid and semi-arid regions. However, the soil salinization process requires assessment on regional spatial and decadal time scales, as it is a continuous but slow change. Therefore, a water and salt balance model (WSBM) for well-canal conjunctive irrigation is developed herein to obtain long-term predictions of regional root zone salinity dynamics in canal- and well-irrigated areas. In the developed model, the characteristic length of the well-canal conjunctive irrigated area (Lc) is used to couple the canal- and well-irrigated areas. The performance of the WSBM as well as a sensitivity analysis and the value rule of the special parameter Lc are evaluated by comparing the simulation results with those derived from the MODFLOW. The results demonstrate the validity of the developed model, and the special parameter Lc is found to be insensitive, with a value approximately two-thirds of the center distance when the canal and well irrigation districts are regularly adjacent or centrosymmetric. Moreover, when a real-world application is adopted, the water table depth and root-zone soil salinity are simulated in the Longsheng well-canal irrigation area in the Hetao Irrigation District, Inner Mongolia, China. Water table depth and soil salinity collected from 2002–2005 and from 2006–2020 are used to calibrate and validate the model. The calibrated model is subsequently used to predict soil salinity dynamics in the next 100 years under current and future water-saving conditions. The predictions indicate that the soil salinity is basically stable at a relatively low level (<0.2 kg/100 kg) under current irrigation practices. The study could support planning making before implementation of well-canal conjunctive irrigation.