Since 2005, dozens of geographical observational stations have been established in the Heihe River Basin (HRB), and by now a large amount of meteorological, hydrological, and ecological observations as well as data pertaining to water resources, soil and vegetation have been collected. To adequately analyze these available data and data to be further collected in future, we present a perspective from complexity theory. The concrete materials covered include a presentation of adaptive multiscale filter, which can readily determine arbi- trary trends, maximally reduce noise, and reliably perform fractal and multifractal analysis, and a presentation of scale-dependent Lyapunov exponent (SDLE), which can reliably dis- tinguish deterministic chaos from random processes, determine the error doubling time for prediction, and obtain the defining parameters of the process examined. The adaptive filter is illustrated by applying it to obtain the global warming trend and the Atlantic multidecadal os- cillation from sea surface temperature data, and by applying it to some variables collected at the HRB to determine diurnal cycle and fractal properties. The SDLE is illustrated to deter- mine intermittent chaos from river flow data.
Air temperature (AT) is a subsystem of a complex climate. Long-range correlation (LRC) is an important feature of complexity. Our research attempt to evaluate AT’s complexity differences in different land-use types in the Heihe River Basin (HRB) based on the stability and LRC. The results show the following: (1) AT’s stability presents differences in different land-use types. In agricultural land, there is no obvious variation in the trend throughout the year. Whereas in a desert, the variation in the trend is obvious: the AT is more stable in summer than it is in winter, with Ta ranges of [8, 20]°C and SD of the AT residual ranges of [0.2, 0.7], respectively. Additionally, in mountainous areas, when the altitude is beyond a certain value, AT’s stability changes. (2) AT’s LRC presents differences in different land-use types. In agricultural land, the long-range correlation of AT is the most persistent throughout the year, showing the smallest difference between summer and winter, with the Hs range of [0.8, 1]. Vegetation could be an important factor. In a desert, the long-range correlation of AT is less persistent, showing the greatest difference between summer and winter, with the Hs range of [0.54, 0.96]. Solar insolation could be a dominant factor. In an alpine meadow, the long-range correlation of AT is the least persistent throughout the year, presenting a smaller difference between summer and winter, with the Hs range of [0.6, 0.85]. Altitude could be an important factor. (3) Usually, LRC is a combination of the Ta and SD of the AT residuals. A larger Ta and smaller SD of the AT residual would be conducive to a more persistent LRC, whereas a smaller Ta and larger SD of the AT residual would limit the persistence of LRC. A larger Ta and SD of the AT residual would create persistence to a degree between those of the first two cases, as would a smaller Ta and SD of the AT residual. In addition, the last two cases might show the same LRC.
The Babao River Basin is the “water tower” of the Heihe River Basin. The combination of vulnerable ecosystems and inhospitable natural environments substantially restricts the existence of humans and the sustainable development of society and environment in the Heihe River Basin. Soil temperature (ST) is a critical soil variable that could affect a series of physical, chemical and biological soil processes, which is the guarantee of water conservation and vegetation growth in this region. To measure the temporal variation and spatial pattern of ST fluctuation in the Babao River Basin, fluctuation of ST at various depths were analyzed with ST data at depths of 4, 10 and 20 cm using classical statistical methods and permutation entropy. The study results show the following: 1) There are variations of ST at different depths, although ST followed an obvious seasonal law. ST at shallower depths is higher than at deeper depths in summer, and vice versa in winter. The difference of ST between different depths is close to zero when ST is near 5℃ in March or -5℃ in September. 2) In spring, ST at the shallower depths becomes higher than at deeper depths as soon as ST is above -5℃; this is reversed in autumn when ST is below 5℃. ST at a soil depth of 4 cm is the first to change, followed by ST at 10 and 20 cm, and the time that ST reaches the same level is delayed for 10-15 days. In chilling and warming seasons, September and February are, respectively, the months when ST at various depths are similar. 3) The average PE values of ST for 17 sites at 4 cm are 0.765 in spring > 0.764 in summer > 0.735 in autumn > 0.723 in winter, which implies the complicated degree of fluctuations of ST. 4) For the variation of ST at different depths, it appears that Max, Ranges, Average and the Standard Deviation of ST decrease by depth increments in soil. Surface soil is more complicated because ST fluctuation at shallower depths is more pronounced and random. The average PE value of ST for 17 sites are 0.863 at a depth of 4 cm > 0.818 at 10 cm > 0.744 at 20 cm. 5) For the variation of ST at different elevations, it appears that Max, Ranges, Average, Standard Deviation and ST fluctuation decrease with increasing elevation at the same soil depth. And with the increase of elevation, the decrease rates of Max, Range, Average, Standard Deviation at 4 cm are -0.89℃/100 m, -0.94℃/100 m, -0.43℃/100 m, and -0.25℃/100 m, respectively. In addition, this correlation decreased with the increase of soil depth. 6) Significant correlation between PE values of ST at depths of 4, 10 and 20 cm can easily be found. This finding implies that temperature can easily be transmitted within soil at depths between 4 and 20 cm. 7) For the variation of ST on shady slope and sunny slope sides, it appears that the PE values of ST at 4, 10 and 20 cm for 8 sites located on shady slope side are 0.868, 0.824 and 0.776, respectively, whereas they are 0.858, 0.810 and 0.716 for 9 sites located on sunny slope side.
Analyses of the soil moisture evolution trend and the influence of different types of radiation on soil moisture are of great significance to the simulation and prediction of soil moisture. In this paper, soil moisture (2-60 cm) and various radiation data from 2014-2015 at the A’rou superstation were selected. The radiation data include the net radiation (NR), shortwave and longwave radiation (SR and LR). Using adaptive fractal analysis (AFA), the long-range correlation (LRC) of soil moisture and long-range cross correlation (LRCC) between moisture and three types of radiation were analyzed at different timescales and soil depths. The results show that: (1) Persistence of soil moisture and consistency between soil moisture and radiation mutate at 18-d and 6-d timescales, respectively. The timescale variation of soil moisture persistence is mainly related to the influence process of radiation on soil moisture; (2) Both the soil moisture persistence and soil moisture-radiation consistency vary substantially with soil depth. The soil depth variation of soil moisture persistence is related to the influence intensity of radiation; (3) From 2-6 day timescales, LR displays the strongest influence on soil moisture at depths of 2-10 cm through negative feedback of radiation on the soil temperature. The influence intensity decreases with depth from 2-15 cm. Therefore, the soil moisture persistence is weak and increases with depth from 2-15 cm; and (4) At more than 6 day timescales, SR and NR display a stronger influence on the soil moisture persistence at depths of 2-40 cm through positive feedback of radiation on the soil temperature, especially at depths of 2-10 cm. This influence also weakens with depth. The soil moisture persistence at depths of 2-10 cm is the weakest and increases with depth from 2-40 cm. The research results are instructive for determining timescales and soil depths related to soil water in hydrological models.
In many arid ecosystems, vegetation frequently occurs in high-cover patches interspersed in a matrix of low plant cover. However, theoretical explanations for shrub patch pattern dynamics along climate gradients remain unclear on a large scale. This context aimed to assess the variance of the Reaumuria soongorica patch structure along the precipitation gradient and the factors that affect patch structure formation in the middle and lower Heihe River Basin (HRB). Field investigations on vegetation patterns and heterogeneity in soil properties were conducted during 2014 and 2015. The results showed that patch height, size and plant-to-patch distance were smaller in high precipitation habitats than in low precipitation sites. Climate, soil and vegetation explained 82.5% of the variance in patch structure. Spatially, R. soongorica shifted from a clumped to a random pattern on the landscape towards the MAP gradient, and heterogeneity in the surface soil properties (the ratio of biological soil crust (BSC) to bare gravels (BG)) determined the R. soongorica population distribution pattern in the middle and lower HRB. A conceptual model, which integrated water availability and plant facilitation and competition effects, was revealed that R. soongorica changed from a flexible water use strategy in high precipitation regions to a consistent water use strategy in low precipitation areas. Our study provides a comprehensive quantification of the variance in shrub patch structure along a precipitation gradient and may improve our understanding of vegetation pattern dynamics in the Gobi Desert under future climate change.
Rainfall interception is of great significance to the fully utilization of rainfall in water limited areas. Until now, studies on rainfall partitioning process of typical ecosystems in Heihe River Basin, one of the most important inland river basins in China, is still insufficient. In this study, six typical ecosystems were selected, namely alpine meadow, coniferous forest, mountain steppe, desert, cultivated crop, and riparian forest, in Heihe River Basin for investigation of the rainfall interception characteristics and their influencing factors, including rainfall amount, duration, and intensity, based on the gross rainfall and high temporal resolution soil moisture data obtained from 12 automatic observation sites. The results show that the average interception amount and average interception rate of the six ecosystems are significantly different: alpine meadow 6.2 mm and 45.9%, coniferous forest 7.4 mm and 69.1%, mountain steppe 3.5 mm and 37.3%, desert 3.5 mm and 57.2%, cultivated crop 4.5 mm and 69.1%, and riparian forest 2.6 mm and 66.7%, respectively. The rainfall amount, duration, and intensity all had impact on the process of rainfall interception. Among these three factors, the impact of rainfall amount was most significant. The responses of these ecosystems to the rainfall characteristics were also different. Analyzing rainfall interception with high temporal resolution soil moisture data is proved to be a feasible method and need further development in the future.
The Heihe River Basin is located in the arid and semi-arid regions of Northwest China. Here, the terrestrial ecosystem is vulnerable, making it necessary to identify the factors that could affect the ecosystem. In this study, MODIS-NDVI data with a 250-m resolution were used as a proxy for the terrestrial ecosystem. By combining these with environmental factors, we were able to explore the spatial features of NDVI and identify the factors influencing the NDVI distribution in the Heihe River Basin during the period of 2000-2016. A geographical detector (Geodetector) was employed to examine the spatial heterogeneity of the NDVI and to explore the factors that could potentially influence the NDVI distribution. The results indicate that: (1) the NDVI in the Heihe River Basin appeared high in the southeast while being low in the north, showing spatial heterogeneity with a q-statistic of 0.38. The spatial trend of the vegetation in the three sub-basins generally increased in the growing seasons from 2000 to 2016; (2) The results obtained by the Geodetector (as denoted by the q-statistic as well as the degree of spatial association between the NDVI and environmental factors) showed spatial heterogeneity in the associations between the NDVI and the environmental factors for the overall basin as well as the sub-basins. Precipitation was the dominant factor for the overall basin. In the upper basin, elevation was found to be the dominant factor. The dominant factor in the middle basin was precipitation, closely followed by the soil type. In the lower basin, the dominant factor was soil type with a lower q-statistic of 0.13, and the dominant interaction between the elevation and soil type was nonlinearly enhanced (q-statistic = 0.22).
Understanding the controls on seasonal variation of energy partitioning and separation between canopy and soil surface are important for qualifying the vegetation feedback to climate system. Using observed day-to-day variations of energy balance components including net radiation, sensible heat flux, latent heat flux ground heat flux, and meteorological variables combined with an energy-balanced two-source model, energy partitioning were investigated at six sites in Heihe River Basin from 2014 to 2016. Bowen ratio (β) among the six sites exhibited significant seasonal variations while showed smaller inter-annual fluctuations. All ecosystems exhibit a “U-shaped” pattern, characterized by smaller value of β in growing season, with a minimum value in July, and fluctuating day to day. During the growing season, average Bowen ratio was the highest for the alpine swamp meadow (0.60 ± 0.30), followed by the desert riparian forest Populus euphratica (0.47 ± 0.72), the alpine desert(0.46 ± 0.10), the Tamarix ramosissima desert riparian shrub ecosystem (0.33 ± 0.57), alpine meadow ecosystem (0.32 ± 0.17), and cropland ecosystem (0.27 ± 0.46). The agreement of Bowen ratio between simulated and observed values demonstrated that the two-source model is a promising tool for energy partitioning and separation between canopy and soil surface. The importance of biophysical control explains the convergence of seasonal and annual patterns of Bowen ratio for all ecosystems, and the changes in Bowen ratio showed divergence among varied ecosystems because of different physiological responses to energy flow pathways between canopy and soil surface.
Land use and cover change (LUCC) is an important indicator of the human-earth system under climate/environmental change, which also serves as a key impact factor of carbon balance, and a major source/sink of soil carbon cycles. The Heihe River Basin (HRB) is known as a typical ecologically fragile area in the arid/semi-arid regions of northwestern China, which makes it more sensitive to the LUCC. However, its sensitivity varies in a broad range of controlling factors, such as soil layers, LUCCs and calculation methods (e.g. the fixed depth method, FD, and the equivalent mass method, ESM). In this study, we performed a meta-analysis to assess the response of soil organic carbon (SOC) and total nitrogen (TN) storage to the LUCC as well as method bias based on 383 sets of SOC data and 148 sets of TN data from the HRB. We first evaluated the calculation methods and found that based on the FD method, the LUCC caused SOC and TN storage to decrease by 17.39% and 14.27%, respectively; while the losses estimated using the ESM method were 19.31% and 18.52%, respectively. The deviations between two methods were mainly due to the fact that the FD method ignores the heterogeneity of soil bulk density (BD), which may underestimate the results subsequently. We then analyzed the response of SOC and TN storage to various types of the LUCC. In particular, when woodland and grassland were converted into cultivated land or other land types, SOC and TN suffered from heavy losses, while other LUCCs had minor influences. Finally, we showed that increasing the depth of the soil layers would reduce the losses of SOC and TN storage. In summary, we identified a series of controlling factors (e.g. soil layer, the LUCC and calculation method) to evaluate the impact of the LUCC on SOC and TN storage in the HRB, which should be considered in future research.
