Heihe River Basin Research Articles

Spatiotemporal dynamics and driving mechanism of arable ecosystem stability in arid and semi-arid areas based on Pressure-Buffer-Response process

The stability of the arable ecosystem plays a crucial role in the sustainable utilization of land resources and the efficiency of agricultural production. However, irrigated agriculture has led to severe desertification and salinization in arid and semi-arid areas, resulting in long-term low ecological stability of arable land. This study focuses on the arable ecosystem stability (AES) in the Hexi Corridor and constructs an AES evaluation model based on the pressure-buffer-response process. Using methods such as the revised wind erosion equation, remote sensing ecological index and path analysis to explore the spatiotemporal dynamics and driving mechanism of AES during the growing season (April–October). The results showed that the AES in the area was transitioning from a critical to stable state, with vulnerable arable land gradually decreasing while stable land increased. Stable arable land was concentrated in the middle reaches of the Heihe River basin, while arable land in the Shule River Basin was basically at critical level, a crucial area for wind erosion prevention. In the lower reaches of the Shiyang River Basin, much arable land was vulnerable to salinization, requiring urgent treatment. Complex climatic conditions and irrational irrigation patterns are the main causes of AES fluctuations. Agricultural engineering construction and favorable policy implementation can significantly improve AES. In addition, seasonal variability in wind erosion, salinization and AES was significant, with the worst in spring and the best in summer. Climate and vegetation factors have become the main factors affecting wind erosion and salinization. The fallow period before spring is a critical time for wind erosion control and salinity soil improvement. This study aims to provide theoretical guidance for the construction of agricultural production and ecological management in arid and semi-arid areas.

Climate-warming-driven changes in the cryosphere and their impact on groundwater–surface-water interactions in the Heihe River basin

Abstract. The Heihe River basin in northwest China depends heavily on both anthropogenic and natural storage (e.g., surface reservoirs, rivers and groundwater) to support economic and environmental functions. The Qilian Mountain cryosphere in the upper basin is integral to recharging these storage supplies. It is well established that climate warming is driving major shifts in high-elevation water storage through loss of glaciers and permafrost. However, the impacts on groundwater–surface-water interactions and water supply in corresponding lower reaches are less clear. We built an integrated hydrologic model of the middle basin, where most water usage occurs, in order to explore the hydrologic response to the changing cryosphere. We simulate the watershed response to loss of glaciers (glacier scenario), advanced permafrost degradation (permafrost scenario), both of these changes simultaneously (combined scenario) and projected temperature increases in the middle basin (warming scenario) by altering streamflow inputs to the model to represent cryosphere-melting processes, as well as by increasing the temperature of the climate forcing data. Net losses to groundwater storage in the glacier scenario and net gains in the permafrost and combined scenarios show the potential of groundwater exchanges to mediate streamflow shifts. The result of the combined scenario also shows that permafrost degradation has more of an impact on the system than glacial loss. Seasonal differences in groundwater–surface-water partitioning are also evident. The glacier scenario has the highest fraction of groundwater in terms of streamflow in early spring. The permafrost and combined scenarios meanwhile have the highest fraction of streamflow infiltration in late spring and summer. The warming scenario raises the temperature of the combined scenario by 2 ∘C. This results in net groundwater storage loss, a reversal from the combined scenario. Large seasonal changes in evapotranspiration and stream network connectivity relative to the combined scenario show the potential for warming to overpower changes resulting from streamflow. Our results demonstrate the importance of understanding the entire system of groundwater–surface-water exchanges to assess water resources under changing climatic conditions. Ultimately, this analysis can be used to examine the cascading impact of climate change in the cryosphere on the resilience of water resources in arid basins downstream of mountain ranges globally.

Separate retrievals of soil and vegetation temperatures using two methods

ABSTRACT Soil temperature (Ts) and vegetation temperature (Tv) provide significant information for various practical applications such as evapotranspiration estimation, water requirement analysis for vegetation, and drought monitoring during the crop growth. In this study, we estimated Ts and Tv with a land surface temperature (LST)-vegetation index (VI) trapezoidal space-based method (hereafter abbreviated as the trapezoidal method) and a dual-angle algorithm in which the estimation equations for Ts and Tv were established with thermal infrared observations from two angles and solved to using the least squares method. The Ts and Tv estimations were conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 data from the Google Earth Engine platform, together with auxiliary meteorological data from eight sites with various land cover types in the Heihe River Basin in 2019, and compared with corresponding in-situ measurements and temperatures from Landsat. The results showed that (1) both estimation methods outperformed the Ts retrievals compared to those of Tv; (2) the trapezoidal method overall had a similar but slightly better performance compared to the dual-angle algorithm, with the root mean square error for Ts retrievals of 3.7 K and 5.7 K, and for Tv retrievals of 6.5 K and 7.2 K, respectively, in comparison with temperatures from Landsat; (3) the trapezoidal method had higher accuracy over sites with grassland, desert, or meadow for Ts retrieval and sites with forests for Tv retrieval. This study provides advice about the method for Ts and Tv estimations and might be useful for rational utilization and more accurate monitoring of soil and vegetation.

Middle to Late Holocene lake evolution and its links with westerlies and Asian monsoon in the middle part of the Hexi Corridor, NW China

AbstractThe interpretation and understanding of the relationship between Middle to Late Holocene climate change in monsoon margins of northwest China with the westerlies and Asian monsoon (AM) remain controversial. Here we present a new multi-proxy sedimentary dataset from the Heihe River basin in the middle part of the Hexi Corridor on the northern margin of the Qinghai-Tibet Plateau (QTP), which is a sensitive zone for the interaction between the westerlies and AM. Fluctuations in grain size, δ13Corg, δ18O, magnetic susceptibility, total organic carbon, total nitrogen, and C/N ratio document regional lake and climate evolution since 5334 cal yr BP. Results show that climate conditions on the millennial timescale are humid in the late Middle Holocene (MH) and dry to wet in the Late Holocene (LH). Combined with the multi-model ensemble simulation from PMIP3-CMIP5, high lake levels and wetter climate in the late MH are closely linked to the strengthening Asian summer monsoon. Simultaneously, the slight wetting trend since the late LH may be the superimposing effect of enhanced westerlies and the weakening Asian winter monsoon. These findings can provide insights into the interpretation of the interaction between the westerlies and AM during the Holocene in East Asia.

The role of human activities in the weakening of the propagation relationship between meteorological and hydrological droughts in the Heihe River Basin

AbstractMeteorological droughts could propagate into hydrological droughts and cause agricultural and societal damage. However, the quantitative impact of human activities on the drought propagation has not been well understood. Based on the precipitation and streamflow observational data and hydrological model simulations during 1961–2013, this study analysed the drought propagation characteristics and quantified the impact of human activities on the propagation in the upper and middle reaches of Heihe River Basin. The matched meteorological and hydrological drought events indicated that the drought propagation was characterized by a lagged onset, weakened drought severity and reduced development and recovery speeds. In the upstream area, the correlations between meteorological and hydrological droughts were significant, and human activities had a negligible impact on the drought propagation. In contrast, the impact from human activities was significant in the midstream area, and the effect was two‐fold. Specifically, human activities (e.g., reservoir regulation) reduced the propagation probability of one or several meteorological droughts propagating into a hydrological drought by 16%. Meanwhile, human activities had remarkable effects on long‐lasting and extreme hydrological droughts through reducing their duration and severity by 18% and 37% respectively. On the other hand, the occurrence probability of mismatched drought events, that is, meteorological droughts without causing hydrological droughts and hydrological droughts without preceding meteorological droughts, increased by 34% due to human interventions. Human activities also accelerated the development speed of slowly developing hydrological droughts. In addition, human activities increased the mean duration and severity of moderate hydrological droughts by 32% and 18%. The results are expected to provide scientific bases for understanding the formation and development process of hydrological drought and improving drought forecasting under global change.

Projection and Analysis of Floods in the Upper Heihe River Basin under Climate Change

The projection of future hydrological processes can provide insights into the risks associated with potential hydrological events in a changing environment and help develop strategies to cope with and prevent them. The Heihe River basin in Northwest China is crucial for providing water resources to water-scarce regions. Thus, understanding the future runoff trends in the context of climate change can optimize water allocation, alleviate water shortages, and mitigate flood risks in the region. In this study, we use meteorological data from 10 general circulation models under two future scenarios to drive the Soil and Water Assessment Tool (SWAT) model and project hydrological processes in the upper Heihe River basin from 2026 to 2100. After examining the future changes in total runoff in the basin, we assess the magnitude, frequency, and timing of daily flood events in the future. The results of the multi-model ensemble averaging (MMEA) method show that the change in the multi-year average annual runoff is −4.5% (2026–2050), −1.8% (2051–2075), and +2.0% (2076–2100) under the SSP245 scenario and −1.0% (2026–2050), +0.4% (2051–2075), and +0.2% (2076–2100) under the SSP585 scenario compared to the historical period. The analysis of flood magnitudes indicates that the basin will experience higher-magnitude floods in the future, with the largest increase rates of 61.9% and 66.4% for the 1-day maximum flows under the SSP245 and SSP585 scenarios, respectively. The flood return period is projected to be shorter in the future, and the 1-day maximum flows of a 100-year flood are expected to increase by 44.7% and 63.7% under the SSP245 and SSP585 scenarios, respectively. Furthermore, a significant shift in the flood timing is expected, with the highest frequency moving from July to August, representing a one-month lag compared to the historical period. Our findings suggest that the hydrological characteristics of the upper Heihe River basin may be significantly altered in the future due to the effects of climate change, resulting in floods with higher magnitudes and frequencies and different timings. Therefore, it is imperative to consider these changes carefully when developing risk prevention measures.

Turbulent fluxes at kilometer scale determined by optical-microwave scintillometry in a heterogeneous oasis cropland of the Heihe River Basin

Observations of kilometer-scale turbulent fluxes of sensible (H) and latent heat (LE) are required for the validation of flux estimate algorithms from satellite remote-sensing data and the development of parameterization schemes in the hydro-meteorological models. Since 2019, two sets of Optical and Microwave scintillometer (OMS) systems have been operated in the Heihe River Basin of northwestern China, one on an alpine grassland of upper reaches, another on an oasis cropland of middle reaches, to measure both the areal H and LE. Combined with the observations of eddy-covariance (EC) and meteorological tower systems in both sites, an improved procedure for OMS data processing is proposed. The newly proposed procedure especially improves the preprocessing of raw scintillation data, properly uses the current probably better Lüdi et al. (2005) method in deriving meteorological structure parameters, and chooses the coefficients of similarity functions by Kooijmans and Hartogensis (2016) in calculating fluxes. Evaluated with the results of rather homogeneous grassland, the area-averaged H and LE over the heterogeneous oasis are then determined. Estimates of H and LE agree reasonably well with those obtained from EC in most cases. However, the most interesting is that LE over the oasis during the early crop growing stages is clearly larger than that of EC; while both agree well during the longer crop grown periods. Footprint analysis shows that, compared with EC, the OMS has clearly larger source area that contains a slight area of orchard and shelterbelts distributed near the light path, leading to larger LE during the early stages of crop growth. The area-averaged evapotranspiration (ET) over the oasis is then analyzed more acceptably, which varies from 3 to 5 mm day−1 depending on meteorological conditions during the 39 days of the crop growing period. These results are used to validate the Penman-Monteith-Leuning Version 2 (PML-V2) scheme.

Precipitation-type discrimination and changes in related climate indices in the upper Heihe River Basin during 1960–2021

Introduction: Precipitation in the upstream region of the Heihe River basin (UHRB) in the northeastern of the Tibetan Plateau, which is the main water source of the basin, has undergone drastic changes in extreme climate events in recent decades. In addition to the amount of precipitation, the type of precipitation has a substantial impact on hydrological processes.Methods: In this study, we compared the results from three methods aimed at improving precipitation type estimation based on daily precipitation type records for 24 discontinuous years. Based on the precipitation type distinction, we examined the spatial and temporal changes in the total precipitation, rainfall, snowfall and air temperature at the six stations as well as the spatial average of the UHRB during the past 62 years. We also analyzed changes in the quantity, duration magnitude, and frequency of extreme precipitation using the RClimDex model and statistical analysis.Results: The probability of detection value of the T3.6_4.5 method was 1.9%, indicating the estimation was closest to actual records. The analyses based on precipitation type diving showed that rainfall accounts for an average of 81.9% of the total precipitation received in a year. In the context of large scale climate warming, temperatures at all six stations increased significantly, but precipitation changes were only apparent at about half of the stations which were located in regions of higher elevation and influenced by both ENSO and the East Asian monsoon. Analysis of the spatial averages in the UHRB revealed that the annual drought events (CDD) were significantly alleviated, and that the growing season length (GSL) was significantly extended. The annual total precipitation, rainfall, and extreme precipitation indices (P99P, P95P, R95P, and SDII) increased in magnitude, and the frequency of extreme precipitation events (P10mm) also significantly increased.Discussion: The findings of this study indicate that under the background of climate warming, the changes of precipitation patterns in the UHRB which may have resulted in bringing better vegetation growth, but also the increasingly frequent extreme rainfall events may pose challenges to growing extreme rainfall events to agriculture and other human activities in local and downstream areas.

Assessment of gridded datasets of various near surface temperature variables over Heihe River Basin: Uncertainties, spatial heterogeneity and clear-sky bias

Near-surface temperatures, such as air, land surface, and soil temperatures, play significant roles in surface radiation and energy balance. This study assessed nine gridded near-surface temperature products and analyzed the spatial heterogeneity and clear-sky bias of these temperature variables, using extensive measurements collected at Heihe River Basin. The MXD21 (MOD21 and MYD21) product had the lowest root mean square error (RMSE) (3.35 K) among all skin temperature products but a high percentage of missing values (48.4 %). All-weather skin temperature products had comparable accuracy for the interpolated cloudy-sky cases (RMSE 4.92 K) and observed clear-sky pixels (RMSE 3.42 K). For air temperature, AMSR2 had the lowest RMSE (2.48 K), but a high percentage of invalid data (32.5 %); and ERA5 had a worse accuracy (RMSE 3.87 K) but a high spatial resolution and gap-free data coverage. Comparing products from the same data source, air and soil temperatures had higher accuracies than skin temperature. Among the different variables of temperature, the 0 cm soil temperature and skin temperature had higher spatiotemporal heterogeneity than the air temperature and the soil temperatures at greater depths. The skin temperature, 0 cm soil temperature, and air temperature had higher clear-sky biases compared to soil temperatures.

Dense flux observations reveal the incapability of evapotranspiration products to capture the heterogeneity of evapotranspiration

Accurate terrestrial evapotranspiration (ET) estimation over complex surfaces with spatial heterogeneity is crucial for local, regional, and global applications. Currently, a variety of global ET products with different spatiotemporal resolutions have been developed and evaluated. However, little is known about their performance in capturing the heterogeneity of ET over complex surfaces. Focusing on the Heihe River Basin (HRB), a typical arid and semi-arid region that is hydrologically vulnerable, this study compared ET from eleven global ET products (six remotely- sensed ET, two land surface model ET, one hydrological model-based ET, one reanalysis ET, and one synthesis ET) and one regional ET dataset against dense eddy covariance observations in terms of the magnitude, seasonal cycle, and spatial pattern. In general, the remotely-sensed ET and synthesis ET outperformed other categories, with the operational Simplified Surface Energy Balance (SSEBop), Penman-Monteith–Leuning (PML), Moderate Resolution Imaging Spectroradiometer (MOD16) and Global LAnd Surface Satellite (GLASS) performing relatively better (root mean square error ranging from 1.22 to 1.57 mm d-1). Across all the land cover types, ET products reproduced a relatively feasible ET over the desert steppe, meadow, and barren, but substantially underestimated ET over ecosystems with high ET values but a low land area fraction over HRB (cropland, wetland, and forest). This highlights the importance of accurately representing the heterogeneity and local climates in the complex land surfaces for ET quantification in regional water resource management.

Improving the SM2RAIN-derived rainfall estimation using Bayesian optimization

The rainfall product derived from the SM2RAIN (Soil Moisture to Rain) algorithm has been widely used. However, there is still a large uncertainty partly due to the soil moisture input and parameters estimation of the SM2RAIN algorithm, which limits the application of the model in alpine regions. Here, the SM2RAIN-BayesOpt algorithm was developed by integrating the SM2RAIN algorithm and Bayesian optimization to improve the estimation of parameters (Z, a, b, Tbase, Tpot), subsequently incorporating SMAP Level-3 soil moisture products for rainfall estimation. The performance of the SM2RAIN-BayesOpt algorithm was evaluated based on observed rainfall data under different environmental conditions in three typical alpine regions, namely Tibetan Plateau, Heihe River Basin, and Shandian River Basin. Moreover, SM2RAIN-BayesOpt, IMERG-V06B, and ERA5 reanalysis rainfall estimates were also compared with in-situ rainfall observations. The results showed that the proposed SM2RAIN-BayesOpt algorithm can obtain more accurate rainfall estimates in all studied areas in terms of different evaluation metrics. It was also found that our proposed SM2RAIN-BayesOpt algorithm performs better in alpine meadows and grassland than in desert and forestland. SM2RAIN-BayesOpt algorithm can considerably improve the accuracy of rainfall estimation, and it is of significant value for rainfall monitoring in alpine regions where observational data are scarce.

A Full-Scale Optimization of a Crop Spatial Planting Structure and its Associated Effects

Driven by the concept of agricultural sustainable development, crop planting structure optimization (CPSO) has become an effective measure to reduce regional crop water demand, ensure food security, and protect the environment. However, traditional optimization of crop planting structures often ignores the impact on regional food supply–demand relations and interprovincial food trading. Therefore, using a system analysis concept and taking virtual water output as the connecting point, this study proposes a theoretical CPSO framework based on a multi-aspect and full-scale evaluation index system. To this end, a water footprint (WF) simulation module denoted as soil and water assessment tool–water footprint (SWAT-WF) is constructed to simulate the amount and components of regional crop WFs. A multi-objective spatial CPSO model with the objectives of maximizing the regional economic water productivity (EWP), minimizing the blue water dependency (BWFrate), and minimizing the grey water footprint (GWFgrey) is established to achieve an optimal planting layout. Considering various benefits, a full-scale evaluation index system based on region, province, and country scales is constructed. Through an entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) comprehensive evaluation model, the optimal plan is selected from a variety of CPSO plans. The proposed framework is then verified through a case study of the upper–middle reaches of the Heihe River Basin in Gansu province, China. By combining the theory of virtual water trading with system analysis, the optimal planting structure is found. While sacrificing reasonable regional economic benefits, the optimization of the planting structure significantly improves the regional water resource benefits and ecological benefits at different scales.

A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River

River runoff simulation and prediction are important for controlling the water volume and ensuring the optimal allocation of water resources in river basins. However, the instability of medium- and long-term runoff series increases the difficulty of runoff forecasting work. In order to improve the prediction accuracy, this research establishes a hybrid deep learning model framework based on variational mode decomposition (VMD), the mutual information method (MI), and a long short-term memory network (LSTM), namely, VMD-LSTM. First, the original runoff data are decomposed into a number of intrinsic mode functions (IMFs) using VMD. Then, for each IMF, a long short-term memory (LSTM) network is applied to establish the prediction model, and the MI method is used to determine the data input lag time. Finally, the prediction results of each subsequence are reconstructed to obtain the final forecast result. We explored the predictive performance of the model with regard to monthly runoff in the upper Heihe River Basin, China, and compared its performance with other single and hybrid models. The results show that the proposed model has obvious advantages in terms of the performance of point prediction and interval prediction compared to several comparative models. The Nash–Sutcliffe efficiency coefficient (NSE) of the prediction results reached 0.96, and the coverage of the interval prediction reached 0.967 and 0.908 at 95% and 90% confidence intervals, respectively. Therefore, the proposed model is feasible for simulating the monthly runoff of this watershed.

Propagation characteristics from meteorological drought to agricultural drought over the Heihe River Basin, Northwest China

Propagation characteristics from meteorological drought to agricultural drought over the Heihe River Basin, Northwest China

Improving regional climate simulations based on a hybrid data assimilation and machine learning method

Abstract. The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land–atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.

Upscaling of Latent Heat Flux in Heihe River Basin Based on Transfer Learning Model

Latent heat flux (LE) plays an essential role in the hydrological cycle, surface energy balance, and climate change, but the spatial resolution of site-scale LE extremely limits its application potential over a regional scale. To overcome the limitation, five transfer learning models were constructed based on artificial neural networks (ANNs), random forests (RFs), extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (LightGBM) to upscale LE from site scale to regional scale in Heihe River basin (HRB). The instance-transfer approach that utilizes data samples outside of HRB was used in the transfer learning models. Moreover, the Bayesian-based three-cornered hat (BTCH) method was used to fuse the best three upscaling results from ANN, RF, and XGBoost models to improve the accuracy of the results. The results indicated that the transfer learning models perform best when the transfer ratio (the data samples ratio between external and HRB dataset) was 0.6. Specifically, the coefficient of determination (R2) and root mean squared errors (RMSE) of LE upscaled by ANN model was improved or reduced by 6% or 17% than the model without external data. Furthermore, the BTCH method can effectively improve the performance of single transfer learning model with the highest accuracy (R2 = 0.83, RMSE = 18.84 W/m2). Finally, the LE upscaling model based on transfer learning model demonstrated great potential in HRB, which may be applicable to similar research in other regions.

Using InSAR for Surface Deformation Monitoring and Active Layer Thickness Retrieval in the Heihe River Basin on the Northeast Qinghai‐Tibet Plateau

AbstractIn cold regions characterized by perennially frozen soil, climate warming has caused permafrost degradation, which is manifested by surface deformation and deepening of the active layer. Interferometric synthetic aperture radar (InSAR) is a common method to obtain large‐scale surface deformation, especially in mountainous terrain where it is difficult to install a large number of monitoring sites. Here, we used Sentinel‐1A SAR data acquired from March 2017 to March 2021 to monitor surface deformation and estimate active layer thickness (ALT) using two methods—temperature‐based and soil moisture‐based—in the permafrost regions of the Heihe River Basin on the northeast Qinghai‐Tibetan Plateau. We find that the seasonal deformation amplitude ranges 10–60 mm, and the annual mean vertical deformation trend ranges from −40 to 30 mm/a. We verify the ALT estimates with observations and find that the method based on soil moisture has higher accuracy and is therefore more applicable in this particular study area. The ALT gradually decreases from the southeast to the northwest, ranging from 0.3 to 6.5 m, with an average value of 2.47 m. These results contribute to better quantifying the response of permafrost change and to understanding the spatial distribution of ALT on a large scale, serving as guidance for engineering and other applications.

Agroecological Risk Assessment Based on Coupling of Water and Land Resources—A Case of Heihe River Basin

In the arid zone of northwest China, the Heihe River Basin (HRB), as a typical inland river basin, has a fragile regional ecological environment, obvious ecological degradation characteristics, and extremely serious problems in the utilization of agricultural land resources. Meanwhile, the shortage of water resources, the low reduction of land quality, and excessive agricultural activities have greatly increased the local water and land pressure. In this paper, firstly, using the Malmquist DEA model and coupling coordination degree model, the agroecological risk assessment system on account of the coupling of water and land resources (WLR) is constructed. Secondly, taking HRB from 1995 to 2020 as an example, we carry out spatial correlation analysis based on the degree of risk-correlated WLR. Thirdly, we analyze the evolution process and spatial correlation of ecological risk of agricultural WLR in the HRB at the county scale, then we conclude and put forward policy suggestions for improvement. The results show that: (1) On the whole, the average ecological risk of agricultural water resources in the HRB from 1995 to 2020 was 0.933, indicating that the risk was declining; the average ecological risk of agricultural land resources in the HRB from 1995 to 2020 was 0.938, indicating that the risk was declining also. (2) The degree of ecological risk coupling and coordination of agricultural soil and water resources upstream of the HRB is on the rise, while that in the middle and lower reaches is on the decline. (3) Through panel model analysis, the matching suitability of WLR drives agroecological risk. The correlation between them is positive. In conclusion, this method can effectively evaluate the agroecological risk of WLR and provide technical support for agricultural production and management in arid areas.

Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China

The accurate mapping of soil organic carbon (SOC) distribution is important for carbon sequestration and land management strategies, contributing to mitigating climate change and ensuring agricultural productivity. The Heihe River Basin in China is an important region that has immense potential for SOC storage. Phenological variables are effective indicators of vegetation growth, and hence are closely related to SOC. However, few studies have incorporated phenological variables in SOC prediction, especially in alpine areas such as the Heihe River Basin. This study used random forest (RF) and extreme gradient boosting (XGBoost) to study the effects of phenological variables (e.g., Greenup, Dormancy, etc.) obtained from MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) product (MCD12Q2) on SOC content prediction in the middle and upper reaches of Heihe River Basin. The current study also identified the dominating variables in SOC prediction and compared model performance using a cross validation procedure. The results indicate that: (1) when phenological variables were considered, the R2 (coefficient of determination) of RF and XGBoost were 0.68 and 0.56, respectively, and RF consistently outperforms XGBoost in various cross validation experiments; (2) the environmental variables MAT, MAP, DEM and NDVI play the most important roles in SOC prediction; (3) the phenological variables can account for 32–39% of the spatial variability of SOC in both the RF and XGBoost models, and hence were the most important factor among the five categories of predictive variables. This study proved that the introduction of phenological variables can significantly improve the performance of SOC prediction. They should be used as indispensable variables for accurately modeling SOC in related studies.

Assessing Integrated Hydrologic Model: From Benchmarking to Case Study in a Typical Arid and Semi-Arid Basin

Groundwater-surface water interactions play a crucial role in hydrologic cycles, especially in arid and semi-arid basins. There is a growing interest in developing integrated hydrologic models to describe groundwater-surface water interactions and the associated processes. In this study, an integrated process-based hydrologic model, ParFlow, was tested and utilized to quantify the hydrologic responses, such as changes in surface runoff and surface/subsurface storage. We progressively conducted a complexity-increasing series of benchmarking cases to assess the performance of ParFlow in simulating overland flow and integrated groundwater-surface water exchange. Meanwhile, the overall performance and the computational efficiency were quantitatively assessed using modified Taylor diagrams. Based on the benchmarking cases, two case studies in the Heihe River Basin were performed for further validation and to diagnose the hydrologic responses under disturbance, named the Bajajihu (BJH) and Dayekou (DYK) cases, respectively. Both cases were 2D transects configured with in-situ measurements in the mid- and downstream of the Heihe River Basin. In the BJH case, simulated soil moisture by ParFlow was shown to be comparable with in-situ observations in general, with Pearson’s correlation coefficient (R) > 0.93 and root mean square difference (RMSD) < 0.007. In the DYK case, seven scenarios driven by remote sensing and reanalysis data were utilized to study hydrological responses influenced by natural physical processes (i.e., precipitation) and groundwater exploitations (i.e., pumping) that are critical to surface and subsurface storage. Results show that subsurface storage is sensitive to groundwater exploitation before an obvious stationary point. Moreover, a correlation analysis was additionally provided demonstrating the impacts of different factors on subsurface storage timeseries. It was found that pumping influences subsurface storage remarkably, especially under short-term but large-volume pumping rates. The study is expected to provide a powerful tool and insightful guidance in understanding hydrological processes’ effects in arid and semi-arid basins.