Search Results (55)

Land Surface Temperature (LST) is a parameter retrieved through the thermal infrared band of remote sensing satellites, and it is a crucial parameter in various climate and environmental models. Compared to other multispectral bands, the thermal infrared bands have lower spatial resolution, which limits their practical applications. Taking the Heihe River Basin in China as a case study, this research focuses on LST data retrieved from the SDGSAT-1 using the three-channel split-window algorithm. In this paper, we propose a novel approach, the Information-Guided Diffusion Model (IGDM), and apply it to downscale the SDGSAT-1 LST image. The results indicate that the downscaling accuracy of the SDGSAT-1 LST image using the proposed IGDM model outperforms that of Linear, Enhanced Deep Super-Resolution Network (EDSR), Super-Resolution Convolutional Neural Network (SRCNN), Discrete Cosine Transform and Local Spatial Attention (DCTLSA), and Denoising Diffusion Probabilistic Models (DDPM). Specifically, the RMSE of IGDM is reduced by 55.16%, 51.29%, 48.39%, 52.88%, and 17.18%. By incorporating auxiliary information, particularly when using NDVI and NDWI as auxiliary inputs, the performance of the IGDM model is significantly improved. Compared to DDPM, the RMSE of IGDM decreased from 0.666 to 0.574, MAE dropped from 0.517 to 0.376, and PSNR increased from 38.55 to 40.27. Overall, the results highlight the effectiveness of the auxiliary information-guided SDGSAT-1 LST downscaling diffusion model in generating high-resolution remote sensing LST data. Additionally, the study reveals the spatial feature impact of different auxiliary information in LST downscaling and the variations in features across different regions and temperature ranges. Full article

As global climate change intensifies, arid land ecosystems face increasing challenges. Vegetation, a key indicator of climate variation, is highly responsive to meteorological factors such as temperature (Tem), precipitation (Pre), and soil moisture (SM). Understanding how fractional vegetation cover (FVC) responds to climate change in arid regions is critical for mitigating its impacts. This study utilizes MOD13Q1-NDVI data from 2000 to 2022, alongside corresponding Tem, Pre, and SM data, to explore the dynamics and underlying mechanisms of SM and FVC in the context of climate change. The results reveal that both climate change and human activities exacerbate vegetation degradation, underscoring its vulnerability. A strong correlation between FVC and both Tem and Pre suggests that these factors significantly influence FVC variability. In conclusion, FVC in the lower reaches of the Heihe River is shaped by a complex interplay of Tem, Pre, SM, and human activities. The findings provide a scientific basis and decision-making support for ecological conservation and water resource management in the lower reaches of the Heihe River, aiding in the development of more effective strategies to address future climate challenges. Full article

In order to anticipate residual errors and improve accuracy while reducing uncertainties, this work integrates the long short-term memory (LSTM) with the Soil and Water Assessment Tool (SWAT) to create a deep learning (DL) model that is guided by physics. By forecasting the residual errors of the SWAT model, the SWAT-informed LSTM model (LSTM-SWAT) differs from typical LSTM approaches that predict the streamflow directly. Through numerical tests, the performance of the LSTM-SWAT was evaluated with both LSTM-only and SWAT-only models in the Upper Heihe River Basin. The outcomes showed that the LSTM-SWAT performed better than the other models, showing higher accuracy and a lower mean absolute error (MAE = 3.13 m³/s). Sensitivity experiments further showed how the quality of the training dataset affects the performance of the LSTM-SWAT. The results of this study demonstrate how the LSTM-SWAT may improve streamflow prediction greatly by remote sensing and in situ observations. Additionally, this study emphasizes the need for detailed consideration of specific sources of uncertainty to further improve the predictive capabilities of the hybrid model. Full article

Our study examined the relationships between riparian plant communities and their soil properties along the midstream of the Heihe River in northwestern China’s arid region. Significant variations in species composition were observed across the upper, middle, and lower reaches of this midstream (adonis2 and anosim, p < 0.001). The lower reaches exhibited higher species diversity (Shannon index up to 2.12) compared to the other reaches. Gramineous plants, particularly Agropyron cristatum (L.) Gaertn. and Equisetum ramosissimum Desf., dominated all reaches, with relative abundances exceeding 50% in the upper reach sites. The soil ionic concentration showed distinct spatial heterogeneity, peaking at site 9 (upper reaches) and lowest at site 3 (lower reaches). Species diversity indices negatively correlated with SO₄²⁻, Mg²⁺, and Ca²⁺ concentrations, while salt-tolerant species like Agropyron cristatum (L.) Gaertn. and Phragmites australis Trin. positively correlated with Na⁺ and Cl⁻ levels. Soil nutrients had weaker but notable effects on the distribution of Onopordum acanthium L. and Artemisia argyi H. Lév. and Vaniot. These findings suggest that riparian plant community distribution along the Heihe River is influenced by complex interactions between hydrological processes, salt dynamics, and soil physicochemical properties, such as anion and cation concentrations and electrical conductivity (EC). Our research provides valuable insights for understanding and managing riparian ecosystems in arid regions. Full article

Monitoring the status and dynamics of desertification is one of the most important parts of combating it. In this study, 30 m high-resolution information on land desertification and restoration in the Heihe River basin (HRB) was extracted from the land cover database. The results indicate that land desertification coexists with land restoration in the HRB. In different periods, the area of land restoration was much larger than the area of land desertification in the HRB, and the HRB has undergone land restoration. Upstream of the HRB, there is a continuing trend of increasing land desertification associated with overgrazing in a context where climate change favors desertification reversal. In the middle and lower reaches, although climate variability and human activities favor land desertification, land desertification is still being reversed, and land restoration dominates. Implementing the eco-environmental protection project and desertification control measures, especially the Ecological Water Distribution Project (EWDP), contributes to the reversal of desertification in the middle and lower reaches of the HRB. However, the EWDP has indirectly led to the lowering of the water table in the middle reaches, resulting in local vegetation degradation. Therefore, there is an urgent need to transform the economic structure of the middle reaches to cope with water scarcity and land desertification. Full article

Inland arid basins face the challenge of ecological deterioration due to insufficient water availability. The irrigation water consumption depletes the water flowing into the downstream tailrace ecological wetland, leading to increasing ecological deterioration. It is urgent to optimize the management of irrigation water resources in the middle reaches and improve the ecological sustainability of the lower reaches. To ensure sustainable development, improving water use efficiency and preserving the health of basin ecosystems should be simultaneously considered in the agricultural water management of these regions. Therefore, a 0–1 integer multi-objective programming approach was proposed to optimize midstream crop planting. This method has advantages in (1) effectively balancing ecological sustainability, agricultural production, and water-saving goals; (2) linking irrigation district management with grid geographic information to develop land use strategies; and (3) obtaining optimal solutions for multi-objective synergies. The proposed approach is applied to a typical inland river basin in China, the Heihe River Basin in Gansu Province. Results indicate that the optimization schemes can increase agricultural benefits, crop suitability, water use efficiency, and ecological quality by 12.37%, 6.82%, 13.00%, and 8.04% (compared to 2022), respectively, while irrigation water can be saved about 7.53%. The optimization results and proposed approach can help decision-makers manage water resources in the Heihe River Basin and similar regions. Full article

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions. Full article

Accurate estimation of surface evapotranspiration (ET) in the Heihe River Basin using remote sensing data is crucial for understanding water dynamics in arid regions. In this paper, by coupling physical constraints and machine learning for hybrid modeling, we develop a hybrid model based on surface conductance optimization. A hybrid modeling algorithm, two physical process-based ET algorithms (Penman–Monteith-based and Priestley–Taylor-based ET algorithms), and three pure machine learning algorithms (Random Forest, Extreme Gradient Boosting, and K Nearest Neighbors) are comparatively analyzed for estimating the ET. The results showed that, in general, the machine learning model optimized by parameters was able to better predict the surface conductance of the hybrid model. Driver analyses showed that radiation, normalized difference vegetation index (NDVI), and air temperature had high correlations with ET. The hybrid model had a better prediction performance for ET than the other five models, and it improved the R₂ of the two physical process-based algorithms to 0.9, reduced the root mean square error (RMSE) to 0.5 mm/day, reduced the BIAS to 0.2 mm/day, and improved the Kling–Gupta efficiency (KGE) to 0.9. The hybrid model outperformed the others across different time scales, displaying lower BIAS, RMSE, and higher KGE. Spatially, its ET patterns aligned with regional vegetation changes, with superior accuracy in annual ET estimation compared to the other models. Comparison with other ET products shows that the estimation results based on the hybrid model have better performance. This approach not only improves the accuracy of ET estimation but also improves the understanding of the physical mechanism of ET estimation by pure machine learning models. This study can provide important support for understanding ET and hydrological processes under different climatic and biotic vegetation in other arid and semi-arid regions. Full article

Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct “single-peak” pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB. Full article

Remote sensing-based models usually have difficulty in generating spatio-temporally continuous terrestrial evapotranspiration (ET) due to cloud cover and model failures. To overcome this problem, machine learning methods have been widely used to reconstruct ET. Therefore, studies comparing and evaluating the accuracy and effectiveness of reconstruction among different machine learning methods at the basin scale are necessary. In this study, four popular machine learning methods, including deep forest (DF), deep neural network (DNN), random forest (RF) and extreme gradient boosting (XGB), were used to reconstruct the ET product, addressing gaps resulting from cloud cover and model failure. The ET reconstructed by the four methods was evaluated and compared for Heihe River Basin. The results showed that the four methods performed well for Heihe River Basin, but the RF method was particularly robust. It not only performed well compared with ground measurements (R = 0.73) but also demonstrated the ability to fully reconstruct gaps generated by the TSEB model across the entire basin. Validation based on ground measurements showed that the DNN and XGB models performed well (R > 0.70). However, some gaps still existed in the desert after reconstruction using the DNN and XGB models, especially for the XGB model. The DF model filled these gaps throughout the basin, but this model had lower consistency compared with ground measurements (R = 0.66) and yielded many low values. The results of this study suggest that machine learning methods have considerable potential in the reconstruction of ET at the basin scale. Full article

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors. Full article

Watershed ecosystems are crucial to the overall sustainable development of a region, and a scientific and effective grasp of the characteristics of land-use change in a watershed, and the factors affecting land change, is an important prerequisite for the high-quality construction of watershed ecology, which needs to be emphasized. As the second largest inland river in the arid zone of Western China, the Heihe River Basin (HRB) has been affected by human and natural factors in recent years, and the ecological environment is relatively fragile, and there is an urgent need to analyze the ecological characteristics of the basin and to explore the relevant influencing factors in order to provide a basis for subsequent ecological management. Therefore, this article applies the landscape index, the landscape ecological risk index (ERI) model and the geodetector tools to analyze the land-use data from 2000 to 2020 in the Zhangye area of the HRB to study the characteristics of the ecological risk evolution and the driving forces affecting the ecological risk differentiation. The results show the following: (1) the area of the regional land-use change accounts for 4.99% of the total area, and the landscape pattern as a whole shows an increasing degree of fragmentation and a decreasing trend of aggregation; (2) the distribution of the ERI in the region shows a trend of being low in the center and high in the periphery, with an increase of 2.11% in the area of the lowest and lower risk and a decrease of 1.77% in the highest and higher, and the temporal change shows an increase followed by a significant decrease; (3) the human interference degree is the dominant factor influencing the spatial differentiation of the ERI in the basin area. There are significant differences between social factors, climate factors and land factors. Full article

The Xin’anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model lacks such a functional module. In this study, the improved XHM coupling snowmelt module is presented to complete the existing XHM for better suitability for flood simulation in areas dominated by snowmelt. The improved model includes four sub-models: evapotranspiration, runoff yield, runoff separation, and runoff routing, where the snowmelt runoff module is introduced in both the runoff yield and separation sub-models. The watershed is divided into two types, non-snow areas with lower altitudes and snow-covered areas with higher altitudes, to study the mechanism of runoff production and separation. The evaluation index, determination coefficients (R²), mean square error (MSE), and Nash efficiency coefficients (NSE) are used to assess the improved XHM’s effect by comparing it with the traditional model. Results show that the R² of the improved XHM coupled with snowmelt are around 0.7 and 0.8 at the Zamashk and Yingluoxia stations, respectively, while the MSE and NSE are also under 0.4 and above 0.6, respectively. The absolute value of error of both flood peaks in the Yingluoxia station simulated by improved XHM is only 10% and 6%, and that of traditional XHM is 32% and 40%, indicating that the peak flow and flood process can be well simulated and showing that the improved XHM coupled with snowmelt constructed in this paper can be applied to the flood forecasting of the Heihe River Basin. The critical temperature of snow melting and degree-day factor of snow are more sensitive compared with other parameters related to snow melting, and the increasing trend of peak flow caused by both decreased critical temperature and increased degree-day factor occurs only when the value of the model’s state (snow reserve) is higher. These results can expand the application scope in snow-dominated areas of the XHM, providing certain technical references for flood forecasting and early warning of other snowmelt-dominated river basins. Full article

The arid endorheic basin of northwest China is characterized by rich land resources, water shortage, and a fragile ecological environment. The establishment of a credible coupling model of groundwater and surface water based on multi-source observation data is an effective means to study the change in basin water cycles and the sustainable utilization of water resources in the past and future. Based on the latest understanding of hydrogeological conditions, hydrology and water resource utilization data in the middle reaches of the Heihe River Basin (HRB), this paper constructs an up-to-date coupling model of surface water and groundwater to study the water balance change of the basin. The water resources data series under historical replay and CMIP5 climate model prediction are constructed to predict future changes in water resources. The study shows that, under the joint influence of natural conditions and human activities, the average annual recharge of groundwater in the study area from 1990 to 2020 is 17.98 × 10⁸ m³/a, the average annual discharge is 18.62 × 10⁸ m³/a, and the difference between recharge and discharge is −0.64 × 10⁸ m³/a. The total groundwater storage is −19.99 × 10⁸ m³, of which the groundwater storage from 1990 to 2001 was −17.52 × 10⁸ m³ and from 2002 to 2020 was −2.47 × 10⁸ m³. Abundant water from 2002 to 2020 in the basin significantly improved the loss of groundwater storage. Under the prediction of historical reappearance and the CMIP5 CNRM-CM5 model RCP4.5 and RCP8.5 pathways, the groundwater level of the Heihe River–Liyuanhe River inclined plain falls first because the HRB has just experienced a wet season and then rises according to future climate change. The groundwater level of the inclined plain east of the Heihe River and Yanchi basin decreases continuously because of the change in water cycle caused by human activities. The erosion accumulation plain is located in the groundwater discharge zone, and the water level is basically stable. Under the conditions of water resource development and utilization, the runoff of Zhengyixia hydrological station cannot meet the requirements of the “97 Water Dividing Plan” of the State Council in most years in the future, and the ecological and production water in the lower reaches of HRB cannot be effectively guaranteed. With the implementation of water-saving irrigation under the RCP4.5 and RCP8.5 scenarios, the runoff of Zhengyixia can meet the “97 Water Diversion Plan”. It is suggested to further improve the level of agricultural water savings in the middle reaches of the HRB and control the reasonable scale of cultivated land in order to reduce water consumption in the middle reaches of the HRB and implement sustainable utilization of water resources in the HRB. Full article

China faces challenges of food security and sustainable agricultural production. However, current studies rarely address the spatial distribution patterns of water consumption and carbon emissions. We studied the irrigation water use efficiency and carbon emission differences of crops in arid areas and their spatial distribution using wheat and maize, two major food crops in the middle reaches of the Heihe River, as examples. Furthermore, we have optimized low-carbon cropping of crops under the multiple objectives of water conservation and economic development. The results show that: (1) The carbon emissions per unit of water consumption for maize are 0.03 × 10⁻⁶ t mm⁻¹ and 0.49 × 10⁻⁶ t mm⁻¹ for wheat. Irrigation water consumption per unit yield is 515.6 mm t⁻¹ for maize and 426.7 mm t⁻¹ for wheat. (2) The spatial distribution patterns of irrigation water consumption were opposites for maize and wheat. The former has lower irrigation water consumption in the planting area upstream of the Heihe River and higher in the lower reaches. In contrast, the pattern of wheat irrigation is the opposite. (3) After optimizing the cropping mix for both crops, the area planted with wheat should be reduced to 59% of the current size, while maize should be expanded to 104%. The results of the research hold immense importance in guiding the future grain crop planting patterns for water-saving agriculture and low-carbon agriculture development in arid zones worldwide, aligning with the United Nations’ Sustainable Development Goals. Full article