Kaidu River Research Articles

Runoff simulation of the Kaidu River Basin based on the GR4J-6 and GR4J-6-LSTM models

Study regionThe Kaidu River Basin originates from the southern slope of the Tienshan Mountains in the Xinjiang Uygur Autonomous Region, China. Study focusAccurate runoff simulation and prediction significantly affect flood control, drought resilience, and water resource allocation decisions. This study establishes the GR4J-6 model (modèle du Génie Rural à 4 paramètres Journalier-6, including a snowmelt module) and integrates it with the LSTM (Long Short-Term Memory) model to construct the hybrid GR4J-6-LSTM model and enhance the simulation accuracy of snowmelt runoff. A case study is conducted in the Kaidu River Basin to demonstrate the applicability of these models in cold and arid regions. The accuracy of the GR4J-6, LSTM, and GR4J-6-LSTM models is evaluated using Nash-Sutcliffe efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Squared Error (RMSE) metrics. In addition, the contributions of each feature variable in the models are analyzed using the SHapley Additive exPlanations (SHAP) method to enhance the reliability of the results. New hydrological insights for the regionThe GR4J-6 model demonstrated good applicability in the Kaidu River Basin, with NSE, KGE, and RMSE values of 0.69, 0.79, and 39.39 m3/s during the validation period, respectively. The hybrid model GR4J-6-LSTM exhibited the highest comprehensive accuracy among all the models, with NSE, KGE, and RMSE values of 0.84, 0.87, and 28.79 m3/s, respectively. In the LSTM model, temperature and precipitation were found to significantly influence the simulated runoff, indicating that higher temperature and precipitation lead to increased runoff. In the GR4J-6-LSTM model, Tmin (minimum temperature) and the hydrological feature variable Qsim exhibited a strong positive correlation with simulated runoff, as Tmin and Qsim increased, they promoted stronger flow production. This study provides a framework for runoff simulation in snowmelt river basins, offering a reference for projecting extreme hydrological events under climate change.

Combining geomorphological and kinematic models to analyze tectonic deformation rates: A case study of the Bayin anticline in the eastern Tian Shan Mountains

Quantitatively characterizing the geometry, kinematics, and deformation rate of fold-thrust belts in intermontane basins is the key to understanding strain partitioning within the Tian Shan range. This work focuses on the Bayin anticline in the Youludusi Basin, a typical intermontane basin located within the eastern Tian Shan. The Kaidu River cuts through the Bayin anticline and has developed three levels of terraces (T1–T3) across the structure. By using cosmogenic nuclide and optically stimulated luminescence dating methods, the formation ages of terraces T1 and T3 are constrained to 11.54 ± 0.55 ka and 42 + 7.0/−7.1 ka, respectively. When applying a listric thrust fault model to the Bayin anticline and using terraces as references, the vertical displacements are estimated to be 16.45 + 6.46/−3.19 m (T1), 32.08 + 12.85/−6.19 m (T2), and 95.93 + 38.94/−18.6 m (T3), and the shortening amounts are 10.56 + 8.33/−5.04 m (T1), 20.46 + 16.68/−9.64 m (T2), and 61.24 + 50.22/−28.93 m (T3). Based on this listric thrust fault model and terrace T1–T3 ages, the rate of fault slip controlling the growth of the Bayin anticline is determined to be 1.6 ± 1.0 mm/yr, and the crustal shortening rate of the anticline is 1.0 + 0.7/−0.6 mm/yr. The estimated crustal shortening deformation of the Bayin anticline accounts for ∼12 % of the total deformation in the Youludusi Basin. In terms of the entire orogenic belt, the crustal shortening absorbed in the southern, central, and northern parts accounts for 24 %–56 %, 46 %–71 %, and 19 %–74 %, respectively, of the total strain across the eastern Tian Shan. Therefore, we believe that the Eastern Tianshan undergoes uniform deformation.

Assessment and Prediction of Future Climate Change in the Kaidu River Basin of Xinjiang under Shared Socioeconomic Pathway Scenarios

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China’s largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people’s daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase.

Spatio-temporal variability and trend of blue-green water resources in the Kaidu River Basin, an arid region of China

Study regionKaidu River Basin, an arid region of China Study focusWater resources are scarce and unstable. Adverse changes in blue-green water resources can disrupt water balance and availability in arid regions. Therefore, it is crucial to effectively assess and predict future blue-green water resources in arid areas. This study utilizes the Soil and Water Assessment Tool (SWAT), Patch-generating Land Use Simulation (PLUS), and Global Circulation Models (GCMs) to analyze the interactive impacts of climate and land use/cover changes (LUCC) on blue-green water in arid basin from 1990 to 2050. New hydrological insights for the regionThis work identified key factors influencing blue-green water changes, including forests, grasslands, and snowmelt. In the historical scenarios, the Kaidu River basin exhibited significant interannual variations in blue water and precipitation. In the future scenarios, blue water increases (+3.6%), and green water decreases (−1.8%) under SSP245 (medium emissions). Under SSP585 (high emissions), blue water decreases (−2.4%), and green water increases (+1.8%). Under SSP126 (low emissions), both blue water (+8.5%) and green water (+0.73%) show an increasing trend. The change in snowmelt is the main reason for the first peak of blue water in April May. Under historical and future snowmelt conditions, blue water shows a significant upward or downward trend with the amount of snowmelt. The findings provided practical hints for the enhancement of the management and allocation of precious water resources.

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

Projecting the Impact of Climate Change on Runoff in the Tarim River Simulated by the Soil and Water Assessment Tool Glacier Model

Analyzing the future changes in runoff is crucial for efficient water resources management and planning in arid regions with large river systems. This paper investigates the future runoffs of the headwaters of the Tarim River Basin under different emission scenarios by forcing the hydrological model SWAT-Glacier using six regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project. Results indicate that compared to the period of 1976~2005, temperatures are projected to increase by 1.22 ± 0.72 °C during 2036~2065 under RCP8.5 scenarios, with a larger increment in the south Tianshan mountains and a lower increment in the north Kunlun Mountains. Precipitation is expected to increase by 3.81 ± 14.72 mm and 20.53 ± 27.65 mm during 2036–2065 and 2066–2095, respectively, under the RCP8.5 scenario. The mountainous runoffs of the four headwaters that directly recharge the mainstream of the Tarim River demonstrate an overall increasing trend in the 21st century. Under the RCP4.5 and RCP8.5 scenarios, the runoff is projected to increase by 3.2% and 3.9% (amounting to 7.84 × 108 m3 and 9.56 × 108 m3) in 2006–2035. Among them, the runoff of the Kaidu River, which is dominated by rainfall and snowmelt, is projected to present slightly decreasing trends of 3~8% under RCP4.5 and RCP8.5 scenarios. For catchments located in the north Kunlun Mountains (e.g., the Yarkant and Hotan Rivers which are mix-recharged by glacier melt, snowmelt, and rainfall), the runoff will increase significantly, especially in summer due to increased glacier melt and precipitation. Seasonally, the Kaidu River shows a forward shift in peak flow. The summer streamflow in the Yarkant and Hotan rivers is expected to increase significantly, which poses challenges in flood risk management.

Machine learning method is an alternative for the hydrological model in an alpine catchment in the Tianshan region, Central Asia

Study regionKaidu River catchment in the Tianshan Mountain, northwestern China. Study focusThis paper compared the applicability and accuracy of four machine learning models and two hydrological ones to simulate the daily streamflow and extreme streamflow of the Kaidu River catchment. The machine learning models are Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Random Forests (RF), and Long Short-Term Memory (LSTM), while the hydrological models are the Soil and Water Assessment Tool (SWAT) and the extended SWAT with a glacier dynamic module (SWAT-Glacier). New hydrological insights for the regionLSTM achieved better model performance in simulating daily streamflow than SWAT and SWAT-Glacier, with Kling-Gupta efficiency of 0.92, 0.82, and 0.80, respectively. Meanwhile, SVR, XGBoost, and RF showed satisfactory performance, with KGE of 0.67, 0.71, and 0.70, respectively. LSTM, SWAT and SWAT-Glacier could well simulate the annual peak flow (i.e., annual maximum 1-day streamflow and 5-day average streamflow) but failed to mimic the annual minimum 7-day average streamflow, with PBIAS exceeding 28%. Furthermore, all the models failed to reproduce the dates of hydrological extremes. Nevertheless, using the quantile loss function in the LSTM model resulted in significantly improved model performance in the low streamflow indices, compared to that using mean squared error as the loss function. Overall, LSTM could be a good alternative for simulating daily streamflow and extreme streamflow in data-scarce catchments.

Determining the main contributing factors to nutrient concentration in rivers in arid northwest China using partial least squares structural equation modeling

Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.

Calibration and Evaluation of the WRF-Hydro Model in Simulating the Streamflow over the Arid Regions of Northwest China: A Case Study in Kaidu River Basin

In this study, the hydrological system of the Weather Research and Forecasting model (WRF-Hydro) is applied to simulate the streamflow at the Kaidu River Basin, which is vital to the ecological system in the lower reaches of the Tarim River in Northwest China. The offline WRF-Hydro model is coupled with the Noah multi-parameterization land surface model (Noah-MP) and is forced by the China Meteorological Forcing Dataset (CMFD), with the grid spacing of the hydrological routing modules being 250 m. A 3-year period (1983–1985) is used for calibration and a 17-year period (1986–2002) for the evaluation. Several key parameters of WRF-Hydro and four Noah-MP parameterization options are calibrated, and the performance of WRF-Hydro with the optimized model setting is evaluated using the daily streamflow observations. The results indicate that WRF-Hydro can reproduce the observed streamflow reasonably, with underestimation of the streamflow peaks. The simulated streamflow is sensitive to the parameters of bexp, dksat, smcmax, REFKDT, slope, OVROUGHRTAC and mann in the Kaidu River Basin. At the same time, the parameterization options of Noah-MP also have a large influence on the streamflow simulation. The WRF-Hydro model with optimized model settings can achieve correlation coefficient (CC) and Nash efficiency coefficient (NSE) statistical scores of 0.78 and 0.61, respectively, for the calibration period. Meanwhile, for the evaluation period, the scores are 0.7 and 0.50, respectively. This study indicates the importance of applying the physical-based WRF-Hydro model over Northwest China and provides a reference for the nearby regions.

Enhancing SWAT model with modified method to improve Eco-hydrological simulation in arid region

The Soil and Water Assessment Tool (SWAT) model is diffusely applied to areas with uniform precipitation distribution and gentle slopes. However, the SWAT has apparent defects in the process of ecohydrological simulation in mid-high latitudes and arid regions. This model can merely be used in agricultural catchments with gentle slopes and uniform rainfall, and the simulation is not suitable for high-altitude alpine catchment. To this point, this paper makes appropriate improvements to the runoff module, and specifically corrects the key sensitive parameters. The global sensitivity and performance assessment of the model's parameters were carried out before and after the improvement using the monthly data of runoff and evaporation from 1990 to 2018 in the Kaidu River basin in northwestern China. It is indicated that the Modified SWAT simulates the performance of monthly runoff and evapotranspiration better than the original SWAT, which overestimates the runoff volume throughout hydrological changes in different periods. Compared with the monthly water production under different land use/cover conditions, the highest water production was found in forest and grassland. By modifying the SWAT algorithm, the structural non-determinacy of the Modified SWAT is significantly reduced, and the adaptability of the model is improved. Thus, the Modified SWAT shows a better simulation performance in arid areas with considerable terrain variation and scarce rainfall data.

Total Streamflow Variation for the Upper Catchment of Bosten Lake Basin in China Inferred from Tree-Ring Width Records

Bosten Lake Basin not only is a major source of drinking water for the residents of the surrounding area, but also maintains the ecological balance of the region. However, with the influence of climate change and human activities, the water level of Bosten Lake fluctuates sharply and has a great impact on the surrounding ecological environment. Therefore, the study of its historical water flow changes as a reference has become a focus of research. In this study, the radial growth of Schrenk spruces (Picea schrenkiana Fisch. et Mey.) significantly correlated with the tributary streamflow coming from the mountainous region near Bosten Lake Basin. On the basis of this good coherence, the tree-ring chronologies were used to reconstruct the streamflow for Huangshuigou River from the previous August to the present July (r = 0.766, p < 0.0001, n = 50). The reconstructed streamflow series matched observations well, explaining 63.3% of the variation in the observed streamflow of 1956–2005. Then, the sum of the streamflow reconstruction of Huangshuigou River and another two tree-ring-based streamflow reconstructions (Kaidu River and Qingshui River) was used to represent the hydrological variation of the upper catchment of Bosten Lake Basin, and the reconstruction sequence was 306 years. The 10.7, 5.5, and 2.1 year cycles of the power spectrum and wavelet analysis revealed that the runoff series reconstructed from tree-ring hydrometeorology was related to solar activity. Some dry and wet years in the reconstructed streamflow series of the upper catchment of Bosten Lake Basin corresponded to the historical record. During the wet years, the Indian Ocean was probably the main source of precipitation.

Analysis of Water Chemistry Characteristics and Hydraulic Relationships of Different Water Bodies in Typical Mountain-oasis Systems in the Northwest Inland Area

Kaidu River basin is a typical mountain oasis ecosystem in the northwest inland. Its hydrochemical environment information is of great significance to understand the regional hydrological process and optimize the allocation of water resources. Based on the collection of samples of various water bodies in mountainous and oasis areas in different seasons in 2020, this study analyzed the water chemical characteristics and hydraulic links in this region. The results showed that:① the water body in the study area was slightly alkaline as a whole, and the pH and TDS values showed the spatial changes of low in the oasis area and high in the mountain area; HCO3- and Ca2+ were the main anions and cations. The regional hydrochemical type was mostly HCO3--Ca2+, and the hydrochemical type of groundwater in the oasis area was more complex than that in the mountainous area. Regional hydrochemical changes were mainly controlled by rock weathering and human disturbance. ② The δ18O and δD values of the river water showed the seasonal changes of dilution in summer and enrichment in spring, whereas groundwater was depleted in winter and spring and enriched in autumn. Precipitation and glacial water did not change significantly within a year. In space, the δ18O and δD values of river water and groundwater showed the law of enrichment in the oasis area and dilution in the mountainous area. ③ The relationship between surface water and groundwater in the study area was close, and the transformation was frequent in summer. The relationship between surface water and groundwater in the oasis area was closer than that in the mountainous area. Groundwater in the oasis area was recharged by groundwater in the previous period, river water, and precipitation, and the recharge rate was usually groundwater in the previous period>river water>precipitation.

River–Lake System Connectivity Effectively Reduced the Salinity of Lake Water in Bosten Lake, Northwest China

High salinity in water constitutes a serious problem for the aquatic environment management of Bosten Lake. Weak water exchange and water movement are the essential factors for the high total dissolved solids (TDS) content of lake water. To improve the water quality of Bosten Lake, a river–lake system connectivity project (water diversion) was introduced starting at the end of 2018, which diverted fresh water from the Kaidu River and the Huangshuigou River to Bosten Lake. In this study, the effect and its mechanism of water diversion on the TDS content of Bosten Lake were evaluated using continuous-field monitoring data. The results showed that the water diversion effectively reduced the TDS content of the lake water and changed Bosten Lake from a brackish lake back to a freshwater lake. Water diversion also improved the spatial distribution of TDS content. One year, two years, and three years after the implementation of the water diversion project, the TDS content of northern, southern, and eastern lake water significantly decreased by more than 20%, 25%, and 30%, respectively. Our study demonstrated that water diversion significantly increased the annual endogenous TDS pollutant amount discharged from the lake and reduced and homogenized the TDS content of the whole lake. TDS content reduction of the lake was realized by water diversion accelerating water movement and water exchange in the lake, especially for the northern, southern, and eastern waters. Therefore, water diversion could be used as an effective measure for water quality improvement in semi-closed inland lakes in arid areas.

Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System

Chlorophyll-a(chl-a) has been used as an important indicator of water quality. Great efforts have been invested to develop remote-sensing-based chl-a retrieval models. However, due to the spatial difference in chl-a concentration, a single model usually cannot accurately predict the whole range of chl-a concentration. To test the performance of precedent chl-a models, we carried out an experiment along the upper and middle reaches of the Kaidu River and around some small ponds in the Bayanbulak Wetland. We measured water surface reflectance in the field and analyzed the chl-a concentration in the laboratory. Initially, we performed a sensitivity analysis of the spectrum band to chl-a concentration with the aim of identifying the most suitable bands for various chl-a models. We found that the water samples could be divided into two groups with a threshold of 4.50 mg/m3. Then, we tested the performance of 11 precedent chl-a retrieval models and 7 spectral index-based regression models from this study for all the sample datasets and the two separate datasets with relatively high and low chl-a concentrations. Through a complete comparison of the performance of these models, we selected the D3B model for water bodies with high chl-a concentration and OC2 model (ocean color 2) for low chl-a concentration waters, resulting in the hierarchical and piecewise retrieval algorithm OC2-D3B. The chl-a concentration of 4.50 mg/m3 corresponded to the D3B value of −0.051; therefore, we used −0.051 as the threshold value of the OC2-D3B model. The result of the OC2-D3B model showed a better performance than the other algorithms. Finally, we mapped the spatial distribution and seasonal pattern of chl-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicated that the chl-a concentration in the riparian ponds was generally in the range of 8–10 mg/m3, which was higher than that in rivers with a range of 2–4 mg/m3. The highest chl-a concentration usually appears in summer, followed by spring and autumn, and the lowest in winter. The correlation between meteorological data and chl-a concentration showed that temperature is the dominant factor for chl-a concentration changes. Our analytical framework could provide a better way to accurately map the spatial distribution of chl-a concentration in complex river systems.

Characterizing Spatiotemporal Patterns of Snowfall in the Kaidu River Basin from 2000–2020 Using MODIS Observations

Characterizing spatiotemporal patterns of snowfall is essential for understanding cryosphere responses to warming climate stress. The changes in snowfall and topographic controls in mountain regions still need to be clarified. This study proposes a general parsimonious methodology to obtain the frequency of snowfall in mountainous areas. The methodology employed is easily transferable to any other mountain region. Utilizing daily MODIS observations from June 2000 to May 2020 and the snowfall event detection algorithm, we monitored the frequency of snowfall in a long time series in the Kaidu river basin. The results are as follows: (1) The method for detecting the frequency of snowfall has high accuracy. The annual detected results agreed with surface observations, with an R2 of 0.65 and RMSE of 3.39. (2) The frequency of snowfall events increased monotonically with elevation. The influence of slope angle on snowfall gradually decreased with increasing elevation. (3) The frequency of snowfall events in the Kaidu river basin was dominated by an increasing trend. The trends showed a pronounced topographic dependence. This study reveals the distribution characteristics and changing snowfall trends in mountain regions. The results provide a reference for snowfall research in mountainous areas.

Research on Sediment Discharge Variations and Driving Factors in the Tarim River Basin

Sediment discharge is widely regarded as a critical indicator of soil and water loss. The Mann–Kendall (M-K) test was applied to analyze the trends of temperature, precipitation, annual runoff, annual sediment discharge (ASD), and snow cover area proportion (SCAP). Sensitivity coefficient and contribution rate were adopted to assess the sensitivity of ASD to driving factors, and the contribution of driving factors to ASD. The results showed: (1) ASD of the Kaidu River and the Aksu River originating from Tien Shan decreased at rates of 3.8503 × 107 kg per year (p < 0.01) and 47.198 × 107 kg per year, respectively, from 2001 to 2019. The ASD there was also found to be more sensitive to SCAP changes in autumn and winter, respectively. (2) ASD of the Yarkand River and the Yulong Kashgar River originating from the Karakoram Mountains increased at rates of 21.807 × 107 kg per year and 27.774 × 107 kg per year, respectively, during 2001–2019. The ASD there was determined to be more sensitive to annual runoff. (3) In terms of contribution rate, except for the Kaidu River, annual runoff of the other three rivers made the largest contribution. (4) In addition, the proportion of glacial-melt water, slope, glacierization and human activities are also possible factors affecting sediment discharge.

Characteristics of Dissolved Organic Matter and Its Relationship with Water Quality along the Downstream of the Kaidu River in China

The variability in the quality of water that runs along the course of a river, flowing out of a mountain pass, through an agricultural oasis and into a lake, has been a key topic of research in recent years. In this study, the characteristics of dissolved organic matter (DOM) along the river flow, and its relationship with water quality, were analyzed using the Canadian water quality index (CWQI), parallel factor (PARAFAC) and self-organizing map (SOM). The study results include: (1) The conclusion of field sampling along the lower reaches of the Kaidu River and laboratory measurements of water quality parameters, using CWQI to determine the water quality index of the lower Kaidu River, ranging between 59.58 and 93.47. The water quality of the lower reaches of the Kaidu River generally ranges between moderate and good, and can meet the water use requirements of Class II water function standards. (2) The DOM composition of the river predominantly contained three fluorescence components, while the three fluorescence indices of the water body varied less in different river sections. Based on the SOM training model, the fluorescence intensity of the C1 component was larger among the three fluorescence components, followed by the C2 component, and the smallest fluorescence intensity of the C3, which was dominated by humic-like substances, with a high authigenic origin and humification degree. (3) The fluorescence index and fluorescence components were correlated with water quality parameters, and it was found that C1, C2 and C3 were negative and correlated significantly with SO42- and Total-dissolved solids (TDS) concentrations; FI, HIX and BIX showed strong positive correlations with SAL and Cu and negative correlations with dissolved oxygen (DO). This study provides a scientific basis for surface water quality monitoring and water quality pollution management in the Kaidu River.

Monitoring the Spatiotemporal Dynamics of Habitat Quality and Its Driving Factors Based on the Coupled NDVI-InVEST Model: A Case Study from the Tianshan Mountains in Xinjiang, China

Globally, mountains have suffered enormous biodiversity loss and habitat degradation due to climate change and human activities. As an agent of biodiversity, evaluating habitat quality (HQ) change is an indispensable key step for regional ecological security and human well-being enhancement, especially for fragile mountain ecosystems in arid areas. In this study, we aimed to propose an integrated framework coupled with the Normalized Difference Vegetation Index (NDVI) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST)-HQ module to improve the effectiveness and accuracy of HQ estimation. We highlighted the Tianshan Mountains in Xinjiang as an example to validate the model, as it is a typical representative of mountain ecosystems in the temperate arid zone. Specifically, we aimed to assess the spatiotemporal dynamics of HQ over the past two decades and investigate its influencing factors using a geographical detector model. The results show that, first, grassland and unused land were the main land-use types in the study area. The land-use transitions were mainly concentrated in grassland, woodland, water body, and unused land. Second, the total area of very important habitats and general habitats accounted for over 70% of the Tianshan Mountains. The average HQ decreased from 0.5044 to 0.4802 during 1995–2015. Third, the HQ exhibited significant spatial differentiation, and the Ili River Valley and Kaidu River Basin were the hot spots, while the south and east of the Tianshan Mountains were the cold spots. Habitat quality was strongly related to the terrain gradient, with an inverted “U” type. Protected areas of different categories played a vital role in biodiversity conservation. Finally, soil type, land-use change, precipitation, temperature, and grazing intensity were the dominant factors in response to HQ change for both the total Tianshan Mountains and sub-regions, followed by elevation, the relief degree of the land surface, gross domestic product, population density, and distance to tourism attractions. The interaction effects of the influencing factors were improved compared to the effects of the individual factors in each zone. Furthermore, these results provide decision-making criteria for formulating a scientific and systematic protection of ecology and restoration planning systems to enhance the capacity to address climate change.

Attribution Analysis of Runoff Change in the Upper Reaches of the Kaidu River Basin Based on a Modified Budyko Framework

The Kaidu River plays an important role in the water development and utilization in the Tarim River basin in northwestern China. In this study, we used a modified Budyko framework, which considered the snowmelt to analyze and attribute the runoff change in the upper Kaidu River basin based on the observations during the period of 1960–2010. The time series was divided into two periods: 1960–1995 and 1996–2010. The contribution rate of runoff change between these two periods and the elasticity coefficient of runoff were estimated to quantify the effect of climatic variables and landscape changes on runoff alteration. The results show that the increase in precipitation was the major cause of increase in runoff, whose contribution accounted for 81.42%. The contribution rate of the landscape change was lower than that of the precipitation change, accounting for 9.07%. The elasticity coefficient of runoff to precipitation was 1.24, and the elasticity coefficient of runoff to the landscape was −0.74. Compared with the original Budyko framework, without considering the snowmelt, the contribution rates of precipitation and potential evaporation to runoff change would decrease after considering the snowmelt in the modified Budyko framework, while the contribution rate of landscape would increase. The increased snow ratio would cause more fluctuations in the runoff. This study provides a valuable reference for the water resources management in the upper Kaidu River basin and deepens our understanding of the response of runoff to climate change in snowmelt-affected regions.

Simulation of water and salt transport in the Kaidu River Irrigation District using the modified SWAT-Salt

Soil salinization is the major factor affecting the sustainable development of agriculture in the Kaidu River Irrigation District (KRID) in Xinjiang Province, China. To better understand water and salt variation regularity in the KRID, this study established a watershed-scale distributed water and salt transport model for the KRID based on the new Soil and Water Assessment Tool with a salinity module (SWAT-Salt). Its point source salt module and irrigation water salt module were modified in the study to obtain more accurate simulation results. Based on evaluation indices, the simulation results of streamflow, salt loading, and crop yield showed that the modified model can more accurately depict water and salt transport processes in the KRID and has good applicability. According to the simulation results, the average annual amount of water and salt entering Bosten Lake through the drainage canal accounted for 15 % and 51 %, reaching 4.29 × 108 m3 and 57.87 × 104 t respectively. The drainage and salt discharge during winter irrigation accounted for 69 % and 74 %, reaching 2.95 × 108 m3 and 42.47 × 104 t, respectively. However, the regions along Bosten Lake had high groundwater and soil salinity, facing a risk of increased salinization and necessitating land or water management to decrease salinization. In conclusion, the modified SWAT-Salt can be applied in agricultural irrigation areas with more diverse irrigation sources and is a useful tool for investigating and assessing watershed-scale salinization, as well as implementing targeted management strategies to reduce salinization.