Kaidu River Basin Research Articles

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

In the 21st century, heavier rainfall events and warmer temperatures in mountainous regions have significant impacts on hydrological processes and the occurrence of flood/drought extremes. Long-term modeling and peak flow detection of streamflow series are crucial in understanding the behavior of flood and drought. This study was conducted to analyze the impacts of future climate change on extreme flows in the Kaidu River Basin, northwestern China. The soil water assessment tool (SWAT) was used for hydrological modeling. The projected future precipitation and temperature under Intergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP) scenarios were downscaled and used to drive the validated SWAT model. A generalized extreme value (GEV) distribution was employed to assess the probability distribution of flood events. The modeling results showed that the simulated discharge well matched the observed ones both in the calibration and validation periods. Comparing with the historical period, the ensemble with 15 general circulation models (GCMs) showed that the annual precipitation will increase by 7.9–16.1% in the future, and extreme precipitation events will increase in winter months. Future temperature will increase from 0.42 °C/10 a to 0.70 °C/10 a. However, with respect to the hydrological response to climate change, annual mean runoff will decrease by 21.5–40.0% under the mean conditions of the four RCP scenarios. A reduction in streamflow will occur in winter, while significantly increased discharge will occur from April to May. In addition, designed floods for return periods of five, 10 and 20 years in the future, as predicted by the GEV distribution, will decrease by 3–20% over the entire Kaidu watershed compared to those in the historical period. The results will be used to help local water resource management with hazard warning and flood control.

Possible Hydrochemical Processes Influencing Dissolved Solids in Surface Water and Groundwater of the Kaidu River Basin, Northwest China

Hydrochemical processes under intense human activities were explored on the basis of the hydrochemical characteristics of 109 surface water samples and 129 groundwater samples collected during August 2015 to September 2016, in the Kaidu River Basin. Results obtained in this study indicated that the water in the basin was neutral to slightly alkaline with low total dissolved solids. Rock weathering and evaporation controlled the natural hydrochemical mechanisms. Mountain groundwater and stream water were dominated by Ca2+-HCO3− type water, whereas the plains groundwater was dominated by mixed type water. The results of principal component analysis demonstrated that water-rock interaction and human activity explained 71.6% and 12.9% of surface water hydrochemical variations, respectively, and 75.1% and 14.2% of groundwater hydrochemical variations, respectively. Sulfate, chloride, and carbonate weathering were the major water-rock interaction processes. Livestock farming and agricultural activities were the primary human activities influencing the water hydrochemistry. In addition, cation exchange is another important process influencing the hydrochemical characteristics in the study area. This study would be helpful in forecasting of water quality in arid areas.

Impacts of climate change and agricultural activities on water quality in the Lower Kaidu River Basin, China

In the context of climate change and over-exploitation of water resources, water shortage and water pollution in arid regions have become major constraints to local sustainable development. In this study, we established a Soil and Water Assessment Tool (SWAT) model for simulating non-point source (NPS) pollution in the irrigation area of the lower reaches of the Kaidu River Basin, based on spatial and attribute data (2010–2014). Four climate change scenarios (2040–2044) and two agricultural management scenarios were input into the SWAT model to quantify the effects of climate change and agricultural management on solvents and solutes of pollutants in the study area. The simulation results show that compared to the reference period (2010–2014), with a decline in streamflow from the Kaidu River, the average annual irrigation water consumption is expected to decrease by 3.84x108 m3 or 8.87% during the period of 2040–2044. Meanwhile, the average annual total nitrogen (TN) and total phosphorus (TP) in agricultural drainage canals will also increase by 10.50% and 30.06%, respectively. Through the implementation of agricultural management measures, the TN and TP in farmland drainage can be reduced by 14.49% and 16.03%, respectively, reaching 661.56 t and 12.99 t, accordingly, and the increasing water efficiency can save irrigation water consumption by 4.41 x108 m3 or 4.77%. The results indicate that although the water environment in the irrigation area in the lower reaches of the Kaidu River Basin is deteriorating, the situation can be improved by implementing appropriate agricultural production methods. The quantitative analysis results of NPS pollutants in the irrigation area under different scenarios provide a scientific basis for water environmental management in the Kaidu River Basin.

Development of a Sparse Polynomial Chaos Expansions Method for Parameter Uncertainty Analysis

Incorporating uncertainty assessment into hydrological simulation is of vital significance for providing valuable information for conserving and restoring the ecology environment in arid and semiarid regions. In this study, a sparse polynomial chaos expansions method was developed to quantify hydrological model parameter uncertainties on model performance in Kaidu river basin, China. A four dimension two order polynomial chaos expansions model was built and the effect of four parameters were quantified based on the coefficients of the polynomial chaos expansions model. Results indicated that precipitation in summer has more significant influence on model output than that in other seasons. High Sobol sensitivity indices values (0.22 in spring, 0.17 in summer, 0.21 in autumn and 0.29) for the interaction of precipitation and maximum capacity for fast store demonstrate that they are the major factors affecting runoff generation. These results can help reveal the flow processes and provide valuable information for water resources management.

Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites

Turbidity, relating to underwater light attenuation, is an important optical parameter for water quality evaluation. Satellite estimation of turbidity in alpine rivers is challenging for common ocean color retrieval models due to the differences in optical properties of the water bodies. In this study, we present a simple two-band semi-analytical turbidity (2BSAT) retrieval model for estimating turbidity in five alpine rivers with varying turbidity from 1.01 to 284 NTU. The model was calibrated and validated, respectively, while using one calibration dataset that was obtained from the Three Parallel Rivers basin and two independent validation datasets that were obtained from the Kaidu River basin and the Yarlung Zangbo River basin. The results show that the model has excellent performance in deriving turbidity in alpine rivers. We verified the consistency of the simulated reflectance and satellite-based reflectance and calibrated the 2BSAT model for the specified bands of high spatial resolution satellites in order to achieve the goal of remote sensing monitoring. It is concluded that the model can be used for the quantitative monitoring of turbidity in alpine rivers using satellite images. Based on the model, we used the Sentinel-2 images from one year to identify the seasonal patterns of turbidity of five alpine rivers and the Landsat series images from 1989 to 2018 to analyze the turbidity variation trends of these rivers. The results indicate that the turbidity of these alpine rivers usually presents the highest level in summer, followed by spring and autumn, and the lowest in winter. Meanwhile, the variation trends of turbidity over the past 30 years present distinctly different characteristics in the five rivers.

An approach to simulate the climate-driven streamflow in the data-scarce mountain basins of Northwest China

With global warming, the inland river basin in the arid region of Northwest China is facing a serious water supply situation. The headwater basin of the inland river is located in the high-altitude mountainous region, and there are few meteorological observation sites, so it is difficult to apply distributed hydrological models and other models based on the physical mechanism of runoff generation to evaluate climate change and its impact on streamflow. To simulate the climate-driven streamflow in data-scarce mountain basins of Northwest China, we developed an integrated approach by using downscaled reanalysis data, Mann–Kendall test, ensemble empirical mode decomposition and backpropagation artificial neural networks together with the weights connection method. We validated the approach in the Kaidu River basin located in the Tianshan mountains. The results showed that the streamflow increased 12.9% by $$2.5 \times 10^{8}\,\hbox {m}^{3}$$ per decade with the warm and wet climate, while the average annual temperature increased 5.2% at a rate of $$0.3{^{\circ }}\hbox {C}$$ per decade and the precipitation increased 37.3% at a rate of 16.4 mm per decade during the period from 1980 to 2015. The impact of temperature variability on streamflow was 44.21 ± 2.08% and the impact of precipitation variability on streamflow was 55.79 ± 2.08%.

Development of a Maximum Entropy-Archimedean Copula-Based Bayesian Network Method for Streamflow Frequency Analysis—A Case Study of the Kaidu River Basin, China

Frequency analysis of streamflow is critical for water-resources system planning, water conservancy projects and the mitigation of hydrological extremes events. In this study, a maximum entropy-Archimedean copula-based Bayesian network (MECBN) method has been proposed for frequency analysis of monthly streamflow in the Kaidu River Basin, which integrates the maximum entropy-Archimedean copula (MEAC) and Bayesian network methods into a general framework. MECBN is effective for representing the uncertainties that exist in model representation, preserving the distributional characteristics of streamflow records and addressing the correlation structure between streamflow pairs. Application to the Kaidu River Basin shows a good performance of MECBN in describing the historical data of this basin in China. The results indicate that the interactions between two adjacent monthly streamflow pairs are non-linear. There is upper tail dependence between monthly streamflow pairs. The dependence coefficients including Spearman’s rho, Kendall’s tau, and the upper tail dependence coefficient are in inverse proportion of monthly streamflow values in the Kaidu River Basin, due to the fact that other factors (i.e., rainfall, snow melting, evapotranspiration rate and requirement of water use) provide more contributions to the streamflow in the flooding season. These findings can be used for providing vital information in the prevention and control of hydrological extremes and to further water resources planning in Kaidu River Basin.

Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin, Xinjiang, China

Climate change may affect water resources by altering various processes in natural ecosystems. Dynamic and statistical downscaling methods are commonly used to assess the impacts of climate change on water resources. Objectively, both methods have their own advantages and disadvantages. In the present study, we assessed the impacts of climate change on water resources during the future periods (2020–2029 and 2040–2049) in the upper reaches of the Kaidu River Basin, Xinjiang, China, and discussed the uncertainties in the research processes by integrating dynamic and statistical downscaling methods (regional climate models (RCMs) and general circulation modes (GCMs)) and utilizing these outputs. The reference period for this study is 1990–1999. The climate change trend is represented by three bias-corrected RCMs (i.e., Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA), Regional Climate Model version 4 (RegCM4), and Seoul National University Meso-scale Model version 5 (SUN-MM5)) and an ensemble of GCMs on the basis of delta change method under two future scenarios (RCP4.5 and RCP8.5). We applied the hydrological SWAT (Soil and Water Assessment Tool) model which uses the RCMs/GCMs outputs as input to analyze the impacts of climate change on the stream flow and peak flow of the upper reaches of the Kaidu River Basin. The simulation of climate factors under future scenarios indicates that both temperature and precipitation in the study area will increase in the future compared with the reference period, with the largest increase of annual mean temperature and largest percentage increase of mean annual precipitation being of 2.4°C and 38.4%, respectively. Based on the results from bias correction of climate model outputs, we conclude that the accuracy of RCM (regional climate model) simulation is much better for temperature than for precipitation. The percentage increase in precipitation simulated by the three RCMs is generally higher than that simulated by the ensemble of GCMs. As for the changes in seasonal precipitation, RCMs exhibit a large percentage increase in seasonal precipitation in the wet season, while the ensemble of GCMs shows a large percentage increase in the dry season. Most of the hydrological simulations indicate that the total stream flow will decrease in the future due to the increase of evaporation, and the maximum percentage decrease can reach up to 22.3%. The possibility of peak flow increasing in the future is expected to higher than 99%. These results indicate that less water is likely to be available in the upper reaches of the Kaidu River Basin in the future, and that the temporal distribution of flow may become more concentrated.

Comparing Bias Correction Methods Used in Downscaling Precipitation and Temperature from Regional Climate Models: A Case Study from the Kaidu River Basin in Western China

The systemic biases of Regional Climate Models (RCMs) impede their application in regional hydrological climate-change effects analysis and lead to errors. As a consequence, bias correction has become a necessary prerequisite for the study of climate change. This paper compares the performance of available bias correction methods that focus on the performance of precipitation and temperature projections. The hydrological effects of these correction methods are evaluated by the modelled discharges of the Kaidu River Basin. The results show that all used methods improve the performance of the original RCM precipitation and temperature simulations across a number of levels. The corrected results obtained by precipitation correction methods demonstrate larger diversities than those produced by the temperature correction methods. The performance of hydrological modelling is highly influenced by the choice of precipitation correction methods. Furthermore, no substantial differences can be identified from the results of the temperature-corrected methods. The biases from input data are often greater from the works of hydrological modelling. The suitability of these approaches depends upon the regional context and the RCM model, while their application procedure and a number of results can be adapted from region to region.

Multi-temporal scale modeling on climatic-hydrological processes in data-scarce mountain basins of Northwest China

Previous studies showed that the climatic processes drive the streamflow of the inland river in Northwest China. However, it is difficult to quantitatively assess the climatic-hydrological processes in the ungauged mountainous basins because of the scarce data. This research developed an integrated approach for multi-temporal scale modeling the climatic-hydrological processes in data-scarce mountain basins of Northwest China by combining downscaling method (DM), backpropagation artificial neural network (BPANN), and wavelet regression (WR). To validate the approach, we also simulated the climatic-hydrological processes at different temporal scales in a typical data-scarce mountain basin, the Kaidu River Basin in Northwest China. The main results are as follows: (i) the streamflow is related with regional climatic change as well as atmosphere-ocean variability, (ii) the BPANN model well simulated the nonlinear relationship between the streamflow and temperature and precipitation at the monthly temporal scale, and (iii) although the annual runoff (AR) appears to have fluctuations, there are significant correlations among AR, annual average temperature (AAT), annual precipitation (AP), and oscillation indices, which can be simulated by equations of WR at different temporal scales of years.

Identifying evaporation fractionation and streamflow components based on stable isotopes in the Kaidu River Basin with mountain–oasis system in north‐west China

AbstractBased on stable isotopes in stream water, groundwater, and meltwater in the Kaidu River Basin, NW China, we estimated the evaporation enrichment of stable oxygen isotopes in different types of water and separated the contribution of each streamflow component in river run‐off. Our results indicated that δ18O and δ2H in stream water did not vary with altitude regularly but with seasons, with low concentrations in spring and high concentrations in summer. However, the seasonal variations of δ18O and δ2H in groundwater were not as obvious. The mean evaporation enrichment was between 26% and 44% for δ18O. Of the various water types under investigation, we found glaciers were influenced the most, showing an evaporation enrichment of 44%, followed by oasis groundwater (37%), stream water (36%), and mountain groundwater (26%). Overall, meltwater and groundwater were the predominant streamflow components, with their contributions were governed by temperature, and varied both temporally and specially. In the oasis region, groundwater was the predominant contributor (64% in spring, 50% in summer, and 66% in autumn), whereas in the mountains, groundwater was the dominant in spring (53%) and autumn (51%), and meltwater contributed the most in summer (52%). Precipitation contributed less than 15% to the streamflow.

A probabilistic method for streamflow projection and associated uncertainty analysis in a data sparse alpine region

Abstract Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide.

Impact of GCM structure uncertainty on hydrological processes in an arid area of China

Abstract Quantifying the uncertainty sources in assessment of climate change impacts on hydrological processes is helpful for local water management decision-making. This paper investigated the impact of the general circulation model (GCM) structural uncertainty on hydrological processes in the Kaidu River Basin. Outputs of 21 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under two representative concentration pathway (RCP) scenarios (i.e., RCP4.5 and RCP8.5), representing future climate change under uncertainty, were first bias-corrected using four precipitation and three temperature methods and then used to force a well-calibrated hydrological model (the Soil and Water Assessment Tool, SWAT) in the study area. Results show that the precipitation will increase by 3.1%–18% and 7.0%–22.5%, the temperature will increase by 2.0 °C–3.3 °C and 4.2 °C–5.5 °C and the streamflow will change by −26% to 3.4% and −38% to −7% under RCP4.5 and RCP8.5, respectively. Timing of snowmelt will shift forward by approximately 1–2 months for both scenarios. Compared to RCPs and bias correction methods, GCM structural uncertainty contributes most to streamflow uncertainty based on the standard deviation method (55.3%) while it is dominant based on the analysis of variance approach (94.1%).

Estimation of land surface evapotranspiration over complex terrain based on multi‐spectral remote sensing data

AbstractLand surface evapotranspiration (ET) plays an important role in energy and water balances. ET can significantly affect the runoff yield of a basin and the available water resources in mountainous areas. The existing models to estimate ET are typically applicable to plains, and excessive data are required to calculate the surface fluxes accurately. This study established a simple and practical model capable of depicting the surface fluxes, while using relatively less parameters. Considering the complex terrain, solar radiation was corrected by importing a series of topographic factors. The water deficit index, a measure of land surface wetness, was calculated by applying the fc (vegetation fractional cover)‐Trad (land surface temperature) framework in the two‐source trapezoid model for evapotranspiration model to mountainous areas after corrections of temperature based on altitude variations. The model was successfully applied to the Kaidu River Basin, a basin with few gauges located in the east Tien Shan Mountains of China. Based on the time scale extensions, ET was analyzed at different time scales from 2000 to 2013. The results demonstrated that the corrected solar radiation and water deficit index were reasonably distributed in space and that this model is applicable to ungauged catchments, such as the Kaidu River Basin.

Analysis of interactive effects of DEM resolution and basin subdivision level on runoff simulation in Kaidu River Basin, China

Uncertainties in spatial data associated with basin topography, drainage networks, and land cover characteristics may affect the performance of runoff simulation. Such uncertainties are mainly derived from selection of digital elevation model (DEM) resolution and basin subdivision level. This study focuses on assessing the effects of DEM resolution and basin subdivision level on runoff simulation with a semi-distributed land use-based runoff process model. Twenty-four scenarios based on various DEM resolutions and subdivision levels are analyzed for the Kaidu River Basin. Results can be used for quantifying the uncertainty of input data about spatial information on model simulation, disclosing the interaction between DEM resolution and subdivision level, as well as identifying the optimal system inputs. Results show that the model performance could be enhanced with the increased subdivision level. Results also reveal that the interaction of DEM resolution and subdivision level has slight effects on modeling outputs. Multi-objective fuzzy evaluation is used to further examine the uncertainty in DEM resolution and basin subdivision level on model performance. The results indicate an optimal combination of input parameters is suitable for Kaidu River Basin which could lead to more reliable results of the hydrological simulation.

Scenario-based runoff prediction for the Kaidu River basin of the Tianshan Mountains, Northwest China

Based on the hydro-meteorological data over the past 50 years (1961–2010), the runoff change of the Kaidu River was predicted for the future 30 years (2011–2040). Two statistical downscaling models, the Statistical DownScaling Model (SDSM) and the Statistical Analog Resampling Scheme (STARS), were used to downscale the HadCM3 outputs for projecting the future climate scenarios of the basin. The Soil and Water Assessment Tool (SWAT) hydrological model was driven by the projected climate scenarios to generate the future runoff. Modeling results suggested that the SWAT model can well duplicate the recorded runoff changes in the basin and thus can be applied to simulation of future runoff changes. Both the SDSM and the STARS models performed well in simulating the temperature but relatively poorly in simulating the precipitation. Under the A2 and B2 scenarios the basin will experience a significant increasing trend in temperature and an indistinctive change trend in precipitation during the entire forecast period. Under the S1–S3 scenarios, both temperature and precipitation do not exhibit distinctive changes. In terms of river runoff, the predicted average annual runoff will be relatively abundant during the period from 2010s to 2020s but obviously short after 2020s under A2 scenario and will be kept relatively steady under B2 scenario. The predicted runoff will fluctuate drastically with no any significant trend under S1–S3 scenarios. The relatively high runoffs under S2–S3 scenarios seem to indicate the importance of temperature increasing in generating runoff. The scenario-based predictions suggest that moderate emission (e.g., B2) or moderate warming (e.g., S2) is beneficial to maintaining the expected level of runoff in the future.

A hybrid factorial stepwise-cluster analysis method for streamflow simulation – a case study in northwestern China

ABSTRACTIn this study, a hybrid factorial stepwise-cluster analysis (HFSA) method is developed for modelling hydrological processes. The HFSA method employs a cluster tree to represent the complex nonlinear relationship between inputs (predictors) and outputs (predictands) in hydrological processes. A real case of streamflow simulation for the Kaidu River basin is applied to demonstrate the efficiency of the HFSA method. After training a total of 24 108 calibration samples, the cluster tree for daily streamflow is generated based on a stepwise-cluster analysis (SCA) approach and is then used to reproduce the daily streamflows for calibration (1995–2005) and validation (2008–2010) periods. The Nash-Sutcliffe coefficients for calibration and validation are 0.68 and 0.65, respectively, and the deviations of volume are 1.68% and 4.11%, respectively. Results show that: (i) the HFSA method can formulate a SCA-based hydrological modelling system for streamflow simulation with a satisfactory fitting; (ii) the variability and peak value of streamflow in the Kaidu River basin can be effectively captured by the SCA-based hydrological modelling system; (iii) results from 26 factorial experiments indicate that not only are minimum temperature and precipitation key drivers of system performance, but also the interaction between precipitation and minimum temperature significantly impacts on the streamflow. The findings are useful in indicating that the streamflow of the study basin is a mixture of snowmelt and rainfall water.EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR G. Thirel

RELATIONS BETWEEN GRACE-DERIVED WATER STORAGE CHANGE WITH PRECIPITATION AND TEMPERATURE OVER KAIDU RIVER BASIN, CHINA

Abstract. Water is essential for human survival and well-being, and important to virtually all sectors of the economy. In the aridzone of China’s west, water resource is the controlling factor on the distribution of human settlements. Water cycle variation is sensitive to temperature and precipitation, which are influenced by human activity and climate change. Satellite observations of Earth’s time-variable gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission, which enable direct measurement of changes of total terrestrial water storage, could be useful to aid this modelling. In this pilot study, TWS change from 2002 to 2013 obtained from GRACE satellite mission over the Kaidu River Basin in Xinjiang, China is presented. Precipitation and temperature data from in-situ station and National Satellite Meteorological Centre of China (NSMC) are analysed to examine whether there is a statistically significant correlation between them.

RELATIONS BETWEEN GRACE-DERIVED WATER STORAGE CHANGE WITH PRECIPITATION AND TEMPERATURE OVER KAIDU RIVER BASIN, CHINA

Water is essential for human survival and well-being, and important to virtually all sectors of the economy. In the aridzone of China’s west, water resource is the controlling factor on the distribution of human settlements. Water cycle variation is sensitive to temperature and precipitation, which are influenced by human activity and climate change. Satellite observations of Earth’s time-variable gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission, which enable direct measurement of changes of total terrestrial water storage, could be useful to aid this modelling. In this pilot study, TWS change from 2002 to 2013 obtained from GRACE satellite mission over the Kaidu River Basin in Xinjiang, China is presented. Precipitation and temperature data from in-situ station and National Satellite Meteorological Centre of China (NSMC) are analysed to examine whether there is a statistically significant correlation between them.

Transitional variations and risk of hydro-meteorological droughts in the Tarim River basin, China

The Tarim River basin, China, is a typical arid inland river basin in China. Management of water resources and agricultural development rely heavily on the understanding of droughts. In this study, an integrated drought index is proposed, based on the Standardized Precipitation Evapotranspiration Index and the Standardized Runoff Index, and then the changing properties of drought regimes have been analyzed using a Markov Chain model. Results indicate that: (1) the Kaidu and Aksu Rivers are dominated by prompt transition between hydrological and meteorological droughts. Long-lasting droughts heavily impact agricultural development in the Kaidu and Yarkand River basins; (2) the Kaidu and Aksu River basins are influenced mainly by hydro-meteorological droughts and hydrological drought is dominant in the Yarkand River basin; (3) different drought conditions are the results of different sources of river runoff. Increasing precipitation alleviate droughts in the Tarim River basin. However, a drying tendency can still be found in the Kaidu River basin; (4) higher probability is detected for the transition from both meteorological and hydrological wet to both meteorological and hydrological dry, implying that the Tarim River basin is sensitive to both meteorological and hydrological droughts. Results of this study are of practical value for the regional management of water resources, planning of agricultural irrigation, and measures for mitigation of hydrological and meteorological droughts.