Drought has become an important natural disaster, affecting the development of Inner Mongolia, as an important animal husbandry region in China. In this study, the characteristics and trends of the Inner Mongolia drought are thoroughly analysed by calculating the standardised precipitation evapotranspiration index (SPEI) at different time scales, based on monthly precipitation and temperature data from 40 national meteorological stations in Inner Mongolia from 1958 to 2019. Subsequently, the area drought intensity (ADI), which is a comprehensive evaluation indicator for evaluating drought intensity within the region, is proposed, taking into account the effects of the persistent drought on drought intensity. The results show that drought has increased during this period, with a remarkable increase in the frequency and the area of drought. The areas with stronger drought intensity are mainly located in the west, north central, and the western area of the east. Since 2000, March to October are identified as drought-prone months and April is characterised as the month with the highest frequency of drought. The inflection points of SPEI and climate conditions both appeared in 1990s and it is speculated that the increase in drought may have been caused by excessive temperature rise. The frequency, coverage area, and continuous duration of drought have increased greatly after climate mutation in this region. According to the changes in the spatial distribution of the ADI and frequency of drought occurrence, the drought-stricken areas shifted from the southeast to the northwest after climate mutations. The findings from this study provide a theoretical basis for the drought management of Inner Mongolia.

It is of great significance to study the characteristics and change trends of drought in Xinjiang to provide a basis for implementing local strategies. Based on monthly precipitation and temperature data from 95 meteorological stations in Xinjiang, from 1960 to 2018, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated, and the characteristics and trends of drought in Xinjiang were analysed, in details. Furthermore, a comprehensive evaluation index, i.e., Regional Drought Severity (RDS), was proposed to analyse the effects of duration of the drought and the extent of the drought affected area. The results from our study suggested: (1) In consideration of global warming, droughts in Xinjiang have intensified during the past 59 years, and the frequency and range of droughts have increased significantly; (2) During the plant growing season, spring, summer, and autumn, a drying trend was observed, while, a wetting trend was identified for winter season; (3) The drought-prone months shifted from January and December to March-November in the 1970s, and April was identified as a month with the highest frequency of droughts; (4) The meteorological change occurred a period near 1997. It can be speculated that the intensified droughts can be triggered by the excessive temperature rise, through comparing the changes in SPEI and the Standardized Precipitation Index (SPI), before and after the meteorological change; (5) After the meteorological change, the frequency of droughts with different levels had significantly increased, in addition, the drought-prone areas shifted from the north-west to the south-east. The results from this research provide important support for drought management in Xinjiang, also offer scientific basis for the formulation of relevant policies on agricultural and animal husbandry production.

Under the influence of climate change, the hydrological processes of glaciers have undergone significant changes, a fact which is seriously affecting agricultural production in the downstream region of the Tianshan Mountains, China. In order to explore the intrinsic relationship between climate change and hydrological elements, we proposed an “evaluation-driving-prediction” system to study it. First, we constructed a glacier-enhanced soil and water assessment tool model (GE-SWAT) and used a two-stage calibration method to optimize the model parameters. Next, a scenario analysis was used to evaluate the driving factors of historical runoff changes. Finally, we projected future runoff changes using bias-corrected regional climate model (RCM) outputs. The results of the case study on the Jinghe River Basin in the Tianshan Mountains show that from 1963 to 2016, total runoff increased by 13.3%, 17.7% of which was due to increasing precipitation and 1.8% of which was negated by rising temperatures. The glacier runoff increased by 14.5%, mainly due to the rising temperatures. A 3.4% reduction in snowmelt was caused by a lower snowfall/precipitation ratio, which significantly reduced the snowfall from June to August. The RCM projection indicated that the warming and humidification phenomenon in the study area will continue at least through to the mid-21st century. A consistent increase in glacier runoff and total runoff is projected, but the contribution rate of the glacier runoff will have little to no change under the RCP4.5 and RCP8.5 emission scenarios. Our research demonstrates the simulation performance of the GE-SWAT model in a basin with moderate glacier cover. This method is shown to be efficient in quantifying the impact of climate change on glacier hydrological processes and predicting future streamflow changes, providing a good research reference for similar regions.

The rational allocation of water resources in the basin/region can be better assisted and performed using a suitable water resources allocation model. Rule-based and optimization-based simulation methods are utilized to solve medium- and long-term water resources allocation problems. Since rule-based allocation methods requires more experience from expert practice than optimization-based allocation methods, it may not be utilized by users that lack experience. Although the optimal solution can be obtained via the optimization-based allocation method, the highly skilled expert experience is not taken into account. To overcome this deficiency and employ the advantages of both rule-based and optimization-based simulation methods, this paper proposes the optimal allocation model of water resources where the highly skilled expert experience has been considered therein. The “prospect theory” is employed to analyze highly skilled expert behavior when decision-making events occur. The cumulative prospect theory value is employed to express the highly skilled expert experience. Then, the various elements of the cumulative prospect theory value can be taken as the variables or parameters in the allocation model. Moreover, the optimal water allocation model developed by the general algebraic modeling system (GAMS) has been improved by adding the decision reversal control point and defining the inverse objective function and other constraints. The case study was carried out in the Wuyur River Basin, northeast of China, and shows that the expert experience considered as the decision maker’s preference can be expressed in the improved optimal allocation model. Accordingly, the improved allocation model will contribute to improving the rationality of decision-making results and helping decision-makers better address the problem of water shortage.

To facilitate water management and efficient utilization of water resources, the allocation of water rights to individual industries must be underpinned by a rational and defensible process. This study aimed to develop an improved fuzzy analytic hierarchy process method of allocating water rights to different industries and focused on Qing’an County, northeast China as a case study. An evaluation index system for allocation of initial water rights was established, and incorporated physiographic, societal, economic, and ecological criteria. The system classifies four categories of second-level indices, 14 third-level indices, and 30 fourth-level indices. The order of priority of the evaluation index was determined and the total weight of initial water rights for different industries was calculated using the fuzzy analytic hierarchy process method. Results showed that the indices for the allocation of initial water rights ranked in descending order of their total weights coefficient were: (1) agricultural water rights: 0.9508; (2) residential water rights: 0.0240; (3) water rights for non-agricultural production: 0.0173; (4) environmental water rights: 0.0078. Agricultural water consumption accounted for the largest proportion of total water because the study area is a major grain production area. The study provides a theoretical basis for the allocation of water rights and water rights trading in northeast China.

Accurate prediction of river runoff is significant for flood control, water resource allocation, and basin ecological management. Despite the promise of integrating signal decomposition with deep learning, current decomposition-based hybrid models face critical forward data contamination: decomposition algorithms improperly access future test data in full-series applications, artificially inflating prediction accuracy. In contrast, the stepwise decomposition method currently proposed leads to high computational costs. To address this limitation, we introduce a novel framework integrating segmented decomposition sampling with a multi-input neural network. Specifically, a hybrid forecasting model combining Seasonal-Trend decomposition using Loess (STL) and Convolutional Long Short-Term Memory (CNN-LSTM) networks was implemented for daily runoff estimation. Method reliability was evaluated using historical runoff data from Huaxian Station in China’s Weihe River Basin, with comparative experiments conducted against established single and hybrid models. The results showed that the proposed framework can effectively avoid future information leakage and simultaneously improve prediction accuracy. For 1–3-day-ahead Nash-Sutcliffe efficiency (NSE) at Huaxian Station, the STL-CNN-LSTM model achieved values of 0.96, 0.83, and 0.80, respectively—representing improvements of 5.49%, 5.06%, and 12.68% over the VMD-CNN-LSTM model. This STL-based configuration outperformed the standalone LSTM counterpart by 23.08%, 9.21%, and 17.65% in NSE, respectively. Therefore, the proposed framework, which incorporates the segmented decomposition sampling method and a multi-input neural network, proves to be both practical and reliable.