Coupling reconstruction of atmospheric hydrological profile and dry-up risk prediction in a typical lake basin in arid area of China

Wang, Jie; Liu, Dongwei; Tian, Songni; Ma, Jun; Wang, Lixin

doi:10.1038/s41598-022-10284-y

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…Arid and semi-arid mountainous areas in northwest China are highly vulnerable to glacial snowmelt flooding under extreme climate change [7]. Floods can cause damage to local transportation facilities, downstream river runoff processes, and related hydrological forecasting systems such as the bursting of the Kayagil glacial dam in the Kunlun Mountains of Xinjiang [8,9]. Achieving sustainable use and scientific management of water resources in the HCMA is directly linked to the green and high quality development of the region downstream [10].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Wang,

Sun,

Wang

et al. 2023

Water

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Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρXY) and random forest feature importance (FI) (ρXY = 0.63, FI = 0.723), followed by soil temperature (ρXY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.

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Section: Introductionmentioning

confidence: 99%

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Wang,

Sun,

Wang

et al. 2023

Water

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“…Climate change will certainly cause the change of hydrological information (water level, flow, groundwater level, etc.) in time and space (Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Climate Change in Natural Areas of the Turkestan Region of the Republic of Kazakhstan for the Purposes of Sustainable Agricultural and Recreational Nature Management

MUSTAFAYEV¹,

SKORINTSEVA²,

TOLETAYEV³

et al. 2023

GTG

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The article presents the scientific results of a study for assessing climate change trends in natural areas of the Turkestan region of the Republic of Kazakhstan based on the use of long-term climate data (1940-2020) of sixteen meteorological stations located in the region. In the course of the study, tested domestic, international and proprietary methodology for assessing climate change in natural areas were used based on plotting a fixed time series of climatic indicators and the resulting equations of linear trends. The results of the study showed that the average annual air temperature in all natural areas of the Turkestan region tend to increase, and the amount of annual precipitation to decrease, this has a negative impact on the development of recreational and agricultural nature management. On the basis of the developed mathematical models of changes in climatic indicators that characterize the coefficient of coincidence of the growth rates of average annual air temperature and annual precipitation, their quantitative values were determined in the territories of the natural areas of the region, which influence the formation of the environment-forming functions of the natural system. The results of the study enable economic entities, especially farms, to organize sustainable agricultural environmental management, and management bodies to develop plans for the development of recreational tourism in the Turkestan region.

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“…As a result, it can better describe the relationship between water and energy fluxes at the atmospheric-terrestrial interface. The WRF-Hydro model can be applied as an offline model for model calibration and flood simulation [21] and as a coupled model for studying the land-atmosphere feedback [22][23][24][25][26][27]. A growing number of studies have been conducted with WRF-Hydro over China, either in the uncoupled or coupled mode [22,[24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…The WRF-Hydro model can be applied as an offline model for model calibration and flood simulation [21] and as a coupled model for studying the land-atmosphere feedback [22][23][24][25][26][27]. A growing number of studies have been conducted with WRF-Hydro over China, either in the uncoupled or coupled mode [22,[24][25][26][27][28][29][30][31]. For example, Wang et al [22] studied the spatiotemporal characteristics of rainfall and several key water cycle elements, including soil moisture, evapotranspiration and runoff over North China, the results show that the fully coupled WRF/WRF-Hydro system produced slightly less runoff but more frequent infiltration and higher soil moisture than the WRF-only simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [30] investigated the effects of four key parameters on the streamflow simulation over North China and found that the runoff infiltration parameter and the channel Manning roughness parameter are the most sensitive parameters, particularly under unsaturated soil conditions. Wang et al [26] used WRF-Hydro to simulate the climate hydrological coupling situation of the Daihai Basin in North China from 1980 to 2020, and the simulations showed that the inflow of Daihai Lake decreased during the study period and that the runoff into the lake tended to be flat since 2013.…”

Section: Introductionmentioning

confidence: 99%

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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

Liu

et al. 2023

Sustainability

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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.

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Coupling reconstruction of atmospheric hydrological profile and dry-up risk prediction in a typical lake basin in arid area of China

Cited by 8 publications

References 25 publications

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Assessment of Climate Change in Natural Areas of the Turkestan Region of the Republic of Kazakhstan for the Purposes of Sustainable Agricultural and Recreational Nature Management

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

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