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Dual factors of climate and human on the hydrological process are reflected not only in changes in the spatiotemporal distribution of water resource amounts but also in the various characteristics of river flow regimes. Isolating and quantifying their contributions to these hydrological alterations helps us to comprehensively understand the response mechanism and patterns of hydrological process to the two kinds of factors. Here we develop a general framework using hydrological model and 33 indicators to describe hydrological process and quantify the impact from climate and human. And we select the Upper Minjiang River (UMR) as a case to explore its feasibility. The results indicate that our approach successfully recognizes the characteristics of river flow regimes in different scenarios and quantitatively separates the climate and human contributions to multi-dimensional hydrological alterations. Among these indicators, 26 of 33 indicators decrease over the past half-century (1961–2012) in the UMR, with change rates ranging from 1.3% to 33.2%, and the human impacts are the dominant factor affecting hydrological processes, with an average relative contribution rate of 58.6%. Climate change causes an increase in most indicators, with an average relative contribution rate of 41.4%. Specifically, changes in precipitation and reservoir operation may play a considerable role in inducing these alterations. The findings in this study help us better understand the response mechanism of hydrological process under changing environment and is conducive to climate change adaptation, water resource planning and ecological construction.
Yuhang Zhang; Aizhong Ye; Jinjun You; Xiangyang Jing. Quantification of human and climate contributions to multi-dimensional hydrological alterations: A case study in the Upper Minjiang River, China. Journal of Geographical Sciences 2021, 31, 1102 -1122.
AMA StyleYuhang Zhang, Aizhong Ye, Jinjun You, Xiangyang Jing. Quantification of human and climate contributions to multi-dimensional hydrological alterations: A case study in the Upper Minjiang River, China. Journal of Geographical Sciences. 2021; 31 (8):1102-1122.
Chicago/Turabian StyleYuhang Zhang; Aizhong Ye; Jinjun You; Xiangyang Jing. 2021. "Quantification of human and climate contributions to multi-dimensional hydrological alterations: A case study in the Upper Minjiang River, China." Journal of Geographical Sciences 31, no. 8: 1102-1122.
Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km2. The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km2, the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.
Yuhang Zhang; Aizhong Ye; Phu Nguyen; Bita Analui; Soroosh Sorooshian; Kuolin Hsu. Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling. Remote Sensing 2021, 13, 3061 .
AMA StyleYuhang Zhang, Aizhong Ye, Phu Nguyen, Bita Analui, Soroosh Sorooshian, Kuolin Hsu. Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling. Remote Sensing. 2021; 13 (16):3061.
Chicago/Turabian StyleYuhang Zhang; Aizhong Ye; Phu Nguyen; Bita Analui; Soroosh Sorooshian; Kuolin Hsu. 2021. "Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling." Remote Sensing 13, no. 16: 3061.
Gross primary productivity (GPP) is a vital variable of the global carbon cycle, but the quantification of global GPP is subject to significant uncertainty due to the lack of direct observations at a global scale. Here, we evaluated and compared 45 GPP products in terms of their applicability to different vegetation types at various spatiotemporal scales. The results show that 44 GPP products and obsGPP (Model Tree Ensemble GPP derived from observations and named obsGPP) have similar global patterns with correlation coefficients greater than 0.8 except for NGT, where GOSIF, RS, and BESS are prominent. GPP products have the greatest variation in Suriname, with a mean 75th and 25th percentile difference value of 0.4748 (normalized), and we recommend RS, SDGVM and LPJ-wsl as they provide GPP estimates close to the average GPP. In terms of seasonal estimations, considerable disagreement occurs among the GPP products in winter, with a range from 118.76 to 314.95 gC/m2/season, among which JULES has the closest GPP value to the average GPP estimation. For studies concerning vegetation types preference is given to the LUE average GPP. The 45 GPP products are more consistent on grasslands but, have obvious differences for savannas. All GPP products have their own specific spatiotemporal scales, such as global or national scales or different seasons and different vegetation types (forest, grasslands, etc.). This study provides guidelines for selecting GPP products.
Yahai Zhang; Aizhong Ye. Would the obtainable gross primary productivity (GPP) products stand up? A critical assessment of 45 global GPP products. Science of The Total Environment 2021, 783, 146965 .
AMA StyleYahai Zhang, Aizhong Ye. Would the obtainable gross primary productivity (GPP) products stand up? A critical assessment of 45 global GPP products. Science of The Total Environment. 2021; 783 ():146965.
Chicago/Turabian StyleYahai Zhang; Aizhong Ye. 2021. "Would the obtainable gross primary productivity (GPP) products stand up? A critical assessment of 45 global GPP products." Science of The Total Environment 783, no. : 146965.
Soil moisture (SM), a vital variable in the climate system, is applied in many fields. But the existing SM data sets from different sources have great uncertainty, hence need comprehensive verification. In this study, we collected and evaluated ten latest commonly used SM products over China, including four reanalysis data (ERA-Interim, ERA5, NCEP R2 and CFSR/CFSV2), three land surface model products (GLDAS 2.1 Noah, CLSM and VIC) and three remote sensing products (ESA CCI ACTIVE, COMBINED and PASSIVE). These products in their overlap period (2000-2018) were inter-compared in spatial and temporal variation. In addition, their accuracy was verified by a large quantity of in-situ observations. The results show that the ten SM products have roughly similar spatial patterns and small inter-annual differences, but there are still some deviations varying in regions and products. ERA5 displays the most encouraging overall performance in China. The estimates of SM in the northwest of China among all products generally perform poorly on capturing in-situ SM variability due to less coverage of observations. CLSM and ERA5 have a satisfactory correlation coefficient with the observed SM (R>0.7) in the northeast and south of China, respectively. ESA CCI ACTIVE performs with the optimal mean Equitable Threat Score (ETS) value, which indicates the promising ability to drought assessment, followed by CFSR/CFSV2 and ERA5. Specifically, ESA CCI ACTIVE expresses higher ETS in the Yellow River Basin, while CFSR/CFSV2 and ERA5 are more applicable in most areas of the eastern China. This study provides a reasonable reference for the application of SM products in China.
Huiqing Li; Aizhong Ye; Yuhang Zhang; Wenwu Zhao. Evaluation of multiple soil moisture products using in-situ observations over China. 2021, 1 .
AMA StyleHuiqing Li, Aizhong Ye, Yuhang Zhang, Wenwu Zhao. Evaluation of multiple soil moisture products using in-situ observations over China. . 2021; ():1.
Chicago/Turabian StyleHuiqing Li; Aizhong Ye; Yuhang Zhang; Wenwu Zhao. 2021. "Evaluation of multiple soil moisture products using in-situ observations over China." , no. : 1.
The hydrological forecasting system coupled with precipitation forecasting can bring us a longer forecast period of early warning information, but it is also accompanied by higher uncertainty. With the improvement of hydrological models, the precipitation forecast may be the largest source of uncertainty. Therefore, before incorporating it into the hydrological model, the precipitation forecast needs post-processing to reduce its uncertainty. Meteorological post-processing corrects the bias of future precipitation forecasts by establishing a linear or non-linear relationship between historical observation and simulation. Machine learning (ML) can fit this relationship and process higher-dimensional predictor features, which is a promising method to improve the accuracy of precipitation forecasts. In this study, we selected the Yalong River basin of China as the cast study and compared the performance of 20 different machine learning algorithms (e.g., ridge regression, random forest, and artificial neural network). The daily hindcast data (1985-2018) from NOAA’s Global ensemble forecast system and corresponding observations from the China Meteorological Administration were selected to construct our data set. To improve the accuracy of the precipitation forecasts, we also screened different combinations of predictors to optimize the model configuration of machine learning, including space, time, and ensemble members. Comparative experiments show that all ML models can improve the accuracy of the raw precipitation forecast, but the performance is different. The extra-trees model has the best results, followed by LightGBM. However, linear regression models perform relatively poorly. The predictor combination of 11 ensemble members and a 2-day time window can achieve the best precipitation forecast. The post-processing of precipitation forecasts based on ML can significantly improve the accuracy of the raw forecasts, and it can also help us build a more advanced hydrological forecast system. In addition, the conclusions of this study and experimental design methods can provide references for the same type of research.
Yuhang Zhang; Aizhong Ye. Improve short-term precipitation forecasts using numerical weather prediction model output and machine learning. 2021, 1 .
AMA StyleYuhang Zhang, Aizhong Ye. Improve short-term precipitation forecasts using numerical weather prediction model output and machine learning. . 2021; ():1.
Chicago/Turabian StyleYuhang Zhang; Aizhong Ye. 2021. "Improve short-term precipitation forecasts using numerical weather prediction model output and machine learning." , no. : 1.
In recent decades, vegetation has faced the dual challenges posed by climate change and human activities. Quantitatively distinguishing the influences of climate change and human activities on vegetation changes is key to developing adaptive ecological protection policies. This study examined changes in temperature and precipitation to determine if anthropogenic land use changes have affected vegetation in mainland China. The contribution rates of temperature and precipitation changes and land use changes to vegetation dynamics are further calculated by the improved residual trend method, which considers the nonlinear relationship between vegetation and climate factors and time-lag effects from a spatiotemporal perspective and sets the base period for the equation. The results show that 68.81% of the vegetation in mainland China is in a state of sustained growth, where cultivated vegetation and grasses are the main greening vegetation types. The contribution of land use changes to vegetation changes in mainland China is higher than that of temperature and precipitation changes. Planting trees and grasses and returning farmlands to forests and grassland has increased the area covered by grasses and mixed coniferous broad-leaved forests, while cultivated vegetation coverage has decreased. Swamps are more sensitive to temperature and precipitation changes. We show that the improved residual trend method that considers temporal and spatial dimensions can reduce the uncertainty in quantifying the effects of climatic and anthropogenic factors on vegetation dynamics. This study provides a theoretical basis and a useful tool for future governmental implementation of ecological management strategies.
Yahai Zhang; Aizhong Ye. Quantitatively distinguishing the impact of climate change and human activities on vegetation in mainland China with the improved residual method. GIScience & Remote Sensing 2021, 58, 235 -260.
AMA StyleYahai Zhang, Aizhong Ye. Quantitatively distinguishing the impact of climate change and human activities on vegetation in mainland China with the improved residual method. GIScience & Remote Sensing. 2021; 58 (2):235-260.
Chicago/Turabian StyleYahai Zhang; Aizhong Ye. 2021. "Quantitatively distinguishing the impact of climate change and human activities on vegetation in mainland China with the improved residual method." GIScience & Remote Sensing 58, no. 2: 235-260.
Quantitatively figuring out the effects of climate and land-use change on water resources and their components is essential for water resource management. This study investigates the effects of climate and land-use change on blue and green water and their components in the upper Ganjiang River basin from the 1980s to the 2010s by comparing the simulated changes in blue and green water resources by using a Soil and Water Assessment Tool (SWAT) model forced by five climate and land-use scenarios. The results suggest that the blue water flow (BWF) decreased by 86.03 mm year−1, while green water flow (GWF) and green water storage (GWS) increased by 8.61 mm year−1 and 12.51 mm year−1, respectively. The spatial distribution of blue and green water was impacted by climate, wind direction, topography, and elevation. Climate change was the main factor affecting blue and green water resources in the basin; land-use change had strong effects only locally. Precipitation changes significantly amplified the BWF changes. The proportion of surface runoff in BWF was positively correlated with precipitation changes; lateral flow showed the opposite tendency. Higher temperatures resulted in increased GWF and decreased BWF, both of which were most sensitive to temperature increases up to 1 °C. All agricultural land and forestland conversion scenarios resulted in decreased BWF and increased GWF in the watershed. GWS was less affected by climate and land-use change than GWF and BWF, and the trends in GWS were not significant. The study provides a reference for blue and green water resource management in humid areas.
Yongfen Zhang; Chongjun Tang; Aizhong Ye; Taihui Zheng; Xiaofei Nie; Anguo Tu; Hua Zhu; Shiqiang Zhang. Impacts of Climate and Land-Use Change on Blue and Green Water: A Case Study of the Upper Ganjiang River Basin, China. Water 2020, 12, 2661 .
AMA StyleYongfen Zhang, Chongjun Tang, Aizhong Ye, Taihui Zheng, Xiaofei Nie, Anguo Tu, Hua Zhu, Shiqiang Zhang. Impacts of Climate and Land-Use Change on Blue and Green Water: A Case Study of the Upper Ganjiang River Basin, China. Water. 2020; 12 (10):2661.
Chicago/Turabian StyleYongfen Zhang; Chongjun Tang; Aizhong Ye; Taihui Zheng; Xiaofei Nie; Anguo Tu; Hua Zhu; Shiqiang Zhang. 2020. "Impacts of Climate and Land-Use Change on Blue and Green Water: A Case Study of the Upper Ganjiang River Basin, China." Water 12, no. 10: 2661.
Understanding the changes in Greenland's temperature is important for assessing and predicting the mass of the Greenland ice sheet, which plays an important role in sea level rise. In this study, we analyzed the annual and seasonal coastal Greenland's temperatures during the period 1952–2017 (focusing on the period 2013–2017) based on a dataset obtained from the Danish Meteorological Institute (DMI). Overall, the annual coastal Greenland's temperature increased during 1952–2017 at a rate of 0.23 °C decade−1, especially in the southeastern (0.70 °C decade−1) and northern (0.42 °C decade−1) regions of the island. From the changes in the seasonal coastal Greenland's composite temperature (CT), winter exhibited the largest change rate (0.28 °C decade−1), and the summer CT increased by 0.25 °C decade−1, while the spring CT increased by 0.17 °C decade−1 with less variation. The temperature increase accelerated during 2013–2017 according to Mann-Kendall (M-K) tests, especially in the northeastern and northern regions of the island. The seasonal temperature change of the whole island decreased in the following order: annual > autumn > summer > winter > spring. We also analyzed the annual inland temperature change during the period 1997–2017 based on a dataset obtained from the Greenland Climate Network; the results indicated that the inland temperature increased by 0.13 °C decade−1. Pearson correlation analysis was used to determine the teleconnection relationship between the coastal temperatures and large-scale atmosphere-ocean climate indexes, and we found that the Greenland Blocking Index (GBI), Atlantic Multidecadal Oscillation (AMO), Tropical Northern Atlantic Index (TNA), North Tropical Atlantic Index (NTA), Caribbean Index (CAR), Atlantic Meridional Mode (AMM), East Atlantic (EA) and Western Hemisphere warm pool (WHWP) have significant positive correlations with the coastal temperature in most months, except in February and May. However, the North Atlantic Oscillation (NAO), Arctic Oscillation (AO) and Eastern Asia/Western Russia (EAWR) show significant negative correlations with temperature. Overall, there exists a time lag effect between the climate indexes (except for the GBI, AO and NAO) and temperature. From the application of the random forest model, we found that the GBI, NAO, CO2, AMO, N2O, SF6, CH4, and Northern Oscillation Index (NOI) are the most important variables that influenced the CT changes during 1979–2017. Finally, we calculated the contribution rates of the most important variables to temperature change during the period 1979–2017 and showed that the contribution rates of the GBI, CO2 and NOI to temperature change were 47.30%, 35.68%, and 17.02%, respectively.
Saiping Jiang; Aizhong Ye; Cunde Xiao. The temperature increase in Greenland has accelerated in the past five years. Global and Planetary Change 2020, 194, 103297 .
AMA StyleSaiping Jiang, Aizhong Ye, Cunde Xiao. The temperature increase in Greenland has accelerated in the past five years. Global and Planetary Change. 2020; 194 ():103297.
Chicago/Turabian StyleSaiping Jiang; Aizhong Ye; Cunde Xiao. 2020. "The temperature increase in Greenland has accelerated in the past five years." Global and Planetary Change 194, no. : 103297.
Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions.
Yang Lang; Lifeng Luo; Aizhong Ye; Qingyun Duan. Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China? Water 2020, 12, 2010 .
AMA StyleYang Lang, Lifeng Luo, Aizhong Ye, Qingyun Duan. Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China? Water. 2020; 12 (7):2010.
Chicago/Turabian StyleYang Lang; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2020. "Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?" Water 12, no. 7: 2010.
Human activities and climate change have changed the vegetation in China. The analysis of the changes in vegetation that have occurred over the past 30 years in China remains a great challenge due to intense human activity and lack of field observations. The use of various Normalized Difference Vegetation Index (NDVI) datasets to study vegetation coverage changes has received much attention. In this paper, we selected the early versions of Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Monitoring and Modelling Studies (GIMMS), GIMMS3g (third generation GIMMS NDVI from AVHRR sensors) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data including the fusion data (GIMMS+MODIS). We analysed spatial and temporal changes in vegetation cover in different ecosystems and basins in the mainland of China. Different contributions of ecosystems and variations in NDVI trends exist in different ecosystems in 17 basins. The results show that different NDVI from different data sources yield different results: (1) Vegetation increased in 74.62–77.7% of the area of the Chinese mainland during 1982–2015, mainly in the Yellow River and the middle reaches of the Yangtze River basin; (2) 2000–2017 MODIS NDVI in mainland China has increased more area (79.67%). (3) Farmland and Forest ecosystems were significantly enhanced in the eastern monsoon region; (4) High-resolution NDVI can provide more information than domain average NDVI. GIMMS and MODIS NDVI data have complementary spatial and temporal distributions. Our study improves the understanding of vegetation dynamics over long time periods and large areas and, moreover, has potential for supporting ecological managers in mainland China.
Yahai Zhang; Aizhong Ye. Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China. International Journal of Remote Sensing 2020, 41, 4238 -4268.
AMA StyleYahai Zhang, Aizhong Ye. Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China. International Journal of Remote Sensing. 2020; 41 (11):4238-4268.
Chicago/Turabian StyleYahai Zhang; Aizhong Ye. 2020. "Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China." International Journal of Remote Sensing 41, no. 11: 4238-4268.
Statistical post-processing methods have been applied in hydrometeorological forecasting to correct the bias and spread error in raw forecasts. Among various post-processing methods, the meta-Gaussian distribution model (MGD) is one of the early successful methods for post-processing of precipitation forecasts and has been applied in the National Weather Service’s Hydrologic Ensemble Forecast System (HEFS), together with the mix-type meta-Gaussian distribution model (MMGD). However, recent studies have shown that the original MGD cannot yield reliable forecasts especially for sub-daily precipitation forecasts (e.g., 6-hourly). In this paper, we improved the MGD model by applying the censored maximum likelihood estimation (CMLE) method. We conducted experiments using GEFS reforecasts in Huai river basin in China to evaluate its performance. The results show that the proposed method performs better than the original MGD for sub-daily precipitation forecasts. The proposed method also achieves similar forecast skill with the state-of-the-art censored, shifted Gamma distribution-based ensemble MOS (CSGD-EMOS) if both use ensemble mean as the only predictor. The results indicate that the proposed CMLE-MGD can be useful for further applications such as flood forecasting that needs forecasts of high temporal resolution.
Wentao Li; Qingyun Duan; Aizhong Ye; Chiyuan Miao. An improved meta-Gaussian distribution model for post-processing of precipitation forecasts by censored maximum likelihood estimation. Journal of Hydrology 2019, 574, 801 -810.
AMA StyleWentao Li, Qingyun Duan, Aizhong Ye, Chiyuan Miao. An improved meta-Gaussian distribution model for post-processing of precipitation forecasts by censored maximum likelihood estimation. Journal of Hydrology. 2019; 574 ():801-810.
Chicago/Turabian StyleWentao Li; Qingyun Duan; Aizhong Ye; Chiyuan Miao. 2019. "An improved meta-Gaussian distribution model for post-processing of precipitation forecasts by censored maximum likelihood estimation." Journal of Hydrology 574, no. : 801-810.
Meteorological and hydrological droughts can bring different socioeconomic impacts. In this study, we investigated meteorological and hydrological drought characteristics and propagation using the standardized precipitation index (SPI) and standardized streamflow index (SSI), over the upstream and midstream of the Heihe River basin (UHRB and MHRB, respectively). The correlation analysis and cross-wavelet transform were adopted to explore the relationship between meteorological and hydrological droughts in the basin. Three modeling experiments were performed to quantitatively understand how climate change and human activities influence hydrological drought and propagation. Results showed that meteorological drought characteristics presented little difference between UHRB and MHRB, while hydrological drought events are more frequent in the MHRB. In the UHRB, there were positive relationships between meteorological and hydrological droughts, whereas drought events became less frequent but longer when meteorological drought propagated into hydrological drought. Human activities have obviously changed the positive correlation to negative in the MHRB, especially during warm and irrigation seasons. The propagation time varied with seasonal climate characteristics and human activities, showing shorter values due to higher evapotranspiration, reservoir filling, and irrigation. Quantitative evaluation showed that climate change was inclined to increase streamflow and propagation time, contributing from −57% to 63%. However, more hydrological droughts and shorter propagation time were detected in the MHRB because human activities play a dominant role in water consumption with contribution rate greater than (−)89%. This study provides a basis for understanding the mechanism of hydrological drought and for the development of improved hydrological drought warning and forecasting system in the HRB.
Feng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. Drought Characteristics and Propagation in the Semiarid Heihe River Basin in Northwestern China. Journal of Hydrometeorology 2019, 20, 59 -77.
AMA StyleFeng Ma, Lifeng Luo, Aizhong Ye, Qingyun Duan. Drought Characteristics and Propagation in the Semiarid Heihe River Basin in Northwestern China. Journal of Hydrometeorology. 2019; 20 (1):59-77.
Chicago/Turabian StyleFeng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2019. "Drought Characteristics and Propagation in the Semiarid Heihe River Basin in Northwestern China." Journal of Hydrometeorology 20, no. 1: 59-77.
High resolution modeling became popular in recent years due to the availability of multisource observations, advances in understanding fine‐scale processes, and improvements in computing facilities. However, modeling of hydrological changes over mountainous regions is still a great challenge due to the sensitivity of highland water cycle to global warming, tightly coupled hydro‐thermal processes, and limited observations. Here we show a successful high resolution (3 km) land surface modeling over the Sanjiangyuan region located in the eastern Tibetan Plateau, which is the headwater of three major Asian rivers. By developing a new version of a Conjunctive Surface‐Subsurface Process model named as CSSPv2, we increased Nash‐Sutcliffe efficiency by 62%‐130% for streamflow simulations due to the introduction of a storage‐based runoff generation scheme, reduced errors by up to 31% for soil moisture modeling after considering the effect of soil organic matter on porosity and hydraulic conductivity. Compared with ERA‐Interim and GLDAS1 reanalysis products, CSSPv2 reduced errors by up to 30%, 69%, 92% and 40% for soil moisture, soil temperature, evapotranspiration (ET) and terrestrial water storage change (TWSC) respectively as evaluated against in‐situ and satellite observations. Moreover, CSSPv2 well captured the elevation‐dependent ground temperature warming trends and the decreased frozen dates during 1979‐2014, and significant increasing trends (p<0.05) in ET and TWSC during 1982‐2011 and 2003‐2014 respectively, while ERA‐Interim and GLDAS1 showed no trends or even negative trends. This study implies the necessity of developing high resolution land surface models in realistically representing hydrological changes over highland areas that are sentinels to climate change.
Xing Yuan; Peng Ji; Linying Wang; Xin‐Zhong Liang; Kun Yang; Aizhong Ye; Zhongbo Su; Jun Wen. High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 1. Model Development and Evaluation. Journal of Advances in Modeling Earth Systems 2018, 10, 2806 -2828.
AMA StyleXing Yuan, Peng Ji, Linying Wang, Xin‐Zhong Liang, Kun Yang, Aizhong Ye, Zhongbo Su, Jun Wen. High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 1. Model Development and Evaluation. Journal of Advances in Modeling Earth Systems. 2018; 10 (11):2806-2828.
Chicago/Turabian StyleXing Yuan; Peng Ji; Linying Wang; Xin‐Zhong Liang; Kun Yang; Aizhong Ye; Zhongbo Su; Jun Wen. 2018. "High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 1. Model Development and Evaluation." Journal of Advances in Modeling Earth Systems 10, no. 11: 2806-2828.
Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from the North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below −1 indicate drought events. The skill of the forecasting systems was evaluated in terms of anomaly correlation (AC) and Brier score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model” assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanisms. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological drought predictability during the wet seasons in the MHRB.
Feng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China. Hydrology and Earth System Sciences 2018, 22, 5697 -5709.
AMA StyleFeng Ma, Lifeng Luo, Aizhong Ye, Qingyun Duan. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China. Hydrology and Earth System Sciences. 2018; 22 (11):5697-5709.
Chicago/Turabian StyleFeng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2018. "Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China." Hydrology and Earth System Sciences 22, no. 11: 5697-5709.
Aizhong Ye. Response to reviewers2. 2018, 1 .
AMA StyleAizhong Ye. Response to reviewers2. . 2018; ():1.
Chicago/Turabian StyleAizhong Ye. 2018. "Response to reviewers2." , no. : 1.
Aizhong Ye. Response to reviewers1. 2018, 1 .
AMA StyleAizhong Ye. Response to reviewers1. . 2018; ():1.
Chicago/Turabian StyleAizhong Ye. 2018. "Response to reviewers1." , no. : 1.
Climate change and human activities have changed the spatial-temporal distribution of water resources, especially in a fragile ecological area such as the upper reaches of the Minjiang River (UMR) basin, where they have had a more profound effect. The average of double-mass curve (DMC) and Distributed Time-Variant Gain Hydrological Model (DTVGM) are applied to distinguish between the impacts of climate change and human activities on water resources in this paper. Results indicated that water resources decreased over nearly 50 years in the UMR. At the annual scale, contributions of human activities and climate change to changes in discharge were -77% and 23%, respectively. In general, human activities decreased the availability of water resources, whereas climate change increased the availability of water resources. However, the impacts of human activities and climate change on water resources availability were distinctly different on annual versus seasonal scales, and they showed more inconsistency in summer and autumn. The main causes of decreasing water resources are reservoir regulation, and water use increases due to population growth. The results of this study can provide support for water resource management and sustainable development in the UMR basin.
Jingwen Hou; Aizhong Ye; Jinjun You; Feng Ma; Qingyun Duan. An estimate of human and natural contributions to changes in water resources in the upper reaches of the Minjiang River. Science of The Total Environment 2018, 635, 901 -912.
AMA StyleJingwen Hou, Aizhong Ye, Jinjun You, Feng Ma, Qingyun Duan. An estimate of human and natural contributions to changes in water resources in the upper reaches of the Minjiang River. Science of The Total Environment. 2018; 635 ():901-912.
Chicago/Turabian StyleJingwen Hou; Aizhong Ye; Jinjun You; Feng Ma; Qingyun Duan. 2018. "An estimate of human and natural contributions to changes in water resources in the upper reaches of the Minjiang River." Science of The Total Environment 635, no. : 901-912.
Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below -1 indicate drought events. The skill of the forecasting systems was evaluated in terms of Anomaly Correlation (AC) and Brier skill score (BSS). The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanism. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of initial hydrologic conditions (ICs) during cold and dry seasons. During wet seasons, there's no skillful hydrological predictions since lead-2 month because the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions is the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological predictability during the wet seasons in the MHRB.
Feng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in Northwestern China. 2018, 2018, 1 -28.
AMA StyleFeng Ma, Lifeng Luo, Aizhong Ye, Qingyun Duan. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in Northwestern China. . 2018; 2018 ():1-28.
Chicago/Turabian StyleFeng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2018. "Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in Northwestern China." 2018, no. : 1-28.
Quantifying and reducing uncertainties in physics-based hydrological model parameters will improve model reliability for hydrological forecasting. We present an uncertainty quantification framework that combines the strengths of stepwise sensitivity analysis and adaptive surrogate-based multi-objective optimization to facilitate practical assessment and reduction of model parametric uncertainties. Framework performance was tested using the distributed hydrological model Coupled Routing and Excess Storage (CREST) for daily streamflow simulation over ten watersheds. By identifying sensitive parameters stepwisely, we reduced the number of parameters requiring calibration from twelve to seven, thus limiting the dimensionality of calibration problem. By updating surrogate models adaptively, we found the optimal sets of sensitive parameters with the surrogate-based multi-objective optimization. The calibrated CREST was able to satisfactorily simulate observed streamflow for all watersheds, improving one minus Nash-Sutcliffe efficiency (1−NSE) by 65–90% and percentage absolute relative bias (|RB|) by 60–95% compared to the default. The validation result demonstrated that the calibrated CREST was also able to reproduce observed streamflow outside the calibration period, improving 1−NSE by 40–85% and |RB| by 35–90% compared to the default. Overall, this uncertainty quantification framework is effective for assessment and reduction of model parametric uncertainties, the results of which improve model simulations and enhance understanding of model behaviors.
Yanjun Gan; Xin-Zhong Liang; Qingyun Duan; Aizhong Ye; Zhenhua Di; Yang Hong; Jianduo Li. A systematic assessment and reduction of parametric uncertainties for a distributed hydrological model. Journal of Hydrology 2018, 564, 697 -711.
AMA StyleYanjun Gan, Xin-Zhong Liang, Qingyun Duan, Aizhong Ye, Zhenhua Di, Yang Hong, Jianduo Li. A systematic assessment and reduction of parametric uncertainties for a distributed hydrological model. Journal of Hydrology. 2018; 564 ():697-711.
Chicago/Turabian StyleYanjun Gan; Xin-Zhong Liang; Qingyun Duan; Aizhong Ye; Zhenhua Di; Yang Hong; Jianduo Li. 2018. "A systematic assessment and reduction of parametric uncertainties for a distributed hydrological model." Journal of Hydrology 564, no. : 697-711.
The features of hydro-climate anomalies in China in 2015–2016 were analyzed in great detail, together with possible responses to the super 2015–16 El Niño event. The 2015–16 El Niño is characterized as a “strong” event in terms of the duration, intensity, and coverage of warming sea surface temperature (SST) in the central and east-central equatorial Pacific in comparison to the 1982–83 and 1997–98 events. The floods and droughts frequency were incidence of floods and droughts per year, respectively. The results show several significant anomalies in China: 1) About 9%–173% of precipitation variance in 2015–16 can be attributed to this El Niño; 2) There was significant inconformity between hydro-climate anomalies and the occurrence of floods and droughts; 3) Flood frequency has increased, especially over Southeast China and the Yangtze River in the summer of 2016; 4) Drought frequency has also increased, especially over Northeast China in summer of 2015, Northwest China in spring of 2016, and most parts in winter of 2015. The response of China hydro-climate anomalies to the 2015–16 El Niño was significant via El Niño and warm Indian Ocean induced circulation anomalies, which were characterized by stronger and more westward-extending western Pacific subtropical high and anomalous water vapor transport. Knowledge of the response of hydro-climate extremes to El Niño can provide valuable information to improve flood and drought forecasting in China.
Feng Ma; Aizhong Ye; Jinjun You; Qingyun Duan. 2015–16 floods and droughts in China, and its response to the strong El Niño. Science of The Total Environment 2018, 627, 1473 -1484.
AMA StyleFeng Ma, Aizhong Ye, Jinjun You, Qingyun Duan. 2015–16 floods and droughts in China, and its response to the strong El Niño. Science of The Total Environment. 2018; 627 ():1473-1484.
Chicago/Turabian StyleFeng Ma; Aizhong Ye; Jinjun You; Qingyun Duan. 2018. "2015–16 floods and droughts in China, and its response to the strong El Niño." Science of The Total Environment 627, no. : 1473-1484.