Improved understanding of snowmelt runoff from the headwaters of China's Yangtze River using remotely sensed snow products and hydrological modeling

Han, Pengfei; Long, Di; Han, Zhuo Rachel; Du, Mingliang; Dai, Liyun; Hao, Xiaohua

doi:10.1016/j.rse.2019.01.041

Cited by 113 publications

(56 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Li et al, 2016), soil moisture (Girotto et al, 2019;Tian et al, 2019), evapotranspiration (X. Li, Long, et al, 2019;Long, Longuevergne, & Scanlon et al, 2014), and snow and glaciers (Castellazzi et al, 2019;Chen et al, 2017;Enzminger et al, 2018), by assimilating GRACE observations (Brookfield et al, 2018;Han et al, 2019;Houborg et al, 2012;Kumar et al, 2016;. However, they cannot provide complete information brought by the GRACE satellites; some aquifers and/or human water use are often overlooked, such as deep groundwater storage (GWS) and cropland irrigation (Sun et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Sun

Long

Yang

et al. 2020

Water Resources Research

Self Cite

149

View full text Add to dashboard Cite

Launched in May 2018, the Gravity Recovery and Climate Experiment Follow-On mission (GRACE-FO)-the successor of the erstwhile GRACE mission-monitors changes in total water storage, which is a critical state variable of the regional and global hydrologic cycles. However, the gap between data of the two missions is breaking the continuity of the observations and limiting its further application. In this study, we used three learning-based models, that is, deep neural network, multiple linear regression (MLR), and seasonal autoregressive integrated moving average with exogenous variables, and six GRACE solutions (i.e., Jet Propulsion Laboratory spherical harmonics (JPL-SH), Center for Space Research SH (CSR-SH), GeoforschungsZentrum Potsdam SH (GFZ-SH), JPL mass concentration blocks (mascons) (JPL-M), CSR mascons (CSR-M), and Goddard Space Flight Center mascons (GSFC-M)) to reconstruct the missing monthly data at a grid cell scale. Evaluation showed that the three learning-based models were reliable for the reconstruction of GRACE data in areas with humid and no/low human interventions. The deep neural network models slightly outperformed the seasonal autoregressive integrated moving average with exogenous variables models and significantly outperformed the multiple linear regression models in most of 60 basins studied. The three GRACE mascon data sets performed better than the SH data sets at the basin scale. The models with SH solutions showed similar performance, but the models with the mascon solutions varied markedly in some basins. Results of this study are expected to provide a reference for bridging the data gaps between the GRACE and GRACE-FO satellites and for selecting suitable GRACE solutions for regional hydrologic studies.

show abstract

Section: Introductionmentioning

confidence: 99%

Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Sun

Long

Yang

et al. 2020

Water Resources Research

Self Cite

149

View full text Add to dashboard Cite

show abstract

“…Correlation coefficient (R), root mean square error (RMSE), NSE, and RB are commonly used in snow related evaluations (Chen et al, 2017;Han et al, 2019;Henn et al, 2018;Koch et al, 2019), and therefore, they are used in the snow simulation evaluations in this study:…”

Section: Criteria For Assessmentmentioning

confidence: 99%

Snow as an Important Natural Reservoir for Runoff and Soil Moisture in Northeast China

Feng

Liu

et al. 2020

JGR Atmospheres

View full text Add to dashboard Cite

As one of the major agricultural regions in the world, water scarcity problems in Northeast China have drawn much attention recently. Because of cold and long winter period, snow is an important component in the hydrological system. Yet few studies have been conducted to systematically assess its role. This study quantified the effects of snow on runoff and soil moisture in the entire region in a 30-year time period (1982-2011) for the first time. A water and energy budget-based distributed biosphere hydrological model with improved snow physics after calibration and validation is employed. A Standardized Snow depth Index (SSdI) is also proposed to quantify snow variations. Result shows that snow contributes 11% to runoff annually on average and 66% and 33% in April and May (main months for crop planting). Soil moisture could decrease by at least 20% in March-May if there would be no snow, and the major agriculture area suffers more than other regions. We also found that SSdI is indicative of standardized soil moisture index and standardized runoff index in spring, particularly useful for supporting water management in agriculture. These results indicate that snow performs like an important reservoir: redistribute water resources among months. This study provides unique insights into the importance of snow in the entire region. The results improve the awareness of the importance of snow to water resources management and indicate that it is worth paying attention to snow in water resources management in this region. Plain Language Summary Because of cold and long winter period in Northeast China, snow plays a role as natural reservoir to store water in winter and to release water to ameliorate water shortage in spring. In addition, snow can influence agriculture through affecting soil moisture. However, studies on the role of snow in the region are limited so far. Here, we evaluate the importance of snow as a reservoir in entire Northeast China for the first time. We found on average snow contributes 11% to the annual runoff and 66% and 33% in April and May (the main crop planting months). Soil moisture in the region can decrease by at least 20% in March-May if there would be no snow, and the major agriculture area suffers more than other regions. Therefore, snow is an important source of water resources in this region and worth paying attention to in water resources management.

show abstract

“…However, they do not require a lot of physical property data and are easy to use, and have been widely used in forecasting streamflow [38,39]. The runoff in both Lancang River and Jinsha River is mainly from rainfall and groundwater, with little glacier and snowmelt runoff for the middle and lower reaches of these rivers [25,40,41]. Therefore, this paper takes the monthly precipitation of each meteorological station in the upstream of each hydropower station and the previous monthly inflow as inputs, without considering temperature change effects on seasonal streamflow.…”

Section: Model Training and Validationmentioning

confidence: 99%

Climate Change Impacts on Hydropower in Yunnan, China

Liu

Lund

Liu

et al. 2020

Water

View full text Add to dashboard Cite

Climate change could have dire effects on hydropower systems, especially in southwest China, where hydropower dominates the regional power system. This study examines two large cascade hydropower systems in Yunnan province in southwest China for 10 climate change projections made with 5 global climate models (GCMs) and 2 representative concentration pathways (RCPs) under Coupled Model Intercomparison Project Phase 5 (CMIP5). First, a back propagation neural network rain-runoff model is built for each hydropower station to estimate inflows with climate change. Then, a progressive optimality algorithm maximizes hydropower generation for each projection. The results show generation increasing in each GCM projection, but increasing more in GCMs under scenario RCP8.5. However, yearly generation fluctuates more: generation decreases dramatically with potential for electricity shortages in dry years and more electricity as well as spill during wet years. Average annual spill, average annual inflow and average storage have similar trends. The analysis indicates that a planned large dam on the upper Jinsha River would increase seasonal regulation ability, increase hydropower generation, and decrease spill. Increased turbine capacity increases generation slightly and decreases spill for the Lancang River. Results from this study demonstrate effects of climate change on hydropower systems and identify which watersheds might be more vulnerable, along with some actions that could help adapt to climate change.

show abstract

Improved understanding of snowmelt runoff from the headwaters of China's Yangtze River using remotely sensed snow products and hydrological modeling

Cited by 113 publications

References 87 publications

Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Snow as an Important Natural Reservoir for Runoff and Soil Moisture in Northeast China

Climate Change Impacts on Hydropower in Yunnan, China

Contact Info

Product

Resources

About