scholarly journals Seasonal cycles and trends of water budget components in 18 river basins across Tibetan Plateau: a multiple datasets perspective

2016 ◽  
Author(s):  
Wenbin Liu ◽  
Fubao Sun ◽  
Yanzhong Li ◽  
Guoqing Zhang ◽  
Yan-Fang Sang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Water Budget ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
East Asian Monsoon ◽  
Aridity Index ◽  
Western China ◽  
Surface Model ◽  
Seasonal Cycles

Abstract. The insights of water budgets over Tibetan Plateau (TP) are not fully understood so far due to the lack of quantitative observations of the land surface processes. Here, we investigated the seasonal cycles and trends of water budget components in 18 TP basins through the use of multi-source datasets during the period 1982–2011. A two-step bias correction procedure was applied to calculate the basin-wide evapotranspiration (ET) through the water balance considering the influences of glacier and water storage change. The results indicated that precipitation, which mainly concentrated during June–October (varied among different monsoons impacted basins), is the major contributor to the runoff in TP basins. The basin-wide snow water equivalent (SWE) was relatively higher from mid-autumn to spring for most TP basins. The water cycles intensified under a global warming in most basins except for the upper Yellow and Yalong Rivers, which were significantly influenced by the weakening East Asian monsoon. Corresponded to the climate warming and moistening in the TP and western China, the aridity index (PET/P) in most basins decreased. The general hydrological regimes could be inferred from the perspective of multi-source datasets although there are considerable uncertainties from different datasets, which are comparable to some existing studies using the field observations and complex modeling approaches. The results highlighted the usefulness of integrating the multi-source data (e.g., in situ observations, remote sensing products, reanalysis, land surface model simulations and climate model outputs) for hydrological applications in the data-sparse environments and could be benefit for understanding the water and energy budgets, sustainable management of water resources under a warming climate in the harsh and data-sparse Tibetan Plateau.

scholarly journals Investigating basin-scale water budget dynamics in 18 rivers across Tibetan Plateau through multiple datasets

2017 ◽  
Author(s):  
Wenbin Liu ◽  
Fubao Sun ◽  
Yanzhong Li ◽  
Guoqing Zhang ◽  
Yan-Fang Sang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Water Budget ◽  
Water Resource Management ◽  
Management Practices ◽  
Yellow River ◽  
Aridity Index ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Basin Scale

Abstract. The dynamics of basin-scale water budgets are not well understood nowadays over the Tibetan Plateau (TP) due to the lack of hydro-climatic observations. In this study, we investigate seasonal cycles and trends of water budget components (e.g., precipitation-P, evapotranspiration-ET and runoff-Q) in eighteen TP river basins during the period 1982–2011 through the use of multi-source datasets (e.g., in situ observations, satellite retrievals, reanalysis outputs and land surface model simulations). A water balance-based two-step procedure, which considers the changes in basin-scale water storage at the annual scale, is also adopted to calculate actual ET. The results indicated that precipitation (mainly snowfall from mid-autumn to next spring), which mainly concentrated during June–October (varied among different monsoons-impacted basins), was the major contributor to the runoff in TP basins. Increased P, ET and Q were found in most TP basins during the past 30 years except for the upper Yellow River basin and some sub-basins of Yalong River, which were mainly affected by the weakening East Asian Monsoon. Moreover, the aridity index (PET/P) and runoff coefficient (Q/P) decreased in most basins, which were in agreement with the warming and moistening climate in the Tibetan Plateau. The results obtained demonstrated the usefulness of integrating multi-source datasets to hydrological applications in the data-sparse regions. More generally, such approach might offer helpful insights towards understanding the water and energy budgets and sustainability of water resource management practices of data-sparse regions in a changing environment.

scholarly journals Investigating water budget dynamics in 18 river basins across the Tibetan Plateau through multiple datasets

2018 ◽  
Vol 22 (1) ◽  
pp. 351-371 ◽  
Author(s):  
Wenbin Liu ◽  
Fubao Sun ◽  
Yanzhong Li ◽  
Guoqing Zhang ◽  
Yan-Fang Sang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Water Budget ◽  
Water Resource Management ◽  
Yellow River ◽  
Aridity Index ◽  
River Basins ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Basin Scale

Abstract. The dynamics of basin-scale water budgets over the Tibetan Plateau (TP) are not well understood nowadays due to the lack of in situ hydro-climatic observations. In this study, we investigate the seasonal cycles and trends of water budget components (e.g. precipitation P, evapotranspiration ET and runoff Q) in 18 TP river basins during the period 1982–2011 through the use of multi-source datasets (e.g. in situ observations, satellite retrievals, reanalysis outputs and land surface model simulations). A water balance-based two-step procedure, which considers the changes in basin-scale water storage on the annual scale, is also adopted to calculate actual ET. The results indicated that precipitation (mainly snowfall from mid-autumn to next spring), which are mainly concentrated during June–October (varied among different monsoons-impacted basins), was the major contributor to the runoff in TP basins. The P, ET and Q were found to marginally increase in most TP basins during the past 30 years except for the upper Yellow River basin and some sub-basins of Yalong River, which were mainly affected by the weakening east Asian monsoon. Moreover, the aridity index (PET/P) and runoff coefficient (Q/P) decreased slightly in most basins, which were in agreement with the warming and moistening climate in the Tibetan Plateau. The results obtained demonstrated the usefulness of integrating multi-source datasets to hydrological applications in the data-sparse regions. More generally, such an approach might offer helpful insights into understanding the water and energy budgets and sustainability of water resource management practices of data-sparse regions in a changing environment.

scholarly journals Evaluation of Snow Water Equivalent in NARCCAP Simulations, Including Measures of Observational Uncertainty

2017 ◽  
Vol 18 (9) ◽  
pp. 2425-2452 ◽  
Author(s):  
Rachel R. McCrary ◽  
Seth McGinnis ◽  
Linda O. Mearns
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Dominant Role ◽  
Regional Climate ◽  
Land Surface Model ◽  
Surface Model ◽  
Observational Uncertainty ◽  
Snow Water ◽  
Noah Land Surface Model

Abstract This study evaluates snow water equivalent (SWE) over North America in the reanalysis-driven NARCCAP regional climate model (RCM) experiments. Examination of SWE in these runs allows for the identification of bias due to RCM configuration, separate from inherited GCM bias. SWE from the models is compared to SWE from a new ensemble observational product to evaluate the RCMs’ ability to capture the magnitude, spatial distribution, duration, and timing of the snow season. This new dataset includes data from 14 different sources in five different types. Consideration of the associated uncertainty in observed SWE strongly influences the appearance of bias in RCM-generated SWE. Of the six NARCCAP RCMs, the version of MM5 run by Iowa State University (MM5I) is found to best represent SWE despite its use of the Noah land surface model. CRCM overestimates SWE because of cold temperature biases and surface temperature parameterization options, while RegCM3 (RCM3) does so because of excessive precipitation. HadRM3 (HRM3) underestimates SWE because of warm temperature biases, while in the version of WRF using the Grell scheme (WRFG) and ECPC-RSM (ECP2), the misrepresentation of snow in the Noah land surface model plays the dominant role in SWE bias, particularly in ECP2 where sublimation is too high.

Data assimilation of GRACE terrestrial water storage to improve sea level rise estimates and isolate anthropogenic influences

2021 ◽  
Author(s):  
Ann Scheliga ◽  
Manuela Girotto
Keyword(s):  
Data Assimilation ◽  
Sea Level Rise ◽  
Sea Level ◽  
Land Surface ◽  
Water Budget ◽  
Water Storage ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Land Hydrology

<p>Sea level rise (SLR) projections rely on the accurate and precise closure of Earth’s water budget. The Gravity Recovery and Climate Experiment (GRACE) mission has provided global-coverage observations of terrestrial water storage (TWS) anomalies that improve accounting of ice and land hydrology changes and how these changes contribute to sea level rise. The contribution of land hydrology TWS changes to sea level rise is much smaller and less certain than contributions from glacial melt and thermal expansion. Although land hydrology TWS plays a smaller role, it is still important to investigate to improve the precision of the overall global water budget. This study analyzes how data assimilation techniques improve estimates of the land hydrology contribution to sea level rise. To achieve this, three global TWS datasets were analyzed: (1) GRACE TWS observations alone, (2) TWS estimates from the model-only simulation using Catchment Land Surface Model, and (3) TWS estimates from a data assimilation product of (1) and (2). We compared the data assimilation product with the GRACE observations alone and the model-only simulation to isolate the contribution to sea level rise from anthropogenic activities. We assumed a balanced water budget between land hydrology and the ocean, thus changes in global TWS are considered equal and opposite to sea level rise contribution.  Over the period of 2003-2016, we found sea level rise contributions from each dataset of +0.35 mm SLR eq/yr for GRACE, -0.34 mm SLR eq/yr for model-only, and a +0.09 mm SLR eq/yr for DA (reported as the mean linear trend). Our results indicate that the model-only simulation is not capturing important hydrologic processes. These are likely anthropogenic driven, indicating direct anthropogenic and climate-driven TWS changes play a substantial role in TWS contribution to SLR.</p>

scholarly journals Towards a more objective evaluation of modelled land-carbon trends using atmospheric CO<sub>2</sub> and satellite-based vegetation activity observations

2013 ◽  
Vol 10 (6) ◽  
pp. 4189-4210 ◽  
Author(s):  
D. Dalmonech ◽  
S. Zaehle
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Spatial Scales ◽  
Evaluation Framework ◽  
Atmospheric Co2 ◽  
Evaluation Methodology ◽  
Surface Model ◽  
System Modelling ◽  
Earth System ◽  
Vegetation Activity

Abstract. Terrestrial ecosystem models used for Earth system modelling show a significant divergence in future patterns of ecosystem processes, in particular the net land–atmosphere carbon exchanges, despite a seemingly common behaviour for the contemporary period. An in-depth evaluation of these models is hence of high importance to better understand the reasons for this disagreement. Here, we develop an extension for existing benchmarking systems by making use of the complementary information contained in the observational records of atmospheric CO2 and remotely sensed vegetation activity to provide a novel set of diagnostics of ecosystem responses to climate variability in the last 30 yr at different temporal and spatial scales. The selection of observational characteristics (traits) specifically considers the robustness of information given that the uncertainty of both data and evaluation methodology is largely unknown or difficult to quantify. Based on these considerations, we introduce a baseline benchmark – a minimum test that any model has to pass – to provide a more objective, quantitative evaluation framework. The benchmarking strategy can be used for any land surface model, either driven by observed meteorology or coupled to a climate model. We apply this framework to evaluate the offline version of the MPI Earth System Model's land surface scheme JSBACH. We demonstrate that the complementary use of atmospheric CO2 and satellite-based vegetation activity data allows pinpointing of specific model deficiencies that would not be possible by the sole use of atmospheric CO2 observations.

Evaluation of Temperature Simulation over Northern Thailand from Regional Climate Model Coupled with Land Surface Model

2017 ◽  
Vol 866 ◽  
pp. 108-111
Author(s):  
Theerapan Saesong ◽  
Pakpoom Ratjiranukool ◽  
Sujittra Ratjiranukool
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Global Analysis ◽  
Regional Climate ◽  
Land Surface Model ◽  
Analysis Data ◽  
Surface Model ◽  
Northern Thailand ◽  
Weather Model

Numerical Weather Model called The Weather Research and Forecasting model, WRF, developed by National Center for Atmospheric Research (NCAR) is adapted to be regional climate model. The model is run to perform the daily mean air surface temperatures over northern Thailand in 2010. Boundery dataset provided by National Centers for Environmental Prediction, NCEP FNL, (Final) Operational Global Analysis data which are on 10 x 10. The simulated temperatures by WRF with four land surface options, i.e., no land surface scheme (option 0), thermal diffusion (option 1), Noah land-surface (option 2) and RUC land-surface (option 3) were compared against observational data from Thai Meteorological Department (TMD). Preliminary analysis indicated WRF simulations with Noah scheme were able to reproduce the most reliable daily mean temperatures over northern Thailand.

The Common Land Model

2003 ◽  
Vol 84 (8) ◽  
pp. 1013-1024 ◽  
Author(s):  
Yongjiu Dai ◽  
Xubin Zeng ◽  
Robert E. Dickinson ◽  
Ian Baker ◽  
Gordon B. Bonan ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Transfer Scheme ◽  
Soil System ◽  
Common Land Model ◽  
The Common ◽  
Academy Of Sciences ◽  
Ice Water

The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other land models [the Biosphere-Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94)].

Quantifying Snow Mass Mission Concept Trade-Offs Using an Observing System Simulation Experiment

2019 ◽  
Vol 20 (1) ◽  
pp. 155-173 ◽  
Author(s):  
Camille Garnaud ◽  
Stéphane Bélair ◽  
Marco L. Carrera ◽  
Chris Derksen ◽  
Bernard Bilodeau ◽  
...  
Keyword(s):  
Land Surface ◽  
Simulation Experiment ◽  
Snow Water Equivalent ◽  
System Simulation ◽  
Surface Model ◽  
Trade Offs ◽  
Wet Snow ◽  
Observing System Simulation Experiment ◽  
The Impact ◽  
Snow Mass

Abstract Because of its location, Canada is particularly affected by snow processes and their impact on the atmosphere and hydrosphere. Yet, snow mass observations that are ongoing, global, frequent (1–5 days), and at high enough spatial resolution (kilometer scale) for assimilation within operational prediction systems are presently not available. Recently, Environment and Climate Change Canada (ECCC) partnered with the Canadian Space Agency (CSA) to initiate a radar-focused snow mission concept study to define spaceborne technological solutions to this observational gap. In this context, an Observing System Simulation Experiment (OSSE) was performed to determine the impact of sensor configuration, snow water equivalent (SWE) retrieval performance, and snow wet/dry state on snow analyses from the Canadian Land Data Assimilation System (CaLDAS). The synthetic experiment shows that snow analyses are strongly sensitive to revisit frequency since more frequent assimilation leads to a more constrained land surface model. The greatest reduction in spatial (temporal) bias is from a 1-day revisit frequency with a 91% (93%) improvement. Temporal standard deviation of the error (STDE) is mostly reduced by a greater retrieval accuracy with a 65% improvement, while a 1-day revisit reduces the temporal STDE by 66%. The inability to detect SWE under wet snow conditions is particularly impactful during the spring meltdown, with an increase in spatial RMSE of up to 50 mm. Wet snow does not affect the domain-wide annual maximum SWE nor the timing of end-of-season snowmelt timing in this case, indicating that radar measurements, although uncertain during melting events, are very useful in adding skill to snow analyses.

scholarly journals Evaluation of CLASS Snow Simulation over Eastern Canada

2017 ◽  
Vol 18 (5) ◽  
pp. 1205-1225 ◽  
Author(s):  
Diana Verseghy ◽  
Ross Brown ◽  
Libo Wang
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern Canada ◽  
Fractional Snow Cover ◽  
Forest Regions ◽  
Snow Water ◽  
Snow Model

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

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