scholarly journals Multisource Estimation of Long-Term Terrestrial Water Budget for Major Global River Basins

2012 ◽  
Vol 25 (9) ◽  
pp. 3191-3206 ◽  
Author(s):  
Ming Pan ◽  
Alok K. Sahoo ◽  
Tara J. Troy ◽  
Raghuveer K. Vinukollu ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Water Balance ◽  
Error Analysis ◽  
Land Surface ◽  
Water Budget ◽  
Large Scale ◽  
Land Surface Model ◽  
Data Sources ◽  
Surface Model ◽  
Terrestrial Water

A systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984–2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems.

scholarly journals On the Use of a Water Balance to Evaluate Interannual Terrestrial ET Variability

2015 ◽  
Vol 16 (3) ◽  
pp. 1102-1108 ◽  
Author(s):  
Eunjin Han ◽  
Wade T. Crow ◽  
Christopher R. Hain ◽  
Martha C. Anderson
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Land Surface Model ◽  
Past Research ◽  
Surface Model ◽  
Strongly Correlated ◽  
Thermal Infrared Remote Sensing ◽  
Cast Doubt ◽  
Terrestrial Water ◽  
Balance Analysis

Abstract Accurately measuring interannual variability in terrestrial evapotranspiration ET is a major challenge for efforts to detect trends in the terrestrial hydrologic cycle. Based on comparisons with annual values of terrestrial evapotranspiration derived from a terrestrial water balance analysis, past research has cast doubt on the ability of existing products to accurately capture variability. Using a variety of estimates, this analysis reexamines this conclusion and finds that estimates of variations obtained from a land surface model are more strongly correlated with independently acquired from thermal infrared remote sensing than derived from water balance considerations. This tendency is attributed to significant interannual variations in terrestrial water storage neglected by the water balance approach. Overall, results demonstrate the need to reassess perceptions concerning the skill of estimates derived from land surface models and show the value of accurate remotely sensed ET products for the validation of interannual ET.

Variability of Basin-Scale Terrestrial Water Storage from a PER Water Budget Method: The Amazon and the Mississippi

2008 ◽  
Vol 21 (2) ◽  
pp. 248-265 ◽  
Author(s):  
Ning Zeng ◽  
Jin-Ho Yoon ◽  
Annarita Mariotti ◽  
Sean Swenson
Keyword(s):  
Time Scales ◽  
Land Surface ◽  
Seasonal Cycle ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Basin Scale ◽  
Terrestrial Water

Abstract In an approach termed the PER method, where the key input variables are observed precipitation P and runoff R and estimated evaporation, the authors apply the basin water budget equation to diagnose the long-term variability of the total terrestrial water storage (TWS). Unlike the typical offline land surface model estimate where only atmospheric variables are used as input, the direct use of observed runoff in the PER method imposes an important constraint on the diagnosed TWS. Although there is a lack of basin-scale observations of evaporation, the tendency of E to have significantly less variability than the difference between precipitation and runoff (P − R) minimizes the uncertainties originating from estimated evaporation. Compared to the more traditional method using atmospheric moisture convergence (MC) minus R (MCR method), the use of observed precipitation in the PER method is expected to lead to general improvement, especially in regions where atmospheric radiosonde data are too sparse to constrain the atmospheric model analyzed MC, such as in the remote tropics. TWS was diagnosed using the PER method for the Amazon (1970–2006) and the Mississippi basin (1928–2006) and compared with the MCR method, land surface model and reanalyses, and NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite gravity data. The seasonal cycle of diagnosed TWS over the Amazon is about 300 mm. The interannual TWS variability in these two basins is 100–200 mm, but multidecadal changes can be as large as 600–800 mm. Major droughts, such as the Dust Bowl period, had large impacts, with water storage depleted by 500 mm over a decade. Within the short period 2003–06 when GRACE data were available, PER and GRACE show good agreement both for seasonal cycle and interannual variability, providing potential to cross validate each other. In contrast, land surface model results are significantly smaller than PER and GRACE, especially toward longer time scales. While the authors currently lack independent means to verify these long-term changes, simple error analysis using three precipitation datasets and three evaporation estimates suggest that the multidecadal amplitude can be uncertain up to a factor of 2, while the agreement is high on interannual time scales. The large TWS variability implies the remarkable capacity of land surface in storing and taking up water that may be underrepresented in models. The results also suggest the existence of water storage memories on multiyear time scales, significantly longer than typically assumed seasonal time scales associated with surface soil moisture.

Climate change impact on terrestrial water balance components at continental and global scales

2020 ◽  
Author(s):  
Olga Nasonova ◽  
Yeugeniy Gusev ◽  
Evgeny Kovalev
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Land Surface ◽  
Large Scale ◽  
Regional Scale ◽  
Global Climate Models ◽  
Surface Model ◽  
Water Balance Components ◽  
Rcp Scenarios ◽  
Terrestrial Water

<p>This work is a continuation of our previous investigations performed within the framework of the International Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) on a regional scale when hydrological projections and their uncertainties were obtained for 11 large-scale river basins using the physically based land surface model Soil Water – Atmosphere – Plants (SWAP) driven by meteorological projections from five Global Climate Models (GCMs). In the present work, we decided to spread our investigations to continental and global scales. The main goals are as follows: (i) projecting changes in terrestrial water balance components in the 21<sup>st</sup> century due to possible climate change for different continents and for the whole globe, (ii) evaluation of uncertainties in the obtained projections sourced from application of different GCMs and different climatic scenarios, (iii) studying the patterns of spatial distribution of changes in the water balance components and their uncertainties.</p><p>Simulations of the water balance components (evapotranspiration and runoff) for the entire land surface of the globe (with the exception of Antarctica) were performed by the SWAP model with a spatial resolution of 0.5<sup>o</sup>×0.5<sup>o</sup> for the period of 1961-2099. The model was driven by daily meteorological outputs from five GCMs (including GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) obtained for each of four Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). As a result, 20 variants of daily values of evapotranspiration, runoff, and precipitation were obtained for each calculational grid cell. Then, the climatic annual values of the water balance components for four periods (historical and three prognostic ones: 2006-2036, 2037-2067, 2068-2099) were obtained and their changes for different prognostic periods compared to historical values were calculated. Besides, uncertainties in the projected changes of the water balance components resulted from application of different GCMs and RCP scenarios were estimated. The obtained results were mapped and averaged over the continents, latitudinal zones, and the globe that allowed us to identify spatio-temporal patterns of changes in the water balance components and their uncertainties due to possible climate changes.</p>

scholarly journals Diagnosing hydrological limitations of a land surface model: application of JULES to a deep-groundwater chalk basin

2016 ◽  
Vol 20 (1) ◽  
pp. 143-159 ◽  
Author(s):  
N. Le Vine ◽  
A. Butler ◽  
N. McIntyre ◽  
C. Jackson
Keyword(s):  
Land Surface ◽  
Expert Knowledge ◽  
Water Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Deep Groundwater ◽  
Resolution Model ◽  
Terrestrial Water ◽  
Water Redistribution ◽  
Types Of Information

Abstract. Land surface models (LSMs) are prospective starting points to develop a global hyper-resolution model of the terrestrial water, energy, and biogeochemical cycles. However, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. A diagnostic approach to model evaluation and improvement is taken here that exploits hydrological expert knowledge to detect LSM inadequacies through consideration of the major behavioural functions of a hydrological system: overall water balance, vertical water redistribution in the unsaturated zone, temporal water redistribution, and spatial water redistribution over the catchment's groundwater and surface-water systems. Three types of information are utilized to improve the model's hydrology: (a) observations, (b) information about expected response from regionalized data, and (c) information from an independent physics-based model. The study considers the JULES (Joint UK Land Environmental Simulator) LSM applied to a deep-groundwater chalk catchment in the UK. The diagnosed hydrological limitations and the proposed ways to address them are indicative of the challenges faced while transitioning to a global high resolution model of the water cycle.

scholarly journals Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

2011 ◽  
Vol 8 (2) ◽  
pp. 2555-2608 ◽  
Author(s):  
E. H. Sutanudjaja ◽  
L. P. H. van Beek ◽  
S. M. de Jong ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Large Scale ◽  
Groundwater Modeling ◽  
Land Surface Model ◽  
Water Levels ◽  
Surface Model ◽  
Groundwater Model ◽  
Model Output ◽  
Global Datasets

Abstract. Large-scale groundwater models involving aquifers and basins of multiple countries are still rare due to a lack of hydrogeological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Although the method that we used to couple the land surface and MODFLOW groundwater model is considered as an offline-coupling procedure (i.e. the simulations of both models were performed separately), results are promising. The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydrogeological parameter settings, we observe that the model can reproduce the observed groundwater head time series reasonably well. However, we note that there are still some limitations in the current approach, specifically because the current offline-coupling technique simplifies dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

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>

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