Land Data Assimilation System Version Research Articles

Estimation of water balance components by Noah-MP land surface model for the Neyshaboor watershed, Khorasan Razavi, Iran

The numerical modeling of the land surface can make up for the insufficient station data in terms of number, dispersion, and temporal continuity. In this research, to evaluate the Noah-MP land surface model, the water balance components were estimated in the Neyshaboor watershed in the monthly time step during 2000–2009. Model input data were obtained from the global land data assimilation system version 1 (GLDAS-1), and the SWAT (soil and water assessment tool, a semi-distributed for small watershed to river basin-scale model) model output was used for the evaluation of the Noah-MP model. In this study, the ability of the Noah-MP model in simulating vegetation dynamically was studied. The precipitation was corrected before running the model for a more reliable evaluation. The time between 2000 and 2001 was considered a spin-up period and 2002–2009 for calibration and validation. The model has the best simulation in the mountainous areas; the runoff simulated by the Noah-MP model is in good agreement with the modeled runoff by SWAT in these areas. (R2 = 0.78, NSE = 0.62, RMSE = 1.98 m3/s). The R2 for simulated soil moisture for soil layers (0–10, 10–40 cm) was 0.62 and 0.57, and RMSE was 0.059 (m3/m3) and 0.052 (m3/m3), respectively, in Motamedieh field. The annual amount of evapotranspiration estimated by the two models is comparable to the average annual evapotranspiration in the watershed (about 300 mm). Based on the results from the research, the model has well simulated: the runoff in the mountainous areas, the moisture in the upper layer of the soil, and the average annual evapotranspiration in the study area.

Evaluation of ERA5 Reanalysis Precipitation Data in the Yarlung Zangbo River Basin of the Tibetan Plateau

Abstract Atmospheric simulation-based gridded precipitation datasets have been widely used in hydrological and land surface modeling, but may contain larger uncertainties in mountainous regions. This study compared the performance of the fifth European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) precipitation data with two fused precipitation datasets [China Meteorological Administration Land Data Assimilation System version 2.0 (CLDAS2.0) and China Meteorological Forcing Dataset (CMFD)] in the Yarlung Zangbo River basin (YZRB), which has a complex terrain and climate. Compared to in situ observations, ERA5 could capture the spatial–temporal pattern of precipitation but showed high precipitation, especially in the downstream region (lower Nuxia discharge station). In terms of the correlation coefficient, the overall performance of the ERA5 data was slightly worse than that for CMFD data at both the monthly and yearly scales, and was comparable to that of the CLDAS2.0 data. Given that the spatial mismatch between the gridded and in situ data may influence the evaluation, we also employed the water balance method to constrain basinwide precipitation amounts. We found that CLDAS2.0 and CMFD precipitation data tended to cause long-term water imbalance, and ERA5, with a much larger multiyear average annual precipitation, could better close the water budget. Further analysis showed that the differences in multiyear average annual precipitation between ERA5 and in situ observations were closely related to the slope and standard deviation of the subgrid-scale orography, indicating the substantial influence of subgrid topography on precipitation simulation. These findings highlight that ERA5 could be a potential reference dataset for hydrological modeling of the YZRB.

Terrestrial water storage anomaly estimating using machine learning techniques and satellite‐based data (a case study of Lake Urmia Basin)

AbstractIn this study, the Terrestrial Water Storage Anomaly (TWSA) in the Lake Urmia Basin (LUB) was obtained by using the GRACE satellites. The whole study area was covered by 10 GRACE/GFO pixels, and the TWSA value was calculated for these 10 pixels. Examining the changing trend showed that the value of TWSA in the LUB has a decreasing trend and fluctuates from approximately −200 to +200 compared to the average value. The TWSA was modelled using the group method of data handling (GMDH) by considering six different parameters obtained from the European Centre for Medium‐Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). Finally, the best models with two to six input variables were selected. Among the models with different inputs, the GMDH Model 2, with three inputs, had the best performance compared to the other models. After choosing the best inputs in calculating the TWSA value by the GMDH, the TWSA value was also modelled by using the Adaptive Neuro‐Fuzzy Inference System (ANFIS), the Extreme Learning Machine (ELM) and the Artificial Neural Network (ANN). The results showed that GMDH not only outperformed other models but also provided some simple equations to apply in practical tasks.

Assessing the Performance of the South American Land Data Assimilation System Version 2 (SALDAS-2) Energy Balance across Diverse Biomes

Understanding the exchange of energy between the surface and the atmosphere is important in view of the climate scenario. However, it becomes a challenging task due to a sparse network of observations. This study aims to improve the energy balance estimates for the Amazon, Cerrado, and Pampa biomes located in South America using the radiation and precipitation forcing obtained from the Clouds and the Earth’s Radiant Energy System (CERES) and the precipitation CPTEC/MERGE datasets. We employed three surface models—Noah-MP, Community Land Model (CLSM), and Integrated Biosphere Simulator (IBIS)—and conducted modeling experiments, termed South America Land Data Assimilation System (SALDAS-2). The results showed that SALDAS-2 radiation estimates had the smallest errors. Moreover, SALDAS-2 precipitation estimates were better than the Global Land Data Assimilation System (GLDAS) in the Cerrado (MBE = −0.16) and Pampa (MBE = −0.19). Noah-MP presented improvements compared with CLSM and IBIS in 100% of towers located in the Amazon. CLSM tends to overestimate the latent heat flux and underestimate the sensible heat flux in the Amazon. Noah-MP and Ensemble outperformed GLDAS in terms latent and sensible heat fluxes. The potential of SALDAS-2 should be emphasized to provide more accurate estimates of surface energy balance.

Amplified future risk of compound droughts and hot events from a hydrological perspective

Global warming can result in changes in droughts and hot events (or compound droughts and hot events, CDHEs), which can take a heavy toll on the society and environment. Recent studies have made substantial progress in the projection of these events. However, previous projection studies mostly focus on the concurrences of meteorological droughts and hot events but ignore the difference among various CDHEs. Specifically, the concurrence of hot events and different types of droughts (e.g., agricultural droughts and hydrological droughts) has been seldom explored from a hydrological perspective. Based on phase six of the Coupled Model Intercomparison Project (CMIP6), we evaluate changes in different types of CDHEs, including compound meteorological drought-hot events (CMDHEs), compound agricultural drought-hot events (CADHEs) and compound hydrological drought-hot events (CHDHEs), for different future periods at the global scale. Based on comparisons with data from Global Land Data Assimilation System Version 2 (GLDAS-2.0), CMIP6 can reproduce the overall spatial distribution and temporal variation of different CDHEs at the global scale. In addition, the frequency and spatial extent of the three compound events show a marked increase during different future periods relative to the base period 1995–2014. The projected increase in global average frequency of CMDHEs in the long term period is lower than that of CADHEs (increase by 73.74% and 113.95% for CMDHEs and CADHEs, respectively). The uncertainty in the simulation of CADHEs and CHDHEs is relatively larger than CMDHEs in the future periods over most regions. The results of this study highlight the urgent demand for adaptation measures of CDHEs to cope with compound extremes in the future.

A high-resolution soil moisture content analysis product in Jing-Jin-Ji using the successive correction method

Two soil moisture content analysis products, each with a spatial resolution of 0.0625° and a temporal resolution of 1 h from 1 October 2015 to 1 October 2021 in the Jing-Jin-Ji area, have been developed and evaluated. Using a quality control (QC) scheme based on the biweight method and a successive correction method (SCM) with different weighting functions introduced from the Cressman and/or Barnes methods, the soil moisture collected from China Land Data Assimilation System version 2.0 was taken as the background data. Two soil analysis products with induced hourly station soil observations were constructed and further compared with in situ observations and satellite products. Results show that with the biweight method, the outliers of soil moisture could be identified, and that the number of these was less than 10% of the actual reported samples. The analysis product is more accurate with the Cressman weighting function than with the Barnes as the root mean square error was reduced by 30% to 0.06, while the correlation coefficient increased by 73% to 0.59, compared to the control. These results indicate that the Cressman product is a useful and promising soil moisture data set. The proposed Cressman method is computationally simple and efficient, so the product can be easily updated to keep pace with significant increases in the volume of soil moisture data. The methods used here for QC, SCM, and comparison could help evaluate and improve land surface models and their soil moisture products.

A dataset of 10-year regional-scale soil moisture and soil temperature measurements at multiple depths on the Tibetan Plateau

Abstract. Soil moisture and soil temperature (SMST) are important state variables for quantifying the exchange of heat and water between land and atmosphere. Yet, long-term, regional-scale in situ SMST measurements are scarce on the Tibetan Plateau (TP), with even fewer are available for multiple soil depths. Tibet-Obs is such a long-term, regional-scale SMST observatory in the TP that has been established 10 years ago and includes three SMST monitoring networks, i.e. Maqu, Naqu, and Ngari (including Ali and Shiquanhe), located in the cold humid area covered by grassland, the cold semiarid area dominated by tundra, and the cold arid area dominated by desert, respectively. This paper presents a long-term (∼ 10 years) SMST profile dataset collected from the Tibet-Obs, which includes the original in situ measurements at a 15 min interval collected between 2008 and 2019 from all the three networks and the spatially upscaled data (SMups and STups) for the Maqu and Shiquanhe networks. The quality of the upscaled data is proved to be good, with errors that are generally better than the measured accuracy of adopted SMST sensors. Long-term analysis of the upscaled SMST profile data shows that the amplitudes of SMST variations decrease with increasing soil depth, and the deeper soil layers present a later onset of freezing and an earlier start of thawing and, thus, a shorter freeze–thaw duration in both the Maqu and Shiquanhe networks. In addition, there are notable differences between the relationships of SMups and STups under freezing conditions for the Maqu and Shiquanhe networks. No significant trend can be found for the SMups profile in the warm season (from May to October) for both networks that is consistent with the tendency of precipitation. A similar finding is also found for the STups profile and air temperature in the Shiquanhe network during the warm season. For the cold season (from November to April), a drying trend is noted for the SMups above 20 cm in the Maqu network, while no significant trend is found for those in the Shiquanhe network. Comparisons between the long-term upscaled data and five reanalysis datasets, including the ECMWF reanalysis v5 (ERA5), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), Global Land Data Assimilation System version 2 Catchment Land Surface Model (GLDAS-2.1 CLSM), GLDAS-2.1 Noah, and GLDAS-2.1 variable infiltration capacity (GLDAS-2.1 VIC), indicate that none of the current model-based products can reproduce the seasonal variations and interannual trend changes in the measured SMST profile dynamics in both networks. All the products underestimate the STups at every depth, leading to an earlier onset of freezing and a later onset of thawing, which essentially demonstrates that the current models are not able to adequately simulate winter conditions on the TP. In short, the presented dataset would be valuable for evaluation and improvement in long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/20141567.v1 (Zhang et al., 2022).

A harmonized global land evaporation dataset from model-based products covering 1980–2017

Abstract. Land evaporation (ET) plays a crucial role in the hydrological and energy cycle. However, the widely used model-based products, even though helpful, are still subject to great uncertainties due to imperfect model parameterizations and forcing data. The lack of available observed data has further complicated estimation. Hence, there is an urgency to define the global proxy land ET with lower uncertainties for climate-induced hydrology and energy change. This study has combined three existing model-based products – the fifth-generation ECMWF reanalysis (ERA5), Global Land Data Assimilation System Version 2 (GLDAS2), and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) – to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25∘. Here, we use the reliability ensemble averaging (REA) method, which minimizes errors using reference data, to combine the three products over regions with high consistencies between the products using the coefficient of variation (CV). The Global Land Evaporation Amsterdam Model Version 3.2a (GLEAM3.2a) and flux tower observation data were selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well over a range of vegetation cover scenarios. The merged product also captured the trend of land evaporation over different areas well, showing the significant decreasing trend in the Amazon Plain in South America and Congo Basin in central Africa and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to demonstrating a good performance, the REA method also successfully converged the models based on the reliability of the inputs. The resulting REA data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).

Evaluation of the Performance of Multi-Source Precipitation Data in Southwest China

The number of precipitation products at the global scale has increased rapidly, and the accuracy of these products directly affects the accuracy of hydro-meteorological simulation and forecast. Therefore, the applicability of these precipitation products should be comprehensively evaluated to improve their application in hydrometeorology. This paper evaluated the performances of six widely used precipitation products in southwest China by quantitative assessment and contingency assessment. The precipitation products were Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 version 7 (TRMM 3B42 V7), Global Satellite Mapping of Precipitation (GSMaP MVK), Integrated Multi-satellitE Retrievals for GPM final run (GPM IMERG Final), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network—Climate Data Record (PERSIANN-CDR), Climate Hazards Infrared Precipitation with Stations version 2.0 (CHIRPS V2.0), and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). From the above six products, the daily-scale precipitation data from 2001 to 2019 were chosen to compare with the measured data of the rain gauge, and the data from the gauges were classified by river basin and elevation. All precipitation products and measured data were evaluated by statistical indicators. Results showed that (1) GPM IMERG Final and CHIRPS V2.0 performed well in the Yarlung Zangbo River (YZ) basin, while GPM IMERG Final and GLDAS V2.0 performed well in the Lantsang River (LS), Nujiang River (NJ), Yangtze River (YT), and Yellow River (YL) basins; (2) in the upper and middle reaches of the YZ basin, GPM IMERG Final and CHIRPS V2.0 were outstanding in all evaluated products; downstream of the YZ basin, all six products performed well; and upstream of the LS and NJ, GPM IMERG Final, TRMM 3B42 V7, CHIRPS V2.0, and GLDAS V2.0 can be recommended as a substitute for measured data; and (3) GPM IMERG Final and GLDAS V2.0 can be seen as substitutes for measured data when elevation is below 4000 m. GPM IMERG Final and CHIRPS V2.0 were recommended when elevation is above 4000 m. This study provides a reference for data selection of hydro-meteorological simulation and forecast in southwest China and also provides a basis for multi-source data assimilation and fusion.

Attributing snow cover extent changes over the Northern Hemisphere for the past 65 years

Snow is a crucial component of the cryosphere and its has been experiencing a significant decline for the Northern Hemisphere (NH) (35–90°N) snow cover extent (SCE) in March-April over the 1948–2012 period. However, the causes of this historical snow cover decline are still uncertain. Here, based on the Global Land Data Assimilation System version 2.0 (GLDAS) reanalysis reanalyzed snow cover extent (SCE) and climate model simulations that separate different anthropogenic forcings, we attribute changes of spring SCE over the NH to anthropogenic and natural climate forcings using an optimal fingerprint technique. We find that fingerprints from greenhouse-gases (GHG) and stratospheric aerosols can be clearly detected in the historical SCE records, whereas natural forcing has not contributed to the long-term SCE trend in a discernible way. The GHG-induced warming is primarily responsible for the NH SCE decline, which, however, has been partly offset (by ∼16%) by aerosol-induced climatic cooling. Our findings confirm the negative effect of warming on SCE changes yet highlight the positive role of aerosols in slowing SCE decline over the past 65 years.

Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis

Knowledge about spatiotemporal error characteristics of remotely sensed soil moisture (SM) products is essential for correctly interpreting observational information and optimally assimilating them into hydrological models. This work aims to (i) investigate the relative difference between time-invariant and time-variant daily SM errors of Advanced Scatterometer (ASCAT) and Soil Moisture Active Passive (SMAP) products and (ii) analyze correlation of the daily SM errors with surface biomass quantified by the Leaf Area Index (LAI) and rainfall. The time-invariant error denotes an aggregate error magnitude during the whole investigation period and the time-variant error at daily time scale also refers to an aggregate error magnitude but for the period of the 100-day time window centered at the day to be processed. The time-invariant and daily SM errors are estimated using the Triple Collocation Analysis (TCA) applied to ASCAT and SMAP SM retrievals along with the Global Land Data Assimilation System version 2.1 (GLDAS2) SM product for the period of April 2015 – January 2020. Results indicate the relative difference between time-invariant and time-variant daily errors is notable for both ASCAT and SMAP SM products. The relative difference in units percentage denotes the ratio of difference value between time-invariant and daily errors to time-invariant error itself. The daily TCA error fluctuated at 43% value from time-invariant error for ASCAT SM and at 47% value for SMAP SM on a global average. When averaged globally, temporal mean of time-variant daily errors was relatively smaller than time-invariant error by -27% for ASCAT SM and -18% for SMAP SM, respectively. In tropical areas, the relative difference between time-invariant and daily errors is large during the dry season and becomes small when rainy season comes. When the peak lagged correlation is used, daily errors exhibit a stronger correlation with rainfall than they do with LAI in 61% of landmass pixels for ASCAT SM and 66% of landmass pixels for SMAP SM. LAI cannot be used to predict temporal variability of time-variant SM errors in barren land. Lagged correlation analysis reveals rainfall peak coincides with SM error peak in areas featured with low vegetation cover, including barren land, grasslands, and open shrublands. By contrast, the LAI peak comes after the SM error peak in all cases. Savannas and woody savannas are a special case as SM error peak comes first, followed by rainfall then LAI peaks. In summary, ASCAT and SMAP time-variant error varies with a large deviation from time-invariant errors and its temporal variation shows a stronger association with rainfall than changing LAI.

Long-term changes in evapotranspiration over China and attribution to climatic drivers during 1980–2010

Evapotranspiration (ET) is one of the most important variables in terrestrial ecosystems, linking the carbon-water-energy cycles. In this study, we first analyze the spatial patterns of annual ET changes during 1980–2010 across China using four ET products: (i) the Global Land Evaporation Amsterdam Model version 3.0a (GLEAMv3.0), (ii) the EartH2Observe ensemble (EartH2Observe-En), (iii) the Global Land Data Assimilation System version 2.0 with Noah model (GLDAS2.0-Noah), and (iv) the Modern Era Retrospective-Analysis for Research and Application-Land (MERRA-Land). The results show that the spatial distribution of annual mean ET values and long-term changes derived from these four ET products are similar. Overall, large-scale increases in ET are observed in southeastern China, while decreases in ET over the northeast. Furthermore, we apply a newly developed separation method with the Budyko framework to quantify the individual contribution of five climatic factors to ET changes, including precipitation (P), net radiation (Rn), air temperature (T), vapour pressure deficit (VPD), and wind speed (u). It is found that the dynamics of P, Rn, and VPD are all strongly correlated with ET, suggesting that they are the major climatic factors influencing ET changes. Specifically, precipitation is the dominant factor for ET in water-limited regions, while ET changes in energy-limited regions are dominated by VPD according to all ET products except the EartH2Observe-En in which Rn and VPD have comparable performance. Our study highlights the importance of VPD in ET changes across energy-limited regions of China and suggests that the role of VPD in land surface-atmosphere interactions should be considered in future studies.

Inferring the potential impact of human activities on evapotranspiration in the Tumen River Basin based on LANDSAT imagery and historical statistics

AbstractUnderstanding the spatial and temporal changes in actual evapotranspiration (ETa) caused by different human activities is important for water management in water‐sensitive areas, because ETa plays a major role in regulating the availability of water on land. Thus, ETa observation is particularly important for observing land degradation in the watershed. This study used the operational simplified surface energy balance (SSEBop) model to perform ETa estimations and observations in the Tumen River Basin (TRB), covering the border of China and Democratic People's Republic of Korea (DPRK), over the last 30 years by using eight periods of clear‐sky LANDSAT Thematic Mapper/Operational Land Imager (TM/OLI) and thermal infrared sensor (TIRS) data. To assess the reliability of the ETa estimations, multilayer soil temperatures from the China Meteorological Administration (CMA) Land Data Assimilation System Version 2.0 (CLDAS‐V2.0) were utilized. The highest R2 (.71) between ETa and soil temperature was observed in layers shallower than 10 cm; this value of R2 gradually decreased to .57 at a depth of 200 cm. The average ETa of the TRB declined from 1.65 mm in 1985 to 1.03 mm in 2017. The impact of human activities on water loss (ETa) was determined by comparing land cover changes observed by an object‐based image analysis (OBIA) approach and economic activities between 1994 and 2008. Varying economic activities and water depletion in different soil layers may have led to the variable ETa values between the northern (China) and southern (DPRK) part of the TRB during the last three decades.

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS), we blended the Climate Prediction Center (CPC) morphing technique (CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China (CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2 (GLDAS-V2.1) precipitation, and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement (GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.

Evaluation of long-term Northern Hemisphere snow water equivalent products

Abstract. Nine gridded Northern Hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS-2; the European Centre for Medium-Range Weather Forecasts (ECMWF) interim land surface reanalysis – ERA-Interim/Land and ERA5; the NASA Modern-Era Retrospective Analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2); the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) stand-alone passive microwave retrievals (NASA AMSR-E SWE versions 1.0 and 2.0) which do not utilize surface snow observations. Evaluation included validation against independent snow course measurements from Russia, Finland, and Canada and product intercomparison through the calculation of spatial and temporal correlations in SWE anomalies. The stand-alone passive microwave SWE products (AMSR-E v1.0 and v2.0 SWE) exhibit low spatial and temporal correlations to other products and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides performance comparable to the reanalysis-based products; RMSE over Finland and Russia for all but the AMSR-E products is ∼50 mm or less, with the exception of ERA-Interim/Land over Russia. Using a seven-dataset ensemble that excluded the stand-alone passive microwave products reduced the RMSE by 10 mm (20 %) and increased the correlation from 0.67 to 0.78 compared to any individual product. The overall performance of the best multiproduct combinations is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.

A Physically Based Atmospheric Variables Downscaling Technique

AbstractThis study proposes a physically based downscaling approach for a set of atmospheric variables that relies on correlations with landscape information, such as topography, surface roughness, and vegetation. A proof-of-concept has been implemented over Oklahoma, where high-resolution, high-quality observations are available for validation purposes. Hourly North America Land Data Assimilation System version 2 (NLDAS-2) meteorological data (i.e., near-surface air temperature, pressure, humidity, wind speed, and incident longwave and shortwave radiation) have been spatially downscaled from their original 1/8° resolution to a 500-m grid over the study area during 2015. Results show that correlation coefficients between the downscaled products and ground observations are consistently higher than the ones between the native resolution NLDAS-2 data and ground observations. Furthermore, the downscaled variables present smaller biases than the original ones with respect to ground observations. Results are therefore encouraging toward the use of the 500-m dataset for land surface and hydrological modeling. This would be especially useful in regions where ground-based observations are sparse or not available altogether, and where downscaled global reanalysis products may be the only option for model inputs at scales that are useful for decision-making.

Spatiotemporal Changes in China's Terrestrial Water Storage From GRACE Satellites and Its Possible Drivers

AbstractTerrestrial water storage (TWS) changes are driven by internal climate variability, external natural climate change, external human‐caused climate change, and human water management. Spatiotemporal patterns of TWS change and attribution can help understand the water cycle process and refine water management in a region. Here, the spatiotemporal changes in the TWS in China between 2003 and 2016 are analyzed based on three monthly mass concentration (mascon) data products from the Gravity Recovery and Climate Experiment (GRACE) satellites, and the possible drivers are investigated using multiple precipitation products, land surface models, and multisource remote sensing and socioeconomic data. Six major TWS change regions are detected, including negative trends in northwestern China, the southeastern Tibetan Plateau, and northern China and positive trends in western China, southern China, and northeastern China. Two global hydrological models, WaterGAP and PCR‐GLOBWB, substantially underestimate the TWS changes relative to GRACE. Four land surface models (CLM 2.0, VIC, MOSAIC, and NOAH 3.3) from the Global Land Data Assimilation System version 2.1 show large model uncertainties in simulating the TWS trend. A statistical attribution indicates that ice melting under human‐caused climate change is a driver of decreasing TWS in northwestern China and the southeastern Tibetan Plateau that cannot be ignored, while human water use is largely responsible for groundwater depletion in northern China. The increasing TWS in southern China and northeastern China is likely caused by precipitation increases, and the increasing TWS in western China is probably a result of precipitation increases and water supplementation from ice melting.

Monitoring drought using composite drought indices based on remote sensing

Drought is one of the most frequent disasters occurring in North China and has a great influence on agriculture, ecology and economy. To monitor drought of typical dry areas in North China, Shandong Province, this paper proposed composite drought indices using multivariable linear regression (MCDIs) to integrate Tropical Rainfall Measuring Mission (TRMM) derived precipitation, Global Land Data Assimilation System Version 2.1 (GLDAS-2.1) derived soil moisture, Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST) and normalized difference vegetation index (NDVI) from 2013 to 2017 (March to September). Pearson correlation analyses were performed between single remote sensing drought indices and in-situ drought indices, standardized precipitation evapotranspiration index (SPEI), in different time scales to assess the capability of single indices over Shandong Province. The multivariable linear regression method was used to established MCDIs, and mediator and moderator variables were introduced to optimize the model. The correlation coefficients (r) between MCDIs and SPEIs was higher than that between each single index and SPEIs. Additionally, when we investigate the correlations of different MCDIs with both standardized precipitation index (SPI) and moisture index (MI), the highest r values with both 1-month SPI and MI were acquired by the MCDI based on 1-month SPEI (MCDI-1). This suggested MCDI-1 was suitable to monitor meteorological drought. Also, the comparison between MCDI based on 9-month SPEI (MCDI-9) and soil moisture showed MCDI-9 was a good indicator for agricultural drought. Therefore, multivariable linear regression and MCDIs were recommended to be an effective method and indices for monitoring drought across Shandong Province and similar areas.

Attribution of Global Soil Moisture Drying to Human Activities: A Quantitative Viewpoint

AbstractAnthropogenic impacts on widespread global soil moisture (SM) drying in the root zone layer during 1948–2005 were evaluated based on the Global Land Data Assimilation System version 2 (GLDAS‐2) and global climate models from the Coupled Model Intercomparison Project Phase 5 using trend analysis and optimal fingerprint methods. Both methods show agreement that natural forcing alone cannot drive significant SM drying. There is a high probability (≥90%) that the anthropogenic climate change signal is detectable in global SM drying. Specifically, anthropogenic greenhouse gas forcing can lead to global SM drying by 2.1 × 10−3 m3/m3, which is comparable to the drying trend seen in Global Land Data Assimilation System version 2 (2.4 × 10−3 m3/m3) over the past 58 years. Global SM drying is expected to continue in the future, given continuous greenhouse gas emissions.

High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 1. Model Development and Evaluation

AbstractHigh‐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 hydrothermal 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 Global Land Data Assimilation System version 1.0 reanalysis products, CSSPv2 reduced errors by up to 30%, 69%, 92%, and 40% for soil moisture, soil temperature, evapotranspiration, and terrestrial water storage change, 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 evapotranspiration and terrestrial water storage during 1982–2011 and 2003–2014 respectively, while ERA‐Interim and Global Land Data Assimilation System version 1.0 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.