Analysis of groundwater storage changes in main Polish river basins using GRACE observations, in-situ data, and hydrological and climate models

2020 ◽  
Author(s):  
Jolanta Nastula ◽  
Justyna Śliwińska ◽  
Zofia Rzepecka ◽  
Monika Birylo
Keyword(s):  
Climate Models ◽  
Water Storage ◽  
Coupled Model ◽  
Research Programme ◽  
Climate Data ◽  
Groundwater Storage ◽  
In Situ Data ◽  
Snow Water ◽  
Land Data Assimilation System

<p>The Gravity Recovery and Climate Experiment (GRACE) measurements have provided global observations of total water storage (TWS) changes at monthly intervals for almost 20 years. They are useful for estimating changes in groundwater storage (GWS) after extracting other water storage components like soil water or snow water.</p><p>In this study, we analyse the GWS variations of two main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ wells measurements, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 5) climate data. The research is conducted for the period between September 2006 and October 2015.</p><p>Here, TWS is taken directly from GRACE measurements and also computed from all considered models. GWS is obtained by subtracting the modelled sum of soil moisture and snow water from the GRACE-based TWS. The resultant GWS series are validated by comparing with appropriately calibrated in-situ wells measurements. For each GWS time series, the trends, spectra, amplitudes, and seasonal components were computed and analysed. The results suggest that in Poland there has been generally no major GWS depletion. The results can contribute toward selection of an appropriate model that, in combination with GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate in-situ groundwater storage measurements.</p>

scholarly journals Analysis of Groundwater and Total Water Storage Changes in Poland Using GRACE Observations, In-situ Data, and Various Assimilation and Climate Models

2019 ◽  
Vol 11 (24) ◽  
pp. 2949 ◽  
Author(s):  
Justyna Śliwińska ◽  
Monika Birylo ◽  
Zofia Rzepecka ◽  
Jolanta Nastula
Keyword(s):  
Climate Models ◽  
Water Storage ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Climate Data ◽  
Total Water ◽  
In Situ Data ◽  
Grace Data ◽  
Land Data Assimilation System

The Gravity Recovery and Climate Experiment (GRACE) observations have provided global observations of total water storage (TWS) changes at monthly intervals for over 15 years, which can be useful for estimating changes in GWS after extracting other water storage components. In this study, we analyzed the TWS and groundwater storage (GWS) variations of the main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ data, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 5) climate data. The research was conducted for the period between September 2006 and October 2015. The TWS data were taken directly from GRACE measurements and also computed from four GLDAS (VIC, CLM, MOSAIC, and NOAH) and six CMIP5 (FGOALS-g2, GFDL-ESM2G, GISS-E2-H, inmcm4, MIROC5, and MPI-ESM-LR) models. The GWS data were obtained by subtracting the model TWS from the GRACE TWS. The resulting GWS values were compared with in-situ well measurements calibrated using porosity coefficients. For each time series, the trends, spectra, amplitudes, and seasonal components were computed and analyzed. The results suggest that in Poland there has been generally no major TWS or GWS depletion. Our results indicate that when comparing TWS values, better compliance with GRACE data was obtained for GLDAS than for CMIP5 models. However, the GWS analysis showed better consistency of climate models with the well results. The results can contribute toward selection of an appropriate model that, in combination with global GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate ground measurements.

Exploring meso-scale soil water and groundwater storage changes within the USA through a Bayesian combination of GRACE data with monthly 12.5 km model simulations

2020 ◽  
Author(s):  
Nooshin Mehrnegar ◽  
Owen Jones ◽  
Michael B. Singer ◽  
Maike Schumacher ◽  
Thomas Jagdhuber ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Soil Water ◽  
Water Storage ◽  
Balance Model ◽  
Soil Water Storage ◽  
Groundwater Storage ◽  
In Situ Data ◽  
Grace Data ◽  
The Usa

<p>Climatic changes in precipitation intensity across the United States (USA) may also affect the frequency and magnitude of drought and flooding events, with potentially serious consequences for water supply across this country. Reliable estimation of water storage changes in the soil root zone and groundwater aquifers is important for predicting future water availability, drought and flood monitoring and weather prediction. In this study, we assimilate Terrestrial Water Storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into a water balance model with 12.5-km spatial resolution. Our goal is to explore meso-scale surface and deep-level soil water storage, as well as groundwater changes within the USA covering the period 2003-2017. A new Bayesian approach is formulated and implemented in this study, which provides a dynamic solution for a state-space equation between hydrological model outputs and TWS observations, while considering their error structures. The unknown state parameters and temporal dependency between them are estimated through a combination of forward/backward Kalman Filtering and Markov Chain Monto Carlo (MCMC) methods.</p><p>The outputs of this methodological approach are evaluated using in situ data from historical USGS groundwater data (over 6600 wells) and the ESA CCI surface soil moisture data. The results indicate that our GRACE data assimilation generally improves the simulation of groundwater and soil moisture across the USA. For example, the long-term linear trend fitted to the Bayesian-derived groundwater and soil water storage are in a same direction as those of in situ data in 63% and 58% of regions studied across the USA, respectively. However, this vale is estimated less than 51% for both water storage estimates derived from the original water balance model, which suggesting that the data assimilation modulates the hydrological models to perform more realistically. The biggest improvements are observed in the southeast USA with considerably large inter-annual variability in precipitation, where modelled groundwater apparently responded too strongly to the changes in atmospheric forcing. The Bayesian data assimilation method also improves the temporal correlation coefficients between the in situ USGS and ESA CCI data and model outputs after merging with GRACE TWS estimates. For instance, the correlation coefficient between groundwater storage and USGS observation increased from -0.52 to 0.48 and from -0.28 to 0.25 in southeast and southwest of USA, respectively. Finally, we will explore changes in Bayesian-derived groundwater and soil water storage within the Florida, California and South of Mississippi regions and interpret their relations with climate-induced factors such as precipitation and ENSO index.</p><p><strong>Keywords:</strong> USA; Data Assimilation; Bayesian Method; Kalman Filtering; MCMC; GRACE; W3RA; groundwater storage; soil water storage; USGS; ESA CCI.</p><p> </p>

Groundwater storage change in the Jinsha River basin from GRACE, hydrologic models, and in situ data

2020 ◽  
Author(s):  
Nengfang Chao
Keyword(s):  
Sediment Transport ◽  
River Basin ◽  
Human Activities ◽  
Water Storage ◽  
Hydrologic Models ◽  
Groundwater Storage ◽  
Jinsha River ◽  
In Situ Data ◽  
Storage Change

<p>Groundwater plays a major role in the hydrological processes driven by climate change and human activities, particularly in upper mountainous basins. The Jinsha River Basin (JRB) is the uppermost region of the Yangtze River and the largest hydropower production region in China. With the construction of artificial cascade reservoirs increasing in this region, the annual and seasonal flows are changing and affecting the water cycles. Here, we first infer the groundwater storage changes (GWSC), accounting for sediment transport in JRB, by combining the Gravity Recovery and Climate Experiment (GRACE) mission, hydrologic models and in situ data. The results indicate: (1) the average estimation of the GWSC trend, accounting for sediment transport in JRB, is 0.76±0.10 cm/year during the period 2003–2015, and the contribution of sediment transport accounts for 15%; (2) precipitation (P), evapotranspiration (ET), soil moisture change (SMC), GWSC and land water storage changes (LWSC) show clear seasonal cycles; the interannual trends of LWSC and GWSC increase, but P, runoff (R), surface water storage change (SWSC) and SMC decrease, and ET remains basically unchanged; (3) the main contributor to the increase in LWSC in JRB is GWSC, and the increased GWSC may be dominated by human activities, such as cascade damming, and climate variations (such as snow and glacier melt due to increased temperatures). This study can provide valuable information regarding JRB in China for understanding GWSC patterns and exploring their implications for regional water management.</p>

scholarly journals Recent changes in terrestrial water storage in the Upper Nile Basin: an evaluation of commonly used gridded GRACE products

2017 ◽  
Vol 21 (9) ◽  
pp. 4533-4549 ◽  
Author(s):  
Mohammad Shamsudduha ◽  
Richard G. Taylor ◽  
Darren Jones ◽  
Laurent Longuevergne ◽  
Michael Owor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Lake Victoria ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Nile Basin ◽  
Lake Victoria Basin ◽  
Groundwater Storage ◽  
Terrestrial Water ◽  
Substantial Uncertainty

Abstract. GRACE (Gravity Recovery and Climate Experiment) satellite data monitor large-scale changes in total terrestrial water storage (ΔTWS), providing an invaluable tool where in situ observations are limited. Substantial uncertainty remains, however, in the amplitude of GRACE gravity signals and the disaggregation of TWS into individual terrestrial water stores (e.g. groundwater storage). Here, we test the phase and amplitude of three GRACE ΔTWS signals from five commonly used gridded products (i.e. NASA's GRCTellus: CSR, JPL, GFZ; JPL-Mascons; GRGS GRACE) using in situ data and modelled soil moisture from the Global Land Data Assimilation System (GLDAS) in two sub-basins (LVB: Lake Victoria Basin; LKB: Lake Kyoga Basin) of the Upper Nile Basin. The analysis extends from January 2003 to December 2012, but focuses on a large and accurately observed reduction in ΔTWS of 83 km3 from 2003 to 2006 in the Lake Victoria Basin. We reveal substantial variability in current GRACE products to quantify the reduction of ΔTWS in Lake Victoria that ranges from 80 km3 (JPL-Mascons) to 69 and 31 km3 for GRGS and GRCTellus respectively. Representation of the phase in TWS in the Upper Nile Basin by GRACE products varies but is generally robust with GRGS, JPL-Mascons, and GRCTellus (ensemble mean of CSR, JPL, and GFZ time-series data), explaining 90, 84, and 75 % of the variance respectively in "in situ" or "bottom-up" ΔTWS in the LVB. Resolution of changes in groundwater storage (ΔGWS) from GRACE ΔTWS is greatly constrained by both uncertainty in changes in soil-moisture storage (ΔSMS) modelled by GLDAS LSMs (CLM, NOAH, VIC) and the low annual amplitudes in ΔGWS (e.g. 1.8–4.9 cm) observed in deeply weathered crystalline rocks underlying the Upper Nile Basin. Our study highlights the substantial uncertainty in the amplitude of ΔTWS that can result from different data-processing strategies in commonly used, gridded GRACE products; this uncertainty is disregarded in analyses of ΔTWS and individual stores applying a single GRACE product.

scholarly journals Influence of Precipitation Forcing Uncertainty on Hydrological Simulations with the NASA South Asia Land Data Assimilation System

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 57 ◽  
Author(s):  
Debjani Ghatak ◽  
Benjamin Zaitchik ◽  
Sujay Kumar ◽  
Mir A. Matin ◽  
Birendra Bajracharya ◽  
...  
Keyword(s):  
South Asia ◽  
Hydrological Simulation ◽  
Meteorological Forcing ◽  
In Situ Data ◽  
Precipitation Estimates ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

: Accurate meteorological estimates are critical for process-based hydrological simulation and prediction. This presents a significant challenge in mountainous Asia where in situ meteorological stations are limited and major river basins cross international borders. In this context, remotely sensed and model-derived meteorological estimates are often necessary inputs for distributed hydrological analysis. However, these datasets are difficult to evaluate on account of limited access to ground data. In this case, the implications of uncertainty associated with precipitation forcing for hydrological simulations is explored by driving the South Asia Land Data Assimilation System (South Asia LDAS) using a range of meteorological forcing products. MERRA2, GDAS, and CHIRPS produce a wide range of estimates for rainfall, which causes a widespread simulated streamflow and evapotranspiration. A combination of satellite-derived and limited in situ data are applied to evaluate model simulations and, by extension, to constrain the estimates of precipitation. The results show that available gridded precipitation estimates based on in situ data may systematically underestimate precipitation in mountainous regions and that performance of gridded satellite-derived or modeled precipitation estimates varies systematically across the region. Since no station-based data or product including station data is satisfactory everywhere, our results suggest that the evaluation of the hydrological simulation of streamflow and ET can be used as an indirect evaluation of precipitation forcing based on ground-based products or in-situ data. South Asia LDAS produces reasonable evapotranspiration and streamflow when forced with appropriate meteorological forcing and the choice of meteorological forcing should be made based on the geographical location as well as on the purpose of the simulations.

scholarly journals Recent changes in terrestrial water storage in the Upper Nile Basin: an evaluation of commonly used gridded GRACE products

2017 ◽  
Author(s):  
Mohammad Shamsudduha ◽  
Richard G. Taylor ◽  
Darren Jones ◽  
Laurent Longuevergne ◽  
Michael Owor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Lake Victoria ◽  
Water Storage ◽  
Series Data ◽  
Terrestrial Water Storage ◽  
Nile Basin ◽  
Groundwater Storage ◽  
Terrestrial Water ◽  
Substantial Uncertainty

Abstract. GRACE (Gravity Recovery and Climate Experiment) satellite data monitor large-scale changes in total terrestrial water storage (ΔTWS) providing an invaluable tool where in situ observations are limited. Substantial uncertainty remains, however, in the amplitude of GRACE gravity signals and the disaggregation of ΔTWS into individual terrestrial water stores (e.g. groundwater storage). Here, we test the phase and amplitude of GRACE ΔTWS signals from 5 commonly-used gridded products (i.e., NASA's GRCTellus: CSR, JPL GFZ; JPL-Mascons; GRGS GRACE) using in situ data and modelled soil-moisture from the Global Land Data Assimilation System (GLDAS). The focus of this analysis is a large and accurately observed reduction in ΔTWS of 75 km3 from 2004 to 2006 in Lake Victoria in the Upper Nile Basin. We reveal substantial variability in current GRACE products to quantify the reduction of ΔTWS in Lake Victoria that ranges from 68 km3 (GRGS) to 50 km3 and 26 km3 for JPL-Mascons and GRCTellus, respectively. Representation of the phase in ΔTWS in the Upper Nile Basin by GRACE products varies but is generally robust with GRGS, JPL-Mascons and GRCTellus (ensemble mean of CSR, JPL and GFZ time-series data) explaining 91 %, 85 %, and 77 % of the variance, respectively, in in-situ ΔTWS. Resolution of changes in groundwater storage (ΔGWS) from GRACE ΔTWS is greatly constrained by both uncertainty in modelled changes in soil-moisture storage (ΔSMS) and the low annual amplitudes in ΔGWS (e.g., 3.5 to 4.4 cm) observed in deeply weathered crystalline rocks underlying the Upper Nile Basin. Our study highlights the substantial uncertainty in the amplitude of ΔTWS that can result from different data-processing strategies in commonly used, gridded GRACE products.

Towards a long term global snow climate data record from satellite data generated within the Snow Climate Change Initiative

2020 ◽  
Author(s):  
Gabriele Schwaizer ◽  
Lars Keuris ◽  
Thomas Nagler ◽  
Chris Derksen ◽  
Kari Luojus ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Process Models ◽  
Climate Data ◽  
Seasonal Snow ◽  
Snow Water

<p>Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.</p><p>In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.</p><p>Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.</p><p>The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.</p><p>In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.</p>

scholarly journals Numerical experiments in calculating snow water equivalent and estimating their impact on the formation of spring floods

2020 ◽  
Vol 163 ◽  
pp. 06003
Author(s):  
Evgenii Churiulin ◽  
Vladimir Kopeykin ◽  
Natalia Frolova ◽  
Inna Krylenko
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Hydrological Models ◽  
In Situ Data ◽  
Use Of Data ◽  
Complex Approach ◽  
Snow Water ◽  
Seasonal Snow Cover

Seasonal snow cover has a significant impact on forming spring floods. Sparse snow course-measuring network does not meet the requirements of modern tasks related to the technologies of numerical weather prediction (NWP) systems and runoff formation models. Moreover, insufficient volume of hydrometeorological data creates a need to improve spring floods forecasting methods by means of available modern hydrometeorological information related to snow cover. To work out an efficient solution to the issue of initial snow data preparation we need a complex approach including the use of data from satellite, atmospheric models, physical-mathematical models of snow cover and insitu information. This approach will provide modern NWP and hydrological models with reliable initial data on snow cover (snow water equivalent – SWE, snow density – SD). The main purpose of our investigation is related to approbation of satellite data and development of snow cover calculation methods for NWP and hydrological models. Numerous SWE and SD experiments have been performed in order to achieve this aim. A regional snow data assimilation system for COSMORu was implemented during the research. Moreover, a new method of hydrological modelling of spring floods based on ECOMAG model with initial information from COSMO-Ru, SnoWE and in-situ data has been proposed and tested.

scholarly journals On biotic and abiotic drivers of the microphytobenthos seasonal cycle in a temperate intertidal mudflat: a modelling study

2018 ◽  
Vol 15 (23) ◽  
pp. 7243-7271 ◽  
Author(s):  
Raphaël Savelli ◽  
Christine Dupuy ◽  
Laurent Barillé ◽  
Astrid Lerouxel ◽  
Katell Guizien ◽  
...  
Keyword(s):  
Spring Bloom ◽  
Coupled Model ◽  
Simulated Data ◽  
Grazing Pressure ◽  
Early Spring ◽  
Trophic Levels ◽  
Intertidal Mudflat ◽  
In Situ Data ◽  
Primary Production Rate

Abstract. Microphytobenthos (MPB) from intertidal mudflats are key primary producers at the land–ocean interface. MPB can be more productive than phytoplankton and sustain both benthic and pelagic higher trophic levels. The objective of this study is to assess the contribution of light, mud temperature, and gastropod Peringia ulvae grazing pressure in shaping the seasonal MPB dynamics on the Brouage mudflat (NW France). We use a physical–biological coupled model applied to the sediment first centimetre for the year 2008. The simulated data compare to observations, including time-coincident remotely sensed and in situ data. The model suggests an MPB annual cycle characterised by a main spring bloom, a biomass depression in summer, and a moderate fall bloom. In early spring, simulated photosynthetic rates are high due to mud surface temperature (MST) values close to the MPB temperature optimum for photosynthesis and because increasing solar irradiance triggers the onset of the MPB spring bloom. Simulated peaks of high P. ulvae grazing (11 days during which ingestion rates exceed the primary production rate) mostly contribute to the decline of the MPB bloom along with the temperature limitation for MPB growth. In late spring–summer, the MPB biomass depression is due to the combined effect of thermo-inhibition and a moderate but sustained grazing pressure. The model ability to infer biotic and abiotic mechanisms driving the seasonal MPB dynamics could open the door to a new assessment of the export flux of biogenic matter from the coast to the open ocean and, more generally, of the contribution of productive intertidal biofilms to the coastal carbon cycle.

scholarly journals When El Niño Rages: How Satellite Data Can Help Water-Stressed Islands

2016 ◽  
Vol 97 (12) ◽  
pp. 2249-2255 ◽  
Author(s):  
Nicholas T. Luchetti ◽  
Jessica R. P. Sutton ◽  
Ethan E. Wright ◽  
Michael C. Kruk ◽  
John J. Marra
Keyword(s):  
El Niño ◽  
Pacific Islands ◽  
El Nino ◽  
Southern Oscillation ◽  
Climate Data ◽  
In Situ Data ◽  
Exclusive Economic Zones ◽  
Economic Zones ◽  
Climate Data Record

Abstract There are more than 2,000 islands across Hawaii and the U.S.-Affiliated Pacific Islands (USAPI), where freshwater resources are heavily dependent upon rainfall. Many of the islands experience dramatic variations in precipitation during the different phases of the El Niño–Southern Oscillation (ENSO). Traditionally, forecasters in the region relied on ENSO climatologies based on spatially limited in situ data to inform their seasonal precipitation outlooks. To address this gap, a unique NOAA/NASA collaborative project updated the ENSO-based rainfall climatology for the Exclusive Economic Zones (EEZs) encompassing Hawaii and the USAPI using NOAA’s PERSIANN Climate Data Record (CDR). The PERSIANN-CDR provides a 30-yr record of global daily precipitation at 0.25° resolution (∼750 km2 near the equator). This project took place over a 10- week NASA DEVELOP National Program term and resulted in a 478-page climatic reference atlas. This atlas is based on a 30-yr period from 1 January 1985 through 31 December 2014 and complements station data by offering an enhanced spatial representation of rainfall averages. Regional and EEZ-specific maps throughout the atlas illustrate the percent departure from average for each season based on the Oceanic Niño Index (ONI) for different ENSO phases. To facilitate intercomparisons across locations, this percentage-based climatology was provided to regional climatologists, forecasters, and outreach experts within the region. Anomalous wet and dry maps for each ENSO phase are used by the regional constituents to better understand precipitation patterns across their regions and to produce more accurate forecasts to inform adaptation, conservation, and mitigation options for drought and f looding events.

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