scholarly journals A Hydrologic Monitoring Dataset for Food and Water Security Applications in Central Asia

2021 ◽  
Author(s):  
Amy McNally ◽  
Jossy Jacob ◽  
Kristi Arsenault ◽  
Kimberly Slinski ◽  
Daniel Sarmiento ◽  
...  
Keyword(s):  
Central Asia ◽  
Data Streams ◽  
Land Surface ◽  
Data Stream ◽  
Snow Water Equivalent ◽  
Value Added ◽  
Water Security ◽  
Humanitarian Assistance ◽  
Economic Market ◽  
Land Data Assimilation System

Abstract. From the Hindu Kush Mountains to the Registan desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The FEWS NET Land Data Assimilation System (FLDAS) global and Central Asia data streams described here combine meteorological reanalysis datasets and land surface models to generate routine estimates of snow-covered fraction, snow water equivalent, soil moisture, runoff and other variables representing the water and energy balance. This approach allows us to fill the gap created by the lack of in situ hydrologic data in the region. First, we describe the configuration of the FLDAS and the two resultant data streams: one, global, at ~1 month latency, provides monthly average outputs on a 10 km2 grid from 1982–present. The second data stream, Central Asia, at ~1 day latency, provides daily average outputs on a 1 km2 grid from 2001–present. We describe our verification of these data that are compared to other remotely sensed estimates as well as qualitative field reports. These data and value-added products (e.g., anomalies and interactive time series) are hosted by NASA and USGS data portals for public use. The global data stream with a longer record, is useful for exploring interannual variability, relationships with atmospheric-oceanic teleconnections (e.g., ENSO), trends over time, and monitoring drought. Meanwhile, the higher spatial resolution Central Asia data stream, with lower latency, is useful for simulating snow-hydrologic dynamics in complex topography for monitoring snowpack and flood risk.

scholarly journals Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

2021 ◽  
Author(s):  
Vincent Vionnet ◽  
Colleen Mortimer ◽  
Mike Brady ◽  
Louise Arnal ◽  
Ross Brown
Keyword(s):  
Newfoundland And Labrador ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Water Security ◽  
Monitoring And Evaluation ◽  
Surface Models ◽  
Snow Water ◽  
The Government ◽  
Different Sources

Abstract. In situ measurements of snow water equivalent (SWE) – the depth of water that would be produced if all the snow melted – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth and derived bulk snow density are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations, and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020 and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro Quebec were included. CanSWE includes over one million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734372 (Vionnet et al., 2021).

scholarly journals Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

2021 ◽  
Vol 13 (9) ◽  
pp. 4603-4619
Author(s):  
Vincent Vionnet ◽  
Colleen Mortimer ◽  
Mike Brady ◽  
Louise Arnal ◽  
Ross Brown
Keyword(s):  
Snow Cover ◽  
Newfoundland And Labrador ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Water Security ◽  
Monitoring And Evaluation ◽  
Surface Models ◽  
Snow Water ◽  
The Government

Abstract. In situ measurements of water equivalent of snow cover (SWE) – the vertical depth of water that would be obtained if all the snow cover melted completely – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth (SD) and bulk snow density (defined as the ratio of SWE to SD) are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020, and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro-Québec were included. CanSWE includes over 1 million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734371 (Vionnet et al., 2021).

scholarly journals Consistent assimilation of multiple data streams in a carbon cycle data assimilation system

2016 ◽  
Vol 9 (10) ◽  
pp. 3569-3588 ◽  
Author(s):  
Natasha MacBean ◽  
Philippe Peylin ◽  
Frédéric Chevallier ◽  
Marko Scholze ◽  
Gregor Schürmann
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Data Streams ◽  
Land Surface ◽  
Data Stream ◽  
Correlated Errors ◽  
Multiple Data ◽  
Model Dynamics ◽  
Error Distributions ◽  
Multiple Data Streams

Abstract. Data assimilation methods provide a rigorous statistical framework for constraining parametric uncertainty in land surface models (LSMs), which in turn helps to improve their predictive capability and to identify areas in which the representation of physical processes is inadequate. The increase in the number of available datasets in recent years allows us to address different aspects of the model at a variety of spatial and temporal scales. However, combining data streams in a DA system is not a trivial task. In this study we highlight some of the challenges surrounding multiple data stream assimilation for the carbon cycle component of LSMs. We give particular consideration to the assumptions associated with the type of inversion algorithm that are typically used when optimising global LSMs – namely, Gaussian error distributions and linearity in the model dynamics. We explore the effect of biases and inconsistencies between the observations and the model (resulting in non-Gaussian error distributions), and we examine the difference between a simultaneous assimilation (in which all data streams are included in one optimisation) and a step-wise approach (in which each data stream is assimilated sequentially) in the presence of non-linear model dynamics. In addition, we perform a preliminary investigation into the impact of correlated errors between two data streams for two cases, both when the correlated observation errors are included in the prior observation error covariance matrix, and when the correlated errors are ignored. We demonstrate these challenges by assimilating synthetic observations into two simple models: the first a simplified version of the carbon cycle processes represented in many LSMs and the second a non-linear toy model. Finally, we provide some perspectives and advice to other land surface modellers wishing to use multiple data streams to constrain their model parameters.

scholarly journals Updating a Land Surface Model with MODIS-Derived Snow Cover

2004 ◽  
Vol 5 (6) ◽  
pp. 1064-1075 ◽  
Author(s):  
M. Rodell ◽  
P. R. Houser
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Surface Air Temperature ◽  
Surface Model ◽  
Mountainous Regions ◽  
Snow Water ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Data Assimilation System

Abstract A simple scheme for updating snow-water storage in a land surface model using snow cover observations is presented. The scheme makes use of snow cover observations retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites. Simulated snow-water equivalent is adjusted when and where the model and MODIS observation differ, following an internal accounting of the observation quality, by either removing the simulated snow or adding a thin layer. The scheme is tested in a 101-day global simulation of the Mosaic land surface model driven by the NASA/NOAA Global Land Data Assimilation System. Output from this simulation is compared to that from a control (not updated) simulation, and both are assessed using a conventional snow cover product and data from ground-based observation networks over the continental United States. In general, output from the updated simulation displays more accurate snow coverage and compares more favorably with in situ snow time series. Both the control and updated simulations have serious deficiencies on occasion and in certain areas when and where the precipitation and/or surface air temperature forcing inputs are unrealistic, particularly in mountainous regions. Suggestions for developing a more sophisticated updating scheme are presented.

Food and Water Security in West Asia

2018 ◽  
pp. 512-529
Author(s):  
Eckart Woertz
Keyword(s):  
Role Model ◽  
Value Added ◽  
Water Security ◽  
Virtual Water ◽  
Agricultural Trade ◽  
Economic Sectors ◽  
Trade Surplus ◽  
Self Sufficiency ◽  
Water Scarce ◽  
Policy Solutions

West Asia is one of the most water-scarce regions of the world and one of its foremost importers of virtual water despite sustained efforts at self-sufficiency, especially in cereal production. Technology-oriented policy solutions eye a reorientation of agriculture towards fruit and vegetables that are less water-intensive than cereals and provide more value added per water unit consumed. Turkey is a role model here; the country has an agricultural trade surplus and ranks among the top ten agricultural economies globally in value terms. Yet technology-oriented policy prescriptions overlook the sociopolitical ‘problemsheds’ that emerge (along with new agro-lobbies) and agriculture as the main water consumer has to compete with other economic sectors and sprawling urbanization. This article looks at the different categories of countries and their specific challenges.

Characteristics and Predictability of Midwestern United States Drought

2021 ◽  
Author(s):  
Andrew Hoell ◽  
Trent W. Ford ◽  
Molly Woloszyn ◽  
Jason A. Otkin ◽  
Jon Eischeid
Keyword(s):  
United States ◽  
Great Plains ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Northern Great Plains ◽  
Ohio Valley ◽  
Midwestern United States ◽  
Wave Trains

AbstractCharacteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Constraining a land surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System

2016 ◽  
Author(s):  
G. J. Schürmann ◽  
T. Kaminski ◽  
C. Köstler ◽  
N. Carvalhais ◽  
M. Voßbeck ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Land Surface ◽  
Data Stream ◽  
Atmospheric Co2 ◽  
Surface Model ◽  
Terrestrial Biosphere ◽  
Multiple Observations ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the land surface scheme of the MPI-Earth System Model v1 (JSBACH). The simulated terrestrial biosphere processes (phenology and carbon balance) were constrained by observations of the fraction of photosynthetically active radiation (TIP-FAPAR product) and by observations of atmospheric CO2 at a global set of monitoring stations for the years 2005–2009. The system successfully, and computationally efficiently, improved average foliar area and northern extra-tropical seasonality of foliar area when constrained by TIP-FAPAR. Global net and gross carbon fluxes were improved when constrained by atmospheric CO2, although the system tended to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, therefore overall increasing the appropriateness of the resultant parameter values and biosphere dynamics. Our study thus highlights the role of the TIP-FAPAR product in stabilising the underdetermined atmospheric inversion problem and demonstrates the value of multiple-data stream assimilation for the simulation of terrestrial biosphere dynamics. The constraint on regional gross and net CO2 flux patterns is limited through the parametrisation of the biosphere model. We expect improvement on that aspect through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

scholarly journals Evaluation of Groundwater Storage Variations in Northern China Using GRACE Data

Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjie Yin ◽  
Litang Hu ◽  
Jiu Jimmy Jiao
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Groundwater Resources ◽  
Dynamic Change ◽  
Northern China ◽  
Groundwater Storage ◽  
Grace Data ◽  
Depletion Rate ◽  
Terrestrial Water ◽  
Storage Change

Dynamic change of groundwater storage is one of the most important topics in the sustainable management of groundwater resources. Groundwater storage variations are firstly isolated from the terrestrial water storage change using the Global Land Data Assimilation System (GLDAS). Two datasets are used: (1) annual groundwater resources and (2) groundwater storage changes estimated from point-based groundwater level data in observation wells. Results show that the match between the GRACE-derived groundwater storage variations and annual water resources variation is not good in six river basins of Northern China. However, it is relatively good between yearly GRACE-derived groundwater storage data and groundwater storage change dataset in Huang-Huai-Hai Plain and the Song-Liao Plain. The mean annual depletion rate of groundwater storage in the Northern China was approximately 1.70 billion m3 yr−1 from 2003 to 2012. In terms of provinces, the yearly depletion rate is higher in Jing-Jin-Ji (Beijing, Tianjin, and Hebei province) and lowest in Henan province from 2003 to 2012, with the rate of 0.70 and 0.21 cm yr−1 Equivalent Water Height (EWH), respectively. Different land surface models suggest that the patterns from different models almost remain the same, and soil moisture variations are generally bigger than snow water equivalent variations.

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