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Metadata for: Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States
,Data reported in the csv files are gridded monthly time-series used in the article “Sohoulande, C.D., Martin, J., Szogi, A. and Stone, K., 2020. Climate-Driven Prediction of Land Water Storage Anomalies: An Outlook for Water Resources Monitoring Across the Conterminous United States. Journal of Hydrology, p.125053”.,The study focused on the conterminous United States (CONUS) which extends over a region of contrasting climates with an uneven distribution of freshwater resources. Under climate change, an exacerbation of the contrast between dry and wet regions is expected across the CONUS and could drastically affect local ecosystems, agriculture practices, and communities. Hence, efforts to better understand long-term spatial and temporal patterns of freshwater resources are needed to plan and anticipate responses. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations provide estimates of large-scale land water storage changes with an unprecedented accuracy. However, the limited lifetime and observation gaps of the GRACE mission have sparked research interest for GRACE-like data reconstruction. The study developed a predictive modeling approach to quantify monthly land liquid water equivalence thickness anomaly (LWE) using climate variables including total precipitation (PRE), number of wet day (WET), air temperature (TMP), and potential evapotranspiration (PET). The approach builds on the achievements of the GRACE mission by determining LWE footprints using a multivariate regression on principal components model with lag signals. Methods are described in the manuscript https://doi.org/10.1016/j.jhydrol.2020.125053. Descriptions corresponding to each figure and table in the manuscript are placed in the Read Me.docx file that is included as part of the Dryad dataset.,,
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SGMA Climate Change Resources
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This dataset includes processed climate change datasets related to climatology, hydrology, and water operations. The climatological data provided are change factors for precipitation and reference evapotranspiration gridded over the entire State. The hydrological data provided are projected stream inflows for major streams in the Central Valley, and streamflow change factors for areas outside of the Central Valley and smaller ungaged watersheds within the Central Valley. The water operations data provided are Central Valley reservoir outflows, diversions, and State Water Project (SWP) and Central Valley Project (CVP) water deliveries and select streamflow data. Most of the Central Valley inflows and all of the water operations data were simulated using the CalSim II model and produced for all projections. These data were originally developed for the California Water Commission’s Water Storage Investment Program (WSIP). The WSIP data used as the basis for these climate change resources along with the technical reference document are located here: https://data.cnra.ca.gov/dataset/climate-change-projections-wsip-2030-2070. Additional processing steps were performed to improve user experience, ease of use for GSP development, and for Sustainable Groundwater Management Act (SGMA) implementation. Furthermore, the data, tools, and guidance may be useful for purposes other than sustainable groundwater management under SGMA. Data are provided for projected climate conditions centered around 2030 and 2070. The climate projections are provided for these two future climate periods, and include one scenario for 2030 and three scenarios for 2070: a 2030 central tendency, a 2070 central tendency, and two 2070 extreme scenarios (i.e., one drier with extreme warming and one wetter with moderate warming). The climate scenario development process represents a climate period analysis where historical interannual variability from January 1915 through December 2011 is preserved while the magnitude of events may be increased or decreased based on projected changes in precipitation and air temperature from general circulation models. ## 2070 Extreme Scenarios Update, September 2020 DWR has collaborated with Lawrence Berkeley National Laboratory to improve the quality of the 2070 extreme scenarios. The 2070 extreme scenario update utilizes an improved climate period analysis method known as "quantile delta mapping" to better capture the GCM-projected change in temperature and precipitation. A technical note on the background and results of this process is provided here: https://data.cnra.ca.gov/dataset/extreme-climate-change-scenarios-for-water-supply-planning/resource/f2e1c61a-4946-4863-825f-e6d516b433ed. *Note: the original version of the 2070 extreme scenarios can be accessed in the archive posted here: https://data.cnra.ca.gov/dataset/sgma-climate-change-resources/resource/51b6ee27-4f78-4226-8429-86c3a85046f4*
SGP97 GCIP/NESOB-97 Sub-Surface: National Climatic Data Center (NCDC) Daily Soil Temperature Dataset
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,The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The Continental-scale International Project (GCIP) Enhanced Observing Period (EOP) takes place in the Mississippi River basin, which provides a number of watershed areas that are potentially useful for hydrologic focused studies. The National Climatic Data Center (NCDC) Soil Temperature Dataset is one of several surface datasets provided for the Global Energy and Water-Balance Experiment (GEWEX) Continental-Scale International Project (GCIP) Near Surface Observation Data Set (NESOB) 1997 project. This dataset was formed by extracting soil temperature data from the GCIP/Enhanced Seasonal Observing Period 1997 (GCIP/ESOP-97) NCDC Summary of the Day Co-operative Dataset (TD-3200) for the NESOB 1997 area and time of interest. This NCDC Soil Temperature Dataset contains data from approximately 12 stations reporting soil temperature data for the NESOB 1997 time period (01 April 1997 through 31 March 1998) and in a domain slightly beyond that of NESOB 1997 (approximately 94.5W to 102W longitude and 34N to 39.5N latitude). The NCDC Soil Temperature Dataset contains seven metadata parameters and eighteen data parameters and flags. The metadata parameters describe the date, network, station and location at which the data were collected. Data values are valid for the 24 hours preceding the time of observation, and all times are UTC. Some stations may report soil temperatures at observation time twice a day. Separate records will occur for both observation times.,
Satellite-based Water Use Dynamics Using Historical Landsat Data (1984-2014) in the Southwestern United States
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Historical (1984-2014) Landsat-based ET maps were generated for Palo Verde Irrigation District (PVID) and eight other sub-basins in parts of Middle and Lower Central Valley, California. A total of 3,396 Landsat images were processed using the Operational Simplified Surface Energy balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study areas over the 31 years. Model output evaluation and validation using gridded-flux data and water balance ET approaches indicated relatively strong association between SSEBop ET and validation datasets. Historical trend analysis of seven agro-hydrologic variables were done using the Seasonal Mann-Kendall test.
SGP97 ARM Soil Water Retention Data Set
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,The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The core of the 1997 experiment involves the deployment of the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) for daily mapping of surface soil moisture. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The temporal coverage for this dataset is as follows: Begin datetime: 1995-10-01 00:00:00, End datetime: 2001-03-31 23:59:59. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Soil Water Retention Data Set is one of the various sub-surface data sets developed for the ARM/GCIP (Global Energy and Water Cycle Experiment (GEWEX) Continental-scale International Project) 1996 Near-Surface Observation (NESOB-96) Data Set. This data set contains a table for each of the ARM SWATS (Soil Water and Temperature System) sites at the SGP site containing the observed soil water retention data as obtained from laboratory tests using pressure plates and hanging columns. The soil characterizations were perfomed by Oklahoma State University.,
SWAT Reach Output Seasonal Change Scenarios for: Modeling Future Land Cover and Water Quality Change in Minneapolis, MN, USA to Support Drinking Water Source Protection Decisions
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We developed four 2011-2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long-term planning for the city of Minneapolis, MN. Baseline land cover was from the NLCD2011 database. IPCC SRES scenarios (https://www.ipcc.ch/site/assets/uploads/2018/03/sres-en.pdf) interpreted by Sohl et al. (2014) (https://doi.org/10.1890/13-1245.1) for the conterminous United States were downscaled from 250 meters to 30 meters. The baseline and scenario land cover data were used as input into the Soil Water and Assessment Tool (SWAT) model to assess the effect of outyear projected land cover change on the source of raw water for the city of Minneapolis, MN. SWAT was used to assess the effect of land cover change on raw water concentrations of sediment, total nitrogen, and total phosphorus. The IPCC SRES evaluated were A1B, A2, B1, and B2. The four scenarios were implemented with (A1Bf) and without (A1B) forest recovery (e.g., conversion of cropland (2011) to forest (2050). Units for the MeanDifference variable are different for TN/TP and sediment due to SWAT reporting conventions; TN and TP are in kilograms (kg) and sediment is in megagrams (Mg). 1 Mg = 1,000 kg. Differences are scenario minus baseline; negative values indicate constituent loads were less for the scenario relative to the baseline. NLCD2001 was included as a scenario to assess the effect of land cover change. Negative values for the NLCD2001 scenario indicate that a constituent load was less in 2001 relative to 2011.
SWAT Reach Output Seasonal Change Scenarios for: Modeling Future Land Cover and Water Quality Change in Minneapolis, MN, USA to Support Drinking Water Source Protection Decisions
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We developed four 2011-2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long-term planning for the city of Minneapolis, MN. Baseline land cover was from the NLCD2011 database. IPCC SRES scenarios (https://www.ipcc.ch/site/assets/uploads/2018/03/sres-en.pdf) interpreted by Sohl et al. (2014) (https://doi.org/10.1890/13-1245.1) for the conterminous United States were downscaled from 250 meters to 30 meters. The baseline and scenario land cover data were used as input into the Soil Water and Assessment Tool (SWAT) model to assess the effect of outyear projected land cover change on the source of raw water for the city of Minneapolis, MN. SWAT was used to assess the effect of land cover change on raw water concentrations of sediment, total nitrogen, and total phosphorus. The IPCC SRES evaluated were A1B, A2, B1, and B2. The four scenarios were implemented with (A1Bf) and without (A1B) forest recovery (e.g., conversion of cropland (2011) to forest (2050). Units for the MeanDifference variable are different for TN/TP and sediment due to SWAT reporting conventions; TN and TP are in kilograms (kg) and sediment is in megagrams (Mg). 1 Mg = 1,000 kg. Differences are scenario minus baseline; negative values indicate constituent loads were less for the scenario relative to the baseline. NLCD2001 was included as a scenario to assess the effect of land cover change. Negative values for the NLCD2001 scenario indicate that a constituent load was less in 2001 relative to 2011.
SGP97 GCIP/NESOB Surface: National Climatic Data Center (NCDC) Daily Evaporation Dataset
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,The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The National Climatic Data Center (NCDC) Evaporation Dataset is one of several surface datasets provided for the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Near Surface Observation Data Set (NESOB) 1997 project. This dataset was formed by extracting evaporation data from the GCIP/Enhanced Seasonal Observing Period 1997 (GCIP/ESOP-97) NCDC Summary of the Day Co-operative Dataset (TD-3200) for the NESOB 1997 area and time of interest. This NCDC Evaporation Dataset contains data from approximately 14 stations reporting evaporation data for the NESOB 1997 time period (01 April 1997 through 31 March 1998, and in the NESOB 1997 domain (approximately 94.5W to 100.5W longitude and 34N to 39N latitude). The NCDC Evaporation Dataset contains seven metadata parameters and sixteen data parameters and flags. The metadata parameters describe the date, network, station and location at which the data were collected. Data values are valid for the 24 hours preceding the time of observation, and all times are UTC. The evaporation and temperature parameters are not reported when the temperature is below freezing. The data parameters have two associated NCDC Quality Control (QC) Flags. The NCDC Evaporation Dataset hour of observation varies by station. Quality Control for this data was provided by NCDC. No additional QC was performed by University Corporation for Atmospheric Research/Joint Office for Science Support (UCAR/JOSS) on this dataset.,