This dataset contains all data and code necessary to reproduce the analysis presented in the manuscript: Winzeler, H.E., Owens, P.R., Read Q.D.., Libohova, Z., Ashworth, A., Sauer, T. 2022. 2022. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land 11:2018. DOI: 10.3390/land11112018. There are several steps to this analysis. The relevant scripts for each are listed below. The first step is to use the raw digital elevation data (DEM) to produce different versions of the topographic wetness index (TWI) for the study region (Calculating TWI). Then, these TWI output files are processed, along with soil moisture (volumetric water content or VWC) time series data from a number of sensors located within the study region, to create analysis-ready data objects (Processing TWI and VWC). Next, models are fit relating TWI to soil moisture (Model fitting) and results are plotted (Visualizing main results). A number of additional analyses were also done (Additional analyses). Input data The DEM of the study region is archived in this dataset as SourceDem.zip. This contains the DEM of the study region (DEM1.sgrd) and associated auxiliary files all called DEM1.* with different extensions. In addition, the DEM is provided as a .tif file called USGS_one_meter_x39y400_AR_R6_WashingtonCO_2015.tif. The remaining data and code files are archived in the repository created with a GitHub release on 2022-10-11, twi-moisture-0.1.zip. The data are found in a subfolder called data. 2017_LoggerData_HEW.csv through 2021_HEW.csv: Soil moisture (VWC) logger data for each year 2017-2021 (5 files total). 2882174.csv: weather data from a nearby station. DryPeriods2017-2021.csv: starting and ending days for dry periods 2017-2021. LoggerLocations.csv: Geographic locations and metadata for each VWC logger. Logger_Locations_TWI_2017-2021.xlsx: 546 topographic wetness indexes calculated at each VWC logger location. note: This is intermediate input created in the first step of the pipeline. Code pipeline To reproduce the analysis in the manuscript run these scripts in the following order. The scripts are all found in the root directory of the repository. See the manuscript for more details on the methods. Calculating TWI TerrainAnalysis.R: Taking the DEM file as input, calculates 546 different topgraphic wetness indexes using a variety of different algorithms. Each algorithm is run multiple times with different input parameters, as described in more detail in the manuscript. After performing this step, it is necessary to use the SAGA-GIS GUI to extract the TWI values for each of the sensor locations. The output generated in this way is included in this repository as Logger_Locations_TWI_2017-2021.xlsx. Therefore it is not necessary to rerun this step of the analysis but the code is provided for completeness. Processing TWI and VWC read_process_data.R: Takes raw TWI and moisture data files and processes them into analysis-ready format, saving the results as CSV. qc_avg_moisture.R: Does additional quality control on the moisture data and averages it across different time periods. Model fitting Models were fit regressing soil moisture (average VWC for a certain time period) against a TWI index, with and without soil depth as a covariate. In each case, for both the model without depth and the model with depth, prediction performance was calculated with and without spatially-blocked cross-validation. Where cross validation wasn't used, we simply used the predictions from the model fit to all the data. fit_combos.R: Models were fit to each combination of soil moisture averaged over 57 months (all months from April 2017-December 2021) and 546 TWI indexes. In addition models were fit to soil moisture averaged over years, and to the grand mean across the full study period. fit_dryperiods.R: Models were fit to soil moisture averaged over previously identified dry periods within the study period (each 1 or 2 weeks in length), again for each of the 546 indexes. fit_summer.R: Models were fit to the soil moisture average for the months of June-September for each of the five years, again for each of the 546 indexes. Visualizing main results Preliminary visualization of results was done in a series of RMarkdown notebooks. All the notebooks follow the same general format, plotting model performance (observed-predicted correlation) across different combinations of time period and characteristics of the TWI indexes being compared. The indexes are grouped by SWI versus TWI, DEM filter used, flow algorithm, and any other parameters that varied. The notebooks show the model performance metrics with and without the soil depth covariate, and with and without spatially-blocked cross-validation. Crossing those two factors, there are four values for model performance for each combination of time period and TWI index presented. performance_plots_bymonth.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by month across the five years of data to show within-year trends. performance_plots_byyear.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by year to show trends across multiple years. performance_plots_dry_periods.Rmd: Prediction performance was presented for the models fit to the previously identified dry periods. performance_plots_summer.Rmd: Prediction performance was presented for the models fit to the June-September moisture averages. Additional analyses Some additional analyses were done that may not be published in the final manuscript but which are included here for completeness. 2019dryperiod.Rmd: analysis, done separately for each day, of a specific dry period in 2019. alldryperiodsbyday.Rmd: analysis, done separately for each day, of the same dry periods discussed above. best_indices.R: after fitting models, this script was used to quickly identify some of the best-performing indexes for closer scrutiny. wateryearfigs.R: exploratory figures showing median and quantile interval of VWC for sensors in low and high TWI locations for each water year. Resources in this dataset:Resource Title: Digital elevation model of study region. File Name: SourceDEM.zipResource Description: .zip archive containing digital elevation model files for the study region. See dataset description for more details.Resource Title: twi-moisture-0.1: Archived git repository containing all other necessary data and code . File Name: twi-moisture-0.1.zipResource Description: .zip archive containing all data and code, other than the digital elevation model archived as a separate file. This file was generated by a GitHub release made on 2022-10-11 of the git repository hosted at https://github.com/qdread/twi-moisture (private repository). See dataset description and README file contained within this archive for more details.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions with a geographic _location of Global. The time period coverage is from 220 to -45 in calendar years before present (BP). See metadata information for parameter and study _location details. Please cite this study when using the data.

fit_combos.R: Models were fit to each combination of soil moisture averaged over 57 months (all months from April 2017-December 2021) and 546 TWI indexes. In addition models were fit to soil moisture averaged over years, and to the grand mean across the full study period. fit_dryperiods.R: Models were fit to soil moisture averaged over previously identified dry periods within the study period (each 1 or 2 weeks in length), again for each of the 546 indexes. fit_summer.R: Models were fit to the soil moisture average for the months of June-September for each of the five years, again for each of the 546 indexes. Visualizing main results

Based on 2912 annually resolved proxy series mainly derived from tree-ring and historical documents (Metadata of Proxies.xlsx), we present a set of standard precipitation index (SPI) reconstructions for annual (Nov-Oct) covering entire Asia and for wet season (i.e., Nov-Apr for western Asia and May-Oct for the others) with the spatial resolution of 2.5° since 1700. The dataset of includes 4 SPI reconstructions: (1) the Nov-Oct SPI reconstruction for entire Asia without using tree-ring density chronologies and width chronologies with negative correlations to precipitation (Nov-Oct SPI Version A); (2) the Nov-Oct SPI reconstruction for entire Asia by adding tree-ring density chronologies and width chronologies with negative correlations to precipitation (Nov-Oct SPI Version B); (3) the wet season SPI reconstruction for the extra-tropical Asia (Nov-Apr SPI for western Asia and May-Oct SPI for the rest regions) without using tree-ring density chronologies and width chronologies with negative correlations to precipitation (wet season SPI Version A); (4) the wet season SPI reconstruction for the extra-tropical Asia (Nov-Apr SPI for western Asia and May-Oct SPI for the rest regions) by adding tree-ring density chronologies and width chronologies with negative correlations to precipitation (wet season SPI Version B). Each of them is stored in a NetCDF file (.nc) and contains 5 three-dimension (longitude × latitude × time) variables, including reconstructed SPI, adjusted coefficient of determination (R2a), validation RE, validation CE and the number of proxies used for construction (nPrx).

The Topographic Wetness Index (TWI) is a commonly used proxy for soil moisture. The predictive capability of TWI is influenced by the flow-routing algorithm and the resolution of the Digital Elevation Model (DEM) that TWI is derived from. Here, we examine the predictive capability of TWI using 11 flow-routing algorithms at DEM resolutions 1 - 30 m. We analyze the relationship between TWI and field-quantified soil moisture using statistical modelling methods and 5200 study plots with over 46 000 soil moisture measurements. In addition, we test the sensitivity of the flow-routing algorithms against vertical height errors in DEM at different resolutions. The results reveal that the overall predictive capability of TWI was modest. The highest R2 (23.7%) was reached using a multiple-flow-direction algorithm at 2 m resolution. In addition, the test of sensitivity against height errors revealed that the multiple-flow-direction algorithms were also more robust against DEM errors than single-flow-direction algorithms. The results provide field-evidence indicating that at its best TWI is a modest proxy for soil moisture and its predictive capability is influenced by the flow-routing algorithm and DEM resolution. Thus, we encourage careful evaluation of algorithms and resolutions when using TWI as a proxy for soil moisture.

Riihimäki, Kemppinen, Kopecký & Luoto (Preprint). Topographic Wetness Index as a proxy for soil moisture: the importance of flow-routing algorithm and grid resolution. Zenodo.

These are the data from Riihimäki & Kemppinen et al. (Preprint).

Compilation of 149 published vegetation and hydroclimate records with their respective references. It is updated compilation of hydroclimate records from tropical South America made by Zhang et al. (2016). We considered all their records that cover the Last Glacial Maximum and added new ones, published since then, that showed vegetation, hydroclimate and environmental reconstructions. Original chronologies of all paleorecords were used. To evaluate the dating quality of the compiled records, we applied a chronological reliability index (CRI). In order to compare the results from the compilation with model simulations, we defined categories for precipitation and vegetation (biome) changes based on the original interpretations of the authors. In the case of precipitation, anomalies are expressed as the difference between LGM and present time, and the categories are "drier", "wetter" and "unclear". Also for vegetation anomalies are expressed as the difference between LGM and present time, and the categories are "change", "no change" and "unclear".

Correlation coefficients between proxy variables and the first component.

Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name Proxy Dot.

No description is available. Visit https://dataone.org/datasets/5db3980d706443a2a3c7154877a93360 for complete metadata about this dataset.

Proxy Funds Rate in the United States decreased to 3.89 percent in October from 3.92 percent in September of 2025. This dataset includes a chart with historical data for the United States Proxy Funds Rate.

In this study we have applied different indices based on long chain diols, i.e., the Long chain Diol Index (LDI) as proxy for past SST, the Diol Index as indicator of past upwelling conditions and the Nutrient Diol Index (NDI) as proxy for nitrate and phosphate concentrations in seawater. The proxies were analyzed in marine sediments recovered at ODP Site 1234, located within the Peru-Chile upwelling system, with a 2 kyr resolution, covering the last 150 kyrs. We also generated TEX^H^~86 and U_K'37 temperature and planktonic δ^18^O records, as well as total organic carbon (TOC) and accumulation rates (ARs) of TOC and lipid biomarkers (i.e., C~37~ alkenones, GDGTs, dinosterol and loliolide) to reconstruct past phytoplankton production. The LDI-derived SST record co-varies with TEX^H^86- and UK'37-derived SST records as well as with the planktonic δ^18^O record, implying that the LDI reflects past SST variations at this site. TOC and phytoplankton AR records indicate increased export production during the Last Interglacial (MIS 5), simultaneous with a peak in the abundance of preserved _Chaetoceros diatoms, suggesting intensified upwelling during this period. The Diol Index is relatively low during the upwelling period, but peaks before and after this period, suggesting that Proboscia diatoms were more abundant before and after the period of upwelling. The NDI reveals the same trends as the Diol Index suggesting that the input of nitrate and phosphate was minimal during upwelling, which is unrealistic. We suggest that the Diol Index and NDI should perhaps be considered as indicators for Proboscia productivity instead of upwelling conditions or nutrient concentrations.

This dataset is about: Alteration index and proxy, and oxide mass concetrations of loess-paleosol sequence Hecklingen. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.870512 for more information.

Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name Proxy protection llc.

Mg/Ca-based SST and thermocline temperatures with 1Sigma error at different sites, their difference (DeltaT), and the average DeltaT values for different periods with 95% confidence interval (CI). Also shown are hydrogen isotope (dD) values of n-C31 alkanes relative to the standard mean ocean water (per mil SMOW) with the 95% CI (LGM values are corrected for ice-volume), the C31 n-alkane concentrations in nanogram per gram sediment (ng g-1), the Carbon Preference Index (CPI27-33) as a measure of the degree of terrestrial organic matter degradation (higher CPI reflects less degraded plant waxes and vice versa), and their 1Sigma standard deviations (SD).

The correlation coefficients between proxy variables.

The correlation coefficients between the realized volatility and the proxy variables.

The data published here were gathered in the framework of a multi-proxy-based study of paleotemperature (both marine and terrestrial), -salinity, and -ecosystem changes from the Little Belt (Site M0059). They cover the past ~8,000 years and contain only material from the uppermost subunits 1a and 1b encountered at Site M0059 (see e.g. Andrén et al. 2015). Four environmental zones (EZ1: oldest, freshwater conditions; EZ2 to EZ4 reflecting following salinity and ecosystem changes in the region) were identified in Kotthoff et al. (2017). The age model and the sedimentology are discussed in Kotthoff et al. (2017). The datasets comprise data for salinity proxies (diatoms, aquatic palynomorphs, diol index) and for water temperature proxies (foraminiferal Mg/Ca-ratios, long chain diol index and TEXL86) as well as temperature reconstruction based on pollen grains. It is discussed in Kotthoff et al. (2017) that applying and interpreting proxies in coastal environments and marginal seas needs particular caution. For example, foraminiferal Mg/Ca-ratios may have been influenced by contamination by authigenic coatings in the deeper intervals of the record. Lipid paleothermometers were probably influenced by significant changes in depositional settings in the Little Belt. References: Andrén, T., Jørgensen, B.B., Cotterill, C., and the Expedition 347 Scientists: Baltic Sea Paleoenvironment. Proceedings IODP, 347. College Station, TX (Integrated Ocean Drilling Program), https://doi.org/10.2204/iodp.proc.347.101.2015, 2015. Kotthoff, U., Groeneveld, J., Ash, J. L., Fanget, A.-S., Krupinski, N. Q., Peyron, O., Stepanova, A., Warnock, J., Van Helmond, N. A. G. M., Passey, B. H., Clausen, O. R., Bennike, O., Andrén, E., Granoszewski, W., Andrén, T., Filipsson, H. L., Seidenkrantz, M.-S., Slomp, C. P., and Bauersachs, T.: Reconstructing Holocene temperature and salinity variations in the western Baltic Sea region: a multi-proxy comparison from the Little Belt (IODP Expedition 347, Site M0059), Biogeosciences, 14, 5607–5632, https://doi.org/10.5194/bg-14-5607-2017, 2017.

High-resolution proxy data analyzed on two high-sedimentation shallow water sedimentary sequences (PO287-26B and PO287-28B) recovered off Lisbon (Portugal) provide the means for comparison to long-term instrumental time series of marine and atmospheric parameters (sea surface temperature (SST), precipitation, total river flow, and upwelling intensity computed from sea level pressure) and the possibility to do the necessary calibration for the quantification of past climate conditions. XRF Fe is used as proxy for river flow, and the upwelling-related diatom genus Chaetoceros is our upwelling proxy. SST is estimated from the coccolithophore-synthesized alkenones and Uk'37 index. Comparison of the Fe record to the instrumental data reveals its similarity to a mean average run of the instrumentally measured winter (JFMA) river flow on both sites. The upwelling diatom record concurs with the upwelling indices at both sites; however, high opal dissolution, below 20-25 cm, prevents its use for quantitative reconstructions. Alkenone-derived SST at site 28B does not show interannual variation; it has a mean value around 16°C and compares quite well with the instrumental winter/spring temperature. At site 26B the mean SST is the same, but a high degree of interannual variability (up to 4°C) appears to be determined by summer upwelling conditions. Stepwise regression analyses of the instrumental and proxy data sets provided regressions that explain from 65 to 94% of the variability contained in the original data, and reflect spring and summer river flow, as well as summer and winter upwelling indices, substantiating the relevance of seasons to the interpretation of the different proxy signals. The lack of analogs and the small data set available do not allow quantitative reconstructions at this time, but this might be a powerful tool for reconstructing past North Atlantic Oscillation conditions, should we be able to find continuous high-resolution records and overcome the analog problem.

Abbreviations: ADL = Activities Daily Living, BMI = Body Mass Index, CESD = Center for Epidemiologic Studies Depression Scale, FFQ = Food Frequency Questionnaire, MMSE = Mini Mental State Examination, MNA = Mini Nutritional Assessment.

The SDI(g) dataset complements Hickel's (2020) Sustainable Development Index (SDI) by considering the Governance Index (GI) as a proxy of the countries' governance climate computed with the World Bank-Worldwide Governance Indicators (WGIs).