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10 results for “drought monitoring”
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2020 in China
<p>In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)
<p>The Enhanced Vegetation Index (EVI), Land Surface Temperature (LST) and Precipitation (P) were used as new data sources based on the spatial distance model to construct an optimized multi-source remote sensing dryness index named Temperature-Vegetation-Precipitation Dryness Index based on the shortcomings of the TVPDIorigin (i.e., TVPDI<sub>o</sub>) data source. The TVPDI<sub>n</sub> of the long time series was also compared and analyzed with the classical drought index - Standardized Precipitation Evapotranspiration Index (SPEI-3) on a 3-month scale, different drought response level products of Solar-Induced Chlorophyll Fluorescence (SIF), soil moisture (SM) from ESA CCI (European Space Agency's Climate Change Initiative), and total crop yield, then the sensitivity and validity of the TVPDI<sub>n</sub> for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification:</p> <p>(1) Compared with the original data source TVPDI<sub>o</sub> using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDI<sub>n</sub>, the overall correlation between the two and SPEI-3 was good, with a maximum of 0.57 and 0.56, respectively (p< 0.1), but the overall TVPDI<sub>n</sub> constructed in this study had a better fit compared to the original data source TVPDI<sub>o</sub> and was more sensitive to the monitoring of dry and wet conditions.</p> <p>(2) According to the comparison of TVPDI<sub>n</sub> with ESA CCI sm, TVPDI<sub>n</sub> showed a high correlation of more than 0.9 with soil water content, which proved that TVPDI<sub>n</sub> was highly consistent with soil moisture; compared with SIF, 54.5% of the regional correlation coefficients were greater than 0.8 (p< 0.01), and spatially, the correlation results were better in the northwest than in the east, indicating that the response of TVPDI<sub>n</sub> to vegetation productivity is more agile in regions with continental climate such as the northwest. The results of correlation with grain yield comparison showed that good positive correlations were presented with TVPDI<sub>n</sub> in Liaodong Peninsula, northern North China Plain, and most of Qilian Mountains, southern edge of Qinling Mountains, middle and lower reaches of Yangtze River, and South China, indicating that TVPDI<sub>n</sub> has a high consistency in the changes of agricultural grain production in the above mentioned regions, and also proving the index in monitoring agricultural aridity and guiding agricultural production The good performance of the index in monitoring agricultural aridity and guiding agricultural production.</p> <p> This dataset is version 2.0, and covers all of China's territory, but the temperature-vegetation- precipitation dryness index of the open water surface are often set to a null value. Note:The data format is "TIF", the spatial resolution is "1 km", the time resolution is "1 month" and dimensionless. The pixel value is the NTVPDI value, and the closer the pixel value is to 0, the drier it is, and the larger the data, the wetter the land surface. The practical utility of this dataset is to compare the degree of dryness and wetness of China's land, to monitor short-term and medium-term droughts, and to substitute model parameters related to soil moisture. This is of great value to the impartial formulation of China's environmental and economic policies, regular monitoring and evaluation of drought and flood conditions. This product will be freely available to all users worldwide and will be continuously improved to suit new goals and needs.</p>
2021-2022 West False River Emergency Drought Barrier water quality, flow, and fish monitoring
To manage the critically low 2021 water supply for beneficial uses, DWR installed the temporary emergency drought barrier (EDB) on West False River in the Sacramento–San Joaquin Delta (Delta), approximately 5 miles south of Rio Vista, California, in Contra Costa County in June 2021. To monitor the effectiveness and impacts of the EBD, a monitoring program was initiated to track changes in hydrodynamics, water quality, fish, harmful algal blooms, and aquatic weeds in the vicinity of the EDB. The EDB was left in place during the winter of 2021-2022 and removed in fall of 2022. This data set includes all data collected as part of that monitoring program and subsets of ongoing monitoring programs that were used in the final effectiveness report for the EDB.
Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale
<p><strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen)</p> <p><strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p><strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").</p> <p> </p>
Satellite-driven 10km global root-zone soil moisture analysis for drought monitoring
<p>Root-zone soil moisture condition is an important component of water cycle at all spatial scales, as it controls various hydrological, biological and meteorological processes such as plant transpiration and hydraulic redistribution. Passive microwave remote sensing offers the possibility to access to near-surface (0 - 5cm) soil moisture measurements over large areas, providing valuable information for agricultural and water resource management. However, the spatial resolution of satellite soil moisture estimates from passive microwave sensor is relatively coarse (25 to 50km) and infrequent in time. Data assimilation algorithms are widely used to obtain spatially complete and daily continuous soil moisture estimates from intermittent remotely sensed soil moisture data and numerical models. </p> <p>The dataset contains the most recent global surface and root-zone soil moisture conditions at 10km generated from the Satellite-Guided Root-zone moisture Analysis and Forecasting System (S-GRAFS) from 2015 to 2022. S-GRAFS is a near-real time data assimilation system that combines complementary information from model simulations and satellite observations to provide soil moisture estimates at near surface and root-zone. In S-GRAFS, satellite soil moisture observations from Soil Moisture Active Passive (SMAP) are assimilated into a simple first-order autoregressive model that captures the soil moisture conditions in response to precipitation. Satellite precipitation from Global Precipitation Measurement (GPM) is used to drive the model to simulate near surface soil moisture at 5cm. The assimilation of SMAP data relies on the four-dimensional variational (4DVAR) method to adjust the modelled surface soil moisture towards observations within a 4-day assimilation window. Soil Water Index (SWI) is then derived from the analysed surface soil moisture using an exponential filter and represents the root-zone soil wetness at approximate 1m depth. The surface and root-zone wetness from S-GRAFS can be converted into absolute soil moisture content using soil physical properties to provide essential support for a wide variety of hydrological and agricultural applications.</p>
Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)
<p><strong>1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong></p> <p>CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f,</p> <p>CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f,</p> <p>CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude:</strong></p> <p>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"):</strong></p> <p>Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt</p> <p>GRDC_number_WaterGAP_ArcID_220_stations.txt</p> <p><strong>4) SDHIs for four GRDC gauging stations:</strong></p> <p>time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt</p> <p><strong>5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p> </p>
Flash drought monitor (FDM) datasets
<p>Flash drought monitor (FDM) is the first near-real time monitoring system for operational tracking of flash drought conditions in Spain (<a href="https://flash-drought.csic.es/">https://flash-drought.csic.es</a>). FDM is based on Standardized Precipitation Evapotranspiration Index (SPEI) at short time scale (1-month) and high spatial (1.21 km<sup>2</sup>) and temporal (weekly) resolution data. Flash drought events were identified following the methodology suggested by Noguera et al. (2020). Thus, we defined a flash drought event as: (i) a minimum length of 4 weeks in the development phase; (ii) a ΔSPEI equal to or <−2 z-units; and (iii) a final SPEI value equal to or <−1.28 z-units (corresponding to return periods of 10 years). See additional details in Noguera et al. (2020).</p> <p>Here, we provide the datasets used by FDM to monitor flash drought conditions in Spain for the period 1961-2021. We include the SPEI (1-month time scale) dataset used for flash drought identification, as well as meteorological datasets required for SPEI calculation [i.e., precipitation and reference evapotranspiration (ETo)]. Likewise, we include the dataset with the flash drought conditions recorded in Spain for the period 1961-2021, indicating the number of weeks elapsing from the onset of a flash drought by means of the following encoding: value = 0 (no flash drought), value = 1 (flash drought onset), value = 2 (1st week from onset), value = 3 (2nd week from onset), value = 4 (3rd week from onset). All datasets are provided in netCDF (network Common Data Form) format, and contain high spatial (1.21 km<sup>2</sup>) and temporal (weekly) resolution data for the period 1961-2021.</p> <p>The precipitation and ETo datasets were created using all daily observational information of precipitation, maximum and minimum air temperature, relative humidity, sunshine duration (as a surrogate of solar radiation) and wind speed from the National Spanish Meteorological Service (AEMET). FAO-56 Penman–Monteith equation (Allen et al., 1998) was used to calculate the reference evapotranspiration (ETo), which is a spatially and temporally comparable metric for the atmospheric evaporative demand (AED). Employing precipitation and ETo data we computed SPEI at a 1-month time scale following the methodology proposed by Vicente-Serrano et al. (2010). Additional details of the datasets development and validation are available in Vicente-Serrano et al. (2017).</p> <p><strong>References:</strong></p> <p>Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements.</p> <p>Noguera, I., Domínguez-Castro, F., & Vicente-Serrano, S. M. (2020). Characteristics and trends of flash droughts in Spain, 1961–2018. Annals of the New York Academy of Sciences, 1472(1), 155– 172. https://doi.org/10.1111/nyas.14365</p> <p>Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718. https://doi.org/10.1175/2009JCLI2909.1</p> <p>Vicente-Serrano, S. M., Tomas-Burguera, M., Beguería, S., Reig, F., Latorre, B., Peña-Gallardo, M., et al. (2017). A High Resolution Dataset of Drought Indices for Spain. Data, 2(3), 22. https://doi.org/10.3390/data2030022</p>
NDVI-based monitoring of long-term vegetation dynamics and responses to multi-time scales droughts in Inner Mongolia
<p>The characteristics of vegetation and drought for different seasons between 1982 and 2015 in Inner Mongolia were studied based on the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The response of vegetation to drought over various time scales for different seasons and vegetation types was investigated using the maximum Pearson correlation, allowing a discussion about the possible causes of any changes. The results indicate that the vegetation NDVI in Inner Mongolia showed an increasing trend in different seasons, with spring vegetation NDVI (April to May) having the largest significant increasing rate, followed by the growing season (April to October), autumn (September to October), and summer (June to August). Accordingly, the proportion of stations with decreasing SPEI was, in descending order, summer, growing season, autumn, and spring. Additionally, the magnitude of the SPEI decrease was greater in eastern Inner Mongolia. NDVI and SPEI were positively correlated in most regions of Inner Mongolia, indicating that changes in vegetation in most parts of this region were affected by the spatial and temporal characteristics of drought, the correlation being them being strongest in the growing season, followed by summer, then spring and autumn. Considering the different types of vegetation, forests were less affected by drought, with broadleaf forests more affected than coniferous forests. The meadow steppes and typical steppes were more affected by 12-month droughts in the growing season and summer, 6-month droughts in spring, and 3-month droughts in autumn, with desert steppes mainly affected by 3-month droughts. The shrubs, sandy vegetation, and cropland were mostly affected by droughts in summer, and show a greater response to 3-month droughts in autumn. Finally, the water balance was found to be the most important factor affecting the response of vegetation to drought in Inner Mongolia.</p>
NDVI-based monitoring of long-term vegetation dynamics and responses to multi-time scales droughts in Inner Mongolia
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Long-term monitoring of a highly invaded annual grassland community through drought, before and after an unintentional fire.
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