Global Gridded Datasets Research Articles

Evaluation of the accuracy of satellite-based rainfed wheat yield dataset over an area with complex geography

Global gridded datasets have a potential of being a reliable resource for filling gaps in observed data in global and regional studies. Global gridded cropping system datasets are useful in various fields, such as crop and hydrological simulations. They could serve as a source alternative to observed datasets. This study evaluates data on rainfed wheat yield derived from a satellite-based Global Dataset of Historical Yields (GDHY) over Iran from the years 1983–2016 (in this period, the average rainfed wheat yield over Iran was 0.86 tons/ha). The process of development of this dataset employed satellite products, solar radiation, harvested area, crop calendar, and production share by cropping season. Our evaluations were conducted at the country scale, province scale, and in reference to different climates based on the Köppen-Geiger climate classification. The index of agreement over Iran is approximately 0.79, and according to percentage bias, GDHY has a 1.6% error over Iran. The dataset was reliable at the sub-national level, over the western parts, and particularly in the mountainous region. In reference to the root mean square error (tons/ha), it showed the lowest performance in the humid part of Iran (Mazandaran, Gilan, and Golestan provinces). Drought analysis showed that the reference crop yield dataset over the study area is more consistent with SPI-12, although GDHY agrees with SPI-3. In contrast to drier years in recent years, the crop yield has increased. This positive trend can be justified by increasing the rate of fertilizer applications, better grazing, an overall improvement in farming technologies, and better farm management at the country scale. In conclusion, GDHY can be used at the national scale for different analyses or modeling, but it is inaccurate at the sub-national level.

Possibility of global gridded streamflow dataset correction: applications of large-scale watersheds with different climates

Possibility of global gridded streamflow dataset correction: applications of large-scale watersheds with different climates

A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)

Abstract. Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax⁡ and Tmin⁡) is particularly valuable and critically needed in the scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax⁡ and Tmin⁡ at 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-based Ta measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates ranged from 1.20 to 2.44 ∘C for Tmax⁡ and 1.69 to 2.39 ∘C for Tmin⁡. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship between Ta and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporal patterns of Ta realistically. This global 1 km gridded daily Tmax⁡ and Tmin⁡ dataset is the first of its kind, and we expect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data have been published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).

A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network.

Municipal water withdrawal (MWW) information is of great significance for water supply planning, including water supply pipeline networks planning, optimization and management. Currently most MWW data are reported as spatially aggregated over large-area survey regions or even lack of data, which is unable to meet the growing demand for spatially detailed data in many applications. In this paper, six different models are constructed and evaluated in estimatingglobal MWW using aggregated MWW data and gridded raster covariates. Among the models, the artificial neural network-based indirect model (NNM) shows the best accuracy with higher R2and lower NMAE and NRMSE in different spatial scales. The estimates achieved from the NNMmodel are consistent with census and survey data, and outperforms the existing global gridded MWW dataset. At last, the NNMmodel is applied to mapping global gridded MWW for the year 2015 at 0.1 × 0.1° resolution.The proposed method can be applied to a wider aggregated output learning problemand the high-resolution global gridded MWW data can be used in hydrological models and water resources management.

ENSO Teleconnections More Uncertain in Regions of Lower Socioeconomic Development

AbstractThe El Niño Southern Oscillation (ENSO) impacts climate variability globally and can influence extreme climate and weather events. We quantify the uncertainty in ENSO's atmospheric teleconnections with extremes using the Twentieth Century Reanalysis, showing that uncertainty estimates vary regionally over the historical period. Uncertainty is found to be greater in regions of lower socioeconomic development. This can be linked to the limited availability of observational data in these regions as well as difficulties constraining tropical climate dynamics in global gridded atmospheric data sets. Poorer locations face greater challenges due to lack of understanding of past variability limiting confidence in regional projections.

Attribution of climate change and human activities to streamflow variations with a posterior distribution of hydrological simulations

Abstract. Hydrological simulations are a main method of quantifying the contribution rate (CR) of climate change (CC) and human activities (HAs) to watershed streamflow changes. However, the uncertainty of hydrological simulations is rarely considered in current research. To fill this research gap, based on the Soil and Water Assessment Tool (SWAT) model, in this study, we propose a new framework to quantify the CR of CC and HAs based on the posterior histogram distribution of hydrological simulations. In our new quantitative framework, the uncertainty of hydrological simulations is first considered to quantify the impact of “equifinality for different parameters”, which is common in hydrological simulations. The Lancang River (LR) basin in China, which has been greatly affected by HAs in the past 2 decades, is then selected as the study area. The global gridded monthly sectoral water use data set (GMSWU), coupled with the dead capacity data of the large reservoirs within the LR basin and the Budyko hypothesis framework, is used to compare the calculation result of the novel framework. The results show that (1) the annual streamflow at Yunjinghong station in the Lancang River basin changed abruptly in 2005, which was mainly due to the construction of the Xiaowan hydropower station that started in October 2004. The annual streamflow and annual mean temperature time series from 1961 to 2015 in the LR basin showed significant decreasing and increasing trends at the α= 0.01 significance level, respectively. The annual precipitation showed an insignificant decreasing trend. (2) The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was 42.6 % due to CC, and the remaining 57.4 % was due to HAs, such as the construction of hydropower stations within the study area. (3) The comparison with the other two methods showed that the CR of CC calculated by the Budyko framework and the GMSWU data was 37.2 % and 42.5 %, respectively, and the errors of the calculations of the new framework proposed in this study were within 5 %. Therefore, the newly proposed framework, which considers the uncertainty of hydrological simulations, can accurately quantify the CR of CC and HAs to streamflow changes. (4) The quantitative results calculated by using the simulation results with the largest Nash–Sutcliffe efficiency coefficient (NSE) indicated that CC was the dominant factor in streamflow reduction, which was in opposition to the calculation results of our new framework. In other words, our novel framework could effectively solve the calculation errors caused by the “equifinality for different parameters” of hydrological simulations. (5) The results of this case study also showed that the reduction in the streamflow in June and November was mainly caused by decreased precipitation and increased evapotranspiration, while the changes in the streamflow in other months were mainly due to HAs such as the regulation of the constructed reservoirs. In general, the novel quantitative framework that considers the uncertainty of hydrological simulations presented in this study has validated an efficient alternative for quantifying the CR of CC and HAs to streamflow changes.

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020–2100 by implementing Random Forest (RF) algorithm. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways’ (SSPs) national population. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields.

The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets

Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess its capacity to capture the distribution of population counts in several urban areas, spread across the major world regions. This analysis was performed to assess its suitability for global population modelling. We acquired the most detailed local population data available for several cities and compared this with the GHS-POP layer. Results showed diverse error rates and degrees depending on the geographic context. In general, cities in High-Income (HIC) and Upper-Middle-Income Countries (UMIC) had fewer model errors as compared to cities in Low- and Middle-Income Countries (LMIC). On a global average, 75% of all urban spaces were wrongly estimated. Generally, in central mixed or non-residential areas, the population was overestimated, while in high-density residential areas (e.g., informal areas and high-rise areas), the population was underestimated. Moreover, high model uncertainties were found in low-density or sparsely populated outskirts of cities. These geographic patterns of errors should be well understood when using population models as an input for urban growth models, as they introduce geographic biases.

Global gridded GDP data set consistent with the shared socioeconomic pathways

The vulnerability, exposure and resilience of socioeconomic activities to future climate extremes call for high-resolution gridded GDP in climate change adaptation and mitigation research. While global socioeconomic projections are provided mainly at the national level, and downscaling approaches using nighttime light (NTL) images or gridded population data can increase the uncertainty due to limitations. Therefore, we adopt an NTL-population-based approach, which exhibits higher accuracy in socioeconomic disaggregation. Gross regional product of over 800 provinces, which covering over 60% of the global land surface and accounted for more than 80% of GDP in 2005, were used as input. We present a first set of comparable spatially explicit global gridded GDP projections with fine spatial resolutions of 30 arc-seconds and 0.25 arc-degrees for the historical period of 2005 and for 2030–2100 at 10-year intervals under the five SSPs, accounting for the two-child policy in China. This gridded GDP projection dataset can broaden the applicability of GDP data, the availability of which is necessary for socioeconomic and climate change research.

Global Gridded Argo Dataset Based on Gradient-Dependent Optimal Interpolation

The international Argo Program was launched at the turn of the millennium. It has since collected over 2 million vertical profiles of temperature and salinity from the upper 2000 m of the global ocean. Gridded interpolation is a technology that gives full play to the advantages of these profiles because they are scattered. This study develops a global gridded Argo dataset, called GDCSM-Argo, by using an improved gradient-dependent correlation scale method. The dataset is theoretically verified, its error-related statistics are recorded, and it is compared with other datasets to establish its reliability. The results show that the maximum mean RMSEs are 0.8 °C for temperature and 0.1 for salinity, and more than 90% of the analysis results are reliable under the statistical probability of 95%. Not only can GDCSM-Argo adequately preserve large-scale signals in the ocean but also retain more mesoscale features than other gridded Argo datasets. Preliminary applications also verify that GDCSM-Argo can systematically describe the spatio-temporal features of multiple elements in the global ocean, and is a useful tool in many areas of research.

Mapping long-term and high-resolution global gridded photosynthetically active radiation using the ISCCP H-series cloud product and reanalysis data

Abstract. Photosynthetically active radiation (PAR) is a fundamental physiological variable for research in the ecological, agricultural, and global change fields. In this study, we produced a 35-year (1984–2018) high-resolution (3 h, 10 km) global gridded PAR dataset using an effective physical-based model. The main inputs of the model were the latest International Satellite Cloud Climatology Project (ISCCP) H-series cloud products, MERRA-2 aerosol data, ERA5 surface routine variables, and MODIS and CLARRA-2 albedo products. Our gridded PAR product was evaluated against surface observations measured at 7 experimental stations of the SURFace RADiation budget network (SURFRAD), 42 experimental stations of the National Ecological Observatory Network (NEON), and 38 experimental stations of the Chinese Ecosystem Research Network (CERN). Instantaneous PAR was validated against SURFRAD and NEON data; mean bias errors (MBE) and root mean square errors (RMSE) were, on average 5.8 and 44.9 W m−2, respectively, and the correlation coefficient (R) was 0.94 at the 10 km scale. When upscaled to 30 km, the errors were markedly reduced. Daily PAR was validated against SURFRAD, NEON, and CERN data, and the RMSEs were 13.2, 13.1, and 19.6 W m−2, respectively, at the 10 km scale. The RMSEs were slightly reduced when upscaled to 30 km. Compared with the well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES), our PAR product was found to be a more accurate dataset with higher resolution. This new dataset is now available at https://doi.org/10.11888/RemoteSen.tpdc.271909 (Tang, 2021).

Mapping rainfall erosivity over India using multiple precipitation datasets

Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.

Evaluating the performance of eight global gridded precipitation datasets across Iran

Precipitation is one of the crucial variables in hydrological and water resources studies. Runoff, soil moisture, groundwater recharge, and other hydrological variables change with the amount of precipitation in a region. Thus, accurate precipitation estimation is a major issue in the field of water resources and hydrological modeling. Most hydrological studies and water management decisions in Iran are established using in situ observation of precipitation. However, due to limited spatial distribution and lack of long-term high-quality data in many stations, this information usually does not fully meet the information requirements of a given study. In this study, the accuracies of eight global gridded precipitation datasets including Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU), Climate Prediction Center (CPC), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), PERSIANN-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Project (GPCP), Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2), and ECMWF Reanalysis version 5 (ERA5) were evaluated to identify the strengths and weaknesses of each dataset in different regions and main catchments of Iran. The precipitation of 96 synoptic stations during the years 1987–2016 was used as the basis for the evaluations. The evaluations were made at monthly, seasonal, and annual scales using grid-based evaluation and areal average evaluation at the catchments scale. In addition to the amount of precipitation, the datasets were evaluated using three statistical performance indices (i.e., correlation coefficient, RMSE, and relative bias) and other metrics such as probability distribution similarity with observation data (using Anderson-darling test), empirical cumulative distribution function (ECDF), and Taylor diagrams. The results indicated that dataset performance varied by catchment. Except for central parts, GPCC was the best dataset in 44% of the evaluated grids in terms of time series pattern recognition. It showed correlation coefficients of 0.71, 0.80, and 0.85, in monthly, seasonal, and annual scales, respectively. In central areas, MERRA2 estimated the average annual precipitation in this catchment with + 2 mm difference, and CRU had a correlation coefficient greater than 0.90. Moreover, CHIRPS was the best dataset in terms of relative bias in the south and southwestern catchments, so that the relative bias was negligible in these catchments in most months. This study provides rankings and recommendations for selecting an appropriate alternative precipitation dataset, which, in turn, provides the knowledge required in monitoring systems and hydrological and water resources modeling that need high-resolution input data.

Climate Changes and Their Elevational Patterns in the Mountains of the World

AbstractQuantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation‐dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation‐dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation‐dependency of precipitation, especially in midlatitudes. Agreement on elevation‐dependent changes between gridded data sets is weak for temperature but stronger for precipitation.

Global gridded crop harvested area, production, yield, and monthly physical area data circa 2015

Here we provide an update to global gridded annual and monthly crop datasets. This new dataset uses the crop categories established by the Global Agro-Ecological Zones (GAEZ) Version 3 model, which is based on the Food and Agricultural Organization of the United Nations (FAO) crop production data. We used publicly available data from the FAOSTAT database as well as GAEZ Version 4 global gridded dataset to generate circa 2015 annual crop harvested area, production, and yields by crop production system (irrigated and rainfed) for 26 crops and crop categories globally at 5-minute resolution. We additionally used available data on crop rotations, cropping intensity, and planting and harvest dates to generate monthly gridded cropland data for physical areas for the 26 crops by production system. These data are in standard georeferenced gridded format, and can be used by any global hydrology, land surface, or other earth system model that requires gridded annual or monthly crop data inputs.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin.

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision–making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann–Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the Tmin in the coldest month over the whole basin at a rate of 0.03–0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00477-022-02172-8.

Spatiotemporal characterization of meteorological drought: a global approach using the Drought Exceedance Probability Index (DEPI)

We present a global spatiotemporal characterization of meteorological droughts using historical precipitation data through the Drought Exceedance Probability Index (DEPI). The relationship between meteorological drought characteristics and monthly precipitation is explored at a global level. This study contributes to our understanding of the drought features observed in different areas of the planet, which can help predict the behavior of future droughts. The DEPI was applied to the Climate Research Unit global gridded high-resolution rainfall data set covering the period 1901-2019. Monthly drought index series were examined to extract the number of droughts experienced in each pixel (0.50° × 0.50°) of the globe, as well as their durations, intensities and severities. Results show agreement with other global drought characterization efforts, revealing areas with a greater drought occurrence. This paper demonstrates that regions with less seasonality and less intra- and inter-annual rainfall variability report fewer drought episodes. Duration and severity of droughts are also related to these rainfall features. The last part of the study describes the temporal distribution of droughts throughout the world. We conclude that regions with many events show stable, even distributions over time, but many pixels in the intertropical regions, the Middle East and smaller patches in Mongolia, China, Siberia and Canada currently show higher-intensity and longer-duration drought events than at the beginning of the twentieth century, while the opposite occurs in parts of Scandinavia, Russia, Argentina and Tanzania. The analysis demonstrates that DEPI is easy to use, is applicable to different climates and is effective in detecting the onset, end and intensity of droughts.

Soil carbon-food synergy: sizable contributions of small-scale farmers

BackgroundBenefits to agricultural yield improvement, soil degradation prevention, and climate mitigation are central to the synergies of soil organic carbon (SOC) build-up. However, the contributions of small-scale farmers, the main target of recent agricultural and rural development policies, to SOC enhancement are understudied. Here, we present a global analysis of small-scale farmers’ contributions to the potential of additional SOC stocks and the associated increase in crop production.MethodsWe applied random forest machine learning models to global gridded datasets on crop yield (wheat, maize, rice, soybean, sorghum and millet), soil, climate and agronomic management practices from the 2000s (n = 1808 to 8123). Using the established crop-specific SOC-yield relationships, the potentials of additional SOC build-up and crop production increase were simulated. The estimated SOC increase was converted into global decadal mean temperature change using the temperature sensitivity to cumulative total anthropogenic CO2 emissions from preindustrial levels. The amount of inorganic nitrogen (N) input that would result in the same yield outcome as the SOC build-up was derived from the crop-specific N-yield relationships.ResultsSOC contributes to yields in addition to management and climatic factors. Additional SOC sums up to 12.78 GtC (11.55–14.05 GtC) of global SOC stock, which earns 38.24 Mt (22.88–57.48 Mt) of additional crop production and prevents warming by 0.030 °C (0.019–0.041 °C). This production increase equates to what would be achieved by an inorganic N input of 5.82 Mt N (3.89–7.14 Mt N). Small-scale farmers account for 28% (26–30%) of the additional SOC build-up and 17% (15–20%) of the production increase. Key crops and regions in terms of small-scale farmers’ contributions include Sub-Saharan African maize and rice, Latin American and Caribbean soybean and maize, and South Asian rice and wheat.ConclusionsThe contribution of small-scale farmers to the potential increase in SOC stock and crop production is sizable, which in theory further leads to saving inorganic N input. These findings emphasize the importance of linking soil management to sustainable land and climate mitigation with institutions and policy for small-scale farmers. Such a joint policy would assist multiple development goals.

Comprehensive evaluation of precipitation datasets over Iran

Global gridded precipitation datasets have been developed using rain gauges, satellite observations, and data assimilation techniques to fulfill the need in regions with a limited contribution of ground observations like Iran. This study presents a comprehensive evaluation of currently available precipitation datasets over Iran at monthly (44 datasets) and daily (34 datasets) time scales. To include the maximum number of datasets and in situ data, we consider two periods for the evaluations, namely 2003–2010 for the daily and monthly assessment and 2014–2018 for the daily. For the assessment, a network of more than 1500 rain gauges is utilized within 2003–2010 and 370 rain gauges within 2014–2018. Moreover, we compare the pixel-to-pixel (interpolated in situ data v.s. gridded datasets), and point-to-pixel (in situ data as a point v.s. gridded datasets) approaches in assessing datasets performances. In terms of the Kling-Gupta efficiency (KGE) parameters, the datasets perform worse in bias at monthly time scales and correlation at daily time scales. However, considering in situ precipitation above 5 mm/day, all datasets perform poorly in estimating precipitation variability. We find that, in general, reanalysis products have a higher KGE, ranging between 0.41 (0.21) and 0.91 (0.71), than satellite-based products with a KGE ranging from 0.14 (-0.57) to 0.92 (0.57) over Iran at monthly (daily) scale. Moreover, GPCC overall matches the validation dataset better than other products over Iran’s basins, whereas CPC, ERA5, and IMERG-Final are more suitable for near-real-time studies. Also, if latency is a top criterion, PERSIANN-PDIR will be the first option. Indeed, PERSIANN-PDIR with a KGE value of 0.69 (0.33) at monthly (daily) time scale within 2003–2010 performs remarkably well, as a non-adjusted real-time satellite-based product. The comparison between the point-to-pixel and the pixel-to-pixel approaches shows that the point-to-pixel approach underestimates the quality of the datasets but does not change the ranking of the datasets.

The Effect of Water Vapor Originating from Land on the 2018 Drought Development in Europe

The 2018 summer drought in Europe was particularly extreme in terms of intensity and impact due to the combination of low rainfall and high temperatures. However, it remains unclear how this drought developed in time and space in such an extreme way. In this study we aimed to get a better understanding of the role of land–atmosphere interactions. More specifically, we investigated whether there was a change in water vapor originating from land, if that caused a reduction in rainfall, and by this mechanism possibly the propagation and intensification of the drought in Europe. Our first step was to use remote sensing products for soil moisture content (SMC) and the normalized difference vegetation index (NDVI) to see where the 2018 drought started and how it developed in time and space. Our SMC and NDVI analysis showed that the 2018 drought started to impact the soil and vegetation state in June in Scandinavia and the British Isles. After that it moved towards the west of Europe where it intensified in July and August. In September, it started to decay. In October, drought was observed in Southeast Europe as well. Based on the observed patterns we divided Europe into six regions of similar spatiotemporal characteristics of SMC and NDVI. Then, we used a global gridded dataset of the fate of land evaporation (i.e., where it ends up as precipitation) to investigate whether the drought intensification and propagation was impacted by the reduction in water vapor transported from the regions that first experienced the drought. This impact was investigated by identifying the anomalies in the water vapor originating from land recycling, imports, and exports within Europe during the spring, summer, and autumn seasons. From these regions we identified four drought regions and investigated the changes in water vapor originating from source regions on the development of drought in those regions. It was found that during the onset phase of the 2018 drought in Europe that the water vapor originating from land played an important role in mitigating the precipitation anomalies as, for example, the share of land evaporation contributing to precipitation increased from 27% (normal years) to 38% (2018) during July in the west of Europe. Land evaporation played a minor role in amplifying it during the intensification phase of the drought as the share of land evaporation contribution to precipitation decreased from 23% (normal years) to 21% (2018) during August in the west of Europe. These findings are somewhat in contrast to similar studies in other continents that found the land surface to play a strong amplifying role for drought development. Subsequently, we found that the relative increase in the amount of land water vapor originating from eastern half of Europe played a role in delaying the onset and accelerating the decay of the 2018 drought in the west of Europe.