We introduce a set of global high-resolution (0.05°) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the “true” long-term P using a Budyko curve, which is an empirical equation relating long-term P, Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-art P climatologies [the “WorldClim version 2” database (WorldClim V2); Climatologies at High Resolution for the Earth’s Land Surface Areas, version 1.2 (CHELSA V1.2 ); and Climate Hazards Group Precipitation Climatology, version 1 (CHPclim V1)], after which we used random-forest regression to produce global gap-free bias correction maps for the P climatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors on the basis of gauge catch efficiencies. We found that all three climatologies systematically underestimate P over parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. In addition, all climatologies underestimate P at latitudes >60°N, likely because of gauge undercatch. Exceptionally high long-term correction factors (>1.5) were obtained for all three P climatologies in Alaska, High Mountain Asia, and Chile—regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely used P datasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimate P over Chile, the Himalayas, and along the Pacific coast of North America. Mean P for the global land surface based on the bias-corrected WorldClim V2 is 862 mm yr−1 (a 9.4% increase over the original WorldClim V2). The annual and monthly bias-corrected P climatologies have been released as the Precipitation Bias Correction (PBCOR) dataset, which is available online (http://www.gloh2o.org/pbcor/).

No abstract

No abstract

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use.

No abstract

Abstract. Over the past decades, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (P) products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We set up and calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four P products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each P product. Despite differences in the spatio-temporal distribution of P, all products provided good performance during calibration (median Kling–Gupta efficiencies (KGE′s) > 0.77), two independent verification periods (median KGE′s >0.70 and 0.61, for near-normal and dry conditions, respectively), and regionalisation (median KGE′s for the best method ranging from 0.56 to 0.63). We show how model calibration is able to compensate, to some extent, differences between P forcings by adjusting model parameters and thus the water balance components. Overall, feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that (i) merging P products and ground-based measurements does not necessarily translate into an improved hydrologic model performance; (ii) the spatial resolution of P products does not substantially affect the regionalisation performance; (iii) a P product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; and (iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.

<p>The Intensity-Duration-Frequency (IDF) curves are crucial for urban drainage design and to mitigate the impact of extreme precipitation events and floods. However, many regions lack a high-density network of rain gauges to adequately characterise the spatial distribution of precipitation events. In this work we compute IDF curves for the South-Central Chilean region (26-56°S) using the latest version of the Integrated Multi-satellitE Retrievals for GPM (IMERGv06B) for 2001-2018, with a spatial resolution of 0.10° and half-hourly temporal frequency.</p><p><br>First, we evaluated the performance of IMERGv06B against 344 rain gauge stations at daily, monthly and annual temporal scales using a point-to-pixel approach. The modified Kling-Gupta efficiency (KGE’) and its components (linear correlation, bias, and variability ratio) were selected as continuous indices of performance. Secondly, we fit maximum precipitation intensities from 14 long-term rain gauge stations to three probability density functions (Gumbel, Log-Pearson Type III, and GEV II) to evaluate: i) the impact of using 15-year rainfall time series in the computation of IDF curves instead of using the typical long-term periods (~ 30 years); and ii) to select the best distribution function for the study area. The Gumbel distribution was selected to obtain the maximum annual intensities for each grid-cell within the study area for 12 durations (0.5, 1, 2, 4, 6, 8, 10, 12, 18, 24, 48, and 72 h) and 6 return periods (T=2, 5, 10, 25, 50, and 100 years).</p><p><br>The application of the Wilcoxon Mann-Whitney test indicates that differences between IDF curves obtained from 15 years of records at the 14 long-term rain gauges and those derived from 25 years of record (or more) are not statistically significant, and therefore, 15 years of record are enough (although not optimal) to compute the IDF curves. Also, our results show that IMERGv06B is able to represent the spatial distribution of precipitation at daily, monthly and annual temporal scales over the study area. Moreover, the obtained precipitation intensities showed high spatial variability, mainly over the Near North (26.0-32.2°S) and the Far South (43.7-56.0°S). Additionally, the intensities from Central Chile (32.2-36.4°S) to the Near South (36.4-43.7°S) were systematically higher compared to the intensities described in older official governmental reports, suggesting an increase in precipitation intensities during recent decades.</p>

No abstract