MSWEP : 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data

Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3hourly temporal and 0.25 degrees ffi spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0% of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km(2)) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byrans Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9% of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org

Global-scale regionalization of hydrologic model parameters

Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments ( 10-10,000 km(2)) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5 degrees grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Koppen-Geiger climate types and even for evaluation catchments>5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

Key Points Global bimonthly streamflow forecasts show potentially valuable skill Initial catchment conditions are responsible for most skill Skill can be estimated from model performance and theoretical skill Ideally, a seasonal streamflow forecasting sy

GLEAM v3 : satellite-based land evaporation and root-zone soil moisture

The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980-2015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C-and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003-2015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011-2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at www.GLEAM.eu and may be used for large-scale hydrological applications, climate studies, or research on land-atmosphere feedbacks

The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments

Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastur

River gauging at global scale using optical and passive microwave remote sensing

Recent discharge observations are lacking for most rivers globally. Discharge can be estimated from remotely sensed floodplain and channel inundation area, but there is currently no method that can be automatically extended to many rivers. We examined whether automated monitoring is feasible by statistically relating inundation estimates from moderate to coarse (>0.05°) resolution remote sensing to monthly station discharge records. Inundation extents were derived from optical MODIS data and passive microwave sensors, and compared to monthly discharge records from over 8000 gauging stations and satellite altimetry observations for 442 reaches of large rivers. An automated statistical method selected grid cells to construct “satellite gauging reaches” (SGRs). MODIS SGRs were generally more accurate than passive microwave SGRs, but there were complementary strengths. The rivers widely varied in size, regime, and morphology. As expected performance was low (R  0.6. The best results (R > 0.9) were obtained for large unregulated lowland rivers, particularly in tropical and boreal regions. Relatively poor results were obtained in arid regions, where flow pulses are few and recede rapidly, and in temperate regions, where many rivers are modified and contained. Provided discharge variations produce clear changes in inundated area and gauge records are available for part of the satellite record, SGRs can retrieve monthly river discharge values back to around 1998 and up to present

The Millennium Drought in southeast Australia (2001-2009): Natural and human causes and implications for water resources, ecosystems, economy, and society

The "Millennium Drought" (2001-2009) can be described as the worst drought on record for southeast Australia. Adaptation to future severe droughts requires insight into the drivers of the drought and its impacts. These were analyzed using climate, water

Assessing the Steady-State Assumption in Water Balance Calculation Across Global Catchments

It has long been assumed that over a sufficiently long period of time, changes in catchment water storage (ΔS) are a relatively minor term compared to other fluxes and can be neglected in the catchment water balance equation. However, the validity of this fundamental assumption has rarely been tested, and the associated uncertainties in water balance calculations remain unknown. Here, we use long‐term (1982-2011) observations of monthly streamflow (Q) and precipitation (P) for 1,057 global unimpaired catchments, combined with four independent evapotranspiration (E) estimates to infer ΔS and to provide a global assessment of the steady‐state assumption in catchment water balance calculations. Results show that when the threshold for steady state is set to 5% of the mean monthly P, ~70% of the catchments attain steady state within 10 years while ~6% of the catchments fail to reach a steady state even after 30 years. The time needed for a catchment to reach steady state (τs) shows a close relationship with climatic aridity and vegetation coverage, with arid/semiarid and sparsely vegetated catchments generally having a longer τs. Additionally, increasing snowfall fraction also increases τs. The imbalance (ewb) caused by ignoring ΔS decreases as averaging period for water balance calculations increases as expected. For a typical 10‐year averaging period, ewb accounts for ~7% of P in arid, but that decreases to ~3% of P in humid catchments. These results suggest that catchment properties should be considered when applying the steady‐state assumption and call for caution when ignoring ΔS in arid/semiarid regions.This study is financially supported by the National Natural Science Foundation of China (Grant 41890821), the Ministry of Science and Technology of China (Grant 2019YFC1510604), the Qinghai Department of Science and Technology (Grant 2019‐SF‐A4), and the Guoqiang Institute of Tsinghua University (Grant 2019GQG1020). M. L. Roderick and T. R. McVicar acknowledge support from the Australian Research Council (CE170100023)