Estimating mountain basin‐mean precipitation from streamflow using <scp>B</scp>ayesian inference

Abstract. 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 3-hourly temporal and 0.25 • 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 Byråns 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.

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“…Adam et al, 2006;Henn et al, 2015). For this purpose we employed the Zhang et al (2001) relationship, Figure 1.…”

Section: Bias Correction Based On Q Observationsmentioning

confidence: 99%

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

Beck

Dijk

Levizzani

et al. 2017

Hydrol. Earth Syst. Sci.

873

640

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“…The dataset covers Asia over the period of 1951-2007. Different versions of the APHRODITE dataset have been used to determine Asian monsoon precipitation change, hydrolog-ical modeling (Pechlivanidis and Arheimer, 2015;, verification of high-resolution model simulations and satellite precipitation estimates (Kamiguchi et al, 2010). In this research, we use the latest version (V1101) for monsoon Asia at a spatial resolution of 0.25 • × 0.25 • .…”

Section: Aphrodite Datasetmentioning

confidence: 99%

Precipitation pattern in the Western Himalayas revealed by four datasets

Haugen

2018

Hydrol. Earth Syst. Sci.

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Data scarcity is the biggest problem for scientific research related to hydrology and climate studies in the Great Himalayas region. High-quality precipitation data are difficult to obtain due to a sparse network, cold climate and high heterogeneity in topography. In this paper, we examine four datasets in northern India of the Western Himalayas: interpolated gridded data based on gauge observations (IMD, 1 • × 1 • , and APHRODITE, 0.25 • × 0.25 • ), reanalysis data (ERA-Interim, 0.75 • ×0.75 • ) and high-resolution simulation by a regional climate model (WRF, 0.15 • × 0.15 • ). The four datasets show a similar spatial pattern and temporal variation during the period 1981-2007, though the absolute values vary significantly (497-819 mm year −1 ). The differences are particularly large in July and August at the windward slopes and high-elevation areas. Overall, the datasets show that the summer is getting wetter and the winter is getting drier, though most of the trends in monthly precipitation are not significant. Trend analysis of summer and winter precipitation at every grids confirms the changes. Wetter summers will result in more and bigger floods in the downstream areas. Warmer and drier winters will result in less glacier accumulation. All the datasets show consistency in the period 1981-2007 and can give a spatial overview of the precipitation in the region. Comparing with the Bhuntar gauge data, the WRF dataset gives the best estimates of extreme precipitation. To conclude, we recommend the APHRODITE dataset and the WRF dataset for hydrological studies for their improved spatial variation which match the scale of hydrological processes as well as accuracy in extreme precipitation for flood simulation.

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“…Similarly, patterns of relative humidity (dewpoint temperature) are more complex than those typically represented in empirical equations based on elevation, but can be well captured by high-resolution atmospheric models [Feld et al, 2013]. As years went on, rating curves were developed, and estimated discharge values were used as boundary conditions for simulations of groundwater levels in Tuolumne Meadows [Lowry et al, 2010[Lowry et al, , 2011] and for hydrologic model evaluation [Cristea et al, 2014;Hinkelman et al, 2015] and precipitation evaluation [Henn et al, 2015].…”

Section: History Of the Yosemite Hydroclimate Monitoring Network And mentioning

confidence: 99%

Yosemite Hydroclimate Network: Distributed stream and atmospheric data for the Tuolumne River watershed and surroundings

Lundquist

Roche

Forrester

et al. 2016

Water Resources Research

Self Cite

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Regions of complex topography and remote wilderness terrain have spatially varying patterns of temperature and streamflow, but due to inherent difficulties of access, are often very poorly sampled. Here we present a data set of distributed stream stage, streamflow, stream temperature, barometric pressure, and air temperature from the Tuolumne River Watershed in Yosemite National Park, Sierra Nevada, California, USA, for water years 2002–2015, as well as a quality‐controlled hourly meteorological forcing time series for use in hydrologic modeling. We also provide snow data and daily inflow to the Hetch Hetchy Reservoir for 1970–2015. This paper describes data collected using low‐visibility and low‐impact installations for wilderness locations and can be used alone or as a critical supplement to ancillary data sets collected by cooperating agencies, referenced herein. This data set provides a unique opportunity to understand spatial patterns and scaling of hydroclimatic processes in complex terrain and can be used to evaluate downscaling techniques or distributed modeling. The paper also provides an example methodology and lessons learned in conducting hydroclimatic monitoring in remote wilderness.

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Estimating mountain basin‐mean precipitation from streamflow using Bayesian inference

Cited by 50 publications

References 61 publications

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

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

Precipitation pattern in the Western Himalayas revealed by four datasets

Yosemite Hydroclimate Network: Distributed stream and atmospheric data for the Tuolumne River watershed and surroundings

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