Toward Global Drought Early Warning Capability: Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting

108

Abstract. Hydrological drought is not only caused by natural hydroclimate variability but can also be directly altered by human interventions including reservoir operation, irrigation, groundwater exploitation, etc. Understanding and forecasting of hydrological drought in the Anthropocene are grand challenges due to complicated interactions among climate, hydrology and humans. In this paper, five decades of naturalized and observed streamflow datasets are used to investigate hydrological drought characteristics in a heavily managed river basin, the Yellow River basin in north China. Human interventions decrease the correlation between hydrological and meteorological droughts, and make the hydrological drought respond to longer timescales of meteorological drought. Due to large water consumptions in the middle and lower reaches, there are 118-262 % increases in the hydrological drought frequency, up to 8-fold increases in the drought severity, 21-99 % increases in the drought duration and the drought onset is earlier. The non-stationarity due to anthropogenic climate change and human water use basically decreases the correlation between meteorological and hydrological droughts and reduces the effect of human interventions on hydrological drought frequency while increasing the effect on drought duration and severity. A set of 29-year (1982-2010) hindcasts from an established seasonal hydrological forecasting system are used to assess the forecast skill of hydrological drought. In the naturalized condition, the climate-model-based approach outperforms the climatology method in predicting the 2001 severe hydrological drought event. Based on the 29-year hindcasts, the former method has a Brier skill score of 11-26 % against the latter for the probabilistic hydrological drought forecasting. In the Anthropocene, the skill for both approaches increases due to the dominant influence of human interventions that have been implicitly incorporated by the hydrological postprocessing, while the difference between the two predictions decreases. This suggests that human interventions can outweigh the climate variability for the hydrological drought forecasting in the Anthropocene, and the predictability for human interventions needs more attention.

Section: Introductionmentioning

confidence: 99%

Understanding and seasonal forecasting of hydrological drought in the Anthropocene

Yuan

Zhang

Wang

et al. 2017

108

“…A drought early warning system with seasonal predictions of drought onset, severity, persistence, and spatial extent in a timely manner would provide invaluable information to decision-makers and stakeholders. There are a number of research and operational drought (or hydrologic) prediction systems (Pozzi et al, 2013;Mishra and Singh, 2010;AghaKouchak and Nakhjiri, 2012), including the Climate Prediction Center Seasonal Drought Outlook (Steinemann, 2006), the University of Washington's Surface Water Monitor (Wood and Lettenmaier, 2006;, Princeton University's drought forecast system (Luo and Wood, 2007;Li et al, 2008;, USMexico Drought Prediction Tool , and the Global Integrated Drought Monitoring and Prediction System (GIDMaPS; . Despite all these efforts, a community white paper by the World Climate Research Program identified sub-seasonal to seasonal drought prediction as one of the major research gaps in hydroclimatology (WCRP, 2010).…”

Section: Introductionmentioning

confidence: 99%

A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought

AghaKouchak

2014

145

Abstract. The 2012 drought was one of the most extensive drought events in half a century, resulting in over USD 12 billion in economic loss in the United States and substantial indirect impacts on global food security and commodity prices. An important feature of the 2012 drought was rapid development and intensification in late spring/early summer, a critical time for crop development and investment planning. Drought prediction remains a major challenge because dynamical precipitation forecasts are highly uncertain, and their prediction skill is low. Using a probabilistic framework for drought forecasting based on the persistence property of accumulated soil moisture, this paper shows that the US drought of summer 2012 was predictable several months in advance. The presented drought forecasting framework provides the probability occurrence of drought based on climatology and near-past observations of soil moisture. The results indicate that soil moisture exhibits higher persistence than precipitation, and hence improves drought predictability.

“…E. • global precipitation Pozzi et al, 2013;Serrat-Capdevila et al, 2013;Lettenmaier et al, 2015). However, P is also one of the meteorological variables that is most difficult to estimate, due to its high spatiotemporal heterogeneity (Daly et al, 1994;Herold et al, 2015).…”

mentioning

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

Self Cite

846

640

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.