Abstract

Abstract. South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end users across the region. This system applies the Noah-Multiparameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5 km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made 1 to 2 months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

Highlights

  • South and Southeast Asia is one of the most populated areas in the world, and a significant portion of livelihoods depend directly or indirectly on smallholder agriculture

  • As large-scale networks of root zone soil moisture (RZSM) observations are rare in South and Southeast Asia, we evaluate the prediction skill of the forecast system of SAHFS-S2S by comparing RZSM estimates in hindcast-RIC simulations to RZSM in the retrospective simulations

  • Since the retrospective run and the hindcast-RIC simulations use the same land surface model, these comparisons aim to evaluate the impact of meteorological forcing on the prediction skill of RZSM

Read more

Summary

Introduction

South and Southeast Asia is one of the most populated areas in the world, and a significant portion of livelihoods depend directly or indirectly on smallholder agriculture. Agriculture is one of the most weather-dependent human activities (Hatfield et al, 2011), and smallholder systems are vulnerable to weather variability, including extreme events such as drought. South and Southeast Asia has been experiencing anthropogenic warming since the 1950s (Sivakumar and Stefanski, 2010), and the warming is projected to continue in the near future (Barros and Field, 2014). The frequency of extreme weather events, including droughts, has been increasing under this warming trend, with implications for food security and social stability in a conflict-prone region that already includes extensive marginal agriculture on semi-arid lands (Samaniego et al, 2018). Zhou et al.: Seasonal forecasting system for South and Southeast Asian river basins

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call