Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa. Issue 2 (1st March 2020)

• Record Type: Journal Article
• Authors: Getirana, Augusto; Jung, Hahn Chul; Arsenault, Kristi; Shukla, Shraddhanand; Kumar, Sujay; Peters‐Lidard, Christa; Maigari, Issoufou; Mamane, Bako
• Is Part Of: Water resources research. Volume 56:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-01
• Subjects: seasonal forecast -- GRACE -- data assimilation -- terrestrial water storage -- streawmflow -- West Africa; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR026259
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: West Africa is one of the poorest regions in the world and highly vulnerable to extreme hydrological events due to the lack of reliable monitoring and forecast systems. For the first time, we demonstrate that initial hydrological conditions informed by satellite‐based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts. TWS variability detected by the Gravity Recovery and Climate Experiment (GRACE) satellites is assimilated into a land surface model during 2003–2016 and used to initialize 6‐month hindcasts (i.e., forecasts of past events) during West Africa's wet seasons. We find that GRACE data assimilation (DA) generally increases groundwater and soil moisture storage in the region, resulting in increased evapotranspiration and reduced total runoff. Total runoff is particularly lower at the headwaters of the Niger River, positively impacting streamflow simulations and hindcast initializations. Compared to simulations without GRACE‐DA, hindcasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin, which is consistent with GRACE's coarse spatial resolution. The long memory of groundwater and deep soil moisture, two main TWS components updated by GRACE‐DA, is reflected in prolonged improvements in the streamflow hindcasts. Model accuracy at Niamey, Niger, the most populated city where streamflow observations are available, improved up to 33% during the flood season. These new findings directly contribute to ongoing developments in food security, flood potential forecast, and water‐related disaster warning systems for Africa. Key Points: Initial hydrological conditions informed by satellite‐based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts Streamflow forecasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin The long memory of groundwater and deep soil moisture is reflected in prolonged improvements in streamflow forecasts