Soil Moisture to Runoff (SM2R): A Data‐Driven Model for Runoff Estimation Across Poorly Gauged Asian Water Towers Based on Soil Moisture Dynamics

Abstract

Almost 2 billion people depend on freshwater provided by the Asian water towers, yet long‐term runoff estimation is challenging in this high‐mountain region with a harsh environment and scarce observations. Most hydrologic models rely on observed runoff for calibration, and have limited applicability in the poorly gauged Asian water towers. To overcome such limitations, here we propose a novel data‐driven model, SM2R (Soil Moisture to Runoff), to simulate monthly runoff based on soil moisture dynamics using reanalysis forcing data. The SM2R model was applied and examined in 20 drainage basins across seven Asian water towers during the past four decades of 1981–2020. Without invoking any observations for calibration, the overall good performance of SM2R‐derived runoff (correlation coefficient ≥0.74 and normalized root mean square error ≤0.22 compared to observed runoff at 20 gauges) suggests considerable potential for runoff simulation in poorly gauged basins. Even though the SM2R model is forced by ERA5‐Land (ERA5L) reanalysis data, it largely outperforms the ERA5L‐estimated runoff across the seven Asian water towers, particularly in basins with widely distributed glaciers and frozen soil. The SM2R approach is highly promising for constraining hydrologic variables from soil moisture information. Our results provide valuable insights for not only long‐term runoff estimation over key Asian basins, but also understanding hydrologic processes across poorly gauged regions globally.

Recent intensified erosion and massive sediment deposition in Tibetan Plateau rivers

“…Due to the limited in situ observations on the TP, we supplemented our analysis by collecting additional data to investigate the effects of climate change and geomorphic processes on the sediment yield and transport processes. These data included soil 63 , 64 , land use 65 , and vegetation changes 66 , which are related to the sediment characteristics, as well as land surface temperature 67 , 68 , precipitation 69 , runoff 70 , 71 , permafrost 72 – 74 , glacier 35 , 75 and snow 76 , which are related to thermal and hydraulic erosion. Note that the spatiotemporal resolution of these datasets was not entirely consistent with the sediment data retrieved from satellite imagery.…”

“…Yet, many rivers on the TP feature incomplete records, although the dataset we collected represents the most comprehensive long-term discharge dataset available for the region (Supplementary Table 1 ), as acknowledged in previous studies 1 , 12 . We also supplemented the discharge data by incorporating existing products specific to the entire TP 70 , 71 , further enriched with ERA5 datasets. Temporally, we generated annual discharge time series (Q A ) based on monthly discharge records to align the temporal resolution with the satellite-estimated monthly SSC dataset.…”

Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin

“…Basin runoff data are crucial for water resource management and natural disaster monitoring. Previous research utilized a data-driven model to estimate long-term runoff from soil moisture dynamics, with successful results [59]. On the other hand, a study was based on the correlation between in situ river level records and GRACE-based TWS to reconstruct past TWS on an interannual scale [60].…”

Enhancing hydrological model calibration through hybrid strategies in data‐scarce regions

“…In this study, the widely used Soil and Water Assessment Tool (SWAT) hydrological model was employed in the Manipur River Basin, India, to demonstrate the methodology using multiple techniques based on both in‐situ measured and remote sensing satellite datasets. Several studies have been done to estimate the discharge of an ungauged river basin solely with remote sensing datasets which can be used for model calibration but they have been limited to rivers with large width (Kebede et al, 2020; Xueying et al, 2023). In the past, Getirana (2010) used Envisat‐derived water levels with empirical formulas in Amazon River basin, Sun et al (2012) used TOPEX derived water levels with AHG, Paris et al (2016) used Jason‐2 and Envisat derived water levels with rating curves in the Amazon River basin, Huang et al (2020) used Jason‐2‐derived water levels and Surface Water and Ocean Topography (SWOT)‐like data with developed empirical equations in Upper Brahmaputra River, to derive river discharge.…”