Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as (Saturated hydraulic conductivity), (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

中文翻译：

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利用 SMAP 卫星土壤湿度产品优化土壤特性，改善农业水资源管理


准确的土壤物理和水力特性估计对于农业主导地区的水资源管理至关重要。本研究引入了一个简化的框架，用于估计对于管理农业水资源至关重要的土壤物理和水力特性。开发的框架优化了区域水文极端评估系统（RHEAS）的土壤特性，以增强其核心水文模型可变入渗能力（VIC）的性能。这些土壤特性通过 NASA 土壤湿度主动被动 (SMAP) 任务的六年（2015-2021）卫星土壤湿度观测结果以及改进的混洗复杂进化 (SCE-UA) 优化算法进行了优化。针对湄公河下游 (LMR) 优化了控制模型土壤湿度模拟的三个最敏感的土壤属性，例如（饱和导水率）、（坎贝尔导水率方程中的指数参数）和 bd（堆积密度）盆地。为了更好地评估优化土壤特性的影响，分别使用 RHEAS 框架的 VIC 路由模块和土壤水分亏缺指数 (SMDI) 模块来估计水流模拟和农业干旱严重程度评估。水流模拟涉及四种方法：初始开环设置，一种使用 SMAP 土壤湿度数据 (SMAP) 进行优化，另一种使用实际水流数据 (Runoff) 进行优化，最后一种方法结合前两个数据集 (SMAP_Runoff)。从初始设置切换到 SMAP 优化模型使 Nash-Sutcliffe 效率 (NSE) 提高了 56.4%，从流优化模型升级到组合数据模型使 NSE 提高了 21.9%。 这展示了优化土壤特性以实现更准确模拟的好处。此外，优化后的模型准确地反映了历史农业干旱的严重程度和范围，与LMR流域的区域干旱报告相一致。该框架为水文建模和干旱管理提供了宝贵的工具，特别是在数据稀缺和农业密集的地区，为LMR流域内外的农业水资源管理、灌溉决策和粮食安全举措提供信息。