Development of water and energy Budget-based Rainfall-Runoff-Inundation model (WEB-RRI) and its verification in the Kalu and Mundeni River Basins, Sri Lanka
Development of water and energy Budget-based Rainfall-Runoff-Inundation model (WEB-RRI) and its verification in the Kalu and Mundeni River Basins, Sri Lanka
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DOI：
10.1016/j.jhydrol.2019.124163
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发表时间：
2019-12
影响因子：
6.4
通讯作者：
M. Rasmy;T. Sayama;T. Koike
中科院分区：
文献类型：
--
作者：
M. Rasmy;T. Sayama;T. Koike
分布式水文模型（DHMs）具有模拟流域尺度水和能量收支以及洪水-径流-淹没过程的能力，是气候变化下水资源综合管理（IWRM）和减少与水有关的灾害风险（WDRR）的重要工具。本研究将RRI模式的扩散波流方程整合到陆面模式中，发展出一个新的DHM模式--WEB-RRI模式（水文SiB 2）结合水和能量收支过程，土地-植被-大气相互作用，土壤水分动态和2-D横向水流，以改善拦截，蒸散（ET），土壤水分，径流，洪水过程。新模型的性能进行了评估，使用河流流量数据，中分辨率成像光谱仪和GLEAM ET数据，以及地面和卫星的卡卢（湿）和蒙代尼（干）在斯里兰卡河流域的淹没程度。该模型得到了很好的校准和验证（纳什> 0.9），并证实是高度能够再现的长期（~20年）观察到的河流流量（纳什> 0.89）和水文水流状态属性的两个流域。特别是，模拟的洪水前的低流量和洪水期间的洪峰流量，以及它们的时间，以及在两个流域，这表明该模型是能够再现土壤和植被的水存储合理，因此，它可以用于实时和预测应用程序的重新启动能力，观测流量吻合得很好。模式模拟的流域平均ET通量及其趋势与GLEAM（RMSE：~0.7-0.95 mm/day，相关性：~0.35-0.39）的一致性好于与MODIS（RMSE：~0.96-1.04 mm/day，相关性：~0.14-0.25）的一致性。模拟的淹没程度也与地面和MODIS驱动的淹没程度一致。这项研究的未来重点将是扩大模型在全流域水资源综合管理和减少水资源风险方面的适用性，包括与洪水和干旱有关的风险评估，方法是将模型应用于实际应用（例如，洪水预报和季节性流量预测）和长期应用（例如，流域对过去和未来气候、水循环变异、水文极端情况和土地利用变化的响应）。
Distributed Hydrological Models (DHMs) with the capability of simulating catchment-scale water and energy budgets as well as rainfall-runoff-inundation processes are essential tools for Integrated Water Resource Management (IWRM) as well as Water-related Disaster Risk Reduction (WDRR) under changing climate. This research developed a new DHM, the Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model, by integrating the RRI model’s diffusive wave flow equations into a land surface model (hydro-SiB2) to incorporate water and energy budget processes, land-vegetation-atmosphere interactions, soil moisture dynamics, and 2-D lateral water flows to improve interception, evapotranspiration (ET), soil moisture, runoff, and inundation processes. The performance of the new model was assessed using river discharge data, MODIS and GLEAM ET data, and ground as well as satellite inundation extents in the Kalu (wet) and Mundeni (dry) River basins in Sri Lanka. The model was well calibrated and validated (Nash > 0.9) and confirmed to be highly capable of reproducing the long-term (~20 years) observed river discharges (Nash > 0.89) and hydrological flow regime properties for both basins. Particularly, the simulated low flow just before the flood and the peak discharges during the flood, as well as their timings, coincided well with the observed discharges in both basins, which indicates that the model is capable of reproducing soil and vegetation water storages reasonably well, and therefore it can be used for real-time and forecasting applications with the re-starting capability. The model-simulated basin averaged ET fluxes and their trends agreed better with GLEAM (RMSE: ~0.7–0.95 mm/day, correlation: ~0.35–0.39) than with MODIS (RMSE: ~0.96–1.04 mm/day, correlation: ~0.14–0.25). The simulated inundation extents were also consistent with the ground- and MODIS-driven inundation extents. The future focus of this research will be on expanding the model applicability for basin-wide IWRM and WDRR, including flood- and drought-related risk assessments, by employing the model to operational applications (e.g., flood forecasting and seasonal flow prediction) and long-term applications (e.g., catchment responses to past and future climatology, water cycle variability, hydrological extremes, and land-use changes).