RAHMAN MOHAMMAD MAHFUZUR

国立大学法人長岡技術科学大学博士（工学）Articledoctoral thesi

RAHMAN, MOHAMMAD MAHFUZUR

3754

Nagaoka University of Technology Institutional Repository

1 様式  論 文 内 容 の 要 旨  氏 名  RAHMAN MOHAMMAD MAHFUZUR  Knowledge of soil moisture memory (SMM) is important for hydrological modelling exercises. SMM information can improve hydro-climatic prediction efficiency as well as provides useful insight about the speed of model spin-up process. Despite these advantages, SMM studies are restricted in certain areas due to the scarcity of observed soil moisture data. To overcome this limitation, this study explains the variability of SMM with respect to the basin dryness and shows a way to predict basin scale SMM timescale using annual observed precipitation and potential evapotranspiration information only. Later, the linkage between the SMM and the model  spin-up time has been investigated using the Xinanjiang (XAJ) model as a case study.   The study presents the basin average SMM timescale that indicates the duration of significant autocorrelations at 95% confidence intervals. The  soil moisture autocorrelations were calculated using observed precipitation, potential evapotranspiration, streamflow and soil moisture data sets simulated using the XAJ model, for 26 river basins across the USA. In American region, the SMM timescale seems to be highly influenced by precipitation variability and exhibits strong seasonality. Dry basins tend to show longer memory during the winter months (December to February) and shorter in late spring (May). In contrast, wet basins have shorter memory during winter and early spring (December to April) and longer in the late summer and early autumn (July to September). The analysis suggests that SMM timescale holds an exponential relationship with the basin aridity index.  The model spin-up behavior was evaluated by two separate approaches. Firstly, it records the spin-up duration by following a recursive simulation using the single year climatology and setting different initial conditions. The spin-up time was defined based on the percent cut off-based time technique. Secondly, it performs a series of simulations using multi-year input data sets with varying simulation start time and two initial conditions. The spin-up time was defined as the time required for the Mahalanobis Distance between the model soil moisture states of saturated and dry simulations becomes zero (0).  Both the analysis reveals that the model spin-up has a clear association with SMM timescale. Moreover, it reveals that model equilibrium is not only a function of initial conditions, but also is affected by input data sets. Model requires less time to be equilibrated under wetter initial conditions. Moreover, model spin-up time shows distinct variations with the dryness of the climate forcing. Analyzing American basins  suggest that wet basins require less time for model equilibrium in comparison to that of dry basins. In wet basins, model spin-up process is slower during the spring months and faster during winter months. In contrast, in dry basins , spin-up process is faster during the late spring and early autumn and slower during the winter.  The spin-up time also displayed an exponential relationship with the basin aridity index. Both SMM timescale and the model spin-up time can be predicted from widely available observed data sets. 

Characterizing soil moisture memory timescale and the Xinanjiang model spin-up time by basin scale hydro-climatic data

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流域スケール水文気候学的データによる土壌水分メモリ及び新安江モデルスピンアップ時間の特徴に関する研究

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Nagaoka University of Technology Institutional Repository