Within the parlance of Hydrology, forecasting streamflow plays a vital role in water resource management. Based on the temporal scale, streamflow forecasting can be broadly classified into two categories: short-term and longterm forecasts. Short-term forecasting at hourly and daily scale is critical for flood warning, mitigation (Gül et al., 2010;Rogelis & Werner, 2018), and disaster management (Roulin, 2007). Long-term forecasting at monthly and seasonal scales is extensively utilized in reservoir monitoring (Rezaie-Balf et al., 2019), design of hydraulic structures (Lowe, 2006), and irrigation practices (Droogers & Bastiaanssen, 2002). However, uncertainty in hydrological predictions is still a serious concern that needs to be addressed before planning and management activities. These uncertainties may arise due to the error in the forcing variables (e.g., rainfall and temperature data), the model's inherent structural error (parameters and boundary conditions), and improper initial condition (Alvarado-Montero et al., 2017). While the quality of the input data such as the rainfall rate and temperature can be improved by deploying better observation systems, errors in model control variables consisting of the initial and boundary conditions and parameters can be corrected using the well-established tools from the theory of dynamic data assimilation (DDA, Lakshmivarahan et al., 2017;Lewis et al., 2006). In this paper, the goal is to demonstrate the power of a new class of method called forward sensitivity method (FSM, Lakshmivarahan & Lewis, 2010) for assimilating data into a simple conceptual two parameter model (TPM, Xiong & Guo, 1999) in the analysis of streamflow.DDA is the process of combining a model of a process of interest in the analysis with a finite set of relevant but noisy observations of the same process. Existing literature on DDA can be broadly classified into three classes. First is a class of sequential methods known as Kalman filtering (Kalman, 1960b) and many if its extensions (Evensen, 1994;Puente & Bras, 1987;Sakov et al., 2012;Whitaker & Hamill, 2002). This class of methods rests on the basic principle of best, linear unbiased estimation (BLUE), where best is in the sense minimum variance. While Kalman filter-based methods (KF) provide a natural framework for sequential dynamic data assimilation,
Climate change (CC) affects millions of people directly or indirectly. Especially, the effect of CC on the hydrological regime is extensive. Hence, understanding its impact is highly essential. In this study, the Bharathapuzha river basin (BRB) lying in the Western Ghats region of southern India is considered for CC impact assessment, as it is a highly complex and challenging watershed, due to its varying topographical features, such as soil texture, land use/land cover types, slope, and climatology, including rainfall and temperature patterns. To understand the CC impact on the hydrological variables at BRB in the future, five downscaled global circulation models (GCMs) were used, namely BNU-ESM, Can-ESM, CNRM, MPI-ESM MR, and MPI-ESM LR. These GCMs were obtained for two representative concentration pathway (RCP) scenarios: 4.5 representing normal condition and 8.5 representing the worst condition of projected carbon and greenhouse gases concentration on the lower atmosphere. To obtain the continuous simulation of hydrological variables, the SWAT hydrological model was adopted in this study. Results showed that rainfall pattern, evapotranspiration, and soil moisture will increase at moderate to significant levels in the future. This is especially seen during the far future period (i.e., 2071 to 2100). Similar results were obtained for surface runoff. For instance, surface runoff will increase up to 19.2% (RCP 4.5) and 36% (RCP 8.5) during 2100, as compared to the average historical condition (1981–2010). The results from this study will be useful for various water resources management and adaptation measures in the future, and the methodology can be adopted for similar regions.
The accuracy of streamflow forecasts is important for efficient monitoring and mitigation of flood events. Unfortunately, the uncertainty in the model control variable which includes model parameters, initial and boundary conditions, propagates through the model, resulting in the degradation of streamflow forecast. Various studies in the past have shown the potential of soil moisture assimilation in hydrological models resulting in the improved forecast. Further, the efficiency of assimilation is based on the number and the distribution of observations used. This study proposes a new approach called Forward sensitivity method (FSM), which operates in two phases. By running the model and forecast sensitivity dynamics forward in time, the first phase places the observations at or near where the square of the forecast sensitivity with respect to the control takes maximum values. Then using only this subset of observations, the second phase estimates the unknown elements of the control by solving a resulting weighted least squares problem. The power of this approach is demonstrated by assimilating ASCAT soil moisture observations into a conceptual Two Parameter Model in a medium sized watershed lying in the Krishna River Basin, India. The model run extends for four monsoon years from June 2007 to June 2011 and two assimilation scenarios were tested. The first scenario uses all the observations, whereas, the second uses only sensitive observations during assimilation and the results were then compared against open loop simulation (model run without assimilation). Sensitivity results indicate that observations during monsoon time alone are sufficient for assimilation purpose, which accounts for only 37.42 percent of total observations. Also, the estimation and forecast results show improved streamflow performance when using only sensitive observations. From the results, it is concluded that FSM based assimilation can help in reducing the computation time greatly. Further, this study will be critically helpful in the places where data availability remains a major problem.
<p>During Data Assimilation (DA) in a hydrological model, observations of soil moisture (SM) and streamflow (Q) at interior locations are often assimilated together during the multivariate case to improve streamflow estimates at the catchment outlet. In addition to model states, model parameters need to be updated periodically to account for the variations caused by climatic and human factors during the assimilation period. Therefore, in this study, time-varying multivariate assimilation of ASCAT SM observations and streamflow gauge data from interior sites are ingested into a conceptual two-parameter model, which simulates streamflow using a water budget equation. The Bharathapuzha river basin, lying in the Western Ghats of Southern India is chosen as the study area. In this study, the Ensemble Kalman filter (EnKF), a sequential assimilation approach, is utilized to update the model&#8217;s states and parameters at a daily time step. Meanwhile, the computational burden of assimilating such a massive observation needs to be dealt with. A plausible solution is to perform assimilation only at those timesteps when the model is sensitive to the assimilating variable. Consequently, two assimilation scenarios were performed apart from the open-loop (OL) simulations. In the first scenario, all the available SM observations are assimilated irrespective of their sensitivity (DA1). Whereas, in the second scenario, only sensitive SM observations are assimilated into the model (DA2). Results revealed that during both the assimilation scenarios, the model showed improved performance as compared to the open-loop simulations. KGE value improved from 0.68 (during OL) to 0.85 (during DA1) and 0.81 (during DA2). An intriguing fact is that during the second scenario (DA2) when only a subset of sensitive observations was assimilated, the model still showed similar results as DA1. Results highlight that assimilating only spatiotemporally sensitive observations would not affect the model&#8217;s performance substantially. Instead, the assimilation efficiency can be enhanced by abbreviating the computational burden.</p>
Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation The use of accurate streamflow estimates is widely recognized in the hydrological field. However, due to the model’s structural error, they often yield suboptimal streamflow estimates. Past studies have shown that soil moisture assimilation improves the performance of the hydrological model which often results in enhanced model estimates. Due to this reason, it is widely studied in the hydrological field. However, the efficiency of the assimilation largely relies on the correct placement of the observation into the model. Ingesting futile observations often results in the degradation of model performance. On the contrary, performing assimilation only at those time steps when the assimilating variable is sensitive to the model output may yield desirable output. Further, it will avoid the assimilation of spurious observations. In this view, this study proposes a new approach where sensitivity-based sequential assimilation is performed on a conceptual Two Parameter Model (TPM). To demonstrate this approach, ASCAT soil moisture observations are assimilated into TPM using Ensemble Kalman Filter (EnKF) sequential approach. At first, the temporal evolution of the soil moisture sensitivity with respect to streamflow is established. Later, at those time steps when the soil moisture is sensitive, EnKF assimilation is performed. For this purpose, a moderately sized catchment in the Krishna basin, India is selected as the study area. Model calibration and validation are performed between 2000 to 2006 and 2007 to 2011 respectively. Model run without assimilation is considered as open-loop simulation. Streamflow simulation after assimilation showed a significant improvement when compared against the open-loop simulation. KGE value increased from 0.70 to 0.79 and PBIAS value reduced from 18.31 to 1.80. The highlighting factor is that only 39% of the total observations were used during the assimilation process. The initial results are encouraging and looks that the proposed approach shall be highly useful at those locations where data availability for assimilation purpose is a serious concern.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.