Distributed Rainfall Runoff Model Research Articles

Regional event-based flood quantile estimation method for large climate projection ensembles

Emerging large ensemble climate datasets produced by multiple general circulation models and their downscaling products challenge the limits of hydrodynamic models because of the immense data size. To overcome this new challenge and estimate the discharge quantiles corresponding to different return periods at all river sections in an entire region, this study proposes an event-based regional approach that uses a nationwide distributed rainfall–runoff model as well as large climate projection ensembles. This approach addresses the high computational burden associated with continuous simulations and solves the problem of conventional event-based simulations serving only a single outlet of a basin. For our analysis, we extracted 372 annual maximum 48 h rainfall events that cover the entirety of Shikoku Island and its eight major river basins. Peak discharges were estimated using a 150 m resolution rainfall–runoff–inundation model. These discharges were then screened using either the peak-over-threshold (POT) method or block maxima (BM) method, and frequency curves were subsequently constructed and evaluated. The primary reason for the necessity of POT or BM was to avoid interference from extraneous low discharges. The POT-based frequency curves showed good accuracy when using peak discharges in the range of the top 10–50%, and the results remain stable within this threshold range. The BM method, employing block sizes of 2–5 years, can generate relatively accurate frequency curves, but the choice of block size introduces significant variations in results among certain basins. Generally, the accuracy of results based on the POT method surpasses that of the BM method. Considering the accuracy, computational cost, and result stability, the POT method is preferred. The error introduced by the regional approach was acceptable with more than half of the relative root-mean-square errors remaining within 10% and basically all of the results are within 20%. The results of the regional approach exhibited good accuracy across climate scenarios and provided consistent information regarding future flood quantiles. This study serves as the foundation for high-resolution future flood risk assessment.

Subgrid Model of Water Storage in Paddy Fields for a Grid-Based Distributed Rainfall–Runoff Model and Assessment of Paddy Field Dam Effects on Flood Control

Paddy field dams are basin-level flood control measures that promote rainwater storage; however, a general runoff model cannot adequately describe the water balance in paddy fields. This study develops a subgrid model for evaluating paddy water balance considering land use on a computational grid. Subgrid models can account for the storage effect of paddy field dams without disregarding the general grid-based distributed rainfall–runoff model framework. To investigate the effect of current paddy field storage and the introduction of paddy field dams on reducing peak flood discharge, rainfall–runoff analysis was conducted using the proposed model in the Kashima River basin, which flows into Lake Inba-numa in Chiba Prefecture, Japan. The computational results indicated that the rainwater storage effect of current paddy fields reduces the peak river discharge, suggesting that the drainage process of the paddy field should be incorporated into runoff models. Furthermore, the storage effect of paddy fields became more pronounced as the height of the drainage pipe in the paddy field dam increased. The calculated results quantitatively show the flood control effect of paddy field storage over the entire basin; thus, the proposed subgrid model may be a useful tool for promoting basin-level flood control measures.

Flood Runoff Simulation under Changing Environment, Based on Multiple Satellite Data in the Jinghe River Basin of the Loess Plateau, China

Understanding the hydrological surface condition changes, climate change and their combined impacts on flood runoff are critical for comprehending the hydrology under environmental changes and for solving future flood management challenges. This study was designed to examine the relative contributions of the hydrological surface condition changes and climate change in the flood runoff of a 45,421-km2 watershed in the Loess Plateau region. Statistical analytical methods, including Kendall’s trend test, the Theisen median trend analysis, and cumulative anomaly method, were used to detect trends in the relationship between the climatic variables, the normalized difference vegetation index (NDVI), land use/cover change (LUCC) data, and observed flood runoff. A grid-cell distributed rainfall–runoff model was used to detect the quantitative hydrologic responses to the climatic variability and land-use change. We found that climatic variables were not statistically significantly different (p > 0.05) over the study period. From 1985 to 2013, the cropland area continued to decrease, while the forest land, pastures, and residential areas increased in the Jinghe River Basin. Affected by LUCC and climate change, the peak discharges and flood volumes decreased by 8–22% and 5–67%, respectively. This study can provide a reference for future land-use planning and flood runoff control policy formulation and for revision in the study area.

Distributed rainfall-runoff simulation for a large-scale karst catchment by incorporating landform and topography into the DDRM model parameters

Runoff movement in karst catchments is of great complexity. Although many models have been developed for the rainfall-runoff simulation in karst catchments, the distributed ones, capable of taking into consideration the impacts of the spatial variability of hydrogeological factors, are few and can only be applied to well-explored karst catchments of commonly less than 100 km2 in area. In this study, the previously-published DEM (Digital Elevation Model) -based distributed rainfall-runoff model (DDRM) was applied and modified for distributed rainfall-runoff simulation in a large-scale karst catchment by assuming the main parameters of runoff generation and routing for each grid cell to be a function of karst landform and topographic index (i.e., the mathematical expression of topographic features) to represent the hydrological effects of epikarst and underground river system. For model structure investigation, three versions of the modified DDRMs, i.e., the DDRM considering only karst landform (DDRM_K), the DDRM considering only topographic index (DDRM_T), and the DDRM considering both karst landform and topographic index (DDRM_KT), were set up and compared with the original DDRM (i.e., the original DDRM, termed as DDRM_O). All four DDRMs were calibrated with a multi-objective optimization framework that uses the Kling-Gupta Efficiency of streamflow (KGEQ) and the autocorrelation function of streamflow (KGEACF) as objective functions to improve parameter identifiability. The investigation was conducted in the Xijiang Basin, which is a typical karst catchment in Southwest China with an area of 309300 km2. The results show that, compared with DDRM_O, DDRM_T barely benefits the rainfall-runoff simulation, while both DDRM_K and DDRM_KT noticeably enhance the simulation accuracy in terms of KGEQ. Due to the better performance in high flow and autocorrelation function of streamflow, DDRM_KT outperforms DDRM_K in general and has the potential to be successfully applied to large-scale karst catchments.

A Daily Water Balance Model Based on the Distribution Function Unifying Probability Distributed Model and the SCS Curve Number Method

A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model.

Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling

Abstract. Despite showing great success of applications in many commercial fields, machine learning and data science models generally show limited success in many scientific fields, including hydrology (Karpatne et al., 2017). The approach is often criticized for its lack of interpretability and physical consistency. This has led to the emergence of new modelling paradigms, such as theory-guided data science (TGDS) and physics-informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on genetic programming (GP), namely the Machine Learning Rainfall–Runoff Model Induction (ML-RR-MI) toolkit. ML-RR-MI is capable of developing fully fledged lumped conceptual rainfall–runoff models for a watershed of interest using the building blocks of two flexible rainfall–runoff modelling frameworks. In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall–runoff models. The meaningfulness and reliability of hydrological inferences gained from lumped models may tend to deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties is significant. This was the motivation behind developing our machine learning approach for distributed rainfall–runoff modelling titled Machine Induction Knowledge Augmented – System Hydrologique Asiatique (MIKA-SHA). MIKA-SHA captures spatial variabilities and automatically induces rainfall–runoff models for the catchment of interest without any explicit user selections. Currently, MIKA-SHA learns models utilizing the model building components of two flexible modelling frameworks. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA's model induction capabilities have been tested on the Rappahannock River basin near Fredericksburg, Virginia, USA. MIKA-SHA builds and tests many model configurations using the model building components of the two flexible modelling frameworks and quantitatively identifies the optimal model for the watershed of concern. In this study, MIKA-SHA is utilized to identify two optimal models (one from each flexible modelling framework) to capture the runoff dynamics of the Rappahannock River basin. Both optimal models achieve high-efficiency values in hydrograph predictions (both at catchment and subcatchment outlets) and good visual matches with the observed runoff response of the catchment. Furthermore, the resulting model architectures are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists. MIKA-SHA-induced semi-distributed model performances were compared against existing lumped model performances for the same basin. MIKA-SHA-induced optimal models outperform the lumped models used in this study in terms of efficiency values while benefitting hydrologists with more meaningful hydrological inferences about the runoff dynamics of the Rappahannock River basin.

High spatial resolution simulation of profile soil moisture by assimilating multi-source remote-sensed information into a distributed hydrological model

Profile soil moisture (PSM), which represents soil moisture content over the whole soil layer depth, is a key variable to control plant growth, biological interactions, and streamflow generation, and its information plays a crucial role in hydrological analysis and agricultural water management. Recent studies have assimilated multi-source satellite PSM information into hydrological modelling to more accurately estimate real PSM. However, the PSM estimated from these studies are normally at coarse spatial resolution (i.e., larger than 25 × 25 km2). In this study, the high-resolution (1 × 1 km2) PSM are generated by assimilating multiple remote-sensed PSM data of coarse resolution into the Digital Elevation Model (DEM) based distributed rainfall-runoff model (DDRM) in three catchments (two humid catchments and one semiarid catchment) in China with the grid scale of 1 × 1 km2. The remote-sensed PSM data are pre-processed from two remote-sensed surface soil moisture datasets, i.e. the multi-satellite-retrieved soil moisture dataset released by the Europe Space Agency Climate Change Initiative (ESA CCI), and the soil moisture product from the Soil Moisture Active Passive (SMAP) satellite. The influence of remote-sensed datasets selection schemes (i.e. only ESA CCI, only SMAP, and ESA CCI and SMAP combined) on assimilation results are investigated. In the assimilation process, two updating schemes are considered, one is to only update the DDRM’s state variable, i.e. PSM, and the other updating both the parameters and PSM variable of the DDRM. Thus, six assimilation scenarios are set in the study, whose performances are compared with the DDRM without assimilation for different time periods, including the whole period, the dormant period and the growing period. Results indicate that in any periods, for any of remote-sensed datasets used for assimilation, either the state update or the parameter-state update can improve the accuracy of high-resolution (1 × 1 km2) PSM simulations by the DDRM. Besides, assimilating the SMAP PSM dataset into the DDRM has the potential to improve streamflow simulations for the three catchments. This study has shown that, by assimilating multi-source remote-sensed PSM into a high spatial resolution distributed hydrological model, i.e. DDRM, estimation of PSM can be improved over both the original remote-sensed PSM and the DDRM-simulated PSM.

Testing the Efficiency of Parameter Disaggregation for Distributed Rainfall-Runoff Modelling

A variety of hydrological models is currently available. Many of those employ physically based formulations to account for the complexity and spatial heterogeneity of natural processes. In turn, they require a substantial amount of spatial data, which may not always be available at sufficient quality. Recently, a top-down approach for distributed rainfall-runoff modelling has been developed, which aims at combining accuracy and simplicity. Essentially, a distributed model with uniform model parameters (base model) is derived from a calibrated lumped conceptual model. Subsequently, selected parameters are disaggregated based on links with the available spatially variable catchment properties. The disaggregation concept is now adjusted to better account for non-linearities and extended to incorporate more model parameters (and, thus, larger catchment heterogeneity). The modelling approach is tested for a catchment including several flow gauging stations. The disaggregated model is shown to outperform the base model with respect to internal catchment dynamics, while performing similarly at the catchment outlet. Moreover, it manages to bridge on average 44% of the Nash–Sutcliffe efficiency difference between the base model and the lumped models calibrated for the internal gauging stations. Nevertheless, the aforementioned improvement is not necessarily sufficient for reliable model results.

A distributed simple dynamical systems approach (dS2 v1.0) for computationally efficient hydrological modelling at high spatio-temporal resolution

Abstract. In this paper, we introduce a new numerically robust distributed rainfall–runoff model for computationally efficient simulation at high spatio-temporal resolution: the distributed simple dynamical systems (dS2) model. The model is based on the simple dynamical systems approach as proposed by Kirchner (2009), and the distributed implementation allows for spatial heterogeneity in the parameters and/or model forcing fields at high spatio-temporal resolution (for instance as derived from precipitation radar data). The concept is extended with snow and routing modules, where the latter transports water from each pixel to the catchment outlet. The sensitivity function, which links changes in storage to changes in discharge, is implemented by a new three-parameter equation that is able to represent the widely observed downward curvature in log–log space. The simplicity of the underlying concept allows the model to calculate discharge in a computationally efficient manner, even at high temporal and spatial resolution, while maintaining proven model performance. The model code is written in Python in order to be easily readable and adjustable while maintaining computational efficiency. Since this model has short runtimes, it allows for extended sensitivity and uncertainty studies with relatively low computational costs. A test application shows good and consistent model performance across scales ranging from 3 to over 1700 km2.

Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls – Part 2: Flood forecasting using 1600-member 4D-EnVar-predicted rainfalls

Abstract. This paper is a continuation of the authors' previous paper (Part 1) on the feasibility of ensemble flood forecasting for a small dam catchment (Kasahori dam; approx. 70 km2) in Niigata, Japan, using a distributed rainfall–runoff model and rainfall ensemble forecasts. The ensemble forecasts were given by an advanced four-dimensional, variational-ensemble assimilation system using the Japan Meteorological Agency nonhydrostatic model (4D-EnVar-NHM). A noteworthy feature of this system was the use of a very large number of ensemble members (1600), which yielded a significant improvement in the rainfall forecast compared to Part 1. The ensemble flood forecasting using the 1600 rainfalls succeeded in indicating the necessity of emergency flood operation with the occurrence probability and enough lead time (e.g., 12 h) with regard to an extreme event. A new method for dynamical selection of the best ensemble member based on the Bayesian reasoning with different evaluation periods is proposed. As the result, it is recognized that the selection based on Nash–Sutcliffe efficiency (NSE) does not provide an exact discharge forecast with several hours lead time, but it can provide some trend in the near future.

Impact of Different Green-Ampt Model Parameters on the Distributed Rainfall-Runoff Model FLO-2D owing to Scale Heterogeneity

Impact of Different Green-Ampt Model Parameters on the Distributed Rainfall-Runoff Model FLO-2D owing to Scale Heterogeneity

A Study on a Simple Algorithm for Parallel Computation of a Grid-Based One-Dimensional Distributed Rainfall-Runoff Model

This paper presents an algorithm that can efficiently simulate a grid-based one-dimensional distributed rainfall-runoff model by performing parallel computations using flow accumulation values for individual grid cells, which are calculated through an eight flow direction method. This parallel computation algorithm uses information about flow accumulation to automatically find parallel computation target grid cells within the overall area and perform parallel computation on the grid by unit. The Microsoft.NET Parallel class was used to apply and evaluate the parallel computation algorithm independently on two machines. The results showed that the time reduction effect of parallel computation differed for each target domain, because flow accumulation values varied depending on the domain. Parallel computation reduced computation time by around 40% to 78% in virtual domains and around 63% in the real domain compared to sequential computation. The results of this study can be utilized to reduce the computation time of distributed models.

Development of a Distributed Rainfall-runoff Model Incorporated with Tank Models for Several Land Utilizations and Quantitative Evaluation of Runoff Change Induced by Rural Development and Climate Change

Development of a Distributed Rainfall-runoff Model Incorporated with Tank Models for Several Land Utilizations and Quantitative Evaluation of Runoff Change Induced by Rural Development and Climate Change

Flood Simulations in Mid-Latitude Agricultural Land Using Regional Current and Future Extreme Weathers

Recent extreme weather events like the August 2016 flood disaster have significantly affected farmland in mid-latitude regions like the Tokachi River (TR) watershed, the most productive farmland in Japan. The August 2016 flood disaster was caused by multiple typhoons that occurred in the span of two weeks and dealt catastrophic damage to agricultural land. This disaster was the focus of our flood model simulations. For the hydrological model input, the rainfall data with 0.04° grid space and an hourly interval were provided by a regional climate model (RCM) during the period of multiple typhoon occurrences. The high-resolution data can take account of the geographic effects, hardly reproduced by ordinary RCMs. The rainfall data drove a conceptual, distributed rainfall–runoff model, embedded in the integrated flood analysis system. The rainfall–runoff model provided discharges along rivers over the TR watershed. The RCM also provided future rainfall data with pseudo-global warming climate, assuming that the August 2016 disaster could reoccur again in the late 21st century. The future rainfall data were used to conduct a future flood simulation. With bias corrections, current and future flood simulations showed the potential inundated areas along riverbanks based on flood risk levels. The crop field-based agricultural losses in both simulations were estimated. The future cost may be two to three times higher as indicated by slightly higher simulated future discharge peaks in tributaries.

Analyse hydrologique de la crue-éclair catastrophique du 15 juin 2010 dans la région de Draguignan (VAR, France)

Suite à la crue exceptionnelle qui s'est produite le 15 juin 2010 dans la région de Draguignan (Var), plusieurs équipes de chercheurs et d'ingénieurs sont intervenues sur le terrain afin de documenter et de caractériser cet événement, en proposant notamment des estimations des débits de pointe sur le cours principal de l'Argens ainsi que sur l'ensemble des affluents touchés. Des informations sur la chronologie des crues ont également été obtenues auprès de témoins oculaires. Un travail de coordination et d'échanges entre ces équipes a été organisé par le SCHAPI et la DREAL PACA, et a permis de confronter et de mettre en cohérence les résultats obtenus, et d'engager une discussion sur les périodes de retour de la crue. Cette comparaison a révélé que malgré les différentes sources possibles d'erreurs d'estimation, les valeurs de débits proposées par les différentes équipes restaient globalement assez proches. Cette première étape a permis de cartographier les débits sur la base d'une quarantaine d'estimations au total. La répartition spatiale obtenue apparaît, de premier abord, cohérente avec l'estimation des cumuls pluviométriques fournie par les lames d'eau radar. L'analyse hydrologique de l'événement a été complétée en comparant les débits estimés à des hydrogrammes simulés à l'aide d'un modèle pluie-débit simple. Ces résultats mettent en évidence une grande disparité des comportements hydrologiques des affluents de l'Argens. La Nartuby se distingue notablement avec une réponse hydrologique très progressive et atténuée ce qui peut être attribué à l'effet du karst très présent sur ce bassin. Enfin, le travail sur les périodes de retour a permis d'identifier les secteurs pour lesquels la crue a dépassé un niveau centennal.

2D GPU-Accelerated High Resolution Numerical Scheme for Solving Diffusive Wave Equations

We developed a GPU-accelerated 2D physically based distributed rainfall runoff model for a PC environment. The governing equations were derived from the diffusive wave model for surface flow and the Horton infiltration model for rainfall loss. A numerical method for the diffusive wave equations was implemented based on a Godunov-type finite volume scheme. The flux at the computational cell interface was reconstructed using the piecewise linear monotonic upwind scheme for conservation laws with a van Leer slope total variation diminishing limiter. Parallelization was implemented using CUDA-Fortran with an NVIDIA GeForce GTX 1060 GPU. The proposed model was tested and verified against several 1D and 2D rainfall runoff processes with various topographies containing depressions. Simulated hydrographs, water depth, and velocity were compared to analytical solutions, dynamic wave modeling results, and measurement data. The diffusive wave model reproduced the runoff processes of impermeable basins with results similar to those of analytical solutions and the numerical results of a dynamic wave model. For ideal permeable basins containing depressions such as furrows and ponds, physically reasonable rainfall runoff processes were observed. From tests on a real basin with complex terrain, reasonable agreement with the measured data was observed. The performance of parallel computing was very efficient as the number of grids increased, achieving a maximum speedup of approximately 150 times compared to a CPU version using an Intel i7 4.7-GHz CPU in a PC environment.

Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework

The traditional calibration objective of hydrological models is to optimize streamflow simulations. To identify the value of satellite soil moisture data in calibrating hydrological models, a new objective of optimizing soil moisture simulations has been added to bring in satellite data. However, it leads to problems: (i) how to consider the trade-off between various objectives; (ii) how to consider the uncertainty these satellite data bring in. Among existing methods, the multi-objective Bayesian calibration framework has the potential to solve both problems but is more suitable for lumped models since it can only deal with constant variances (in time and space) of model residuals. In this study, to investigate the utilization of a soil moisture product from the Soil Moisture Active Passive (SMAP) satellite in calibrating a distributed hydrological model, the DEM (Digital Elevation Model) -based Distributed Rainfall-Runoff Model (DDRM), a multi-objective Bayesian hierarchical framework is employed in two humid catchments of southwestern China. This hierarchical framework is superior to the non-hierarchical framework when applied to distributed models since it considers the spatial and temporal residual heteroscedasticity of distributed model simulations. Taking the streamflow-based single objective calibration as the benchmark, results of adding satellite soil moisture data in calibration show that (i) there is less uncertainty in streamflow simulations and better performance of soil moisture simulations either in time and space; (ii) streamflow simulations are largely affected, while soil moisture simulations are slightly affected by weights of objectives. Overall, the introduction of satellite soil moisture data in addition to observed streamflow in calibration and putting more weights on the streamflow calibration objective lead to better hydrological performance. The multi-objective Bayesian hierarchical framework implemented here successfully provides insights into the value of satellite soil moisture data in distributed model calibration.

The accuracy of ecological flow metrics derived using a physics‐based distributed rainfall–runoff model in the Great Plains, USA

AbstractThe development of a hydrologic foundation, essential for advancing our understanding of flow‐ecology relationships, was developed using the high‐resolution physics‐based distributed rainfall–runoff model Vflo in a semi‐arid region. We compared the accuracy and bias associated with flow metrics that were generated using Vflo, gauge data, and drainage area ratios at both a daily and monthly time step in the Canadian River basin, USA. First, we calibrated and applied bias correction to the Vflo model to simulate streamflow at ungauged catchment locations. Next, flow metrics were calculated using simulated and observed data from stream gauge locations. We found discharge predictions using Vflo were more accurate than drainage area ratios. General correspondence between predicted discharge and the gauge data was apparent; however, flow metrics calculated using the Vflo output did not accurately represent flow variability. Results from the Vflo model showed systematic discharge over‐predictions in the upper basin and isolated over‐predictions in the lower basin, likely due to hail events and sparse rainfall data across the large catchment. Goodness‐of‐fit statistics (Nash–Sutcliffe efficiency, root‐mean square error, and the coefficient of variation) indicated the drainage area ratio and Vflo were more accurate at a monthly rather than daily time step, even after quantile mapping. This finding limits the number of streamflow metrics available to develop ecological models, but more importantly, the coarser resolution may hinder our understanding of ecological processes that occur at a submonthly time step. Our approach provides a framework for selecting flow metrics that best represent hydrologic patterns across a large semi‐arid catchment with the necessary accuracy to address the ecological questions of interest.

Propagation of uncertainties in interpolated rainfields to runoff errors

ABSTRACT Conditional daily rainfields were generated using collocated raingauge radar data by a kriging interpolation method, and disaggregated into hourly rainfields using variants of the method of fragments. A geographic information system (GIS)-based distributed rainfall–runoff model was used to convert the hourly rainfields into hydrographs. Using the complete radar rainfall as input, the rainfall–runoff model was calibrated based on storm events taken from nested catchments. Performance statistics were estimated by comparing the observed and the complete radar rainfall simulated hydrographs. Degradation in the hydrograph performance statistics by the simulated hourly rainfields was used to identify runoff error propagation. Uncertainty in daily rainfall amounts alone caused higher errors in runoff (depth, peak, and time to peak) than those caused by uncertainties in the hourly proportions alone. However, the degradation, which reduced with runoff depth, caused by the combined uncertainties was not significantly different from that caused by the uncertainty of amounts alone.

Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation

With the increased availability of remote sensing products, more hydrological variables (e.g., soil moisture and evapotranspiration) other than streamflow data are introduced into the calibration procedure of a hydrological model. However, how the incorporation of these hydrological variables influences the calibration results remains unclear. This study aims to analyze the impact of remote sensing soil moisture data in the joint calibration of a distributed hydrological model. The investigation was carried out in Qujiang and Ganjiang catchments in southern China, where the Dem-based Distributed Rainfall-runoff Model (DDRM) was calibrated under different calibration schemes where the streamflow data and the remote sensing soil moisture are assigned to different weights in the objective function. The remote sensing soil moisture data are from the SMAP L3 soil moisture product. The results show that different weights of soil moisture in the objective function can lead to very slight differences in simulation performance of soil moisture and streamflow. Besides, the joint calibration shows no apparent advantages in terms of streamflow simulation over the traditional calibration using streamflow data only. More studies including various remote sensing soil moisture products are necessary to access their effect on the joint calibration.