Runoff Error Research Articles

Modeling of runoff formation processes with aufeis feed in mountain cryosphere of the north-east of Russia

Aufeis are a key element in the chain of water exchange processes in the permafrost zone. The hydrological role of aufeis in the formation of river flow can be comparable to that of glaciers. Observations of aufeis during the construction of the Baikal-Amur Mainline showed that the share of aufeis runoff in the annual river flow can reach 35% for watersheds with an area of up to 500 km2. Despite the long history of studying aufeis and the results achieved, there are no methods for predicting the development of aufeis processes, as well as hydrological models that take into account the share of aufeis feeding in river runoff. This is due to the lack of observational data on the dynamics and development factors of aufeis. In the last century, long-term studies were carried out on some aufeis in Siberia and the Far East. One of these unique objects is the giant Anmangynda aufeis, which forms in the upper reaches of the Kolyma River basin. The aim of the research is to study the hydrological regime of the Anmangynda river basin and develop a method for accounting of aufeis runoff (module «Aufeis») in the distributed hydrological model «Hydrograph». The module “Aufeis” takes into account two factors of aufeis destruction – under the influence of solar radiation and thermal erosion destruction. The input data is the area of aufeis at the beginning of the warm season and daily meteorological data. The calculation parameters are the coefficients of ice melting and evaporation from ice, as well as the coefficient of the relative area of aufeis depending on the period of destruction, calculated for the modern climatic period. The result of the calculation is the aufeis runoff and the characteristics of aufeis for a given interval. Based on historical data and materials obtained during own field research, the module «Aufeis» was tested. The deviation of the calculated and observed values was 2—10% and 1—9% for the maximum values of the area and volume of the Anmangynda aufeis, respectively. Results of numerical calculations for the period 1967–2022 revealed the dynamics of the contribution of the Anmangynda aufeis to the river flow in different seasons. The expanded functionality of the Hydrograph model has improved the quality of modeling for rivers where aufeis form. For the river basin Anmangynda average Nash-Sutcliffe coefficient and annual runoff error were 0,57 and 13,0% compared to 0,41 and 18,0% when the module «Aufeis» was not used.

Correction of River Bathymetry Parameters Using the Stage–Discharge Rating Curve

AbstractRiver bathymetry is an important parameter for hydrodynamic modeling; however, it is associated with large bias because direct large‐scale measurements are impractical. Recent studies adjusted river bathymetry data based on assessment of the difference between modeled and observed water surface elevation (WSE); however, model uncertainties in river discharge can lead to unintended errors in correcting river bathymetry. In this study, we propose a simple but robust and rational correction method of river bathymetry using the bias between stage–discharge rating curves rather than WSE time series data. The rating curve represents the internal characteristics of the river section, and is not sensitive to the instantaneous simulated discharge errors. Our results showed that the corrected river bathymetry are robust to bias in runoff as they converged among experiments driven by noise‐corrupted or multimodel runoff forcing. Evaluation with the corrected river bathymetry against virtual truth demonstrated that the new method reduced 0.85–1.12 m of the absolute bias than the result from the conventional method. The deviation among the results reduced by more than 70% particularly in river sections with no backwater effects. Evaluation of the corrected river model output also showed the advantage of rating‐curve bias correction, as the simulated WSE is reasonably better only with better runoff and it does not conceal errors in runoff inputs. Given the difficulty of eliminating discharge errors and bias in runoff, a method for correcting river bathymetry that is free from discharge and runoff errors is important for improving hydrodynamic modeling.

Coordinated operation method of cascade hydropower stations considering runoff error

The traditional collaborative scheduling method of cascade hydropower stations group has the problem of imperfect regression model, which leads to excessive water consumption rate. A collaborative scheduling method of cascade hydropower stations group considering runoff error is designed. The influence factors of runoff forecast were obtained, the network parameters were called, and the regression model was built based on the runoff error according to the total power generation of cascade hydropower stations in the degree period. The objective function of hydropower system with hydropower stations as the center was established to optimize the cooperative dispatching mode. Experimental results: The average water consumption rates of the coordinated operation method of cascade hydropower stations group in this paper and the other two kinds of coordinated operation method of cascade hydropower stations group are 34.86%, 57.26% and 59.47% respectively, indicating that the coordinated operation method of cascade hydropower stations group with runoff error is more perfect.

Applicability Analysis of Different Runoff Schemes of the Gulang River Basin in Arid Region of Gansu Province, China

[Background] Mountain torrent disasters are one of the deadliest weather-related natural disasters in the world, often causing extremely serious economic and property losses and casualties; in recent years, due to heavy rainfall and complex geological and geomorphological conditions, mountain torrent disasters have occurred frequently in Gansu Province. An effective rainfall-runoff model is the key to prevent them. There are still many problems about how to establish suitable hydrological models in the arid basin of China. [Methods] Therefore, the Gulang River Basin in Gansu Province was selected as the research area to construct the HEC-HMS rainfall-runoff model; through the screening of different runoff methods, different runoff schemes were established to explore the optimal runoff algorithm for the arid basin; there are mainly SCS-CN method, initial and constant method, Green and Ampt method and exponential loss method in runoff generation; SCS unit hydrograph and Clark unit hydrograph methods are selected for slope transform, kinematic wave and lag algorithms for channel routing, totally 16 different runoff schemes. Three floods in the Gulang River Basin are selected to analyze the simulation effect of different runoff schemes in Gulang River Basin. [Results]1) If the Nash-Sutcliffe efficiency coefficient is selected as the evaluation index, the best simulation results are schemes 1 and 16 in the 20180826 flood, schemes 2 and 1 in the 20190626 flood and schemes 1 and 3 in the 20190911 flood. If the percentage of flood peak error is selected as the evaluation index, the best simulation results are schemes 16 and 1 in the 20180826 flood, schemes 9 and 6 in the 20190626 flood and schemes 13 and 4 in the 20190911 flood.If the percentage of runoff depth error is selected as indicator, the best simulation results are schemes 1 and 8 in the 20180826 flood, schemes 7 and 8 in the 20190626 flood and schemes 6 and 13 in the 20190911 flood. 2)The mean value of Nash-Sutcliffe efficiency coefficient obtained by the SCS-CN method for runoff generation, the SCS unit hydrograph for slope transform and the lag algorithm for channel routing are 0.8, 0.65 and 0.65, respectively; the mean absolute percentage errors of flood peak are 9.29%, 9.71% and 8.47%, respectively; the mean absolute percentage errors of runoff depth are 6.07%, 7.17% and 7.74%, respectively; the mean time difference of flood peak of SCS unit hydrograph for slope transform and lag algorithm for channel routing are 1.21 hour and 1.5 hour, respectively.[Conclusion]The most suitable scheme is the combination of the SCS-CN method for runoff generation, SCS unit hydrograph for slope transform, and lag algorithm for channel routing. The results can provide a certain reference for the prevention of flood disasters in the arid region of Gansu Province.

Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation

Accurate runoff prediction is one of the important tasks in various fields such as agriculture, hydrology, and environmental studies. Recently, with massive improvements of computational system and hardware, the deep learning-based approach has recently been applied for more accurate runoff prediction. In this study, the long short-term memory model with sequence-to-sequence structure was applied for hourly runoff predictions from 2015 to 2019 in the Russian River basin, California, USA. The proposed model was used to predict hourly runoff with lead time of 1–6 h using runoff data observed at upstream stations. The model was evaluated in terms of event-based performance using the statistical metrics including root mean square error, Nash-Sutcliffe Efficiency, peak runoff error, and peak time error. The results show that proposed model outperforms support vector machine and conventional long short-term memory models. In addition, the model has the best predictive ability for runoff events, which means that it can be effective for developing short-term flood forecasting and warning systems. The results of this study demonstrate that the deep learning-based approach for hourly runoff forecasting has high predictive power and sequence-to-sequence structure is effective method to improve the prediction results.

System response curve correction method of runoff error for real-time flood forecast

Abstract Multiple factors including rainfall and underlying surface conditions make river basin real-time flood forecasting very challenging. It is often necessary to use real-time correction techniques to modify the forecasting results so that they reach satisfactory accuracy. There are many such techniques in use today; however, they tend to have weak physical conceptual basis, relatively short forecast periods, unsatisfactory correction effects, and other problems. The mechanism that affects real-time flood forecasting error is very complicated. The strongest influencing factors corresponding to this mechanism affect the runoff yield of the forecast model. This paper proposes a feedback correction algorithm that traces back to the source of information, namely, modifies the watershed runoff. The runoff yield error is investigated using the principle of least squares estimation. A unit hydrograph is introduced into the real-time flood forecast correction; a feedback correction model that traces back to the source of information. The model is established and verified by comparison with an ideal model. The correction effects of the runoff yield errors are also compared in different ranges. The proposed method shows stronger correction effect and enhanced prediction accuracy than the traditional method. It is also simple in structure and has a clear physical concept without requiring added parameters or forecast period truncation. It is readily applicable in actual river basin flood forecasting scenarios.

Improvement and application research of the SRM in alpine regions.

The simulation of snowmelt runoff in alpine mountainous areas is of great significance not only for the risk assessment of snowmelt flood in spring and summer, but also for the development and management of water resources in the basin. An improved snowmelt runoff model (SRM) is constructed based on the analysis of change characteristics of climate, runoff, and snow and ice cover in the middle and upper reaches of the Taxkorgan River in Xinjiang Province, China. Because of the large evaporation in the study basin, the evaporation loss is added to the model. The SRM and the improved SRM are calibrated and verified by using data such as temperature, precipitation, water vapor pressure, and snow-covered area (SCA) ratio in the study basin from 2002 to 2012. The results show that, compared with the SRM, the average Nash-Sutcliffe coefficient (NSE) of annual runoff simulation increases from 0.80 to 0.86 in the calibration and increases from 0.74 to 0.83 in the validation through the improved model, and the average runoff error reduces from - 12.8 to 1.32% in the calibration and reduces from - 20.0 to - 11.51% in the validation. After adding the measured flow rate for real-time correction, the average NSE of annual runoff simulation increases from 0.91 to 0.93 and the average annual runoff error reduces from - 7.76 to - 3.91% in the calibration. The average NSE increases from 0.85 to 0.89 and the average runoff error reduces from - 12.35 to - 2.76% in the validation. It indicates that the SRM structure with increased evaporation loss is more in line with the actual situation. The short-term simulation effect of the model is greatly improved by adding the measured flow rate for real-time correction. At the same time, the improved SRM and the hypothetical climate change scenario are used to analyze the impact analysis of the snowmelt runoff simulation in the partial wet year. The results show that in the case of rising temperature, the ice and snow ablation period is prolonged, and the annual runoff also changes significantly in time distribution. It is of guiding significance for the influence of climate change on the runoff of recharged rivers with ice-snow meltwater in the other alpine regions.

Enhancing SWOT discharge assimilation through spatiotemporal correlations

The highly intermittent and sparse nature of the Surface Water and Ocean Topography (SWOT) river observations in both space and time poses a major challenge to users for most applications. While interpolation/assimilation efforts exist to assemble discrete SWOT river observations into a spatially complete and temporally continuous record, the efficacy of such efforts is severely compromised by large observation gaps. In this study, we introduced spatiotemporal error correlations in runoff fields into the Inverse Streamflow Routing (ISR) model to improve the gap-filling capability for creating continuous daily discharge. This enhanced ISR model was first investigated in the Ohio River basin, which showed very significant improvements with better reproduced spatial and temporal dynamics of the runoff fields and the discharge time series. Results using 25 validation gauges showed a more concentrated Kling-Gupta Efficiency distribution, where the median was improved from 0.494 to 0.750. The enhanced ISR model was further tested over 16 representative global river basins to verify the effectiveness and robustness of the proposed method. The best fitted e-fold correlation length and time leading to the greatest improvements varied largely among the 16 basins. While all basins benefitted from the spatiotemporal correlations in the SWOT gap-filling, basins suffering from poorer initial guess and fewer SWOT observations, such as the Dnieper, Kolyma and Murray basins, showed larger performance gains than others. Although the experiments were idealized and did not reflect all potential errors that may be present in a real-world application, we demonstrate that the utility of SWOT river products and their value in hydrologic sciences may be boosted by expanding a local/intermittent observation platform into a far-reaching source of hydrologic information through both river hydrodynamics in channel networks and spatiotemporal coherence of runoff errors.

Analytical Propagation of Runoff Uncertainty Into Discharge Uncertainty Through a Large River Network

AbstractThe transport of freshwater from continents to oceans through rivers has traditionally been estimated by routing runoff from land surface models within river models to obtain discharge. This paradigm imposes that errors are transferred from runoff to discharge, yet the analytical propagation of uncertainty from runoff to discharge has never been derived. Here we apply statistics to the continuity equation within a river network to derive two equations that propagate the mean and variance/covariance of runoff errors independently. We validate these equations in a case study of the rivers in the western United States and, for the first time, invert observed discharge errors for spatially distributed runoff errors. Our results suggest that the largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors. Our findings significantly advance the science of error quantification in model‐based estimates of river discharge.

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.

A Framework for Diagnosing Factors Degrading the Streamflow Performance of a Soil Moisture Data Assimilation System

Abstract Data assimilation (DA) techniques have been widely applied to assimilate satellite-based soil moisture (SM) measurements into hydrologic models to improve streamflow simulations. However, past studies have reached mixed conclusions regarding the degree of runoff improvement achieved via SM state updating. In this study, a synthetic diagnostic framework is designed to 1) decompose the random error components in a hydrologic simulation, 2) quantify the error terms that originate from SM states, and 3) assess the effectiveness of SM DA to correct these random errors. The general framework is illustrated through a case study in which surface Soil Moisture Active Passive (SMAP) data are assimilated into a large-scale land surface model in the Arkansas–Red River basin. The case study includes systematic error in the simulated streamflow that imposes a first-order limit on DA performance. In addition, about 60% of the random runoff error originates directly from rainfall and cannot be corrected by SM DA. In particular, fast-response runoff dominates in much of the basin but is relatively unresponsive to state updating. Slow-response runoff is strongly controlled by the bottom-layer SM and therefore only modestly improved via the assimilation of surface measurements. Combined, the total runoff improvement in the synthetic analysis is small (<10% over the basin). Improvements in the real SMAP-assimilated case are further limited due to systematic error and other factors such as inaccurate error assumptions and SMAP rescaling. Findings from the diagnostic framework suggest that SM DA alone is insufficient to substantially improve streamflow estimates in large basins.

Development and Integration of Sub-Daily Flood Modelling Capability within the SWAT Model and a Comparison with XAJ Model

To date, floods have become one of the most severe natural disasters on Earth. Flood forecasting with hydrological models is an important non-engineering measure for flood control and disaster reduction. The Xin’anjiang (XAJ) model is the most widely used hydrological model in China for flood forecasting, while the Soil and Water Assessment Tool (SWAT) model is widely applied for daily and monthly simulation and has shown its potential for flood simulation. The objective of this paper is to evaluate the performance of the SWAT model in simulating floods at a sub-daily time-scale in a slightly larger basin and compare that with the XAJ model. Taking Qilijie Basin (southeast of China) as a study area, this paper developed the XAJ model and SWAT model at a sub-daily time-scale. The results showed that the XAJ model had a better performance than the sub-daily SWAT model regarding relative runoff error (RRE) but the SWAT model performed well according to relative peak discharge error (RPE) and error of occurrence time of peak flow (PTE). The SWAT model performed unsatisfactorily in simulating low flows due to the daily calculation of base flow but behaved quite well in simulating high flows. We also evaluated the effect of spatial scale on the SWAT model. The results showed that the SWAT model had a good applicability at different spatial scales. In conclusion, the sub-daily SWAT model is a promising tool for flood simulation though more improvements remain to be studied further.

Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes

Small reservoirs represent a critical water supply to millions of farmers across semi-arid regions, but their hydrological modelling suffers from data scarcity and highly variable and localised rainfall intensities. Increased availability of satellite imagery provide substantial opportunities but the monitoring of surface water resources is constrained by the small size and rapid flood declines in small reservoirs. To overcome remote sensing and hydrological modelling difficulties, the benefits of combining field data, numerical modelling and satellite observations to monitor small reservoirs were investigated. Building on substantial field data, coupled daily rainfall-runoff and water balance models were developed for 7 small reservoirs (1–10 ha) in semi arid Tunisia over 1999–2014. Surface water observations from MNDWI classifications on 546 Landsat TM, ETM+ and OLI sensors were used to update model outputs through an Ensemble (n = 100) Kalman Filter over the 15 year period. The Ensemble Kalman Filter, providing near-real time corrections, reduced runoff errors by modulating incorrectly modelled rainfall events, while compensating for Landsat’s limited temporal resolution and correcting classification outliers. Validated against long term hydrometric field data, daily volume root mean square errors (RMSE) decreased by 54% to 31200 m3 across 7 lakes compared to the initial model forecast. The method reproduced the amplitude and timing of major floods and their decline phases, providing a valuable approach to improve hydrological monitoring (NSE increase from 0.64 up to 0.94) of flood dynamics in small water bodies. In the smallest and data-scarce lakes, higher temporal and spatial resolution time series are essential to improve monitoring accuracy.

Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling

A hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC) model with artificial neural networks (ANNs). In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation). The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River.

Evaluation of rainfall spatial correlation effect on rainfall-runoff modeling uncertainty, considering 2-copula

Lack of accuracy of rainfall-runoff simulation (RRS) remains critical for some applications. Among various sources of uncertainty, precipitation plays a particular role. Rainfall rates as the main input data of RRS are of the first factors controlling the accuracy. In addition to the depth, spatial and temporal distributions of rainfall impact the flood discharge. Most of the previous studies on RRS uncertainty have ignored rainfall spatial distribution, where in large catchments, it is necessary to be modeled explicitly. Karoon III is one most important basin of the Iran because of the Karoon III dam in the outlet. In the present work, effect of spatial correlation of rainfall on HEC-HMS (SMA) continuous RRS uncertainty is evaluated using 2variate copula (2copula). Monte Carlo simulation (MCS) approach was used to consider the rainfall spatial dependence. To reduce the computational expense, sampling efficiency and convergence for MCS, Latin hypercube sampling (LHS) was used. Copula functions consider wide range of marginal probability distribution functions (PDFs), eliminating limits of regular join PDFs. For this aim, two scenarios were investigated. In the first scenario, sub-basin rainfall was considered independent, and in the second scenario, 2copula was adopted to model spatial correlation of rainfall. Dimensionless rainfall depths were calculated for each sub-basin, and the PDFs were determined. The generated random dimensionless rainfalls were reweighted and multiplied by watershed’s mean rainfall value. Stochastic Climate Library was used to generate continuous daily rainfalls. Sampling from dimensionless rainfalls using LHS algorithm, 100 runs of calibrated model-simulated 100 flows for each day following MCS, and 80 % certainty bound was calculated. Results showed that considering dependence decreased 18 % of the maximum uncertainty bound width, so the methodology could be recommended for decreasing predicted runoff error.

The distributed model intercomparison project – Phase 2: Experiment design and summary results of the western basin experiments

Summary The Office of Hydrologic Development (OHD) of the U.S. National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) conducted the two phases of the Distributed Model Intercomparison Project (DMIP) as cost-effective studies to guide the transition to spatially distributed hydrologic modeling for operational forecasting at NWS River Forecast Centers (RFCs). Phase 2 of the Distributed Model Intercomparison Project (DMIP 2) was formulated primarily as a mechanism to help guide the U.S. NWS as it expands its use of spatially distributed watershed models for operational river, flash flood, and water resources forecasting. The overall purpose of DMIP 2 was to test many distributed models forced by high quality operational data with a view towards meeting NWS operational forecasting needs. At the same time, DMIP 2 was formulated as an experiment that could be leveraged by the broader scientific community as a platform for the testing, evaluation, and improvement of distributed models. DMIP 2 contained experiments in two regions: in the DMIP 1 Oklahoma basins, and second, in two basins in the Sierra Nevada Mountains in the western USA. This paper presents the overview and results of the DMIP 2 experiments conducted for the two Sierra Nevada basins. Simulations from five independent groups from France, Italy, Spain and the USA were analyzed. Experiments included comparison of lumped and distributed model streamflow simulations generated with uncalibrated and calibrated parameters, and simulations of snow water equivalent (SWE) at interior locations. As in other phases of DMIP, the participant simulations were evaluated against observed hourly streamflow and SWE data and compared with simulations provided by the NWS operational lumped model. A wide range of statistical measures are used to evaluate model performance on a run-period and event basis. Differences between uncalibrated and calibrated model simulations are assessed. Results indicate that in the two study basins, no single model performed best in all cases. In addition, no distributed model was able to consistently outperform the lumped model benchmark. However, one or more distributed models were able to outperform the lumped model benchmark in many of the analyses. Several calibrated distributed models achieved higher correlation and lower bias than the calibrated lumped benchmark in the calibration, validation, and combined periods. Evaluating a number of specific precipitation-runoff events, one calibrated distributed model was able to perform at a level equal to or better than the calibrated lumped model benchmark in terms of event-averaged peak and runoff volume error. However, three distributed models were able to provide improved peak timing compared to the lumped benchmark. Taken together, calibrated distributed models provided specific improvements over the lumped benchmark in 24% of the model-basin pairs for peak flow, 12% of the model-basin pairs for event runoff volume, and 41% of the model-basin pairs for peak timing. Model calibration improved the performance statistics of nearly all models (lumped and distributed). Analysis of several precipitation/runoff events indicates that distributed models may more accurately model the dynamics of the rain/snow line (and resulting hydrologic conditions) compared to the lumped benchmark model. Analysis of SWE simulations shows that better results were achieved at higher elevation observation sites. Although the performance of distributed models was mixed compared to the lumped benchmark, all calibrated models performed well compared to results in the DMIP 2 Oklahoma basins in terms of run period correlation and %Bias, and event-averaged peak and runoff error. This finding is noteworthy considering that these Sierra Nevada basins have complications such as orographically-enhanced precipitation, snow accumulation and melt, rain on snow events, and highly variable topography. Looking at these findings and those from the previous DMIP experiments, it is clear that at this point in their evolution, distributed models have the potential to provide valuable information on specific flood events that could complement lumped model simulations.

Evaluating Surface Water Cycle Simulated by the Australian Community Land Surface Model (CABLE) across Different Spatial and Temporal Domains

Abstract The terrestrial water cycle in the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model has been evaluated across a range of temporal and spatial domains. A series of offline experiments were conducted using the forcing data from the second Global Soil Wetness Project (GSWP-2) for the period of 1986–95, but with its default parameter settings. Results were compared against GSWP-2 multimodel ensembles and a range of observationally driven datasets. CABLE-simulated global mean evapotranspiration (ET) and runoff agreed well with the GSWP-2 multimodel climatology and observations, and the spatial variations of ET and runoff across 150 large catchments were well captured. Nevertheless, at regional scales it underestimated ET in the tropics and had some significant runoff errors. The model sensitivity to a number of selected parameters is further examined. Results showed some significant model uncertainty caused by its sensitivity to soil wilting point as well as to the root water uptaking efficiency and canopy water storage parameters. The sensitivity was large in tropical rain forest and midlatitude forest regions, where the uncertainty caused by the model parameters was comparable to a large part of its difference against the GSWP-2 multimodel mean. Furthermore, the discrepancy among the CABLE perturbation experiments caused by its sensitivity to model parameters was equivalent to about 20%–40% of the intermodel difference among the GSWP-2 models, which was primarily caused by different model structure/processes. Although such results are model dependent, they suggest that soil/vegetation parameters could be another source of uncertainty in estimating global surface energy and water budgets.

Understanding the Dependence of Satellite Rainfall Uncertainty on Topography and Climate for Hydrologic Model Simulation

A quantitative and physical understanding of satellite rainfall uncertainties provides meaningful guidance on improving algorithms to advance hydrologic prediction. The aim of this study is to characterize satellite rainfall errors and their impact on hydrologic fluxes based on fundamental governing factors that dictate the accuracy of passive remote sensing of precipitation. These governing factors are land features-comprising topography (elevation)-and climate type, representing the average ambient atmospheric conditions. First, the study examines satellite rainfall errors of three major products, 3B42RT, Climate prediction center MORHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), by breaking the errors down into independent components (hit, miss-rain, and false-rain biases) and investigating their contribution to runoff and soil moisture errors. The uncertainties of three satellite rainfall products are explored for five regions of the Mississippi River basin that are categorized grid cell by grid cell (at the native spatial resolution of satellite products) based on topography and climate. It is found that total and hit biases dictate the temporal trend of soil moisture and runoff errors, respectively. Miss-rain and hit biases are the leading errors in the 3B42RT and CMORPH products, respectively, whereas false-rain bias is a pervasive problem of the PERSIANN product. For 3B42RT and CMORPH, about 50%-60% of grid cells are influenced by the total bias during winter and 60%-70% of grid cells during summer. For PERSIANN, about 70%-80% of the grid cells are marked by total bias during the summer and winter seasons. False-rain bias gradually increases from lowland to highland regions universally for all three satellite rainfall products. Overall, the study reveals that satellite rainfall uncertainty is dependent more on topography than the climate of the region. This study's results indicate that it is now worthwhile to assimilate the static knowledge of topography in the satellite estimation of precipitation to minimize the uncertainty in anticipation of the Global Precipitation Measurement mission.

Tracing hydrologic model simulation error as a function of satellite rainfall estimation bias components and land use and land cover conditions

The key question that is asked in this study is “how are the three independent bias components of satellite rainfall estimation, comprising hit bias, missed, and false precipitation, physically related to the estimation uncertainty of soil moisture and runoff for a physically based hydrologic model?” The study also investigated the performance of different satellite rainfall products as a function of land use and land cover (LULC) type. Using the entire Mississippi river basin as the study region and the variable infiltration capacity (VIC)‐3L as the distributed hydrologic model, the study of the satellite products (CMORPH, 3B42RT, and PERSIANN‐CCS) yielded two key findings. First, during the winter season, more than 40% of the rainfall total bias is dominated by missed precipitation in forest and woodland regions (southeast of Mississippi). During the summer season, 51% of the total bias is governed by the hit bias, and about 42% by the false precipitation in grassland‐savanna region (western part of Mississippi basin). Second, a strong dependence is observed between hit bias and runoff error, and missed precipitation and soil moisture error. High correlation with runoff error is observed with hit bias (∼0.85), indicating the need for improving the satellite rainfall product's ability to detect rainfall more consistently for flood prediction. For soil moisture error, it is the total bias that correlated significantly (∼0.78), indicating that a satellite product needed to be minimized of total bias for long‐term monitoring of watershed conditions for drought through continuous simulation.

Uncertainty of areal average rainfall and its effect on runoff simulation: A case study for the Chungju Dam Basin, Korea

This study investigated the relationship between the errors involved in the areal average rainfall estimation and the runoff simulation (hereafter, rainfall and runoff errors). The error statistics of the observed areal average rainfall were estimated and used for the generation of input data for the runoff simulation. The Clark instantaneous unit hydrograph was used for this runoff simulation. The runoff model parameters were estimated using several sets of rainfall-runoff data observed, whose statistics were then used for the sensitive analysis of the model simulation on the parameter sets. The rainfall error in this study was defined as the relative difference between the observed and generated areal average rainfall. On the other hand, the runoff error was designed to consider the runoff volume, peak flow and peak time, respectively. This study was applied to the Chugnju Dam Basin, Korea. The results obtained are as follows: (1) The variation of model parameters estimated in this study were about 30% of their means. Also, the effect of this error in model parameter estimation on runoff simulation was found to be maximum 15% of the peak flow. (2) The estimation error of areal average rainfall was found roughly proportional to the areal average rainfall itself. Both the rainfall and runoff errors were found to have no obvious biases. However, the variance of the peak flow error was found to be significantly higher. (3) The relationship between rainfall error and runoff volume error was roughly one to one, however, the rainfall error has become amplified by more than 50 % and transferred to the peak flow error.