Distributed Hydrological Model Research Articles

A new numerical model for simulating top surface soil moisture and runoff

PurposeThis paper aims to construct a simplified distributed hydrological model based on the surveyed watershed soil properties database.Design/methodology/approachThe new established model requires fewer parameters to be adjusted than needed by former hydrological models. However, the achieved stream-flow simulation results are similar and comparable to the classic hydrological models, such as the Xinanjiang model and the TOPMODEL.FindingsGood results show that the discharge and the top surface soil moisture can be simultaneously simulated, and that is the exclusive character of this new model. The stream-flow simulation results from two moderate hydrological watershed models show that the daily stream-flow simulation achieved the classic hydrological results shown in the TOPMODEL and Xinanjiang model. The soil moisture validation results show that the modeled watershed scale surface soil moisture has general agreement with the obtained measurements, with a root-mean-square error (RMSE) value of 0.04 (m3/m3) for one of the one-measurement sites and an averaged RMSE of 0.08 (m3/m3) over all measurements.Originality/valueIn this paper, a new simplified distributed hydrological model was constructed.

Water Flux Tracking With a Distributed Hydrological Model to Quantify Controls on the Spatio‐temporal Variability of Transit Time Distributions

AbstractWater transit times and flow pathways are crucial elements in characterizing catchment hydrologic response. Understanding their variability in space and time sheds light on the link between discharge formation and water quality at the catchment scale. Here, we introduce a novel modeling framework to explore water transport mechanisms using the Hafren catchment in Wales (UK) as a case study. We show that a fully distributed hydrological model coupled with a transport component for conservative tracers is useful in analyzing how hydrometeorological conditions and spatial heterogeneity may affect water transit times and age distributions in a real catchment. We use the model to track the paths of water parcels that entered the catchment as rainfall over 2 years, labeling each day of rain individually. There is a reasonable agreement between tracer simulations and observations, suggesting that dynamic transit time distributions (TTDs) both forward and backward in time can be approximated using a high spatial and temporal resolution hydrochemical model, without assuming a priori any transit and storage selection functions at the catchment scale. TTDs are quantified for the modeled internal dynamics of the study catchment. TTDs conditional on a given rainfall time are mostly correlated to the season in which the rain event occurs, whereas TTDs conditional on a given exit time are mostly affected by catchment wetness. When TTDs for individual rainfall events are re‐scaled as functions of cumulative discharge, they collapse around a single common distribution, suggesting a potential characteristic catchment function.

Hydrological impacts in La Plata basin under 1.5, 2 and 3 °C global warming above the pre‐industrial level

The assessment of regional climate and hydrological impacts at different levels of global‐mean temperature increase became vital to guide decision makers and water management planners after the adoption of the Paris Agreement in 2015. With current emission trends close to a pathway of 3 °C warming by the end of the present century, this study investigates the projected regional hydrology impacts over La Plata basin (LPB) for the warming thresholds of 1.5 and 2 °C established in Paris and for 3 °C above the pre‐industrial level. We also explore the consequences from following a medium or high‐emission representative concentration pathway (RCP) to achieve the different warming targets. In order to determine the possible changes in precipitation, evapotranspiration, runoff and river discharges, we use the variable infiltration capacity (VIC)‐distributed hydrology model in combination of bias‐corrected GCM outputs from the Inter‐Sectorial Impact Model Intercomparison Project phase 2a (ISIMIP). Because the behaviour of the hydrometeorological variables is not homogeneous over LPB, we analysed impacts of the different changes in mean global temperature and RCPs over four sub‐basins: Paraguay, Paraná, Iguazú and Uruguay. Overall, most of the changes over the sub‐basins suggest moister conditions with increasing temperatures. The comparison of the RCPs indicates that increases in precipitation, evapotranspiration and runoff would be larger under the medium‐emission scenario. When the different responses of the various components of the terrestrial water cycle were integrated, results show that variation of annual mean streamflow in all sub‐basins ranges between ±20%. However, in most cases, the sign of the changes highly depends on the RCP chosen to achieve a warming level.

Application of the MacCormack scheme to overland flow routing for high-spatial resolution distributed hydrological model

Although process-based distributed hydrological models (PDHMs) are evolving rapidly over the last few decades, their extensive applications are still challenged by the computational expenses. This study attempted, for the first time, to apply the numerically efficient MacCormack algorithm to overland flow routing in a representative high-spatial resolution PDHM, i.e., the distributed hydrology-soil-vegetation model (DHSVM), in order to improve its computational efficiency. The analytical verification indicates that both the semi and full versions of the MacCormack schemes exhibit robust numerical stability and are more computationally efficient than the conventional explicit linear scheme. The full-version outperforms the semi-version in terms of simulation accuracy when a same time step is adopted. The semi-MacCormack scheme was implemented into DHSVM (version 3.1.2) to solve the kinematic wave equations for overland flow routing. The performance and practicality of the enhanced DHSVM-MacCormack model was assessed by performing two groups of modeling experiments in the Mercer Creek watershed, a small urban catchment near Bellevue, Washington. The experiments show that DHSVM-MacCormack can considerably improve the computational efficiency without compromising the simulation accuracy of the original DHSVM model. More specifically, with the same computational environment and model settings, the computational time required by DHSVM-MacCormack can be reduced to several dozen minutes for a simulation period of three months (in contrast with one day and a half by the original DHSVM model) without noticeable sacrifice of the accuracy. The MacCormack scheme proves to be applicable to overland flow routing in DHSVM, which implies that it can be coupled into other PHDMs for watershed routing to either significantly improve their computational efficiency or to make the kinematic wave routing for high resolution modeling computational feasible.

Hydrometeorology of the catastrophic Blanco river flood in South Texas, May 2015

Study regionThis study was carried out along the Blanco River in south-central Texas which drains approximately 1100 km2. Study focusUnprecedented rainfall across the state of Texas in May 2015 produced flooding that claimed at least 24 lives across the state. The most devastating single event over this period occurred on 23–24 May along the Blanco River, where a fast moving floodwave resulted in eleven fatalities in the town of Wimberley. The storm event resulted in the flood of record at the USGS gauge in Wimberley which has collected data since the 1920s. New hydrological insightsMeteorological observations, high-resolution rainfall estimates, and physics-based distributed hydrological modeling provide the opportunity to examine the hydrometeorological mechanisms accompanying the extreme storm and subsequent flood. Additionally, transpositions of the radar rain fields forced through the hydrologic model allow for analysis of the effect of position of the centroid of the storm relative to the catchment on floodwave propagation. Two major controls on the flood response are identified and analyzed: the position of heavy storm cells over the headwaters with subsequent slow movement downstream and the extremely moist antecedent conditions prior to the onset of the event.

Improving streamflow simulations and forecasting performance of SWAT model by assimilating remotely sensed soil moisture observations

This article presents a study carried out using EnKF based assimilation of coarser-scale SMOS soil moisture retrievals to improve the streamflow simulations and forecasting performance of SWAT model in a large catchment. This study has been carried out in Munneru river catchment, India, which is about 10,156 km2. In this study, an EnkF based new approach is proposed for improving the inherent vertical coupling of soil layers of SWAT hydrological model during soil moisture data assimilation. Evaluation of the vertical error correlation obtained between surface and subsurface layers indicates that the vertical coupling can be improved significantly using ensemble of soil storages compared to the traditional static soil storages based EnKF approach. However, the improvements in the simulated streamflow are moderate, which is due to the limitations in SWAT model in reflecting the profile soil moisture updates in surface runoff computations. Further, it is observed that the durability of streamflow improvements is longer when the assimilation system effectively updates the subsurface flow component. Overall, the results of the present study indicate that the passive microwave-based coarser-scale soil moisture products like SMOS hold significant potential to improve the streamflow estimates when assimilating into large-scale distributed hydrological models operating at a daily time step.

Forecasting and Providing Warnings of Flash Floods for Ungauged Mountainous Areas Based on a Distributed Hydrological Model

Flash floods occur in mountainous catchments with short response times, which are among the most devastating natural hazards in China. This paper intends to forecast and provide warnings of flash floods timely and precisely using the flash flood warning system, which is established by a new distributed hydrological model (the China flash flood hydrological model, CNFF-HM). Two ungauged mountainous regions, Shunchang and Zherong, are chosen as the study areas. The CNFF-HM is calibrated in five well-monitored catchments. The parameters for the ungauged regions are estimated by regionalization. River water stage data and reservoir water stage data from Shunchang, and reservoir water stage data from Zherong are used to validate the model. The model performs well and the average Nash–Sutcliffe efficiency (NSE) is above 0.8 for the five catchments. The validation shows the difference in the timing of flood peaks using the two types of water stage data is less than 1 h. The rising and declining trends of the floods correspond to the observed trends over the entire validation process. Furthermore, the flash flood warning system was effectively applied in flash flood event on 28 September 2016 in Zherong. Thus, the CNFF-HM with regionalization is effective in forecasting flash floods for ungauged mountainous regions.

Applying SMOS soil moisture data into the National Weather Service (NWS)’s Research Distributed Hydrologic Model (HL-RDHM) for flash flood guidance application

After rainfall, soil moisture is the most important factor dictating flash flooding, since rainfall infiltration and runoff are based on the saturation of the soil. However, continuous and regional soil moisture data acquisition is difficult by ground-based measurement. As such, soil moisture is often derived from land surface models and used by agencies such as the National Oceanic and Atmospheric Administration's National Weather Service (NOAA/NWS) as a proxy for estimates of soil moisture at the surface in order to support operational flood forecasting. The current Flash Flood Guidance (FFG) system at the Arkansas Red Basin River Forecast Center (ABRFC) provides gridded flash flood guidance (GFFG) by using the soil moisture from the NWS Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) to scale Natural Resources Conservation Service curve numbers. This study evaluates the contribution of remote sensing technology to quantifiable improvements in HL-RDHM soil moisture as well as adding a satellite-based soil moisture component to the NWS FFG Algorithm. The Soil Moisture and Ocean Salinity (SMOS) satellite of European Space Agency operates at an L-band (1.4GHz) wavelength which offers relatively deeper penetration and has lower sensitivity to vegetation impacts than other microwave satellite platforms. It has been shown to be well-suited for observing surface soil moisture. The purpose of this paper is to determine, execute, and assess a method of SMOS data assimilation applicable for use with the HL-RDHM modeling system. The value of remote sensing data in constraining modeled soil moisture states is evaluated. Results from the technique developed in this study imply a potential for SMOS-based improvement of the GFFG product. The technique is also expected to be useful for assimilating soil moisture data from the Soil Moisture Active Passive (SMAP).

Potential implications of climate change and urbanization on watershed hydrology

This paper proposes a modeling framework able to analyze the alterations in watershed hydrology induced by two recurrent drivers for hydrological changes: climate change and urbanization. The procedure is based on the coupling of a stochastic weather generator with a land use change model for the generation of some hypothetical scenarios. The generated scenarios are successively used to force a physically-based and spatial distributed hydrological model to reconstruct the basin response under different conditions. Several potential climate alterations are simulated by imposing negative and positive variations in the mean annual precipitation and a simultaneous temperature increase. Urbanization is conceptualized by an increase in the impervious fraction of the basin. The procedure is applied to a large basin and a much smaller sub-basin; the results show how climate and land use changes may interact and affect the fundamental hydrological dynamics and how the processes governing basin hydrological response may change with spatial scale.

Distributed Hydrological Modeling: Determination of Theoretical Hydraulic Potential & Streamflow Simulation of Extreme Hydrometeorological Events

The progressive change in climatic conditions worldwide has increased frequency and severity of extreme hydrometeorological events (EHEs). México is an example that has been affected by the occurrence of EHE leading to economic, social, and environmental losses. The objective of this research was to apply a Canadian distributed hydrological model (DHM) to tropical conditions and to evaluate its capacity to simulate flows in a basin in the central Gulf of Mexico. In addition, the DHM (once calibrated and validated) was used to calculate the theoretical hydraulic power (THP) and the performance to predict streamflow before the presence of an EHE. The results of the DHM show that the goodness of fit indicators between the observed and simulated flows in the calibration process Nash-Sutcliffe efficiency (NSE) = 0.83, ratio of the root mean square error to the standard deviation of measured data (RSR) = 0.41, and percent bias (PBIAS) = −4.3) and validation (NSE = 0.775, RSR = 0.4735, and PBIAS = 2.45) are satisfactory. The DHM showed its applicability: determination of THP showed that the mean flows are in synchrony with the order of the river reaches and streamflow simulation of 13 EHEs (NSE = 0.78 ± 0.13, RSR = 0.46 ± 0.14 and PBIAS = −0.48 ± 7.5) confirmed a reliable efficiency. This work can serve as a tool for identifying vulnerabilities before floods and for the rational and sustainable management of water resources.

Ensemble Streamflow Forecasting across the U.S. Mid-Atlantic Region with a Distributed Hydrological Model Forced by GEFS Reforecasts

Abstract The quality of ensemble streamflow forecasts in the U.S. mid-Atlantic region (MAR) is investigated for short- to medium-range forecast lead times (6–168 h). To this end, a regional hydrological ensemble prediction system (RHEPS) is assembled and implemented. The RHEPS in this case comprises the ensemble meteorological forcing, a distributed hydrological model, and a statistical postprocessor. As the meteorological forcing, precipitation, and near-surface temperature outputs from the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast, version 2 (GEFSRv2), are used. The Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is used as the distributed hydrological model, and a statistical autoregressive model with an exogenous variable is used as the postprocessor. To verify streamflow forecasts from the RHEPS, eight river basins in the MAR are selected, ranging in drainage area from ~262 to 29 965 km2 and covering some of the major rivers in the MAR. The verification results for the RHEPS show that, at the initial lead times (1–3 days), the hydrological uncertainties have more impact on forecast skill than the meteorological ones. The former become less pronounced, and the meteorological uncertainties dominate, across longer lead times (>3 days). Nonetheless, the ensemble streamflow forecasts remain skillful for lead times of up to 7 days. Additionally, postprocessing increases forecast skills across lead times and spatial scales, particularly for the high-flow conditions. Overall, the proposed RHEPS is able to improve streamflow forecasting in the MAR relative to the deterministic (unperturbed GEFSRv2 member) forecasting case.

A watershed decision support tool for managing invasive species on Hawai‘i Island, USA

Non-native species invasions, growing human populations, and climate change are central ecological concerns in tropical island communities. The combination of these threats have led to losses of native biota, altered hydrological and ecosystem processes, and reduced ecosystem services. These threats pose complex problems to often underfunded management entities. We developed a watershed decision support tool (WDST) for the windward coast of Hawai‘i Island aimed at prioritizing catchments for invasive species removal and native forest protection from non-native species invasions. Using the Ecosystem Management Decision Support (EMDS) system, we integrated spatial data from four sources: (i) native and invasive species coverage; (ii) modeled water yield; (iii) treatment cost and efficacy; and (iv) native species conservation value. We used a distributed hydrology model (DHSVM) to estimate catchment-level (∼90ha) water yield under six climate and non-native species invasion scenarios to identify where (1) invasive species removal and (2) protection from invasion would have the greatest benefit to increasing or maintaining native biodiversity and hydrologic functioning. The hydrology model predicted a 30% decline (386Glyr−1) in total water yield under a drier future climate (20% reduction in rainfall), with an additional 2% reduction when catchments were fully invaded by non-native species. Increased temperatures had a small compensatory effect on water yield. The WDST identified 6.3% of the study area as high priority for invasive species removal, based on characteristics of large hydrological response to the removal treatment (concentrated in high rainfall areas), high quality road or trail access, and high conservation value. High protection priority from invasive species (5.9% by area) occurred in higher elevation catchments, near the upper range of strawberry guava (the main invasive species), where water yield was most sensitive to invasion. Climate change scenarios had little influence on the spatial distribution of priority scores despite large changes in overall water yield. In contrast, priority scores were sensitive to very high variation in treatment costs, which were influenced largely by travel times to catchments via road and trail networks. This last finding suggests that future management feasibility will hinge on improvements to road and trail networks, or development of alternative management strategies that reduce travel costs and time.

A framework to improve hyper-resolution hydrological simulation in snow-affected regions

Snow processes in mid- and north-latitude basins and their interaction with runoff generation at hyperresolution (<1km and <hourly) pose challenges in current state-of-the-art distributed hydrological models. These models run typically at macro to moderate scales (>5km), representing land surface processes based on simplified couplings of snow thermal physics and the water cycle in the soil-vegetation-atmosphere (SVA) layers. This paper evaluates a new hydrological model capable of simulating river flows for a range of basin scales (100km2 to >10,000km2), and a particular focus on mid- and north-latitude regions. The new model combines the runoff generation and fully distributed routing framework of the Coupled Routing and Excess STorage (CREST) model with a new land surface process model that strictly couples water and energy balances at the SVA layer, imposing closed energy balance solutions. The model is vectorized and parallelized to achieve long-term (>30years) high-resolution (30m to 500m and subhourly) simulations of large river basins utilizing high-performance computing. The model is tested in the Connecticut River basin (20,000km2), where flooding is frequently associated with interactions of snowmelt triggered by rainfall events. Model simulations of distributed evapotranspiration (ET) and snow water equivalence (SWE) at daily time step are shown to match accurately ET estimates from MODIS (average NSCE and bias are 0.77 and 6.79%) and SWE estimates from SNODAS (average correlation and normalized root mean square error are 0.94 and of 19%); the modeled daily river flow simulations exhibit an NSCE of 0.58 against USGS streamflow observations.

Daily streamflow simulation based on the improved machine learning method

Kan, G., He, X., Ding, L., Li, J., Hong, Y., Ren, M., Lei, T., Liang, K., Zuo, D., & Huang, P. (March-April, 2017). Daily streamflow simulation based on the improved machine learning method. Water Technology and Sciences (in Spanish), 8(2), 51-60.Daily streamflow simulation has usually been implemented by conceptual or distributed hydrological models. Nowadays, hydrological data, which can be easily obtained from automatic measuring systems, are more than enough. Therefore, machine learning turns into an effective and popular tool which is highly suited for the streamflow simulation task. In this paper, we propose an improved machine learning method referred to as PKEK model based on the previously proposed NU-PEK model for the purpose of generating daily streamflow simulation results with better accuracy and stability. Comparison results between the PKEK model and the NU-PEK model indicated that the improved model has better accuracy and stability and has a bright application prospect for daily streamflow simulation tasks.

Large-watershed flood forecasting with high-resolution distributed hydrological model

Abstract. A distributed hydrological model has been successfully used in small-watershed flood forecasting, but there are still challenges for the application in a large watershed, one of them being the model's spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model's computation efficiency in a large watershed, the other is implementing the model on a high-performance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil and land use are downloaded from the website freely, and the model structure with a high resolution of 200 m × 200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the Particle Swarm Optimization (PSO) algorithm; the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m × 200 m model resolution is proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500 m × 500 m grid cell, but the model spatial resolution with a 200 m × 200 m grid cell is recommended in this study to keep the model at a better performance.

Assessment on the Effect of Climate Change on Streamflow in the Source Region of the Yangtze River, China

Tuotuo River basin, known as the source region of the Yangtze River, is the key area where the impact of climate change has been observed on many of the hydrological processes of this central region of the Tibetan Plateau. In this study, we examined six Global Climate Models (GCMs) under three Representative Concentration Pathways (RCPs) scenarios. First, the already impacted climate change was analyzed, based on the historical data available and then, the simulation results of the GCMs and RCPs were used for future scenario assessments. Results indicated that the annual mean temperature will likely be increased, ranging from −0.66 °C to 6.68 °C during the three future prediction periods (2020s, 2050s and 2080s), while the change in the annual precipitation ranged from −1.18% to 66.14%. Then, a well-known distributed hydrological soil vegetation model (DHSVM) was utilized to evaluate the effects of future climate change on the streamflow dynamics. The seasonal mean streamflows, predicted by the six GCMs and the three RCPs scenarios, were also shown to likely increase, ranging from −0.52% to 22.58%. Watershed managers and regulators can use the findings from this study to better implement their conservation practices in the face of climate change.

Adequacy of TRMM satellite rainfall data in driving the SWAT modeling of Tiaoxi catchment (Taihu lake basin, China)

Spatial rainfall data is an essential input to Distributed Hydrological Models (DHM), and a significant contributor to hydrological model uncertainty. Model uncertainty is higher when rain gauges are sparse, as is often the case in practice. Currently, satellite-based precipitation products increasingly provide an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment is required before implementation. Accordingly, the twofold objective of this work paper was the real-world assessment of both (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, and (b) the TRMM product’s role in forcing data for hydrologic simulations in the area of the Tiaoxi catchment (Taihu lake basin, China). The TRMM rainfall products used in this study are the Version-7 real-time 3B42RT and the post-real-time 3B42. It was found that the TRMM rainfall data showed a superior performance at the monthly and annual scales, fitting well with surface observation-based frequency rainfall distributions. The Nash-Sutcliffe Coefficient of Efficiency (NSCE) and the relative bias ratio (BIAS) were used to evaluate hydrologic model performance. The satisfactory performance of the monthly runoff simulations in the Tiaoxi study supports the view that the implementation of real-time 3B42RT allows considerable room for improvement. At the same time, post-real-time 3B42 can be a valuable tool of hydrologic modeling, water balance analysis, and basin water resource management, especially in developing countries or at remote locations in which rainfall gauges are scarce.

Case Studies of a MODIS-Based Potential Evapotranspiration Input to the Sacramento Soil Moisture Accounting Model

Abstract A satellite-based potential evapotranspiration (PET) estimate derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations was tested for input to the spatially lumped and gridded Sacramento Soil Moisture Accounting (SAC-SMA) model. The 15 forecast points within the National Weather Service (NWS) North Central River Forecast Center (NCRFC) forecasting region were the basis for this analysis. Through a series of case studies, the MODIS-derived PET estimate (M-PET) was evaluated for input to the SAC-SMA model by comparing streamflow simulations with those from traditional SAC-SMA evapotranspiration (ET) demand. Two prior studies have evaluated the M-PET data 1) to compute new long-term average ET demand values and 2) to input a time series (i.e., daily time-varying PET) to the NWS Hydrology Laboratory–Research Distributed Hydrologic Model (HL-RDHM), a spatially distributed version of the SAC-SMA model. This current paper presents results from a third test in which the M-PET time series is input to the lumped SAC-SMA model. In all cases, evaluation is between the M-PET data and the long-term average values used by the NWS. Similar to prior studies, results of the current analysis are mixed with improved model evaluation statistics for 4 of 15 basins tested. Of the three cases, using the time-varying M-PET as input to the distributed SAC-SMA model led to the most promising results, with model simulations that are at least as good as those when using the SAC-SMA ET demand. Analyses of the model-simulated ET suggest that the time-varying M-PET input may produce a more physically realistic representation of ET processes in both the lumped and distributed versions of the SAC-SMA model.

Impact of spatially-variable soil thickness and texture on simulated hydrologic conditions in a semiarid watershed in northwest Mexico

Soil depth and texture exert strong controls on the spatial distribution of water and energy fluxes and states in semiarid watersheds. As a result, realistic representations of the spatial soil characteristics within watersheds are important for the improvement of process-based distributed hydrologic modeling applications. In this study, we evaluated the effects of combinations of soil thickness and soil texture products with varying spatial distributions to assess their effects on the simulated hydrologic response in the semiarid Sierra Los Locos watershed in Sonora, Mexico. The main findings of the hydrological simulations show that soil texture exerted a strong control on evapotranspiration and soil moisture patterns, while soil thickness was directly related with the magnitude and range of the simulated values. Furthermore, soil texture patterns were an important factor controlling the spatial and temporal persistence of soil moisture which is highly evident during the transition from dry to wet conditions in the North American monsoon region. Once vegetation cover increases in the watershed in response to seasonal rainfall, the influence of soil texture decreases for determining the spatial distribution of the simulated hydrologic response and soil thickness becomes more important. Spatially-variable soil thickness tends to create soil depressions that store and transmit subsurface water, leading to large spatial variations in soil moisture, evapotranspiration and runoff generation. The results of this study highlight the sensitivity of estimated water fluxes and states with respect to the spatial distribution of soil depth and texture as obtained in a distributed hydrologic model representative of modern approaches. Finally, this research work offers insight into the importance of field studies and remote sensing approaches to better characterize these watershed properties.

Implementation of Real-Time Flood Prediction and its Application to Dam Operations by Data Integration Analysis System

Despite recent advances in hydrological models and observation technology, the prediction of floods using advanced models and data has not yet been fully implemented for practical use. The major issues in prediction originate from the underlying uncertainty of the initial conditions of the basin and the accuracy of the precipitation forecast. Effective transmission of flood information to corresponding authorities is also necessary when considering countermeasures against an oncoming flood. We present in this article a data archive and model integrated system to overcome these issues. The system realizes flood forecasting by employing a land surface model coupled with hydrological model and an ensemble precipitation forecast model to address the accuracy of initial conditions and precipitation. While the Water and Energy Budget Based Distributed Hydrological Model (WEB-DHM) rigorously estimates the physical state of the basin, the ensemble precipitation forecast model analyzes historical errors in forecasts and returns precipitation ensembles reflecting the uncertainty in the forecast specifically regarding the target basin. A combination of these models yields an ensemble of streamflow forecasts. We further develop a virtual reservoir simulator to enhance the proactive use of forecast information to support decision-making by reservoir managers. These models are integrated into the Data Integration Analysis System (DIAS). The feasibility of the system for practical use is tested against data from recent typhoon events.