scholarly journals Impact of model complexity and precipitation data products on modeled streamflow

2013 ◽  
Vol 16 (3) ◽  
pp. 588-599 ◽  
Author(s):  
Kenneth J. Tobin ◽  
Marvin E. Bennett
Keyword(s):  
Assessment Tool ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
South Texas ◽  
Model Complexity ◽  
Precipitation Data ◽  
Hydrologic Models ◽  
Rain Gauges ◽  
Sensing Platforms ◽  
Southern Georgia

With the proliferation of remote sensing platforms as well as numerous ground products based on weather radar estimation, there are now multiple options for precipitation data beyond traditional rain gauges for which most hydrologic models were originally designed. This study evaluates four precipitation products as input for generating streamflow simulations using two hydrologic models that significantly vary in complexity. The four precipitation products include two ground products from the National Weather Service: the Multi-sensor Precipitation Estimator (MPE) and rain gauge data. The two satellite products come from NASA's Tropical Rainfall Measurement Mission (TRMM) and include the TRMM 3B42 Research Version 6, which has a built-in ground bias correction, and the real-time TRMM Multi-Satellite Precipitation Analysis. The two hydrologic models utilized include the Soil and Water Assessment Tool (SWAT) and Gridded Surface and Subsurface Hydrologic Analysis (GSSHA). Simulations were conducted in three, moderate- to large-sized basins across the southern United States, the San Casimiro (South Texas), Skuna (northern Mississippi), Alapaha (southern Georgia), and were run for over 2 years. This study affirms the realization that input precipitation is at least as important as the choice of hydrologic model.

scholarly journals Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1006 ◽  
Author(s):  
Xiuna Wang ◽  
Yongjian Ding ◽  
Chuancheng Zhao ◽  
Jian Wang
Keyword(s):  
Bias Correction ◽  
Arid Region ◽  
Rain Gauge ◽  
Climatic Conditions ◽  
Monthly Precipitation ◽  
Observation Data ◽  
Precipitation Data ◽  
Rain Gauges ◽  
Hexi Region ◽  
Precipitation Events

Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009–2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0–1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009–2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

scholarly journals Quality-Based Combination of Multi-Source Precipitation Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1709 ◽  
Author(s):  
Anna Jurczyk ◽  
Jan Szturc ◽  
Irena Otop ◽  
Katarzyna Ośródka ◽  
Piotr Struzik
Keyword(s):  
Spatial Resolution ◽  
Flash Flood ◽  
Radar Data ◽  
Rain Gauge ◽  
Quality Information ◽  
Precipitation Data ◽  
Spatial Error ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Temporal And Spatial

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

scholarly journals Improved input to distributed hydrologic model in areas with sparse subdaily rainfall data using multivariate daily rainfall disaggregation

2018 ◽  
Vol 20 (4) ◽  
pp. 784-797 ◽  
Author(s):  
Marija Ivković ◽  
Andrijana Todorović ◽  
Jasna Plavšić
Keyword(s):  
Daily Rainfall ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Rain Gauges ◽  
Observation Network ◽  
Hourly Rainfall ◽  
Extreme Flood ◽  
Flood Simulations

Abstract Flood forecasting relies on good quality of observed and forecasted rainfall. In Serbia, the recording rain gauge network is sparse and rainfall data mainly come from dense non-recording rain gauges. This is not beneficial for flood forecasting in smaller catchments and short-duration events, when hydrologic models operating on subdaily scale are applied. Moreover, differences in rainfall amounts from two types of gauges can be considerable, which is common in operational hydrological practice. This paper examines the possibility of including daily rainfall data from dense observation networks in flood forecasting based on subdaily data, using the extreme flood event in the Kolubara catchment in May 2014 as a case study. Daily rainfall from a dense observation network is disaggregated to hourly scale using the MuDRain multivariate disaggregation software. The disaggregation procedure results in well-reproduced rainfall dynamics and adjusts rainfall volume to the values from the non-recording gauges. The fully distributed wflow_hbv model, which is under development as a forecasting tool for the Kolubara catchment, is used for flood simulations with two alternative hourly rainfall data. The results show an improvement when the disaggregated rainfall from denser network is used, thus indicating the significance of better representation of rainfall temporal and spatial variability for flood forecasting.

scholarly journals An Operation-Based Scheme for a Multiyear and Multipurpose Reservoir to Enhance Macroscale Hydrologic Models

2012 ◽  
Vol 13 (1) ◽  
pp. 270-283 ◽  
Author(s):  
Yiping Wu ◽  
Ji Chen
Keyword(s):  
Land Surface ◽  
Reservoir Operation ◽  
Flood Control ◽  
Assessment Tool ◽  
Southern China ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Time Step ◽  
Reservoir Storage ◽  
Multipurpose Reservoir

Abstract This paper develops an operation-based numerical scheme for simulating storage in and outflow from a multiyear and multipurpose reservoir at a daily time step in order to enhance the simulation capacity of macroscale land surface hydrologic models. In the new scheme, besides the purpose of flood control, three other operational purposes—hydropower generation, downstream water supply, and water impoundment—are considered, and accordingly three related decision-based parameters are introduced. The new scheme is then integrated into the Soil and Water Assessment Tool (SWAT), which is a macroscale hydrologic model. The observed water storage and outflow from a multiyear and multipurpose reservoir, the Xinfengjiang Reservoir in southern China, are used to examine the new scheme. Compared with two other reservoir operation schemes—namely, a modified existing reservoir operation scheme in SWAT (i.e., the target release scheme) and a multilinear regression scheme—the new scheme can give a consistently better simulation of the reservoir storage and outflow. Furthermore, through a sensitivity analysis, this study shows that the three decision-based parameters can represent the significance of each operational purpose in different periods and the new scheme can advance the flexibility and capability of the simulation of the reservoir storage and outflow.

scholarly journals Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

scholarly journals Comparison of Hydrologic Model Performance Statistics Using Rain Gauge and NEXRAD Precipitation Input at Different Watershed Spatial Scales and Rainfall Return Frequencies for the Upper St. Johns River, Florida USA

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Amanda Bredesen ◽  
Christopher J. Brown
Keyword(s):  
Spatial Scale ◽  
Model Simulation ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Precipitation Data ◽  
Remotely Sensed Data ◽  
Model Input

Water resources numerical models are dependent upon various input hydrologic field data. As models become increasingly complex and model simulation times expand, it is critical to understand the inherent value in using different input datasets available. One important category of model input is precipitation data. For hydrologic models, the precipitation data inputs are perhaps the most critical. Common precipitation model input includes either rain gauge or remotely-sensed data such next-generation radar-based (NEXRAD) data. NEXRAD data provides a higher level of spatial resolution than point rain gauge coverage, but is subject to more extensive data pre and post processing along with additional computational requirements. This study first documents the development and initial calibration of a HEC-HMS model of a subtropical watershed in the Upper St. Johns River Basin in Florida, USA. Then, the study compares calibration performance of the same HEC-HMS model using either rain gauge or NEXRAD precipitation inputs. The results are further discretized by comparing key calibration statistics such as Nash–Sutcliffe Efficiency for different spatial scale and at different rainfall return frequencies. The study revealed that at larger spatial scale, the calibration performance of the model was about the same for the two different precipitation datasets while the study showed some benefit of NEXRAD for smaller watersheds. Similarly, the study showed that for smaller return frequency precipitation events, NEXRAD data was superior.

scholarly journals Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Hyojin Lee ◽  
Kwangmin Kang
Keyword(s):  
Hydrologic Modeling ◽  
Assessment Tool ◽  
Hydrologic Model ◽  
Kernel Functions ◽  
Precipitation Data ◽  
Important Research Topic ◽  
Daily Precipitation Data ◽  
Data Assessment ◽  
Regression Approach ◽  
Kernel Estimations

Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data throughKth nearest neighborhood (KNN) regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT) hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with theKNN regression approach, in terms of both statistical data assessment and hydrologic modeling performance.

scholarly journals Parameter-dependent convergence bounds and complexity measure for a class of conceptual hydrological models

2011 ◽  
Vol 14 (2) ◽  
pp. 443-463 ◽  
Author(s):  
Saket Pande ◽  
Luis A. Bastidas ◽  
Sandjai Bhulai ◽  
Mac McKee
Keyword(s):  
Model Selection ◽  
Linear Models ◽  
Convergence Rates ◽  
Model Performance ◽  
Hydrologic Model ◽  
Complexity Measure ◽  
Model Complexity ◽  
Finite Sample ◽  
Hydrologic Models ◽  
Complexity Measures

We provide analytical bounds on convergence rates for a class of hydrologic models and consequently derive a complexity measure based on the Vapnik–Chervonenkis (VC) generalization theory. The class of hydrologic models is a spatially explicit interconnected set of linear reservoirs with the aim of representing globally nonlinear hydrologic behavior by locally linear models. Here, by convergence rate, we mean convergence of the empirical risk to the expected risk. The derived measure of complexity measures a model's propensity to overfit data. We explore how data finiteness can affect model selection for this class of hydrologic model and provide theoretical results on how model performance on a finite sample converges to its expected performance as data size approaches infinity. These bounds can then be used for model selection, as the bounds provide a tradeoff between model complexity and model performance on finite data. The convergence bounds for the considered hydrologic models depend on the magnitude of their parameters, which are the recession parameters of constituting linear reservoirs. Further, the complexity of hydrologic models not only varies with the magnitude of their parameters but also depends on the network structure of the models (in terms of the spatial heterogeneity of parameters and the nature of hydrologic connectivity).

scholarly journals ESOLIP – estimate of solid and liquid precipitation at sub-daily time resolution by combining snow height and rain gauge measurements

2013 ◽  
Vol 10 (7) ◽  
pp. 8683-8714 ◽  
Author(s):  
E. Mair ◽  
G. Bertoldi ◽  
G. Leitinger ◽  
S. Della Chiesa ◽  
G. Niedrist ◽  
...  
Keyword(s):  
Time Resolution ◽  
Hydrological Cycle ◽  
Winter Precipitation ◽  
Rain Gauge ◽  
High Temporal Resolution ◽  
Precipitation Data ◽  
Rain Gauges ◽  
Alpine Catchments ◽  
Liquid Precipitation ◽  
Daily Time

Abstract. Measuring precipitation in mountain areas is a demanding task, but essential for hydrological and environmental themes. Especially in small Alpine catchments with short hydrological response, precipitation data with high temporal resolution are required for a better understanding of the hydrological cycle. Since most climate/meteorological stations are situated at the easily accessible bottom of valleys, and the few heated rain gauges installed at higher elevation sites are problematic in winter conditions, an accurate quantification of winter (snow) precipitation at high elevations remains difficult. However, there are an increasing number of micro-meteorological stations and snow height sensors at high elevation locations in Alpine catchments. To benefit from data of such stations, an improved approach to estimate solid and liquid precipitation (ESOLIP) is proposed. ESOLIP allows gathering hourly precipitation data throughout the year by using unheated rain gauge data, careful filtering of snow height sensors as well as standard meteorological data (air temperature, relative humidity, global shortwave radiation, wind speed). ESOLIP was validated at a well-equipped test site in Stubai Valley (Tyrol, Austria), comparing results to winter precipitation measured with a snow pillow and a heated rain gauge. The snow height filtering routine and indicators for possible precipitation were tested at a field site in Matsch Valley (South Tyrol, Italy). Results show a good match with measured data because variable snow density is taken into account, which is important when working with freshly fallen snow. Furthermore, the results show the need for accurate filtering of the noise of the snow height signal and they confirm the unreliability of heated rain gauges for estimating winter precipitation. The described improved precipitation estimate ESOLIP at sub-daily time resolution is helpful for precipitation analysis and for several hydrological applications like monitoring systems and rainfall-runoff models.

scholarly journals Hydrologic Utility of Satellite-Based and Gauge-Based Gridded Precipitation Products in the Huai Bang Sai Watershed of Northeastern Thailand

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 165
Author(s):  
Miyuru B. Gunathilake ◽  
M. N. M. Zamri ◽  
Tharaka P. Alagiyawanna ◽  
Jayanga T. Samarasinghe ◽  
Pavithra K. Baddewela ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Rain Gauges ◽  
Governing Factor ◽  
North Eastern ◽  
The World ◽  
Potential Applications ◽  
Basin Topography

Accurate rainfall estimates are important in many hydrologic activities. Rainfall data are retrieved from rain gauges (RGs), satellites, radars, and re-analysis products. The accuracy of gauge-based gridded precipitation products (GbGPPs) relies on the distribution of RGs and the quality of rainfall data records obtained from these. The accuracy of satellite-based precipitation products (SbPPs) depends on many factors, including basin climatology, basin topography, precipitation mechanism, etc. The hydrologic utility of different precipitation products was examined in many developed regions; however, less focused on the developing world. The Huai Bang Sai (HBS) watershed in north-eastern Thailand is a less focused but an important catchment that significantly contributes to the water resources in Thailand. Therefore, this research presents the investigation results of the hydrologic utility of SbPPs and GbGPPs in the HBS watershed. The efficiency of nine SbPPs (including 3B42, 3B42-RT, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, CHIRPS, CMORPH, IMERG, and MSWEP) and three GbGPPs (including APHRODITE_V1801, APHRODITE_V1901, and GPCC) was examined by simulating streamflow of the HBS watershed through the Soil & Water Assessment Tool (SWAT), hydrologic model. Subsequently, the streamflow simulation capacity of the hydrological model for different precipitation products was compared against observed streamflow records by using the same set of calibrated parameters used for an RG simulated scenario. The 3B42 product outperformed other SbPPS with a higher Nash–Sutcliffe Efficiency (NSEmonthly > 0.55), while APHRODITE_V1901 (NSEmonthly > 0.53) performed fairly well in the GbGPPs category with closer agreements with observed streamflow. In addition, the CMORPH precipitation product has not performed well in capturing observed rainfall and subsequently in simulating streamflow (NSEmonthly < 0) of the HBS. Furthermore, MSWEP and CHIRPS products have performed fairly well during calibration; however, they showcased a lowered performance for validation. Therefore, the results suggest that accurate precipitation data is the major governing factor in streamflow modeling performances. The research outcomes would capture the interest of all stakeholders, including farmers, meteorologists, agriculturists, river basin managers, and hydrologists for potential applications in the tropical humid regions of the world. Moreover, 3B42 and APHRODITE_V1901 precipitation products show promising prospects for the tropical humid regions of the world for hydrologic modeling and climatological studies.

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