scholarly journals Small Catchment Runoff Sensitivity to Station Density and Spatial Interpolation: Hydrological Modeling of Heavy Rainfall Using a Dense Rain Gauge Network

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1381
Author(s):  
Clara Hohmann ◽  
Gottfried Kirchengast ◽  
Sungmin O ◽  
Wolfgang Rieger ◽  
Ulrich Foelsche
Keyword(s):  
Hydrological Modeling ◽  
Nearest Neighbor ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Large Set ◽  
Small Catchment ◽  
Long Duration ◽  
Runoff Modeling ◽  
Thiessen Polygons ◽  
Station Density

Precipitation is the most important input to hydrological models, and its spatial variability can strongly influence modeled runoff. The highly dense station network WegenerNet (0.5 stations per km2) in southeastern Austria offers the opportunity to study the sensitivity of modeled runoff to precipitation input. We performed a large set of runoff simulations (WaSiM model) using 16 subnetworks with varying station densities and two interpolation schemes (inverse distance weighting, Thiessen polygons). Six representative heavy precipitation events were analyzed, placing a focus on small subcatchments (10–30 km2) and different event durations. We found that the modeling performance generally improved when the station density was increased up to a certain resolution: a mean nearest neighbor distance of around 6 km for long-duration events and about 2.5 km for short-duration events. However, this is not always true for small subcatchments. The sufficient station density is clearly dependent on the catchment area, event type, and station distribution. When the network is very dense (mean distance < 1.7 km), any reasonable interpolation choice is suitable. Overall, the station density is much more important than the interpolation scheme. Our findings highlight the need to study extreme precipitation characteristics in combination with runoff modeling to decompose precipitation uncertainties more comprehensively.

scholarly journals Runoff sensitivity to spatial rainfall variability: A hydrological modeling study with dense rain gauge observations

2020 ◽  
Author(s):  
Clara Hohmann ◽  
Gottfried Kirchengast ◽  
Sungmin O ◽  
Wolfgang Rieger ◽  
Ulrich Foelsche
Keyword(s):  
Spatial Variability ◽  
Short Duration ◽  
Hydrological Modeling ◽  
Rainfall Variability ◽  
Heavy Precipitation ◽  
Rainfall Events ◽  
Long Duration ◽  
Runoff Modeling ◽  
Thiessen Polygon ◽  
The Impact

Abstract. Precipitation is a key input to hydrological models. While rain gauges provide the most direct precipitation measurements, their accuracy in capturing rain patterns highly depends on the spatial variability of rainfall events and the gauge network density. In this study, we employ a high-resolution meteorological station network (mean station distance of 1.4 km), the WegenerNet in southeastern Austria, to investigate the impact of station density and interpolation schemes on runoff simulations. We first simulate runoff during heavy precipitation (three short-duration and three long-duration events) using a physically based hydrological model with precipitation input obtained from a full network of 158 stations. The same simulations are then repeated with precipitation inputs from subnetworks of 5, 8, 16, 32, and 64 stations, using three different interpolation schemes – Inverse Distance Weighting with a weighting power of 2 and of 3, respectively, and Thiessen polygon interpolation. We find that the performance of runoff simulations is greatly influenced by the spatial variability of precipitation input, especially for short-duration rainfall events and in small catchments. For long-duration events, reliable runoff simulations in the study area can be obtained with a subnetwork of 16 or more well-distributed gauges (mean station distance of about 6 km). We find a clear effect of interpolation schemes on runoff modeling as well, but only for low-density gauge networks. The sensitivity to the precipitation input is smaller for long-duration heavy precipitation events and bigger catchments. As a next step we suggest to study an ensemble of precipitation datasets in combination with runoff modeling to be able to decompose the effects of precipitation measurement uncertainties and its spatial variability.

scholarly journals Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

2021 ◽  
Author(s):  
Salman Khan ◽  
Farhan Khan ◽  
Yiqing Guan
Keyword(s):  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Critical Role ◽  
Cost Effective ◽  
Rain Gauge ◽  
Runoff Modeling ◽  
Daily Precipitation Data ◽  
Huai River ◽  
Perform Sensitivity Analysis

Abstract Precipitation plays a critical role in hydrometeorological studies. A predictive analysis of gridded rainfall datasets may provide a cost-effective alternative to conventional rain gauge observations. Here, our objective is to evaluate the performance of satellite and reanalysis precipitation products in the hydrological modeling of a mesoscale watershed. The research also examines the accuracy of hydrological simulations in a sizeable flood-prone watershed in the absence of observed data associated with the myriad water retaining structures present in the catchment. We use three precipitation products, namely Tropical Rainfall Measurement Missions (TRMM) 3B42 Version 7, Climate Forecast System Reanalysis (CFSR), and daily precipitation data recorded at multiple rain gauges in the upper Huai River Basin to simulate streamflow. The Soil & Water Assessment Tool (SWAT) is utilized for runoff modeling, while SWAT-CUP is used to perform sensitivity analysis and to calibrate and validate the simulation results. Nash–Sutcliffe efficiency, percent bias, and Kling-Gupta efficiency (KGE) are employed to evaluate modeling efficiency for three precipitation datasets on different temporal scales. The results indicate that TRMM and CFSR datasets provide satisfactory results on both daily and monthly scales. Specifically, the SWAT model performs better at monthly simulations than daily simulations for all precipitation datasets used.

scholarly journals Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate

2021 ◽  
Author(s):  
Andrés Merino ◽  
Eduardo García‐Ortega ◽  
Andrés Navarro ◽  
Sergio Fernández‐González ◽  
Francisco J. Tapiador ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Rain Gauge ◽  
Altitude Gradient ◽  
Station Density

scholarly journals Seasonal Variation of the Diurnal Cycle of Rainfall in Southern Contiguous China

2008 ◽  
Vol 21 (22) ◽  
pp. 6036-6043 ◽  
Author(s):  
Jian Li ◽  
Rucong Yu ◽  
Tianjun Zhou
Keyword(s):  
Seasonal Variation ◽  
Diurnal Cycle ◽  
Radiative Forcing ◽  
Early Morning ◽  
Rain Gauge ◽  
Rainfall Events ◽  
Late Afternoon ◽  
Long Duration ◽  
China Region ◽  
Afternoon Peak

Abstract Hourly station rain gauge data are employed to study the seasonal variation of the diurnal cycle of rainfall in southern contiguous China. The results show a robust seasonal variation of the rainfall diurnal cycle, which is dependent both on region and duration. Difference in the diurnal cycle of rainfall is found in the following two neighboring regions: southwestern China (region A) and southeastern contiguous China (region B). The diurnal cycle of annual mean precipitation in region A tends to reach the maximum in either midnight or early morning, while precipitation in region B has a late-afternoon peak. In contrast with the weak seasonal variation of the diurnal phases of precipitation in region A, the rainfall peak in region B shifts sharply from late afternoon in warm seasons to early morning in cold seasons. Rainfall events in south China are classified into short- (1–3 h) and long-duration (more than 6 h) events. Short-duration precipitation in both regions reaches the maximum in late afternoon in warm seasons and peaks in either midnight or early morning in cold seasons, but the late-afternoon peak in region B exists during February–October, while that in region A only exists during May–September. More distinct differences between regions A and B are found in the long-duration rainfall events. The long-duration events in region A show dominant midnight or early morning peaks in all seasons. But in region B, the late-afternoon peak exists during July–September. Possible reasons for the difference in the diurnal cycle of rainfall between the two regions are discussed. The different cloud radiative forcing over regions A and B might contribute to this difference.

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

scholarly journals Analysis of intense rainfall events on Madeira Island during the 2009/2010 winter

2012 ◽  
Vol 12 (7) ◽  
pp. 2225-2240 ◽  
Author(s):  
F. T. Couto ◽  
R. Salgado ◽  
M. J. Costa
Keyword(s):  
Precipitable Water ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Horizontal Resolution ◽  
Pressure System ◽  
Orographic Effects ◽  
Madeira Island ◽  
High Rainfall ◽  
High Precipitation Events ◽  
Precipitation Events

Abstract. This paper constitutes a step towards the understanding of some characteristics associated with high rainfall amounts and flooding on Madeira Island. The high precipitation events that occurred during the winter of 2009/2010 have been considered with three main goals: to analyze the main atmospheric characteristics associated with the events; to expand the understanding of the interaction between the island and the atmospheric circulations, mainly the effects of the island on the generation or intensification of orographic precipitation; and to evaluate the performance of high resolution numerical modeling in simulating and forecasting heavy precipitation events over the island. The MESO-NH model with a horizontal resolution of 1 km is used, as well as rain gauge data, synoptic charts and measurements of precipitable water obtained from the Atmospheric InfraRed Sounder (AIRS). The results confirm the influence of the orographic effects on precipitation over Madeira as well as the tropical–extratropical interaction, since atmospheric rivers were detected in six out of the seven cases analyzed, acting as a low level moisture supplier, which together with the orographic lifting induced the high rainfall amounts. Only in one of the cases the presence of a low pressure system was identified over the archipelago.

scholarly journals First Evaluation of the Day-1 IMERG over the Upper Blue Nile Basin

2016 ◽  
Vol 17 (11) ◽  
pp. 2875-2882 ◽  
Author(s):  
Dejene Sahlu ◽  
Efthymios I. Nikolopoulos ◽  
Semu A. Moges ◽  
Emmanouil N. Anagnostou ◽  
Dereje Hailu
Keyword(s):  
Time Scales ◽  
Spatial Scales ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Nile Basin ◽  
Wet Season ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Hourly Rainfall ◽  
Upper Blue Nile Basin

Abstract This work presents a first evaluation of the performance of the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product over the upper Blue Nile basin of Ethiopia. One of the unique features of this study is the availability of hourly rainfall measurements from an experimental rain gauge network in the area. Both the uncalibrated and calibrated versions of IMERG are evaluated, and their performance is contrasted against another high-resolution satellite product, which is the Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH). The analysis is performed for hourly and daily time scales and at spatial scales that correspond to the nominal resolution of satellite products, which is 0.1° spatial resolution. The period analyzed is focused on a single wet season (May–October 2014). Evaluation is performed using several statistical and categorical error metrics, as well as spatial correlation analysis to assess the ability of satellite products to represent spatial variability of precipitation in the area. Results show that both IMERG products have a better bias ratio and correlation coefficient on both time scales as compared to CMORPH. Comparison statistics show a slight improvement in the skill of detecting rainfall events in IMERG products compared to CMORPH. Results also show a decreasing trend in the detection ability of satellite products for increasing threshold values, highlighting the need to further improve detection during heavy precipitation.

scholarly journals Rainfall-Runoff Modeling Using NRCS-CN method and GIS approach

2020 ◽  
Author(s):  
Rekha Verma ◽  
Azhar Husain ◽  
Mohammed Sharif
Keyword(s):  
Hydrological Modeling ◽  
Curve Number ◽  
Geographical Information ◽  
Rainfall Runoff ◽  
Moisture Condition ◽  
Natural Resource Conservation ◽  
Antecedent Moisture ◽  
Natural Resource Conservation Service ◽  
Runoff Modeling ◽  
Runoff Depth

Rainfall-Runoff modeling is a hydrological modeling which is extremely important for water resources planning, development, and management. In this paper, Natural Resource Conservation Service-Curve Number (NRCS-CN) method along with Geographical Information System (GIS) approach was used to evaluate the runoff resulting from the rainfall of four stations, namely, Bilodra, Kathlal, Navavas and Rellawada of Sabarmati River basin. The rainfall data were taken for 10 years (2005-2014). The curve number which is the function of land use, soil and antecedent moisture condition (AMC) was generated in GIS platform. The CN value generated for AMC- I, II and III were 57.29, 75.39 and 87.77 respectively. Using NRCS-CN method, runoff depth was calculated for all the four stations. The runoff depth calculated with respect to the rainfall for Bilodra, Kathlal, Navavas and Rellawada shows a good correlation of 0.96. The computed runoff was compared with the observed runoff which depicted a good correlation of 0.73, 0.70, 0.76 and 0.65 for the four stations. This method results in speedy and precise estimation of runoff from a watershed.

scholarly journals Reply to “Comments on ‘Incorporating the Effects of Moisture into a Dynamical Parameter: Moist Vorticity and Moist Divergence’”

2016 ◽  
Vol 31 (4) ◽  
pp. 1397-1405
Author(s):  
Weihong Qian ◽  
Ning Jiang ◽  
Jun Du
Keyword(s):  
Large Scale ◽  
Value Added ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Small Scale ◽  
Precipitation Efficiency ◽  
Long Duration ◽  
Advection Term ◽  
Rain Events ◽  
Mathematical Derivation

Abstract Mathematical derivation, meteorological justification, and comparison to model direct precipitation forecasts are the three main concerns recently raised by Schultz and Spengler about moist divergence (MD) and moist vorticity (MV), which were introduced in earlier work by Qian et al. That previous work demonstrated that MD (MV) can in principle be derived mathematically with a value-added empirical modification. MD (MV) has a solid meteorological basis. It combines ascent motion and high moisture: the two elements necessary for rainfall. However, precipitation efficiency is not considered in MD (MV). Given the omission of an advection term in the mathematical derivation and the lack of precipitation efficiency, MD (MV) might be suitable mainly for heavy rain events with large areal coverage and long duration caused by large-scale quasi-stationary weather systems, but not for local intense heavy rain events caused by small-scale convection. In addition, MD (MV) is not capable of describing precipitation intensity. MD (MV) worked reasonably well in predicting heavy rain locations from short to medium ranges as compared with the ECMWF model precipitation forecasts. MD (MV) was generally worse than (though sometimes similar to) the model heavy rain forecast at shorter ranges (about a week) but became comparable or even better at longer ranges (around 10 days). It should be reiterated that MD (MV) is not intended to be a primary tool for predicting heavy rain areas, especially in the short range, but is a useful parameter for calibrating model heavy precipitation forecasts, as stated in the original paper.

scholarly journals Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple Extents: The Potential of Operational Radar

2007 ◽  
Vol 8 (6) ◽  
pp. 1204-1224 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens ◽  
E. J. Pebesma ◽  
R. Uijlenhoet
Keyword(s):  
High Resolution ◽  
Hydrological Modeling ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Added Value ◽  
Rain Gauge Data ◽  
Geostatistical Methods ◽  
Gauge Data ◽  
Event Based ◽  
Better Than

Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

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