Areal Precipitation Estimates Research Articles

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Accurate mean areal precipitation (MAP) estimates are essential input forcings for hydrologic models. However, the selection of the most accurate method to estimate MAP can be daunting because there are numerous methods to choose from (e.g., proximate gauge, direct weighted average, surface-fitting, and remotely sensed methods). Multiple methods (n = 19) were used to estimate MAP with precipitation data from 11 distributed monitoring sites, and 4 remotely sensed data sets. Each method was validated against the hydrologic model simulated stream flow using the Soil and Water Assessment Tool (SWAT). SWAT was validated using a split-site method and the observed stream flow data from five nested-scale gauging sites in a mixed-land-use watershed of the central USA. Cross-validation results showed the error associated with surface-fitting and remotely sensed methods ranging from −4.5 to −5.1%, and −9.8 to −14.7%, respectively. Split-site validation results showed the percent bias (PBIAS) values that ranged from −4.5 to −160%. Second order polynomial functions especially overestimated precipitation and subsequent stream flow simulations (PBIAS = −160) in the headwaters. The results indicated that using an inverse-distance weighted, linear polynomial interpolation or multiquadric function method to estimate MAP may improve SWAT model simulations. Collectively, the results highlight the importance of spatially distributed observed hydroclimate data for precipitation and subsequent steam flow estimations. The MAP methods demonstrated in the current work can be used to reduce hydrologic model uncertainty caused by watershed physiographic differences.

Simulation of Ship-Track versus Satellite-Sensor Differences in Oceanic Precipitation Using an Island-Based Radar

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h − 1 (55%) compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24–34 km h − 1 depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data.

Estimation of areal precipitation in the Qilian Mountains based on a gridded dataset since 1961

Based on a 0.5°×0.5° daily gridded precipitation dataset and observations in meteorological stations released by the National Meteorological Information Center, the interannual variation of areal precipitation in the Qilian Mountains during 1961–2012 is investigated using principal component analysis (PCA) and regression analysis, and the relationship between areal precipitation and drought accumulation intensity is also analyzed. The results indicate that the spatial distribution of precipitation in the Qilian Mountains can be well reflected by the gridded dataset. The gridded data-based precipitation in mountainous region is generally larger than that in plain region, and the eastern section of the mountain range usually has more precipitation than the western section. The annual mean areal precipitation in the Qilian Mountains is 724.9×108 m3, and the seasonal means in spring, summer, autumn and winter are 118.9×108 m3, 469.4×108 m3, 122.5×108 m3 and 14.1×108 m3, respectively. Summer is a season with the largest areal precipitation among the four seasons, and the proportion in summer is approximately 64.76%. The areal precipitation in summer, autumn and winter shows increasing trends, but a decreasing trend is seen in spring. Among the four seasons, summer have the largest trend magnitude of 1.7×108 m3•a–1. The correlation between areal precipitation in the mountainous region and dry-wet conditions in the mountains and the surroundings can be well exhibited. There is a negative correlation between drought accumulation intensity and the larger areal precipitation is consistent with the weaker drought intensity for this region.

Assimilation of weather radar and binary ubiquitous sensor measurements for quantitative precipitation estimation

Assimilation of data from heterogeneous sensors and sensor networks is critical for achieving accurate measurements of environmental processes at the time and space scales necessary to improve forecasting and decision-making. Owing to different measurement accuracies and types of spatial and/or temporal measurement support of the component sensors, it is often unclear how best to combine these data. This study explores the utility of ubiquitous sensors producing categorical wet/dry rainfall measurements for improving the resolution of areal quantitative precipitation estimates through fusion with weather radar observations. The model developed in this study employs a Markov random field model to compute the probability of rainfall at sub-grid pixels. These likelihoods are used to ‘unmix’ the cell-averaged rainfall rate measured by the radar. Simulation studies using synthetic and known rainfall fields reveal that the model can improve remotely sensed quantitative rainfall intensity measurements by 40% using networks of ubiquitous sensors with a density of 56 sensors per square kilometer, and for denser networks, the accuracy can increase by as much as 50%.

Improving real-time estimation of heavy-to-extreme precipitation using rain gauge data via conditional bias-penalized optimal estimation

Summary A new technique for gauge-only precipitation analysis for improved estimation of heavy-to-extreme precipitation is described and evaluated. The technique is based on a novel extension of classical optimal linear estimation theory in which, in addition to error variance, Type-II conditional bias (CB) is explicitly minimized. When cast in the form of well-known kriging, the methodology yields a new kriging estimator, referred to as CB-penalized kriging (CBPK). CBPK, however, tends to yield negative estimates in areas of no or light precipitation. To address this, an extension of CBPK, referred to herein as extended conditional bias penalized kriging (ECBPK), has been developed which combines the CBPK estimate with a trivial estimate of zero precipitation. To evaluate ECBPK, we carried out real-world and synthetic experiments in which ECBPK and the gauge-only precipitation analysis procedure used in the NWS’s Multisensor Precipitation Estimator (MPE) were compared for estimation of point precipitation and mean areal precipitation (MAP), respectively. The results indicate that ECBPK improves hourly gauge-only estimation of heavy-to-extreme precipitation significantly. The improvement is particularly large for estimation of MAP for a range of combinations of basin size and rain gauge network density. This paper describes the technique, summarizes the results and shares ideas for future research.

Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling

Abstract. In data sparse mountainous regions it is difficult to derive areal precipitation estimates. In addition, their evaluation by cross validation can be misleading if the precipitation gauges are not in representative locations in the catchment. This study aims at the evaluation of precipitation estimates in data sparse mountainous catchments. In particular, it is first tested whether monthly precipitation fields from downscaled reanalysis data can be used for interpolating gauge observations. Secondly, precipitation estimates from this and other methods are evaluated by comparing simulated and observed discharge, which has the advantage that the data are evaluated at the catchment scale. This approach is extended here in order to differentiate between errors in the overall bias and the temporal dynamics, and by taking into account different sources of uncertainties. The study area includes six headwater catchments of the Karadarya Basin in Central Asia. Generally the precipitation estimate based on monthly precipitation fields from downscaled reanalysis data showed an acceptable performance, comparable to another interpolation method using monthly precipitation fields from multi-linear regression against topographical variables. Poor performance was observed in only one catchment, probably due to mountain ridges not resolved in the model orography of the regional climate model. Using two performance criteria for the evaluation by hydrological modelling allowed a more informed differentiation between the precipitation data and showed that the precipitation data sets mostly differed in their overall bias, while the performance with respect to the temporal dynamics was similar. Our precipitation estimates in these catchments are considerably higher than those from continental- or global-scale gridded data sets. The study demonstrates large uncertainties in areal precipitation estimates in these data sparse mountainous catchments. In such regions with only very few precipitation gauges but high spatial variability of precipitation, important information for evaluating precipitation estimates may be gained by hydrological modelling and a comparison to observed discharge.

Spatial Disaggregation of Mean Areal Rainfall Using Gibbs Sampling

Abstract Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data (<10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP’s stage IV analysis were used to estimate the model parameters (2002–04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated.

An improved statistical approach to merge satellite rainfall estimates and raingauge data

Deriving high quality daily rainfall estimates are required not only for successful hydrological modelling but also for its application in ungauged basins. At present, there are two resources to estimate rainfall fields: raingauge stations and remote sensing devices (such as satellites and radar). The observations obtained from raingauges are accurate and reliable, but are usually limited to interpolate daily rainfall as input into hydrological models by spatial coverage. Hence, the areal precipitation estimates constructed solely by raingauges exhibit a great deal of uncertainty where the raingauge network is sparse, especially for local convective events. Satellite-based gridded precipitation estimates are produced at high temporal (3 h) and reasonably high spatial resolution (0.25° × 0.25°). However, the accuracy of these surfaces is lower than using ground based observations. The main aim of this paper is to present a novel method of generating a synthesis of a discrete set of point raingauge observations and a satellite derived rainfall product. This method stems from some ideas of data assimilation and focuses on the difference between gridded and point data. A nonparametric kernel smoothing method is employed in this merging strategy with emphasis on discontinuity correction and spatial interpolation adapting for sparse design. A cross-validation study was undertaken to blend observations from the Australian raingauge network and satellite derived TRMM Multisatellite Precipitation Analysis (TMPA) 3B42. In order to test the proposed method, several methods including ordinary kriging and cokriging were trialled. The comparison showed that the bias results produced by the proposed method were far superior to the other methods trialled and other error statistics such as root mean square error were comparable or better (although the kriging methods tended to perform better in densely gauged areas). In particular, the bias of the proposed method was reduced consistently over the whole country, whilst the kriging methods trialled produced relatively large biases across the country leading to near overall zero bias. In addition to improving quantitative efficiency, the estimated rainfall field showed good visual performance in the presence of sparse gauge stations.

Climate change effects on drought severity

Abstract. This paper evaluates climate change effects on drought severity in the region of Thessaly, Greece. The Standardized Precipitation Index (SPI) has been used for estimation of drought severity. A geographical information system is applied for the division of Thessaly region to twelve hydrological homogeneous areas based on their geomorphology. Mean monthly precipitation values from 50 precipitation stations of Thessaly for the hydrological period October 1960–September 1990 were used for the estimation of mean areal precipitation. These precipitation timeseries have been used for the estimation of Standardized Precipitation Index (SPI) for multiple time scales (1-, 3-, 6-, 9-, and 12-months) for each sub-basin or area. The outputs of Global Circulation Model CGCM2 were applied for two socioeconomic scenarios, namely, SRES A2 and SRES B2 for the assessment of climate change impact on droughts. The GCM outputs were downscaled to the region of Thessaly using a statistical methodology to estimate precipitation time series for two future periods 2020–2050 and 2070–2100. A method has been proposed for the estimation of annual cumulative drought severity-time scale-frequency curves. These curves integrate the drought severity and frequency for various types of drought. The SPI timeseries and annual weighted cumulative drought severity were estimated and compared with the respective timeseries and values of the historical period 1960–1990. The results showed that the annual drought severity is increased for all hydrological areas and SPI time scales, with the socioeconomic scenario SRES A2 being the most extreme.

A regional monthly precipitation simulation model based on an L-moment smoothed statistical regionalization approach

Summary Using rain-gauge station records for the statistical characterization and simulation modeling of spatio-temporal precipitation field involves many issues and simplistic assumptions. One major issue is related to dealing with uncertainty at-site sample statistical inference, because of the limited length of records. Regional frequency analysis uses the idea of substituting space for time in order to reduce uncertainty. It assumes equal shapes of the precipitation statistical distributions in a region. However, this assumption limits the area of the analyzed region where this assumption is valid. The extension is dependent on terrain complexity. This work presents a new approach for the statistical regionalization of a large precipitation field, replacing the shape constancy assumption for the hypothesis of smooth spatial variation. The approach accounts for every uncertainty on site information, using an L-moment method for inference analysis. Additionally, the orographic effect is introduced in the regionalization, which substantially improves the interpolation performance and estimation of areal precipitation. The approach is used for modeling the monthly precipitation field in the Jucar River Basin Authority Demarcation (Spain), incorporating its stochastic structure, and spatial dependency coming from a geostatistical analysis. Issues related to the estimation of regional precipitation, and mean areal precipitation are discussed in the exposition.

Adjustment of Radar-Based Precipitation Estimates for Great Lakes Hydrologic Modeling

For the Great Lakes region, radar-based precipitation estimates provide valuable data for over-lake areas and may help to improve mean areal precipitation estimates over land areas, particularly where precipitation gauges are sparse. To this end, potential biases in daily radar-based precipitation data are evaluated for several Great Lakes watersheds. Statistical analysis of gauge data (MAP) and radar-based data (MAPX) for the period 1996–2004 shows that, except for some summer months, MAPX values were consistently lower than MAP values. Possible reasons for this underestimation include truncation error, beam blockage and overshoot, and variability in radar reflectivity-surface precipitation relationships. Application of the precipitation data to continuous simulation and forecasting models of the Great Lakes shows that the observed biases are generally magnified by the hydrology models, i.e., even larger discrepancies are observed between simulated runoff values generated with MAP and MAPX inputs. Discre...

Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting

Abstract This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance.

A multiple linear regression GIS module using spatial variables to model orographic rainfall

This paper aims to document the development of a new GIS-based spatial interpolation module that adopts a multiple linear regression technique. The functionality of the GIS module is illustrated through a test case represented by the island of Crete, Greece, where the models generated were applied to locations where estimates of annual precipitation were required. The response variable is ‘precipitation’ and the predictor variables are ‘elevation’, ‘longitude’ and ‘latitude’, or any combination of these. The module is capable of performing a sequence of tasks which will eventually lead to an estimation of mean areal precipitation and the total volume of precipitation. In addition, it can generate up to nine predictor variables and their parameters, and can estimate areal rainfall for a user-specified three-dimensional extent. The developed module performed satisfactorily. Precipitation estimates at ungauged locations were obtained using the multiple linear regression method in addition to some conventional spatial interpolation techniques (i.e. IDW, Spline, Kriging, etc.). The multiple linear regression models provided better estimates than the other spatial interpolation techniques.

RAINGAGE NETWORK DESIGN USING NEXRAD PRECIPITATION ESTIMATES1

ABSTRACT: A general framework is proposed for using precipitation estimates from NEXRAD weather radars in raingage network design. NEXRAD precipitation products are used to represent space time rainfall fields, which can be sampled by hypothetical raingage networks. A stochastic model is used to simulate gage observations based on the areal average precipitation for radar grid cells. The stochastic model accounts for subgrid variability of precipitation within the cell and gage measurement errors. The approach is ideally suited to raingage network design in regions with strong climatic variations in rainfall where conventional methods are sometimes lacking. A case study example involving the estimation of areal average precipitation for catchments in the Catskill Mountains illustrates the approach. The case study shows how the simulation approach can be used to quantify the effects of gage density, basin size, spatial variation of precipitation, and gage measurement error, on network estimates of areal average precipitation. Although the quality of NEXRAD precipitation products imposes limitations on their use in network design, weather radars can provide valuable information for empirical assessment of rain‐gage network estimation errors. Still, the biggest challenge in quantifying estimation errors is understanding subgrid spatial variability. The results from the case study show that the spatial correlation of precipitation at subgrid scales (4 km and less) is difficult to quantify, especially for short sampling durations. Network estimation errors for hourly precipitation are extremely sensitive to the uncertainty in subgrid spatial variability, although for storm total accumulation, they are much less sensitive.

Areal Precipitation and Temperature in the Swedish Mountains

This paper presents an evaluation of three different methods for estimation of areal precipitation and temperature, with special emphasis on their applicability for runoff modelling in the Swedish mountains. All three methods estimate the areal values as a weighted mean of the observations at nearby meteorological stations. The weights are determined by: 1) a manual subjective selection of the most representative stations 3) inverse square distance weighting 4) optimal interpolation The methods were tested in an area with complex topography and precipitation gradients. The evaluation included comparison of areal estimates, verification against point observations and the water balance equation, and sensitivity analyses with respect to method parameters and network changes. The evaluation showed that for simple runoff modelling the subjective and optimal interpolation methods performed equally well, and considerably better than inverse-distance weighting. The evaluation also showed that none of the methods correctly described the spatial variation in precipitation and temperature in the investigated region. They are thus not directly applicable for non-routine modelling applications where the estimation of runoff is not the sole objective. All methods proved to be sensitive to the selection of parameter values, which pointed to possible improvements of the estimates. The optimal interpolation method seemed to be the least sensitive to changes in the meteorological network.

Estimation of Daily Area-Average Rainfall during the CaPE Experiment in Central Florida

The principal component of the water cycle over land areas is precipitation. Knowledge of the accuracy of areal precipitation estimates, therefore, is imperative. The manifold observations of hydrometeorological quantities in the Convection and Precipitation/Electrification Experiment (CaPE) that took place in central Florida during the summer of 1991 have provided an opportunity to examine the various water fluxes. This paper deals with only one of them, the daily area-average rainfall as derived from rain gauges. The theory for spatial sampling errors for random gauge design is extended to include rain gauge bias and random errors. The requirement for randomly located gauges turns out to be quite well met with the approximately 100 gauges available each day over the 17 300-km2 CaPE study area. Rain gauge bias, due mainly to wind effects, is estimated to be 6% based on a previous study and random measurement error is calculated from seven pairs of collocated gauges. For the category of daily area-average rainfall less than 1 mm, the standard error was found to be 0.27 mm or a 53% variation with respect to the category mean. For the highest rainfall category, 11–15 mm, the standard error was 1.49 mm or 11%. For this application, the standard error is essentially a consequence of the limited number of gauges. Given that the rain gauges are randomly distributed and arithmetic averaging is used to estimate the daily area-average rainfall, application of the appropriate standard error provides a useful measure of the best accuracy achievable in assessing the daily water budget of this area.

Accuracy of precipitation network in different geophysical regions in India

For any type of hydro meteorological studies it Is imperative that an optimum design of network of raingauge stations is determined taking into consideration various factors influencing specific purpose for which such designs are envisaged. In the present paper an attempt has been made to determine the relative accuracy of the precipitation network designed for estimation of normal areal precipitation in comparison to the standard prescribed by World Meteorological Orgamsatlon. It is observed that III the present case the proposed network is fairly accurate for the purpose for which it has been designed.

Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data

The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.

Unbiased minimum-variance linear state estimation

A method is developed for linear estimation in the presence of unknown or highly non-Gaussian system inputs. The state update is determined so that it is unaffected by the unknown inputs. The filter may not be globally optimum in the mean square error sense. However, it performs well when the unknown inputs take extreme or unexpected values. In many geophysical and environmental applications, it is performance during these periods which counts the most. The application of the filter is illustrated in the real-time estimation of mean areal precipitation.