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

2012 ◽  
Vol 9 (9) ◽  
pp. 10719-10773 ◽  
Author(s):  
D. Duethmann ◽  
J. Zimmer ◽  
A. Gafurov ◽  
A. Güntner ◽  
B. Merz ◽  
...  
Keyword(s):  
Reanalysis Data ◽  
Hydrological Modelling ◽  
Mean Annual Precipitation ◽  
Model Parameters ◽  
Data Sets ◽  
Precipitation Data ◽  
Data Set ◽  
Station Data ◽  
Precipitation Estimates ◽  
Areal Precipitation

Abstract. In data sparse regions, as in many mountainous catchments, it is a challenge to generate suitable precipitation input fields for hydrological modelling, as station data do not provide enough information to derive areal precipitation estimates. This study presents a method using the spatial variation of precipitation from downscaled reanalysis data for the interpolation of gauge observations. The second aim of this study is the evaluation of different precipitation estimates by hydrological modelling. Study area is the Karadarya catchment in Central Asia (11 700 km2). ERA-40 reanalysis data are downscaled with the regional climate model Weather Research and Forecasting Model (WRF). Precipitation data from gauge observations are interpolated (i) using monthly accumulated WRF precipitation data, (ii) using monthly fields from multiple linear regression against topographical variables and (iii) with the inverse distance approach. These precipitation data sets are also compared to (iv) the direct use of the precipitation output from the WRF downscaled ERA-40 data and (v) precipitation from the APHRODITE data set. Our study suggests that using monthly fields from downscaled reanalysis data can be a good approach for the interpolation of station data in data sparse mountainous regions. Compared to mean annual precipitation from continental and global scale gridded data sets our precipitation estimates for the study area are considerably higher. The introduction of a calibrated precipitation bias factor for the comparison of different precipitation estimates by hydrological modelling allows for a more informed differentiation with regard to the temporal dynamics, on the one hand, and the overall bias, on the other hand. Uncertainty and sensitivity analyses suggest that our results are robust against uncertainties in the calibration parameters, other model parameters and inputs, and the selected calibration period.

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

2013 ◽  
Vol 17 (7) ◽  
pp. 2415-2434 ◽  
Author(s):  
D. Duethmann ◽  
J. Zimmer ◽  
A. Gafurov ◽  
A. Güntner ◽  
D. Kriegel ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Reanalysis Data ◽  
Hydrological Modelling ◽  
Performance Criteria ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Mountainous Catchments ◽  
Precipitation Estimates ◽  
Areal Precipitation

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.

scholarly journals Precipitation Trends over the Indus Basin

Climate ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 116 ◽  
Author(s):  
Nir Y. Krakauer ◽  
Tarendra Lakhankar ◽  
Ghulam H. Dars
Keyword(s):  
Remote Sensing ◽  
Large Population ◽  
Tropical Rainfall Measuring Mission ◽  
Indus Basin ◽  
Data Sets ◽  
Precipitation Trend ◽  
Precipitation Data ◽  
Data Set ◽  
Station Data ◽  
Precipitation Trends

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

scholarly journals Evaluation of two precipitation data sets for the Rhine River using streamflow simulations

2011 ◽  
Vol 15 (11) ◽  
pp. 3355-3366 ◽  
Author(s):  
C. S. Photiadou ◽  
A. H. Weerts ◽  
B. J. J. M. van den Hurk
Keyword(s):  
Annual Cycle ◽  
Reanalysis Data ◽  
Data Sets ◽  
Precipitation Data ◽  
Rhine River ◽  
Extended Version ◽  
Data Set ◽  
Rhine Basin ◽  
Temporal Correlations ◽  
Lower Uncertainty

Abstract. This paper presents an extended version of a widely used precipitation data set and evaluates it along with a recently released precipitation data set, using streamflow simulations. First, the existing precipitation data set issued by the Commission for the Hydrology of the Rhine basin (CHR), originally covering the period 1961–1995, was extended until 2008 using a number of additional precipitation data sets. Next, the extended version of the CHR, together with E-OBS Version 4 (ECA & D gridded data set) were evaluated for their performance in the Rhine basin for extreme events. Finally, the two aforementioned precipitation data sets and a meteorological reanalysis data set were used to force a hydrological model, evaluating the influence of different precipitation forcings on the annual mean and extreme discharges compared to observational discharges for the period from 1990 until 2008. The extended version of CHR showed good agreement in terms of mean annual cycle, extreme discharge (both high and low flows), and spatial distribution of correlations with observed discharge. E-OBS performed well with respect to extreme discharge. However, its performance of the mean annual cycle in winter was rather poor and remarkably well in the summer. Also, CHR08 outperformed E-OBS in terms of temporal correlations in most of the analyzed sub-catchment means. The length extension for the CHR and the even longer length of E-OBS permit the assessment of extreme discharge and precipitation values with lower uncertainty for longer return periods. This assessment classifies both of the presented precipitation data sets as possible reference data sets for future studies in hydrological applications.

scholarly journals Validation of two precipitation data sets for the Rhine River

2011 ◽  
Vol 8 (3) ◽  
pp. 5465-5496
Author(s):  
C. S. Photiadou ◽  
A. H. Weerts ◽  
B. J. J. M. van den Hurk
Keyword(s):  
Annual Cycle ◽  
Reanalysis Data ◽  
Data Sets ◽  
Precipitation Data ◽  
Rhine River ◽  
Data Set ◽  
Rhine Basin ◽  
The Mean ◽  
Lower Uncertainty ◽  
Good Agreement

Abstract. This paper evaluates a number of recently constructed or extended precipitation data sets used for hydrological applications and climate change studies in the Rhine basin. Firstly, the existing precipitation data set issued by the Commission for the Hydrology of the Rhine basin (CHR), originally covering the period 1961–1995, was extended until 2008 using a number of additional precipitation data sets. The length extension permits the assessment of extreme discharge and precipitation values with lower uncertainty than the original version. Secondly, the E-OBS Version 4 (ECA&D gridded data set) was evaluated for its performance in the Rhine basin for extreme events. The two extended precipitation data sets and a meteorological reanalysis data set were used to force a hydrological model, evaluating the influence of different precipitation forcings on the annual mean and extreme discharges compared to observational discharges for the period from 1990 until 2008. The extended version of CHR showed good agreement in terms of mean annual cycle, extreme discharge (both high and low flows), and spatial distribution of correlations with observed discharge. E-OBS performed well with respect to extreme discharge, but its performance of the mean annual cycle was rather poor in winter and remarkably well in the summer.

scholarly journals Bayesian Inference of Species Trees using Diffusion Models

2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Numerical Algorithms ◽  
Diffusion Models ◽  
Model Parameters ◽  
Data Sets ◽  
Species Trees ◽  
Computationally Efficient ◽  
Data Set ◽  
Snp Data ◽  
Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

scholarly journals High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2

2013 ◽  
Vol 6 (2) ◽  
pp. 779-809 ◽  
Author(s):  
B. Geyer
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Data Set ◽  
Horizontal Grid ◽  
Global Reanalysis ◽  
Marine Climate

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and long-term changes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges etc.) over many decades. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013, doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for entire Europe, including Baltic and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with a regional climate model and a horizontal grid size of 0.22° in rotated coordinates. Global reanalysis data were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

scholarly journals Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes

2019 ◽  
Vol 34 (9) ◽  
pp. 1369-1383 ◽  
Author(s):  
Dirk Diederen ◽  
Ye Liu
Keyword(s):  
Large Scale ◽  
Spatial Coherence ◽  
Original Data ◽  
Return Level ◽  
Data Sets ◽  
Large Set ◽  
Precipitation Data ◽  
Data Set ◽  
Spatio Temporal ◽  
Synthetic Precipitation

Abstract With the ongoing development of distributed hydrological models, flood risk analysis calls for synthetic, gridded precipitation data sets. The availability of large, coherent, gridded re-analysis data sets in combination with the increase in computational power, accommodates the development of new methodology to generate such synthetic data. We tracked moving precipitation fields and classified them using self-organising maps. For each class, we fitted a multivariate mixture model and generated a large set of synthetic, coherent descriptors, which we used to reconstruct moving synthetic precipitation fields. We introduced randomness in the original data set by replacing the observed precipitation fields in the original data set with the synthetic precipitation fields. The output is a continuous, gridded, hourly precipitation data set of a much longer duration, containing physically plausible and spatio-temporally coherent precipitation events. The proposed methodology implicitly provides an important improvement in the spatial coherence of precipitation extremes. We investigate the issue of unrealistic, sudden changes on the grid and demonstrate how a dynamic spatio-temporal generator can provide spatial smoothness in the probability distribution parameters and hence in the return level estimates.

scholarly journals Doppler radar rainfall prediction and gauge data

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jesse W. Lansford ◽  
Tyson H. Walsh ◽  
T. V. Hromadka ◽  
P. Rao
Keyword(s):  
Doppler Radar ◽  
Weather Forecasting ◽  
Rain Gauge ◽  
Data Sets ◽  
Multiple Sources ◽  
Data Set ◽  
Radar Rainfall ◽  
Rainfall Prediction ◽  
Doppler Data ◽  
Precipitation Estimates

Abstract Objective The data herein represents multiple gauge sets and multiple radar sites of like-type Doppler data sets combined to produce populations of ordered pairs. Publications spanning decades yet specific to Doppler radar sites contain graphs of data pairs of Doppler radar precipitation estimates versus rain gauge precipitation readings. Data description Taken from multiple sources, the data set represents several radar sites and rain gauge sites combined for 8830 data points. The data is relevant in various applications of hydrometeorology and engineering as well as weather forecasting. Further, the importance of accuracy in radar and precipitation estimates continues to increase, necessitating the incorporation of as much data as possible.

scholarly journals The Adjusted Log-logistic Generalized Exponential Distribution with Application to Lifetime Data

2017 ◽  
Vol 5 (4) ◽  
pp. 1
Author(s):  
I. E. Okorie ◽  
A. C. Akpanta ◽  
J. Ohakwe ◽  
D. C. Chikezie ◽  
C. U. Onyemachi ◽  
...  
Keyword(s):  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Data Sets ◽  
Lifetime Data ◽  
Generalized Exponential Distribution ◽  
Data Set ◽  
Method Of Maximum Likelihood ◽  
The One ◽  
Special Case

This paper introduces a new generator of probability distribution-the adjusted log-logistic generalized (ALLoG) distribution and a new extension of the standard one parameter exponential distribution called the adjusted log-logistic generalized exponential (ALLoGExp) distribution. The ALLoGExp distribution is a special case of the ALLoG distribution and we have provided some of its statistical and reliability properties. Notably, the failure rate could be monotonically decreasing, increasing or upside-down bathtub shaped depending on the value of the parameters $\delta$ and $\theta$. The method of maximum likelihood estimation was proposed to estimate the model parameters. The importance and flexibility of he ALLoGExp distribution was demonstrated with a real and uncensored lifetime data set and its fit was compared with five other exponential related distributions. The results obtained from the model fittings shows that the ALLoGExp distribution provides a reasonably better fit than the one based on the other fitted distributions. The ALLoGExp distribution is therefore ecommended for effective modelling of lifetime data sets.

Apple Disease Recognition Based on Small-scale Data Sets

2021 ◽  
Vol 37 (3) ◽  
pp. 481-490
Author(s):  
Chenyong Song ◽  
Dongwei Wang ◽  
Haoran Bai ◽  
Weihao Sun
Keyword(s):  
Identification Accuracy ◽  
Model Complexity ◽  
Geometric Transformation ◽  
Small Scale ◽  
Model Parameters ◽  
Data Sets ◽  
List Type ◽  
Data Set ◽  
Disease Identification ◽  
Scale Data

HighlightsThe proposed data enhancement method can be used for small-scale data sets with rich sample image features.The accuracy of the new model reaches 98.5%, which is better than the traditional CNN method.Abstract: GoogLeNet offers far better performance in identifying apple disease compared to traditional methods. However, the complexity of GoogLeNet is relatively high. For small volumes of data, GoogLeNet does not achieve the same performance as it does with large-scale data. We propose a new apple disease identification model using GoogLeNet’s inception module. The model adopts a variety of methods to optimize its generalization ability. First, geometric transformation and image modification of data enhancement methods (including rotation, scaling, noise interference, random elimination, color space enhancement) and random probability and appropriate combination of strategies are used to amplify the data set. Second, we employ a deep convolution generative adversarial network (DCGAN) to enhance the richness of generated images by increasing the diversity of the noise distribution of the generator. Finally, we optimize the GoogLeNet model structure to reduce model complexity and model parameters, making it more suitable for identifying apple tree diseases. The experimental results show that our approach quickly detects and classifies apple diseases including rust, spotted leaf disease, and anthrax. It outperforms the original GoogLeNet in recognition accuracy and model size, with identification accuracy reaching 98.5%, making it a feasible method for apple disease classification. Keywords: Apple disease identification, Data enhancement, DCGAN, GoogLeNet.

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