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Hieu, Bui Thi. "Study on quantification of areal mean precipitation using satellite-gauge merging precipitation." Journal of Science and Technology in Civil Engineering (STCE) - NUCE 12, no. 5 (August 30, 2018): 117–26. http://dx.doi.org/10.31814/stce.nuce2018-12(5)-12.
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Satellite based precipitation product (GSMaP-MVK) can be reliably used to estimate the Areal Mean Precipitation error based on “Sample Design method” (Esdd) with the effort to mitigate the problem of sparse data, especially severe in poorly gauged river basins. In addition, the satellite-gauge merging precipitation would reduce significantly the magnitude gaps between the satellite rainfall estimations and the rain gauge data. In this study, the capability of satellite-gauge merging precipitation using GSMaP-MVK and local dense rain gauge data with bias reduction approach to evaluate the AMP is investigated. The main finding is that satellite-gauge blending data which incorporates a dense rain gauge measurements shows the better capability to evaluate AMP using Esdd index than the original satellite only precipitation estimations. However, Esdd quantification performances of satellite-gauge blending precipitation are inferior to the original satellite only precipitation product GSMaP-MVK when the number of blended rain gauges is not large enough.
Keywords: areal mean precipitation; remote sensed precipitation product; satellite-gauge merging; rainfall runoff simulations.
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Fontaine, Thomas A. "PREDICTING MEASUREMENT ERROR OF AREAL MEAN PRECIPITATION DURING EXTREME EVENTS." Journal of the American Water Resources Association 27, no. 3 (June 1991): 509–20. http://dx.doi.org/10.1111/j.1752-1688.1991.tb01451.x.
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Garen, David C., Gregory L. Johnson, and Clayton L. Hanson. "MEAN AREAL PRECIPITATION FOR DAILY HYDROLOGIC MODELING IN MOUNTAINOUS REGIONS." Journal of the American Water Resources Association 30, no. 3 (June 1994): 481–91. http://dx.doi.org/10.1111/j.1752-1688.1994.tb03307.x.
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Xu, Guoyin, Zhongjing Wang, and Ting Xia. "Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region." Applied Sciences 9, no. 11 (June 4, 2019): 2294. http://dx.doi.org/10.3390/app9112294.
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Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data.
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Moulin, L., E. Gaume, and C. Obled. "Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations." Hydrology and Earth System Sciences Discussions 5, no. 4 (August 1, 2008): 2067–110. http://dx.doi.org/10.5194/hessd-5-2067-2008.
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Abstract. This paper investigates the influence of mean areal rainfall estimation errors on a specific case study: the use of lumped conceptual rainfall-runoff models to simulate the flood hydrographs of three small to medium-sized catchments of the upper Loire river. This area (3200 km2) is densely covered by an operational network of stream and rain gauges. It is frequently exposed to flash floods and the improvement of flood forecasting models is then a crucial concern. Particular attention has been drawn to the development of an error model for rainfall estimation consistent with data in order to produce realistic streamflow simulation uncertainty ranges. The proposed error model combines geostatistical tools based on kriging and an autoregressive model to account for temporal dependence of errors. It has been calibrated and partly validated for hourly mean areal precipitation rates. Simulated error scenarios were propagated into two calibrated rainfall-runoff models using Monte Carlo simulations. Three catchments with areas ranging from 60 to 3200 km2 were tested to reveal any possible links between the sensitivity of the model outputs to rainfall estimation errors and the size of the catchment. The results show that a large part of the rainfall-runoff (RR) modelling errors can be explained by the uncertainties on rainfall estimates, especially in the case of smaller catchments. These errors are a major factor limiting accuracy and sharpness of rainfall-runoff simulations, and thus their operational use for flood forecasting.
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Moulin, L., E. Gaume, and C. Obled. "Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations." Hydrology and Earth System Sciences 13, no. 2 (February 4, 2009): 99–114. http://dx.doi.org/10.5194/hess-13-99-2009.
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Abstract. This paper investigates the influence of mean areal rainfall estimation errors on a specific case study: the use of lumped conceptual rainfall-runoff models to simulate the flood hydrographs of three small to medium-sized catchments of the upper Loire river. This area (3200 km2) is densely covered by an operational network of stream and rain gauges. It is frequently exposed to flash floods and the improvement of flood forecasting models is then a crucial concern. Particular attention has been drawn to the development of an error model for rainfall estimation consistent with data in order to produce realistic streamflow simulation uncertainty ranges. The proposed error model combines geostatistical tools based on kriging and an autoregressive model to account for temporal dependence of errors. It has been calibrated and partly validated for hourly mean areal precipitation rates. Simulated error scenarios were propagated into two calibrated rainfall-runoff models using Monte Carlo simulations. Three catchments with areas ranging from 60 to 3200 km2 were tested to reveal any possible links between the sensitivity of the model outputs to rainfall estimation errors and the size of the catchment. The results show that a large part of the rainfall-runoff (RR) modelling errors can be explained by the uncertainties on rainfall estimates, especially in the case of smaller catchments. These errors are a major factor limiting accuracy and sharpness of rainfall-runoff simulations, and thus their operational use for flood forecasting.
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Johnson, Dennis, Michael Smith, Victor Koren, and Bryce Finnerty. "Comparing Mean Areal Precipitation Estimates from NEXRAD and Rain Gauge Networks." Journal of Hydrologic Engineering 4, no. 2 (April 1999): 117–24. http://dx.doi.org/10.1061/(asce)1084-0699(1999)4:2(117).
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Gagnon, P., A. N. Rousseau, A. Mailhot, and D. Caya. "Spatial Disaggregation of Mean Areal Rainfall Using Gibbs Sampling." Journal of Hydrometeorology 13, no. 1 (February 1, 2012): 324–37. http://dx.doi.org/10.1175/jhm-d-11-034.1.
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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.
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Guven, Aytac, and Abdulhadi Pala. "Comparison of different statistical downscaling models and future projection of areal mean precipitation of a river basin under climate change effect." Water Supply 22, no. 3 (October 27, 2021): 2424–39. http://dx.doi.org/10.2166/ws.2021.372.
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Abstract Investigation of the hydrological impacts of climate change at the local scale requires the use of a statistical downscaling technique. In order to use the output of a Global Circulation Model (GCM), a downscaling technique is used. In this study, statistical downscaling of monthly areal mean precipitation in the Göksun River basin in Turkey was carried out using the Group Method of Data Handling (GMDH), Support Vector Machine (SVM) and Gene Expression Programming (GEP) techniques. Large-scale weather factors were used for the basin with a monthly areal mean precipitation (PM) record from 1971 to 2000 used for training and testing periods. The R2-value for precipitation in the SVM, GEP and GMDH models are 0.62, 0.59, and 0.6 respectively, for the testing periods. The results show that SVM has the best model performance of the three proposed downscaling models, however, the GEP model has the lowest AIC value. The simulated results for the Canadian GCM3 (CGCM3) A1B and A2 scenarios show a similarity in their average precipitation prediction. Generally, both these scenarios anticipate a decrease in the average monthly precipitation during the simulated periods. Therefore, the results of the future projections show that mean precipitation might decrease during the period of 2021–2100.
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Bumke, Karl, Robin Pilch Kedzierski, Marc Schröder, Christian Klepp, and Karsten Fennig. "Validation of HOAPS Rain Retrievals against OceanRAIN In-Situ Measurements over the Atlantic Ocean." Atmosphere 10, no. 1 (January 7, 2019): 15. http://dx.doi.org/10.3390/atmos10010015.
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The satellite-derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) precipitation estimates have been validated against in-situ precipitation measurements from optical disdrometers, available from OceanRAIN (Ocean Rainfall And Ice-phase precipitation measurement Network) over the open-ocean by applying a statistical analysis for binary estimates. In addition to using directly collocated pairs of data, collocated data were merged within a certain temporal and spatial threshold into single events, according to the observation times. Although binary statistics do not show perfect agreement, simulations of areal estimates from the observations themselves indicate a reasonable performance of HOAPS to detect rain. However, there are deficits at low and mid-latitudes. Weaknesses also occur when analyzing the mean precipitation rates; HOAPS underperforms in the area of the intertropical convergence zone, where OceanRAIN observations show the highest mean precipitation rates. Histograms indicate that this is due to an underestimation of the frequency of moderate to high precipitation rates by HOAPS, which cannot be explained by areal averaging.
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Johansson, Barbro. "Areal Precipitation and Temperature in the Swedish Mountains." Hydrology Research 31, no. 3 (June 1, 2000): 207–28. http://dx.doi.org/10.2166/nh.2000.0013.
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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.
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Stellman, Keith M., Henry E. Fuelberg, Reggina Garza, and Mary Mullusky. "An Examination of Radar and Rain Gauge–Derived Mean Areal Precipitation over Georgia Watersheds." Weather and Forecasting 16, no. 1 (February 2001): 133–44. http://dx.doi.org/10.1175/1520-0434(2001)016<0133:aeorar>2.0.co;2.
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Chen, Cheng-Ta, and Thomas Knutson. "On the Verification and Comparison of Extreme Rainfall Indices from Climate Models." Journal of Climate 21, no. 7 (April 1, 2008): 1605–21. http://dx.doi.org/10.1175/2007jcli1494.1.
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Abstract The interpretation of model precipitation output (e.g., as a gridpoint estimate versus as an areal mean) has a large impact on the evaluation and comparison of simulated daily extreme rainfall indices from climate models. It is first argued that interpretation as a gridpoint estimate (i.e., corresponding to station data) is incorrect. The impacts of this interpretation versus the areal mean interpretation in the context of rainfall extremes are then illustrated. A high-resolution (0.25° × 0.25° grid) daily observed precipitation dataset for the United States [from Climate Prediction Center (CPC)] is used as idealized perfect model gridded data. Both 30-yr return levels of daily precipitation (P30) and a simple daily intensity index are substantially reduced in these data when estimated at coarser resolution compared to the estimation at finer resolution. The reduction of P30 averaged over the conterminous United States is about 9%, 15%, 28%, 33%, and 43% when the data were first interpolated to 0.5° × 0.5°, 1° × 1°, 2° × 2°, 3° × 3°, and 4° × 4° grid boxes, respectively, before the calculation of extremes. The differences resulting from the point estimate versus areal mean interpretation are sensitive to both the data grid size and to the particular extreme rainfall index analyzed. The differences are not as sensitive to the magnitude and regional distribution of the indices. Almost all Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) models underestimate U.S. mean P30 if it is compared directly with P30 estimated from the high-resolution CPC daily rainfall observation. On the other hand, if CPC daily data are first interpolated to various model resolutions before calculating the P30 (a more correct procedure in our view), about half of the models show good agreement with observations while most of the remaining models tend to overestimate the mean intensity of heavy rainfall events. A further implication of interpreting model precipitation output as an areal mean is that use of either simple multimodel ensemble averages of extreme rainfall or of intermodel variability measures of extreme rainfall to assess the common characteristics and range of uncertainties in current climate models is not appropriate if simulated extreme rainfall is analyzed at a model’s native resolution. Owing to the large sensitivity to the assumption used, the authors recommend that for analysis of precipitation extremes, investigators interpret model precipitation output as an area average as opposed to a point estimate and then ensure that various analysis steps remain consistent with that interpretation.
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BUI, THI HIEU, HIROSHI ISHIDAIRA, YUTAKA ICHIKAWA, and JUN MAGOME. "EVALUATION OF POTENTIAL ERROR IN MEAN AREAL PRECIPITATION AND ITS IMPACT ON RAINFALL-RUNOFF SIMULATION USING SATELLITE PRECIPITATION PRODUCT." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 70, no. 4 (2014): I_199—I_204. http://dx.doi.org/10.2208/jscejhe.70.i_199.
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Gabriele, Salvatore, Francesco Chiaravalloti, and Antonio Procopio. "Radar–rain-gauge rainfall estimation for hydrological applications in small catchments." Advances in Geosciences 44 (July 12, 2017): 61–66. http://dx.doi.org/10.5194/adgeo-44-61-2017.
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Abstract. The accurate evaluation of the precipitation's time–spatial structure is a critical step for rainfall–runoff modelling. Particularly for small catchments, the variability of rainfall can lead to mismatched results. Large errors in flow evaluation may occur during convective storms, responsible for most of the flash floods in small catchments in the Mediterranean area. During such events, we may expect large spatial and temporal variability. Therefore, using rain-gauge measurements only can be insufficient in order to adequately depict extreme rainfall events. In this work, a double-level information approach, based on rain gauges and weather radar measurements, is used to improve areal rainfall estimations for hydrological applications. In order to highlight the effect that precipitation fields with different level of spatial details have on hydrological modelling, two kinds of spatial rainfall fields were computed for precipitation data collected during 2015, considering both rain gauges only and their merging with radar information. The differences produced by these two precipitation fields in the computation of the areal mean rainfall accumulation were evaluated considering 999 basins of the region Calabria, southern Italy. Moreover, both of the two precipitation fields were used to carry out rainfall–runoff simulations at catchment scale for main precipitation events that occurred during 2015 and the differences between the scenarios obtained in the two cases were analysed. A representative case study is presented in detail.
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Pereira Filho, Augusto José, Felipe Vemado, Guilherme Vemado, Fábio Augusto Gomes Vieira Reis, Lucilia do Carmo Giordano, Rodrigo Irineu Cerri, Cláudia Cristina dos Santos, et al. "A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements." Advances in Meteorology 2018 (December 17, 2018): 1–24. http://dx.doi.org/10.1155/2018/2095304.
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Accurate daily rainfall estimation is required in several applications such as in hydrology, hydrometeorology, water resources management, geomorphology, civil protection, and agriculture, among others. CMORPH daily rainfall estimations were integrated with rain gauge measurements in Brazil between 2000 and 2015, in order to reduce daily rainfall estimation errors by means of the statistical objective analysis scheme (SOAS). Early comparisons indicated high discrepancies between daily rain gauge rainfall measurements and respective CMORPH areal rainfall accumulation estimates that tended to be reduced with accumulation time span (e.g., yearly accumulation). Current results show CMORPH systematically underestimates daily rainfall accumulation along the coastal areas. The normalized error variance (NEXERVA) is higher in sparsely gauged areas at Brazilian North and Central-West regions. Monthly areal rainfall averages and standard deviation were obtained for eleven Brazilian watersheds. While an overall negative tendency (3 mm·h−1) was estimated, the Amazon watershed presented a long-term positive tendency. Monthly areal mean precipitation and respective spatial standard deviation closely follow a power-law relationship for data-rich watersheds, i.e., with denser rain gauge networks. Daily SOAS rainfall accumulation was also used to calculate the spatial distribution of frequencies of 3-day rainfall episodes greater than 100 mm. Frequencies greater than 3% were identified downwind of the Peruvian Andes, the Bolivian Amazon Basin, and the La Plata Basin, as well as along the Brazilian coast, where landslides are recurrently triggered by precipitation.
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Duethmann, D., J. Zimmer, A. Gafurov, A. Güntner, B. Merz, and S. Vorogushyn. "Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling." Hydrology and Earth System Sciences Discussions 9, no. 9 (September 20, 2012): 10719–73. http://dx.doi.org/10.5194/hessd-9-10719-2012.
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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.
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Naoum, S., and I. K. Tsanis. "A multiple linear regression GIS module using spatial variables to model orographic rainfall." Journal of Hydroinformatics 6, no. 1 (January 1, 2004): 39–56. http://dx.doi.org/10.2166/hydro.2004.0004.
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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.
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Amburn, Steven A., Andrew S. I. D. Lang, and Michael A. Buonaiuto. "Precipitation Forecasting with Gamma Distribution Models for Gridded Precipitation Events in Eastern Oklahoma and Northwestern Arkansas*." Weather and Forecasting 30, no. 2 (April 1, 2015): 349–67. http://dx.doi.org/10.1175/waf-d-14-00054.1.
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Abstract An elegant and easy to implement probabilistic quantitative precipitation forecasting model that can be used to estimate the probability of exceedance (POE) is presented. The model was built using precipitation data collected across eastern Oklahoma and northwestern Arkansas from late 2005 through early 2013. The dataset includes precipitation analyses at 4578 contiguous, 4 km × 4 km grid cells for 1800 precipitation events of 12 h. The dataset is unique in that the meteorological conditions for each 12-h event were relatively homogeneous when contrasted with single-point data obtained over months or years where the meteorological conditions for each rain event could have varied widely. Grid cells were counted and stratified by precipitation amount in increments of 0.05 in. (1.27 mm) up to 10 in. (254 mm), yielding histograms for each event. POEs were computed from the observed precipitation distributions and compared to POEs computed from two gamma probability density functions ( and ). The errors between the observed POEs and gamma-computed POEs ranged between 2% and 10%, depending on the threshold POE selected for the comparison. This accuracy suggests the gamma models could be used to make reasonably accurate estimates of POE, given the percent areal coverage and the mean precipitation over the area. Finally, it is suggested that the areal distribution for each event is representative of the distribution at any point in the area over a large number of similar events. It then follows that the gamma models can be used to make forecasts for the probability of exceedance at a point, given the probability of rain and the expected mean rainfall at that same point.
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Berne, A., M. ten Heggeler, R. Uijlenhoet, L. Delobbe, Ph Dierickx, and M. de Wit. "A preliminary investigation of radar rainfall estimation in the Ardennes region and a first hydrological application for the Ourthe catchment." Natural Hazards and Earth System Sciences 5, no. 2 (March 4, 2005): 267–74. http://dx.doi.org/10.5194/nhess-5-267-2005.
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Abstract. This paper presents a first assessment of the hydrometeorological potential of a C-band doppler weather radar recently installed by the Royal Meteorological Institute of Belgium near the village of Wideumont in the southern Ardennes region. An analysis of the vertical profile of reflectivity for two contrasting rainfall events confirms the expected differences between stratiform and convective precipitation. The mean areal rainfall over the Ourthe catchment upstream of Tabreux estimated from the Wideumont weather radar using the standard Marshall-Palmer reflectivity-rain rate relation shows biases between +128% and –42% for six selected precipitation events. For two rainfall events the radar-estimated mean areal rainfall is applied to the gauge-calibrated (lumped) HBV-model for the Ourthe upstream of Tabreux, resulting in a significant underestimation with respect to the observed discharge for one event and a closer match for another. A bootstrap analysis using the radar data reveals that the uncertainty in the hourly discharge from the ~1600km2} catchment associated with the sampling uncertainty of the mean areal rainfall estimated from 10 rain gauges evenly spread over the catchment amounts to ±25% for the two events analyzed. This uncertainty is shown to be of the same order of magnitude as that associated with the model variables describing the initial state of the model.
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Loukas, A., L. Vasiliades, and J. Tzabiras. "Climate change effects on drought severity." Advances in Geosciences 17 (June 20, 2008): 23–29. http://dx.doi.org/10.5194/adgeo-17-23-2008.
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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.
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Zeiger, Sean, and Jason Hubbart. "An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment." Water 9, no. 7 (June 24, 2017): 459. http://dx.doi.org/10.3390/w9070459.
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Rudolf, B., H. Hauschild, M. Reiss, and U. Schneider. "The calculation of areal mean precipitation totals on a 2.5 ° grid by an objective analysis method." Meteorologische Zeitschrift 1, no. 1 (March 10, 1992): 32–50. http://dx.doi.org/10.1127/metz/1/1992/32.
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Zhang, Yu, and Dong-Jun Seo. "Recursive estimators of mean-areal and local bias in precipitation products that account for conditional bias." Advances in Water Resources 101 (March 2017): 49–59. http://dx.doi.org/10.1016/j.advwatres.2017.01.002.
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Dabhi, Hetal P., Mathias W. Rotach, and Michael Oberguggenberger. "A gridded multi-site precipitation generator for complex terrain: an evaluation in the Austrian Alps." Hydrology and Earth System Sciences 27, no. 11 (June 7, 2023): 2123–47. http://dx.doi.org/10.5194/hess-27-2123-2023.
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Abstract. For climate change impact assessment, many applications require very high-resolution, spatiotemporally consistent precipitation data on current or future climate. In this regard, stochastic weather generators are designed as a statistical downscaling tool that can provide such data. Here, we adopt the precipitation generator framework of Kleiber et al. (2012), which is based on latent and transformed Gaussian processes, and propose an extension of that framework for a mountainous region with complex topography by allowing elevation dependence in the model. The model is used to generate two-dimensional fields of precipitation with a 1 km spatial resolution and a daily temporal resolution in a small region with highly complex terrain in the Austrian Alps. This study aims to evaluate the model with respect to its ability to simulate realistic precipitation fields over the region using historical observations from a network of 29 meteorological stations as input. The model's added value over the original setup and its limitations are also discussed. The results show that the model generates realistic fields of precipitation with good spatial and temporal variability. The model is able to generate some of the difficult areal statistics useful for impact assessment, such as the areal dry and wet spells of different lengths and the areal monthly mean of precipitation, with great accuracy. The model also captures the inter-seasonal and intra-seasonal variability very well, while the inter-annual variability is well captured in summer but largely underestimated in autumn and winter. The proposed model adds substantial value over the original modeling framework, specifically with respect to the precipitation amount. The model is unable to reproduce the realistic spatiotemporal characteristics of precipitation in autumn. We conclude that, with further development, the model is a promising tool for downscaling precipitation in complex terrain for a wide range of applications in impact assessment studies.
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Jia, Yao, Huimin Lei, Hanbo Yang, and Qingfang Hu. "Terrestrial Water Storage Change Retrieved by GRACE and Its Implication in the Tibetan Plateau: Estimating Areal Precipitation in Ungauged Region." Remote Sensing 12, no. 19 (September 24, 2020): 3129. http://dx.doi.org/10.3390/rs12193129.
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The Tibetan Plateau (TP) is referred to as the water tower of Asia, where water storage and precipitation have huge impacts on most major Asian rivers. Based on gravity recovery and climate experiment data, this study analyzed the terrestrial water storage (TWS) changes and estimated areal precipitation based on the water balance equation in four different basins, namely, the upper Yellow River (UYE), the upper Yangtze River (UYA), the Yarlung Zangbo River (YZ), and the Qiangtang Plateau (QT). The results show that the TWS change exhibits different patterns in the four basins and varies from −13 to 2 mm/year from 2003 to 2017. The estimated mean annual precipitation was 260 ± 19 mm/year (QT), 697 ± 26 mm/year (UYA), 541 ± 36 mm/year (UYE), and 1160 ± 39 mm/year (YZ) which performed better than other precipitation products in the TP. It indicates a potential method for estimating basin-scale precipitation through integrating basin average precipitation from the water balance equation in the poorly gauged and ungauged regions.
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Vrochidou, A. E. K., and I. K. Tsanis. "Assessing precipitation distribution impacts on droughts on the island of Crete." Natural Hazards and Earth System Sciences 12, no. 4 (April 25, 2012): 1159–71. http://dx.doi.org/10.5194/nhess-12-1159-2012.
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Abstract. Precipitation records from 56 stations on the island of Crete (Greece) revealed that areal mean annual precipitation is of a strong orographic type and its magnitude decreases in west-east direction by as much as 400 mm on average. Amongst many parameters that influence precipitation, the elevation and longitude were the most important and provided the highest spatial correlation. It was found that during the year with minimum precipitation, the precipitation shortage was greater at high elevations while the precipitation excess during the year with maximum precipitation was greater in the western part of the island. The assessment of the spatial and temporal distribution of droughts was carried out with the aid of the Spatially Normalized Standardized Precipitation Index (SN-SPI) for the period 1974–2005 in order to compare drought conditions between neighbouring areas of differing precipitation heights. The analysis showed that severe droughts occurred around the year 1992–1993, with a duration of up to 3 yr. Multiple linear regression (MLR) modeling of precipitation in conjunction with cluster analysis of drought duration exhibits the linkage between precipitation, droughts and geographical factors. This connection between spatial precipitation distribution and geographical parameters provides an important clue for the respective spatial drought pattern. The above findings on the spatio-temporal drought distribution will update the current~drought management plans by developing more precise drought warning systems.
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Kobold, M., and M. Brilly. "The use of HBV model for flash flood forecasting." Natural Hazards and Earth System Sciences 6, no. 3 (May 24, 2006): 407–17. http://dx.doi.org/10.5194/nhess-6-407-2006.
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Abstract. The standard conceptual HBV model was originally developed with daily data and is normally operated on daily time step. But many floods in Slovenia are usually flash floods as result of intense frontal precipitation combined with orographic enhancement. Peak discharges are maintained only for hours or even minutes. To use the HBV model for flash flood forecasting, the version of HBV-96 has been applied on the catchment with complex topography with the time step of one hour. The recording raingauges giving hourly values of precipitation have been taken in calibration of the model. The uncertainty of simulated runoff is mainly the result of precipitation uncertainty associated with the mean areal precipitation and is higher for mountainous catchments. Therefore the influence of number of raingauges used to derive the areal precipitation by the method of Thiessen polygons was investigated. The quantification of hydrological uncertainty has been performed by analysis of sensitivity of the HBV model to error in precipitation input. The results show that an error of 10% in the amount of precipitation causes an error of 17% in the peak of flood wave. The polynomial equations showing the relationship between the errors in rainfall amounts and peak discharges were derived for two water stations on the Savinja catchment. Simulated discharges of half-yearly runs demonstrate the applicability of the HBV model for flash flood forecasting using the mesoscale meteorological forecasts of ALADIN/SI model as input precipitation data.
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Diodato, Nazzareno, and Gianni Bellocchi. "From Past to Present: Decoding Precipitation Patterns in a Complex Mediterranean River Basin." Climate 11, no. 7 (July 4, 2023): 141. http://dx.doi.org/10.3390/cli11070141.
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Enhancing spatial data attributes is crucial for effective basin-scale environmental modelling and improving our understanding and management of precipitation patterns. In this study, we focused on reconstructing homogeneous areal precipitation data in the complex terrain of the Calore River Basin (CRB) in Southern Italy. Until 1869, weather observations in the region were inconsistent, unstandardised, and lacked coordination, but the establishment of meteorological observatories brought a more unified approach to weather monitoring. We relied on the rainfall data obtained from two of these historical observatories: Benevento (1869–present) and Montevergine (1884–present). We utilised a statistical regression framework that considered rainfall measurements and temporal properties from specific locations to reconstruct and visually analyse the evolution patterns of annual mean areal precipitation (MAP) in the CRB from 1869 to 2020. The analysis revealed that mean MAP decreased from 1153 mm yr−1 (1869–1951) to 998 mm yr−1 (1952–2020). This decrease was accompanied by a reduction in interannual variability (from 168 mm yr−1 to 147 mm yr−1 standard deviation), and the difference between the means was significant (p < 0.0001), suggesting a sudden shift in the time-series. These findings provide a basis for CRB water resource management and insights for modelling other complex Mediterranean basins.
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Raupach, Timothy H., and Alexis Berne. "Small-Scale Variability of the Raindrop Size Distribution and Its Effect on Areal Rainfall Retrieval." Journal of Hydrometeorology 17, no. 7 (July 1, 2016): 2077–104. http://dx.doi.org/10.1175/jhm-d-15-0214.1.
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Abstract The drop size distribution (DSD) describes the microstructure of liquid precipitation. The high variability of the DSD reflects the variety of microphysical processes controlling raindrop properties and affects the retrieval of rainfall. An analysis of the effects of DSD subgrid variability on areal estimation of precipitation is presented. Data used were recorded with a network of disdrometers in Ardèche, France. DSD variability was studied over two typical scales: 5 km × 5 km, similar to the ground footprint size of the Global Precipitation Measurement (GPM) spaceborne weather radar, and 2.8 km × 2.8 km, an operational pixel size of the Consortium for Small-Scale Modeling (COSMO) numerical weather model. Stochastic simulation was used to generate high-resolution grids of DSD estimates over the regions of interest, constrained by experimental DSDs measured by disdrometers. From these grids, areal DSD estimates were derived. The error introduced by assuming a point measurement to be representative of the areal DSD was quantitatively characterized and was shown to increase with the size of the considered area and with drop size and to decrease with the integration time. The controlled framework allowed for the accuracy of retrieval algorithms to be investigated. Rainfall variables derived by idealized simulations of GPM- and COSMO-style algorithms were compared to subgrid distributions of the same variables. While rain rate and radar reflectivity were well represented, the estimated drop concentration and mass-weighted mean drop diameter were often less representative of subgrid values.
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Zhang, Yanping, Min Luo, Yu Hu, Hongbin Wang, and Duofu Chen. "An Areal Assessment of Subseafloor Carbon Cycling in Cold Seeps and Hydrate-Bearing Areas in the Northern South China Sea." Geofluids 2019 (March 17, 2019): 1–14. http://dx.doi.org/10.1155/2019/2573937.
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Gas hydrates, acting as a dynamic methane reservoir, store methane in the form of a solid phase under high-pressure and low-temperature conditions and release methane through the sediment column into seawater when they are decomposed. The seepage of methane-rich fluid (i.e., cold hydrocarbon seeps) fuels the chemosynthetic biota-inhabited surface sediments and represents the major pathway to transfer carbon from sediments to the water column. Generally, the major biogeochemical reactions related to carbon cycling in the anoxic marine sediments include organic matter degradation via sulfate reduction (OSR), anaerobic oxidation of methane (AOM), methanogenesis (ME), and carbonate precipitation (CP). In order to better understand the carbon turnover in the cold seeps and gas hydrate-bearing areas of the northern South China Sea (SCS), we collected geochemical data of 358 cores from published literatures and retrieved 37 cores and corresponding pore water samples from three areas of interest (i.e., Xisha, Dongsha, and Shenhu areas). Reaction-transport simulations indicate that the rates of organic matter degradation and carbonate precipitation are comparable in the three areas, while the rates of AOM vary over several orders of magnitude (AOM: 8.3-37.5 mmol·m-2·yr-1 in Dongsha, AOM: 12.4-170.6 mmol·m-2·yr-1 in Xisha, and AOM: 9.4-30.5 mmol·m-2·yr-1 in Shenhu). Both the arithmetical mean and interpolation mean of the biogeochemical processes were calculated in each area. Averaging these two mean values suggested that the rates of organic matter degradation in Dongsha (25.7 mmol·m-2·yr-1) and Xisha (25.1 mmol·m-2·yr-1) are higher than that in Shenhu (12 mmol·m-2·yr-1) and the AOM rate in Xisha (135.2 mmol·m-2·yr-1) is greater than those in Dongsha (27.8 mmol·m-2·yr-1) and Shenhu (17.5 mmol·m-2·yr-1). In addition, the rate of carbonate precipitation (32.3 mmol·m-2·yr-1) in Xisha is far higher than those of the other two regions (5.3 mmol·m-2·yr-1 in Dongsha, 5.8 mmol·m-2·yr-1 in Shenhu) due to intense AOM sustained by gas dissolution. In comparison with other cold seeps around the world, the biogeochemical rates in the northern SCS are generally lower than those in active continental margins and special environments (e.g., the Black sea) but are comparable with those in passive continental margins. Collectively, ~2.8 Gmol organic matter was buried and at least ~0.82 Gmol dissolved organic and inorganic carbon was diffused out of sediments annually. This may, to some extent, have an impact on the long-term deep ocean carbon cycle in the northern SCS.
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Zhang, Yu, Dong-Jun Seo, Emad Habib, and Jeffrey McCollum. "Differences in scale-dependent, climatological variation of mean areal precipitation based on satellite and radar-gauge observations." Journal of Hydrology 522 (March 2015): 35–48. http://dx.doi.org/10.1016/j.jhydrol.2014.11.077.
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Nguyen, Duc Hai, and Deg-Hyo Bae. "Correcting mean areal precipitation forecasts to improve urban flooding predictions by using long short-term memory network." Journal of Hydrology 584 (May 2020): 124710. http://dx.doi.org/10.1016/j.jhydrol.2020.124710.
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Mishra, Vimal, Reepal Shah, and Bridget Thrasher. "Soil Moisture Droughts under the Retrospective and Projected Climate in India*." Journal of Hydrometeorology 15, no. 6 (December 1, 2014): 2267–92. http://dx.doi.org/10.1175/jhm-d-13-0177.1.
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Abstract Changes in precipitation, air temperature, and model-simulated soil moisture were examined for the observed (1950–2008) and projected (2010–99) climate for the sowing period of Kharif and Rabi [KHARIF_SOW (May–July) and RABI_SOW (October–December)] and the entire Kharif and Rabi [KHARIF (May–October) and RABI (October–April)] crop-growing periods in India. During the KHARIF_SOW and KHARIF periods, precipitation declined significantly in the Gangetic Plain, which in turn resulted in declines in soil moisture. Statistically significant warming trends were noticed as all-India-averaged air temperature increased by 0.40°, 0.90°, and 0.70°C in the KHARIF, RABI_SOW, and RABI periods, respectively, during 1950–2008. Frequency and areal extent of soil moisture–based droughts increased substantially during the latter half (1980–2008) of the observed period. Under the projected climate (2010–99), precipitation, air temperature, and soil moisture are projected to increase in all four crop-growing seasons. In the projected climate, all-India ensemble mean precipitation, air temperature, and soil moisture are projected to increase up to 39% (RABI_SOW period), 2.3°C, and 5.3%, respectively, in the crop-growing periods. While projected changes in air temperature are robust across India, robust increases in precipitation and soil moisture are projected to occur in the end-term (2070–99) climate. Frequency and areal extents of soil moisture–based severe, extreme, and exceptional droughts are projected to increase in the near- (2010–39) and midterm (2040–69) climate in the majority of crop-growing seasons in India. However, frequency and areal extent of droughts during the crop-growing period are projected to decline in the end-term climate in the entire crop-growing period because of projected increases in the monsoon season precipitation.
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Chardon, Jérémy, Anne-Catherine Favre, and Benoît Hingray. "Effects of Spatial Aggregation on the Accuracy of Statistically Downscaled Precipitation Predictions." Journal of Hydrometeorology 17, no. 5 (May 1, 2016): 1561–78. http://dx.doi.org/10.1175/jhm-d-15-0031.1.
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Abstract The effects of spatial aggregation on the skill of downscaled precipitation predictions obtained over an 8 × 8 km2 grid from circulation analogs for metropolitan France are explored. The Safran precipitation reanalysis and an analog approach are used to downscale the precipitation where the predictors are taken from the 40-yr ECMWF Re-Analysis (ERA-40). Prediction skill—characterized by the continuous ranked probability score (CRPS), its skill score, and its decomposition—is generally found to continuously increase with spatial aggregation. The increase is also greater when the spatial correlation of precipitation is lower. This effect is shown from an empirical experiment carried out with a fully uncorrelated dataset, generated from a space-shake experiment, where the precipitation time series of each grid cell is randomly assigned to another grid cell. The underlying mechanisms of this effect are further highlighted with synthetic predictions simulated using a stochastic spatiotemporal generator. It is shown 1) that the skill increase with spatial aggregation jointly results from the higher and lower values obtained for the resolution and uncertainty terms of the CRPS decomposition, respectively, and 2) that the lower spatial correlation of precipitation is beneficial for both terms. Results obtained for France suggest that the prediction skill indefinitely increases with aggregation. A last experiment is finally proposed to show that this is not expected to be always the case. A prediction skill optimum is, for instance, obtained when the mean areal precipitation is estimated over a region where local precipitations of different grid cells originate from different underlying meteorological processes.
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Storm, B., K. Høgh Jensen, and J. C. Refsgaard. "Estimation of Catchment Rainfall Uncertainty and its Influence on Runoff Prediction." Hydrology Research 19, no. 2 (April 1, 1988): 77–88. http://dx.doi.org/10.2166/nh.1988.0006.
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Interpolation of spatially varying point precipitation depths introduces uncertainties in the estimated mean areal precipitation (MAP). This paper describes a geostatistical approach – the Kriging method – to calculate the daily MAP on real-time basis. The procedure provides a linear unbiased estimate with minimum estimation variance. The structural analysis of the random precipitation field is automatized by relating the time-varying semivariogram model to the sample variance. This is illustrated on data from a Danish IHD catchment. The conceptual rainfall-runoff model NAM incorporated into a Kalman-filter algortithm is applied to investigate the effects of uncertainties in MAP on the runoff predictions. Measurement and processing errors are not included in the investigation.
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Singh, Shweta, and Norbert Kalthoff. "Process Studies of the Impact of Land-Surface Resolution on Convective Precipitation Based on High-Resolution ICON Simulations." Meteorology 1, no. 3 (July 31, 2022): 254–73. http://dx.doi.org/10.3390/meteorology1030017.
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This study investigated the relevant processes responsible for differences of convective precipitation caused by land-surface resolution. The simulations were performed with the ICOsahedral Nonhydrostatic model (ICON) with grid spacing of 156 m and Large Eddy Simulation physics. Regions of different orographic complexity, days with weak synoptic forcing and favourable convective conditions were selected. The resolution of land-surface properties (soil type, vegetation) and/or the orography was reduced from 156 to 5000 m. Analyses are based on backward trajectories (Lagrangian Analysis Tool (LAGRANTO)), heat budget and convective organisation potential (COP) calculations. On average, the relative difference of areal mean daily precipitation at 1250 and 5000 m land-surface resolutions compared to 156 m were 6% and 15%, respectively. No consistent dependency of precipitation on orography or land-surface properties was found. Both factors impact convective initiation over areas with embedded mesoscale-sized land-surface heterogeneities. The position of convective precipitation was often influenced by the resolution of orography. Coarsening from 156 to 5000 m considerably changed the location of wind convergence and associated convection initiation. It also affects the onset times of clouds (<20 min) and precipitation (≈1 h). Cloud aggregation and microphysical processes proved to be important for further development towards convective precipitation.
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Aminyavari, Saghafian, and Sharifi. "Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran." Remote Sensing 11, no. 23 (November 21, 2019): 2741. http://dx.doi.org/10.3390/rs11232741.
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Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models.
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Wen, Jet‐Chau. "A study of mean areal precipitation and spatial structure of rainfall distribution in the Tsen‐Wen river basin." Journal of the Chinese Institute of Engineers 24, no. 5 (July 2001): 649–58. http://dx.doi.org/10.1080/02533839.2001.9670662.
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Pardo-Igúzquiza, Eulogio. "Comparison of geostatistical methods for estimating the areal average climatological rainfall mean using data on precipitation and topography." International Journal of Climatology 18, no. 9 (July 1998): 1031–47. http://dx.doi.org/10.1002/(sici)1097-0088(199807)18:9<1031::aid-joc303>3.0.co;2-u.
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Hatzianastassiou, N., B. Katsoulis, J. Pnevmatikos, and V. Antakis. "Spatial and Temporal Variation of Precipitation in Greece and Surrounding Regions Based on Global Precipitation Climatology Project Data." Journal of Climate 21, no. 6 (March 15, 2008): 1349–70. http://dx.doi.org/10.1175/2007jcli1682.1.
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Abstract In this study, the spatial and temporal distribution of precipitation in the broader Greek area is investigated for the 26-yr period 1979–2004 by using monthly mean satellite-based data, with complete spatial coverage, taken from the Global Precipitation Climatology Project (GPCP). The results show that there exists a clear contrast between the more rainy western Greek area (rainside) and the drier eastern one (rainshadow), whereas there is little precipitation over the islands, particularly in the southern parts. The computed long-term areal mean annual precipitation amount averaged for the study area is equal to P = 639.8 ± 44.8 mm yr−1, showing a decreasing trend of −2.32 mm yr−1 or −60.3 mm over the 26-yr study period, which corresponds to −9.4%. This decrease of precipitation, arising primarily in winter and secondarily in spring, is the result of a decreasing trend from 1979 through the 1980s, against an increase during the 1990s through the early 2000s, followed again by a decrease up to the year 2004. The performed analysis reveals an increasing trend of precipitation in the central and northern parts of the study region, contrary to an identified decreasing trend in the southern parts, which is indicative of threatening desertification processes in those areas in the context of climatic changes in the climatically sensitive Mediterranean basin. In addition, the analysis shows that the precipitation decrease is due to a corresponding decrease of maximum precipitation against rather unchanged minimum precipitation amounts. The analysis indicates that the changing precipitation patterns in the region during winter are significantly anticorrelated with the North Atlantic Oscillation (NAO) index values, against a positive correlation during summer, highlighting thus the role of large-scale circulation patterns for regional climates. The GPCP precipitation data are satisfactorily correlated with instrumental measurements from 36 stations uniformly distributed over the study area (correlation coefficient R = 0.74 for all stations; R = 0.63–0.91 for individual stations).
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Zhao, Shuyun, Hua Zhang, Zhili Wang, and Xianwen Jing. "Simulating the Effects of Anthropogenic Aerosols on Terrestrial Aridity Using an Aerosol–Climate Coupled Model." Journal of Climate 30, no. 18 (August 22, 2017): 7451–63. http://dx.doi.org/10.1175/jcli-d-16-0407.1.
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Abstract The comprehensive effects of anthropogenic aerosols (sulfate, black carbon, and organic carbon) on terrestrial aridity were simulated using an aerosol–climate coupled model system. The results showed that the increase in total anthropogenic aerosols in the atmosphere from 1850 to 2010 had caused global land annual mean precipitation to decrease by about 0.19 (0.18, 0.21) mm day−1, where the uncertainty range of the change (minimum, maximum) is given in parentheses following the mean change, and reference evapotranspiration ET0 (representing evapotranspiration ability) to decrease by about 0.33 (0.31, 0.35) mm day−1. The increase in anthropogenic aerosols in the atmosphere from 1850 to 2010 had caused land annual mean terrestrial aridity to decrease by about 3.0% (2.7%, 3.6%). The areal extent of global total arid and semiarid areas had reduced due to the increase in total anthropogenic aerosols in the atmosphere from preindustrial times. However, it was found that the increase in anthropogenic aerosols in the atmosphere had enhanced the terrestrial aridity and thus resulted in an expansion of arid and semiarid areas over East and South Asia. The projected decrease in anthropogenic aerosols in the atmosphere from 2010 to 2100 will increase global land annual mean precipitation by about 0.15 (0.13, 0.16) mm day−1 and ET0 by about 0.26 (0.25, 0.28) mm day−1, thereby producing a net increase in terrestrial aridity of about 2.8% (2.1%, 3.6%) and an expansion of global total arid and semiarid areas.
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Guven, Aytac, Abdulhadi Pala, and Mohamad Sheikhvaisi. "Investigation of impact of climate change on small catchments using different climate models and statistical approaches." Water Supply 22, no. 3 (November 5, 2021): 3540–52. http://dx.doi.org/10.2166/ws.2021.383.
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Abstract The use of a statistical downscaling technique is needed to investigate the hydrological consequences of climate change on the local hydropower capacity. Global Circulation Models (GCMs) are crucial tools used in various simulations for potential climate change effects, including precipitation and temperature. Statistical downscaling methods comprise the improvement of relations between the large-scale climatic parameters and the local variables. This study presents the trend analysis of the observed variables compared to the statistically downscaled emission scenarios that are adopted from the Canadian Second Generation Earth Systems Model (CanESM2) in the basin of Göksu River which is located in Turkey. The key purpose of the research is to evaluate both the predicted monthly precipitation and the projections of GCMs within the three simulated scenarios of RCP2.6, RCP4.5, and RCP8.5 by Gene Expression Programming (GEP). In addition, the findings of statistical downscaling of monthly mean precipitation will be compared to the Linear Regression (LR) model. The R-value is 0.827 and 0.755 for precipitation of the GEP model for the periods of calibrating and validation. In comparison with the LR model for the validation and calibration periods (1971–2005), the results of the GEP model prove its applicability in projecting the data for monthly mean rainfall. Generally, in the simulated periods of 2021–2100, the mentioned scenarios forecast a decline in the monthly mean precipitation in the basin. Moreover, the scenario of RCP8.5 projected more suitably for the case study than expected under the scenarios RCP4.5 and RCP2.6. The mean statistically downscaled CanESM2 model was compared with the trend analysis of the areal mean precipitation over the case study area, and the trend was shown as decreasing. However, the RCP 8.5 scenario was the more quasi-asymptotic for trend.
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Hirose, M., and K. Okada. "A 0.01° Resolving TRMM PR Precipitation Climatology." Journal of Applied Meteorology and Climatology 57, no. 8 (August 2018): 1645–61. http://dx.doi.org/10.1175/jamc-d-17-0280.1.
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AbstractIn this study, rainfall data are prepared at a 0.01° scale using 16-yr spaceborne radar data over the area of 36.13°S–36.13°N as provided by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A spatial resolution that is finer than the field of view is obtained by assuming rainfall uniformity within an instantaneous footprint centered on the PR footprint geolocation. These ultra-high-resolution data reveal local rainfall concentrations over slope areas. A new estimate of the maximum rainfall at Cherrapunji, India, was observed on the valley side, approximately 5 km east of the gauge station, and is approximately 50% higher than the value indicated by the 0.1°-scale data. A case study of Yakushima Island, Japan, indicates that several percent of the sampling error arising from the spatial mismatch may be contained in conventional 0.05°-scale datasets generated without footprint areal information. The differences attributable to the enhancement in the resolution are significant in complex terrain such as the Himalayas. The differences in rainfall averaged for the 0.1° and 0.01° scales exceed 10 mm day−1 over specific slope areas. In the case of New Guinea, the mean rainfall on a mountain ridge can be 30 times smaller than that on an adjacent slope at a distance of 0.25°; this is not well represented by other high-resolution datasets based on gauges and infrared radiometers. The substantial nonuniformity of rainfall climatology highlights the need for a better understanding of kilometer-scale geographic constraints on rainfall and retrieval approaches.
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Giangrande, Scott E., Scott Collis, Adam K. Theisen, and Ali Tokay. "Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign." Journal of Applied Meteorology and Climatology 53, no. 9 (September 2014): 2130–47. http://dx.doi.org/10.1175/jamc-d-13-0321.1.
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AbstractThis study presents radar-based precipitation estimates collected during the 2-month U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM)–NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR and XSAPR, respectively) for rainfall estimation products to distances within 100 km of the Lamont, Oklahoma, ARM facility. The study utilizes a dense collection of collocated ARM, NASA Global Precipitation Measurement, and nearby surface Oklahoma Mesonet gauge records to evaluate radar-based hourly rainfall products and campaign-optimized methods over individual gauges and for areal rainfall characterizations. Rainfall products are also evaluated against the performance of a regional NWS Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band dual-polarization radar product. Results indicate that the CSAPR system may achieve similar point– and areal–gauge bias and root-mean-square (RMS) error performance to a WSR-88D reference for the variety of MC3E deep convective events sampled. The best campaign rainfall performance was achieved when using radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The XSAPRs demonstrate limited capabilities, having modest success in comparison with the WSR-88D reference for hourly rainfall accumulations that are under 10 mm. All rainfall estimation methods exhibit a reduction by a factor of 1.5–2.5 in RMS errors for areal accumulations over a 15-km2 NASA dense gauge network, with the smallest errors typically associated with dual-polarization radar methods.
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Woodson, Adams, and Dymond. "Precipitation Estimation Methods in Continuous, Distributed Urban Hydrologic Modeling." Water 11, no. 7 (June 28, 2019): 1340. http://dx.doi.org/10.3390/w11071340.
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Quantitative precipitation estimation (QPE) remains a key area of uncertainty in hydrological modeling and prediction, particularly in small, urban watersheds, which respond rapidly to precipitation and can experience significant spatial variability in rainfall fields. Few studies have compared QPE methods in small, urban watersheds, and studies that have examined this topic only compared model results on an event basis using a small number of storms. This study sought to compare the efficacy of multiple QPE methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and QPE forcings. The research distributed hydrologic model (RDHM) was used to model a basin in Roanoke, Virginia, USA, forced with QPEs from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a six-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by root mean squared error (RMSE) and peak flow relative error, despite systematic underprediction of the mean areal precipitation (MAP). Simulations forced with MFB-corrected radar data consistently and significantly overpredicted discharge, but had the highest accuracy in predicting the timing of peak flows.
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BUI, Thi Hieu, and Hiroshi ISHIDAIRA. "QUANTIFICATION AND MAPPING OF AREAL MEAN PRECIPITATION ERROR USING SATELLITE OBSERVATIONS FOR IMPROVEMENT OF RAIN GAUGE NETWORK IN VIETNAM." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 74, no. 4 (2018): I_67—I_72. http://dx.doi.org/10.2208/jscejhe.74.i_67.
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Eastman, Ryan, and Robert Wood. "Factors Controlling Low-Cloud Evolution over the Eastern Subtropical Oceans: A Lagrangian Perspective Using the A-Train Satellites." Journal of the Atmospheric Sciences 73, no. 1 (December 30, 2015): 331–51. http://dx.doi.org/10.1175/jas-d-15-0193.1.
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Abstract A Lagrangian technique is developed to sample satellite data to quantify and understand factors controlling temporal changes in low-cloud properties (cloud cover, areal-mean liquid water path, and droplet concentration). Over 62 000 low-cloud scenes over the eastern subtropical/tropical oceans are sampled using the A-Train satellites. Horizontal wind fields at 925 hPa from the ERA-Interim are used to compute 24-h, two-dimensional, forward, boundary layer trajectories with trajectory locations starting on the CloudSat/CALIPSO track. Cloud properties from MODIS and AMSR-E are sampled at the trajectory start and end points, allowing for direct measurement of the temporal cloud evolution. The importance of various controls (here, boundary layer depth, lower-tropospheric stability, and precipitation) on cloud evolution is evaluated by comparing cloud evolution for different initial values of these controls. Viewing angle biases are removed and cloud anomalies (diurnal and seasonal cycles removed) are used throughout to quantify cloud evolution relative to the climatological-mean evolution. Cloud property anomalies show temporal changes similar to those expected for a stochastic red noise process, with linear relationships between initial anomalies and their mean 24-h changes. This creates a potential bias when comparing the evolutions of sets of trajectories with different initial anomalies; three methods are introduced and evaluated to account for this. Results provide statistically robust observational support for theoretical/modeling studies by showing that low clouds in deep boundary layers and under weak inversions are prone to break up. Precipitation shows a more complex and less statistically significant relationship with cloud breakup. Cloud cover in shallow precipitating boundary layers is more persistent than in deep precipitating boundary layers. Liquid water path and cloud droplet concentration decrease more rapidly for precipitating clouds and in deep boundary layers.
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Roux, Christian, Anne Guillon, and Anne Comblez. "Space-time heterogeneities of rainfalls on runoff over urban catchments." Water Science and Technology 32, no. 1 (July 1, 1995): 209–15. http://dx.doi.org/10.2166/wst.1995.0047.
Full textAbstract:
Simulated outlet flow series are compared downstream of a 14 sq. km. urban watershed. They are generated either with design rainfall, with long-term point rainfall series, or with long-term space distributed rainfall series (measured with 3 raingauges or with radar). The use of the double-triangle shaped hyetograph as design rainfall provides the same T-year flow as the long term 3 raingauge series, if it is used with a areal reduction factor and uniformly applied to the watershed. For the same T-year mean areal precipitation depth during the time of concentration, the rainfall spatial and temporal heterogenities may induce differences up to +/− 30% on the peak flow, compared to the T-year peak flow.
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Worqlul, A. W., B. Maathuis, A. A. Adem, S. S. Demissie, S. Langan, and T. S. Steenhuis. "Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground-observed data for the Lake Tana basin in Ethiopia." Hydrology and Earth System Sciences 18, no. 12 (December 5, 2014): 4871–81. http://dx.doi.org/10.5194/hess-18-4871-2014.
Full textAbstract:
Abstract. Planning for drought relief and floods in developing countries is greatly hampered by the lack of a sufficiently dense network of weather stations measuring precipitation. In this paper, we test the utility of three satellite products to augment the ground-based precipitation measurement to provide improved spatial estimates of rainfall. The three products are the Tropical Rainfall Measuring Mission (TRMM) product (3B42), Multi-Sensor Precipitation Estimate–Geostationary (MPEG) and the Climate Forecast System Reanalysis (CFSR). The accuracy of the three products is tested in the Lake Tana basin in Ethiopia, where 38 weather stations were available in 2010 with a full record of daily precipitation amounts. Daily gridded satellite-based rainfall estimates were compared to (1) point-observed ground rainfall and (2) areal rainfall in the major river sub-basins of Lake Tana. The result shows that the MPEG and CFSR satellites provided the most accurate rainfall estimates. On average, for 38 stations, 78 and 86% of the observed rainfall variation is explained by MPEG and CFSR data, respectively, while TRMM explained only 17% of the variation. Similarly, the areal comparison indicated a better performance for both MPEG and CFSR data in capturing the pattern and amount of rainfall. MPEG and CFSR also have a lower root mean square error (RMSE) compared to the TRMM 3B42 satellite rainfall. The bias indicated that TRMM 3B42 was, on average, unbiased, whereas MPEG consistently underestimated the observed rainfall. CFSR often produced large overestimates.