scholarly journals A Nonparametric Wet/Dry Spell Model for Resampling Daily Precipitation

1996 ◽  
Vol 32 (9) ◽  
pp. 2803-2823 ◽  
Author(s):  
Upmanu Lall ◽  
Balaji Rajagopalan ◽  
David G. Tarboton
Keyword(s):  
Daily Precipitation ◽  
Dry Spell

scholarly journals Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation

2013 ◽  
Vol 17 (11) ◽  
pp. 4481-4502 ◽  
Author(s):  
S. Hwang ◽  
W. D. Graham
Keyword(s):  
Bias Correction ◽  
Daily Precipitation ◽  
Transition Probabilities ◽  
Small Scale ◽  
Analog Method ◽  
Dry Spell ◽  
Spatial Disaggregation ◽  
Dry Seasons ◽  
Stochastic Analog ◽  
Wet And Dry Seasons

Abstract. There are a number of statistical techniques that downscale coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias-Correction and Stochastic Analog method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these characteristics, (2) the BCSD and BCCA methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.

scholarly journals Identifying hydro-meteorological events from precipitation extremes indices and other sources over northern Namibia, Cuvelai Basin

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Frans C. Persendt ◽  
Christopher Gomez ◽  
Peyman Zawar-Reza
Keyword(s):  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Economic Losses ◽  
Precipitation Data ◽  
Rainfall Station ◽  
Extreme Precipitation Events ◽  
Daily Precipitation Data ◽  
Dry Spell ◽  
Flood Disasters ◽  
Precipitation Indices

Worldwide, more than 40% of all natural hazards and about half of all deaths are the result of flood disasters. In northern Namibia flood disasters have increased dramatically over the past half-century, along with associated economic losses and fatalities. There is a growing concern to identify these extreme precipitation events that result in many hydro-meteorological disasters. This study presents an up to date and broad analysis of the trends of hydrometeorological events using extreme daily precipitation indices, daily precipitation data from the Grootfontein rainfall station (1917–present), regionally averaged climatologies from the gauged gridded Climate Research Unit (CRU) product, archived disasters by global disaster databases, published disaster events in literature as well as events listed by Mendelsohn, Jarvis and Robertson (2013) for the data-sparse Cuvelai river basin (CRB). The listed events that have many missing data gaps were used to reference and validate results obtained from other sources in this study. A suite of ten climate change extreme precipitation indices derived from daily precipitation data (Grootfontein rainfall station), were calculated and analysed. The results in this study highlighted years that had major hydro-meteorological events during periods where no data are available. Furthermore, the results underlined decrease in both the annual precipitation as well as the annual total wet days of precipitation, whilst it found increases in the longest annual dry spell indicating more extreme dry seasons. These findings can help to improve flood risk management policies by providing timely information on historic hydro-meteorological hazard events that are essential for early warning and forecasting.

scholarly journals Estimating extreme dry spell risk in Ichkeul Lake Basin (Northern Tunisia): a comparative analysis of annual maxima series with a Gumbel distribution

2020 ◽  
Vol 383 ◽  
pp. 241-248
Author(s):  
Majid Mathlouthi ◽  
Fethi Lebdi
Keyword(s):  
Daily Precipitation ◽  
Gumbel Distribution ◽  
Lake Basin ◽  
Climatic Variable ◽  
Rainfall Events ◽  
Northern Tunisia ◽  
Dry Spell ◽  
Ichkeul Lake ◽  
Threshold Amount ◽  
Annual Maximum Series

Abstract. This paper analyses a 42 year time series of daily precipitation in Ichkeul Lake Basin (northern Tunisia) in order to predict extreme dry-spell risk. Dry events are considered as a sequence of dry days separated by rainfall events from each other. Thus the rainy season is defined as a series of rainfall and subsequent dry events. Rainfall events are defined as the uninterrupted sequence of rainy days, when at last on one day more than a threshold amount of rainfall has been observed. A comparison of observed and estimated maximum dry events (42 year return period) showed that Gumbel distribution fitted to annual maximum series gives better results than the exponential (E) distribution combined with partial duration series (PDS). Indeed, the classical Gumbel approach slightly underestimated the empirical duration of dry events. The AMS–G approach was successfully applied in the study of extreme hydro-climatic variable values. The results reported here could be applied in estimating climatic drought risks in other geographical areas.

scholarly journals Markov chain as a tool for forecasting daily precipitation in the vicinity of the city of Bydgoszcz, Poland

2018 ◽  
Vol 23 ◽  
pp. 00003 ◽  
Author(s):  
Waldemar Bojar ◽  
Leszek Knopik ◽  
Jacek Żarski ◽  
Renata Kuśmierek-Tomaszewska ◽  
Wojciech Żarski
Keyword(s):  
Markov Chain ◽  
Daily Precipitation ◽  
Vegetation Period ◽  
Information Criteria ◽  
Precipitation Pattern ◽  
Weather Factors ◽  
Measuring Point ◽  
Dry Spell ◽  
The University ◽  
The City

The crop yield depends on numerous weather factors, but mainly on the rainfall pattern and course of air temperature during vegetation period. Investigating the dependence of yields on rainfall, apart from its amount, there also should be taken into account dry spell periods. The two-state Markov chain was considered as a precipitation pattern in the investigation, since it is generally recognized as a simple and effective model of the precipitation occurrence. Based on the daily precipitation totals from the period 1971—2013, the Markov chain was designated. The data were derived from a measuring point of the University of Science and Technology in Bydgoszcz, Poland. As one of the objectives was to determine the order of the Markov chain examined describing the change of precipitation in subsequent days. Another aim was to investigate rainfall dependencies on a month of a year. An analysis of this data leads to the conclusion that the chain is second order. This is confirmed by the two criteria used: BIC (Bayesian Information Criteria) and AIC (Akaike Information Criteria). The research regarded the precipitation volume dependence on a month of the year.

scholarly journals A Markov Chain-Based Bias Correction Method for Simulating the Temporal Sequence of Daily Precipitation

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 109
Author(s):  
Han Liu ◽  
Jie Chen ◽  
Xun-Chang Zhang ◽  
Chong-Yu Xu ◽  
Yu Hui
Keyword(s):  
Markov Chain ◽  
Bias Correction ◽  
Daily Precipitation ◽  
Correction Method ◽  
Temporal Sequence ◽  
Cumulative Frequency ◽  
First Order ◽  
Precipitation Occurrence ◽  
Bias Correction Method ◽  
Dry Spell

Bias correction methods are routinely used to correct climate model outputs for hydrological and agricultural impact studies. Even though superior bias correction methods can correct the distribution of daily precipitation amounts, as well as the wet-day frequency, they usually fail to correct the temporal sequence or structure of precipitation occurrence. To solve this problem, we presented a hybrid bias correction method for simulating the temporal sequence of daily precipitation occurrence. We did this by combining a first-order two-state Markov chain with a quantile-mapping (QM) based bias correction method. Specifically, a QM-based method was used to correct the distributional attributes of daily precipitation amounts and the wet-day frequency simulated by climate models. Then, the sequence of precipitation occurrence was simulated using the first-order two-state Markov chain with its parameters adjusted based on linear relationships between QM-corrected mean monthly precipitation and the transition probabilities of precipitation occurrence. The proposed Markov chain-based bias correction (MCBC) method was compared with the QM-based method with respect to reproducing the temporal structure of precipitation occurrence over 10 meteorological stations across China. The results showed that the QM-based method was unable to correct the temporal sequence, with the cumulative frequency of wet- and dry-spell length being considerably underestimated for most stations. The MCBC method can could reproduce the temporal sequence of precipitation occurrence, with the generated cumulative frequency of wet- and dry-spell lengths fitting that of the observation well. The proposed method also performed reasonably well with respect to reproducing the mean, standard deviation, and the longest length of observed wet- and dry-spells. Overall, the MCBC method can simulate the temporal sequence of precipitation occurrence, along with correcting the distributional attributes of precipitation amounts. This method can be used with crop and hydrological models in climate change impact studies at the field and small watershed scales.

scholarly journals Implementation and validation of a Wilks-type multi-site daily precipitation generator over a typical Alpine river catchment

2015 ◽  
Vol 19 (5) ◽  
pp. 2163-2177 ◽  
Author(s):  
D. E. Keller ◽  
A. M. Fischer ◽  
C. Frei ◽  
M. A. Liniger ◽  
C. Appenzeller ◽  
...  
Keyword(s):  
Time Series ◽  
Daily Precipitation ◽  
Climate Impact ◽  
Added Value ◽  
Monthly Precipitation ◽  
Climate Change Scenarios ◽  
Climate Conditions ◽  
River Catchment ◽  
Dry Spell ◽  
Alpine River

Abstract. Many climate impact assessments require high-resolution precipitation time series that have a spatio-temporal correlation structure consistent with observations, for simulating either current or future climate conditions. In this respect, weather generators (WGs) designed and calibrated for multiple sites are an appealing statistical downscaling technique to stochastically simulate multiple realisations of possible future time series consistent with the local precipitation characteristics and their expected future changes. In this study, we present the implementation and validation of a multi-site daily precipitation generator re-built after the methodology described in Wilks (1998). The generator consists of several Richardson-type WGs run with spatially correlated random number streams. This study aims at investigating the capabilities, the added value and the limitations of the precipitation generator for a typical Alpine river catchment in the Swiss Alpine region under current climate. The calibrated multi-site WG is skilful at individual sites in representing the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as monthly precipitation sums. It reproduces realistically the multi-day statistics such as the frequencies of dry and wet spell lengths and precipitation sums over consecutive wet days. Substantial added value is demonstrated in simulating daily areal precipitation sums in comparison to multiple WGs that lack the spatial dependency in the stochastic process. Limitations are seen in reproducing daily and multi-day extreme precipitation sums, observed variability from year to year and in reproducing long dry spell lengths. Given the performance of the presented generator, we conclude that it is a useful tool to generate precipitation series consistent with the mean climatic aspects and likely helpful to be used as a downscaling technique for climate change scenarios.

scholarly journals Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation

2013 ◽  
Vol 10 (2) ◽  
pp. 2141-2181 ◽  
Author(s):  
S. Hwang ◽  
W. D. Graham
Keyword(s):  
Spatial Variability ◽  
Bias Correction ◽  
Temporal Variability ◽  
Daily Precipitation ◽  
Transition Probabilities ◽  
Small Scale ◽  
Analog Method ◽  
Dry Spell ◽  
Spatial Disaggregation ◽  
Stochastic Analog

Abstract. There are a number of statistical techniques that downscale coarse climate information from global circulation models (GCM). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data which can be an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (bias-correction and stochastic analog method, BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve the spatial autocorrelation structure of observed daily precipitation sequences. This approach was designed to reproduce observed spatial and temporal variability as well as mean climatology. We used the BCSA method to downscale 4 GCM precipitation predictions from 1961 to 1999 over the state of Florida and compared the skill of the method to the results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, bias-correction and constructed analog (BCCA) method, and a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC). Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean climatology of daily precipitation while the BCSD, SDBC and BCSA methods accurately reproduced it, (2) the BCSD and BCCA methods underestimated temporal variability because of the interpolation and regression schemes used for downscaling and thus, did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in precipitation resulting in under-prediction of spatial variance and over-prediction of spatial correlation, whereas the new stochastic technique (BCSA) accurately reproduces observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be accurately predicted. For low-relief, rainfall-dominated watersheds where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.

Adoption and Commercialization of Green Corn, Green Corn –Based Silage, Haylage and Ummb Production for Dairy Cattle in Cagayan Valley, Philippines

2019 ◽  
Vol 4 (4) ◽  
pp. 82-92
Author(s):  
Eva U. Cammayo ◽  
Nilo E. Padilla
Keyword(s):  
Dairy Cattle ◽  
Nutritive Value ◽  
Crop Residues ◽  
Smallholder Farmers ◽  
Corn Silage ◽  
Small Scale ◽  
Dairy Farmers ◽  
Feed Resources ◽  
Dry Spell ◽  
Dairy Animals

This research aimed to improve dairy production and increase the income of dairy farmers using locally available feed resources. Small-scale milk producers rely heavily on available feed resources in the locality which are either indigenous in the area or introduced species for feed and nutrition of their dairy cattle and buffalos. Their milk output depends mainly on seasonal fluctuations in the quality and quantity of natural forage. Crop residues such as corn stover and rice straw which are high in fiber but low in nutrients serve as a feed supplement and filler to the daily diets of dairy cattle and buffalos. Cagayan Valley is an ear of top corn and rice-producing region. The potential of crop residues as feed supplements or raw materials of dairy cattle/buffalo feed mix is great. But dairy farmers still face the scarcity problem of quality feed resources for dairy animals especially during the dry season. The supply of forage is very low during the dry spell. Inadequate feed mix and low nutritive value of feed mix result in low or no milk production. Producing green corn and ensiling it to produce green corn silage preserves and prolong the storage life of forages. In this way, a stable supply of feed mix for dairy animals is assured year-round. Type of Paper: Empirical. Keywords: adoption and commercialization, dairy industry, financial viability, green-corn silage production, indigenous grasses, smallholder farmers.

Annual Variability in Summer Evapotranspiration and Water Balance at a Subarctic Forest Site

1994 ◽  
Vol 25 (5) ◽  
pp. 331-344 ◽  
Author(s):  
Peter M. Lafleur
Keyword(s):  
Water Balance ◽  
Daily Precipitation ◽  
The Other ◽  
Forest Site ◽  
Net Radiation ◽  
Atmospheric Conditions ◽  
Surface Moisture ◽  
Equilibrium Evaporation ◽  
Subarctic Forest ◽  
Single Regression

Evapotranspiration (ET) and precipitation were measured during five summers (1989-1993 inclusive) at a subarctic forest site near Churchill, Manitoba, Canada. Mean daily ET varied from 2.14-3.18 mm d−1 during the five summers, while mean daily precipitation (P) ranged from 1.46-3.15 mm d−1. Yearly variability in summer ET was most influenced by availability of surface moisture, then by atmospheric conditions (i.e. temperature), and least of all by net radiation. In four of the five years total summer ET exceeded P resulting in significant soil water deficits and in the other year summer ET and P were similar in magnitude. The use of equilibrium evaporation (EE) as a predictor of ET was explored. Separate relationships between ET and EE were computed for all five years. Three statistically dissimilar groups of equations were found: 1989/1990, 1991/ 1992, and 1993. A single regression equation describing all years is presented.

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