Impact of number of realizations on the suitability of simulated weather data for hydrologic and environmental applications

Guo, Ten-Huei; Mehan, Sushant; Gitau, Margaret W.; Wang, Qi; Kuczek, Thomas; Flanagan, D. C.

doi:10.1007/s00477-017-1498-5

Cited by 18 publications

(12 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These 100 realizations per emission scenario were used in the WEAP model for computing water fluxes which were averaged afterwards. Although Guo et al [36] concluded that 25 realizations are sufficient and there is no significant difference between 25 and 100 realizations, we followed the recommendation of the LARS-WG manual.…”

Section: Climate Changementioning

confidence: 99%

Analysis of the Effects of Water Management Strategies and Climate Change on the Environmental and Agricultural Sustainability of Urmia Lake Basin, Iran

Ahmadaali

Barani

Qaderi

et al. 2018

Water

View full text Add to dashboard Cite

Abstract:In arid and semi-arid areas, unsustainable development of irrigated agriculture has reduced the water level of large lakes such as Aral, Urmia, Hamoon, and Bakhtegan. Urmia Lake, as a hyper saline and very shallow lake, located in the northwest of Iran, has water level reductions of about 40 cm each year over the past two decades. In this research, the indices of environmental and agricultural sustainability are evaluated using performance criteria influenced by climate change and water management strategies for the Zarrinehrud and Siminehrud River basins as the largest sub-basin of Urmia Lake basin. Modeling of hydrologic behavior of these basins is performed using WEAP21 model. The model is analyzed for three future emission scenarios (A2, A1B, and B1), for the period of 2015-2040 and five water management scenarios: (1) keeping the existing situation; (2) crop pattern change; (3) improving the conveyance and distribution efficiency; (4) combining the improvement of conveyance and distribution efficiency with improving the application efficiency using modern technology; and (5) the combination of crop pattern change with the improvement of total irrigation efficiency. The results show that the highest values of indices of environmental sustainability and agricultural sustainability are related to the scenario of combining the crop pattern change with improving the total irrigation efficiency under the B1 emission scenario (B1S4).

show abstract

Section: Climate Changementioning

confidence: 99%

Analysis of the Effects of Water Management Strategies and Climate Change on the Environmental and Agricultural Sustainability of Urmia Lake Basin, Iran

Ahmadaali

Barani

Qaderi

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…One method was a conventional one that included power transformation [57,58] and variance scaling of temperature [59]. The other bias correction method was novel and based on conclusions and discussions from previous studies [27,60] where Stochastic Weather Generators (SWGs) performed better at simulating greater depths of precipitation. We postulated that SWGs could be used to redistribute the precipitation and simulate greater daily precipitation depths.…”

Section: Discussionmentioning

confidence: 99%

Reliable Future Climatic Projections for Sustainable Hydro-Meteorological Assessments in the Western Lake Erie Basin

Mehan¹,

Gitau

Flanagan

2019

Water

Self Cite

View full text Add to dashboard Cite

Modeling efforts to simulate hydrologic processes under different climate conditions rely on accurate input data. Among other inaccuracies, errors in climate projections can lead to incorrect decisions. This study aimed to develop a reliable climate (precipitation and temperature) database for the Western Lake Erie Basin for the 21st century. Two statistically downscaled bias-corrected sources of climate projections (GDO: Global Downscaled Climate and Hydrology Projections and MACA: Multivariate Adaptive Constructed Analogs) were tested for their effectiveness in simulating historic climate (1966–2005) using ground-based station data from the National Climatic Data Center. MACA was found to have less bias than GDO and was better at simulating selected climate indices; thus, its climate projections were subsequently tested with different bias correction methods including the power transformation method, variance scaling of temperature, and Stochastic Weather Generators. The power transformation method outperformed the other methods and was used in bias corrections for 2006 to 2099. From the analysis, mean daily precipitation values were expected to remain more or less the same under both RCP (Representative Concentration Pathway) 4.5 and RCP 8.5 scenarios, ranging between 2.4 mm and 3.2 mm, while standard deviations were expected to increase, pointing to a rescaling of the distribution. Maximum one-day precipitation was expected to increase and could vary between 120 and 650 mm across the basin, while the number of wet days could potentially increase under the effects of RCP 4.5 and RCP 8.5. Both mean maximum and mean minimum daily air temperatures were expected to increase by up to 5.0 °C across the basin, while absolute maximum and minimum values could increase by more than 10 °C. The number of days in which precipitation could potentially fall as snow was expected to decrease, as was the annual number of days for optimal corn growth, although an earlier start to the growing season could be expected. Results from this study were very useful in creating a reliable climate database for the entire Western Lake Erie Basin (WLEB), which can be used for hydrologic, water resources, and other applications in the basin. The resulting climate database is published and accessible through the Purdue University Research Repository (Mehan et al., 2019), which is an open-access repository.

show abstract

“…One method was a conventional one that included power transformation (Leander and Buishand, 2007;Leander et al, 2008) and variance scaling of temperature (Chen et al, 2011a;Chen et al, 2011b). The other bias-correction method was novel and based on conclusions and discussions from previous studies (Guo et al, 2017;Mehan et al, 2017a), where Stochastic Weather Generators (SWGs) performed better at simulating greater depths of precipitation. We postulated that SWGs could 20 be used to redistribute the precipitation and simulate greater daily precipitation depths, which otherwise would be distributed to dry days or days with lower or no precipitation, adversely affecting the simulation outputs from crop growth and hydrologic models.…”

Section: Discussionmentioning

confidence: 99%

“…The weather generators were used in their default state without changing their parametrization for the historic period for analysis. Twenty-five different realizations (Guo et al, 2017) were generated for all nine GCMs at the three stations, to capture the variability and correct for bias or reduce error. Since the interest was to redistribute the precipitation to capture the high magnitude precipitation events, the extreme percentiles (75th and 90th) from the 25 different realizations were used for precipitation 30 depth comparisons and means were used for temperature comparisons.…”

Section: Discussionmentioning

confidence: 99%

Development of reliable future climatic projections to assess hydro-meteorological implications in the Western Lake Erie Basin

Mehan

Gitau

Flanagan

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Modeling efforts to simulate hydrologic processes under different climate conditions rely on accurate input data; inaccuracies in climate projections can lead to incorrect decisions. This study aimed to develop a reliable climate 10 (precipitation and temperature) database for the Western Lake Erie Basin (WLEB) for the 21 st century. Two statistically downscaled bias-corrected sources of climate projections (GDO and MACA) were tested for their effectiveness in simulating historic climate using ground-based station data from the National Climatic Data Center (NCDC). MACA was found to have less bias than GDO and was better in simulating certain climate indices, thus, its climate projections were subsequently tested with different bias correction methods including the power transformation method, variance scaling of 15 temperature, and Stochastic Weather Generators. The power transformation method outperformed the other methods and was used in bias corrections for 2006 to 2099. From the analysis, maximum one-day precipitation could vary between 120 and 650 mm across the basin, while the number of days with no precipitation could reduce by 5-15 % under the RCP 4.5 and RCP 8.5. The number of wet sequences could increase up to 9 times and the conditional probability of having a wet day followed by wet day could decrease by 25%. The maximum and minimum daily air temperatures could increase by 2-12 % 20 while the annual number of days for optimal corn growth could decrease by 0-10 days. The resulting climate database will be made accessible through an open-access platform.

show abstract

Impact of number of realizations on the suitability of simulated weather data for hydrologic and environmental applications

Cited by 18 publications

References 51 publications

Analysis of the Effects of Water Management Strategies and Climate Change on the Environmental and Agricultural Sustainability of Urmia Lake Basin, Iran

Analysis of the Effects of Water Management Strategies and Climate Change on the Environmental and Agricultural Sustainability of Urmia Lake Basin, Iran

Reliable Future Climatic Projections for Sustainable Hydro-Meteorological Assessments in the Western Lake Erie Basin

Development of reliable future climatic projections to assess hydro-meteorological implications in the Western Lake Erie Basin

Contact Info

Product

Resources

About