scholarly journals Hydrological Modelling of the Mono River Basin at Athiémé

2021 ◽  
Vol 384 ◽  
pp. 57-62
Author(s):  
Eliézer Iboukoun Biao ◽  
Ezéchiel Obada ◽  
Eric Adéchina Alamou ◽  
Josué Esdras Zandagba ◽  
Amédée Chabi ◽  
...  
Keyword(s):  
River Basin ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Stochastic Approach ◽  
Projection Data ◽  
Rainfall Runoff ◽  
Future Projection ◽  
The Individual ◽  
Modelling Approach

Abstract. The objective of this study is to model the Mono River basin at Athiémé using stochastic approach for a better knowledge of the hydrological functioning of the basin. Data used in this study consist of observed precipitation and temperature data over the period 1961–2012 and future projection data from two regional climate models (HIRHAM5 and REMO) over the period 2016–2100. Simulation of the river discharge was made using ModHyPMA, GR4J, HBV, AWBM models and uncertainties analysis were performed by a stochastic approach. Results showed that the different rainfall-runoff models used reproduce well the observed hydrographs. However, the multi-modelling approach has improved the performance of the individual models. The Hermite orthogonal polynomials of order 4 are well suited for the prediction of flood flows in this basin. This stochastic modeling approach allowed us to deduce that extreme events would therefore increase in the middle of the century under RCP8.5 scenario and towards the end of the century under RCP4.5 scenario.

scholarly journals Solar Irradiance and Temperature Variability and Projected Trends Analysis in Burundi

Climate ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 83 ◽  
Author(s):  
Agnidé Emmanuel Lawin ◽  
Marc Niyongendako ◽  
Célestin Manirakiza
Keyword(s):  
Solar Irradiance ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Historical Period ◽  
Projection Data ◽  
Data Set ◽  
Variability Analysis ◽  
The Future

This paper assessed the variability and projected trends of solar irradiance and temperature in the East of Burundi. Observed temperature from meteorological stations and the MERRA-2 data set provided by NASA/Goddard Space Flight Center are used over the historical period 1976–2005. In addition, solar irradiance data provided by SoDa database were considered. Furthermore, projection data from eight Regional Climate Models were used over the periods 2026–2045 and 2066–2085. The variability analysis was performed using a standardized index. Projected trends and changes in the future climate were respectively detected through Mann-Kendall and t-tests. The findings over the historical period revealed increase temperature and decrease in solar irradiance over the last decades of the 20th century. At a monthly scale, the variability analysis showed that excesses in solar irradiance coincide with the dry season, which led to the conclusion that it may be a period of high production for solar energy. In the future climate, upward trends in temperature are expected over the two future periods, while no significant trends are forecasted in solar irradiance over the entire studied region. However, slight decreases and significant changes in solar irradiance have been detected over all regions.

Integrating Model Evaluation and Observations into a Production-Release Pipeline

2020 ◽  
Author(s):  
Philipp S. Sommer ◽  
Ronny Petrik ◽  
Beate Geyer ◽  
Ulrike Kleeberg ◽  
Dietmar Sauer ◽  
...  
Keyword(s):  
Evaluation System ◽  
Large Scale ◽  
Climate Models ◽  
Model Simulation ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Active Communication ◽  
The Individual ◽  
Scientific Questions

<p>The complexity of Earth System and Regional Climate Models represents a considerable challenge for developers. Tuning but also improving one aspect of a model can unexpectedly decrease the performance of others and introduces hidden errors. Reasons are in particular the multitude of output parameters and the shortage of reliable and complete observational datasets. One possibility to overcome these issues is a rigorous and continuous scientific evaluation of the model. This requires standardized model output and, most notably, standardized observational datasets. Additionally, in order to reduce the extra burden for the single scientist, this evaluation has to be as close as possible to the standard workflow of the researcher, and it needs to be flexible enough to adapt it to new scientific questions.</p><p>We present the Free Evaluation System Framework (Freva) implementation within the Helmholtz Coastal Data Center (HCDC) at the Institute of Coastal Research in the Helmholtz-Zentrum Geesthacht (HZG). Various plugins into the Freva software, namely the HZG-EvaSuite, use observational data to perform a standardized evaluation of the model simulation. We present a comprehensive data management infrastructure that copes with the heterogeneity of observations and simulations. This web framework comprises a FAIR and standardized database of both, large-scale and in-situ observations exported to a format suitable for data-model intercomparisons (particularly netCDF following the CF-conventions). Our pipeline links the raw data of the individual model simulations (i.e. the production of the results) to the finally published results (i.e. the released data). </p><p>Another benefit of the Freva-based evaluation is the enhanced exchange between the different compartments of the institute, particularly between the model developers and the data collectors, as Freva contains built-in functionalities to share and discuss results with colleagues. We will furthermore use the tool to strengthen the active communication with the data and software managers of the institute to generate or adapt the evaluation plugins.</p>

scholarly journals Estimation of groundwater recharge using multiple climate models in Bayesian frameworks

2021 ◽  
Author(s):  
Kevin O. Achieng ◽  
Jianting Zhu
Keyword(s):  
Groundwater Recharge ◽  
River Basin ◽  
Empirical Bayes ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Bayesian Framework ◽  
Vital Role ◽  
Bias Error ◽  
Bayesian Priors

Abstract Groundwater recharge plays a vital role in replenishing aquifers, sustaining demand, and reducing adverse effects (e.g. land subsidence). In order to manage climate change-induced effects on groundwater dynamics, climate models are increasingly being used to predict current and future recharges. Even though there has been a number of hydrological studies that have averaged climate models’ predictions in a Bayesian framework, few studies have been related to the groundwater recharge. In this study, groundwater recharge estimates from 10 regional climate models (RCMs) are averaged in 12 different Bayesian frameworks with variations of priors. A recession-curve-displacement method was used to compute recharge from measured streamflow data. Two basins of different sizes located in the same water resource region in the USA, the Cedar River Basin and the Rainy River Basin, are selected to illustrate the approach and conduct quantitative analysis. It has been shown that groundwater recharge prediction is affected by the Bayesian priors. The non-Empirical Bayes g-Local-based Bayesian priors result in posterior inclusion probability values that are consistent with the performance of the climate models outside the Bayesian framework. With the proper choice of priors, the Bayesian frameworks can produce good results of groundwater recharge with R2, percent bias error, and Willmott's index of agreement of >0.97, <2%, and >0.97, respectively, in the two basins. The Bayesian framework with an appropriate prior provides opportunity to estimate recharge from multiple climate models.

scholarly journals Quantification of climate change impact on dam failure risk under hydrological scenarios: a case study from a Spanish dam

2019 ◽  
Vol 19 (10) ◽  
pp. 2117-2139 ◽  
Author(s):  
Javier Fluixá-Sanmartín ◽  
Adrián Morales-Torres ◽  
Ignacio Escuder-Bueno ◽  
Javier Paredes-Arquiola
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Safety Management ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Dam Failure ◽  
Dam Safety ◽  
Risk Management Framework ◽  
Failure Risk ◽  
The Individual

Abstract. Dam safety is increasingly subjected to the influence of climate change. Its impacts must be assessed through the integration of the various effects acting on each aspect, considering their interdependencies, rather than just a simple accumulation of separate impacts. This serves as a dam safety management supporting tool to assess the vulnerability of the dam to climate change and to define adaptation strategies under an evolutive dam failure risk management framework. This article presents a comprehensive quantitative assessment of the impacts of climate change on the safety of a Spanish dam under hydrological scenarios, integrating the various projected effects acting on each component of the risk, from the input hydrology to the consequences of the outflow hydrograph. In particular, the results of 21 regional climate models encompassing three Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) have been used to calculate the risk evolution of the dam until the end of the 21st century. Results show a progressive deterioration of the dam failure risk, for most of the cases contemplated, especially for the RCP2.6 and RCP4.5 scenarios. Moreover, the individual analysis of each risk component shows that the alteration of the expected inflows has the greater influence on the final risk. The approach followed in this paper can serve as a useful guidebook for dam owners and dam safety practitioners in the analysis of other study cases.

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