scholarly journals Assessment of the impacts of climate change on maize production in the Wami Ruvu basin of Tanzania

2016 ◽  
Vol 8 (1) ◽  
pp. 142-164 ◽  
Author(s):  
Philbert Luhunga ◽  
Ladslaus Chang'a ◽  
George Djolov
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Projections ◽  
Intergovernmental Panel ◽  
Lower Altitude ◽  
Rcp 8.5 ◽  
Maize Yields ◽  
The Impact ◽  
Impacts Of Climate Change

The IPCC (Intergovernmental Panel on Climate Change) assessment reports confirm that climate change will hit developing countries the hardest. Adaption is on the agenda of many countries around the world. However, before devising adaption strategies, it is crucial to assess and understand the impacts of climate change at regional and local scales. In this study, the impact of climate change on rain-fed maize (Zea mays) production in the Wami-Ruvu basin of Tanzania was evaluated using the Decision Support System for Agro-technological Transfer. The model was fed with daily minimum and maximum temperatures, rainfall and solar radiation for current climate conditions (1971–2000) as well as future climate projections (2010–2099) for two Representative Concentration Pathways: RCP 4.5 and RCP 8.5. These data were derived from three high-resolution regional climate models, used in the Coordinated Regional Climate Downscaling Experiment program. Results showed that due to climate change future maize yields over the Wami-Ruvu basin will slightly increase relative to the baseline during the current century under RCP 4.5 and RCP 8.5. However, maize yields will decline in the mid and end centuries. The spatial distribution showed that high decline in maize yields are projected over lower altitude regions due to projected increase in temperatures in those areas.

scholarly journals Evaluation of the impacts of climate variability on rainfed maize production over the Wami-Ruvu basin of Tanzania

2017 ◽  
Vol 9 (1) ◽  
pp. 207-222 ◽  
Author(s):  
Philbert Luhunga
Keyword(s):  
Climate Variability ◽  
Climate Models ◽  
Pearson Correlation ◽  
Regional Climate ◽  
Maximum Temperature ◽  
Climate Projections ◽  
Historical Climate ◽  
Climate Simulations ◽  
Maize Yields ◽  
The Impact

AbstractIn this study, the impact of inter-seasonal climate variability on rainfed maize (Zea mays) production over the Wami-Ruvu basin of Tanzania is evaluated. Daily high-resolution climate simulations from the Coordinated Regional Climate Downscaling Experiment_Regional Climate Models (CORDEX_RCMs) are used to drive the Decision Support System for Agro-technological Transfer (DSSAT) to simulate maize yields. Climate simulations for the base period of 35 years (1971–2005) are used to drive DSSAT to simulate maize yields during the historical climate. On the other hand, climate projections for the period 2010–2039 (current), 2040–2069 (mid), and 2070–2099 centuries for two Representative Concentration Pathway (RCP45 and 85) emission scenarios are used to drive DSSAT to simulate maize yields in respective centuries. Statistical approaches based on Pearson correlation coefficient and the coefficients of determination are used in the analysis. Results show that rainfall, maximum temperature, and solar radiation are the most important climate variables that determine variation in rainfed maize yields over the Wami-Ruvu basin of Tanzania. They explain the variability in maize yields in historical climate condition (1971–2005), present century under RCP 4.5, and mid and end centuries under both RCP 4.5 and RCP 8.5.

scholarly journals Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach

2013 ◽  
Vol 26 (10) ◽  
pp. 3394-3414 ◽  
Author(s):  
C. Adam Schlosser ◽  
Xiang Gao ◽  
Kenneth Strzepek ◽  
Andrei Sokolov ◽  
Chris E. Forest ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Projections ◽  
Massachusetts Institute Of Technology ◽  
Adaptation To Climate Change ◽  
Intergovernmental Panel ◽  
Near Surface ◽  
Institute Of Technology

Abstract The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

Is Institutional Democracy a Good Proxy for Model Independence?

2016 ◽  
Vol 29 (23) ◽  
pp. 8301-8316 ◽  
Author(s):  
Martin Leduc ◽  
René Laprise ◽  
Ramón de Elía ◽  
Leo Šeparović
Keyword(s):  
Climate Change ◽  
Model Comparison ◽  
Climate Models ◽  
Regional Climate ◽  
Burden Of Proof ◽  
Climate Projections ◽  
Climate Change Projections ◽  
Cross Model ◽  
Structural Differences ◽  
The Impact

Abstract Climate models developed within a given research group or institution are prone to share structural similarities, which may induce resembling features in their simulations of the earth’s climate. This assertion, known as the “same-center hypothesis,” is investigated here using a subsample of CMIP3 climate projections constructed by retaining only the models originating from institutions that provided more than one model (or model version). The contributions of individual modeling centers to this ensemble are first presented in terms of climate change projections. A metric for climate change disagreement is then defined to analyze the impact of typical structural differences (such as resolution, parameterizations, or even entire atmosphere and ocean components) on regional climate projections. This metric is compared to a present climate performance metric (correlation of error patterns) within a cross-model comparison framework in terms of their abilities to identify the same-center models. Overall, structural differences between the pairs of same-center models have a stronger impact on climate change projections than on how models reproduce the observed climate. The same-center criterion is used to detect agreements that might be attributable to model similarities and thus that should not be interpreted as implying greater confidence in a given result. It is proposed that such noninformative agreements should be discarded from the ensemble, unless evidence shows that these models can be assumed to be independent. Since this burden of proof is not generally met by the centers participating in a multimodel ensemble, the authors propose an ensemble-weighting scheme based on the assumption of institutional democracy to prevent overconfidence in climate change projections.

scholarly journals Assessment of climate change impact on hydrology of a transboundary river of Bhutan and India

2021 ◽  
Author(s):  
Phub Zam ◽  
Sangam Shrestha ◽  
Aakanchya Budhathoki
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Assessment Tool ◽  
Regional Climate ◽  
Flow Simulation ◽  
Water Security ◽  
Regional Climate Models ◽  
Transboundary River ◽  
Rcp 8.5 ◽  
The Impact

Abstract Assessing the impacts of climate change on a transboundary river plays an important role in sustaining water security within as well as beyond the national boundaries. At times, the unilateral decision taken by one country can increase the risk of negative effect on the riparian countries and if the impact is felt strongly by the other country, it can lead to international tension between them. This study examines the impact of climate change on hydrology between a shared river which is Wangchu river in Bhutan and Raidak river in India. The river is mainly used to produce hydropower in the two largest hydropower plants on which the majority of Bhutan's economic development depends and is mainly used for agriculture in India. The Soil and Water Assessment Tool (SWAT) was used for future flow simulation. Future climate was projected for near future (NF) from 2025–2050 and far future (FF) from 2074–2099 using an ensemble of three regional climate models (ACCESS, CNRM-CM5 and MPI-ESM-LR) for two RCPs (Representative Concentration Pathways), RCP 4.5 and RCP 8.5 scenario. The ensemble results indicated that, in future, the study area would become warmer with temperature increase of 1.5 °C under RCP 4.5 and 3.6 °C under RCP 8.5. However, as per RCP 4.5 and RCP 8.5, rainfall over the study area is projected to decrease by 1.90% and 1.38% respectively. As a consequence of the projected decrease in rainfall, the flow in river is projected to decrease by 5.77% under RCP 4.5 and 4.73% under RCP 8.5. Overall, the results indicated that the degree of hydrological change is expected to be higher, particularly for low flows in both Wangchu and Raidak River. Since transboundary water is a shared for economic growth, climate change adaptation and opportunities should also be considered by both the nations for better water management.

scholarly journals Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241147
Author(s):  
Sridhar Gummadi ◽  
M. D. M. Kadiyala ◽  
K. P. C. Rao ◽  
Ioannis Athanasiadis ◽  
Richard Mulwa ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climatic Conditions ◽  
Adaptation Strategies ◽  
Model Intercomparison ◽  
Climate Variability And Change ◽  
Cmip5 Climate Models ◽  
Rcp 8.5 ◽  
Maize Yields ◽  
Impacts Of Climate Change

In this study, we assessed the possible impacts of climate variability and change on growth and performance of maize using multi-climate, multi-crop model approaches built on Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols in five different agro-ecological zones (AEZs) of Embu County in Kenya and under different management systems. Adaptation strategies were developed that are locally relevant by identifying a set of technologies that help to offset potential impacts of climate change on maize yields. Impacts and adaptation options were evaluated using projections by 20 Coupled Model Intercomparison Project—Phase 5 (CMIP5) climate models under two representative concentration pathways (RCPs) 4.5 and 8.5. Two widely used crop simulation models, Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate the potential impacts of climate change on maize. Results showed that 20 CMIP5 models are consistent in their projections of increased surface temperatures with different magnitude. Projections by HadGEM2-CC, HadGEM2-ES, and MIROC-ESM tend to be higher than the rest of 17 CMIP5 climate models under both emission scenarios. The projected increase in minimum temperature (Tmin) which ranged between 2.7 and 5.8°C is higher than the increase in maximum temperature (Tmax) that varied between 2.2 and 4.8°C by end century under RCP 8.5. Future projections in rainfall are less certain with high variability projections by GFDL-ESM2G, MIROC5, and NorESM1-M suggest 8 to 25% decline in rainfall, while CanESM2, IPSL-CM5A-MR and BNU-ESM suggested more than 85% increase in rainfall under RCP 8.5 by end of 21st century. Impacts of current and future climatic conditions on maize yields varied depending on the AEZs, soil type, crop management and climate change scenario. Impacts are largely negative in the low potential AEZs such as Lower Midlands (LM4 and LM5) compared with the high potential AEZs Upper Midlands (UM2 and UM3). However, impacts of climate change are largely positive across all AEZs and management conditions when CO2 fertilization is included. Using the differential impacts of climate change, a strategy to adapt maize cultivation to climate change in all the five AEZs was identified by consolidating those practices that contributed to increased yields under climate change. We consider this approach as more appropriate to identify operational adaptation strategies using readily available technologies that contribute positively under both current and future climatic conditions. This approach when adopted in strategic manner will also contribute to further strengthen the development of adaptation strategies at national and local levels. The methods and tools validated and applied in this assessment allowed estimating possible impacts of climate change and adaptation strategies which can provide valuable insights and guidance for adaptation planning.

scholarly journals Assessment of the Impacts of Climate Change on Hydrological Characteristics of the Mbarali River Sub Catchment Using High Resolution Climate Simulations from CORDEX Regional Climate Models

2018 ◽  
Vol 10 (5) ◽  
pp. 61
Author(s):  
Edmund Mutayoba ◽  
Japhet J. Kashaigili ◽  
Frederick C. Kahimba ◽  
Winfred Mbungu ◽  
Nyemo A. Chilagane
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Climate Models ◽  
Regional Climate ◽  
Water Balance Components ◽  
Mean Values ◽  
Stream Flows ◽  
The Future ◽  
Rcp 8.5 ◽  
Impacts Of Climate Change

This study assesses the impacts of climate change on water resources over Mbarali River sub-catchment using high resolution climate simulations from the Coordinated Regional Climate Downscaling Experiment Regional Climate Models (CORDEX_RCMs). Daily rainfall, minimum and maximum temperatures for historical climate (1971-2000) and for the future climate projection (2011-2100) under two Representative Concentration Pathways RCP 8.5 and RCP 4.5 were used as input into the Soil and Water Assessment Tool (SWAT) hydrological model to simulate stream flows and water balance components for the Mbarali River sub-catchment. The impacts of climate change on hydrological conditions over Mbarali river catchment were assessed by comparing the mean values of stream flows and water balance components during the present (2011-2040), mid (2041-2070) and end (2071-2100) centuries with their respective mean values in the baseline (1971-2000) climate condition. The results of the study indicate that, in the future, under both RCP 4.5 and RCP 8.5 emission scenarios, the four main components that determine change in catchment water balance (rainfall, ground water recharge, evaporation and surface runoff) over Mbarali river catchment are projected to increase. While the stream flows are projected to decline in the future by 13.33% under RCP 4.5 and 13.67% under RCP 8.5 emission scenarios, it is important to note that simulated surface runoff under RCP8.5 emission scenario is higher than that which is obtained under the RCP4.5 emission scenario.

Realised added value in dynamical downscaling of Australian climate change

2021 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Jason P. Evans ◽  
Alejandro Di Luca ◽  
Michael R. Grose ◽  
Vanessa Round ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Future Climate ◽  
Added Value ◽  
Climate Projections ◽  
Eastern Australia ◽  
Australian Alps ◽  
Future Climate Projections

<p>Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.</p>

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Extremes ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

scholarly journals Impact of climate change on hydrological conditions in a tropical West African catchment using an ensemble of climate simulations

2017 ◽  
Vol 21 (4) ◽  
pp. 2143-2161 ◽  
Author(s):  
Yacouba Yira ◽  
Bernd Diekkrüger ◽  
Gero Steup ◽  
Aymar Yaovi Bossa
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
West African ◽  
Climate Projections ◽  
Impact Of Climate Change ◽  
Budget Analysis ◽  
High Uncertainty

Abstract. This study evaluates climate change impacts on water resources using an ensemble of six regional climate models (RCMs)–global climate models (GCMs) in the Dano catchment (Burkina Faso). The applied climate datasets were performed in the framework of the COordinated Regional climate Downscaling Experiment (CORDEX-Africa) project.After evaluation of the historical runs of the climate models' ensemble, a statistical bias correction (empirical quantile mapping) was applied to daily precipitation. Temperature and bias corrected precipitation data from the ensemble of RCMs–GCMs was then used as input for the Water flow and balance Simulation Model (WaSiM) to simulate water balance components.The mean hydrological and climate variables for two periods (1971–2000 and 2021–2050) were compared to assess the potential impact of climate change on water resources up to the middle of the 21st century under two greenhouse gas concentration scenarios, the Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results indicate (i) a clear signal of temperature increase of about 0.1 to 2.6 °C for all members of the RCM–GCM ensemble; (ii) high uncertainty about how the catchment precipitation will evolve over the period 2021–2050; (iii) the applied bias correction method only affected the magnitude of the climate change signal; (iv) individual climate models results lead to opposite discharge change signals; and (v) the results for the RCM–GCM ensemble are too uncertain to give any clear direction for future hydrological development. Therefore, potential increase and decrease in future discharge have to be considered in climate change adaptation strategies in the catchment. The results further underline on the one hand the need for a larger ensemble of projections to properly estimate the impacts of climate change on water resources in the catchment and on the other hand the high uncertainty associated with climate projections for the West African region. A water-energy budget analysis provides further insight into the behavior of the catchment.

Evaluating the effect of climate change on snow load on structures

2019 ◽  
Author(s):  
Pietro Croce ◽  
Paolo Formichi ◽  
Filippo Landi ◽  
Francesca Marsili
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Extremes ◽  
Regional Climate Models ◽  
Extreme Weather Events ◽  
Uncertainty Range ◽  
Greenhouse Gasses ◽  
Weather Events ◽  
The Impact

<p>As consequence of global warming extreme weather events might become more frequent and severe across the globe. The evaluation of the impact of climate change on extremes is then a crucial issue for the resilience of infrastructures and buildings and is a key challenge for adaptation planning. In this paper, a suitable procedure for the estimation of future trends of climatic actions is presented starting from the output of regional climate models and taking into account the uncertainty in the model itself. In particular, the influence of climate change on ground snow loads is discussed in detail and the typical uncertainty range is determined applying an innovative algorithm for weather generation. Considering different greenhouse gasses emission scenarios, some results are presented for the Italian Mediterranean region proving the ability of the method to define factors of change for climate extremes also allowing a sound estimate of the uncertainty range associated with different models.</p>

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