Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)
Published: Dec. 23, 2024
Language: Английский
Climate, Journal Year: 2025, Volume and Issue: 13(2), P. 31 - 31
Published: Feb. 2, 2025
The pastoral areas of Ethiopia are facing a recurrent drought crisis that significantly affects the availability water resources for communities dependent on livestock. Despite urgent need effective early warning systems, Ethiopia’s have limited capacities to monitor variations in intensity–duration–frequency droughts. This study intends drive (IDF) curves account climate-model uncertainty and spatial variability, with goal enhancing management Borana, Ethiopia. To achieve this, employed quantile delta mapping bias-correct outputs from five climate models. A novel multi-model ensemble approach, known as spatiotemporal reliability averaging, was utilized combine outputs, exploiting strengths each model while discounting their weaknesses. Standardized Precipitation Evaporation Index (SPEI) used quantify meteorological (3-month SPEI), agricultural (6-month hydrological (12-month SPEI) Overall, analysis historical (1990–2014) projected (2025–2049, 2050–2074, 2075–2099) periods revealed change exacerbates conditions across all three changes being more pronounced than mean precipitation. prevailing rise droughts’ IDF features is linked an anticipated decline precipitation increase temperature. From derived curves, projections 2025–2049 2050–2074 indicate significant occurrences, 2075–2099 demonstrate greater vulnerability systems. While frequency droughts decrease between 2075 2099 duration increases, 2025 2049 2050 2074 expected experience intense Generally, findings underscore critical timely interventions mitigate vulnerabilities associated drought, particularly like Borana depend heavily availability.
Language: Английский
Citations
2
Heliyon, Journal Year: 2024, Volume and Issue: 10(10), P. e31332 - e31332
Published: May 1, 2024
Crop water requirement and irrigation scheduling in Lower Kulfo Catchment of southern Ethiopia have not assessed under climate change scenarios, the allocation crop land also optimal that signifcantly challenges to productivity.Therefore, this study was conducted evaluate effects on future requirements, scheduling, allocate cropland optimally. Bias projected precipitation temperature were corrected by utilizing Climate Model data with hydrologic modeling tool (CMhyd). Alongside, requirements using Water Assessment Tool. After estimating requirement, allocated optimally General Algebraic Modeling System programming non-negativity constraints (scenario 1), based farmers adaptation 2). Average reference evapotranspiration from 2030 2050 2060 2080 increased 11.9%, 16.2%, respectively compared period (2010-2022). The total seasonal 4,529mm, 4,866.7mm, 5,272.2mm 2010 2022, 2050, respectively. meean interval scenarios 8 days, 7 5 This decreased 14% (2030 2050), 34% (2060 2080) period. In 2026 required at inlet main canal 6.8%, 18%, area for tomato (60.4%), maize (20.8%), watermelon (18.8%) scenario 1 net benefit 1.47*108 Ethiopian Birr. areas 2 (48%) maize, (31.6%) tomato, (20.4%) 1.34*108 Birr it reduced 19.1% 1. Fruit crops alone may suffice local food needs address this, small should grow watermelon. research aids policymakers encouraging climate-resilient agriculture improving small-scale farmers' awareness through conducting workshops training. Agriculture plays an important role driving economic growth within economy covers 40% gross domestic product [1Tesema T. Gebissa B. Multiple Agricultural Production Efficiency Horro District Guduru Wollega Zone, Western Ethiopia, Using Hierarchical-Based Cluster Data Envelopment Analysis.Sci. World J. 2022; 2022https://doi.org/10.1155/2022/4436262Crossref Scopus (1) Google Scholar]. Irrigation dry or semi-dry environments is used sustain agricultural productivity when available rainfall insufficient [2Zhang et al.Challenges opportunities precision decision-support systems center pivots.Environ. Res. Lett. 2021; 16https://doi.org/10.1088/1748-9326/abe436Crossref (39) Effective management across conveyance system demand-based are basic activities improve schemes [3Létourneau G. Caron scale application method yield field-grown strawberries.Agronomy. 2019; 9https://doi.org/10.3390/agronomy9060286Crossref (14) amount equal what lost cropped field [4Adamtie Temesgen F. selie Abeba H. Mitku Demeke season irrigated Pawe district, lowland hot humid Ethiopia.Int. Sch. Life Sci. 1: 009-021https://doi.org/10.56781/ijsrls.2022.1.1.0022Crossref Soil type, change, topographical location, type highly affect quantity [5Mirzaei A. Azarm Naghavi S. Optimization cropping pattern fluctuations surface multistage stochastic programming.Water Supply. 22: 5716-5728https://doi.org/10.2166/ws.2022.224Crossref (9) denotes significant enduring shifts Earth's climate, driven human due emitting greenhouse gases like CO2, CH4, N2O, altering temperature, precipitation, wind patterns [6Boatemaa Incoom M. Kwadwo E. Odai S.N. Impacts Savannah regions Ghana.J. Clim. Chang. 13: 3338-3356https://doi.org/10.2166/wcc.2022.129Crossref estimation change's impact suggest possible mitigation measures sustainable resources development [7Soares D. Paço T.A. Rolim Assessing Change Requirements Mediterranean Conditions—A Review Methodological Approaches Focusing Maize Crop.Agronomy. 2023; 13https://doi.org/10.3390/agronomy13010117Crossref (10) Coupled Intercomparison Project (CMIP) model organized Research Program (WCRP) produces ensembles Earth (ESM) conditions different CO2 emission [8Tian X. Dong Jin He Yin Chen impacts regional use semi-arid environments.Agric. Manag. 281108239https://doi.org/10.1016/j.agwat.2023.108239Crossref (8) comparison CMIP5, CMIP6 most recent phase high spatial temporal resolutions offer more intricate representation processes [9Oyelakin Analysing Urban Flooding Risk CMIP5 Projections.Water. 2024; 16Crossref (0) To forecasting (SSP585) demonstrates better performance contrasted [10Feyissa Demissie Saathoff Evaluation Circulation Models Performance Future over Omo River Basin , Ethiopia.Sustainability. 15Crossref (3) Tool (CropWat model) estimate historical [11Sen Determining Changing Demands Cukurova Plain Scenarios CROPWAT Model.Water. (CMhyd) utilize bias correction between [12Yeboah K.A. Akpoti K. Kabo-bah A.T. Ofosu E.A. Siabi E.K. projections Volta CORDEX- Africa simulations statistical bias-correction CORDEX-Africa bias-correction.Environ. Challenges. 6100439https://doi.org/10.1016/j.envc.2021.100439Crossref (21) Scholar] as input CropWat model. Proper increase yields manage amount, frequency [13Betele Gebul M.A. Andries Plessis allocation.Koftu Ethiopia. 18: 1331-1342https://doi.org/10.2166/wpt.2023.080Crossref gives a direction adapted resilience agriculture. Optimizing utilization satisfy household security providing economical specific [14Pal J.S. al.Regional developing world: ICTP RegCM3 RegCNET.Bull. Am. Meteorol. Soc. 2007; 88: 1395-1409https://doi.org/10.1175/BAMS-88-9-1395Crossref (835) optimization contains objective function, decision variables [15Zenis F.M. Supian Lesmana farms Sumedang regency linear models.IOP Conf. Ser. Mater. Eng. 2018; 332https://doi.org/10.1088/1757-899X/332/1/012021Crossref Scholar], depending nature problems [16Sofi N.A. Ahmed Ahmad Bhat B.A. Decision Making Agriculture: A Linear Programming Approach.Int. Mod. Math. homepage www.ModernScientificPress.com. 2015; 13 ([Online]. Available:): 160-169www.ModernScientificPress.com/Journals/ijmms.aspxGoogle can consider availability [17Nimah M.N. Bsaibes Alkahl Darwish M.R. Bashour I. maximize productivity.River Ii. 2003; 7: 187-198Google aims per unit [18Hao L. Su Singh V.P. Cropping considering uncertainty saving potential.Int. Agric. Biol. 11: 178-186https://doi.org/10.25165/j.ijabe.20181101.3658Crossref (20) (GAMS) code best strategy considers water, land, crop, [19Jayne T.S. Chamberlin Headey D.D. Land pressures, evolution farming systems, strategies Africa: synthesis.Food Policy. 2014; 48: 1-17https://doi.org/10.1016/j.foodpol.2014.05.014Crossref (336) During season, there conflict among users scarcity demand showed increasing trend observed during problem investigation be change. Absence estimated lack proper practices significantly disturbed distribution. Addition variability shifting wet major lower catchment hinder rainfed/irrigated area. Stream flow will 2.99% 2050s 5.28% 2080s [20N. Demmissie, Demissie, Tufa, "Predicting Impact Flow," vol. 6, no. 3, pp. 78–87, 2018, doi: 10.11648/j.hyd.20180603.11.Google But no any scheduling. Both Arba Minch low [21Reta B.G. Hatiye S.D. Finsa M.M. Management Indicators Mitigation Measure Irrigation.Adv. knowledge about user-friendly tools identifying Traditional adopted understanding regarding affordable GAMS code. Poor multiple undermines effectiveness scheme, leading yields, operational costs, market value fluctuations, environmental degradation [22Yubing Fan S.C.P. R. M.Multi-Crop Decisions Economic Use : Effects Climatic Determinants.Water. 10https://doi.org/10.3390/w10111637Crossref (12) These agricultural-related solved reasonable identify Therefore, worst three programming. significance lies its potential critical catchment, offering solutions optimizing resources, productivity, fostering face located 6 2' 0" 5' North latitude 37 33'0" 36'0" East longitudes Southern (Figure 1). Elevation thestudy varied 1200 1203.8m above mean sea. near town, running alongside road connecting Mirab Abaya Wolayita Sodo location holds importance efficient transportation fruit production market. irrigation, University (AMU) farmland, smallhold farmer Kola Shara private farmland airport included farm, Kolla shara 1, 835.22ha, 109.17ha, 160.23ha, 18.44ha, 52.76ha, respectively, irrigable 1175.82ha source annual minimum, maximum 2.35m3/s 50.73m3/s, Market survey collect price dominant both sellers buyers which helps people since only prices justify whether profitable not. Field observation around investigate practical understand agronomic quantitative qualitative such size, costs production, existing practices, hectare collected questionnaires, key informant interviews, focus group discussions. revenues each crop. Based [23A. Wright, Hudson, Mutuc, "A Spatial Analysis Technology," 2013, 307–318, 2013.Google simplified formula, sample size interviews Kebele calculated described Eq 1.(1) Where n N population e expected error (5%) 95% confidence level. Zuria Woreda office. ArcGIS software after collecting ground control point (Table 1).Table 1population conduct interview shareTotal population10,794Number farmers886Available (ha)1,974Probable (ha)160.2Number (N)72Expected (e)5% @95% levelSample interview61 Open table new tab Methods soil sampling composite techniques depth 0.9m. texture evaluateing hydrometer test bulk density evaluated dividing mass volume drying oven 105 24 hours. chemical properties organic matter electric conductivity laboratory present status fertility. capacity permanent wilting pressure plate apparatus laboratory. [24Goebel Lascano R.J. Acosta-Martinez V. Stable Isotopes Determine Rainwater Infiltration Soils Conservation Reserve Program.J. Chem. Environ. 2016; 05: 179-190https://doi.org/10.4236/jacen.2016.54019Crossref infiltration characteristics determined double-ring infiltrometer. physical [25Chen C. Hsu Liang simulating extreme Pacific Asia.Weather Extrem. 31https://doi.org/10.1016/j.wace.2021.100303Crossref (92) (CMIP6) has good Phase 3 (CMIP3) five (CMIP5) predicting trends. As result, current derived sixth (CMIP6). utilized correction, meteorological station. Temperature, network Common Form (netCDF) files extracted coordinate elevation employed [26Leander Buishand Resampling output simulation river flows.J. Hydrol. 332: 487-496https://doi.org/10.1016/j.jhydrol.2006.08.006Crossref (353) addressed 3.(2) p* bias-corrected rainfall, P uncorrected & b power regression factors.(3) T*, Tobs, Trcm, δ stand standard deviation where P* amount; factors assessment (CropWat) computer program soil, input. [27R. Allen, Pereira, Raes, Smith, "FAO Drainage Paper No. 56 - Evapotranspiration," November 2017, 1998.Google (ETo), (CWR), effective (Pe), (IWR), (i) scenarios. 2022 (reference), 2060-2080 rainfall. Projected solar radiation, humidity, speed sunshine hours result all driest January April evapotranspiration, condition coefficients initial, mid, late stages drainage manual paper number (FAO 56) presented Table but teff found FAO paper. Length growing stage, root depth, reduction factor, allowable depletion, planting harvesting date central Rift Valley Lake Basin, 0.46 (initial stage), 0.88 (development 1.03 (mid-stage), 0.57 (late stage) [28T. Hordofa, "Crop Requirement Coefficient Tef ( Eragrostis tef ) Central Ethiopia," 11, 15, 34–39, 2020, 10.7176/JNSR/11-15-0.Google Reference evapotranspartion, 4, 5, 6/7, 9, respectively.Where; Rn=net radiation (MJ m-2 day-1), G=Soil heat flux T=Mean daily air 2m height (oc), U2=Wind (ms-1), es =Saturation vapour (kPa), ea= actual (kpa), (es -ea) =Saturated deficit, Δ=slope curve (kPa oc-1) r=psychrometric constant oc-1), Kc=crop (-), (mm), d CWR (mm/day).Table 2crop function stage 27R. Scholar(4) (5) (6) (7) Crop/Growth stageInitial stageMid-stageLate-stageWheat0.31.150.25-0.4 (0.325)Maize0.31.20.6Watermelon0.410.75Pepper0.61.050.9Onion0.71.050.75Banana0.61.11.05Tomato0.151.10.6-0.8 (0.7) solve mixed-integer, linear, nonlinear [29Hooper B.P. Integrated Resources Governance.Water Resour. (Updat): 12-20Google Objective profits develop [30Bowen R.L. Young R.A. Financial Net Benefit Functions Egypt's Northern Delta.Water 1985; 21: 1329-1335https://doi.org/10.1029/WR021i009p01329Crossref (23) 10..(10) Where; Pi, Yi, Xi, Ci "i" (birr/ton), (ton/ha), (ha) cost (birr/ha), Total including labor, fertilizer, pesticides, insecticides availability, outcome, function. Lengths various periods start according guidelines outlined banana persist year-round. Onions harvested end March, while wheat require months time stope four except perennial sum exceed (At) describe 11.(11) X1, X2, X3, X4, X5, X6, X7, X8 onion, watermelon, pepper, wheat, banana, Teff, At=total (ha). (mm) calculate water. less than minimum could obtained sources. (2010 2022) Equation constraint (Eq 12).(12) CWR=crop (m), Peff=effective Vmin= supply (ha-m). represents highest achievable quality optimal, managed effectively, sufficient available. sourced Manual Number 33 33). primary aim overall 13).(13) YI average (ton/ha) TYc=Expected (ton). 14.(14) PCi TPC I (Birr/ha) (Birr) Non-negativity had two 1) remaining depend small-hold (such yield, cost) common considered 1).X1>=0, X2>=0, X3>=0, X4>=0, X5>=………………………………………………………………………….X8>=0 interview, covered 564.9ha (48% area). Because vegetable cover consumption maize. other 48% ordered 2).X1>=0, X2>=564.9ha, X5>=………………………………………..……X8>=0 clay density, matter, conductivity, capacity, point, 1.32 gm/cm3, 0.87%, 0.16ds/m, 38.3%, 25.9%, 124mm/m, 3). 110 160mm/m, recommended 127mm/m. suitable uncompacted 1.63gm/cm3 [31Twum E.K.A. Nii-Annang Compaction Bulk Density Root Biomass Quercus petraea Reclaimed Post-Lignite Mining Site Lusatia, Germany.Appl. 2015https://doi.org/10.1155/2015/504603Cross
Language: Английский
Citations
8
Heliyon, Journal Year: 2024, Volume and Issue: 10(7), P. e28433 - e28433
Published: March 21, 2024
Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as primary livelihood population. New global climate model (GCM) simulations have recently been released established shared socioeconomic pathways (SSPs). requires evaluating projected under new scenarios and subsequent policy updates. research employed six GCMs from CMIP6 project spatial temporal across Afghanistan all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5. The were bias-corrected using Precipitation Climatological Center's (GPCC) monthly gridded data with a 1.0° resolution. Subsequently, change factor was calculated both near future (2020-2059) distant (2060-2099). projections' multi-model ensemble (MME) revealed increased most of SSPs higher emissions scenarios. showed substantial increase summer around 50%, SSP1-1.9 southwestern region, while decline over 50% northwestern region until 2100. annual northwest up 15% SSP1-2.6. SSP2-4.5 20% certain eastern regions far future. Furthermore, rise approximately SSP3-7.0 expected central western However, it crucial note that exhibit considerable uncertainty among different GCMs.
Language: Английский
Citations
7
Discover Environment, Journal Year: 2023, Volume and Issue: 1(1)
Published: Aug. 31, 2023
Abstract The Accuracy of model simulations is critical for climate change and its socio-economic impact. This study evaluated23 Global models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). main objective was to identify top 10 best performance capturing patterns rainfall 1981–2014 period over Intergovernmental Authority on Development (IGAD) region Eastern Africa. total rainfall, annual cycle, continuous, categorical Volumatic statistical metrics, scatter plots, Cumulative Distribution Function (CDF), colored code portrait were used assess . Results indicate that most CMIP6 generally capture characteristics observed climatology pattern bimodal unimodal regimes. majority Arid Semi-Arid Lands (ASALs) Kenya, Somalia, Ethiopia, Sudan scored lowest skills, highest bias, over-estimated lower skills June–September (JJAS) compared March–May (MAM) October-December (OND). Quantitatively, a high percent bias exceeding 80% ASALs, correlation coefficient ranging between 0.6 0.7 across Ethiopia’s highlands, 5–40 as Root Mean Squared Error region. In addition, 21 out 23 parts ACCESS-ESM1-5 MIROC6 are opposed CNRM-CM6-1HR under-estimated RMSE values. regional sub-national analysis showed it inconclusive select best-performed based individual metrics analysis. Out models, INM-CM5-0, HadGEM3-GC31-MM, CMCC-CM2-HR4, IPSL-CM6A-LR, KACE-1-0-G, EC-Earth3, NorESM2-MM, GFDL-ESM4, TaiESM1, KIOST-ESM IGAD These findings highlight importance selecting mapping present future hotspots extreme events
Language: Английский
Citations
13
Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 53, P. 101756 - 101756
Published: March 27, 2024
Kobo-Golina River, Upper Danakil Basin, Ethiopia. It is crucial to understand the spatiotemporal distribution of blue water (BW) and green (GW) for optimal use resources, especially in data-scarce regions. This study aims evaluate extent which future climate changing, its impact on blue-green resources area. Projected changes were predicted based latest Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) three periods (2015–2044, 2045–2075, 2076–2100) under two shared socio-economic pathways (SSP2–4.5 & SSP5–8.5). Compromise programming technique was employed rank select best performing GCMs. The multi-variable calibrated SWAT+ model forced with projections from top-ranked CMIP6 GCMs ensemble simulate projected Compared baseline period (1984–2014), declined while exhibited an increasing trend all SSPs. also noted that spatial BW GW remains uneven Precipitation significantly impacted than resources. provides valuable insights into utilization recent coupled hydrological models better simulation Blue-Green basins changing climate.
Language: Английский
Citations
5
Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 8(2), P. 265 - 279
Published: April 10, 2024
Language: Английский
Citations
5
Environmental Research Climate, Journal Year: 2024, Volume and Issue: 3(2), P. 025011 - 025011
Published: April 17, 2024
Abstract This study evaluated the performance of 50 global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating statistical features precipitation and air temperature five subdomains South America during historical period (1995–2014). Monthly simulations were validated with data Climate Prediction Center Merged Analysis Precipitation, Global Precipitation Climatology Project, ERA5 reanalysis. The models’ was using a ranking analysis metrics such as mean, standard deviation, Pearson’s spatial correlation, annual cycle amplitude, linear trend. analyses considered representation separately for each subdomain, all regions together, joint subdomains. In Brazilian Amazon, best-performing EC-Earth3-Veg, INM-CM4-8, INMCM5-0 (precipitation), IPSL-CM6A-LR, MPI-ESM2-0, IITM-ESM (temperature). La Plata Basin, KACE-1-0-G, ACCESS-CM2, IPSL-CM6A-LR GFDL-ESM4, TaiESM1, EC-Earth3-Veg (temperature) yielded best simulations. Northeast Brazil, SAM0-UNICON, CESM2, MCM-UA-1-0 BCC-CSM2-MR, CESM2 showed results. Argentine Patagonia, GCMs ACCESS-ESM1-5 EC-Earth3-Veg-LR CAMS-CSM1-0, CMCC-CM2-HR4, GFDL-ESM4 outperformed. Finally, Southeast ACCESS-ESM1-5, evaluation variables indicated that are FIO-ESM-2-0, MRI-ESM2-0.
Language: Английский
Citations
4
Heliyon, Journal Year: 2024, Volume and Issue: 10(12), P. e32442 - e32442
Published: June 1, 2024
The most suitable multi-model ensemble set of general circulation models is used to reduce the uncertainty associated with GCM selection and improve accuracy model simulations. This study evaluated performance 20 global climate participating in Coupled Model Intercomparison Project Phase 6 (CMIP6) reproducing precipitation patterns over Abaya-Chamo Sub-basin, Ethiopia. For validation models' capabilities, datasets from Climate Hazards Infrared Precipitation Stations (CHIRPS) were after comparing them ground observational datasets. objective was identify (MME) a subset CMIP6 GCMs capture rainfall for 1981–2014 period region. Data Operators (CDOs) data processing extraction, Mann-Kendall test Theil-Sen slope estimator methods utilized analyze trends Four statistical metrics (Nash-Sutcliffe coefficient, percent bias, normalized root mean square error, Kling-Gupta efficiency) further assess models. A multi-criteria decision analysis approach, namely, technique order preferences by similarity an ideal solution (TOPSIS) method, obtain overall ranks select best-performing results indicated that CHIRPS simulations generally reproduced bimodal CESM2-WACCM, NorESM2-MM, NorESM2-LM, NorESM2-LM performed better than other seasonal winter, spring, summer, autumn seasons, respectively. On hand, FGOALS-f3-L revealed reference all seasons. In terms NSE, PB, NRMSE, KGE metrics, EC-Earth3-C, EC-Earth3, EC-Earth-C, respectively, considered good at representing observed features EC-Earth3-C,EC-Earth3, EC-Earth3-Veg-LR, ACCESS-CM2, MPI-ESM1-2-HR, CNRM-CM6-1-HR exhibited best performances Sub-basin.
Language: Английский
Citations
3
Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102708 - 102708
Published: Aug. 9, 2024
Climate change has a negative impact on the basin's hydrological processes and water resources. In this study, projected impacts of climate in Omo River Basin was evaluated under two Shared Socioeconomic Pathways (SSP245 SSP585) scenarios. The latest Coupled Model Inter-comparison Project (CMIP6) model dataset precipitation temperature were used to assess anticipated basin. SWAT simulate effects throughout baseline (1990–2019), near (2031–2060), far future (2071–2100) periods. predicted stream flow will increase annually monthly June, July, August, September (JJAS) both scenarios except decrease months March, April, May (MAM) SSP245 scenario. basin mean annual seasonal (JJAS MAM) surface runoff SSP585 scenarios; however, it decreases groundwater decline MAM Likewise, yield scenario, nevertheless, increases potential evapotranspiration with over all circumstances. There be significant spatial variations balance components future. results study essential for managing resources future, creating plans coping change, reducing risk flooding scarcity.
Language: Английский
Citations
3
Climate, Journal Year: 2024, Volume and Issue: 12(11), P. 169 - 169
Published: Oct. 22, 2024
The projection and identification of historical future changes in climatic systems is crucial. This study aims to assess the performance CMIP6 climate models projections precipitation temperature variables over Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. bias model data was adjusted using from meteorological stations. Additionally, this uses daily under SSP1-2.6, SSP2-4.5, SSP5-8.5 scenarios for near (2015–2044), mid (2045–2074), far (2075–2100) periods. Power transformation distribution mapping correction techniques were used adjust biases seven models. To validate against observed data, statistical evaluation employed. Mann–Kendall (MK) Sen’s slope estimator also performed identify trends magnitudes variations rainfall temperature, respectively. revealed that INM-CM5-0 INM-CM4-8 best all agro-climatic zones show a significant (p < 0.01) positive trend. mean annual maximum UBNB estimated increase by 1.8 °C, 2.1 2.8 °C between 2015 2100, Similarly, annually minimum 1.5 3.1 SSP5-8.5, These are anticipated alter incidence severity extremes. Hence, communities should adopt various adaptation practices mitigate effects rising temperatures.
Language: Английский
Citations
3