General Circulation Model Projections Research Articles

Climate change and cover crop effects on water use efficiency of a corn-soybean rotation system

Climate change led to increased temperature and variable rainfall, which may pose great threats to both agricultural productions and environmental impacts. In this study, we aim to explore how changing climate and its extremes in the 20th and 21st-century influence system water use efficiency (sWUE) of a corn-soybean cropping rotation in a humid sub-tropic environment, and how much can cover crops mitigate these impacts. Different from the traditional yield-focused water use efficiency (WUE), sWUE addresses both production and environmental quality goals by considering grain yields and all major system water losses (evapotranspiration, runoff, and drainage). A calibrated crop simulation model, Root Zone Water Quality Model version 2 (RZWQM2), was applied to simulate grain yields and all major system water losses. The model was forced by daily climate data from in situ observations during 1956–2015 and 10 downscaled and bias corrected General Circulation Model (GCMs) projections under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios during 2020–2079. The results showed that, under the historical baseline and the future RCP4.5 and RCP8.5 scenarios, due to the growth of cover crops, the sWUE for corn were improved by 1.7%, 2.6% and 2.3%, respectively (p-value < 0.001), and for soybean by 0.7% (p = 0.06), 1.0% (p-value < 0.001) and 0.9% (p-value < 0.001). Soil evaporation, as the largest source of water loss from the cropping system, was significantly decreased by 1.7%, 2.6% and 2.3% during the corn growing season, and by 0.7%, 1.0% and 0.9% during the soybean growing season. The annual drainage was decreased by 38 mm, 53 mm and 67 mm, under the baseline, RCP4.5 and RCP8.5, respectively. With the incorporation of wheat cover crops, the correlations of temperature or precipitation extremes with grain yields and major water losses were mostly decreased, suggesting that growing cover crop is an effective means to mitigate the impact of climate extremes on sWUE of a corn-soybean cropping.

Regional sustainable development of renewable natural resources using Net Primary Productivity on a global scale

Renewable natural resources and ecosystem services constitute the fundamental support needed for the sustainable development of human beings. Emergy assessment coupled with Geographic Information System (GIS) has supported the creation of an effective framework needed towards sustainable development. Unprecedented global maps depicting the emergy distribution of renewable natural resources (e.g., solar radiation, wind, rainfall, and geothermal heat flow) are presented in this work, and a composite empower density (Em; sej/km2/year) map was generated. The entire world was classified based on land cover types, United Nations (UN) geoscheme regions, major river basins, and climate types. We identified the regions with abundant renewable emergy and compared these regions based on different renewable resources. We also used 24 different General Circulation Models (GCMs) to examine the impact of climate change on distribution of global resources. Moreover, the distribution of Net Primary Productivity (NPP) was related to emergy to understand the interactions between energy and the environment. Our results suggest that the Himalayan region and highland areas of South America, European Russia and Eastern Europe are rich in overall renewable resources, GCM projections suggest that a major part of the globe is expected to observe an increment in renewable resources. Most of the regions projecting a decrease in renewable resources (critical regions as the empower density is expected to decrease) have a moderate empower density/NPP ratio and therefore, climate change is expected to cause a moderate effect on the ecosystem of these regions. The results from this study can provide useful information to decision-makers in formulating the policy of renewable resources exploitation while ensuring minimal disturbance to the ecosystem.

Simulated climate change effects on soybean production using two crop modules in RZWQM2

AbstractThe ability to predict climate change effects on crop yield through field experiments and crop modeling is essential for developing mitigation strategies. The objective of this study was to compare two different crop modules (CROPGRO and HERMES) in the Root Zone Water Quality Model 2 (RZWQM2) for predicting climate change effects on soybean [Glycine max (L.) Merr.] production. The modules were previously calibrated for measured temperature responses using data from a 4‐yr open‐top chamber experiment (2015–2018) in North Carolina. Both crop modules simulated similar climate change effects in terms of yield and biomass by the end of Year 2100 (2083–2099) using 40 general circulation model (GCM) projections and two Representative Concentration Pathways (RCP4.5 and RCP8.5), compared with the simulations using current baseline (2002–2018). For both modules, much greater reductions in biomass and seed yield were simulated under RCP8.5 than under RCP4.5 due to higher air temperature. In addition, both modules predicted lower variability of biomass and seed yield across these GCMs under irrigated than under rainfed conditions. CROPGRO predicted a greater positive climate change effect in response to the projected higher precipitation and increased atmospheric CO2 (compared with baseline conditions) than HERMES. Soybean production will likely benefit more from the projected high precipitation and elevated CO2 under rainfed conditions than under irrigated conditions. Due to much higher simulated yield under irrigation than under rainfed conditions, supplementary irrigation may be an effective mitigation strategy to maintain soybean yield; however, adjusting sowing dates appear to have little effect on soybean production.

Local effects of climate change on row crop production and irrigation adoption

Global climate change will affect crop productivity, technology adoption, and commodity food and fiber prices. This research investigates the ex-ante effects of climate change on cropping mix and irrigation decisions at a watershed scale by integrating a downscaled General Circulation Model (GCM) projections, a crop growth model, and an economic model of the row crop sector typical of corn, soybean, wheat, and sorghum operations in Tennessee, United States. The downscaled GCM is used to generate weather patterns for Tennessee’s watersheds to 2049, under moderate and high greenhouse gas (GHG) emission assumptions. Crop yields and commodity prices were also estimated under these prevailing climate scenarios. Compared with the moderate emission level scenario, greater GHG emissions decrease dryland crop productivity and cause commodity prices to trend upward. A row crop optimization model was developed under the assumption that producers are profit maximizers who base their cropping decisions on the previous yield performance of a cropping system, commodity prices, and production functions, subject to resource constrains. Results suggest that producers in some watersheds adopt irrigation to minimize variability in net returns if water is available. Dryland and irrigated soybeans, dryland corn, and dryland soybean-wheat double cropping could become the dominant cropping systems in watersheds located in western Tennessee while irrigated and dryland soybeans could become the dominant row crop in watersheds located in middle Tennessee.

An Efficient Statistical Approach to Develop Intensity-Duration-Frequency Curves for Precipitation and Runoff under Future Climate.

Ongoing and potential future changes in precipitation will affect water management infrastructure. Urban drainage systems are particularly vulnerable. Design standards for many stormwater practices rely on precipitation intensity-duration-frequency (IDF) curves based on extreme value analysis. General Circulation Models (GCMs) project increases in future average temperature but are less clear on changes in precipitation. In many areas, climate projections suggest relatively small changes in total precipitation volume, but also suggest increased magnitude of extreme events. Model skill in predicting extreme precipitation events, however, is limited. We develop an approach for estimating future IDF curves that is efficient, uses widely available statistically downscaled GCM output, and is consistent with published IDF curves for the United States that are often incorporated into local stormwater regulations and design guides (and are GCM model agnostic). The method provides a relatively simple way to develop scenarios in a format directly useful to assessing risk to stormwater management infrastructure. Model biases are addressed through equidistant quantile mapping, in which the modeled change in the cumulative distribution of storm events from historical to future conditions is used to adjust the extreme value fit used for IDF curve development. The approach is efficient because it requires only annual maxima and is readily automated, allowing rapid examination of results across projections. We estimate future IDF curves at locations throughout the United States and link IDF-derived design storms to a rainfall-runoff model to evaluate the potential change in storage volume requirements for capture-based stormwater management practices by 2065.

Intercomparison of Bias-Correction Data Sources and Their Influence on Watershed-Specific Downscaling Climate Projections

HighlightsBias correction from three historical data sources (NCDC, PRISM, and NEXRAD) were assessed.Bias correction removed watershed-average bias in general circulation model (GCM) data for a historical period.Subbasin-specific variability was detected in future projections for each bias-correction data source.Data source had less impact on uncertainty in future projections than GCM or a representative concentration pathway.Uncertainty from bias-correction data sources was higher for precipitation than for temperature.Abstract. Climate projections developed by general circulation models (GCM) are often used in watershed modeling applications to project future hydrologic changes. In many models, the climate projections are downscaled to individual map units represented by grid cells or subbasins. Uncertainty of downscaled climate projections are a product of uncertainties arising mainly from the model itself, from the representative concentration pathway (RCP), and from the downscaling procedure. Other sources of uncertainty may include the historical observations used for GCM bias correction and data aggregation from GCM grids to map (often subbasin) units. This study evaluated effects of three sources of historical data (ground-based weather station network, NCDC, and two gridded datasets, NEXRAD and PRISM) on historical variability, and shifts and uncertainty in precipitation and temperature projections. Climate projections from six GCMs and three RCPs were evaluated in 54 subbasins of the Smoky Hill River watershed in the U.S. Central Great Plains. Bias correction of GCM projections reduced bias of watershed-average annual precipitation in the historical period to near zero, but subbasin-specific variability remained in future projections with little difference among bias-correction data sources. For minimum and maximum temperatures, the GCM ensemble statistics for basin-average and subbasin-specific future projections were similar for all bias-correction data sources. Increase in RCP forcing was found to widen the uncertainty in future projections. Overall, the uncertainty due to data source selection was smaller than the uncertainty due to GCM model and RCP forcing selection. The results demonstrate that statistical downscaling is essential to account for local climate factors within a watershed, and that both weather station-based and gridded bias-correction data sources can be used effectively, but that future climate projections may inherit the historical bias in a selected data source. These inherent uncertainties associated with application of GCMs in hydrological and geospatial modeling should be carefully considered for understanding climate projections when building watershed models and interpreting the results. Keywords: Bias correction, Climate change, Downscaling, GCM, Uncertainty, Watershed.

A flow-weighted ensemble strategy to assess the impacts of climate change on watershed hydrology

Evaluation of hydrological response to future climate change is essential for water quality risk assessment and adaptive management implementation within a watershed context. In this study, we present a modeling framework that integrates a hydrological model with projections of General Circulation Models (GCMs). Temperature and precipitation projections of six GCMs with two downscaling methods are used to force the Soil and Water Assessment Tool (SWAT) model in the Hamilton Harbour watershed in Ontario, Canada. A flow-based weighting strategy was developed to integrate the projections of multiple GCMs based on their ability to recreate empirical flow frequency distributions in multiple monitoring sites. Our study renders support to the ability of the weighted model ensemble to draw meaningful hydrological forecasts. Nonetheless, we also note that the ensemble strategy understates the frequency and magnitude of flow extremes, and therefore the domain that is collectively delineated by individual GCMs can still provide complementary planning information. Climate change is projected to trigger a distinct increase in air temperature and precipitation during the late winter-early spring period, which in turn will likely result in an earlier snowmelt and changes in the magnitude and timing of peak flow events. Analysis of the water cycle shows that the sensitivity of the individual hydrological components to climate change may vary along the urban-to-agriculture gradient. The projected declining soil–water content in agricultural catchments highlights the likelihood of more intensified drought conditions in the croplands. Evapotranspiration rates will likely increase across the entire watershed, whereas surface runoff could be reduced but less so in urbanized locations. Overall, our findings suggest that any future planning exercise to ameliorate the prevailing water quality conditions will only be insightful if we consider the interplay between climate change and urbanization processes in the area.

Non-analog increases to air, surface, and belowground temperature extreme events due to climate change

Air temperatures (Ta) are rising in a changing climate, increasing extreme temperature events. Examining how Ta increases are influencing extreme temperatures at the soil surface and belowground in the soil profile can refine our understanding of the ecological consequences of rising temperatures. In this paper, we validate surface and soil temperature (Ts: 0–100-cm depth) simulations in the SOILWAT2 model for 29 locations comprising 5 ecosystem types in the central and western USA. We determine the temperature characteristics of these locations from 1980 to 2015, and explore simulations of Ta and Ts change over 2030–2065 and 2065–2100 time periods using General Circulation Model (GCM) projections and the RCP 8.5 emissions scenario. We define temperature extremes using a nonstationary peak over threshold method, quantified from standard deviations above the mean (0-σ: an event $>\sim $ 51% of extreme events; 2- $\sigma :>\sim 98\%$ ). Our primary objective is to contrast the magnitude (∘C) and frequency of occurrence of extreme temperature events between the twentieth and twenty-first century. We project that temperatures will increase substantially in the twenty-first century. Extreme Ta events will experience the largest increases by magnitude, and extreme Ts events will experience the largest increases by proportion. On average, 2-σ extreme Ts events will increase by 3.4 ∘C in 2030–2065 and by 5.3 ∘C in 2065–2100. Increases in extreme Ts events will often exceed + 10 ∘C at 0–20 cm by 2065–2100, and at 0–100 cm will often exceed 5.0 standard deviations above 1980–2015 values. 2-σ extreme Ts events will increase from 0.9 events per decade in 1980–2015 to 23 events in 2030–2065 and 38 events in 2065–2100. By 2065–2100, the majority of months will experience extreme events that co-occur at 0–100 cm, which did not occur in 1980–2015. These projections illustrate the non-analog temperature increases that ecosystems will experience in the twenty-first century as a result of climate change.

Future climate impacts on the hydrology of headwater streams in the Amazon River Basin: Implications for migratory goliath catfishes

AbstractClimate‐driven alterations of hydro‐meteorological conditions can change river flow regimes and potentially affect the migration behaviour of fishes and the productivity of important fisheries in the Amazon basin, such as those for the continental‐scale migratory goliath catfishes (Brachyplatystoma, Pimelodidae). In this study, we investigated hydrologic responses to climate change using a hydrologic model forced with climate inputs, which integrate historical (2001–2010) observations and general circulation model (GCM) projections under the emission scenario Representative Concentration Pathway 8.5. We developed an empirical model to characterize future (2090–2099) climate‐change impacts on goliath catfish spawning migrations as a function of river flow depth dynamics at the upstream elevational limit of spawning (250 m) in headwater basins of the Amazon. The model results revealed spatially variable impacts of climate change on the catfish spawning migrations. The Marañón, Ucayali, Juruá, Purus, and Madeira basins had a predicted increase in the annual mean (3–8%) and maximum (1.1–4.9%) spawning migration rate (i.e., the fraction of fish that migrate to the spawning grounds in a day), mainly due to the lengthened rising phase of flow‐driven migratory events during wet seasons. The Caquetá‐Japurá, Putumayo‐Içá, Napo, and Blanco rivers had predicted decreases (3–7%) in the mean migration rate because of decreases in the length of the rising season of flow depth and the frequency of migratory events. The predicted timing of fish spawning migrations (quantified by the temporal centroid of migration rates) was delayed by 7–10 days in the west‐central and southwest regions and was 8 days earlier in the northwest and northcentral areas, due to changes in the onset of the rising season. We established a river depth baseline that controls the onset of goliath catfish spawning migration. This depth varies between 0.9–5.6 m across study sites. We found that the estimated depth baseline was most sensitive to uncertainties in river width and cross‐sectional channel shape. These results may help inform sustainable adaptation strategies for ecosystem conservation and local fisheries management in the Amazon basin.

Uncertainty in hydrological analysis using multi-GCM predictions and multi-parameters under RCP 2.6 and 8.5 scenarios in Manipur River basin, India

Climate change is likely to have adverse impacts on hydrological processes in a river basin, by altered runoff due to modifications in land use/land cover (LULC) and catchment hydrology. Substantial uncertainty exists to evaluate impacts of climate change on river catchment due to uncertainty in general circulation model (GCM) projections. In this study, impact of climate change on water balance and hydrological regime of Manipur River basin was investigated using SWAT model. A comprehensive semi-distributed soil and water assessment tool (SWAT) was used for future projection of changes in hydrological regime of Manipur River basin based on two representative concentration pathways (RCP 2.6 and RCP 8.5). A coupled model intercomparison project’s (CMIP5) based GCM downscaled outputs and future LULC projected data were used in the analysis. The projected temperature is likely to be increased by 2.84°C and increase of 836 mm in annual average precipitation is projected under RCP 8.5 by 2090s. Change in meteorological condition and LULC will lead to increase in runoff, evapotranspiration and water yield by 57.79 m3/s (38.32%), 318.7 mm (54.59%) and 629.72 mm (89.82%), respectively, by the end of 21st century. This study demonstrates the importance of water balance components and its spatial and temporal variation in the Manipur River basin. The key findings of this study reveals that the runoff, evapotranspiration and water yield will increase in the coming decades. Increase in water yield may lead to landslides in the hilly region and flooding in low lying areas in future.

One Thousand Three Hundred Years of Variability in the Position of the South Pacific Convergence Zone

AbstractThe South Pacific Convergence Zone (SPCZ) is the largest rain belt in the Southern Hemisphere and a key driver of precipitation variability, impacting South Pacific island communities. Our millennial‐long reconstruction is based on a trans‐Pacific tree‐ring network, containing chronologies sensitive to changes in the SPCZ because of its pervasive nature, spatial extent, and link to the El Niño‐Southern Oscillation. The reconstruction explains 58% of variance in the instrumental SPCZ index from 1911–1998. El Niño‐Southern Oscillation cycles are identified throughout the reconstruction period. Multidecadal periodicities wax and wane, coinciding with a sustained eastward shift during the Medieval Climate Anomaly (~1,000–1,200 CE). We find large volcanic eruptions increased the tendency for the SPCZ to be displaced eastward. The reconstruction helps improve our understanding of past hydroclimatic behavior in the southwest Pacific and can be used to validate general circulation model projections for Pacific Island communities and the wider region in the 21st century.

FGOALS-g3 Model Datasets for CMIP6 Flux-Anomaly-Forced Model Intercomparison Project

The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) is an endorsed Model Intercomparison Project in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The goal of FAFMIP is to investigate the spread in the atmosphere-ocean general circulation model projections of ocean climate change forced by increased CO2, including the uncertainties in the simulations of ocean heat uptake, global mean sea level rise due to ocean thermal expansion and dynamic sea level change due to ocean circulation and density changes. The FAFMIP experiments have already been conducted with the Flexible Global Ocean-Atmosphere-Land System Model, gridpoint version 3.0 (FGOALS-g3). The model datasets have been submitted to the Earth System Grid Federation (ESGF) node. Here, the details of the experiments, the output variables and some baseline results are presented. Compared with the preliminary results of other models, the evolutions of global mean variables can be reproduced well by FGOALS-g3. The simulations of spatial patterns are also consistent with those of other models in most regions except the North Atlantic and the Southern Ocean, indicating large uncertainties in the regional sea level projections of these two regions.

Spatio-Temporal Analysis of Historical and Future Climate Data in the Texas High Plains

Agricultural production in the Texas High Plains (THP) relies heavily on irrigation and is susceptible to drought due to the declining availability of groundwater and climate change. Therefore, it is meaningful to perform an overview of possible climate change scenarios to provide appropriate strategies for climate change adaptation in the THP. In this study, spatio-temporal variations of climate data were mapped in the THP during 2000–2009, 2050–2059, and 2090–2099 periods using 14 research-grade meteorological stations and 19 bias-corrected General Circulation Models (GCMs) under representative concentration pathway (RCP) scenarios RCP 4.5 and 8.5. Results indicated different bias correction methods were needed for different climatic parameters and study purposes. For example, using high-quality data from the meteorological stations, the linear scaling method was selected to alter the projected precipitation while air temperatures were bias corrected using the quantile mapping method. At the end of the 21st century (2090–2099) under the severe CO2 emission scenario (RCP 8.5), the maximum and minimum air temperatures could increase from 3.9 to 10.0 °C and 2.8 to 8.4 °C across the entire THP, respectively, while precipitation could decrease by ~7.5% relative to the historical (2000–2009) observed data. However, large uncertainties were found according to 19 GCM projections.

Variability in spatial–temporal recharge under the observed and projected climate: A site-specific simulation in the black soil region of Russia

The main purpose of this work is to evaluate diffuse groundwater recharge and its temporal dynamics at sites with different landscapes and soil profiles under observed and projected climate variability. Three typical sites with different landscapes and topsoil profiles were chosen for the field study. Field work consisted of pit development and soil sample collection for laboratory study using the centrifuge method. Soil hydraulic parameters were obtained by the RETC code using the observed values of volumetric water content vs. pressure head. HYDRUS-1D code was used to estimate groundwater recharge based on a 70-year (1945–2015) meteorological dataset with daily resolution (including values of precipitation, air temperature, wind speed, and humidity). This dataset was preprocessed using our SURFBAL code to calculate the surface and topsoil water and energy balance. Retrospective historical simulation results showed that there was a considerable variation in the mean annual groundwater recharge values (49–104 mm/year) for the three studied profiles. The temporal change in groundwater recharge occurred relatively synchronously at all sites and was governed by the change in the current annual aridity index. The results of this historical simulation did not reveal any traces of climate change in the groundwater recharge of the studied region in the last 30–40 years. To predict the recharge variations in the second half of the 21st century, the LARSWG forecast weather generator with the climate projections of the 5th General Circulation Models (GCMs) from the Coupled Model Intercomparison Project 5 (CMIP5) family was used. On average in the case of climate development under the maximum greenhouse gas emissions RCP8.5 scenario, there will be a reduction in groundwater recharge in the studied region in 2060–2080, caused by two factors: change of aridity index, which determines evapotranspiration during a warm season and the accumulation of snow and subsequent meltwater infiltration during the snowmelt periods.

Europe-wide precipitation projections at convection permitting scale with the Unified Model

For the first time, we analyze 2.2 km UK Met Office Unified Model convection-permitting model (CPM) projections for a pan-European domain. These new simulations represent a major increase in domain size, allowing us to examine the benefits of CPMs across a range of European climates. We find a change to the seasonality of extreme precipitation with warming. In particular, there is a relatively muted response for summer, which contrasts with much larger increases in autumn and winter. This flattens the hourly extreme precipitation seasonal cycle across Northern Europe which has a summer peak in the present climate. Over the Western Mediterranean, where autumn is the main extreme precipitation season, there is a regional increase in hourly extreme precipitation frequency, but local changes for lower precipitation thresholds are often insignificant. For mean precipitation, decreases are projected across Europe in summer, smaller decreases in autumn, and increases in winter; comparable changes are seen in the driving general circulation model (GCM) simulations. The winter mean increase is accompanied by a large decrease of winter mean snowfall. Comparing the driving GCM projections with the CPM ones, the CPMs show a robust enhanced intensification of precipitation extremes at the convection-permitting scale compared to coarser resolution climate model projections across various European regions for summer and autumn.

Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios

Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010–2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall.

Multiple bias-correction of dynamically downscaled CMIP5 climate models temperature projection: a case study of the transboundary Komadugu-Yobe river basin, Lake Chad region, West Africa

General circulation model projections are inadequate for impact studies due to their coarse resolution and inherent biases. Therefore, there is a need to accurately correct these biases and examine the abilities of these correction techniques in replicating the observed climate change signals. Over the Komadugu-Yobe Basin, this study investigates the performance of univariate empirical and parametric quantile mapping, as well as multivariate bias-correction (BC) techniques, using; N-dimensional probability density function transform (MBCN), Spearman rank correlation dependency (MBCR) and Pearson correlation dependency (MBCP) in correcting the biases in some selected CMIP5-Coordinated Regional Climate Downscaling Experiment model temperature projections based on the historical period (1975–2005) and the future (2020–2050) for annual, dry and wet periods under two emission scenarios (RCP 4.5 and 8.5). The temporal temperature variability of the BC outputs is further assessed. In correcting the temperature distribution, both univariate BC perform well for all seasons, while MBCN performs best during the dry season. The latitudinal-time cross-section result shows that high temperatures are mismatched either by the years of occurrence or by the latitudes at which they occurred. The univariate BC techniques and MBCN performed best in replicating the observed monthly variability. There is a positive temperature trend in most parts of the basin, however, with increased magnitudes in the future. Overall, the bias-corrected model ensemble mean performs better in replicating the trend in temperature, while the multivariate BC methods correct the joint dependence structure between them modelled variables, thereby providing a general-purpose methodology to the climate community.

Impact of climate change on adaptive management decisions in the face of water scarcity

Reoccurring drought through the early 2000s has caused a serious water scarcity issue in the Colorado River Basin. Previous modeling studies have focused on the impact of climate change without considering the adaptive behaviors of farmers and under-utilized Indian water rights. In this paper, we use a coupled agent-based water resource model (ABM) to investigate how the adaptive decisions of farmers can affect water resource management under both climate change impacts and fully utilized Indian water right conditions. We used five General Circulation Model projections with RCP8.5 scenarios for the study. The results of farm-level decision-making showed different responses in irrigated areas that were changing due to climate change impact. While winter precipitation changes might partially explain the behavior changes, no specific pattern could be concluded based on their location. Also, farmers’ responses about annual water diversion showed more significant inter-year variation compared to irrigated areas. Basin-level metrics showed that climate change impacts will generally worsen water scarcity issues as measured in Navajo Reservoir storage, flow to Lake Powell, and instream flow requirement. But these basin-level water scarcity metrics cannot reflect individual farm-level impacts under climate change, which is why modeling the bottom-up management actions is necessary. When the under-utilized Indian water rights are fully used, it is more likely to trigger the shortage sharing agreement due to the higher tribal water depletion. Evaluation of model uncertainty and a more realistic setup for adaptive actions under drought contingency plans are suggested for future research.

GCM compareR: A web application to assess differences and assist in the selection of general circulation models for climate change research

Abstract Climate change research often relies on downscaled general circulation models (GCM), projections of future scenarios that are used to build ecological and evolutionary models. With more than 35 different GCMs widely available at a resolution of 10 km and finer, standardized methods to understand the differences among GCM projections in a region of interest and to choose which GCM to use for analysis are essential to maximize relevance to policy and to assure a proper treatment of uncertainty. To help researchers and policymakers understand and select form the range of available GCM scenarios, we have developed GCM compareR, an open‐source web application written in r using shiny. GCM compareR is freely accessible with an easy interactive user interface, has preloaded climate scenario data to increase the speed of analysis and is fully documented to ensure reproducibility. Users of the application need no prior experience in coding. GCM compareR is designed to compare GCMs and different climate change scenarios to provide full, documented exploration of the possible alternative futures from within the range of projections in CMIP5 climate models. Designed with a wide group of users in mind, including ecologists, conservationists and policymakers, the application is designed to adapt analyses to any geographic area of interest. Results are provided as figures, tables and maps that clearly communicate the differences among model projections for the region. Additionally, the tool allows for the export of a report that records the parameter choices and results of a session, along with contextual information, to make the analysis fully transparent and replicable.

Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China

Climate and land use/cover changes are the main factors altering hydrological regimes. To understand the impacts of climate and land use/cover changes on streamflow within a specific catchment, it is essential to accurately quantify their changes given many possibilities. We propose an integrated framework to assess how individual and combined climate and land use/cover changes impact the streamflow of Xinanjiang Basin, in East China, in the future. Five bias-corrected and downscaled General Circulation Model (GCM) projections are used to indicate the inter-model uncertainties under three Representative Concentration Pathways (RCPs). Additionally, three land use/cover change scenarios representing a range of tradeoffs between ecological protection (EP) and urban development (UD) are projected by Cellular Automata - Markov (CA-Markov). The streamflow in 2021–2050 is then assessed using the calibrated Soil and Water Assessment Tool (SWAT) with 15 scenarios and 75 possibilities. Finally, the uncertainty and attribution of streamflow changes to climate and land use/cover changes at monthly and annual scale are analyzed. Results show that while both land use/cover change alone and combined changes project an increase in streamflow, there is a disagreement on the direction of streamflow change under climate change alone. Future streamflow may undergo a more blurred boundary between the flood and non-flood seasons, potentially easing the operation stress of Xinanjiang Reservoir for water supply or hydropower generation. We find that the impacts of climate and land use/cover changes on monthly mean streamflow are sensitive to the impermeable area (IA). The impacts of climate change are stronger than those induced by land use/cover change under EP (i.e., lower IA); and land use/cover change has a greater impact in case of UD (i.e., higher IA). However, changes in annual mean streamflow are mainly driven by land use/cover change, and climate change may decrease the influence attributed to land use/cover change.