Long Ashton Research Station Weather Generator Research Articles

Adapting to climate change in arid agricultural systems: An optimization model for water-energy-food nexus sustainability

Sustainable management of water, energy, and food (WEF) under climate change will be a significant challenge for arid agricultural systems. This study developed a fractional non-linear multi-objective programming (FNLMOP) model to optimize resource allocation and improve agricultural sustainability in these systems under climate change. The model was designed in the framework of the WEF nexus to simultaneously improved energy productivity (profit/energy), and water productivity (profit/water), while mitigating environmental damage (damage to groundwater resources/output) and ensuring food security in an arid watershed in Iran. The long Ashton research station weather generator (LARS-WG) and the coupled model intercomparison project 6 (CMIP6) were employed to project climate parameters for both future dry and wet conditions. The sustainability of the optimal solutions was then assessed using a hybrid criteria importance through intercriteria correlation (CRITIC)-VIKOR approach. The optimal solutions revealed a reduction in the land under cultivation and produced less water-intensive crops. The optimization model can ensure WEF security, enhancing agricultural system sustainability by optimizing crop cultivation patterns and resource allocation. Current crop choices were highly inefficient with the bigger changes being from the current crops to optimal crops. Climate change showed a substantial but lesser influence on optimal crop choice.

Assessing the impact of climate change on agricultural production in central Afghanistan

Afghanistan has faced extreme climatic crises such as drought, rising temperature, and scarce precipitation, and these crises will likely worsen in the future. Reduction in crop yield can affect food security in Afghanistan, where the majority of population and economy are completely dependent on agriculture. This study assessed the interaction between climate change and crop yield in Kabul of Afghanistan during the reference (1990–2020) and future (2025–2100) periods. Climate data (1990–2020) were collected from four meteorological stations and three local organizations, and wheat yield data (1990–2020) were acquired from the United States Agriculture Department. Data during the reference period (1990–2020) were used for the validation and calibration of the statistical downscaling models such as the Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG). Furthermore, the auto-regression model was used for trend analysis. The results showed that an increase in the average annual temperature of 2.15°C, 2.89°C, and 4.13°C will lead to a reduction in the wheat yield of 9.14%, 10.20%, and 12.00% under Representative Concentration Pathway (RCP)2.6, RCP4.5, and RCP8.5 during the future period (2025–2100), respectively. Moreover, an increase in the annual maximum temperature of 1.79°C, 2.48°C, and 3.74°C also causes a significant reduction in the wheat yield of 2.60%, 3.60%, and 10.50% under RCP2.6, RCP4.5, and RCP8.5, respectively. Furthermore, an increase in the annual minimum temperature of 2.98°C, 2.23°C, and 4.30°C can result in an increase in the wheat yield of 6.50%, 4.80%, and 9.30% under RCP2.6, RCP4.5, and RCP8.5, respectively. According to the SDSM, the decrease of the average monthly precipitation of 4.34%, 4.10%, and 5.13% results in a decrease in the wheat yield of 2.60%, 2.36%, and 3.18% under RCP2.6, RCP4.5, and RCP8.5, respectively. This study suggests that adaptation strategies can be applied to minimize the consequences of climate change on agricultural production.

The hidden threat of heavy metal leaching in urban runoff: Investigating the long-term consequences of land use changes on human health risk exposure

This study evaluated the potential effects of long-term land use and climate change on the quality of surface runoff and the health risks associated with it. The land use change projection 2030 was derived from the main changes in land use from 2009 to 2019, and rainfall data was obtained from the Long Ashton Research Station Weather Generator (LARS-WG) model. The Long-Term Hydrological Impact Assessment (L-THIA) model was then utilized to calculate the rate of runoff heavy metal (HM) pollutant loading from the urban catchment. It was found that areas with heavy development posed a significantly greater public health risk associated with runoff, with higher risks observed in high-development and traffic areas compared to industrial, residential, and commercial areas. Additionally, exposure to Lead (Pb), Mercury (Hg), and Arsenic (As) was found to contribute significantly to overall non-carcinogenic health risks for possible consumers of runoff. Carcinogenic risk values of As, Cadmium (Cd), and Pb were also observed to increase, particularly in high-development and traffic areas, by 2030. This investigation offers important insight into the health risks posed by metals present in surface runoff in urban catchment areas under different land use and climate change scenarios.

Evaluating the impact of climate change on irrigation canal performance

AbstractIrrigation canals are the main systems that convey water from sources to demand nodes. Their performance is affected by climate change. In this research, the effect of climate change on temperature and precipitation was investigated in the Aghili irrigation network over the base period 1994–2017, and the Aghili east canal performance was consequently assessed. To this end, the climate data were first assessed by the Mann–Kendall test to determine trends. Then, changes in temperature and precipitation were simulated using HadGEM2‐ES in the Long Ashton Research Station Weather Generator (LARS‐WG) under two representative concentration pathways (RCPs) of 2.6 and 8.5 over the periods 2021–2040 and 2041–2060. CROPWAT8 was used to calculate the crop water requirement and irrigation hydromodule, and the turnout flow in the considered canal was calculated next. Finally, the canal was simulated and assessed. The results showed that the maximum temperature, evapotranspiration and turnout flow increases are 3.7°C, 1.45 mm/day and 39 L/s, respectively, related to the base timescale. Additionally, the adequacy performance decreased to 0.768 from 0.986, leading to a maximum extra water requirement of 15.1 million m3/year in the Aghili east canal under a pessimistic scenario.

A model-based evaluation of farmers' income variability under climate change (case study: autumn crops in Iran).

The study strives to analyze the potential variations of farmers' income under climate change by using Ricardian approach. The case study was Mazandaran province of Iran and three autumn crops, i.e. wheat, barley and canola were considered as the investigated crops. The Long Ashton Research Station Weather Generator (LARS-WG) model was selected to downscale the climate data. Three climate variables were downscaled for the years 2020-2080 under three climate scenarios: optimistic (RCP2.6), medium (RCP4.5), and pessimistic (RCP8.5). The Ricardian approach was also employed to predict the economics of climate change. Accordingly, the mean monthly temperature of the province is projected to have an upward trend under all climate scenarios, however, the rainfall pattern would be varied. The results of economic impacts of climate change also approved that the net income of investigated crops would be different trends under climate change scenarios. Accordingly, the variations of air temperature and rainfall would lead that the net income increases for wheat and barley, while it decreases for canola.

Trends, forecasting and adaptation strategies of climate change in the middle and west regions of Iraq

Climate change has placed considerable pressure on the residential environment, agricultural, and water supplies in different areas of the world, especially arid places such as Iraq. Iraq is one of the five most vulnerable countries in the world to climate change, where it has been encountering extremes heat waves during the most recent decades resulted in drought, desertification, and rivers dried up, which led to thousands of hectares to turn dry and yellow. This study aims to investigate the trends of climate change in the middle and western regions of Iraq and future expectations. The daily maximum temperature, minimum temperature, and precipitation are downscaled using the Long Ashton Research Station Weather Generator (LARS-WG) model. Five General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for three future periods: the near future (2021–2040), medium future (2051–2070), and far future (2081–2100), based on two scenarios of the Representative Concentration Pathways (RCP4.5 and RCP8.5) for four selected meteorological stations representing the study area. The outcomes of the calibration and validation of the model supported its skill and reliability to downscale precipitation and temperature time series for statistical indices (R2, RMSE and MBE) ranging between (0.894–0.998), (0.1270–1.9274) and (− 0.6158 to 0.0008), respectively. The results showed that the average minimum and maximum annual temperatures will increase at all selected stations across the three future periods by between 0.94 and 4.98 °C by the end of the twenty-first century. Annual changes in precipitation tend generally towards increase for the study area by average (6.09–14.31%) for RCP4.5 and (11.25–20.97%) for RCP8.5 Compared to the historical data (1990–2020). These findings can contribute to become more acquainted with the effects of climate change on the environment and encourage managers and planners to come up with plans for mitigating and adapting to these effects. They can also serve as a guide for future management of water and agricultural resources in the study region.

Future Climate Prediction Based on Support Vector Machine Optimization in Tianjin, China

Climate is closely related to human life, food security and ecosystems. Forecasting future climate provides important information for agricultural production, water resources management and so on. In this paper, historical climate data from 1962–2001 was used at three sites in Tianjin Baodi, Tianjin and Tanggu districts as baseline and the model parameters were calibrated by the Long Ashton Research Station Weather Generator (LARS-WG). 2m-temperatures in 2011–2020 were verified under two scenarios, representative concentration pathway (RCP) 4.5 and RCP8.5 in different atmospheric circulation models with optimal minimum 2m-temperatures at the three sites. From 2031–2050, Tianjin will be using more moderate minimum 2m-temperatures in future simulations. Support vector machines (SVM) were used to optimize the simulated data to obtain more accurate future maximum and minimum 2m-temperatures for the three sites. The results showed that the determinant coefficient of LARS-WG simulation was 0.8 and SVM optimized determinant coefficient was 0.9 which greatly improved the prediction accuracy. The minimum and maximum future 2m-temperatures optimized under European Community Earth System Model (EC-EARTH) were relatively low and the same future 2m-temperatures optimized under Hadley Centre Global Environment Model Earth System (Had-GEM2-ES4) were high especially in the RCP8.5 scenario which simulated 2051–2070 climate. The SVM optimization showed that the maximum and minimum 2m-temperatures were in general agreement with the original simulation values.

Developing sustainable strategies by LID optimization in response to annual climate change impacts

Designing urban runoff drainage systems is prominent in effectively managing floods due to increasing impermeable regions worldwide. However, although urban runoff drainage systems are configured mainly based on rainfall analysis, climate change influences their hydrological properties substantially. This research introduces an innovative methodology to analyze climate variations on optimal low impact developments (LIDs) of urban drainage systems in historical periods based on annual impacts (AIs) in projection periods considering uncertainties assessments. First, Storm Water Management Model (SWMM) is employed for simulating the process of rainfall-runoff considering quality and quantity analyses. This simulation model is coupled with a non-dominated genetic algorithm- II (NSGA-II) optimization algorithm to minimize the cost of LIDs, flood volume, and pollutant load. Then, future runoff and daily rainfall are projected on a yearly basis, including maximum, minimum, and median rainfall, to identify how climate change affects catchment properties. These projections and an ensemble model are obtained based on the Long Ashton Research Station Weather Generator (LARS-WG), which considers uncertainties. After that, the projected daily precipitation is fragmented into hourly segments utilizing the change factor approach (CFA). Finally, the ideal optimum type, size, and placement of the chosen LIDs are determined using the presented simulation-optimization (SO) model. To prove the effectiveness and appropriateness of the presented framework, it is implemented in a real-world study area in Darabad catchment, Tehran, Iran. Results indicate that the developed optimal LIDs are well-designed and sufficient in both the historical and projection periods. The findings also depict that with the current LIDs established for the historical period, the flooding volume and summation of total suspended solid (TSS) and total Nitrogen (TN) removals are decreased by 55.96% and 60.2% compared to when LIDs are not adopted. In addition, employing the developed LIDs based on an ensemble model results in the runoff volume and pollutants removal decline of up to 31.39% and 46.9%, respectively.

Modelling the impacts of changing land use and climate on sediment and nutrient retention in Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia

Natural ecosystems provide many different services such as carbon sequestration, regulation of climate change, biodiversity, and aesthetic value. Ecosystem functions are significantly impacted by both natural and man-made processes, such as sediment and nutrient deposition. The Lake Tana basin faces significant problems with soil depletion, sedimentation, and degradation. Thus, this study is designed to assess the effects of climate and land use change on the export of sediment and nutrients as well as their retention in the basin. This study used different data sources such as satellite data for land use and land cover and watershed preparation, climate data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Climate Model Intercomparison Project phase five (CMIP5), soil data from Ministry of Water, Irrigation and Electricity (MoWIE), and field data for model calibration. Five different Global Climate Models (GCMs) were employed to produce climate projections using the Long Ashton Research Station Weather Generator (LARS-WG) for the basin. The cellular automata (CA) and Markov chain has then been employed for future projections of changes in land use. Finally, the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) sediment delivery ratio (SDR) and nutrient delivery ratio (NDR) models have been applied to assess the changes in sediment and nutrient export between the baseline and future time periods. The findings indicated that between 1990 and 2019, there was a change in land use that led to an increase in sediment exports of 60.5% and a decrease in sediment retention of 12.8%. Projecting into the future, the amount of exported and retained sediment increases under the RCP4.5 and RCP8.5 scenarios in the 2040s by 8.4% and 17.8%, respectively due to the combined effects of climate and land use change. Equally, nitrogen and phosphorus exports rose under land use change scenarios but are projected to decrease under climate and integrated climate and land use change scenarios. Out of the 12 sub-watersheds that were designated, five were found to have a high mean annual rate of sediment and nutrient export and were then ranked in terms of priority level for conservation planning. Hence, this research shows that the integration of spatial data with the InVEST model can be used to aid land use planning and decision-making to combat nutrient and sediment exports and prioritize watersheds.

The impacts of climate changes on watershed streamflow and total dissolved nitrogen in Danjiang Watershed, China

Abstract The impacts of future climate change on the watershed streamflow and total dissolved nitrogen (TDN) fluxes upstream of the Danjiang River were estimated. The newest shared socioeconomic pathways (SSP) in CMIP6 were used as a climate change scenario. The ensembles of downscaled GCM outputs from WorldClim were used as future climate change information. A combined modeling approach is proposed, including the Long Ashton Research Station Weather Generator (LARS-WG) model as a weather generator and the generalized watershed loading function (GWLF) model for watershed hydrochemical process model and scenario analysis. The results show that there is generally less annual streamflow but more annual TDN flux under future climate change scenarios. The monthly streamflow and TDN flux increased from May to July and decreased from August to October. Changes in streamflow and TDN fluxes were the greatest in the worst uncontrolled scenario of SSP 5-85, with a 12.1% decrease in annual streamflow in the 2070s and a 4.82% increase in annual TDN flux in the 2090s. This indicates that active climate policies can mitigate the impact of climate change on watersheds. Furthermore, the source apportionments of TDN from agricultural sources will increase to nearly 50% by the 2090s, and targeted management strategies should be implemented.

Potential Impact of Global Warming on Climate and Streamflow of Adhaim River Basin, Iraq

Global warming induces to increase of greenhouse gases in the atmosphere and plays a crucial role in determining the future trend in climatology and hydrology of a watershed. This paper aims to investigate the implications of global warming on future climate and its consequents on streamflow of the Adhaim River Basin (ARB). For this purpose, the Long Ashton Research Station-Weather Generator (LARS-WG) and Soil and Water Assessment Tool (SWAT)-Based models were implemented. The climate and hydrologic records for the period 1990-2019 were used as a Reference Period (RP) and projected to 2080 under Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5) and five Global Climate Models (GCMs). The results show that the region of ARB tends to become hotter and drier with an increase in mean temperature by 1.2, 2.9, and 4.6 °C under the considered RCPs, respectively. However, precipitation tends to decrease from 366 mm/y in RP to 320.2, 302, and 300.5 mm/y by 2080 under the considered RCPs. Consequently, the streamflow will decrease to about 28, 26, and 24 m3/s by 2080 under the considered RCPs, respectively, compared with 28.96 m3/s in RP. Therefore, adaptation strategies are highly recommended to alleviate the negative impacts of climate change, and the implications of climate change on groundwater, water demand, and adaptation plans should be investigated in future studies.

Assessing the potential impacts of climate change on streamflow in the data-scarce Upper Ruvu River watershed, Tanzania

Abstract This study assessed the impacts of climate change on streamflow in the data-scarce Upper Ruvu River watershed (URRW). The Long Ashton Research Station Weather Generator (LARS-WG) was employed for generating the future ensemble-mean climate scenario based on six global circulation models (GCMs), under two representative concentration pathways (RCPs: RCP4.5 and RCP8.5). The future projections were made in two periods (2041–2060 and 2081–2100), and the baseline period (1951–1978) was used as a reference. The watershed hydrology was represented by the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) model, which was calibrated and validated by using 5 and 4 years of streamflow data, respectively. Results indicate that the rainfall and minimum and maximum temperatures will increase in both periods, under both scenarios. This will potentially affect the streamflow that is projected to increase from March to August and decrease from September to February. The mean annual streamflow could potentially change from 48 m3/s in the baseline period to 45.6 and 56.5 m3/s during 2041–2060, and 52.4 and 67.4 m3/s during 2081–2100, under RCP4.5 and RCP8.5, respectively. The minimum and maximum streamflows are also predicted to change in both periods, under both scenarios. Considering these results, the climate change will have significant impacts on the streamflows of the URRW.

System Thinking Approach toward Reclamation of Regional Water Management under Changing Climate Conditions

This paper represents a streamflow prediction model with the approach of ensemble multi-GCM downscaling and system dynamics (SD) for the Aji-Chay watershed located in northwest Iran. In this study, firstly, the precipitation and temperature projection for the future was assessed according to the climate change impact using a statistical downscaling technique, i.e., Long Ashton Research Station Weather Generator (LARS-WG); secondly, a rainfall-runoff model for future horizons was developed according to artificial neural networks (ANN); finally, an SD model was developed according to plausible reclamation scenarios, i.e., cloud seeding, increasing the irrigation efficiency and reducing agricultural production, controlling policies on groundwater withdrawal as well as environmental awareness, and cultivation to reduce domestic consumption to achieve sustainable development. For downscaling purposes, the outputs of four general circulation models (GCMs) including EC-EARTH, HadGEM2, MIROC5, MPI-ESM from Coupled Model Intercomparison Project 5 (CMIP5) were applied. The results of multi-GCM downscaling indicated an ascending trend of 0.1 °C to +1.3 °C for temperature and a descending trend of 17 to 23% for precipitation by 2040 under representative concentration pathways (RCPs) of 4.5 and 8.5, respectively. Moreover, the results of the SD model revealed that none of the individual reclamation scenarios were impressive on water balance sustainable conditions; instead, the simultaneous implementation of all plausible scenarios managed to meet the requirements of socio-environment aspects as well as sustainability approaches.

Future Climate Projections Using SDSM and LARS-WG Downscaling Methods for CMIP5 GCMs over the Transboundary Jhelum River Basin of the Himalayas Region

Climate change is one of the leading issues affecting river basins due to its direct impacts on the cryosphere and hydrosphere. General circulation models (GCMs) are widely applied tools to assess climate change but the coarse spatial resolution of GCMs limit their direct application for local studies. This study selected five CMIP5 GCMs (CCSM4, HadCM3, GFDL-CM3, MRI-CGCM3 and CanESM2) for performance evaluation ranked by Nash–Sutcliffe coefficient (NSE) and Kling–Gupta Efficiency (KGE). CCSM4 and HadCM3 large-scale predictors were favored based on ranks (0.71 and 0.68, respectively) for statistical downscaling techniques to downscale the climatic indicators Tmax, Tmin and precipitation. The performance of two downscaling techniques, Statistical Downscaling Methods (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG), were examined using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), bias, NSE and KGE with weights (Wi) for the validation period. The results of statistical measures proved SDSM more efficient (0.67) in comparison to the LARS-WG (0.51) for the validation time for the Jhelum River basin. The findings revealed that the SDSM simulation for Tmax and Tmin are more comparable to the reference data for the validation period except simulation of extreme events by precipitation. The 21st century climatic projections exhibited a significant rise in Tmax (2.37–4.66 °C), Tmin (2.47–4.52 °C) and precipitation (7.4–11.54%) for RCP-4.5 and RCP-8.5, respectively. Overall, the results depicted that winter and pre-monsoon seasons were potentially most affected in terms of warming and precipitation, which has the potential to alter the cryosphere and runoff of the Jhelum River basin.

Policy-Making toward Integrated Water Resources Management of Zarrine River Basin via System Dynamics Approach under Climate Change Impact

In terms of having a comprehensive vision toward supplying the water requirements, a multi-criteria decision-making approach was employed on the Zarrine River Basin (ZRB) in the northwest of Iran. First, the climate change impacts were analyzed with the Long Ashton Research Station Weather Generator (LARS-WG) downscaling approach by using General Circulation Models (GCMs) including the European Consortium Earth System Model (EC-EARTH), Hadley Centre Global Environment Model version 2 (HADGEM2), Model for Interdisciplinary Research on Climate, version 5 (MIROC5), and Max Planck Institute Earth System Model (MPI-ESM), from Coupled Model Intercomparison Project 5 (CMIP5) under Representative Concentration Pathway (RCP4.5, RCP8.5) scenarios for 2021–2080. Afterward, the downscaled variables were utilized as inputs to the Artificial Neural Network (ANN) model to predict future runoff under the climate change impact. Finally, the system dynamics (SD) model was employed to simulate various scenarios for assessing water balance utilizing the Vensim software. The results of downscaling models suggested that the temperature of the basin will increase by 0.47 and 0.91 °C under RCPs4.5 and 8.5 by 2040, respectively. Additionally, the precipitation will decrease by 3.5 percent under RCP4.5 and 14 percent under RCP8.5, respectively. Moreover, simulation results revealed that the water demand in various sectors will be enormously increased. The contribution of the climate change impact on the future run-off was a seven percent decrease, on average, over the basin. The SD model, according to presented plausible scenarios including decreasing agriculture product and shifting irrigation efficiency, cloud-seeding, population control, and household consumption reduction, reducing meat and animal-husbandry production, and groundwater consumption control, resulted in a water balance equilibrium over five years. However, the performance of individual scenarios was not effective; instead, a combination of several scenarios led to effective performance in managing reduced runoff under climate change.

Future precipitation changes in the Central Ethiopian Main Rift under CMIP5 GCMs

Abstract The purpose of this study is to predict future changes in precipitation in the Central Ethiopian Main Rift, which is vulnerable to climate change. The Long Ashton Research Station Weather Generator (LARS-WG) model was applied to project precipitation based on five global climate models (GCMs) (EC-EARTH, MIROC-ESM, HadGEM2-ES, INM-CM4, and CCSM4) from Coupled Model Intercomparison Project phase 5 (CMIP5) under two representative concentration pathways (RCP4.5 and RCP8.5) in the periods of 2041–2060 compared to the baseline period of 1976–2005. The model's calibration and validation results showed that it could predict future precipitation. According to the analysis, the mean rainfall is expected to increase in January (up to 14.2%) and December (up to 27.8%) under the RCP 4.5 and RCP 8.5 scenarios, respectively. However, a drop is anticipated in June (up to 8.2%) and May (up to 7%) under the RCP 4.5 and RCP 8.5 scenarios, respectively. In both scenarios, summer precipitation (usually the rainy season) is predicted to fall, while winter precipitation (usually the dry season) is expected to climb. Furthermore, annual and spring precipitation forecasts are anticipated to decrease in most locations. The findings of this research will be utilized to guide future water resource management in the study region.

Hydrologic response of arid and semi-arid river basins in Iraq under a changing climate

Abstract An assessment of the total hydrologic response of arid and semi-arid river basins to various scenarios of climate change by considering evapotranspiration, streamflow, and snowmelt is essential for sustainable management of water resources. The Diyala River Basin in Iraq has been chosen as a typical case study of dozens of river basins in arid and semi-arid regions. Here, the Long Ashton Research Station-Weather Generator (LARS-WG), the Soil and Water Assessment Tool (SWAT), and the SWAT Calibration and Uncertainty Program (CUP) were used to evaluate the total response by considering three Representative Concentration Pathways (RCPs); RCPs 2.6, 4.5, and 8.5 over three periods, 2021–2040, 2041–2061, and 2061–2080. The results indicate that by the year 2080, the basin will experience a temperature increase by 6.6, 10.1, and 16.6% for RCP 2.6, RCP 4.5, and RCP 8.5, respectively. The corresponding reduction in precipitation will be 3.2, 6.4, and 8.7%, resulting in 38.8, 47.9, and 52.8% fall in streamflow for RCPs 2.6, 4.5, and 8.5, respectively. Due to the increase in temperature, an earlier and less contribution of snowmelt is expected in the projected streamflow. Our findings provide a useful reference and a guide to decision makers for developing adaption plans to sustainably manage water resources in the Diyala River Basin and other similar basins in arid and semi-arid regions.

Assessment of climate change impact on water availability in the upper Dong Nai River Basin, Vietnam

Abstract On a global scale, climate change is projected to have detrimental impacts on water availability. This situation will become more severe owing to accumulated impacts of climate change and anthropogenic activities. This study aims to investigate climate change impact on water availability in the upper Dong Nai River Basin using the Soil and Water Assessment Tool (SWAT) and Water Evaluation and Planning (WEAP) models. Future rainfall scenarios were downscaled from five different general circulation models under RCP4.5 and RCP8.5 using the Long Ashton Research Station Weather Generator (LARS-WG) tool. Under the climate change impact, annual river discharge in the study region is generally projected to have upward trends in the future, except for the near-future period of the 2030s under RCP4.5. In addition, dry-seasonal river discharge is expected to be increased in the future. Considering the baseline condition of water use, there was an annual water shortage of approximately 32.9 × 103 m3, which mostly occurred in the dry season from January to March. Climate change may reduce the water shortage in the study region ranging from 7.0 to 30.1% in the future. Under the combined impacts of climate change and increasing water demand, the water shortage will vary from −18.6 to 6.0% in the future. The results can provide valuable insights to implement appropriate future water resources planning and management in the study region.

Estimation of Watershed Hydrochemical Responses to Future Climate Changes Based on CMIP6 Scenarios in the Tianhe River (China)

The impacts of future climate changes on watershed hydrochemical processes were assessed based on the newest Shared Socioeconomic Pathways (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) in the Tianhe River in the middle area of China. The monthly spatial downscaled outputs of General Circulation Models (GCMs) were used, and a new Python procedure was developed to batch pick up site-scale climate change information. A combined modeling approach was proposed to estimate the responses of the streamflow and Total Dissolved Nitrogen (TDN) fluxes to four climate change scenarios during four future periods. The Long Ashton Research Station Weather Generator (LARS-WG) was used to generate synthetic daily weather series, which were further used in the Regional Nutrient Management (ReNuMa) model for scenario analyses of watershed hydrochemical process responses. The results showed that there would be 2–3% decreases in annual streamflow by the end of this century for most scenarios except SSP 1-26. More streamflow is expected in the summer months, responding to most climate change scenarios. The annual TDN fluxes would continue to increase in the future under the uncontrolled climate scenarios, with more non-point source contributions during the high-flow periods in the summer. The intensities of the TDN flux increasing under the emission-controlled climate scenarios would be relatively moderate, with a turning point around the 2070s, indicating that positive climate policies could be effective for mitigating the impacts of future climate changes on watershed hydrochemical processes.

Testing of Cassava (Manihot esculenta) Varieties for Climate Resilience Under Kerala (India) Conditions

The present study was conducted to identify the climate resilience of two widely used varieties of cassava in one of the major cassava growing areas in Kerala, India. The future projections for 2030, 2050, and 2070 were derived using the Long Ashton Research Station-Weather Generator (LARS-WG) with integrated global climate models (GCMs). The projections for the representative concentration pathway-4.5 (RCP-4.5) were tested in the crop model, World Food Studies (WOFOST) to assess the resilience of cassava varieties. The future projections in the study region indicated an increase of up to 2.1 and 2.3 °C for maximum and minimum temperatures respectively, followed by solar dimming. The crop yield predictions based on the outputs from the GCMs indicated that the yield of the long duration cassava var. H-226 increased during 2030 from 8.6 to 12% and that of short duration var. Sree Vijaya increased from 3.6 to 5.5%. With the 2050 scenario, the yield increased from 3.3 to 6.7% for var. H-226 and −4.3 to 1.9% for var. Sree Vijaya, respectively. Whereas, during 2070 was a decrease in the yield for vars H-226 and Sree Vijaya ranging from −9 to 3.8% and −10 to −5.2% respectively. The results indicated that var. H-226 is more resilient to the changing climate than var. Sree Vijaya. As an outcome of this study, the var. H-226 can be considered as climate-resilient, and this information can assist the decision makers in selecting an appropriate crop variety to ensure food security.