Assessing the impacts of climate change on water resources in the Srepok watershed, Central Highland of Vietnam

A general circulation model (GCM) and hydrological model SWAT (Soil and Water Assessment Tool) under forcing from A1B, B1, and A2 emission scenarios by 2030 were used to assess the implications of climate change on water balance of the Gorganrood River Basin (GRB). The results of MPEH5C models and multi-scenarios indicated that monthly precipitation generally decreases while temperature increases in various parts of the basin with the magnitude of the changes in terms of different stations and scenarios. Accordingly, seasonal ET will decrease throughout the GRB over the 2020s in all seasons except in summer, where a slight increase is projected for A1B and A2 scenarios. At annual scale, average quick flow and average low flow under the B1, A1B, and A2 scenarios are projected to decrease by 7.3 to 12.0% from the historical levels. Over the ensembles of climate change scenarios, the simulations project average autumn total flow declines of ∼10% and an overall range of 6.9 to 13.2%. In summer, the components of flow at the studied basin are expected to increase under A2 and A1B scenarios but will slightly decrease under B1 scenario. The study result addresses a likelihood of inevitable future climate change.

Assessment of future climate and vegetation canopy change impacts on hydrological behavior of Chungju dam watershed using SWAT model

The study highlights the potential characteristics of droughts under future climate change scenarios. For this purpose, the changes in Standardized Precipitation Evapotranspiration Index (SPEI) under the A1B, A2, and B1 climate change scenarios in Iran were assessed. The daily weather data of 30 synoptic stations from 1992 to 2010 were analyzed. The HadCM3 statistical model in the LARS-WG was used to predict the future weather conditions between 2011 and 2112, for three 34-year periods; 2011-2045, 2046-2079, and 2080-2112. In regard to the findings, the upward trend of the potential evapotranspiration in parallel with the downward trend of the precipitation in the next 102 years in three scenarios to the base timescale was transparent. The frequency of the SPEI in the base month indicated that 17.02% of the studied months faced the drought. Considering the scenarios of climate change for three 34-year periods (i.e., 2011-2045, 2046-2079, and 2080-2112) the average percentages of potential drought occurrences for all the stations in the next three periods will be 8.89, 16.58, and 27.27 respectively under the B1 scenario. While the predicted values under the A1B scenario are 7.63, 12.66, and 35.08%respectively. The relevant findings under the A2 scenario are 6.73, 10.16, 40.8%. As a consequence, water shortage would be more serious in the third period of study under all three scenarios. The percentage of drought occurrence in the future years under the A2, B1, and A1B will be 19.23%, 17.74%, and 18.84%, respectively which confirms the worst condition under the A2 scenario. For all stations, the number of months with moderate drought was substantially more than severe and extreme droughts. Considering the A2 scenario as a high emission scenario, the analysis of SPEI frequency illustrated that the proportion of dry periods in regions with humid and cool climate is more than hot and warm climates; however, the duration of dry periods in warmer climates is longer than colder climates. Moreover, the temporal distribution of precipitation and potential evapotranspiration indicated that in a large number of stations, there is a significant difference between them in the middle months of the year, which justifies the importance of prudent water management in warm months.

Introduction: Forecasting the inflow to the reservoir is important issues due to the limited water resources and the importance of optimal utilization of reservoirs to meet the need for drinking, industry and agriculture in future time periods. In the meantime, ignoring the effects of climate change on meteorological and hydrological parameters and water resources in long-term planning of water resources cause inaccuracy. It is essential to assess the impact of climate change on reservoir operation in arid regions. In this research, climate change impact on hydrological and meteorological variables of the Shahcheragh dam basin, in Semnan Province, was studied using an integrated model of climate change assessment. Materials and Methods: The case study area of this study was located in Damghan Township, Semnan Province, Iran. It is an arid zone. The case study area is a part of the Iran Central Desert. The basin is in 12 km north of the Damghan City and between 53° E to 54° 30’ E longitude and 36° N to 36° 30’ N latitude. The area of the basin is 1,373 km2 with average annual inflow around 17.9 MCM. Total actual evaporation and average annual rainfall are 1,986 mm and 137 mm, respectively. This case study is chosen to test proposed framework for assessment of climate change impact hydrological and meteorological variables of the basin. In the proposed model, LARS-WG and ANN sub-models (7 sub models with a combination of different inputs such as temperature, precipitation and also solar radiation) were used for downscaling daily outputs of CGCM3 model under 3 emission scenarios, A2, B1 and A1B and reservoir inflow simulation, respectively. LARS-WG was tested in 99% confidence level before using it as downscaling model and feed-forward neural network was used as raifall-runoff model. Moreover, the base period data (BPD), 1990-2008, were used for calibration. Finally, reservoir inflow was simulated for future period data (FPD) of 2015-2044 and compared to BPD. The best ANN sub-model has minimum Mean Absolute Relative Error (MARE) index (0.27 in test phases) and maximum correlation coefficient (ρ) (0.82 in test phases). Results and Discussion: The tested climate change scenarios revealed that climate change has more impact on rainfall and temperature than solar radiation. The utmost growth of monthly rainfall occurred in May under all the three tested climate change scenarios. But, rainfall under A1B scenario had the maximum growth (52%) whereas the most decrease occurred (–21.5%) during January under the A2 climate change scenario. Rainfall dropped over the period of June to October under the three tested climate change scenarios. Furthermore, in all three scenarios, the maximum temperature increased about 2.2 to 2.6°C in May but the lowest increase of temperature occurred in January under A2 and B1 scenarios as 0.3 and 0.5°C, respectively. The maximum temperature usually increased in all months compared to the baseline period. Minimum and maximum temperatures enlarged likewise in all months, with 2.05°C in September under A2 climate change scenario. Conversely, solar radiation change was comparatively low and the most decreases occurred in February under A1B and A2 climate change scenarios as –4.2% and –4.3% , respectively, and in August under the B1 scenario as –4.2%. The greatest increase of solar radiation occurs in April, November, and March by 3.1%, 3.2%, and 4.9% for A1B, A2, and B1 scenarios, respectively. The impact of climate change on rainfall and temperature can origin changes on reservoir inflow and need new strategies to adapt reservoir operation for change inflows. Therefore, first, reservoir inflow in future period (after climate change impact) should be anticipated for the adaptation of the reservoir. A Feed-Forward (FF) Multilayer-Perceptron (MLP) Artificial Neural Network (ANN) model was nominated for the seven tested ANN models based on minimization of error function. The selected model had 12 neurons in the hidden layer, and two delays. The comparison of forecasted flow hydrograph by selecting an ANN model and observed one proved that forecasted flow hydrograph can follow observed one closely. By comparison with the IHACRES model, this model displayed a 54% and 46% lower error functions for validation data. The selected model was used to forecast flow for the climate change scenarios of the future period. Conclusions: The results show a reduction of monthly flow in most months and annual flow in all studied scenarios. The following main points can be concluded: • By climate change, flow growths in dry years and it declines in wet and normal years. • The studied climate change scenarios showed that climate change has more impact on rainfall and temperature than solar radiation.

Climate change is likely to increase the global mean temperature in future decades, increasing evapotranspiration and affecting precipitation and discharge. However, there is always uncertainty in future climate projection which might be due to the use of different general circulation models (GCMs), greenhouse gas emission scenarios, downscaling techniques, or other factors. This uncertainty in future climates may affect quantification of discharge. This chapter aims to describe the impact of climate change and uncertainty on future climate projection, specifically on discharge in the Nam Ou River Basin, Lao PDR. The Long Ashton Research Station Weather Generator (LARS-WG) was used to downscale daily precipitation, and maximum and minimum temperatures using 8 GCMs for future periods 2011–2030, 2046–2065, and 2080–2099 under scenarios A1B and A2, respectively. Probability density functions (PDFs) were constructed to estimate the uncertainty of future climates. The maximum and minimum temperatures are expected to increase and change for precipitation in future and are observed to be multidirectional. The temperature and precipitation projection due to the GCMs varied by a higher range under both scenarios for the 2090s compared to the 2020s and 2055s. The inter-model variability and variance of the future projections increased in the latter part of the century compared to the early and mid-centuries. In order to assess the impact of these uncertainties in future climates, the soil and water assessment tool (SWAT) model was used. An increase in annual discharge of 5.7 % is observed during the 2055s and 3.13 % during the 2090s under A1B scenario and of 9.23 % during the 2090s under A2 scenario. At the same time, the discharge is estimated to decrease by 0.33 % during the 2020s under A1B and by 1.15 % during the 2055s under A2 scenario. Thus, an increase and decrease in discharge is expected for future periods when all GCMs are considered under A1B and A2 scenarios.

Malaba River in Uganda a focal area to the Lake Kyoga basin is prone to climate change because of its heavy reliance on rainfall as its major flow contributor. The impacts of climate change on streamflow in Malaba River were assessed using LARS-WG downscaling model and Soil and Water Assessment Tool (SWAT) model. This was achieved by downscaling the future (2020-2050) precipitation and temperature variables for A1B and A2 scenarios and simulating the projected climate with calibrated LARS-WG and SWAT models for the two scenarios. The SWAT calibration (1992 - 1999) and validation (2000 - 2004) NSE results were respectively 0.55 and 0.35. Results indicated that the projected areal rainfall will increase by 0.34 mm per year for A1B which is averagely 1% less than the baseline period. Areal rainfall for A2 scenario will increase by 0.41 mm per year which is averagely 9% more than the baseline period. The Flow Duration Curve analyses indicated that the A2 scenario displayed higher flows for all the percentiles as compared to the baseline flows while A1B scenario has lower flows for percentiles less than 50, and equal or slightly higher flows for percentiles greater than 50 as compared to the baseline flows.

A hydro-environmental model chain in the Doam dam basin, Korea, was developed for an impact assessment under the Intergovernmental Panel on Climate Change's A1B scenario. The feasible downscaling scheme composed of an artificial neural network (ANN) and non-stationary quantile mapping was applied to the GCM (Global Climate Model) output. The impacts under climate and land use change scenarios were examined and projected using the Soil and Water Assessment Tool (SWAT) model. The daily SWAT model was calibrated and validated for 2003–2004 and 2006–2008, respectively. Meanwhile the monthly SS (suspended solids) was calibrated and validated for 1999–2001 and 2007–2009, respectively. The simulation results illustrated that under the assumption of 1–5% urbanization of the forest area, the hydrologic impact is relatively negligible and the climate change impacts are dominant over the urbanization impacts. Additionally the partial impacts of land use changes were analyzed under five different scenarios: partial change of forest to urban (PCFUr), to bare field, to grassland, to upland crop (PCFUp), and to agriculture (PCFA). The analysis of the runoff change shows the highest rate of increase, 73.57% in April, for the PCFUp scenario. The second and third highest rate increases, 37.83% and 31.45% in May, occurred under the PCFA and PCFUr scenarios, respectively.

To account for CO2 impacts on the vegetation leaf area index (LAI) and the different responses of stomatal conductance in various vegetation species, the modified Soil and Water Assessment Tool (SWAT) model is calibrated in the Xinjiang Basin with the measured daily streamflow from 1961 to 1970, showing that SWAT accurately reproduces monthly and annual streamflow from 1971 to 2005 and actual evapotranspiration (AET) from 1983 to 2005. AET and streamflow in the basin for the 21st century are assessed using a multi-model ensemble approach in which downscaled and bias corrected outputs are compared to the baseline (1971–1999) and used to drive the SWAT model. The ensemble average annual precipitation decreases in the three scenarios, with maximum decreases of 7.61% during 2011–2050 and 4.80% during 2051–2090 in scenario A2. In contrast, the ensemble average annual and monthly temperature (minimum and maximum) increase by different amounts during the two periods in each scenario. For each emissions scenario, the ensemble average annual AET will increase, while the average annual streamflow will decrease. Under the A2 scenario, the maximum changes of average annual AET (streamflow) are +18.31% (−20.93%) and +20.93% (−23.43%) during 2011–2050 and 2051–2090, respectively. Compared to the baseline, the ensemble average annual minimum streamflow during 2011–2050 will decrease by approximately 20% under the A1B and A2 scenarios and by 7% in the B1 scenario. Thus, the ensemble average annual maximum streamflow will decrease under each scenario during the two periods, except for a 6.42% increase in the A1B scenario during 2051–2090. A Generalized Extreme Value (GEV) analysis, simultaneously, suggests that the maximum annual streamflow will decrease during the two periods under each scenario, with exception during 2051–2090 in the scenario A1B, and the magnitudes will decrease for 2-, 5-, 10-, 20- and 50-year return periods.

Assessing the impacts of climate change and tillage practices on stream flow, crop and sediment yields from the Mississippi River Basin

Climate change is expected to alter streamflow and sediment load in the transboundary Mekong River Basin (MRB), thereby affecting aquatic ecosystem and people’s livelihoods. Thus, future decisions on sustainable management of water resources in the basin should take climate change impact into consideration. In this study, the responses of river discharge and sediment (total suspended sediment [TSS]) load to future climate change in the transboundary MRB have been investigated. The Soil and Water Assessment Tool (SWAT) model is used as a simulation tool, which has been carefully calibrated and validated against the observation data in the period of 1982–2005 before applying to evaluate the impact of climate change scenarios for the near‐future period of 2016–2040. The climate change scenarios, which are acquired from four different Regional Climate Models (RCMs) downscaled from the Hadley Centre Global Environmental Model version 2‐Atmosphere (HadGEM2‐AO), are applied in the present study. In regard to the effect of changing climate, the annual river discharge and TSS load are forecasted to rise from 3.35 to 9.13% and from 7.45 to 13.15%, respectively. Generally, the TSS loading projection varies larger than the streamflow projection concerning changing climate. The findings here are also agreed with the conclusions of previous investigations on the future impacts of changing climate in the MRB.

Aeolesthes sarta or Trirachys sarta is a polyphagous long-horned beetle that has caused severe damage to the Populus alba forests/plantations in its regions of origin. Climate change could accelerate the introduction and spread of invasive pest species, potentially causing ecological damage and economic losses. Furthermore, globalization and increased trade can inadvertently transport pests across borders into regions where they do not already occur. Hence, it is crucial to identify areas where the climate is most suitable for the establishment of A. sarta’s and which areas of the world are suitable for the growth of P. alba under climate change scenarios. This study employed the CLIMEX model to estimate the potential global distribution of A. sarta and its correlation with its dominant host, P. alba, under current climatic conditions and potential future scenarios, namely the A1B and A2 climate change scenarios (CCSs). Under current climatic conditions, the model indicates that the establishment of a climatically suitable habitat for A. sarta extends beyond its current known range. The model estimated that, under the world’s current climatic conditions, 41.06% of the world can provide suitable areas (EI > 0) for the survival of A. sarta. For P. alba, under the current climatic conditions, suitable regions for the growth of P. alba are present in all continents (excluding Antarctica); under the world’s current climatic conditions, 53.52% of the world can provide suitable areas for the growth of P. alba (EI > 0). Climate change will significantly alter the number of suitable habitats for A. sarta development and P. alba growth globally. In future climatic conditions, the number areas capable of supplying suitable habitats (EI > 0) for A. sarta will slightly decrease to 40.14% (under A1B and A2 CCSs), while, for P. alba, the number areas capable of supplying suitable habitats will also marginally decrease to 50.39% (under A1B scenario), and this figure is estimated to drop to 48.41% (under A2 scenario) by the end century (2100). Asia, Europe, North America, South America, and Oceania have a high percentage of highly suitable areas for A. sarta development and P. alba growth under current climatic conditions; however, according to estimates of future climatic conditions, by the end century, only Asia, Europe, North America, and Oceania will have a high percentage of highly suitable areas for A. sarta development and P. alba growth. The range of highly suitable habitats is likely to increase in the northern hemisphere; however, this range is expected to shrink with regards to the southern hemisphere. The range contraction was higher under the A2 climate change scenario due to a higher warming trend than in the A1B scenario. Due to climate change, the range of A. sarta development shifted, as did the P. alba growth range, which, thanks to the suitable environmental conditions for the growth of P. alba, makes all those regions vulnerable to the introduction and development of A. sarta. Strict monitoring, prevention, and control measures at borders, airports, and seaports before the trade of P. alba and other suitable host species wood (logs/billets) are highly recommended to prevent the spread of A. sarta and ensure biodiversity security. It is expected that the A. sarta and P. alba climate models presented here will be useful for management purposes since both can be adapted to guide decisions about imparting resources to regions where the threat of pest invasion remains and away from regions where climate suitability is predicted to decrease in the future.

The Simineh River is heavily reliant on water resources for agricultural aims in the Lake Urmia. However, the hydrological system of the Simineh basin is highly susceptible to the impacts of climate change scenarios, primarily due to the presence of diverse topographical features, limited availability of data, and the complex nature of the local climate. This study aimed to simulate the monthly discharge of the Simineh River using the SWAT and assess the effects of climate change on the monthly discharge. Future climate scenarios for the years 2011-2030 were generated using the HadCM3 weather models under the A2, B1, and A1B scenarios. After evaluating the performance of the LARS-WG model in producing precipitation, minimum and maximum temperatures for the Simineh River watershed, the output of the HadCM3 under the A1B, B1, and A2 scenarios reduced, and the desired meteorological parameters predicted. These predicted values used as inputs for the SWAT model. In this study, assuming no change in land use, the focus was solely on the impact of climate change scenarios. However, appropriate measures can be taken to save the Simineh River's water consumption by optimizing irrigation efficiency through innovative methods. This is crucial because the results indicate that a total reduction of up to 25% in discharge in the Lake Urmia basin under climate change leads to a significant decrease in the annual average inflow to the lake from 570 million cubic meters to 394, 398, and 440 million cubic meters under the A2, B1, and A1B scenarios, respectively. The Simineh River supplies 11% of the water in Lake Urmia, and taking necessary measures to conserve its water resources is essential.

<p>A Soil and Water Assessment Tool (SWAT) model was developed for the Iowa-Cedar River Basin (ICRB), a 32,660 km<sup>2</sup> watershed dominated by agricultural land cover (∼70%) to simulate hydrology and water quality for the prediction of stream discharge, nitrate loads, and nitrate concentration under climate and land use change scenarios. Iowa exports as much as 20% of the nitrogen entering the Gulf of Mexico at the mouth of the Mississippi, contributing to Gulf hypoxia as well as local threats to water quality in the ICRB. The model utilized a combined autocalibration and sensitivity procedure incorporating Sequential Uncertainty Fitting (SUFI) and generalized additive models. This procedure resulted in Nash-Sutcliffe Efficiency (NSE) goodness-of-fit statistics that met literature guidelines for monthly mean stream discharge (NSE≥0.60) and daily nitrate load (NSE≥0.50). Artificial neural networks coupled with SWAT stream discharges aided in the simulation of daily mean nitrate concentrations that met the literature guideline (NSE≥0.50).</p> <p>The North American Regional Climate Change Assessment Program (NARCCAP) provided an ensemble of 11 climate change scenarios. NARCCAP is a multi-institutional effort to simulate climate change at the mesoscale by downscaling global circulation models (GCM) with regional climate models (RCM). The resulting GCM-RCM produced synthetic precipitation and temperature time-series that drove the SWAT simulations and scenarios. The land use scenarios were a collaboration with the U.S. Army Corps of Engineers, using a rule-based GIS method to generate scenarios that (1) maximized agricultural productivity, (2) improved water quality and reduced flooding, and (3) enhanced local biodiversity. The SWAT simulations and ensemble climate change scenarios resulted in a warmer and wetter climate with greater and more extreme discharge in all seasons except summer where the models indicate a somewhat higher probability of extreme low flows (p-value<0.05). The land use scenarios for SWAT showed that nitrate load and discharge positively and linearly scale with percent of agricultural land area (p-value<0.05).</p>

<abstract><title><italic>Abstract.</italic></title> This study employed the Soil and Water Assessment Tool (SWAT) to evaluate the impacts of projected future climate change scenarios on water balance, runoff, sediment, total nitrogen (N), and total phosphorus (P) at the field scale for four locations in the Heartland region: Sioux City (Iowa) and Columbus, Mullen, and Harrison (Nebraska). AÂ conventional two-year corn-soybean rotation was assumed to be grown on each field. All fields were simulated identically in terms of topographic and cover/land management conditions. Model inputs for the fields differed in only three ways: the forcing conditions for existing and future climatic scenarios (SRES A2, A1B, and B1), soil and aquifer properties, and calibrated parameters at each location. Model simulations indicate that for the Columbus and Sioux City sites, where current average annual precipitation is about 740 and 650 mm, respectively, substantial increases in runoff and pollutant loadings from a corn-soybean crop rotation are projected to occur during the spring under future climate scenarios in comparison to existing conditions. At the Sioux City site, for example, increases in runoff of 213%, 124%, and 128% during the month of May are projected for the A2, A1B, and B1 scenarios, respectively, in comparison to the baseline condition. Very large increases in attendant sediment and nutrient losses are also projected for that month at the Sioux City site. Considerably greater attention in coming years will therefore likely be necessary to devise best management practices and adaptation strategies that can be effectively employed to conserve soil and water resources and to protect streams and receiving waters from the harmful effects of higher pollutant loadings. At the Harrison site, where average annual precipitation is less than 450 mm, increases in average annual evapotranspiration of 29, 31, and 46 mm under the A2, A1B, and B1 future climate scenarios are projected to occur for a corn-soybean crop. Relative to the baseline at this site, water requirements are projected to be 37, 39, and 32 mm greater, respectively, for the peak irrigation month of July under the A2, A1B, and B1 scenarios. For regions in western Nebraska with similar or lesser precipitation levels, these anticipated changes could exacerbate already existing challenges for agricultural producers who primarily rely upon groundwater for irrigation. SWAT was employed to simulate the impact of four best management practices (BMPs) on changes in sediment, total N, and total P under the baseline and future climate change scenarios for each site. These four treatments included conversion of the corn-soybean rotation to pasture, switchgrass, and no-till, and implementation of a 10 m wide edge-of-field buffer strip. At all four sites, the pasture and switchgrass BMPs reduced sediment and total P yields by 97% to 99% in comparison to the corn-soybean cover crop. Each of the BMP treatments employed in this study appears to hold promise in providing potential reductions in sediment and nutrients for the two eastern field sites. However, further analyses are needed to not only assess the impact of other types of BMPs, but their cost effectiveness and sustainability as well. Model simulations suggest that, for the Harrison site, moderate decreases in sediment, total N, and total P are projected to occur for the no-till BMP and modest decreases for the 10 m buffer strip BMP. Model simulations also suggest that, of the four BMP types, only the pasture and switchgrass treatments appear to provide appreciable reductions in sediment and nutrients at the Mullen site.

Potential impacts of climate change on streamflow in the Gomti River basin of India were studied using the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated using monthly streamflow data of four gauging stations of the basin. Climate change scenarios were developed using spatially downscaled (0.5×0.5°) MIROC3.2 (HiRes) GCM data for A2, A1b and B1 emission scenarios. The analysis showed that annual rainfall is likely to increase by 10 % to 18 %, 15 % to 24 %, and 19 % to 26 % during the 2020s, 2050s and 2080s, respectively. Mean annual stream-flows were projected to increase by 15 % to 38 %, 25 % to 44 % and 40 % to 55 % during the 2020s, 2050s and 2080s, respectively. Simulation results also indicated spatial and temporal variability in stream-flow in the basin, indicating the need for location-specific adaptation measure for planning of water use in the basin. The findings of the study could be useful for planning and managing water resources in the Gomti river basin for adaptation to climate change.