Abstract:This paper evaluates the possible impacts of climate change and land use change and its combined effects on soil loss and net soil loss (erosion and deposition) in the Mae Nam Nan sub-catchment, Thailand. Future climate from two general circulation models (GCMs) and a regional circulation model (RCM) consisting of HadCM3, NCAR CSSM3 and PRECIS RCM ware downscaled using a delta change approach. Cellular Automata/Markov (CA_Markov) model was used to characterize future land use. Soil loss modeling using Revised … Show more
“…Climate models estimate [2][3][4][5][6][7][8] that the global average temperature is likely to increase 4.0 • C by the conclusion of the 21st century [9]. Reliable prediction of climate is pre-requisite to comprehend its impacts on hydrology and water resources [10].…”
Assessment of climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs). This paper discusses the consequences of climate change on discharge. Historical climatic and gauging data were collected from different stations within a watershed. Bias correction was performed on GCMs temperature and precipitation data. After successful development of the hydrological modeling system (SWAT) for the basin, streamflow was simulated for three future periods (2011-2040, 2041-2070, and 2071-2100) and compared with the baseline data to explore the changes in different flow indicators such as mean flow, low flow, median flow, high flow, flow duration curves, temporal shift in peaks, and temporal shifts in center-of-volume dates. From the results obtained, an overall increase in mean annual flow was projected in the basin under both RCP 4.5 and RCP 8.5 scenarios. Winter and spring showed a noticeable increase in streamflow, while summer and autumn showed a decrease in streamflow. High flows were predicted to increase, but median flow was projected to decrease in the future under both scenarios. Flow duration curves showed that the probability of occurrence of high flow is likely to be more in the future. It was also noted that peaks were predicted to shift from May to July in the future, and the center-of-volume date of the annual flow may vary from −11 to 23 days in the basin, under both RCP 4.5 and RCP 8.5. As a whole, the Mangla basin will face more floods and less droughts in the future due to the projected increase in high and low flows, decrease in median flows and greater temporal and magnitudinal variations in peak flows. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.
“…Climate models estimate [2][3][4][5][6][7][8] that the global average temperature is likely to increase 4.0 • C by the conclusion of the 21st century [9]. Reliable prediction of climate is pre-requisite to comprehend its impacts on hydrology and water resources [10].…”
Assessment of climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs). This paper discusses the consequences of climate change on discharge. Historical climatic and gauging data were collected from different stations within a watershed. Bias correction was performed on GCMs temperature and precipitation data. After successful development of the hydrological modeling system (SWAT) for the basin, streamflow was simulated for three future periods (2011-2040, 2041-2070, and 2071-2100) and compared with the baseline data to explore the changes in different flow indicators such as mean flow, low flow, median flow, high flow, flow duration curves, temporal shift in peaks, and temporal shifts in center-of-volume dates. From the results obtained, an overall increase in mean annual flow was projected in the basin under both RCP 4.5 and RCP 8.5 scenarios. Winter and spring showed a noticeable increase in streamflow, while summer and autumn showed a decrease in streamflow. High flows were predicted to increase, but median flow was projected to decrease in the future under both scenarios. Flow duration curves showed that the probability of occurrence of high flow is likely to be more in the future. It was also noted that peaks were predicted to shift from May to July in the future, and the center-of-volume date of the annual flow may vary from −11 to 23 days in the basin, under both RCP 4.5 and RCP 8.5. As a whole, the Mangla basin will face more floods and less droughts in the future due to the projected increase in high and low flows, decrease in median flows and greater temporal and magnitudinal variations in peak flows. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.
“…The most vulnerable area is steeply sloping land, which is under cultivation (more than 35% of sloping land). In recent times, human encroachment on forest areas in the upper part of the study area and land use changes with respect to agriculture have become problematic [48].…”
Predicting sediment yield is necessary for good land and water management in any river basin. However, sometimes, the sediment data is either not available or is sparse, which renders estimating sediment yield a daunting task. The present study investigates the factors influencing suspended sediment yield using the principal component analysis (PCA). Additionally, the regression relationships for estimating suspended sediment yield, based on the selected key factors from the PCA, are developed. The PCA shows six components of key factors that can explain at least up to 86.7% of the variation of all variables. The regression models show that basin size, channel network characteristics, land use, basin steepness and rainfall distribution are the key factors affecting sediment yield. The validation of regression relationships for estimating suspended sediment yield shows the error of estimation ranging from −55% to +315% and −59% to +259% for suspended sediment yield and for area-specific suspended sediment yield, respectively. The proposed relationships may be considered useful for predicting suspended sediment yield in ungauged basins of Northern Thailand that have geologic, climatic and hydrologic conditions similar to the study area.
“…Climate change is expected to impact soil erosion based on factors like precipitation amount, the impact of precipitation intensity on soil moisture and plant growth [15]. The most direct effect of climate change on erosion by water can be expected to be the effect of changes in rainfall erosivity [16][17][18][19]. Thus, an increase in soil erosion can be expected due to the increase in rainfall erosivity.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, an increase in soil erosion can be expected due to the increase in rainfall erosivity. Table 1 shows earlier studies projecting impacts of climate change on rainfall erosivity [19][20][21][22][23]. Climate change is expected to affect soil erosion based on a variety of factors [24] including changes in precipitation amount and intensity, impacts on soil moisture and plant growth, etc.…”
Section: Introductionmentioning
confidence: 99%
“…Climate change is expected to affect soil erosion based on a variety of factors [24] including changes in precipitation amount and intensity, impacts on soil moisture and plant growth, etc. Several studies have also shown that climate change could significantly affect soil erosion (as shown in Table 2) [19,20,25,26]. One of the direct impacts of climate change on soil erosion is the change in the erosive power of rainfall [23][24][25].…”
This study focuses on the impacts of climate change on rainfall erosivity in the Huai Luang watershed, Thailand. The multivariate climate models (IPCC AR5) consisting of CCSM4, CSIRO-MK3.6.0 and MRI-CGCM3 under RCP4.5 and RCP8.5 emission scenarios are analyzed. The Quantile mapping method is used as a downscaling technique to generate future precipitation scenarios which enable the estimation of future rainfall erosivity under possible changes in climatic conditions. The relationship between monthly precipitation and rainfall erosivity is used to estimate monthly rainfall erosivity under future climate scenarios. The assessment compared values of rainfall erosivity during 1982-2005 with future timescales (i.e., the 2030s, 2050s, 2070s and 2090s). The results indicate that the average of each General Circulation Model (GCM) combination shows a rise in the average annual rainfall erosivity for all four future time scales, as compared to the baseline of 8302 MJ mm ha −1 h −1 year −1 , by 12% in 2030s, 24% in 2050s, 43% in 2070s and 41% in 2090s. The magnitude of change varies, depending on the GCMs (CCSM4, CSIRO-MK3.6.0, and MRI-CGCM3) and RCPs with the largest change being 82.6% (15,159 MJ mm ha −1 h −1 year −1 ) occurring under the MRI-CGCM3 RCP8.5 scenario in 2090s. A decrease in rainfall erosivity has been found, in comparison to the baseline by 2.3% (8114 MJ mm ha −1 h −1 year −1 ) for the CCSM4 RCP4.5 scenario in 2030s and 2.6% (8088 MJ mm ha −1 h −1 year −1 ) for the 2050s period. However, this could be considered uncertain for future rainfall erosivity estimation due to different GCMs. The results of this study are expected to help development planners and decision makers while planning and implementing suitable soil erosion and deposition control plans to adapt climate change in the Huai Luang watershed.
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