ALBERT

Description: The original Gash analytical model and the sparse Gash's model were combined to simulate rainfall interception losses from the top- and sub-canopy layers in Shaoshan evergreen forest located in central-south China in 2003. The total estimated interception loss from the two canopy layers was 334.1 mm with an overestimation of 39.8 mm or 13.5% of the total measured interception (294.3 mm). The simulated interception losses of the top- and sub-canopy suggested that the simulated interception losses in the stages of ''during storms'' and ''after storms'' were in good agreement with the published ones. Both the original Gash model and the sparse model overestimated the interception losses, but the sparse model gave more accurate estimates than the original Gash model.

Description: In this research, a dual-inexact fuzzy stochastic programming (DIFSP) method was developed for supporting the planning of water and farmland use management system considering the non-point source pollution mitigation under uncertainty. The random boundary interval (RBI) was incorporated into DIFSP through integrating fuzzy linear programming (FLP) and chance-constrained programming (CCP) approaches within an interval linear programming (ILP) framework. This developed method could effectively tackle the uncertainties expressed as intervals and fuzzy sets. Moreover, the lower and upper bounds of RBI are continuous random variables, and the correlation existing between the lower and upper bounds can be tackled in RBI through the joint probability distribution function. And thus the subjectivity of decision making is greatly reduced, enhancing the stability and robustness of obtained solutions. The proposed method was then applied to solve a water and farmland use planning model (WFUPM) with non-point source pollution mitigation. The generated results could provide decision makers with detailed water supply–demand schemes involving diversified water-related activities under preferred satisfaction degrees. These useful solutions could allow more in-depth analyses of the trade-offs between humans and environment, as well as those between system optimality and reliability. In addition, comparative analyses on the solutions obtained from ICCP (Interval chance-constraints programming) and DIFSP demonstrated the higher application of this developed approach for supporting the water and farmland use system planning.

Description: The expansion of agriculture to rugged mountains can exacerbate negative impacts of agricultural activities on ecosystem function. In this study, we monitored streamwater and rainfall chemistry of mountain watersheds at the Feitsui Reservoir Watershed in northern Taiwan to examine the effects of agriculture on watershed nutrient cycling. We found that the greater the proportion of tea plantation cover, the higher the concentrations of fertilizer-associated ions (NO3−, K+) in streamwater of the four mountain watersheds examined; on the other hand, the concentrations of the ions that are rich in soils (SO42−, Ca2+, Mg2+) did not increase with the proportion of tea plantation cover, suggesting that agriculture enriched fertilizer-associated nutrients in streamwater. Of the two watersheds for which rainfall chemistry was available, the one with higher proportion of tea plantation cover had higher concentrations of ions in rainfall and retained less nitrogen in proportion to input compared to the more pristine watershed, suggesting that agriculture can influence atmospheric deposition of nutrients and a system's ability to retain nutrients. As expected, we found that a forested watershed downstream of agricultural activities can dilute the concentrations of NO3− in streamwater by more than 70 %, indicating that such a landscape configuration helps mitigate nutrient enrichment in aquatic systems even for watersheds with steep topography. We estimated that tea plantation at our study site contributed approximately 450 kg ha−1 yr−1 of NO3-N via streamwater, an order of magnitude greater than previously reported for agricultural lands around the globe, which can only be matched by areas under intense fertilizer use. Furthermore, we constructed watershed N fluxes to show that excessive leaching of N, and additional loss to the atmosphere via volatilization and denitrification can occur under intense fertilizer use. In summary, this study demonstrated the pervasive impacts of agricultural activities, especially excessive fertilization, on ecosystem nutrient cycling at mountain watersheds.

Description: The expansion of agriculture to rugged mountains can exacerbate negative impacts of agriculture activities on ecosystem function. In this study, we monitored streamwater chemistry of four watersheds with varying proportions of agricultural lands (0.4, 3, 17, 22%) and rainfall chemistry of two of the four watersheds at Feitsui Reservoir Watershed in northern Taiwan to examine the effects of agriculture on watershed nutrient cycling. We found that the greater the proportions of agricultural lands, the higher the ion concentrations, which is evident for fertilizer-associated ions (NO3-, K+) but not for ions that are rich in soils (SO42-, Ca2+, Mg2+), suggesting that agriculture enriched fertilizer-associated nutrients in streamwater. The watershed with the highest proportion of agricultural lands had higher concentrations of ions in rainfall and lower nutrient retention capacity (i.e. higher output–input ratio of ions) compared to the relatively pristine watershed, suggesting that agriculture can influence atmospheric deposition of nutrients and a system's ability to retain nutrients. Furthermore, we found that a forested watershed downstream of agricultural activities can dilute the concentrations of fertilizer-associated ions (NO3−, K+) in streamwater by more than 70%, indicating that specific landscape configurations help mitigate nutrient enrichment to aquatic systems. We estimated that agricultural lands at our study site contributed approximately 400 kg ha−1 yr−1 of NO3-N and 260 kg ha−1 yr−1 of PO4-P output via streamwater, an order of magnitude greater than previously reported around the globe and can only be matched by areas under intense fertilizer use. Furthermore, we re-constructed watershed nutrient fluxes to show that excessive leaching of N and P, and additional loss of N to the atmosphere via volatilization and denitrification, can occur under intense fertilizer use. In summary, this study demonstrated the pervasive impacts of agriculture activities, especially excessive fertilization, on ecosystem nutrient cycling at mountain watersheds.

Description: The original Gash analytical model and the sparse Gash's model have been applied to simulate rainfall interception losses from the two canopy layers in Shaoshan forest of central-south China during 2003. The total estimated interception loss from the two canopy layers is 478.4 mm with an error of 12.4 mm or 2.7% of total measured interception loss (466.0 mm). Both the original Gash model for top-canopy interception loss and the sparse model for sub-canopy loss overestimate interception losses. The simulated results show that the interception losses in top-canopy is 182.6 mm with an overestimation of 4.9% of measured losses and that in sub-canopy is 295.8 mm with an overestimation of 1.3%. The simulated values of the top-canopy suggest that 47% of the simulated interception losses are evaporated in the stage of "during storms" and 38% in "after storms", which is similar to the published results in temperate and tropical forests. However, the modelled losses from the sub-canopy show that 17% of interception losses are evaporated in "during storms" and 70% in "after storms", which is deviated from the reported results. The simulated results of two canopy interception losses in Shaoshan forest indicate that canopy structures may strongly impact hydrological fluxes in forested ecosystems.

Description: The occurrence of climate warming is unequivocal, and is expected to be experienced through increases in the magnitude and frequency of extreme events, including flooding. This paper presents an analysis of the implications of climate change on the future flood hazard in the Beijiang River basin in South China, using a variable infiltration capacity (VIC) model. Uncertainty is considered by employing five global climate models (GCMs), three emission scenarios (representative concentration pathway (RCP) 2.6, RCP4.5, and RCP8.5), 10 downscaling simulations for each emission scenario, and two stages of future periods (2020–2050, 2050–2080). Credibility of the projected changes in floods is described using an uncertainty expression approach, as recommended by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). The results suggest that the VIC model shows a good performance in simulating extreme floods, with a daily runoff Nash–Sutcliffe efficiency coefficient (NSE) of 0.91. The GCMs and emission scenarios are a large source of uncertainty in predictions of future floods over the study region, although the overall uncertainty range for changes in historical extreme precipitation and flood magnitudes are well represented by the five GCMs. During the periods 2020–2050 and 2050–2080, annual maximum 1-day discharges (AMX1d) and annual maximum 7-day flood volumes (AMX7fv) are expected to show very similar trends, with the largest possibility of increasing trends occurring under the RCP2.6 scenario, and the smallest possibility of increasing trends under the RCP4.5 scenario. The projected ranges of AMX1d and AMX7fv show relatively large variability under different future scenarios in the five GCMs, but most project an increase during the two future periods (relative to the baseline period 1970–2000).

Description: The occurrence of climate warming is unequivocal, and is expected to be experienced through increases in the magnitude and frequency of extreme events, including flooding. This paper presents an analysis of the implications of climate change on the future flood hazard in the Beijiang River basin in South China, using a Variable Infiltration Capacity (VIC) model. Uncertainty is considered by employing five Global Climate Models (GCMs), three emission scenarios (RCP2.6, RCP4.5, and RCP8.5), ten downscaling simulations for each emission scenario, and two stages of future periods (2020–2050, 2050–2080). Credibility of the projected changes in floods is described using an uncertainty expression approach, as recommended by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). The results suggest that the VIC model shows a good performance in simulating extreme floods, with a daily runoff Nash and Sutcliffe efficiency coefficient (NSE) of 0.91. The GCMs and emission scenarios are a large source of uncertainty in predictions of future floods over the study region, although the overall uncertainty range for changes in historical extreme precipitation and flood magnitudes are well represented by the five GCMs. During the periods 2020–2050 and 2050–2080, annual maximum 1-day discharges (AMX1d) and annual maximum 7-day flood volumes (AMX7fv) are projected to show very similar trends, with the largest possibility of increasing trends occurring under the RCP2.6 scenario, and the smallest possibility of increasing trends under the RCP4.5 scenario. The projected ranges of AMX1d and AMX7fv show relatively large variability under different future scenarios in the five GCMs, but most project an increase during the two future periods (relative to the baseline period 1970–2000).

Description: In this research, a dual-inexact fuzzy stochastic programming (DIFSP) method was developed for supporting the planning of water and farmland use management system considering the non-point source pollution mitigation under uncertainty. The random boundary interval (RBI) was incorporated into DIFSP through integrating fuzzy programming (FP) and chance-constrained programming (CCP) approaches within an interval linear programming (ILP) framework. The lower and upper bounds of RBI are continuous random variables, and the correlation exiting between the lower and upper bounds can be tackled in RBI through the joint probability distribution function. And thus the subjectivity of decision making is greatly reduced, enhancing the stability and robustness of obtained solutions. The proposed method was then applied to solve a water and farmland use planning model (WFUPM) with non-point source pollution. The generated results could provide decision makers with detailed water supply-demand schemes involving diversified water related activities under various system conditions. These useful solutions could allow more in-depth analyses of the trade-offs between human and environment, as well as those between system optimality and reliability. In addition, comparative analyses on the solutions obtained from ICCP (Interval chance-constraints programming) and DIFSP demonstrated the higher application of this developed approach for supporting the water and farmland use system planning.