Modeling the supply, demand, and stress of water resources using ecosystem services concept in Sirvan River Basin (Kurdistan-Iran)

Water resources modeling can provide valuable information to planners. In this respect, water yield is an ecosystem service with significant roles in the sustainability of societies and ecosystems. The present study aimed to model the supply and demand of water resources and identify their scarcity and stress in the Sirvan river basin. For this purpose, we employed the ecosystem services concept as new thinking in earth sciences and using soil, climate, and land use data. Firstly, the Landsat satellite images of 2019 were prepared after different corrections, and the land use map was produced. Then, precipitation, evapotranspiration, root restricting layer depth, and evapotranspiration coefficients of the land uses were prepared and modeled in InVEST 3.8.9 software environment. The findings indicated that the water yield in this river basin is 5,381 million m3, with sub-basins 5, 11, and 1 having the highest water yield per year and sub-basin 2 having the lowest water yield. Moreover, sub-basins 5 and 11 had the highest water consumption. Based on the estimated water scarcity and stress index, sub-basin 8 has experienced water scarcity and sub-basin 4 water stress. We conclude that applying the InVEST Water Yield model to assess water resource status at the basin and sub-basins level can provide suitable results for planning.

Modelling, control and supervisory optimization of generalized predictive control in catalytic cracking reactor

As gasoline demand increases, the efficiency of operation of Fluidized Catalytic Cracking Unit (FCCU) becomes paramount importance. In this paper, a dynamic model for FCCU is simulated and integrated with yield model in order to estimate the yield of products namely gasoline, light gases and coke. Conventional PI controllers are designed for the control of reactor and regenerator temperature. Since, the complete reaction occurs in a very short duration, the controllers are tuned so as to achieve shorter settling time and minimum overshot. Further in order to increase the yield, optimization of FCCU using Generalized Predictive Controller (GPC) at supervisory level is attempted. Through optimization of objective function, the GPC will provide optimized set point for the PI controller in order to maintain maximum gasoline yield.

Development of agro-climatic grape yield model for Nashik region, Maharashtra, India

Prediction of the crop yield is need of time according to the change in climate conditions. In the present study, the Agro-Climatic Grape Yield (ACGY) model has been developed with monthly climatic parameters using multi-regression analysis approach. The developed model was statistically tested for its predictive ability. The discrepancy ratio, the standard deviation of discrepancy ratio, mean percentage error and standard deviation of mean percentage error for the model was obtained as 1.03, 0.19, 0.03% and 0.19, respectively. Sensitivity analysis was carried out for the developed ACGY model using the parametric sensitivity method. In order to know the future grape yield using ACGY model, climate scenarios were generated under Canadian Earth System Model (CanESM2) for three emissions representative concentration pathways as RCP2.6, RCP4.5, and RCP8.5. According to the analysis using ACGY model, increasing yield was observed in grape up to year 2050 as compared to current yield.

Generation of district level rice crop inventory, growth profile and yield estimation in Orissa using spot-vegetation data

Spot-vegetation 10 day NDVI composites over Orissa state were analysed to study rice crop inventory and condition assessment. A total of 17 images from July to December during the monsoon (kharif) season of 1998 (S1) and 2001 (S2) a drought and normal year, respectively were analysed. A hierarchical decision rule-based approach that successively eliminated data loss, non vegetated land, forest cover, fallow and other crops was adopted for rice inventory. NDVI temporal profiles of rice could distinguish autumn and winter rice. The total monsoon rice area identified by RS in the state was 4.5 M ha in 1998 and 4.05 M ha in 2001 and was within 7 percent of the state level rice estimate given by Directorate of Economic Survey (DES) i.e., 4.26 and 4.22 M ha, respectively. A new profile fit i.e., a six parameter modified Gaussian approach was adopted. The spectral profile indicated higher mean NDVI at peak growth profile of lowland winter rice (sown in June-July) in 2001-02 compared to 1998-1999. Thus, 2001-2002 rice was seen to be normal while in 1998-1999 a drought affected year. District-wise NDVI profiles of rice were generated and peak NDVI and date at peak profile were found to be correlated with rice yield at district and agro-climatic zone level. Use of rainfall with spectral profile parameters in yield model group of districts or zonal level improved coefficient of determination. This study demonstrates the utility of 1 km and 10 day NDVI composite data for rice crop assessment during monsoon season.

Applicability of Constitutive Models to Describing the Compressibility of Mining Backfill: A Comparative Study

The compressibility of mining backfill governs its resistance to the closure of surrounding rock mass, which should be well reflected in numerical modeling. In most numerical simulations of backfill, the Mohr–Coulomb elasto-plastic model is used, but is constantly criticized for its poor representativeness to the mechanical response of geomaterials. Finding an appropriate constitutive model to better represent the compressibility of mining backfill is critical and necessary. In this paper, Mohr–Coulomb elasto-plastic model, double-yield model, and Soft Soil model are briefly recalled. Their applicability to describing the backfill compressibility is then assessed by comparing numerical and experimental results of one-dimensional consolidation and consolidated drained triaxial compression tests made on lowly cemented backfills available in the literature. The comparisons show that the Soft Soil model can be used to properly describe the experimental results while the application of the Mohr–Coulomb model and double-yield model shows poor description on the compressibility of the backfill submitted to large and cycle loading. A further application of the Soft Soil model to the case of a backfilled stope overlying a sill mat shows stress distributions close to those obtained by applying the Mohr–Coulomb model when rock wall closure is absent. After excavating the underlying stope, rock wall closure is generated and exercises compression on the overlying backfill. Compared to the results obtained by applying the Soft Soil model, an application of the Mohr–Coulomb model tends to overestimate the stresses in the backfill when the mine depth is small and underestimate the stresses when the mine depth is large due to the poor description of fill compressibility. The Soft Soil model is recommended to describe the compressibility of uncemented or lightly cemented backfill with small cohesions under external compressions associated with rock wall closure.

Considering uncertainties expands the lower tail of maize yield projections

Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern U.S.in this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty.

Water Security or Water Resilience? Climate Resilient Water Management in Wellington New Zealand

<p>A confluence of factors including population growth, climate change, resource constraints and legacy effects poses significant challenges to the sustainability of cities worldwide. With the deep complexity inherent in socio-ecological systems, 'solutions' sometimes shift the problem in space or time or drive the system in the opposite direction than intended. A case study into climate change adaptation and community resilience in the context of urban water management was undertaken in Wellington, New Zealand, using a 'post normal' science approach. Climate change and water demand scenarios for 2040 and 2090 were analysed using Greater Wellington Water’s 'sustainable yield' model and downscaled general circulation climate model data. Semi-structured interviews and a systems modelling workshop were conducted in order to gain an understanding of the local context for adaptation, resilience and response option selection. With a 20% reduction of aggregate per capita demand and greater storage capacity, Wellington has sufficient water from current sources to smooth increased flow variability due to climate change and to meet increased demand from the projected increase in population. Adaptation pathways and the potential for 'maladaptation' is explored and an integrated framework for optimising urban water resilience developed.</p>