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The Problem of Water Use in Rural Areas of Southwestern Spain: A Local Perspective
Climate Variability and Climate Change Impacts on Land Surface, Hydrological Processes and Water Management
Quantifying the Impacts of Climate Change, Coal Mining and Soil and Water Conservation on Streamflow in a Coal Mining Concentrated Watershed on the Loess Plateau, China
Sridhar, V.
Kang, H.
Ali, S. A.
Venkataramana Sridhar
Hyunwoo Kang
Syed A. Ali
Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Received: 26 April 2019 / Revised: 7 June 2019 / Accepted: 19 June 2019 / Published: 25 June 2019
(This article belongs to the Special Issue Climate Variability and Climate Change Impacts on Land Surface, Hydrological Processes and Water Management)
The Mekong River Basin (MRB) is one of the significant river basins in the world. For political and economic reasons, it has remained mostly in its natural condition. However, with population increases and rapid industrial growth in the Mekong region, the river has recently become a hotbed of hydropower development projects. This study evaluated these changing hydrological conditions, primarily driven by climate as well as land use and land cover change between 1992 and 2015 and into the future. A 3% increase in croplands and a 1–2% decrease in grasslands, shrublands, and forests was evident in the basin. Similarly, an increase in temperature of 1–6 °C and in precipitation of 15% was projected for 2015–2099. These natural and climate-induced changes were incorporated into two hydrological models to evaluate impacts on water budget components, particularly streamflow. Wet season flows increased by up to 10%; no significant change in dry season flows under natural conditions was evident. Anomaly in streamflows due to climate change was present in the Chiang Saen and Luang Prabang, and the remaining flow stations showed up to a 5% increase. A coefficient of variation <1 suggested no major difference in flows between the pre- and post-development of hydropower projects. The results suggested an increasing trend in streamflow without the effect of dams, while the inclusion of a few major dams resulted in decreased river streamflow of 6% to 15% possibly due to irrigation diversions and climate change. However, these estimates fall within the range of uncertainties in natural climate variability and hydrological parameter estimations. This study offers insights into the relationship between biophysical and anthropogenic factors and highlights that management of the Mekong River is critical to optimally manage increased wet season flows and decreased dry season flows and handle irrigation diversions to meet the demand for food and energy production.
hydrology; land cover; land use and climate change; water resources management; macro scale modeling
The Mekong is one of the most important rivers in Asia. Its significance is evident from its geographical location, topographic variability, biodiversity, and large population of inhabitants in the basin. A cascade of dams, population increase, and climate change have also complicated hydrology and water resources management in the Mekong River Basin (MRB). The nexus of food–energy–water is highly pronounced as the basin relies on rice production and fisheries to feed the population. The conversion of lands from forests to agriculture, subsequent expansion and intensification of irrigation, and hydropower development projects have changed the characteristics of the MRB, in which the river previously flowed unhindered for most of its length [1,2]. The Tibetan Plateau in China—where the river originates at about 4000 m—and the downstream regions are going through natural and human-induced climatic changes and experiencing a general increase in precipitation and temperature in the 21st century, and this can affect the basin's hydrology. Low flow days are expected to decrease and flooding potential may also increase, and hence policies to mitigate the impacts are urgently needed [3,4].
Considerable implications of dam constructions, climate change, irrigation, and land use change to downstream ecosystems have resulted in numerous studies to predict floods, droughts, and sediment yield over the past two decades [5,6,7]. The next few paragraphs will cover some of these studies and identify the knowledge gaps that still exist. Specifically, the understanding of unintended consequences of dams require a comprehensive investigation of reservoir management [8]. Construction and initial filling of the upstream dams reduced the annual streamflow in wet seasons and increased the streamflow in dry seasons, resulting in a unique seasonal variation in the streamflow [9], and the dams had significant impacts on the low pulse duration. Besides, study authors [10,11] reported that construction of dams in the basin is expected to decrease total sediment transportation by 40%–80% over the whole basin, which would impact the river's morphology, aquatic biodiversity, ecosystem services, and agriculture.
By employing simulation models, many studies have projected the basin conditions, but the uncertainties in climate model projections are greater than those of the hydrological models; therefore, comparisons of different climate models and hydrological model outputs at a relatively high resolution are necessary to characterize these uncertainties [12,13]. Study authors [14,15] evaluated the climate change impacts on the hydrological characteristics of the Harvey River catchment in western Australia and the Richmond River catchment in eastern Australia using a rainfall-runoff model (HBV model) and climate model outputs from the Coupled Model Intercomparison Project 5 (CMIP5). The results suggested that there were decreases and increases in the mean annual flows due to the precipitation and temperature variabilities in the future. In another study [16], authors compared two different models (conceptual-HBV and distributed-BTOPMC) in several catchments in Australia and assessed the impacts of climate change on streamflow. Both models simulated a decrease in wet and dry season streamflow across the catchments. An evaluation of the water resource development scenarios over different future time periods' horizons by Piman et al. [17] reported reductions in the average wet season flows by 4%–14% and flow reversal to the Tonle Sap Lake by up to 16%. It predicted an increase in flooded areas by 5%–8% and in salinity intrusion areas in the Viet Nam Delta by up to 17% in the future. It was also reported that the small and nonlinear response of annual river discharge to progressive change in global mean temperature, the change in monthly river discharge varying from −16% to +55%—showed the greatest decrease in July–August and increase in May–June for natural flow only. The impacts of climate change for six catchments around the world, including the Mekong Basin, using a global hydrological model (GHM) and catchment-scale hydrological models (CHM) was performed by [18], and this study reported that substantial differences in the projected change of mean annual runoff between GHM and CHM were dependent on climate model outputs and did not evaluate the regulated flow impacted by the reservoirs. Finally, a semi-distributed hydrological model (SLURP) with the pattern-scaled GCM scenarios was used by [19] to assess the impact of climate change on the freshwater resources associated with GCM structure and climate change sensitivity in the Mekong River Basin.
The effect of land use land cover change (LULCC) impact on the water balance studied by Homdee et al. [20] using the soil and water assessment tool (SWAT) in the Chi River basin, Thailand, reported that land use changes impacted annual and seasonal water yield and evapotranspiration (ET). In addition, the conversion of forested area and agricultural lands affected the flow regimes in the basin. Replacing sugarcane with rice paddies resulted in clearly reduced water flows and increased ET by almost 5.0% during the dry season. Also, the increased conversion of rice paddies to farmland showed a significant effect on seasonal flows. Also, the results of this change showed a decrease in ET by 12.0% and an increase in water yield by 5.1% during the dry season. However, the implications of this study for the entire Mekong basin is not well understood. Another study [21] evaluated, the Mae Chaem River—which was subjected to land use change—by developing three plausible future forest-to-crop expansion scenarios and a scenario of crop-to-forest reversal based on the land cover transition from 1989 to 2000. In this study, the resulting hydrologic responses of the basin were simulated using the distributed hydrology soil vegetation model (DHSVM). The authors also reported that the expansion of highland crop fields affected annual and wet-season water yields compared with a similar expansion in the lowland–midland zone and that the downstream sections of the river were sensitive to irrigation diversion.
The effect of irrigation water abstraction on the streamflow, energy state, and fluxes was evaluated using a model simulation to predict changes in the Bowen Ratio, surface temperature, and water resources within the Mekong River Basin based on the variable infiltration capacity (VIC) macroscale hydrological model [22]. Their results revealed a significant decrease in the Bowen Ratio and surface temperature due to irrigation water withdrawal. The irrigation water withdrawals from runoff, river channels, and dams decreased the total monthly runoff by 32%. Study authors [1] identified the relative roles of precipitation and soil moisture in runoff variability in the Mekong River Basin and reported that simulated soil moisture plays an important role in determining the timing and amount of generated runoff.
However, while these studies reported the changing biophysical conditions of the basin, flow regimes, hydroclimatic extremes, and ecosystems, long-term simulation of the basin hydrology highlighting the role of land use and climate change as well as the effect of dams on the downstream flows have been limited. To our knowledge there is no study that compared SWAT and VIC simulations as well as with and without-reservoir effects. Given their differences in model structure and strengths in simulating global river basins, how they characterize the basin responses under changing conditions of land use and climate change needs a periodic reanalysis. Finally, two different hydrology models are implemented to understand how the major reservoirs play a role in modifying the peak flow in the wet season and low flows in the dry season. While management inputs are needed to precisely quantify the impoundment effects, sensitivity analysis of regulated and natural flows has the potential to know the role of human-induced changes to the flow regimes. Due to the range of predictions and uncertainties, it is imperative to evaluate the changing conditions in the basin in the multi-model framework in order to generate an updated assessment for policy decisions. Therefore, our objective is to evaluate two macroscale hydrological models in capturing basin responses and investigate the historical streamflow changes by explicitly considering the effect of dams and future projections of streamflow and other water budget components. We use both SWAT and VIC to evaluate the hydroclimatological behavior by including six major dams and two climate model projections combined with four global circulation models to characterize the peak flow regime shifts in the basin.
2.1. The Mekong River Basin
MRB covers an area of about 800,000 km2 and the mainstem and its tributaries drain six countries: China, Myanmar, Thailand, Laos, Cambodia, and Vietnam. The basin is divided into seven sub-watersheds with flow stations and major dams as shown in Figure 1a. The upper reaches of the Mekong River flow through higher elevations in the Himalayan mountain ranges, through the steep terrain of Laos and Thailand and the lowlands of Cambodia, and into the delta in Vietnam before draining into the South China Sea. For both development and management of this transboundary river basin, a complex river basin agreement was formulated between the member countries and coordinated by the Mekong River Commission; however, rapid changes in this basin have necessitated a comprehensive understanding of conditions in a system modeling framework.
The mean annual discharge from the basin is approximately 15,000 km3/year. The heterogeneous distribution of the precipitation follows an east–west gradient, with the mean annual value of 1200 mm. Nearly 70% of the annual precipitation in the MRB occurs during the monsoon season. However, the temperature and elevation variations follow a north–south gradient. The temperature in the MRB varies from 38 °C during March–April to 15 °C during November–February. Since conditions in the MRB are hot and humid with the glaciated portion for the upper region, the climate is classified as tropical monsoonal. The elevation drop of more than 4900 m in the MRB also affects the climate heterogeneity. A major portion of the MRB is covered with croplands (40%), followed by evergreen broadleaf forest (28%), closed shrublands (10.3%), and grasslands (9.3%). Irrigated wet season rice grown throughout the year and fishing (4.4 million tons per year) provide food security to more than 60 million people residing in the MRB. In addition, the hydropower potential of the MRB amounts to more than 88,000 MW, with only a small portion utilized. Hence, more than 450 dam projects are currently being planned/constructed by the member countries to take advantage of the hydropower capabilities of the MRB.
2.2. Hydrological Models
Both VIC and SWAT have been widely used in our previous studies in several basins around the world and both are currently incorporated in the Mekong River simulation studies. The physical diagrams of these models are available in published literature and websites [23,24]. The range of applications to evaluate water resource problems includes drought [25,26,27,28], water management [29,30], and climate impacts [31,32,33,34]. The VIC model is grid-based, whereas SWAT is a hydrologic response unit (HRU)-based model that is defined using soil, slope, and land-use data. The resolution of the VIC model varies depending on the availability of forcing data. In our study, we used 0.25° (about 25 km), while the SWAT model considered 1153 climate grids at the same resolution but subdivided into 2196 sub-watersheds. The VIC model was implemented to simulate the natural flows in the basin, whereas the SWAT model was used to simulate both natural and managed flows across selected reservoirs.
The SWAT model [35,36,37] is a river basin-scale, semi-distributed, and continuous model that generates hydrologic variables based on hydrologic response units (HRUs), which combine diverse land uses, soil types, and slopes. SWAT has been applied to various river basins around the globe to evaluate climate change impacts on streamflow [38,39,40], agricultural systems [41], and hydrologic extremes [25,26,42,43]. SWAT estimates several hydrologic components—such as surface runoff, baseflow, evapotranspiration (ET), and soil moisture—which are the primary variables for streamflow calculation (Equation (1)).
S W t = S W 0 + ∑ i = 1 t P d a y − Q s u r f − E T a − W s e e p − Q g w ,
where S W t is the final soil water (mm) on day i, t is the time (days), S W 0 is the initial soil water on day i, P d a y is the daily precipitation (mm), Q s u r f is the surface runoff (mm), E T a is the evapotranspiration (mm), W s e e p is the water entering to the vadose zone from the soil layer (mm), and Q g w is the return flow (mm).
The SWAT model needs a meteorological dataset (e.g., daily precipitation, maximum and minimum temperatures), digital elevation model (DEM), soil properties, and land use. For the historic simulation (1951–2015), a 0.25° resolution of the meteorological forcing dataset was applied [44,45]. The MRB was delineated as 2196 sub-watersheds to consider all climate grids (1153 grids). In addition, the Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010; 250-m resolution) [46] was applied, and the soil properties were obtained from the Food and Agriculture Organization of the United Nations dataset [47]. Finally, the Global Land Cover Characterization (GLCC) was used to determine land use [48].
The VIC model was also implemented to estimate the streamflow at the gage station locations for observed and projected future climates. The VIC is a semi-distributed, physically based hydrological model that solves water and energy balance for each grid separately at a designated daily time. The meteorological parameters for the execution of the model include precipitation from the APHRODITE dataset and minimum and maximum temperatures and wind speed from the Global Meteorological Forcing Dataset (GMFD) gridded dataset, available at 0.25° spatial and daily temporal resolution [44,45]. The vegetation texture—containing the land cover type, leaf area index, and albedo—was developed using the Advanced Very High Resolution Radiometer (AVHRR) at a 1 km spatial resolution. The soil class was taken from the United States Department of Agriculture (USDA) classification and pedo-transfer functions [49] applied to the Harmonized World Soil Database (HWSD) were combined to extract soil parameters.
The infiltration mechanism utilized in the Xinanjiang model [50] was adopted for use in the VIC model to generate the runoff from precipitation when it is higher than the available infiltration capacity. This scheme is commonly used in models that are used for flood forecasting, climate change studies, and water resource assessment in the humid and sub-humid regions of the world [51]. The model is capable of catchment response on any scale and can account for nonlinear spatial retention of soil moisture [52]. Also, the Xinanjiang model accounts for soil heterogeneity and assumes the variation of the infiltration capacity within an area [53]. In the VIC, the Xinanjiang formulation is assumed to hold for the upper soil layer only. The Xinanjiang model effectively assumes that runoff is generated by those areas for which precipitation, when added to soil moisture storage at the end of the previous time step, exceeds the storage capacity of the soil. When the precipitation is less than or equal to the available infiltration capacity, overland runoff is not generated. However, the soil moisture transfers from the upper soil layer to the lower soil layer for subsurface runoff generation using the Arno model conceptualization [54]. The top two layers of the three soil layers in the model respond to the rainfall, whereas the bottom layer corresponds to baseflow computed using the Arno model formulation [54]. The variable infiltration curve [55] governs the infiltration of water into the soil layer. The total ET is estimated using the Penman–Monteith approach and defined as the accumulation of evaporation from bare soil and canopy and transpiration from vegetation features. Since VIC is a unidimensional hydrological model, the fluxes are exchanged only in the vertical direction and the lateral movement in the subsurface layer is considered negligible. Moreover, the routing scheme developed by [56,57] is employed on the fluxes simulated by the VIC model for each grid to estimate the monthly streamflow at the gage station locations. The surface and subsurface fluxes of the grids were explicitly routed by the routing scheme using a unit hydrograph of a channel network, in which the node of the channel network represented each grid-cell of the VIC model.
The observed monthly streamflow from the seven gauging stations distributed across the basin—namely Chiang Saen, Luang Prabang, Nakhon Phanom, Vientiane, Mukdahan, Pakse, and Kratie—were used to calibrate and evaluate the VIC model. The VIC model has been used by the various studies for hydrological assessment of the MRB [8,22,58,59,60].
2.3. Choice of General Circulation Models
Figure 2 shows the distribution of wet/dry and cold/hot global circulation models (GCM) from 2 Representative Concentration Pathways (RCPs), 4.5 and 8.5, showing changing precipitation and temperature for 5 future periods F1 through F5 between 2006 and 2099. These models are GFDL-ESM2M, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M. Each model was bias-corrected and statistically downscaled to 0.25° resolution by the Intersectoral Impact Model Intercomparison Project (ISI-MIP) [61]. These models exhibited a wide range of temperatures (1–6 °C) and precipitation changes (−5%–20%) in the basin and were widely used to predict climate change impacts on hydrology as well as in other basins [61,62,63]. Clearly, MIROC showed wetter and hotter conditions for the later part of the century, while GFDL and IPSL projected drier and cooler conditions through 2040.
2.4. Calibration and Simulation of Streamflows and Water Budget Components
The SWAT model was calibrated using the monthly streamflow and the SWAT calibration and uncertainty assessment tool (SWAT-CUP) [64] with 4 parameters (Table 1) at 7 stations. Similarly, VIC was also calibrated, and the results are shown in Table 2. As shown in Table 3, monthly calibration metrics of correlation coefficient (R2) and Nash–Sutcliff (NS) efficiency were above 0.8 for both SWAT and VIC models. The parameters used to calibrate the VIC model included the variable infiltration curve parameter (bi), the depth of the second and third soil layers (D), the fraction of maximum velocity of baseflow where non-linear baseflow begins (Ds), and the fraction of maximum soil moisture where non-linear baseflow occurs (Ws) with allowable ranges of 0.1–0.5, 0.1–1.5, 0–0.4, and 0.5–1.0 respectively. The calibration was carried out for the gage stations stepwise from upstream basins, with the exclusion of the regions already considered for the upstream station. The Nash–Sutcliffe efficiency coefficient [65] and coefficient of determination (R2) between the monthly simulated and observed streamflows was used to evaluate the capability of the VIC model. This exercise was necessary to ensure that the model's parameters were able to characterize the hydrologic responses to changing environmental and bio-physical conditions.
Our approach consisted of the following steps: monthly calibration of the hydrology models for the historic period, simulation of streamflows using the climate model outputs by dividing them into seven sub-basins with the outlets where the observations were available, evaluation of peak flows, assessment of flow changes in the context of reservoirs, and spatial mapping of temperature and precipitation anomalies and water budget components (ET and runoff). Monthly calibration of the hydrology models for the historic period was required in order to understand whether the models could capture the basin scale responses hydrologically and reliably so as to as extend to the other periods of interest [66,67]. Subsequent analysis was aimed to investigate if there were any differences in streamflows and peakflows considering the spatial and temporal variability of the forcings, land use and reservoir management. This sequential approach enabled us to understand and quantify the impact of spatial variability and shift in the flow regimes as shown in Figure 3. Finally, this study is framed to seek an answer for the suitability of these models for changing conditions in the future. To answer this question, we evaluated the differences between them in multiple variables, including peakflows, sub-basin scale hydrologic budgets and see whether they help us decide the suitability for decision making in the context of the sustainable management of the basin and food–energy–water nexus considering the data needs and resolution. For instance, food systems need high resolution, field scale data for decision making while hydropower and water would be decided based on the catchment scale runoff and inflows to the dam. This particular study offers insights based on 0.25° forcings and a relatively high-resolution land use and soil properties with major reservoirs across the basin all of which can be integrated in a simple framework.
3.1. Hydroclimatology of Streamflow
The annual hydrograph was primarily driven by the southwest monsoon in the basin and the typical flood hydrograph consisted of peak flows in the wet season (July–October) and relatively low flows in the dry season (January–May). Generally, the smooth hydrographs reflecting the size of the catchment were evident. Figure 4a–f show the long-term streamflow simulations by SWAT and VIC. The historical simulation period was between 1954 and 2015, and due to limited availability of observational data, a relatively short period between 1984 and 1990 was used for calibration and the remaining period from 1991 to 1996 for validation. The locations distributed across the entire lower Mekong from the upstream point in the basin—Chiang Sean to downstream at Kratie—demonstrated how the annual average streamflow gathered in magnitude from about 2000 m3/s to 10,000 m3/s. Table 1 shows the list of four runoff-, base flow-, and groundwater-related parameters calibrated in SWAT. Similarly, the calibration parameters shown in Table 2 for VIC include the variable infiltration curve parameter, the depth of the second and third soil layers, the fraction of maximum velocity of baseflow where nonlinear baseflow begins, and the fraction of maximum soil moisture where nonlinear baseflow occurs.
Clearly, the multi-decadal simulations showed interannual variability in flows caused mostly by precipitation changes; however, the shifts in flows on annual scales were indistinguishable. The box plot (on the right) shows the mean and spread of annual streamflows captured by SWAT and VIC. While the mean values between these models across all seven stations were close, the spread was greater for SWAT. Also, the coefficient of variation (standard deviation/mean) computed for each of the gage locations showed a low value of less than 0.7 consistently for all the stations and they were comparable between the observed and simulated flows. This could be considered typical for a tropical river basin where variability was minimal.
3.2. Historical Peakflow Assessment
In order to understand the dynamics of drivers of change, particularly climate and land use, we evaluated the peakflow magnitudes simulated by SWAT and VIC. Our assessment of change in land use between 1992 and 2015 suggested a 3% increase in croplands and a 1–2% decrease in grasslands, shrublands and forests. Figure 5 shows the differences in peakflows between the two periods—1956–1965 and 2006–2015—to compare pre-development and post-development conditions in the basin. Other than a reduction in flows of 4–8% for Chiang Sean and less than 1% for Luang Prabang, all of the other flow stations indicated an increase of 8–11%. The decrease in flows in the upstream location can be attributed partly to climate change in the Tibetan Plateau. However, the tropical monsoon impacts on the lower portion of the basin were evident in the increased flows. These increased peakflows can result in flooding, and therefore impoundments of these flows can potentially reduce the risk of flooding in this basin, which is prone to seasonal flooding. This is further highlighted in Figure 5h, where the streamflow anomaly (%) for the seven locations between 1992 and 2015 decreased up to 4% for Chiang Saen and Luang Prabang. The remaining stations showed a positive anomaly of up to 5%. Noticeably, the differences between 1992 and 2015 in both SWAT and VIC showed no difference in anomaly, which suggested that natural flows between pre- and post-development of hydropower projects are not significant. In other words, while the flow alterations in the basin could not be attributed to land use changes in the basin, human-induced changes—such as irrigation diversions—and climate change can affect peakflows.
3.3. Projected Changes in Flows and Comparison of Models
Since the effect of climate change was evident with increased precipitation and temperature in the basin, it was considered appropriate to assess the climate change impacts on streamflow and other water budget components. Figure 6 shows the projected streamflows from the VIC and SWAT models between 2020 and 2099 for the same seven locations where calibration and validation of streamflows were performed for the historic periods. The results included the ensemble average of all four GCMs introduced in the earlier sections. The annual hydrographs resembled historical estimates of flows, with interannual variability and seasonal peaks. Most notably, the differences in SWAT and VIC were also similar to historic simulations, as SWAT produced more flows relative to VIC. While the hydrological model processes that caused the increased flow in SWAT are not discussed here in detail, the role of the calibration parameters that previously estimated higher flows could be substantial. Also, the irrigation extraction for croplands—whereby the streamflows remained mostly natural and hence the attenuation of flows was not obvious—was not explicitly considered.
Due to increased precipitation in the basin, as predicted by most of the GCMs, hydrological flow simulation had shown similar increases in peakflows, ranging from 10%–70% between RCP 4.5 and 8.5 scenarios. The hydrological model responses in the form of streamflow were directly proportional to increased precipitation, typical of a tropical basin. The substantial increases were also expected in the later part of the century across all flow stations between 2060 and 2099. Projected peakflow changes simulated by VIC and SWAT are shown in Figure 7. On the one hand, the reductions in dry season flows were not evident, and counterintuitively, on the other hand, the management of reservoirs and their releases can augment them.
3.4. Projected Peakflow Estimation
Changes in peak flows were analyzed, and the shifts in peakflows with and without reservoirs simulated by the SWAT model for the period 1992–2015 (14 year average) are shown in Figure 8. A similar analysis for future climate projections from 2020–2099 (80 year average) from RCP 4.5 is shown in Figure 9. The results are similar for RCP 8.5, and the percentage changes in flows are shown in Table 4. In general, the reductions in flows in 2015 were lesser when compared to 1992 in simulations in which reservoirs were taken into account with certain parameters. These reductions, ranging from 3%–15%, can only be considered changes due to climate variability, as exact operation and releases of flows were not integrated into this analysis. When the simulations did not include reservoirs, both wet and dry season flows were higher for the same period. However, general reductions of up to 35% in dry season flows and 16% in wet season flows were identical in simulations that considered reservoirs in their analyses. This analysis emphasizes the importance of incorporating the actual reservoir operations to predict wet and dry season flows.
3.5. Basin Scale Water Budget Analysis
Figure 10 presents the bar charts of ET and runoff changes for the historic and future periods from the SWAT and VIC models, and Figure 11 shows the spatial maps of changes in precipitation, temperature, ET, and runoff. The historic changes were the percentage alterations of the last 10 years (2006–2015) compared to the entire historic period (1954–2015), and the future changes were the percent increases of the future period (2020–2099) to the historic period. Both ET and runoff changes were computed for each sub-region and grid in the MRB. As in Figure 10, reductions in runoff of up to 6% in the historic period did not persist in other locations or into the future. Historic reductions were more pronounced in the upper portion. Both RCP 4.5 and RCP 8.5 projected increases in ET of 4–15% and runoff increases of up to 60%. In both historic and future periods, water budget changes were highly influenced by precipitation and temperature alterations in the MRB, as shown in Figure 11a–f. Relative to the past period, both RCPs projected increased precipitation between 10 and 60% across all of the sub-basins, with higher increases in the central and lower portions of the basin. However, temperature increases were notable in the upper and central sub-watersheds, ranging from 1–4 °C. This can potentially impact snowmelt-driven flow in the Tibetan Plateau before the monsoon season begins.
For the historic period, there were overall ET and runoff increases from the SWAT model for the entire MRB, except for the runoff in Upper Mekong. In the VIC model, there were also overall increases, but some regions showed ET and runoff decreases (e.g., Upper Mekong, Delta). The highest increase of runoff occurred in the middle of the basins from both SWAT and VIC (13.7% and 8.5% respectively; Figure 11j,p), and these increases were derived from the highest precipitation increase in the middle Mekong region during the last 10 years (Figure 11a). However, there were runoff decreases in the upper Mekong and Siem Bok (Figure 11j,p), and the precipitation decreases mainly derived from these in the corresponding areas (Figure 11a).
For the future period, there were ET and runoff increases for the entire regions and both models, and the precipitation and temperature increases were the main drivers of those changes. VIC projected decreased runoff in the delta in the future, and in general, estimates of the water budget from the two models were considerably different. The runoff estimations from the SWAT model were more sensitive to the precipitation increase. For instance, in the Tolne Sap region, where more than 40% precipitation increases occurred in the entire area, the runoff increases were 56% and 57% for RCP 4.5 and RCP 8.5 using the SWAT model, but 38% and 46% using the VIC model. In addition, the results of runoff increases in the Siem Bok showed similar results. These results were derived from the different runoff estimations between the two models. The SWAT model is based on the soil conservation service (SCS) curve number (CN) [68], while the VIC model uses the variable infiltration curve method [55]. The SCS CN method has been known to have a higher sensitivity for runoff estimation [69], and this could be credited for those discrepancies.
The Mekong region is at the center of a multitude of changes, including hydropower development, land use, and climate change. While it is important to characterize the basin's responses to these changes at the local level, macroscale changes to climate and hydrology spanning all six basin countries at the sub-watershed domain is critical. This study evaluated historic streamflows and impacts of 21st century hydropower development on those flows through simulation. The limiting factor in considering basic scale changes arose from lack of information about the management of the currently operated dams. Hence, the initial simulation of flows could be used to identify the changes in dry- and wet-season flows driven primarily by both natural and ongoing anthropogenic-induced climatic changes while integrating reservoirs into the models to accommodate the inflows in a simplistic way. Several key insights were gained from this study. More broadly, the comparison of two hydrological model simulations highlighted that basin responses to peakflows for both historic and future periods were in close agreement despite the differences in the model formulations and both can serve as a predictive model for future water resources assessment with some improvements to field-scale crop water estimation. The increase in peakflow estimations due to increased precipitation in a changing climate was quantified for several locations and agreed with several previous studies. These increased peakflows are expected to be harnessed for both hydropower and irrigation water demand, and these new insights are useful for making policy decisions and developing operating procedures for water resources development projects. Additionally, the spatial variability in ET and runoff highlighted the need for a differential approach at the sub-basin level to sustain food and energy production in the context of drought and other anthropogenic-induced changes, including land use and population increase. Perhaps, irrigation water assessment by VIC was providing a more realistic estimates of ET and both hydrological models require improvements to simulate crop-specific, field scale estimates of water balance components.
Our findings from this investigation also suggested that the hydrological models SWAT and VIC were capable of predicting large-scale changes to the system by accounting for up to 90% variability under natural conditions. Relative changes in flows impacted by recent hydropower projects between 1992 and 2015 revealed that anomalies in peakflows during this period were less than 5%. This suggests that system-level changes were not identifiable due to modest land cover changes in croplands and forests unless storage and irrigation diversion were properly considered. Increased precipitation over several sub-watersheds also resulted in increased peakflows, as the monsoon season variability for multiple decades included only nominal changes.
Generally, climate models projected a wide range of temperature (1–6 °C) and precipitation changes (−5–20%) in the basin between 2020 and 2099. Corresponding increases in peakflows—ranging from 10–70% between RCP 4.5 and 8.5 scenarios—were expected to occur, leading to possible flooding and inundation unless the reservoir management for both peakflows and diversions for crop water requirements were optimally handled. Without reservoirs in the modeling assessment, both wet- and dry-season flows were higher, but general reductions of up to 35% in dry-season flows and 16% in wet-season flows were identical to simulations that considered reservoirs in their analysis. However, with expanded irrigated areas in the basins and increased peakflows, not only can conflicts be alleviated to manage dry season flows, but increased crop production and hydropower generation also become feasible.
Conceptualization, V.S.; methodology, H.K. and S.A.A.; software, H.K. and S.A.A.; validation, V.S., H.K. and S.A.A.; formal analysis, V.S., H.K. and S.A.A.; investigation, V.S., H.K. and S.A.A.; resources, V.S.; data curation, V.S., H.K. and S.A.A.; writing—original draft preparation, V.S., H.K. and S.A.A.; writing—review and editing—V.S.; visualization, V.S., H.K. and S.A.A.; supervision, V.S.; project administration, V.S.; funding acquisition, V.S.
This work was supported in part by NASA under the award 80NSSC17K0259.
We thank Jiaguo Qi, William McConnell, Yadu Pokhrel, and David Hyndman from Michigan State University for their indirect assistance. We are grateful for the general discussions on the Mekong Basin with our partners from the Mekong River Commission, Asian Institute of Technology and the Vietnam National Mekong Committee.
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Figure 1. Location map of the Mekong River Basin with reservoirs (in red circles), flow stations (black boxes), and sub-watershed boundaries.
Figure 2. Choice of wet/dry and cold/hot global circulation models (GCM) from two representative concentration pathways (RCPs)—4.5 and 8.5—showing changing precipitation and temperature for five future periods F1 through F5 between 2006 and 2099.
Figure 3. Flow diagram of the overall processes of hydrologic modeling and analyses. SWAT: soil and water assessment; VIC: variable infiltration capacity; APHRODTE: Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation; CMIP5: Coupled Model Intercomparison Project 5; ISI-MIP: Inter-Sectoral Impact Model Intercomparison Project; RCP: representative concentration pathway.
Figure 4. Long-term streamflow simulations (1954–2015) compared against observations (1984–1996) with two different time periods for calibration and validation by the SWAT and the VIC macro scale hydrological models. The box plot (on the right) shows the mean and spread of flows captured by SWAT and VIC.
Figure 5. (a–g) Differences in peakflows between two periods; (h) streamflow anomaly (%) for seven locations between 1992 and 2015.
Figure 6. Projected streamflows from VIC and SWAT models between 2020 and 2099 for seven locations where calibration and validation of streamflows were performed for the historic periods.
Figure 7. Projected peakflow changes between 2020 and 2099, simulated by VIC and SWAT.
Figure 8. Shifts in peakflows with and without reservoirs, simulated by the SWAT model for the period between 1992 and 2015 (14 year average).
Figure 9. Shifts in peakflows (RCP 4.5) with and without reservoirs, simulated by the SWAT model for the period between 2020 and 2099 (80 year average).
Figure 10. Water budget changes for historic and future periods for each sub-watershed; (a) SWAT; (b) VIC.
Figure 11. Spatial plot of precipitation, temperature, and water budget changes for historic and future periods. (a–c) Precipitation changes; (d–f) temperature changes; (g–i) evapotranspiration (ET) and runoff changes from the SWAT model; (m–r). ET and runoff changes from the VIC model.
Table 1. Description of the soil and water assessment tool (SWAT) model input parameters for the calibration.
Best Parameters
r_CN2.mgt Curve number for moisture condition II −0.2 0.2 0.06
v_ALPHA_BF.gw Baseflow alpha factor 0 1 0.35
v_GW_DELAY.gw Groundwater delay time 30 450 177
v_GWQMN.gw Threshold water depth in shallow aquifer for back discharge 0 2000 1500
Notes: v_, denotes the default parameter is replaced by a given value; r_, means the existing parameter value is multiplied by (1 + a given value).
Table 2. Description of the variable infiltration capacity (VIC) model input parameters for the calibration.
Allowable Range
1 bi variable infiltration curve parameter 0.1 0.5
2 D the depth of soil layers 0.1 1.5
3 Ds fraction of maximum velocity of baseflow where non-linear baseflow begins 0 0.4
4 Ws fraction of maximum soil moisture where non-linear baseflow occurs 0.5 1
Table 3. Statistical indicators showing the hydrology model calibration and validation for the historical period between 1984 and 1992 in the Mekong River Basin.
Calibration Period
Validation Period
Chiang Saen 1984–1990 1991–1996 0.92 0.93 0.86 0.83 0.93 0.91 0.85 0.81
Luang Prabang 1984–1990 1991–1997 0.93 0.93 0.81 0.73 0.94 0.89 0.86 0.67
Vientiane 1984–1990 1991–1996 0.92 0.93 0.83 0.91 0.95 0.94 0.88 0.92
Nakhon Phanom 1984–1990 1991–1995 0.93 0.93 0.87 0.90 0.92 0.92 0.86 0.79
Mukdahan 1984–1990 1991–1995 0.93 0.94 0.89 0.86 0.93 0.94 0.88 0.83
Pakse 1984–1990 1991–1998 0.90 0.91 0.84 0.86 0.90 0.93 0.85 0.87
Kratie 1984–1990 1991–1998 0.90 0.90 0.85 0.85 0.91 0.93 0.86 0.86
Table 4. Percentage change in dry- and wet-season flows projected by SWAT and VIC for RCP 4.5 and RCP 8.5 between 2020 and 2099.
RCP4.5
Chiang Saen Wet 10.5 21.9 10.7 25.1 31.4 41.0 31.7 49.1
Dry 17.6 25.7 14.7 21.3 33.6 41.3 32.2 36.6
Luang Prabang Wet 11.3 23.5 11.9 25.8 56.1 68.7 57.1 77.7
Vientiane Wet 14.3 27.6 15.6 29.3 12.1 22.3 13.8 26.8
Dry 20.4 28.6 16.5 22.0 −11.3 −5.3 −12.4 −8.6
Nakhon Phanom Wet 18.8 32.2 21.0 32.7 28.0 39.4 30.1 42.7
Dry 26.0 33.5 21.4 26.9 −8.7 −3.8 −10.8 −6.5
Mukdahan Wet 19.2 32.4 21.4 32.3 38.2 50.5 40.4 53.5
Dry 27.6 35.0 22.8 28.5 −3.3 1.9 -5.5 −0.7
Pakse Wet 18.6 30.4 20.2 29.3 22.6 32.0 23.5 33.2
Dry 31.7 39.0 26.3 33.4 −31.2 −27.0 −32.5 −28.2
Kratie Wet 21.0 30.2 21.5 29.4 32.1 39.7 32.0 40.6
Sridhar, V.; Kang, H.; Ali, S.A. Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin. Water 2019, 11, 1307. https://doi.org/10.3390/w11061307
Sridhar V, Kang H, Ali SA. Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin. Water. 2019; 11(6):1307. https://doi.org/10.3390/w11061307
Sridhar, Venkataramana, Hyunwoo Kang, and Syed A. Ali. 2019. "Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin" Water 11, no. 6: 1307. https://doi.org/10.3390/w11061307


text:
"The Infinite Music of French Horn Rebellion" by French Horn Rebellion
May 23. 2011 | By Tania
The French Horn Rebellion
The Infinite Music of French Horn Rebellion
[Once Upon A Time Records]
The Infinite Music of French Horn Rebellion is certainly a title that takes more than a mouthful to get out. However, while the album's title might give a bit of trouble to the tongue, the album is just a delight on the ears.
One part classic emo-indie, another part technological influences, French Horn Rebellion presents an interesting sound to listeners. In "Last Summer," the track has two minutes of a quaint little indie rock song. But then – alas! – comes the horns. The song morphs into this big band-lofty-and-large sound before wrapping up with a little bit of Queen. It sounds neurotic but somehow the band is able to make it all work together as one. "Brasilla Girl" starts off sounding a bit like a Nintendo dance track. It moves into a sexy, full-on electronic track before getting quiet towards the end. "The Cantor Meets the Alien" is one of the quietest tracks on the album. Taking in a bit of a 1980s feeling, the track sounds a bit like so many songs you've heard before. But there's something in the way the track is performed that changes an otherwise basic track into something special. Another winner on the album is "Up All Night," which is poised to become your next favorite party song. Starting with balls of energy and more synthesizer than should be legally allowed, the falsetto high notes and energy drive their way through this track.
While this might not have been a band that was on your radar before, be sure that they're on your radar now.

redpajama_set_name: RedPajamaCommonCrawl


text:
Faster and Safer
Don't think of a purple elephant.
You thought of a purple elephant, didn't you? For Madison, a 16-year-old Murphysboro, Illinois, resident, something similar happened when radiology technicians told her to stand still for X-rays as she started scoliosis treatment 3 1/2 years ago at Shriners Hospitals for Children — St. Louis.
"When someone tells you to stand still, it's really hard to stand still," she said. "I get kind of twitchy."
Madison is hardly alone, said radiology technician Melissa Warren, a 20-year hospital veteran. She's had to redo many an X-ray on patients who moved at some point during the minute or so of complete stillness that is required in traditional radioography. But thanks to some new technology, many St. Louis Shriners Hospital patients are able to have X-rays taken much quicker. The machine is called EOS, and it reduces the mandated stillness to about 11 seconds. That makes Madison smile.


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text:
Andreoni is an Italian and Corsican surname, derived from the given name Andrea. Notable people with the surname include:

 Cristian Andreoni (born 1992), Italian football player
 Giovanni Battista Andreoni (1720-1797), Italian castrato singer
 James Andreoni (born 1959), American economist
 Luis Andreoni (1853-1936), Italian engineer active in Uruguay
 Serge Andreoni (born 1940), French politician

Italian-language surnames
Patronymic surnames
Surnames from given names

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Elsevier, the information analytics business specialising in science and health, has announced that Kumsal Bayazit has been appointed Chief Executive Officer, effective February 15, 2019. Bayazit succeeds Ron Mobed who, after seven years as Elsevier's Chief Executive Officer, has decided to retire. Mobed will support his successor during the transition, providing counsel and advice until June. Youngsuk (YS) Chi will remain Chairman of Elsevier.
The Institute for Scientific Information, the 'university' of Web of Science Group has launched its Global Research Reports – a new series of publications aimed at those who deal with research in academia, corporations, publishers and governments to inform, to stimulate debate and to demonstrate the rich information potential of research data. The first Global Research Report, 'Profiles not Metrics' draws attention to the information that is lost when data about researchers and their institutions are squeezed into simplified metrics or a league table.
The Publications Division of the American Chemical Society (ACS) has announced the addition of ACS Materials Letters to its portfolio of journals, responding to the needs of the ever-expanding and fast-paced global research community focused on materials science. ACS Materials Letters will serve as a forum for the rapid publication of high-quality and important contributions on the forefront of fundamental and applied research at the interface between materials and other disciplines, including chemistry, engineering and biology.
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Atypon has announced its leadership changes. Atypon's Founder and CEO, Georgios Papadopoulos, has assumed the new role of Chairman, and Marty Picco, Atypon's Senior Vice President of Product, has been promoted to the new role of General Manager.


text:
\section{Introduction}
\label{sec:intro}

Remote sensing image (RSI) change detection is a procedure for extracting changes in the earth's surface, which is significant for map revision, agricultural investigation, and disaster assessment, etc \cite{cd_survey_rsi}. However, besides the semantic changes, which are mainly man-made changes, there are various noise changes in RSI, such as illumination, sensor noise and resolution variations \cite{ccd}. Traditional RSI change detection depends on manual features and time-consuming pre- and post-processing processes, and is difficult to distinguish semantic changes from background noise \cite{cd_survey_1}. The image pair, on the other hand, can be directly fed into the Siamese convolutional network  without preprocessing, and it relies on end-to-end supervised learning to separate semantically changed regions from invariant ones, so it has recently become the mainstream method for RSI change detection \cite{SNUNet,DASNet,cd_optical,chen2019deep,lei2019landslide,zhang2020deeply,peng2019end,FC_siamese}.

\begin{figure}[htb]

	\begin{minipage}[b]{1.0\linewidth}
		\centering
		\centerline{\epsfig{figure=imgs/figure1.png,width=8.5cm}}
	\end{minipage}
	%
	\caption{Computational (GFLOPs) $\text{vs}$ accuracy (F1-Score) of our method  and other Siamese RSI detection networks.}
	\label{fig1}
	%
\end{figure}

\begin{figure*}[htbp]

	\begin{minipage}[b]{1.0\linewidth}
		\centering
		\centerline{\epsfig{figure=imgs/figure2.png,width=18cm}}
	\end{minipage}
	%
	\caption{The pipeline of the proposed LightSiamese Network (LSNet) and the detailed structure of context guide block.}
	\label{fig2}
	%
\end{figure*}

In order to improve the accuracy of semantic changes detection, recent research on Siamese network has primarily focused on improving the feature interaction of Siamese backbone \cite{SNUNet,lei2019landslide,peng2019end,FC_siamese}, introducing attention mechanisms \cite{SNUNet,DASNet} to refine features and improving loss function \cite{DASNet,cd_optical,chen2019deep,zhang2020deeply} to optimize the training process, but has paid little attention to the efficiency. With the rapid development of satellite observation technology (eg, QuickBird, GaoFen, and WorldView), RSI with high spatial and temporal resolution is easier to obtain. In order to handle the change detection challenge of large-scale RSI, it is necessary to improve the effectiveness of the Siamese network.

Therefore, this paper proposes an extremely lightweight Siamese network (LSNet), which outperforms existing techniques in efficiency as shown in Figure 1. The network backbone is constructed using a context-guided module composed of depthwise separable atrous convolution and global feature aggregation as the core components. Furthermore, a differential feature pyramid network (diffFPN) is presented to perform progressive feature pair difference extraction and resolution recovery, eventually separating changed from constant image areas. The main contributions can be summarized as:
\vspace{-2mm}
\begin{itemize}[leftmargin=*]
\setlength{\itemsep}{0pt}
\setlength{\parsep}{0pt}
\setlength{\parskip}{0pt}
\item
 An extremely lightweight Siamese network for RSI change detection is constructed, which includes a 52-layers light backbone with only 3.97\% of the parameters and 32.56\% of the computation of ResNet-50 \cite{ResNet}.
\item
The proposed diffFPN for Siamese feature fusion removes rebundant connections while maintaining valid feature flow.
\item
Experiments on the CCD dataset show that the proposed method produces competitive results with a modest number of parameters and computation. When compared to the first-place model, the parameters and calculation amount of LSNet is only the 9.64\% and 8.67\% respectively, with only a 1.5\% decrease in accuracy.
\end{itemize}

\section{Method}
\label{sec:format}
{\vspace{-2mm}
The proposed LSNet contains a light-Siamese backbone that is built utilizing the context guide block (CGB) \cite{CGNet} intead of the commonly used residual module \cite{ResNet}, and diffFPN is used for efficient Siamese features pair fusion. Figure 2 depicts the network structure.
\vspace{-2mm}}
\subsection{Light-Siamese backbone}
The structure of basic component CGB is shown in Figure 2. The input X passes through parallel dilate convolutions \cite{deeplabv2} to obtain local context information in different ranges. The dilate convolutions here are calculated in a depthwise separable manner, i.e, all channels are grouped, and the convolution operation is only performed in an independent group \cite{Mobilenet}. Considering the input $X\in {{R}^{H\times W\times {{C}_{x}}}}$ (${\text{C}}_{\text{x}}$ is the input channels) and the output $Y\in {{R}^{H\times W\times {{C}_{y}}}}$, the calculation amount of standard convolution $K \in {R^{{{\rm{H}}_k} \times {W_k} \times {C_x} \times {C_y}}}$ used to obtain the row $h$ and column $w$ of the $cth$ channel in Y is
{\setlength\abovedisplayskip{2mm}
	\setlength\belowdisplayskip{2mm}
\begin{equation}
	Y(h,w,c)
	=\underset{i=1}{\overset{{{H}_{k}}}{\mathop{\sum }}}\,\underset{j=1}{\overset{{{W}_{k}}}{\mathop{\sum }}}\,\underset{j=1}{\overset{{{\text{C}}_{\text{x}}}}{\mathop{\sum }}}\,K(i,j,n,c)X(h+i,w+j,n)  \\
\end{equation}}
when depth-wise convolution $K\in {{R}^{{{\text{H}}_{k}}\times {{W}_{k}}\times {{C}_{x}}\times {{C}_{y}}}}$ is used, the calculation amount is
{\setlength\abovedisplayskip{2mm}
	\setlength\belowdisplayskip{2mm}
	\begin{equation}
	Y(h,w,c)
	=\underset{i=1}{\overset{{{H}_{k}}}{\mathop{\sum }}}\,\underset{j=1}{\overset{{{W}_{k}}}{\mathop{\sum }}}\,K(i,j,1,c)X(h+i,w+j,n)  \\
\end{equation}}
which is the $\text{1/}{{\text{C}}_{\text{x}}}$ of standard convolution. And then an attention module is used for channels interaction and global information extraction, which is composed of average pooling, non-linear $f(\cdot)$ and sigmoid layer and can be expressed as
{\setlength\abovedisplayskip{2mm}
	\setlength\belowdisplayskip{2mm}
\begin{equation}
  {{Z}_{c}}=sigmoid(f(\frac{1}{H\times W}\underset{i=1}{\overset{H}{\mathop{\sum }}}\,\underset{j=1}{\overset{W}{\mathop{\sum }}}\,Y(i,j,c)))
\end{equation}}

{The two phases of images T1 and T2 pass through the light-Siamese backbone network with shared weights, which consists of 4 compound layers with $3/3/8/12$ CGBs, each CGB is equivalent to two levels, so there are 4 Group feature output, for a total of 52 layers.
\vspace{-1mm}}

\subsection{Differential feature pyramid network}
\begin{figure}[h!]
	\vspace{-2mm}
	\begin{minipage}[b]{1.0\linewidth}
		\centering
		\centerline{\epsfig{figure=imgs/figure3.png,width=9.0cm}}
	\end{minipage}
	%
	\caption{Siamese Feature Pyramid Network (FPN) Comparison. (a) denseFPN proposed in SNUNet; (b) Our diffFPN.}
	\label{fig3}
	%
\end{figure}
\noindent A densely connected pyramid feature fusion method is proposed in SNUNet \cite{SNUNet}, as shown in Figure 3(a), and has achieved leading results. Considering three output levels ${{\text{d}}_{0,0}}\text{,}{{\text{d}}_{1,0}}\text{,}{{\text{d}}_{2,0}}$ for prediction, here the convolutional layer is abbreviated as $f(\cdot )$, then
\begin{align}
{{d}_{0,0}}&=f({{T}_{1,0}},{{T}_{2,0}},{{T}_{1,1}},{{T}_{2,1}})\\
{{d}_{1,0}}&=f({{T}_{1,0}},{{T}_{2,0}},{{d}_{0,0}},{{d}_{0,1}})\\
{{d}_{2,0}}&=f({{T}_{1,0}},{{T}_{2,0}},{{d}_{0,0}},{{d}_{1,0}},{{d}_{1,1}})
\end{align}

In this structure, there are two issues: (1) Redundant connections. Shallow features such as ${T}_{1,0},{T}_{2,0}$ are repeatedly inputted into ${\text{d}}_{0,0},{\text{d}}_{1,0}$ and ${\text{d}}_{2,0}$, which is
inefficient. (2) Unreasonable feature flow. Despite the presence of redundant connections in denseFPN, the output layers ${{\text{d}}_{0,0}}$ and $\text{d}_{1,0}$ contains incomplete features from the backbone. The proposed diffFPN is shown in Figure 3(b), which can be stated as
\begin{align}
	& {{d}_{0,0}}=f({{T}_{1,0}},{{T}_{2,0}},f({{d}_{0,1}})) \\
	& {{d}_{1,0}}=f({{d}_{0,0}},{{d}_{0,1}}) \\
	& {{d}_{2,0}}=f({{d}_{1,0}},{{d}_{1,1}})
\end{align}
In diffFPN, redundant connections are removed, and the added bottom-up fusion path makes the three output layers contain complete backbone network features.

\section{Experiment and Results}
\label{sec:pagestyle}
\subsection{Dataset and evaluation metrics}
RSI change detection dataset (CDD) proposed by in [9] is used for evaluation, which was obtained from Google Earth with the spatial resolution from 0.03m to 1m per pixel, and the training, validating, and testing sets contains 10000, 3000 and 3000 image-pairs with size of 256$\times$256 pixels respectively.

For accuracy evaluation, Common accuracy including {\bf precision (P)}, {\bf recall (R)}, {\bf F1-score (F1)} and {\bf overall accuracy (OA)} are utilized.
Previous efficiency indicators include the amount of parameters (Params) and Giga floating-point operations per second (GFLOPs). As the focus on the Siamese network effectiveness, we proposes the following normalized efficiency indicators
\vspace{0.5mm}
\begin{align}
	 &\text{F1-P}=\frac{F1}{Params/Ma{{x}_{Params}}} \\
	 &\text{F1-G}=\frac{F1}{GFLOPs/Ma{{x}_{GFLOPs}}} \\
	 &\text{F1-Eff}=(\text{F1-P}+\text{F1-G})/2
\end{align}

$\textbf{F1-G}$ and $\textbf{F1-F}$ quantify the impact of unit parameters and calculations on the F1-score respectively, and $\textbf{F1-Eff}$, which weights $\text{F1-G}$ and $\text{F1-F}$ equally, is used to evaluate the overall efficiency of a model.

\subsection{Accuracy and efficiency comparison}

\begin{table}[tbp]
	\caption{Comparison of module parameters and computation amount. \textbf{Bold} font indicates the best results.}
	\label{table1}
	\vspace{2mm}
	\centering
	\begin{tabular}{cccc}
		\toprule

		Module & Type & Params (M) & GFLOPs \\
		\midrule
	 	\multirow{2}*{Backbone} & ResNet-50 & 23.5080 & 10.7347 \\
	 	~ & LightSiamese-52 & \bf{0.9326} & \bf{3.4956} \\
	 	\cmidrule(r){2-4}
	 	\multirow{2}*{FPN} & DenseFPN & \bf{0.1590} & 2.3348 \\
		~ & DiffFPN & 0.2299 & \bf{1.2464} \\
		\bottomrule
	\end{tabular}
\end{table}

ResNet-50 or residual block is commonly utilized in FC-EF [10], DASNet [11] and SNUNet [12], etc, whose amount of parameters and calculation is the 25 times and 3 times that of the proposed light-Siamese backbone respectively. It can be seen that the compression of parameters is more easier than it of calculation amount. As discussed in \textbf{Sec2.2}, the structure of denseFPN has irrationality of feature flow, and diffFPN is the proposed revised structure. With a small increase in the amount of parameters (0.0709M), the amount of calculation is further reduced by 1.0884 GFLOPs.

\begin{table}[bp]
	\renewcommand\arraystretch{1.1}
	\caption{Comparison with the mainstream methods. The colors {\color{red}{red}}, {\color{green}{green}}, {\color{blue}{blue}} indicate the top 3 results for each metric.}
	\label{table1}
	\vspace{2mm}
	\centering
	\begin{tabular}{ccccc}
		\toprule

		Method&   P&   R&   OA&   F1 \\
		\midrule
		FC-EF \cite{FC_siamese}&   81.00&   73.34&   94.82&   77.00 \\
		FC-Siam-Diff \cite{FC_siamese}&   88.62&   80.29&   96.45&   84.25 \\
		DSMS-FCN \cite{chen2019deep}&   89.34&   82.40&   96.85&   85.73 \\
		FCN-PP \cite{lei2019landslide}&   91.77&   82.21&   97.03&   86.73 \\
		IUNet++ \cite{peng2019end}&   89.54&   87.11&   96.73&   87.56 \\
		DASNet \cite{DASNet}&   92.52&   91.45&   98.07&   91.93 \\
		IFN \cite{zhang2020deeply}&   \color{green}{94.96}&   86.08&   97.71&   90.30 \\
		SNUNet \cite{SNUNet}&   \color{red}{95.6}&   \color{red}{94.9}&   -&   \color{red}{95.3} \\
		Our-denseFPN&   93.84&   \color{green}{94.19}&   \color{blue}{98.52}&   \color{blue}{94.02} \\
		Our-diffFPN&   \color{blue}{94.31}&   \color{blue}{93.95}&   \color{green}{98.55}&   \color{green}{94.13} \\
		\bottomrule
	\end{tabular}
\end{table}

In Table 2, the results of the proposed LSNet outperforms alternative networks using ResNet-50, indicating the high efficiency of the proposed backbone. The structural improvement from denseFPN to diffFPN improves the results of many indicators such as P, OA, and F1. And majority of the proposed LSNet'indicators  are in the second and third places.

\begin{figure*}[htbp]
	\centering
	\begin{minipage}[t]{0.48\textwidth}
		\centering
		\includegraphics[width=8.5cm]{imgs/figure4.png}
		\caption{Efficiency comparision with F1-P, F1-G and F1-Eff}
	\end{minipage}
	\hspace{5mm}
	\begin{minipage}[t]{0.48\textwidth}
		\centering
		\includegraphics[width=8.5cm]{imgs/figure5.png}
		\caption{Visualization results. (a) Image in T1. (b) Image in T2. (c) Ground truth. (d) Prediction. (e) Class score map.}
	\end{minipage}
\end{figure*}

Figure 4 further compares the the efficiency. It can be seen that our method with diffFPN has the highest F1-Eff and F1-G. Compared to SNUNet, the method with highest accuracy, the amount of parameters and calculation of our LSNet has reduced by 90.35\% and 91.34\% respectively, with just a 1.5\% decline in accuracy.

Figure 5 shows that the ground cover has changed greatly between T1 and T2. Even the constant buildings, their colors and textures have also changed, retaining only the basic structures. Changes defined in this dataset are mainly caused by human activity. Comparing column (c) with column (d), the detection results of LSNet are relatively accurate. From column (e), it can be found that the edge of the changing area has higher probability than the inner areas, which indicates that the network leverages the structure of the area as a discriminant feature, which improves its robustness to color and texture changes.

\section{Conclusion}
For effictive RSI change detection, A lightweight Siamese network with a context-guided block based backbone and feature pair fusion module is proposed. The results on the challenging CCD dataset reveal that when compared to other mainstreams, the proposed method achieves competitive results with a limited amount of parameters and computations, demonstrating its efficiency.

\textbf{Acknowledgements} This paper is supported by the "YangFan" major project in Guangdong province of China, No. [2020] 05.

\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


June 2016 - Eye Operation Mission at Trung Vuong Hospital - Saigon. Thank you again Dr. Alex Thuy Nhien Dang and Tiffany Le for supporting entire of our May mission at Trung Vuong hospital. This operation serves 100 underprivileged patients from Xuan Loc, Dong Nai, Phu Quy & Vung Tau. Thanks Tu Thien Hy Vong for coordinating this mission.


text:
Home/News/ICPC To Partner Police, Bauchi State In Fighting Corruption
ICPC To Partner Police, Bauchi State In Fighting Corruption
Elizabeth KarremMay 4, 2022
The Bauchi State office of the Independent Corrupt Practices and Other Related Offences Commission (ICPC) has expressed its intention of partnering with the Nigerian Police, Bauchi State Command in the fight against corruption.
This was made known by the ICPC Bauchi State Resident Anti-Corruption Commission (RACC), Mr. Gali Baba Rogo during a sensitization programme at the Nigeria Police Command Bauchi State recently, which was held to create awareness about ICPC activities and rub minds on how to tackle the menace of corruption that has marred every sector of the country.
Mr. Rogo explained that the Commission considered collaboration with the Nigeria Police Force in combating corruption as a step in the right direction as there are many areas of interest and similarities, just like some other relevant agencies that were partnering with ICPC in the fight against corruption.
He further intimated the officers about the latest initiatives of the Commission to tackle corruption such as the National Ethics and Integrity Policy, Constituency and Executive Projects Tracking Initiative (CEPTI) among others.
Making his remarks, the State Commissioner of Police CP. Umar Mamman Sanda, appreciated the efforts of the Commission especially at this critical time in the nation.
The Bauchi State Police boss expressed displeasure at the negative effects of corruption in our socio-economic lives, and regret that backwardness evidenced by bad roads, unequipped hospitals, dilapidated government structures, etc. were what we would bequeath to our children.
He therefore challenged officers of the Command to have a rethink about doing things right, and admonished everyone to live above board and rise to the occasion by complementing the work of ICPC knowing that collective efforts is required in the war against corruption.
Later, while presenting a paper titled "Corruption, ICPC Act and You – the needed Synergy in the War Against Corruption" an officer of the Bauchi State office Mr. Baba Fika Mohammad took the officers through the history and duties of the Commission, and some offences and their punishments under the ICPC Act.
Baba Fika called on the officers to imbibe the tenet of transparency and be accountable in every business that concerns their official responsibilities.
The sensitization session ended with the presentation of IEC materials by the RACC to the Commissioner of Police, Bauchi State Command.
bauchi State corruption ICPC Police
Osinbajo Graces Durbar Festival, Hails Emir Of Kano
Imo Govt To Build Schools In 305 Wards


text:
Jörðgarð Map of Week #119 - Oar & Pick Inn
Description: With the 119th Map of the Week we take a look at the Slovanian city of Mine Docks with a glimpse inside of the "low Class" Oar and Pick Inn in the North District. Klement, a 67-year-old retired merchant from the Royal Trading Co. coster, purchased the rundown Oar and Pick several years ago and, step-for-step, restored it. Klement is betting that the inn will provide a good income for himself and later his inheriting children once Baron Luboš' cleanup of the North District has been completed. At present, the inn runs on something of a break-even basis. The once decrepit wooden roofed firetrap now has a clay tile roof, but the walls inside still are wooden and any semblance of luxury must be sought elsewhere. That notwithstanding, the inn does enjoy substantial protection. Baron Luboš respects Klement's investment as a signal of support for his planned renewal of the North District. It would be against the baron's own best interests if the remodeled Oar and Pick were to be known as a house where guests are robbed and burglarized. The inn is off limits to all independent operations by the thieves' guild. Any planned theft or burglary in the inn or on the inn's grounds must be approved by the baron's brother-in-law, crime lord Bronislav. The inn is a place where only nobodies stay. Its name comes from its original goal of being a hostelry for visiting miners and raftsmen. They still account for the largest part of the guest registry. The rest includes traveling peddlers, occasional parties of adventurers with few coins in their pockets and other low income transients. The rooms cost a bronze piece nightly. A bed in the open bay accommodations costs 4 copper pieces. The menu offers only a small selection of simple, traditional Slovanian dishes, which means small portions of meat and larger shares of dumplings and vegetables. However, that's exactly the kind of food that most guests seek and want. The quality of the food is good, and the house cook, Nina, a 55-year-old human female artisan, is talented at making the best of simple ingredients. The house's hearty ale, on the other hand, is of the best quality. It's the work of master alewife Klára, a 50-year-old human female artisan. Meals run from two to six bronze pieces, and the ale costs 2 copper pieces per pint/half-liter tankard. The inn's manager is Klement's son, Robin, a 42-year-old human male merchant. He, his wife and their two children live in their own private dwelling. Robin employs a bartender, two waitresses, two chamber maids and a laborer. All are human male and female commoners.
Key to the Floor Plan of the Oar and Pick Inn
If a PC group wants to spend the night in the Oar and Pick, it probably will find beds available in the open bay on the second floor, but most rooms on the third floor usually are occupied.
1. Entry.
2. Public Room.
3. Entry to Guest Area.
4. Inn Reception and Office.
5. Stairway to Upper Floors.
6. Jakes. Note that there are no separate jakes for men and women. The door to the double jakes also has no lock. The black circles are offal shafts from the upper story jakes.
8. Trapdoor to Storage Cellar (not mapped).
9. Rear Door.
10. Beer Tap.
11. Food Serving Area.
Second Story
12. Stairway from the Ground Floor.
13. Second Story Landing.
14. Stairway to the 3rd Story.
15. Tables for Guests.
16. Open Bay Bunk Area.
17. Jakes.
18. Broom Closet. The black circle is the offal shaft from the third story jakes.
Third Story
The game master should note that accomodations on this level are humble but more private than in the open bay area on the second story. Each private room has the same type of bed as is found in the open bay. Each room also has a night table, a foot locker, a bed pan, a small wardrobe and a table with chair in the windowed dormer. In each room, the 6-foot/180 cm space inward from the outer wall is of limited usefulness due to the slope of the inn's mansard roof. Only the dormer areas had full-height ceilings.
19. Stairway to the Second Story.
20. Open Dormer. The walls of this dormer extend all the way to the floor.
22. Lobby Area. Two wooden benches are the only furnishings.
23. Ladder. It leads to a ceiling trapdoor to the attic storage area (unmapped), which has a low ceiling except in the areas where there are dormers. The trapdoor always is locked.
24. Guest Room Area. The numbers in parantheses are individual room numbers.
You can get the floor plans in two versions:
2. As three JPG flat maps of 1360 Pixels x 910 Pixels each (1.3 MB) with the hyperlink below.
Next Week: The Auðarhome Enclaves


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In a parallel circuit, each unit is directly linked to the power supply, so each system gets the exact voltage. There are 3 basic sorts of standard light switches. The circuit needs to be checked with a volt tester whatsoever points. Obp 3 Wiring Diagram. Each circuit displays a distinctive voltage condition. You are able to easily step up the voltage to the necessary level utilizing an inexpensive buck-boost transformer and steer clear of such issues. The voltage is the sum of electrical power produced by the battery. Be sure that the new fuse isn't blown, and carries the very same amperage.
The control box may have over three terminals. After you have the correct size box and have fed the cable to it, you're almost prepared to permit the wiring begin. Then there's also a fuse box that's for the body controls that is situated under the dash. Obp 3 Wiring Diagram. You will find that every circuit has to have a load and every load has to have a power side and a ground side. Make certain that the transformer nameplate power is enough to supply the load that you're connecting.
Schematic wiring diagram guitar wiring diagrams house wiring circuits diagram basic wiring diagram outlet wiring diagram wiring a 400 amp service wiring lighted doorbell button 3 -way switch wiring diagram.


text:
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Headstone available in 30" x 24" x 4" with a 4" x 30" x 12" base. Tropical Green granite with a splayed base for further inscription.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" Base. Dark Grey granite with a contrasting moulded edge.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Fully polished Pink granite with moulding on both headstone and base.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Dark Grey granite with polished moulded edges to emphasise the inscription.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Black granite with rose and double border design.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Dark Grey granite with engraved rose design highlighted with 23½ carat gold leaf.
Headstone available in 30" x 24" x 4" with a 4" x 30" x 12" base. Black granite with central raised heart and hand painted violets.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Green ovolo top granite with gilded inscription.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Black granite with 'a good round' design. Please ask for alternative personal designs.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Black granite with beautiful hand painted lily. Any other flower can be depicted.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Black granite offset peon with rose ornament climbing the left side.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Blue Pearl granite with roses or any other flower depicted. Central gilded cross.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Dark Grey granite. Natural carved roses and natural rock pitched edges.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Balmoral Red granite with deep carved roses left natural.
Headstone available in 33" x 21" x 3" with a 3" x 24" x 12" base. Dark Grey granite with hand painted deep carved roses following the gothic shape.
Headstone available in 27" x 21" x 3" with a 3" x 24" x 12" base. Black granite Heart shaped with rounded shoulders with engraved and printed roses. (shown here in yellow but any colour available).
Headstone available in 33" x 26" x 6" with a 4" x 30" x 12" base. A Ruby Red granite beautifully carved angel cherishing a heart.
Overall size is 2'3" x 30" x 12" Black granite 'Gates of Heaven' memorial.
Available in 30″ x 28″ x 4″. Blue Pearl granite with a finely carved cord, tassel and shaped pages.
Available in 30″ x 28″ x 4″. Light Grey granite with carved rose design on a flat page book.
Available in 30″ x 28″ x 4″. Classic Black granite book with a carved cord, tassel and shaped pages, rest and bow fronted base.
Available in 30″ x 28″ x 4″. A traditional Black granite book with stepped and polished page edges and half ogee moulded base.
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