Abstract:Among several components of watershed prioritization, morphometric parameters are considered to be essential elements for appropriate water resource planning and management. In the current study, nine hilly sub-watersheds are prioritized using novel hybrid model based on morphometric variables analysis at Bino Watershed (BW) located in the upper Ramganga basin, India. The proposed model is based on the hybridization of principal component analysis (PCA) with weighted-sum approach (WSA), presenting a single-frame methodology (PCWSA) for sub-watershed prioritization. The prioritization process was conducted based on several morphometric parameters including linear, areal, and shape. The PCA was performed to identify the significant correlated factor-loading matrix whereas WSA was established to provide the weights for the morphometric parameters and fix their priority ranking (PR) to be categorized based on compound factor value. The findings showed that 37.81% of total area is under highly susceptible zone sub-watersheds (SW-6 and SW-7). This is verifying the necessity for appropriate soil and water conservation measures for the area. The proposed hybrid methodology demonstrated a reliable approach for water resource planning and management, agriculture, and irrigation activities in the study region.Keywords: prioritization; morphometric variables; PCWSA; Bino watershed; Uttarakhand
Abstract:This paper assessed the impact of soil and water conservation practices on farm productivity and risk exposure using data from 1204 plots in the semiarid tropics of India. A probit model was used to assess the determinants of adoption of soil bunds. We employed a moment-based approach for estimating crop revenue, its variability and downside risk exposure, i.e., crop failure. Furthermore, we also used a doubly robust method for assessing the impact of soil bunds on crop revenue, its variability and downside risk. Matching and propensity-based methods were also used to check robustness. The results show that training, access to credit and extension services are key determinants of adoption of soil bunds. Furthermore, the results also suggest that soil bunds not only improve the crop revenue but also reduce its variability. Most interestingly, we show that soil bunds also reduce the chances of downside risk, i.e., crop failure. Therefore, in view of increasing climate change and variability in the semiarid tropics, it can be suggested that soil bunds could be an important adaptation strategy for improving productivity and reducing risk exposure. This paper supports the investment in soil and water conservation technologies for sustaining the livelihood of resource-poor farmers of ecologically fragile regions such as the semiarid tropics.Keywords: soil and water conservation; soil bund; impact; risk exposure; semiarid tropics
Soil And Water Conservation Engineering R.suresh.epub
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Mangroves in the Northern parts of Sri Lanka has been understudied and often ignored by the ecologists due to several reasons. This paper contributes to the assessment of floristic diversity and community structure of mangrove forests in two sites in the Northern Province; Mandaitivu and Arali. Vegetation studies were undertaken with the use of 10m wide belt transect laid across the water-land gradient, within which, 5-7 quadrats were placed for floral identification (nMandaitivu=67, nArali=44). Subsurface water samples (n=57) from adjacent estuarine region were taken to assess environmental parameters. Ten true mangrove species of six families were identified altogether in both sites. Bruguiera cylindrica, Lumnitzera racemosa, Excoecaria agallocha and Avicennia marina showed higher niche width. Zonation exhibited Rhizophora in the fringing zone, Avicennia landward and the others in the mixed zone. Salinity in both areas were relatively high and showed a significantly positive correlation with mangrove abundance along with conductivity, tidal regime and pH. Despite of their important role in coastal dynamics, mangroves have been degraded due to anthropogenic activities, thus demanding actions for conservation in the future.
Water quality parameters displayed a tidal fluctuation along with least freshwater input and increased evaporation 17 in these regions. This have resulted in an extremely high salinity and lower dissolved oxygen (Table 3). Salinity ranges between 25 and 27 ppt whilst pH showed a slight alkalinity with a variation from 7.05 (+ 0.19) to 7.4 (+ 0.1). The soil water content varied between 24 and 26% showing a critical dryness of the region sampled.
W. Mujib-Wala catchment represents the second largest watershed draining to the Rift, and discharging directly into the Dead Sea. The catchment is located between 3039' to 31 to 41'N latitude, and 3530' to 3630' East longitude (Figure 1). The watershed attains a maximum elevation of 1277 m (a.s.l) and a minimum of - 431 m (b.s.l) at the outlet of the wadi. The catchment has been subjected to progressive stages of rejuvenation due to continuous base-level changes along the Dead Sea, the lowest base level in the Jordan Rift [37] [38] . Thus, deeply dissected topography, incised drainage, and over-steepened and interrupted slopes are characteristic. Moreover, the catchment suffers from serious geo-hazards such as landslides activity, high sediment yield, severe soil erosion, and repetitive flooding [13] . Slope categories range from 0 - 10 in the east, to nearly vertical slopes bordering the canyons in the western part of the watershed, and the faulted-erosional escarpment overlooking the Dead Sea. Therefore, slope gradients have a direct impact on surface runoff and geomorphic processes over the demarcated sub-basins.
Using ERDAS Imagine (2015, v. 15), LANDSATE 8 (July 2017), and supervised classification, the Maximum Likelihood Method of classification techniques was employed to classify Land use/cover guided by the classification system proposed by Anderson et al. [50] . The land use/cover classes recognized are: bare land; rainfed farming; irrigated farming, water body (Mujib dam and Wala dam), and irrigation pools, settlement, and major and minor roads. A soil map (Figure 4) was digitized from the National Soil and Land Use maps of Jordan [51] . A slope categories map was derived using ASTER DEM (Figure 5). A rem arkable variation exists in slopes. Slope categories of 0 - 5, 5 - 10, and 10 - 15 characterized the eastern part of the entire watershed, whereas, slope categories of 15 - 20, 20 - 30, 30 - 45, and >45 stand out in the western part of the watershed and the faulted-erosional escarpment overlooking the Dead Sea, and along the canyons and major tributaries downstream (Figure 6).
sub-watersheds (9.2% of the total) as follows: 1, 2, 4, 18, 31, 39, and 46. Five of these sub-basins represent typical rainfed and irrigated farming areas on flat-undulating terrain in the northern part of the catchment east of Madaba, and the eastern part of the watershed. The expansion of cereals cultivation over the marginal areas and rangeland (annual rainfall varies from 200 to 250 mm) accelerate soil erosion [2] . Irrespective of scattered irrigated agriculture, the eastern part of W. Mujib-Wala forms a poor grazing land with high soil erodibility. Thus repetitive intense rainstorms over southern Jordan [54] initiate severe soil erosion where the vegetation cover is highly degraded. Additionally, degraded rangeland and bare land occupy large areas of the watershed due to the low amount of rainfall, marginality (the dominance of semiarid and arid conditions), which in turn accelerate soil erosion. All sub-watersheds ranked under very high and high priority have greater erosional potential with a high erosion risk. Consequently, they are considered potential areas for adapting soil conservation measures [21] . Rill and gully erosion, and landslide activity are common on steep slopes (15 - 25, and >30) where soft carbonate rocks of low shearing resistance are exposed. Sheet erosion is also active on gentle slopes (0 - 5) in the rainfed areas over the table lands bordering the canyons of W. Mujib-Wala downstream (i.e., the Qasr-Rabba, south east of Madaba, and Dhiban areas) where the annual rainfall ranges from 285 to 350 mm. Geological, morphological
Morphological factors contributing to high soil erosion loss are: high relief, steep and long slopes of 10 - 15, 15 - 20, 20 - 30 and >30 slope categories. Sub-watersheds classified as high priority groups display high soil erosion rates and high sediment loads discharging into the Wala dam and the Mujib dam [16] [17] . These 44 sub-basins which are influenced by such processes (58% of the total) need urgent attention for implementing soil conservation practice. The long periods of resource exploitation, land use abuse, and the deterioration of vegetation cover have functioned to maximize soil erosion since the Neolithic and Chalcalithic periods. The existence of agricultural stone terraces over the rainfed highlands is an indicator of severe soil erosion at least since the Nabatean period some 3000 years ago [56] .
It has been argued that prioritization based on the morphometric analysis method by utilizing the erosion risk morphometric parameters is time consuming in comparison with other approaches including the Principal Component Analysis approach, which allows for more effective parameters for prioritizing watersheds [24] . However, a prioritization for the Zarqa River has been carried out recently based on the morphometric analysis method and the PCA approach [13] . The output revealed that both methods did not produce similar results as stated earlier by Gajbhiye and Sharma [24] in a study on Shakkar River Catchment, Madhya Pradesh, India. Such disagreement in the results related to the two case studies is probably attributed to differences in physical conditions between Central India and Northern Jordan. The present study introduces a prioritization scheme for 76 sub-watersheds on the basis of the morphometric analysis method (the linear and shape parameters), and to test the achieved priority classes using Discriminant Analysis (DA) technique. The resultant priority classes displayed in Figure 7 and Table 3, are proved to be statistically valid. Consequently, the utilization of the morphometric analysis method is justified as a successful method of prioritization. Statistical validation also implies that erosion risk morphometric parameters are efficient parameters in prioritization of watersheds for soil and water conservation measures. The intention of statistical testing of sub-watersheds pertaining to the four priority classes is to test the hypothesis that there are significant differences between the four priority classes achieved earlier, and if the hypothesis can be accepted to establish a system of coordinate axis which discriminates between the four priority classes identified (1: low priority to 4: very high priority). Statistical analysis was conducted on four data matrices representing the four priority groups (i.e., 7 11; 25 11:32 11; and 12 11) with the associated ranking values (connected to linear and shape parameters), including the Cp scores. The F test of Wilks Lambda obtained is F ratio 174.9, with the degree of freedom V1 = 3 and V2 = 72. Referring to the table of percentage points of the F-distribution, with V1 = 3 and V2 = 72, it is found that at 99.9 percent of confidence, the tabulated value is 5.78, which is significantly exceeded by the computed F ratio (174.9). Subsequently, their is a great significant difference between each of the priority groups (very high, high, moderate, and low), and the four priority classes are completely separate and distinct. Moreover, 98.0 percent of the difference between the four the four priority classes is attributed to discriminant function 1 (93.7 percent) and discriminant function 2 (4.3 percent). Further, it was observed that the discriminant function 1 is positively correlated with six erosion risk morphometric parameters (the linear and shape parameters). Correlation values range from 0.413 to 0.999, and the Cp values are very strongly correlated with discriminant function 1 (0.970). By contrast, the correlation of discriminant function 2 with erosion risk parameters is relatively weak (0.17 - 0.237). The scores of each sub-basin of the priority groups (shown in Table 3, and illustrated in Figure 7) on the discriminant function 1 and 2 were plotted in Figure 9. The plot displays completely disconnected priority clusters. The present results show that 2ff7e9595c
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