The highest variation in elasticity coefficients was found to be for the watersheds within the Central Great Plains region of MORB. The positive values of e AgLU were found in the watersheds close to
Iowa while negative values clustered closer to the west side of cen- tral states (see Fig. 9 ). Changes in baseflow patterns can be attrib- uted to concurrent changes in agriculturallanduse and precipitation, especially in eastern MORB, where Xu et al. (2013a) reported stronger influence of landusechange than cli- mate on baseflow with corn and soybean production systems in the 1970s and 1980s for Iowa. Agricultural production in 63 undis- turbed watersheds in four central states is significant (average over 31%) but the larger range of variability for agriculturallanduse coefficient suggests that the influence of agricultural production on baseflow vary widely, from low to high, compared to the effect of precipitation on baseflow. This may be due to the fact that upland agricultural activities foster many management practices which exert varying influence on downstream waters. Findings from the present study are consistent with previous studies. While some studies reported stronger climate impact on streamflow than landuse in North-Central US including South Dakota and Min- nesota (e.g. Ryberg et al., 2014; Kibria et al., 2016; Novotny and Heinz, 2006 ), other researchers indicated that landusechange and land management practices was the main driver for stream- flow and baseflow increases in the Upper Mississippi RiverBasin including Iowa (e.g. Schilling et al., 2008; Schilling and Libra, 2003; Xu et al., 2013a ). Selection of the study periods that drive the core datasets for landuse and climate impact studies can also affect the conclusions of the study. For example, Xu et al. (2013a)
The four crops rice, groundnut, sun flower, and sorghum are selected for analysis in this study which are already been included in EPIC simulation model, but needed to be modi ﬁed to reflect local conditions. The model was run for all four crops for Kharif season only. Except Rice remaining three crops are rainfed. Rice being an irrigated crop simulation is carried out based on the prevailing conditions in the ﬁeld. About 47 parameters related to crop phenology, its environment and crop growth in a stressed environment are used in EPIC. Parameter values for the selected crops and the management practices associated with them are based on previous modeling exercises with EPIC and on advice from experts at the Acharya N. G. Ranga Agricultural University (ANGRAU) Hyderabad. EPIC simulated yields are generated at adminstrative blocks falling under four major districts (Kurnool, Chuddapah, Chittor and Ananthpur) of Pennar basin and database developed to describe agricultural practices and environmental conditions in each of these 160 blocks are being used. Soil properties are derived from the National Bureau of Soil Survey and Landuse planning (NBSS&LUP) Nagpur paper maps at 1:250 K scale are employed. Validation of crop simulation model EPIC is carried out at districts level. EPIC is forced at block level and yields are aggregated to district level for the years 1989 through 1996 and the annual reported yields for the selected four crops viz., rice, sorghum, groundnut and sun flower. The validation was done using Kharif simulated crop yield, which were compared with annual (Kharif + Rabi) reported yields, which were the only data available. The crops, other than rice, are majorly a dryland crop dependent on southwest monsoon, extent of irrigation crops under Rabi season have not been covered in this study. Nev- ertheless, the validation test is still powerful since a predominance of annual yield is derived from the Kharif season. For instance statistical analysis on crop growing region shows that in the Ananthpur district of Andhra Pradesh the area planted in the Kharif versus rabi season were for rice 2.7 times, and groundnut 41 times. Rice tended to be irrigated in both seasons.
Abstract: Alteration of landuse and climatechange are among the main variables affecting watershed hydrology. Characterizing the impacts of climate variation and landuse alteration on water resources is essential in managing watersheds. Thus, in this research, streamflow and baseflow responses to climate and landuse variation were modeled in two watersheds, the Upper West Branch DuPage River (UWBDR) watershed in Illinois and Walzem Creek watershed in Texas. The variations in streamflow and baseflow were evaluated using the Soil and Water Assessment Tool (SWAT) hydrological model. The alteration in landuse between 1992 and 2011 was evaluated using transition matrix analysis. The non-parametric Mann-Kendall test was adopted to investigate changes in meteorological data from 1980-2017. Our results indicated that the baseflow accounted for almost 55.3% and 33.3% of the annual streamflow in the UWBDR and Walzem Creek watersheds, respectively. The contribution of both landuse alteration and climatevariability on the flow variation is higher in the UWBDR watershed. In Walzem Creek, the alteration in streamflow and baseflow appears to be driven by the effect of urbanization more than that of climatevariability. The results reported herein are compared with results reported in recent work by the authors in order to provide necessary information for water resources management planning, as well as soil and water conservation, and to broaden the current understanding of hydrological components variation in different climate regions.
agricultural harvests, water and energy supply and demand balances, yields from fisheries, and modulate higher frequency events such as ﬂoods and droughts. Moreover, their low frequency natural variability may obscure human inﬂuences on hydrologic variations and climatechange (Cayan et al. 1998). Other effects on the economy range from the increasing costs of coastal regions protection and the redesign of off-shore oil platforms, to cope with the increases in wave heights (Kushnir et al. 1997), to the higher likelihood of major hurricanes land falling on the east coast of the U.S. (Elsner, Jagger and Niu 2000; Kocher 2000). From an economic perspective, an estimate of the potential welfare gains that could be achieved through early NAO phase announcements and subsequent crop mix, storage and consumption adjustments ranges from 600 million to 1.2 billion dollars a year (Kim and McCarl 2004)
in water extraction from the soil and aquifer by the trees. Trees often have deeper and more extensive rooting systems than other vegetation which enables them to extract groundwater to meet the evapotranspiration demand, especially during the dry seasons when the top soil is dry (Thomas et al., 2012; Doody and Benyon, 2011a; FAO, 2006; Calder, 2005; Benyon et al., 2006). A study by Pinto et al. (2014), for example, estimated that annual soil and groundwater contributions to tree transpiration were about 70% and 30%, respectively. However, during the dry summer months the groundwater contribution became dominant and rose to 73% of transpiration. Additionally, trees have higher aerodynamic roughness than crops that favour higher evapotranspiration rates (Calder, 2005). The differences in leaf, size, shape, thickness, anatomy and chlorophyll content between trees and other plants and even between trees species also affects the rate of transpiration (Muthuri et al., 2009). Consequently, increase in tree cover through agroforestry also increases water use in the watershed in form of evapotranspiration. A study by Muthuri et al. (2004) in central Kenya found that water use in agroforestry systems was higher than for treatments under only maize cultivation. The decrease in groundwater in shallow aquifers, due to increased uptake by trees, decreases the water available and the amount released to the streams as baseflow (Adelana et al., 2015; Fan et al., 2014; Doody and Benyon, 2011b). Generally, the change in baseflow may be either positive or negative depending on the water budget in the aquifer storage (Bruijnzeel, 2004). If the incoming water, as a result of improved infiltration, surpasses the extra water removal by trees, then the extra storage may lead to increase in baseflow. The reverse is also true in the case of negative change in aquifer storage as was the case in our study (Brown et al., 2005; Bruijnzeel, 2004). The overall water yield, which is essentially a summation of surface runoff, lateral flow and groundwater contribution to streamflow, also decreased with an increase in the area under agroforestry.
not protected, 3 and are classified into sub-groups with specific climate requirements: citrus (CIT), which is sensitive to extreme cold events; deciduous (DEC), which need cold doses to produce fruit; and sub-tropical trees (SUBT), which are tolerant to hot conditions — all irrigated; all other types of orchards were assigned to irrigated (OTHI) and rain-fed (OTHR) groups. A 13th land-use category encompasses all the non-cultivated (NC) agriculture-related land, including grazing areas, access roads, and uncultivated farmland. The aforementioned constitutes our reference bundle. The sample averages of the observed bundles‟ land shares are shown in the first column of Table 4. Also shown is the share of total cultivated land (TCL), which, as will be explained later, plays a key role in our proactive analyses. On average, the sample covers 463,000 hectares of land (4,630 square kilometers), or about a quarter of Israel‟s surface.
During monsoon (June, July, August, September and October), due to the heavy rainfall, Mahanadi faces high stream flow as it is a monsoon-fed river. The ground water component with infiltrationis trivial compared to the streamflow during the monsoon season. In the non-monsoon season as there is no rainfall, infiltration to ground water is not considerable, resulting in low stream flow in Mahanadi RiverBasin. Thus, for the monsoon season therunoff prediction can be used to predict floods, manage reservoir operations, or impact on water quality in the basin. There are many literatures in which transformation of rainfall into runoff has been studied in order to extend stream flow series. There are many techniques for predicting the runoff volume.To assess the future runoff in the present study, the black box approach (Artificial Neural Network) and Multiple Linear Regression techniques are used.
Chapter 2 – Background and Literature Review
In order to effectively predict climatechange at global, regional, and local levels, precise methods are imperative that will clearly assess its impacts by developing appropriate adaptation and mitigation guidelines (Giorgi, 2005). A lot has been done in handling the challenges of climatechange by scientists and economists taking into account risks, costs, and also how future changes will look like (Nordhaus, 1994). Knox (2012) states “… one sees climatechange as an urgent problem that threatens our planet; one does not. I want our president to place scientific evidence and risk management above electoral politics.” (Bloomberg, 2012) referenced Knox and added “… our climate is changing, and while the increase in extreme weather we have experienced in New York and around the world may or may not be the result of it, the risk that it might be-given this week’s devastation- should compel all elected leaders to take immediate action.”
the annual temperature and precipitation in the drainage basin and the ENSO events were not correlated.
Further independence test of the seasonal temperature and precipitation with the El Nino and La Nina events revealed that the temperature in autumn and the precipitation in summer were correlated with the El Nino event in the years when the El Nino event develops, and their conﬁdences were as high as 0.1 and 0.05 respectively ( Table 7 ). The temperature in spring is related to the El Nino event in next years after the El Nino event occurs only. The temperature in summer and autumn is related to the La Nina event in the years when the La Nina event occurs. Such results suggest that the anomaly of precipitation in the drainage basin in summer was more signiﬁcant in the years when the El Nino event develops than that in following year after the El Nino event occurs and in the years when the La Nina event occurs. Contrastly, the anomaly of temperature in following year after the El Nino event occurs and in the years when the La Nina event occurs was more signiﬁcant than that in the years when the El Nino event occurs. Temperature change in autumn and precipitation change in summer in the drainage basin are extremely signiﬁcant and dominated by positive anomalies in the years when the El Nino event developed. It is obvious that the temperature in autumn and precipitation in summer were high in the years when the El Nino event occurs. Although ﬂood disasters might occur easily, they become less serious as the temperature and rainfall did not increase coin- stantaneously. Moreover, the high temperature in autumn can maintain the glacier melt and recharge the rivers for a long time. This is beneﬁcial for users of water resources in the drainage basin. But we still need to pay great attention to ﬂood disasters.
Usechange and Forestry – LULUCF) – ŠESD apskaitos sektorius, kuris apima antropogeninės kilmės ŠESD išmetimus iš šaltinių ir šalinimus absorbentais, at- siradusius dėl anglies sankaupų pokyčių sausumoje. ŽNPKM sektorius apima šiuos anglies šaltinius: gyvoji biomasė (antžeminėje ir požeminėje augmeni- jos dalyse), negyvoji organinė medžiaga (negyvi medžiai ir miško paklotė) ir or- ganinio dirvožemio anglies sankaupos visose žemės naudojimo kategorijose (DG CLIMA, 2010). Apskaitomos šešios pagrindinės žemės naudojimo kategori- jos: miško žemė, žemės ūkio naudmenos, pievos, pelkės, užstatyta teritorija, kitos paskirties žemė, kurios veikia klimato kaitos procesus (IPCC, 2003).
2.3. Land-Use/Cover Classification
Supervised classification using a maximum likelihood algorithm was adopted using 6 land-use/ cover categories. Selection of training samples was based on information from available past maps of the area [ 28 , 37 , 38 ], Google Earth images, first author’s knowledge of the study area and additional field surveys carried out in April 2014 and May 2015. Training pixels for different landuse/cover categories were mainly sampled in areas that did not experience change over the study period. Reflectance values corresponding to the various landuse/cover classes were used to create their respective spectral signatures which were then used to perform the classification. The land cover categories are: closed forest, open forest, small-scale agriculture, mechanized (large scale) agriculture, rangeland and tea plantation. Closed forest represents densely forested areas with closed canopies (over 75% tree cover), while open forest represents areas with light tree canopy coverage (less than 75% tree cover) and mosaics of predominantly forested areas with some patches of cleared/cultivated land. Small-scale agriculture includes patterns of small cultivated areas sometimes alternated with fallow land, while mechanized agriculture comprises of large coherent agriculturalland of the same crop stand, mostly in regular shapes. Tea plantation includes both small and large tea plantations. Rangeland includes grassland, shrub land, savanna mainly used for grazing and game reserves. The land-use/cover map of 1976 was resampled using the nearest neighbour technique from originally 60 m to 30 m resolution to conform to the resolution of other maps for easier post classification comparison/analysis.
Acknowledgements. Gina Tsarouchi acknowledges support by the Grantham Institute for ClimateChange (Imperial College London) and HR Wallingford. Wouter Buytaert acknowledges support by the NERC Changing Water Cycle (South Asia) project hydrometeorological feedbacks and changes in water storage and fluxes in northern India (grant number NE/I022558/1). We also acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table A1 of this paper) for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of the software infrastructure in partnership with the Global Organization for Earth System Science Portals. We would like to thank the two anonymous reviewers for their constructive comments, which helped us improve the manuscript. Edited by: Ian Holman
5.2. Gender, internal migration and agriculturalland contraction The logistic regression shows the gendered dimension of the impact of internal migration on agriculturallandusechange. It is interesting to note that with a 1% increase in internal outmigration of men, there are 15% fewer chances of agriculturalland contraction. On the other hand, an increase of 1% in internal outmigration by women is likely to increase the chances of agriculturalland contraction by 36%. This reveals that internal outmigration of women impacts agriculturallandusechange more compared to internal outmigration by men. This is an interesting finding that the community consultations helped understand more in detail. When men migrate the remaining family members, particularly women, continue to cultivate the land. However, when women migrate, often looking for better education and health facilities for their children, there is only older people left in the household, thus leading to aban- donment of agricultural work. For instance, in wards 2 and 3 of Bahundanda VDC in Lamjung district, the development of hydropower plants has provided employment opportunities for men. While men work in the hydropower plants, their wives have migrated to urban areas (mostly Besisahar) with their children, abandoning agriculturalland as they seek better educational opportunities for children. Nonetheless, the major role played by women in agriculture clearly emerged in the gender-differentiated views they held on the performance and future of agriculture. Men tended to be more dismissive and less interested in the future of agriculture. Women’s answers were far more concrete and hopeful. While recognizing the challenges, women respondents were clear in identifying needs and required interventions, leaving more space for positive improvements in mountain agriculture.
Abstract— Landuseland cover change (LULCC) is the result of the long time process of natural and anthropogenic activities that has been practiced on the land. GIS and remote sensing are the best tools that support to generate the relevant landuse/cover change in the basin. This study was conducted in the Akaki Riverbasin to detect landuseland cover changes within the 30 years period (1985-2015) by using landsat imagery data acquired from the GCF. Supervised maximum likelihood algorithm classification were deployed to classify landuse/cover into four prominent landuse groups and data’s were processed by using ERDAS imagine 2014 and ArcGIS10.1 software. In the basin dominant LULC was agriculturallanduse which accounts around 56.28% and the second largest is built-up area by 31.51% and the rest, forest(11.9%) and water body(0.31%) coverage were takes third and fourth position(as 2015 data). The rapid expansion of Addis Ababa city consumes more fertile land near to the city. According to the projected LULCC for 2030 the proportion of agricultural and built-up area near to each other, i.e., agriculturalland reduced to 42.33% and urban or built-up area increased to 41.63%. One good thing observed in the basin was an increment of the forest land in between 2011 and 2015 by 23.85% whereas in between 1985 and 2015 the annual rate of change was by 4.2. This may be due to the implementation of green-economy building strategy of the government and other stakeholders to rehabilitate the degraded lands in order to achieve MDG and SDG goals. Urbanization, industrialization, commercial center enlargement and
1.6 Gap of knowledge
Despite many studies on the assessment of the landuse and climate changes impacts on hydrological processes, quantitative information on possible changes of regional patterns of climate and landuse and their implications for the hydrologic cycle and water resources are still scarce in humid tropical regions like southeast Asia and yet poorly understood. Quantitative assessment of the sensitivity, vulnerability and adaptive capacity to climatechange especially for humid tropical regions are rare at watershed scale where the most important sources and drivers are located. Studies that relate both landuse and climate changes with hydrological processes and streamflow of the watershed are one of the urgent issues and high priorities of today's water management needs to narrow the gaps between current knowledge and policymaking needs in tropical regions.
is at its minimum from July to September, however future minimal values occur earlier in the year near April to June.
8.3 Model Response to LandUseLand Cover Change Only
Figure 17 shows the average monthly flow and sediment loading under future LULC change only for each carbon emission scenario compared to the baseline. Runoff is virtually unaltered by the changes made to landuse; future quantities match the present baseline with monthly differences equaling no more than ± 20 cms, occurring from August – October. Sediment loading is more significantly affected, with monthly averages differing from the baseline ± 185 tonnes / day (Figure 17(b)). Further, a distinct response to the specific carbon scenarios is more easily identifiable. A2 predicts an increase in loading for all months. The largest increase (33%) occurs in March. A1B produces similar monthly averages compared to the baseline, and B1 results in a decrease in sediment loading. The largest deviation between the baseline and B1 also occurs in March, when the sediment loading is projected to reduce by 18%. Over the course of the entire 30 year simulation, the sediment percent change for A2, A1B, and B1 from the baseline are +43.8, -0.4, and -20.8, respectively. Percent change for runoff is < 1% for all three scenarios. In general, the seasonal fluctuations of future streamflow and sediment loading remain consistent with present day conditions.
Real estate is also believed to be a safe long term in- vestment among all sections of the society who has addi- tional surplus income to save. Moreover, it is highly luc- rative for the middlemen and the promoters of real estate ventures who orchestrate and boost up the market value of land. Conversion of wetlands to households is a usual practice in Kerala. Most of the agriculture belts of Pala- kkad have got legally converted as housing plots prior to the Land acquisition (amendment) bill (2007). The new ‘Regulatory Framework for Conservation of Wetlands (2008) by the central government also does not affirm the future of rice paddies, an ecosystem on its own sup- porting a range of species and offering a range of eco- logical services, although it deter filling up wetlands for other uses.
Federal, Brazil. To represent the climatic variability of the historic precipitation series, 5 years were selected: the moistest, the driest, an average, and 2 years representing the standard deviations from the series (half-wet and half-dry). After the calibration of the model with basin rainfall and runoff data, each rainfall event was simulated in each one of the selected years, and sediment yields were computed using software MUSLE for three different land-use and management situations: (a) The present conditions, with predominantly conventional agriculture in the agricultural areas (PC); (b) The former scenario, where native (Cerrado) vegetation existed in the basin (CER); and (c) no-till in the agricultural areas (PD). Although the relationship of the mean annual flow rates with the annual rainfall volume was linear, there was an exponential increase in runoff volume and sediment yield with precipitation in all scenarios; the most significant increases were observed for PC, followed by PD and CER. The exponential increase can be explained by the non-linearity of the MUSLE model regarding both precipitation and runoff volume. Index terms: MUSLE, precipitation variability, sediment yield, surface runoff.
Received: 15 September 2011 / Accepted: 3 April 2012 / Published online: 8 May 2012 # Springer-Verlag 2012
Abstract This paper analyzes climatevariability and change in the Urmia Lake Basin, northwest of Iran. Annual average of the following data time series has been analyzed by statistical methods: dry bulb temperature, maximum and minimum temperature, precipitation, and number of rainy and snowy days. We have also used mean monthly temper- ature and precipitation data for analysis of drought spells for the period 1964–2005 to find out whether fluctuations in the lake level are attributable to natural drought. Our results indicate that mean precipitation has decreased by 9.2 % and the average maximum temperature has increased by 0.8°C over these four decades. The seasonal changes are particularly visible in winter and spring. Results of the Palmer Drought Severity Index show that on average, drought episodes have hit the Urmia Lake Basin every 5 years and most of them reached severe levels, but recent droughts have become more intense and last longer.
Montalvo (39) señala que la cuenca es una unidad lógica de planificación, que obliga explícitamente a reconocer que el desarrollo basado en los recursos naturales o sobre el suelo, depende de la interacción de todas las actividades que tienen lugar en el total de la cuenca, permitiendo la aplicación de enfoques ambientales integrados para su gestión. En ella, la dinámica espacial de los patrones de uso del suelo ejerce un efecto importante sobre la estructura y funcionamiento de los ecosistemas y sobre la biodiversidad, pudiendo potenciar procesos de deterioro de la fertilidad de suelo, de la calidad del agua y pérdida del hábitat que, en consecuencia, afectan la provisión the basis of a combination of economic, legal and environmental, concluding that the human factor has been primarily responsible for driving changes in landuse in the Boroa riverbasin.