Climate change is expected to show unforeseen impacts in the Pacific Northwest and in order to prepare the planning or adaptation techniques sound science is necessary. In this study, we evaluated the hydrologic impacts for a relatively remote but ecologically important basin, SRB, located in central Idaho. Among the hydrological indicators, streamflow and the water balance components were focused in our analysis. In general, the climate model ranges in precipitation and temperature showed a wide margin, however, a consistently increasing pattern was seen in the study basin, SRB. Comparisons of the climate model ensembles from A1B, A2 and B1 scenarios, for the three periods, 2010-2040, 2040-70 and 2070-99 with the historical trends averaged over 1949-99, highlighted an increase of 3.1 °C, 4.2 °C and 5.3 °C while the precipitation trends were up by 1.8, 4.6 and 8% over the same period. As these trends were expected to have significant hydrological impacts, we investigated the response of the basin to these probable changes in the future. We demonstrated the improved macroscale hydrological model, VIC, by linking the climate model forcings to evaluate the basin hydrology. Clearly, on average the snowmelt-induced peakflows were advancing by 10 days from our center of timing analysis when compared with historical periods. The ensemble average of center of timing showed a steep decline for the next 90 years with A2 being the highest. Depletion of SWE and soil moisture just after the peak snowmelt into the growing season appeared to be considerable and this could directly have implications for drought, wildfire risks and low flows. This in turn can impact the stream temperature and salmon migration and spawning. Hence, the consequences of climate change for SRB can be significant. One interesting question to ask by looking at this vast range of predictions is how does it impact our management of water resources? It may not be easy to rule out one model predictions against the other, rather a more guided approach could prove the consistency in altered flow regimes and the resulting advancement of CT among all model scenarios could be construed as consistent enough in reassuring the impacts to develop policies and plans. High resolution modeling as well as multi-model analysis to characterize the uncertainty in streamflow and water balance at the sub-basin scale is required and that will provide further insights into the heterogeneous characteristics of this basin in a warming world.
Parametric or non–parametric trend analysis methods trough a statistical approach is frequently used for trend detection by fixing a certain level of confidence. Several studies have been addressed regarding trend analysis of climatic variables at global scale of a watershed. For example, Yan and Bai  in China found a decrease and an increase of flood precipitation and non-flood precipitation between 1969 to 2011. Afterward, Gocic and Trajkovic  obtained a negative and positive trend of streamflow for the last 50 years and comparing factors due to human activities and climate change. In west and south Africa, Gosling et al.  analyzed the trend in daily climate extremes and have observed an evident warming over most of the region. Oguntunde et al.  determined an increase of runoff trend with significant by analyzing the long-term trend hydro-climatology of Volta riverbasin in west Africa from 1901 to 2002. Diallo et al.  analyzed the inter-annual variability using several climate models over Sahel region and concluded that the models can reproduce rainfall variability with correlation exceeding 0.6 compare to observations. Amoussou et al.  reviewed hydroclimaticvariability and flood risk in two small forests located in the Mono RiverBasin (MRB). Lawin et al. [21,22] studied climate extreme trends of temperature and rainfall using a single model of Coordinated Regional Downscaling Experiment (CORDEX) and later with REgional MOdel (REMO) and consedering a few number of stations in the MRB. These studies pointed out an increase of temperature and a high variability of rainfall for historical and future baseline. According to the literature, in the MRB, fewer studies incorporated water balance components and as well an ensemble of climatic models for future scenarios of extreme indices.
Figure 4. The characterization of hydrological changes associated with climate change requires a consideration of vegetation dis- turbance, as indicated by a number of simulations of San Juan Riverbasin streamflow with the variable infiltration capacity (VIC) model. Several simulations are considered here: one using historical (1970–1999) meteorological forcing (average streamflow shown as a thick black line) and others using future (2070–2099) temperature and precipitation forcing from the Intergovernmental Panel on Cli- mate Change (IPCC) CMIP5 database (four different sets of forcing from four different Earth system models, or ESMs). Future stream- flow conditions are provided for two vegetation disturbance scenar- ios. The thin black line (with gray shading underneath) represents the average seasonal cycle of simulated streamflow from future runs which utilize the historical representation of vegetation. The green envelope (mean is shown as a dashed green line), on the other hand, represents the range of average seasonal cycles produced in future runs (one for each of the four ESMs) that results from the imposed forest mortality of close to 90 % by the 2080s, based on work from McDowell et al. (2016). We see that for the San Juan Riverbasin, a major tributary to the Colorado Riverbasin, spring freshet in the future runs occurs earlier in the season, shifting from mid-May to the end of April. Flows are projected to be higher during late fall, winter, and early spring, and lower during late spring, summer, and early fall. Disturbing the vegetation in addition to using projected temperature and precipitation forcing results in a different pattern of streamflow, with lower flows in early spring and then higher peak flow, and with lower recessional summer flows due to differ- ences in how regrowth vegetation (i.e., shrubs) partitions water and snowpack. Studies on climate change thus require a consideration of changes in vegetation dynamics; otherwise results may be mis- leading or could underestimate impacts. (Contact: Katrina Bennett.)
We investigate the impacts of future climate change on groundwater resources in the RRB, an important agricultural region overlying the High Plains Aquifer. Future precipitation and temperature changes, retrieved from the LOCA downscaled dataset for CMIP5, are used to calculate changes in groundwater pumping and recharge. Projected pumping and recharge are then used in a groundwater flow model to simulate the groundwater responses. The simulation results suggest that in response to climate change: (1) Water stress in the irrigation season will be exaggerated due to increased irrigation water demands; (2) recharge will increase in the non-irrigation season; (3) groundwater levels will decline more in areas with declining trends in the baseline; and (4) baseflow will increase because of increased groundwater recharge in the Republican River Valley. The methodologies and predictions of this study may inform proactive planning and management that increase sustainability and help avoid and resolve conflicts in the RRP and surrounding Great Plains landscapes. Limitations of this study include the lack of representation of the soil water regime and crop physiological responses to other climatic variables. These limitations can be overcome with a physically based model that integrates parameterization of these processes. The improvement of irrigation technology and management practice can also be incorporated into future analyses.
(2012b), Molecular records of climate variability and vegetation response since the Late Pleistocene in the Lake Victoria basin, East Africa, Quaternary Science Reviews, 55, 59–74. Blanchet, C. L., R. Tjallingii, M. Frank, J. Lorenzen, A. Reitz, K. Brown, T. Feseker, and W.
Bru¨ckmann (2013), High-and low-latitude forcing of the Nile River regime during the Holocene inferred from laminated sediments of the Nile deep-sea fan, Earth and Planetary Science Letters, 364, 98–110.
Mountainous environments are considered sensitive to climate change . In spite of the fact that the climate of the Himalayan region is changing rapidly , the majority of precipitation studies of South Asia have excluded the Himalayan belt due to its complex topography and lack of rain-gauge coverage [8,9]. Climate models have less skill in capturing precipitation patterns in high mountains . There is very low consistency among climate models in precipitation predictions in Himalaya region for either the winter or the monsoon seasons; an overall increase in annual precipitation is projected, but the magnitude of change is low [5,11]. The Himalayas are at the headwaters for the major river systems of Asia [12,13], and understanding how climate change is operating in the region is critically important . Change in precipitation is expected to significantly affect cryospheric processes and the hydrology of headwater catchments in the Himalayas . Ice core analysis showed that South Asian monsoon variability in the Himalayas has been significant .
We notice from the above discussion that the models are performed differently for different months. Some models performed well, while others performed poorly, and vice versa. This is explained by compensation or cancellation of the biases between the different RCMs. However, the analysis herein highlighted the multi-models’ mean ability to simulate the Mono riverbasin rainfall adequately and therefore can be used for assessment of future climate projections for this region. To have better comprehension of results of in this paper it is important to look at the results of other regions and countries. The disparity observed in the evolution of precipitation could be due to the type of forcing models or the convection pattern used in West Africa . This divergence of models on cli- mate projections on precipitation in West Africa is still uncertain . The in- consistent trends and changes of rainfall noticed are likely linked to the high he- terogeneousness types seasons. Indeed, the Mono basin is characterized by two types of rainfall regime. In southern basin, there are two rainy seasons which ex- tend from mid-March to mid-July and from mid-August to October. In north- ern basin, there is one rainy season which extends from April to October.
negative social externalities, such as deterioration of respiratory health due to drought-induced fires (Smith et al., 2014), exhaustion of family savings (Brondizio and Moran, 2008), and isolation of communities that are affected by river navigation and drinking water scarcity (Sena et al., 2012), hence affecting the overall livelihood of the local communi- ties. Thus, it is critical to understand the characteristics of historical droughts to better understand the dominant mech- anisms that modulate droughts and their evolution over time. As often is the case, droughts in the Amazon are driven by El Niño events; however, some droughts are suggested to be caused by climate change and variability (Espinoza et al., 2011; Lewis et al., 2011; Marengo et al., 2008; Marengo and Espinoza, 2016; Phillips et al., 2009; Xu et al., 2011; Zeng et al., 2008) and due to accelerating human activi- ties causing rapid changes in the land use and water cycle (Lima et al., 2014; Malhi et al., 2008). Numerous studies have quantified the impacts and spatial extent of these peri- odic droughts on the hydrological and ecological systems in the Amazon (Alho et al., 2015; Brando et al., 2014; Castello et al., 2013, 2015, Chen et al., 2009, 2010; da Costa et al., 2010; Davidson et al., 2012; Fernandes et al., 2011; Lewis
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 Land use 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.
Chapter 2 – Background and Literature Review
In order to effectively predict climate change 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 climate change by scientists and economists taking into account risks, costs, and also how future changes will look like (Nordhaus, 1994). Knox (2012) states “… one sees climate change 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.”
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.
For this purpose, 14 meteorological stations were selected based on the length of the rainfall series and the climatic clas- sification to obtain a representative untreated dataset from the riverbasin. Daily rainfall series from 1957 to 2002 were obtained from each meteorological station. First, classical climatic indexes were analysed with an autoregressive test to study possible trends in rainfall. The results can be ex- plained following the evolution of the NAO and WeMO in- dexes, which indicate that the initial period should be subdi- vided in two periods (1957–1979 and 1980–2002) to assume stationarity and to analyse the rainfall distribution functions. The general results obtained in this study for both sub- periods, through the generalised Pareto distribution (GPD) parameters and the maximum expected return values, do not support the results previously obtained by other authors that affirm a positive trend in extreme rainfall indexes and point to a slight reduction indicated by others. Three ex- treme precipitation indexes show negative statistical signif- icant trends. GPD-scale parameters decrease except for only one rain gauge, although this decrease is only statistically significant for two rain gauges. Another two locations show statistical significance decreased for maximum expected re- turn values.
4.2 Uncertainties in projections of river flow regime Projections of the direction of change relative to the refer- ence period were well constrained for the majority of river discharge signatures, particularly towards the end of the 21st century and for the warmer RCP8.5 emission scenario. Even so, there was considerable spread in the projected magnitude of these changes due to uncertainties in the driving climate data (ES, GCM–RCM, DS) and representation of glacio- hydrological processes (TIM, ROR) in the model chain. Un- certainty in future snow and ice coverage primarily stemmed from the ES due to its control on future near-surface air tem- perature. In fact, the proportional contribution of the ES to projection uncertainties increased throughout the 21st cen- tury and, consequently, the ES was also found to be the dom- inant source of uncertainty for projections of mean monthly flows during the melt season by the 2080s. The growing influence of the ES over time was also shown by Addor et al. (2014) for six alpine catchments in Switzerland and by Duethmann et al. (2016) for two mountain river basins in the Tian Shan. Interestingly though, these studies along with the recent study of Jobst et al. (2018) found that cli- mate model uncertainty was still the dominant source for projections of monthly river flows. Jobst et al. (2018) pos- tulated that this was likely because of the high uncertainty in future precipitation across the climate models. Indeed, oth- ers have also attributed future runoff uncertainty in glaciated river basins to variability in precipitation projections (Lutz et al., 2016), a finding which is compounded by an increas- ingly warm and thus rainfall-dominated precipitation input. In this study, however, the GCM–RCM model chain compo- nent only dominated river flow projection uncertainty during the winter months while summer flow uncertainty was dom- inated by the ES. There are two key reasons that could ex- plain this. Firstly, precipitation
This section describes the conceptual basis, theoretical structure and empirical setup of the RIVERSIM model.
RIVERSIM is an economic and hydrological model incorporating: (a) water demand from agricultural, municipal, industrial, and other types of use; (b) a spatial river flow relationship including influence zones for water diversion, in-stream flow, reservoir storage and evaporation, and return flow, and (c) uncertainty about crop yields and water availability under the DCV influence. The model serves to project water and agricultural land allocations under a given DCV phase combination and will be used to estimate the economic value of forecasting DCV-phase combinations. In order to establish model validity, it is calibrated to 2010 as base year so that it reproduces historical results and, for future research, be capable of including other features such as infrastructure development, operating rules, institutional and policy changes, and water management schemes (Cai 2010).
SWAT is a publically available rainfall–runoff hydrology and water quality model. The model possesses adequate rep- resentation of physical processes governing hydrology and is particularly suitable for application in large river basins. In the SWAT model, a riverbasin is subdivided into mul- tiple sub-catchments, each sub-catchment consisting of at least one representative stream. The sub-catchments are fur- ther divided into hydrologic response units (HRUs), which are lumped land areas within the catchment comprising unique land cover, soil, and slope combinations. For a de- tailed description of the SWAT model, reference is made to Muthuwatta et al. (2014). Various data sets were accumu- lated from global and local sources. The major data sets used in this study are listed in Table 1.
Given the magnitude of projected climatic changes, the importance of water for socio-economic development throughout the region (including the growing influence of hy- dropower), and the increasing (often trans-boundary) compe- tition for water use in the Mekong, there is a clear need for improved understanding of the potential impacts of climate change on future availability of freshwater resources. Only through such understanding can water resource managers (particularly the basin authority, the Mekong River Commis- sion, MRC) fully evaluate proposed developments and im- plement appropriate trans-boundary management strategies. The need for climate change adaptation strategies is particu- larly prescient for the Mekong given the reliance on the river for agriculture and fish, the vulnerability of the low-lying delta region including large flood-prone areas, and the rel- ative absence of river management infrastructure. This situ- ation is likely to be exacerbated by the projected substantial increases in population, in particular in the lower Mekong Basin (from 55 to 90 million by 2025, MRC 2003). Fur- thermore, the precipitation elasticity of Mekong river flow has been estimated as generally greater than zero, meaning that changes in precipitation result in proportionately greater changes in river flow (Hapuarachchi et al., 2008).
Abstract This paper presents a water resources management strategy developed by the Brazilian National Water Agency (ANA) to cope with the conflicts between water users in the Verde Grande Riverbasin, located at the southern border of the Brazilian semi-arid region. The basin is dominated by water-demanding fruit irrigation agriculture, which has grown significantly and without adequate water use control, over the last 30 years. The current water demand for irrigation exceeds water availability (understood as a 95% percentile of the flow duration curve) in a ratio of three to one, meaning that downstream water users are experiencing more frequent water shortages than upstream ones. The management strategy implemented in 2008 has the objective of equalizing risk for all water users and consists of a set of rules designed to restrict water withdrawals according to current river water level (indicative of water availability) and water demand. Under that rule, larger farmers have proportionally larger reductions in water use, preserving small subsistence irrigators. Moreover, dry season streamflow is forecasted at strategic points by the end of every rainy season, providing evaluation of shortage risk. Thus, water users are informed about the forecasts and corresponding restrictions well in advance, allowing for anticipated planning of irrigated areas and practices. In order to enforce restriction rules, water meters were installed in all larger water users and inefficient farmers were obligated to improve their irrigation systems’ performance. Finally, increases in irrigated area are only allowed in the case of annual crops and during months of higher water availability (November to June). The strategy differs from convectional approached based only on water use priority and has been successful in dealing with natural variability of water availability, allowing more water to be used in wet years and managing risk in an isonomic manner during dry years.
Skyline methods are also often limited in their ability to detect recent events. Catastrophic census declines for Columbia basin Chinook salmon were documented during the mid-19 th century. It is difficult to imagine that these declines were without genetic consequence. Yet, no evidence for a coincident decline in genetic diversity is indicated in the EBSP for either the Columbia or the Snake data. Even with heterochronous data, recent events are not always detectable, especially when genetic losses are brief, extreme, or occur very near the sampling events [91, 92]. Further, our analysis is based on a uniparentally inherited marker (mtDNA), which can only provide a limited picture of evolution. Future investigations utilizing addi- tional markers may contribute to increased resolution. However, we cannot exclude the possi- bility that the demographic patterns indicated are true representations of recent history. Examples of sustained diversity, despite periods of largescale population declines, have been demonstrated. For example, Hawaiian petrel (Pterodroma sandwichensis) populations were so reduced in numbers during the 1900s that many believed the species to be extinct [93, 94]. However, comparisons of ancient and contemporary DNA revealed limited losses in genetic diversity and maintenance of effective population size through the period of population decline and recovery . Despite the potential limitations, the analysis of the EBSP as a quali- tative heuristic for model rejection fits with the larger patterns for the Columbia and Snake River sample groups and provides evidence of contrasting patterns.