Using the concept that water runs down hill, a watershed is described as all points enclosed within an area from which rain falling at these points will contribute water to the outlet (Raghunath, 2006; Suresh, 2005). Naturally the earth’s land surface is divided into watersheds based on the drainage of water at which all land within a specified area drains to the same outlet point. This implies that large watersheds are originated from many smaller watersheds. Therefore, it is necessary to define watershed in terms of a point. This point is usually the location at which the hydrologic design is made and referred as the watershed outlet (McCuen, 1989) whereas; hydrology is concerned with the problems of water on the earth. Such problems may involve water quantity and quality, interrelations between water and environment and the impact of man’s activity on occurrence, circulation and distribution of water (Change, 2003; Raghunath, 2006; McCuen, 1989; Suresh, 2005). It looks for the causes and effects of these problems, predicts water related events and problems, studies the adjustment, management and operation of water resources to the benefit of the society and the environment. Therefore, the phrase watershed hydrology deals with the integration of hydrologic processes at the watershed scale to determine the watershed response (McCuen, 1989).
Faced to the above difficulty, the key solution is of course to design some new conceptualisation schemes of hydrologic processes for predicting hydrological extremes which will be our future work. Finding the main driving factors of the tar- get variable and using them in the prediction of its extreme
1979; Kollet & Maxwell, 2006; ˇ Sim˚ unek et al., 2006; Qu & Duffy, 2007; Markstrom et al., 2008), are more sophisticated in their representation of processes and, in prin- ciple, provide predictions of observable parameters (e.g., snow water equivalent, soil moisture, etc.) throughout the watershed rather than only at the watershed outlet. Even though physically based models represent most hydrologic processes with more fidelity to hydrologic processes than lumped parameter models, they suffer from a number of other issues that has made their general use for hydrologic forecasting difficult. First, nonlinearities and closure problems in the underlying processes ul- timately necessitate empirical parameterization (e.g., constitutive relationships be- tween soil water content and matric potential). Second, these models require a cor- respondingly complex and large amount of spatiotemporally varying data related to atmospheric, surface, and subsurface variables as input. In topographically complex watersheds, observations characterizing the environmental forcings required as input to these models (e.g., precipitation, temperature, wind speed, etc.) are sparse and often not representative. The complexity of the terrain, moreover, leads to gaps in or unavailability of radar-retrieved precipitation. As a result, in these watersheds data input to hydrologic models is increasingly derived from the output of numeri- cal weather prediction (NWP) models. The key advantage of these models is that they provide environmental forcings that are internally and physically consistent, and spatiotemporally continuous during the period of interest.
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Abstract. The phase of precipitation when it reaches the ground is a first-order driver of hydrologic processes in a wa- tershed. The presence of snow, rain, or mixed-phase precip- itation affects the initial and boundary conditions that drive hydrological models. Despite their foundational importance to terrestrial hydrology, typical phase partitioning methods (PPMs) specify the phase based on near-surface air tempera- ture only. Our review conveys the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain with large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There is a wide range of op- tions for field observations of precipitation phase, but there is a lack of a robust observation networks in complex ter- rain. New remote sensing observations have the potential to increase PPM fidelity, but generally require assumptions typ- ical of other PPMs and field validation before they are oper- ational. We review common PPMs and find that accuracy is generally increased at finer measurement intervals and by in- cluding humidity information. One important tool for PPM development is atmospheric modeling, which includes mi- crophysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation-phase observations. The review concludes by describing key research gaps and recommendations to im-
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transport of carbon, major nutrients and certain contaminants represent important controls on the structure and function of the terrestrial and aquatic ecosystems that are components of the Neuse watershed. Along with land use change, long term changes in these ecosystems can have a feedback effect on hydrologic processes at the large watershed scale. Further, naturally-occurring or human-introduced solutes can serve as hydrologic tracers that help elucidate the movement of water through the hydrologic cycle and provide an approach that is complementary to physical hydrologic measurements. The Neuse HO should support research on the linkages between hydrologic and biogeochemical cycles in the Neuse watershed by: (1) linking the hydrologic data and analyses discussed earlier with collection of chemical data in groundwater and surface water of the HO (through original HO measurements and gathering data from others’ chemical measurements), and (2) providing a basic interpretation of the data (e.g., computation of chemical fluxes at different spatial and temporal scales, such as the riverine flux of a chemical into the estuary). The chemical parameters that will be studied include (a) those needed for understanding the coupling between hydrologic and biogeochemical processes, (b) those potentially useful for characterizing hydrologic flow paths, and (c) basic water quality
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Another question for future research is: Does the classi- fication of dominant hydrologic processes, both geographi- cal and categorical, as described in this study, apply in other contexts? Comparable findings from other modeling studies, such as those by Newman et al. (2015) and Bock et al. (2016), might indicate that there could be a connection. These other studies use the same input information (i.e., being driven with the same climate data and using the same sources of information for parameter estimation), and thus simulation results and model sensitivity to this information might be similar. Also, can real world watersheds be classified by sen- sitivity analysis using DPHMs? Based on the findings of the work presented so far, the answer is inconclusive. Clearly there are some results that indicate that it might be possible. For example, the methods described here effectively identify “snowmelt watersheds” in the mountainous and northern lat- itudes, but, is all of this necessary to accomplish this? Might simpler methods (e.g., an isohyetal snowfall map) identify snowmelt watersheds just as effectively?
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Inspired by the organization that nature exhibits around a hi- erarchy of spatial scales, i.e., hillslope, catchment, region, etc. (Blöschl and Sivapalan, 1995), there have been concerted efforts to identify whether a characteristic space scale, which reflects the spatial organization and can thus serve as the building block of distributed models, exists in catchment hy- drology. This idea is similar to the continuum or REV con- cept long used in groundwater hydrology with a consider- able degree of success (Bear, 1972; Hassanizadeh and Gray, 1979). The argument has been that aggregating the govern- ing equations or process descriptions to this building block scale might lead to simplified (effective) lumped or contin- uum treatments, obviating the need to split the catchment into smaller elements to capture the effects of heterogeneity. This way of thinking reflects the long-standing conviction, sup- ported by observations, that in spite of the enormous com- plexity of hydrologic processes in landscapes, catchment- scale hydrologic responses can often be described by sim- pler models with only a few parameters (Jakeman and Horn- berger, 1993; Sivapalan, 2003). Dooge (1986) has argued that catchments are complex systems with some level of organi- zation, and indeed “simplicity out of complexity” is a use- ful property of such complex systems (Davies, 1992). How- ever, when one opts for the parameterization approach in- spired by this reasoning, one still needs to know key fea- tures of the underlying heterogeneity, e.g., their statistical distributions or organizational structure (and not necessarily the actual observed patterns). Furthermore, we also need to have information about cross-scale process interactions that might lead to the simplicity we desire (Dunne and Black, 1970; Hassanizadeh and Gray, 1979), and must utilize ef- ficient approaches to incorporate them into models through appropriate model structures and parameterizations (Beven and Kirkby, 1979; Zehe et al., 2014).
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Abstract. Humans have significantly altered the redistribu- tion of water in intensively managed hydrologic systems, shifting the spatiotemporal patterns of surface water. Eval- uating water availability requires integration of hydrologic processes and associated human influences. In this study, we summarize the development and evaluation of an extensi- ble hydrologic model that explicitly integrates water rights to spatially distribute irrigation waters in a semi-arid agri- cultural region in the western US, using the Envision inte- grated modeling platform. The model captures both human and biophysical systems, particularly the diversion of water from the Boise River, which is the main water source that supports irrigated agriculture in this region. In agricultural areas, water demand is estimated as a function of crop type and local environmental conditions. Surface water to meet crop demand is diverted from the stream reaches, constrained by the amount of water available in the stream, the water- rights-appropriated amount, and the priority dates associated with particular places of use. Results, measured by flow rates at gaged stream and canal locations within the study area, suggest that the impacts of irrigation activities on the mag- nitude and timing of flows through this intensively managed system are well captured. The multi-year averaged diverted water from the Boise River matches observations well, re- flecting the appropriation of water according to the water rights database. Because of the spatially explicit implementa- tion of surface water diversion, the model can help diagnose places and times where water resources are likely insufficient
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This dissertation provides a holistic understanding of how climate, topography and vegetation mediate hydrologic processes that influence runoff generation and biogeochemical processes in the Southern Appalachians. These mountains are characterized by a temperate and humid climate, and their headwater basins generally contain steep slopes and deep soils. The topics we investigate are: i) the spatiotemporal patterns of baseflow and its relationship to catchment structure, ii) hillslope-scale controls on soil moisture and shallow groundwater responses to storms, and iii) controls on long-term patterns of dissolved organic carbon in runoff. To addresses these objectives the dissertation makes use of a wealth of datasets that includes hydrometric, isotopic, and water quality data, collected by others and me at Coweeta over time span of past few years to more than two decades. This dissertation builds on
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There may be various types of default parameters used in a physical hydrologic model for development efficiency. Parameters can be classified as sensitive and insensitive or model execution related and process algorithm related. Apart from the model-execution-related parameters and other in- sensitive parameters, the process-algorithm-related sensi- tive parameters are typically critical to model development, which greatly affect the model’s performance. Default values can follow physical laws and be contained in the correspond- ing computation algorithms but not necessarily capture the regional hydrologic characteristics at a study site. Capturing such site-specific features is the process of calibration. As such, the differences between uncalibrated–default-set mod- els and calibrated models are determined by the significance of sensitive parameters affecting the modeling performance. A physical hydrologic model usually cannot generate good results with default values and requires calibration (Chen et al., 2015b; Hay et al., 2006; Hay and Umemoto, 2007b). In the paper, we have two examples showing that default values produce inaccurate results. With the same model and study area, the Table 1 calibrated original PRMS results are much more accurate than the Table 2 uncalibrated original PRMS based on performance evaluation indices. Similarly, the Ta- ble 3 calibrated original HEC-HMS results are much better than the Table 4 uncalibrated original HEC-HMS. Numeri- cal experiments have corroborated the superior performance
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The curve number (CN) is a function of hydrologic soil group, cover type, land use and antecedent moisture condition. In order to determine curve number, the land cover and soil type cover ages were combined through overlay analysis of GIS. The resulting coverage was used to delineate the sub-basin area into sub-areas that have same land use and soil type characteristics. The soils in the study area were broadly classified into hydrologic soil groups B and C. In this study, each sub-basin was assigned the dominant soil type and assumed uniform. The sub-basins with hydrologic soil group B were SBB3, SBB4, SBB5, SBB6 and SBB7 while those with hydrologic soil group C were SBB1, SBB2, SBB8 and SBB9. The curve number for every sub-area was obtained using appropriate tables such as those developed by SCS (1985). The representative curve number of each sub-basin was determined as the weighted average of all CN values of the sub-areas given by the expression:
From many hydrologic software, HEC-RAS (Hydrologic Engeneering Center – River Analysis System) is a good choice to develop the hydraulic model of a given river system in the south of Iraq represented by Al- Kahlaa River by a network of main channel and three reach and a total of 57 cross sections with 3 boundary sections for one of the applications . The model is calibrated using the observed weekly stage and flow data . The results show that a good agreement is achieved between the model predicted and the observed data using the values Manning's(n=0.04) for over bank with the values of Manning's ( n=0.027) for main channel and also with using time weighting factor ( θ ) equal one . Lastly , the AL- Kahlaa River HEC-RAS model has been applied to analyze flows of Al Huwayza marsh feeding rivers ( Al Kahlaa River and its main branches) , evaluation of their hydraulic performance under two hydraulic model scenarios .The results demonstrate that in case of high flow discharge it is found that cross sections flooded and inadequate for such flows. While, flows are remained within cross section extents during drought season .
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Inter-event nitrogen cycling within the saturated pools and sand layer was likely influenced by two factors: media composition and groundwater intrusion. The 20% mulch by volume was included to be a carbon source (an electron donor) to reduce nitrate to nitrogen gas (Brown et al., 2010). Though denitrification was observed, it appears mineralization of mulch organic matter to ammoniacal nitrogen led to a nitrogen release during baseflow. The decomposition of organic matter by microbes will result in mineralization when the C:N ratio of the substrate is less than the microbial biomass, typically 25:1 (Hodge, et al., 2000). Organic matter that degrades faster than assimilation or reduction processes will result in nitrogen export (Kim et al., 2003). TKN release has been observed previously in field SCMs with high media organic content (Bratieres et al., 2008; Hunt et al., 2006). Mulch
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As shown in Table 1, the 33rd and 67th percentiles were computed for all 33 hydrologic alteration indicators as the lower and upper limits of the RVA target range for the Jieshou station. The results show that fall rate ranked first of all hydrologic alteration values followed by July, 1-, 7-, 30-, and 90-day minimum, duration of high pulses, March, 3-day maximum, all with deviation degrees exceeding 67%. They are supposed to be strongly affected by the construction and operation of upstream dams and sluices. In practical regulation, targets should be kept from 10 to 811 m 3 /s for
However, in developing countries and international rivers, it is often very difficult to obtain meteorological and hydrological data which are input into hydrologic models. Also the quality of observation data is extremely poor, and the situation that meteorological and hydrological observations have been stopped happens quite often. Drainage basins in many parts of the world are ungauged or poorly gauged, and in some cases existing mea- surement networks are declining . Therefore, sometimes it is impossible to apply a hydrologic model to si- mulation because of observation, even if there is a hydrologic model.
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All the studies cited above and various others not men- tioned here have addressed the question “What are the sources of seasonal hydrologic predictability and what is their relative influence?” Various methods have been used, however to our knowledge there has been no attempt to an- swer this question for the entire globe with one consistent method. Understanding the relative contributions of the IHCs and FS to seasonal hydrologic predictability at different fore- cast initialization dates and lead times globally is impor- tant for identifying those regions of the globe where use- ful skill can be attained in any given season, given current global hydrologic monitoring capability (the basis for pro- viding the IHCs) and seasonal FS. For example, depending on which one of those factors dominates the seasonal hydro- logic predictability, efforts can be focused toward improv- ing the estimation of the IHCs (e.g., by data assimilation, or model improvement that reduce prediction uncertainty in the land surface models used to estimate IHCs) or improv- ing FS. This knowledge could also lead to better understand- ing of the uncertainty of seasonal hydrologic predictability for any region and season. Hence the primary objective of this study is to provide a consistent estimate of the relative contributions of the IHCs and FS in seasonal hydrologic pre- dictability over the entire globe throughout the year. In this study we only consider the contribution of soil moisture and snow water content as IHCs. We use an ensemble stream- flow prediction (ESP) framework based on an experimental design structure proposed by Wood and Lettenmaier (2008) (described in Sect. 2.1) to conduct this analysis. ESP (Day, 1985; Wood et al., 2002; Wood and Lettenmaier, 2008; Shukla and Lettenmaier, 2011) is a method widely used for seasonal hydrologic prediction. In this method a physi- cally based hydrology model is run up to the time of fore- cast using observation-based atmospheric forcings to set ini- tial conditions. During the forecast period, the hydrology model uses ensembles of observed forcings that are resam- pled from sequences of past observations. This process re- sults in ensemble-based hydrologic forecasts that are based solely on knowledge of the IHCs (no FS). An alternative hy- pothetical structure, termed reverse ESP (Rev-ESP) by Wood and Lettenmaier (2008), runs the model up to the forecast date using ensembles of past observation-based atmospheric forcings sequences, and pairs each with observation-based atmospheric forcings (perfect FS) during the forecast period. The combination of ESP and Rev-ESP includes the two end points of no FS and perfect FS. Variations of the ESP/Rev- ESP approach have since been used in recent studies such as Li et al. (2009), Shukla and Lettenmaier (2011), Paiva et al. (2012) and Singla et al. (2012) to partition the influence of IHCs and FS on seasonal hydrologic predictability.
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Matalas and Fiering (1977) noted that large water resource systems have a high degree of redundancy and robustness that enables them to adapt technologically and institutionally to severe stresses. There is amply empirical evidence of systems having been operated under short-run emergency situations of droughts and floods in manners enabling communities to adapt successfully. The systems have been designed and operated on statistical estimates of the parameters and not on the parameter (population) values themselves. Some of the stresses imposed on the systems derive from statistical sampling err ors under stationary conditions. It may be that other stresses relate to hydrologic changes due to undetected climate shifts. Systems are to varying degrees robust, i.e., they are to, varying degrees, insensitive to errors random (e.g., statistical sampling errors) or otherwise (e.g., computational errors, in design). To varying degrees a system is resilient, i.e., it can be operated technologically or institutionally to simulate over the short run a system of another design such as to limit economic losses. The greater the degree of robustness and of resilience, the greater is the operational merit of the assumption of stationarity.
Water resources management usually requires that hydraulic, ecological, and hydrological models be linked. The Hy- drologic Engineering Center River Analysis System (HEC-RAS) hydraulic model and the Hydrologic Engineering Center Geospatial River Analysis System (HEC-GEORAS), imitates flow and water profiles in the Neka river basin’s downstream flood plain. Hydrograph phases studied during the flood seasons of 1986-1999 and from 2002-2004 were used to calibrate and verify the hydraulic model respectively. Simulations of peak flood stages and hydrographs’ evaluations are congruent with studies and observations, with the former showing mean square errors between 4.8 - 10 cm. HECRAS calculations and forecast flood water levels. Nash-Sutcliffe effectiveness (CR3) is more than 0.92 along with elevated levels of water which were created with some effectiveness (CR5) of 0.94 for the validation period. The coupled two models show good performance in the water level modeling.
The trend towards “hyper” resolution land models (Wood et al., 2011), e.g., 1 km or 100 m over large geographical do- mains, emphasizes the need for general parameterizations of hydrological processes on this scale. However, this is still an unsolved problem: we do not have firm evidence that the structure and parameter values of our element-scale equa- tions correspond to hydrologic reality at those scales. One of the most important causes of this difficulty is the spatial heterogeneity in the initial and boundary conditions, and in the material properties of the medium. This heterogeneity occurs at multiple spatial scales, and has multiple physical causes (Seyfried and Wilcox, 1995). The multiple scales of heterogeneity are manifest as multiple dominant processes (Grayson and Blöschl, 2001), and also as processes without a well-defined spatial scale (e.g. preferential flow in the snow- pack, on the land surface, in the subsurface). These prob- lems cannot be solved solely by numerical integration across space. The next section summarizes recent advances in de- veloping large-scale flux parameterizations and in effectively resolving dominant processes.
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For the last half-century, computational modeling has become a principal tool in the water resource manager’s toolbox. Groundwater models have become indispensable, industry standard methods for estimating the availability and sustainability of groundwater resources (e.g. Young & Bredehoeft, 1972; Cummings & McFarland, 1974; Willis & Yeh, 1987). However, because of the inherent complexity of numerical models and the significant time, effort, and expertise needed for their development, it is often challenging for stakeholders and water resources managers to access models that are appropriate for their needs (Essawy et al., 2018). Within the traditional model development paradigm, water management agencies usually take one of two approaches for obtaining hydrologic models to suit their needs, 1) dedicate significant resources to building internal modeling capacity or 2) contract with outside ‘experts’ to deliver models that typically cannot be interacted with once completed. Drawbacks to the former approach include the high cost of training, software, and salary required for agencies to retain personnel with sufficient skills to assess the validity, conceptualization, calibration, and usefulness of existing models or to create and maintain effective modeling programs. This level of resource dedication is often only possible for larger utility companies or management
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