NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated using observed data from BBMB (Bhakra Beas Management Board) and coefficient of Correlation(R 2 ) measured for (2003)(2004)(2005)(2006) was 0.67 and 0.61 for the year 2006.But as VIC does not consider snowmelt runoff as a part of the total discharge, snowmelt runoff has been estimated for the simulation both with and without D.A. The snow fluxes as generated from VIC gives basin average estimates of Snow Cover, SWE, Snow Depth and Snow melt. It has been observed to be overestimated when model predicted snow cover is compared with MODIS SCA of 500 m resolution from MOD10A2 for each year. So MODIS 8-day snow cover area has been assimilated directly into the model state as well as by using EnKF after every 8 days for the year 2006.D.I Technique performed well as compared to EnKF. R 2 between Model SCA and MODIS SCA is estimated as 0.73 after D.I with Root Mean Square Error (RMSE) of +0.19. After direct Insertion of D.A, SCA has been reduced comparatively which resulted in 7% reduction of annual snowmelt contribution to total discharge.The assimilation of MODIS SCA data hence improved the snow cover area (SCA) fraction and finally updated other snow components.
Abstract. The objective of this study is to assess the impacts of land cover change on the hydrological responses of the Mahanadi river basin, a large river basin in India. Commonly, such assessments are accomplished by using distributed hydrological models in conjunction with different land use scenarios. However, these models, through their complex interactions among the model parameters to generate hydrological processes, can introduce significant uncertainties to the hydrological projections. Therefore, we seek to further understand the uncertainties associated with model parameterization in those simulated hydrological responses due to different land cover scenarios. We performed a sensitivity-guided model calibration of a physically semi-distributed model, the Variable Infiltration Capacity (VIC) model, within a Monte Carlo framework to generate behavioural models that can yield equally good or acceptable model performances for subcatchments of the Mahanadi river basin. These behavioural models are then used in conjunction with historical and future land cover scenarios from the recently released Land-Use Harmonization version 2 (LUH2) dataset to generate hydrological predictions and related uncertainties from behavioural model parameterization. The LUH2 dataset indicates a noticeable increase in the cropland (23.3 % cover) at the expense of forest (22.65 % cover) by the end of year 2100 compared to the baseline year, 2005. As a response, simulation results indicate a median percent increase in the extreme flows (defined as the 95th percentile or higher river flow magnitude) and mean annual flows in the range of 1.8 % to 11.3 % across the subcatchments. The direct conversion of forested areas to agriculture (of the order of 30 000 km2) reduces the leaf area index, which subsequently reduces the evapotranspiration (ET) and increases surface runoff. Further, the range of behavioural hydrological predictions indicated variation in the magnitudes of extreme flows simulated for the different land cover scenarios; for instance, uncertainty in scenario labelled “Far Future” ranges from 17 to 210 m3 s−1 across subcatchments. This study indicates that the recurrent flood events occurring in the Mahanadi river basin might be influenced by the changes in land use/land cover (LULC) at the catchment scale and suggests that model parameterization represents an uncertainty which should be accounted for in the land use change impact assessment.
NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated using observed data from BBMB (Bhakra Beas Management Board) and coefficient of Correlation(R 2 ) measured for (2003)(2004)(2005)(2006) was 0.67 and 0.61 for the year 2006.But as VIC does not consider snowmelt runoff as a part of the total discharge, snowmelt runoff has been estimated for the simulation both with and without D.A. The snow fluxes as generated from VIC gives basin average estimates of Snow Cover, SWE, Snow Depth and Snow melt. It has been observed to be overestimated when model predicted snow cover is compared with MODIS SCA of 500 m resolution from MOD10A2 for each year. So MODIS 8-day snow cover area has been assimilated directly into the model state as well as by using EnKF after every 8 days for the year 2006.D.I Technique performed well as compared to EnKF. R 2 between Model SCA and MODIS SCA is estimated as 0.73 after D.I with Root Mean Square Error (RMSE) of +0.19. After direct Insertion of D.A, SCA has been reduced comparatively which resulted in 7% reduction of annual snowmelt contribution to total discharge.The assimilation of MODIS SCA data hence improved the snow cover area (SCA) fraction and finally updated other snow components.
Abstract. The objective of this study is to assess the impacts of Land Use Land Cover change on the hydrological responses of the Mahanadi river basin, a large river basin in India. Commonly, such assessments are accomplished by using distributed hydrological models in conjunction with different land use scenarios. However, these models through their complex interactions among the model parameters to generate hydrological processes, can introduce significant uncertainties to the hydrological projections. Therefore, we seek to further understand the uncertainties associated with model parameterization in those simulated hydrological responses due to different land cover scenarios. We performed a sensitivity-guided model calibration of a physically semi-distributed model, the Variable Infiltration Capacity (VIC) within a Monte Carlo Framework to generate behavioural models for subcatchments of the Mahanadi river basin. These behavioural models are then used in conjunction with historical and future land cover scenarios from the recently released, Land use Harmonisation (LUH2) to generate hydrological predictions and related uncertainties from behavioural model parameterisation. The LUH2 dataset indicates a noticeable increase in the cropland (23.3 % cover) at the expense of forest (22.65 % cover) by the end of year 2100 compared to the baseline year, 2005. As a response, simulation results indicate a median percent increase in the extreme flows (defined as the 95th percentile or higher river flow magnitude) and mean annual flows in the range of 1.8 to 11.3 % across the subcatchments. The direct conversion of forested areas to agriculture (on the order of 30,000 km2) reduces the Leaf Area Index and which subsequently reduces the Evapotranspiration (ET) and increases surface runoff. Further, the range of behavioural hydrological predictions indicated variation in the magnitudes of extreme flows simulated for the different land cover scenarios, for instance uncertainty in far future scenario ranges from 17 to 210 cumecs across subcatchments. This study indicates that the recurrent flood events occurring in the Mahanadi river basin might be influenced by the changes in LULC at the catchment scale and suggests that model parameterisation represents an uncertainty, which should be accounted for in the land-use change impact assessment.
<p class="paragraph">Scotland is increasingly vulnerable to periods of dry weather, impacting water users and the natural environment. In 2022, large parts of Scotland have experienced water scarcity, resulting in Scotland Environmental Protection Act (SEPA) suspending water abstractions for abstraction licence holders in some Scottish catchments. To understand and manage these water scarcity events in Scotland, we need to monitor and model the drought processes. This research is a part of a Scottish Government funded project &#8216;Understanding the vulnerabilities of Scotland&#8217;s water resources to drought&#8217; which has been co-constructed with a range of national level stakeholders and aims to understand what the specific impacts of droughts are and what are the vulnerabilities that may apply to Scotland under future change. This includes the understanding of the spatial variability and characteristics of future hydrological drought events and short-term forecasting of drought duration to inform adaptive catchment management, while considering water resources requirements of different user sectors. As a first step towards constructing a national short-term drought forecasting framework, w<span xml:lang="EN-GB" data-contrast="none">e</span><span data-ccp-charstyle="normaltextrun"> have</span> reviewed the <span data-ccp-charstyle="normaltextrun">state-of-the art </span><span data-ccp-charstyle="normaltextrun">hydrological modelling approaches</span> <span data-ccp-charstyle="normaltextrun">currently</span> <span data-ccp-charstyle="normaltextrun">applied in</span><span data-ccp-charstyle="normaltextrun"> the UK</span>. <span data-ccp-charstyle="normaltextrun">Our review suggests</span> a lumped conceptual model, <span data-ccp-charstyle="normaltextrun">GR</span>6J and <span data-ccp-charstyle="normaltextrun">a </span><span data-ccp-charstyle="normaltextrun">distributed hydrological response unit-based model, </span><span data-ccp-charstyle="normaltextrun">HYPE,</span> <span data-ccp-charstyle="normaltextrun">are </span><span data-ccp-charstyle="normaltextrun">the most appropriate hydrological models for</span><span data-ccp-charstyle="normaltextrun"> both</span><span data-ccp-charstyle="normaltextrun"> simulating </span>and<span data-ccp-charstyle="normaltextrun"> short-term</span> forecasting of <span data-ccp-charstyle="normaltextrun">droughts</span>, <span data-ccp-charstyle="normaltextrun">based on </span><span data-ccp-charstyle="normaltextrun">the following </span><span data-ccp-charstyle="normaltextrun">criteri</span>a<span data-ccp-charstyle="normaltextrun">: </span><span data-ccp-charstyle="normaltextrun">openly</span> <span data-ccp-charstyle="normaltextrun">available</span> model code, <span data-ccp-charstyle="normaltextrun">proven ability at </span><span data-ccp-charstyle="normaltextrun">simulating and forecasting low flows</span><span data-ccp-charstyle="normaltextrun">, </span><span data-ccp-charstyle="normaltextrun">and widely</span> used and supported <span data-ccp-charstyle="normaltextrun">m</span>odel. <span data-ccp-charstyle="normaltextrun">In </span>next steps, <span data-ccp-charstyle="normaltextrun">w</span>e <span data-ccp-charstyle="normaltextrun">will </span><span data-ccp-charstyle="normaltextrun">design</span> <span data-ccp-charstyle="normaltextrun">a common modelling framework </span><span data-ccp-charstyle="normaltextrun">for drought simulation and forecasti</span><span data-ccp-charstyle="normaltextrun">ng </span><span data-ccp-charstyle="normaltextrun">in </span>Scotland. <span data-ccp-charstyle="normaltextrun">Using both H</span><span data-ccp-charstyle="normaltextrun">YPE and GR6J, </span><span data-ccp-charstyle="normaltextrun">w</span>e <span data-ccp-charstyle="normaltextrun">will </span><span data-ccp-charstyle="normaltextrun">set up and test</span> both<span data-ccp-charstyle="normaltextrun"> models</span> in <span data-ccp-charstyle="normaltextrun">a</span> <span data-ccp-charstyle="normaltextrun">medium size </span><span data-ccp-charstyle="normaltextrun">long-term monitoring </span><span data-ccp-charstyle="normaltextrun">test catchment in</span> Tarland <span data-ccp-charstyle="normaltextrun">in northeast Scotland (~70km</span><span xml:lang="EN-GB" data-contrast="none"><sup>2</sup></span><span data-ccp-charstyle="normaltextrun">) where we&#8239;have good process understanding and recent </span>hydro climatological datasets<span data-ccp-charstyle="normaltextrun">.</span> <span xml:lang="EN-GB" data-contrast="auto">C</span><span data-ccp-charstyle="normaltextrun">omparison of </span><span xml:lang="EN-GB" data-contrast="auto">the model performances</span> of HYPE and GR6J will guide us to take a decision on which model to move forward with for upscaling in Scotland. Machi<span xml:lang="EN-GB" data-contrast="none"><span data-ccp-charstyle="normaltextrun">ne learning approaches </span><span data-ccp-charstyle="normaltextrun">for low-flow forecasting using </span><span data-ccp-charstyle="normaltextrun">long-short-memory networks will also be explored in </span><span data-ccp-charstyle="normaltextrun">develop</span>ing</span><span data-ccp-charstyle="normaltextrun"> a multi-model </span><span data-ccp-charstyle="normaltextrun">drought </span>forecasting ensemble. <span data-ccp-props="{">&#160;</span></p> <p class="paragraph"><span data-ccp-props="{">Keywords: Drought, water scarcity, modelling, HYPE, GR6J, forecasting&#160;</span></p>
Typical implementation of VIC-3L model includes three soil layers and three root zones to represent soil moisture uptake through the plant roots. Depths and fractions of the root zones are user-defined for each land use types so that shorter vegetation draw soil moisture from the upper soil layer and deep-rooted plants from the deeper soil layers. VIC assumes that the roots are linearly distributed within the root zones and computes the root fractions for each soil layer by linear interpolation (Fig S1 left). Depths of soil layers are generally calibrated which requires the model to redistribute the user-defined root fractions specified for each root zone, in the soil layer by linear interpolation. Most of the studies related to VIC model have adopted this approach where the soil depths are calibrated and allocation of roots are
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