Cross-scale evaluation of catchment- and global-scale hydrological model simulations of drought characteristics across eight large river catchments

2021 ◽  
Author(s):  
Amit Kumar ◽  
Simon N Gosling ◽  
Matthew F Johnson ◽  
Jamal Zaherpour ◽  
Guoyong Leng ◽  
...  
Keyword(s):  
Time Series ◽  
Large River ◽  
Global Scale ◽  
Historical Period ◽  
Severe Drought ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Hydrological Extremes ◽  
Intercomparison Project ◽  
Monthly Runoff

Abstract Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated runoff simulations from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed monthly runoff and hydrological droughts over 31 years (1971–2001). Observed and simulated hydrological drought events were identified using the Standardised Runoff Index (SRI) and were classified based on intensity. Our results show that for all eight catchments, CHMs out-performed GHMs in monthly runoff estimation showing a better representation of observed runoff than GHMs. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means present limited ability to accurately simulate severe drought events in all eight catchments, in terms of their timing and intensity. By analysing the monthly runoff time-series for several extreme droughts over the historical period, we identify room for improvement in the models so that extreme droughts may ultimately be better represented by both CHMs and GHMs.

scholarly journals A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models

2010 ◽  
Vol 7 (5) ◽  
pp. 7191-7229 ◽  
Author(s):  
S. N. Gosling ◽  
R. G. Taylor ◽  
N. W. Arnell ◽  
M. C. Todd
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Climate Model ◽  
Annual Runoff ◽  
Structural Uncertainty ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Monthly Runoff ◽  
Global Mean ◽  
Impacts Of Climate Change

Abstract. We present a comparative analysis of projected impacts of climate change on river runoff from two types of distributed hydrological model, a global hydrological model (GHM) and catchment-scale hydrological models (CHM). Analyses are conducted for six catchments that are global in coverage and feature strong contrasts in spatial scale as well as climatic and developmental conditions. These include the Liard (Canada), Mekong (SE Asia), Okavango (SW Africa), Rio Grande (Brazil), Xiangxi (China) and Harper's Brook (UK). A single GHM (Mac-PDM.09) is applied to all catchments whilst different CHMs are applied for each catchment. The CHMs include SLURP v. 12.2 (Liard), SLURP v. 12.7 (Mekong), Pitman (Okavango), MGB-IPH (Rio Grande), AV-SWAT-X 2005 (Xiangxi) and Cat-PDM (Harper's Brook). Simulations of mean annual runoff, mean monthly runoff and high (Q5) and low (Q95) monthly runoff under baseline (1961–1990) and climate change scenarios are presented. We compare the simulated runoff response of each hydrological model to (1) prescribed increases in global-mean air temperature of 1.0, 2.0, 3.0, 4.0, 5.0 and 6.0 °C relative to baseline from the UKMO HadCM3 Global Climate Model (GCM) to explore response to different amounts of climate forcing, and (2) a prescribed increase in global-mean air temperature of 2.0 °C relative to baseline for seven GCMs to explore response to climate model structural uncertainty. We find that the differences in projected changes of mean annual runoff between the two types of hydrological model can be substantial for a given GCM, and they are generally larger for indicators of high and low monthly runoff. However, they are relatively small in comparison to the range of projections across the seven GCMs. Hence, for the six catchments and seven GCMs we considered, climate model structural uncertainty is greater than the uncertainty associated with the type of hydrological model applied. Moreover, shifts in the seasonal cycle of runoff with climate change are represented similarly by both hydrological models, although for some catchments the monthly timing of high and low flows differs. This implies that for studies that seek to quantify and assess the role of climate model uncertainty on catchment-scale runoff, it may be equally as feasible to apply a GHM as it is to apply a CHM, especially when climate modelling uncertainty across the range of available GCMs is as large as it currently is. Whilst the GHM is able to represent the broad climate change signal that is represented by the CHMs, we find however, that for some catchments there are differences between GHMs and CHMs in mean annual runoff due to differences in potential evapotranspiration estimation methods, in the representation of the seasonality of runoff, and in the magnitude of changes in extreme (Q5, Q95) monthly runoff, all of which have implications for future water management issues.

An Evaluation of Probability of Occurrence of Hydrological Extremes

2021 ◽  
pp. 69-78
Author(s):  
M.B. Joisy ◽  
Deepa G.S. Varghese
Keyword(s):  
Severe Drought ◽  
Catchment Scale ◽  
Probability Of Occurrence ◽  
Hydrological Extremes ◽  
Storage Reservoirs ◽  
Scarce Water ◽  
Droughts And Floods ◽  
Modelling Techniques ◽  
Streamflow Data ◽  
Floods And Droughts

The hydrological extremes viz. droughts and floods, are global recurring natural hazards which are dynamic with respect to space and time impacting many people. The increase in the number of instances of these hydrological events in the past has steered the research in the direction towards evaluation of probability of occurrence of droughts and floods on a catchment scale, for proper planning and decision making in ideal allocation of the scarce water resources and mitigation of flood. Understanding and evaluating hydrological extremes becomes important in terms of sizing of storage reservoirs for combating droughts and floods, while its prediction becomes the key in reduction of its consequences. This study presents a summarized evaluation of probability of occurrence of floods and droughts in Bhavani basin of Kerala, using Herbst method, for a period of 40 years from 2002 to 2042, using streamflow data. As per the analysis, the most severe drought is expected to hit the basin in the year 2022- 2023 while the worst flood is expected in the year 2040 -2041. The novelty of the study is in applying the Herbst method for evaluating the probability of occurrence of floods in a catchment area without adopting rigorous hydrological modelling techniques.

scholarly journals A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models

2011 ◽  
Vol 15 (1) ◽  
pp. 279-294 ◽  
Author(s):  
S. N. Gosling ◽  
R. G. Taylor ◽  
N. W. Arnell ◽  
M. C. Todd
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Climate Model ◽  
Annual Runoff ◽  
Structural Uncertainty ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Monthly Runoff ◽  
Global Mean ◽  
Impacts Of Climate Change

Abstract. We present a comparative analysis of projected impacts of climate change on river runoff from two types of distributed hydrological model, a global hydrological model (GHM) and catchment-scale hydrological models (CHM). Analyses are conducted for six catchments that are global in coverage and feature strong contrasts in spatial scale as well as climatic and developmental conditions. These include the Liard (Canada), Mekong (SE Asia), Okavango (SW Africa), Rio Grande (Brazil), Xiangxi (China) and Harper's Brook (UK). A single GHM (Mac-PDM.09) is applied to all catchments whilst different CHMs are applied for each catchment. The CHMs include SLURP v. 12.2 (Liard), SLURP v. 12.7 (Mekong), Pitman (Okavango), MGB-IPH (Rio Grande), AV-SWAT-X 2005 (Xiangxi) and Cat-PDM (Harper's Brook). The CHMs typically simulate water resource impacts based on a more explicit representation of catchment water resources than that available from the GHM and the CHMs include river routing, whereas the GHM does not. Simulations of mean annual runoff, mean monthly runoff and high (Q5) and low (Q95) monthly runoff under baseline (1961–1990) and climate change scenarios are presented. We compare the simulated runoff response of each hydrological model to (1) prescribed increases in global-mean air temperature of 1.0, 2.0, 3.0, 4.0, 5.0 and 6.0 °C relative to baseline from the UKMO HadCM3 Global Climate Model (GCM) to explore response to different amounts of climate forcing, and (2) a prescribed increase in global-mean air temperature of 2.0 °C relative to baseline for seven GCMs to explore response to climate model structural uncertainty. We find that the differences in projected changes of mean annual runoff between the two types of hydrological model can be substantial for a given GCM (e.g. an absolute GHM-CHM difference in mean annual runoff percentage change for UKMO HadCM3 2 °C warming of up to 25%), and they are generally larger for indicators of high and low monthly runoff. However, they are relatively small in comparison to the range of projections across the seven GCMs. Hence, for the six catchments and seven GCMs we considered, climate model structural uncertainty is greater than the uncertainty associated with the type of hydrological model applied. Moreover, shifts in the seasonal cycle of runoff with climate change are represented similarly by both hydrological models, although for some catchments the monthly timing of high and low flows differs. This implies that for studies that seek to quantify and assess the role of climate model uncertainty on catchment-scale runoff, it may be equally as feasible to apply a GHM (Mac-PDM.09 here) as it is to apply a CHM, especially when climate modelling uncertainty across the range of available GCMs is as large as it currently is. Whilst the GHM is able to represent the broad climate change signal that is represented by the CHMs, we find however, that for some catchments there are differences between GHMs and CHMs in mean annual runoff due to differences in potential evapotranspiration estimation methods, in the representation of the seasonality of runoff, and in the magnitude of changes in extreme (Q5, Q95) monthly runoff, all of which have implications for future water management issues.

scholarly journals On the spatio-temporal analysis of hydrological droughts from global hydrological models

2011 ◽  
Vol 15 (9) ◽  
pp. 2963-2978 ◽  
Author(s):  
G. A. Corzo Perez ◽  
M. H. J. van Huijgevoort ◽  
F. Voß ◽  
H. A. J. van Lanen
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Large Scale ◽  
Southern Oscillation ◽  
Spatial Coherence ◽  
Temporal Analysis ◽  
Global Scale ◽  
Hydrological Models ◽  
Context Analysis ◽  
Spatio Temporal

Abstract. The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river basin models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are classified as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA). The NCDA presents time series of percentages of areas in drought at the global scale and for pre-defined regions of known hydroclimatology. The CDA is introduced as a complementary method that generates information on the spatial coherence of drought events at the global scale. Spatial drought events are found through CDA by clustering patterns (contiguous areas). In this study the global hydrological model WaterGAP was used to illustrate the methodology development. Global gridded time series of subsurface runoff (resolution 0.5°) simulated with the WaterGAP model from land points were used. The NCDA and CDA were developed to identify drought events in runoff. The percentages of area in drought calculated with both methods show complementary information on the spatial and temporal events for the last decades of the 20th century. The NCDA provides relevant information on the average number of droughts, duration and severity (deficit volume) for pre-defined regions (globe, 2 selected hydroclimatic regions). Additionally, the CDA provides information on the number of spatially linked areas in drought, maximum spatial event and their geographic location on the globe. Some results capture the overall spatio-temporal drought extremes over the last decades of the 20th century. Events like the El Niño Southern Oscillation (ENSO) in South America and the pan-European drought in 1976 appeared clearly in both analyses. The methodologies introduced provide an important basis for the global characterization of droughts, model inter-comparison of drought identified from global hydrological models and spatial event analyses.

scholarly journals Historical and future changes in global flood magnitude – evidence from a model–observation investigation

2020 ◽  
Vol 24 (3) ◽  
pp. 1543-1564 ◽  
Author(s):  
Hong Xuan Do ◽  
Fang Zhao ◽  
Seth Westra ◽  
Michael Leonard ◽  
Lukas Gudmundsson ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Climate Model ◽  
Global Scale ◽  
Historical Period ◽  
Climate Projections ◽  
Hydrological Models ◽  
Flood Hazards ◽  
Significance Level ◽  
Gas Concentration ◽  
Order Of Magnitude

Abstract. To improve the understanding of trends in extreme flows related to flood events at the global scale, historical and future changes of annual maxima of 7 d streamflow are investigated, using a comprehensive streamflow archive and six global hydrological models. The models' capacity to characterise trends in annual maxima of 7 d streamflow at the continental and global scale is evaluated across 3666 river gauge locations over the period from 1971 to 2005, focusing on four aspects of trends: (i) mean, (ii) standard deviation, (iii) percentage of locations showing significant trends and (iv) spatial pattern. Compared to observed trends, simulated trends driven by observed climate forcing generally have a higher mean, lower spread and a similar percentage of locations showing significant trends. Models show a low to moderate capacity to simulate spatial patterns of historical trends, with approximately only from 12 % to 25 % of the spatial variance of observed trends across all gauge stations accounted for by the simulations. Interestingly, there are statistically significant differences between trends simulated by global hydrological models (GHMs) forced with observational climate and by those forced by bias-corrected climate model output during the historical period, suggesting the important role of the stochastic natural (decadal, inter-annual) climate variability. Significant differences were found in simulated flood trends when averaged only at gauged locations compared to those averaged across all simulated grid cells, highlighting the potential for bias toward well-observed regions in our understanding of changes in floods. Future climate projections (simulated under the RCP2.6 and RCP6.0 greenhouse gas concentration scenarios) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or decrease; at 10 % significance level) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of locations consistently projected with increased flood hazards under the RCP6.0 greenhouse gas concentration scenario by more than an order of magnitude (0.9 % compared to 11.7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change.

scholarly journals How Well Do Large-Scale Models Reproduce Regional Hydrological Extremes in Europe?

2011 ◽  
Vol 12 (6) ◽  
pp. 1181-1204 ◽  
Author(s):  
Christel Prudhomme ◽  
Simon Parry ◽  
Jamie Hannaford ◽  
Douglas B. Clark ◽  
Stefan Hagemann ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Storage Capacity ◽  
Spatial Coherence ◽  
Low Flow ◽  
Water Model ◽  
Hydrological Models ◽  
Flow Measurements ◽  
Hydrological Extremes ◽  
High Flows

Abstract This paper presents a new methodology for assessing the ability of gridded hydrological models to reproduce large-scale hydrological high and low flow events (as a proxy for hydrological extremes) as described by catalogues of historical droughts [using the regional deficiency index (RDI)] and high flows [regional flood index (RFI)] previously derived from river flow measurements across Europe. Using the same methods, total runoff simulated by three global hydrological models from the Water Model Intercomparison Project (WaterMIP) [Joint U.K. Land Environment Simulator (JULES), Water Global Assessment and Prognosis (WaterGAP), and Max Planck Institute Hydrological Model (MPI-HM)] run with the same meteorological input (watch forcing data) at the same spatial 0.5° grid was used to calculate simulated RDI and RFI for the period 1963–2001 in the same European regions, directly comparable with the observed catalogues. Observed and simulated RDI and RFI time series were compared using three performance measures: the relative mean error, the ratio between the standard deviation of simulated over observed series, and the Spearman correlation coefficient. Results show that all models can broadly reproduce the spatiotemporal evolution of hydrological extremes in Europe to varying degrees. JULES tends to produce prolonged, highly spatially coherent events for both high and low flows, with events developing more slowly and reaching and sustaining greater spatial coherence than observed—this could be due to runoff being dominated by slow-responding subsurface flow. In contrast, MPI-HM shows very high variability in the simulated RDI and RFI time series and a more rapid onset of extreme events than observed, in particular for regions with significant water storage capacity—this could be due to possible underrepresentation of infiltration and groundwater storage, with soil saturation reached too quickly. WaterGAP shares some of the issues of variability with MPI-HM—also attributed to insufficient soil storage capacity and surplus effective precipitation being generated as surface runoff—and some strong spatial coherence of simulated events with JULES, but neither of these are dominant. Of the three global models considered here, WaterGAP is arguably best suited to reproduce most regional characteristics of large-scale high and low flow events in Europe. Some systematic weaknesses emerge in all models, in particular for high flows, which could be a product of poor spatial resolution of the input climate data (e.g., where extreme precipitation is driven by local convective storms) or topography. Overall, this study has demonstrated that RDI and RFI are powerful tools that can be used to assess how well large-scale hydrological models reproduce large-scale hydrological extremes—an exercise rarely undertaken in model intercomparisons.

Global scale hydrological modelling at 100 m, 1 h resolution, in Python

2021 ◽  
Author(s):  
Kor de Jong ◽  
Marc van Kreveld ◽  
Debabrata Panja ◽  
Oliver Schmitz ◽  
Derek Karssenberg
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Flow Direction ◽  
Building Blocks ◽  
Global Scale ◽  
Data Availability ◽  
Hydrological Models ◽  
Continental Scale ◽  
Modelling Framework ◽  
Scale Models

<p>Data availability at global scale is increasing exponentially. Although considerable challenges remain regarding the identification of model structure and parameters of continental scale hydrological models, we will soon reach the situation that global scale models could be defined at very high resolutions close to 100 m or less. One of the key challenges is how to make simulations of these ultra-high resolution models tractable ([1]).</p><p>Our research contributes by the development of a model building framework that is specifically designed to distribute calculations over multiple cluster nodes. This framework enables domain experts like hydrologists to develop their own large scale models, using a scripting language like Python, without the need to acquire the skills to develop low-level computer code for parallel and distributed computing.</p><p>We present the design and implementation of this software framework and illustrate its use with a prototype 100 m, 1 h continental scale hydrological model. Our modelling framework ensures that any model built with it is parallelized. This is made possible by providing the model builder with a set of building blocks of models, which are coded in such a manner that parallelization of calculations occurs within and across these building blocks, for any combination of building blocks. There is thus full flexibility on the side of the modeller, without losing performance.</p><p>This breakthrough is made possible by applying a novel approach to the implementation of the model building framework, called asynchronous many-tasks, provided by the HPX C++ software library ([3]). The code in the model building framework expresses spatial operations as large collections of interdependent tasks that can be executed efficiently on individual laptops as well as computer clusters ([2]). Our framework currently includes the most essential operations for building large scale hydrological models, including those for simulating transport of material through a flow direction network. By combining these operations, we rebuilt an existing 100 m, 1 h resolution model, thus far used for simulations of small catchments, requiring limited coding as we only had to replace the computational back end of the existing model. Runs at continental scale on a computer cluster show acceptable strong and weak scaling providing a strong indication that global simulations at this resolution will soon be possible, technically speaking.</p><p>Future work will focus on extending the set of modelling operations and adding scalable I/O, after which existing models that are currently limited in their ability to use the computational resources available to them can be ported to this new environment.</p><p>More information about our modelling framework is at https://lue.computationalgeography.org.</p><p><strong>References</strong></p><p>[1] M. Bierkens. Global hydrology 2015: State, trends, and directions. Water Resources Research, 51(7):4923–4947, 2015.<br>[2] K. de Jong, et al. An environmental modelling framework based on asynchronous many-tasks: scalability and usability. Submitted.<br>[3] H. Kaiser, et al. HPX - The C++ standard library for parallelism and concurrency. Journal of Open Source Software, 5(53):2352, 2020.</p>

Extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau

2021 ◽  
Author(s):  
Junyuan Fei ◽  
Jintao Liu
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Time Scale ◽  
Proper Time ◽  
Cumulative Distribution ◽  
The Tibetan Plateau ◽  
Statistical Features ◽  
Catchment Scale ◽  
Intermittent Rivers ◽  
Intermittent River

<p>Highly intermittent rivers are widespread on the Tibetan Plateau and deeply impact the ecological stability and social development downstream. Due to the highly intermittent rivers are small, seasonal variated and heavy cloud covered on the Tibetan Plateau, their distribution location is still unknown at catchment scale currently. To address these challenges, a new method is proposed for extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau. The proposed method first determines the proper time scale of extracting highly intermittent river, based on which the statistical features are calculated to amplify the difference between land covers. Subsequently, the synoptic cumulative distribution location is extracted through Random Forest model using the statistical features above as explanatory variables. And the precise result is generated by combining the synoptic result with critical flow accumulation area.  The highly intermittent river segments are derived and assessed in an alpine catchment of Lhasa River Basin. The results show that the the intra-annual time scale is sufficient for highly intermittent river extraction. And the proposed method can extract highly intermittent river cumulative distribution locations with total precision of 0.62, distance error median of 64.03 m, outperforming other existing river extraction method.</p>

scholarly journals Hydrology of inland tropical lowlands: the Kapuas and Mahakam wetlands

2017 ◽  
Vol 21 (5) ◽  
pp. 2579-2594 ◽  
Author(s):  
Hidayat Hidayat ◽  
Adriaan J. Teuling ◽  
Bart Vermeulen ◽  
Muh Taufik ◽  
Karl Kastner ◽  
...  
Keyword(s):  
Time Series ◽  
Groundwater Recharge ◽  
Potential Evapotranspiration ◽  
Southern Oscillation ◽  
Tropical Rainfall Measuring Mission ◽  
Threshold Level ◽  
Phase Array ◽  
Catchment Scale ◽  
Tropical Regions ◽  
Rainfall Estimates

Abstract. Wetlands are important reservoirs of water, carbon and biodiversity. They are typical landscapes of lowland regions that have high potential for water retention. However, the hydrology of these wetlands in tropical regions is often studied in isolation from the processes taking place at the catchment scale. Our main objective is to study the hydrological dynamics of one of the largest tropical rainforest regions on an island using a combination of satellite remote sensing and novel observations from dedicated field campaigns. This contribution offers a comprehensive analysis of the hydrological dynamics of two neighbouring poorly gauged tropical basins; the Kapuas basin (98 700 km2) in West Kalimantan and the Mahakam basin (77 100 km2) in East Kalimantan, Indonesia. Both basins are characterised by vast areas of inland lowlands. Hereby, we put specific emphasis on key hydrological variables and indicators such as discharge and flood extent. The hydroclimatological data described herein were obtained during fieldwork campaigns carried out in the Kapuas over the period 2013–2015 and in the Mahakam over the period 2008–2010. Additionally, we used the Tropical Rainfall Measuring Mission (TRMM) rainfall estimates over the period 1998–2015 to analyse the distribution of rainfall and the influence of El-Niño – Southern Oscillation. Flood occurrence maps were obtained from the analysis of the Phase Array type L-band Synthetic Aperture Radar (PALSAR) images from 2007 to 2010. Drought events were derived from time series of simulated groundwater recharge using time series of TRMM rainfall estimates, potential evapotranspiration estimates and the threshold level approach. The Kapuas and the Mahakam lake regions are vast reservoirs of water of about 1000 and 1500 km2 that can store as much as 3 and 6.5 billion m3 of water, respectively. These storage capacity values can be doubled considering the area of flooding under vegetation cover. Discharge time series show that backwater effects are highly influential in the wetland regions, which can be partly explained by inundation dynamics shown by flood occurrence maps obtained from PALSAR images. In contrast to their nature as wetlands, both lowland areas have frequent periods with low soil moisture conditions and low groundwater recharge. The Mahakam wetland area regularly exhibits low groundwater recharge, which may lead to prolonged drought events that can last up to 13 months. It appears that the Mahakam lowland is more vulnerable to hydrological drought, leading to more frequent fire occurrences than in the Kapuas basin.

scholarly journals Kamchia watershed groundwater recharge assessment by the CLM3 model

2018 ◽  
Vol 145 ◽  
pp. 03011
Author(s):  
Olga Nitcheva ◽  
Borislav Milev ◽  
Tanya Trenkova ◽  
Nina Philipova ◽  
Polya Dobreva
Keyword(s):  
Groundwater Recharge ◽  
Global Scale ◽  
Richards Equation ◽  
Hydrological Models ◽  
Filtration Process ◽  
Community Land Model ◽  
Natural Factors ◽  
Meteorological Effect ◽  
Resources Evaluation ◽  
Soil Filtration

Estimating groundwater recharge is an important part of the water resources evaluation. In spite of the numerous existing methods it continues to be not easy value to quantify. This is due to its dependence on many meteorological, hydrogeological, soil type and cover conditions and the impossibility for direct measurement. Employment of hydrological models in fact directly calculates the influence of the above cited natural factors. The Community Land Model (CLM3) being loaded with all land featuring data in global scale, including an adequate soil filtration process simulation by the Richards equation, together with the possibility for input of NCEP/NCAR Reanalyses database, featuring the meteorological effect, gives an opportunity to avoid to great extent the difficulties in groundwater (GW) recharge estimation. The paper presents the results from an experiment concerning GW recharge monthly estimation during 2013, worked out for the Kamchia river watershed in Bulgaria. The computed monthly and annual values are presented on GIS maps and are compared with existing assessments made by other methods. It is proved the good approach and the applicability of the method.

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