Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6339-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-6339-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2021
Research article |
| 16 Dec 2021
| 16 Dec 2021
Quantifying the impacts of land cover change on hydrological responses in the Mahanadi river basin in India
Civil Engineering Department, University of Bristol, Bristol, BS8 1TR, UK
Miguel Angel Rico-Ramirez
Civil Engineering Department, University of Bristol, Bristol, BS8 1TR, UK
Rafael Rosolem
Civil Engineering Department, University of Bristol, Bristol, BS8 1TR, UK
Related authors
No articles found.
Jamie Robert Cameron Brown, Ross Woods, Humberto Ribeiro da Rocha, Debora Regina Roberti, and Rafael Rosolem
EGUsphere, https://doi.org/10.5194/egusphere-2025-883, https://doi.org/10.5194/egusphere-2025-883, 2025
Short summary
Short summary
In recent years, global and regional weather datasets have emerged, but validation with real-world data is crucial, especially in diverse regions like Brazil. This study compares seven key weather variables from five datasets with measurements from 11 sites across Brazil’s main biomes. Results show varying performance across variables and timescales, with one reanalysis product outperforming others overall. Findings suggest it may be a strong choice for multi-variable studies in Brazil.
Yongshin Lee, Andres Peñuela, Francesca Pianosi, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 29, 1429–1447, https://doi.org/10.5194/hess-29-1429-2025, https://doi.org/10.5194/hess-29-1429-2025, 2025
Short summary
Short summary
This study assesses the value of seasonal flow forecasts (SFFs) in informing decision-making for drought management in South Korea and introduces a novel method for assessing values benchmarked against historical operations. Our results showed the importance of considering flow forecast uncertainty in reservoir operations. There was no significant correlation between the forecast accuracy and value. The method for selecting a compromise release schedule was a key control of the value.
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
Short summary
Short summary
Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Yanchen Zheng, Gemma Coxon, Ross Woods, Daniel Power, Miguel Angel Rico-Ramirez, David McJannet, Rafael Rosolem, Jianzhu Li, and Ping Feng
Hydrol. Earth Syst. Sci., 28, 1999–2022, https://doi.org/10.5194/hess-28-1999-2024, https://doi.org/10.5194/hess-28-1999-2024, 2024
Short summary
Short summary
Reanalysis soil moisture products are a vital basis for hydrological and environmental research. Previous product evaluation is limited by the scale difference (point and grid scale). This paper adopts cosmic ray neutron sensor observations, a novel technique that provides root-zone soil moisture at field scale. In this paper, global harmonized CRNS observations were used to assess products. ERA5-Land, SMAPL4, CFSv2, CRA40 and GLEAM show better performance than MERRA2, GLDAS-Noah and JRA55.
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Daniel Sanchez-Rivas and Miguel A. Rico-Ramirez
Atmos. Meas. Tech., 15, 503–520, https://doi.org/10.5194/amt-15-503-2022, https://doi.org/10.5194/amt-15-503-2022, 2022
Short summary
Short summary
In this work, we review the use of quasi-vertical profiles for monitoring the calibration of the radar differential reflectivity ZDR. We validate the proposed method by comparing its results against the traditional approach based on measurements taken at 90°; we observed good agreement as the errors are within 0.2 dB. Additionally, we compare the results of the proposed method with ZDR derived from disdrometers; the errors are reasonable considering factors discussed in the paper.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Daniel Power, Miguel Angel Rico-Ramirez, Sharon Desilets, Darin Desilets, and Rafael Rosolem
Geosci. Model Dev., 14, 7287–7307, https://doi.org/10.5194/gmd-14-7287-2021, https://doi.org/10.5194/gmd-14-7287-2021, 2021
Short summary
Short summary
Cosmic-ray neutron sensors estimate root-zone soil moisture at sub-kilometre scales. There are national-scale networks of these sensors across the globe; however, methods for converting neutron signals to soil moisture values are inconsistent. This paper describes our open-source Python tool that processes raw sensor data into soil moisture estimates. The aim is to allow a user to ensure they have a harmonized data set, along with informative metadata, to facilitate both research and teaching.
E. Andrés Quichimbo, Michael Bliss Singer, Katerina Michaelides, Daniel E. J. Hobley, Rafael Rosolem, and Mark O. Cuthbert
Geosci. Model Dev., 14, 6893–6917, https://doi.org/10.5194/gmd-14-6893-2021, https://doi.org/10.5194/gmd-14-6893-2021, 2021
Short summary
Short summary
Understanding and quantifying water partitioning in dryland regions are of key importance to anticipate the future impacts of climate change in water resources and dryland ecosystems. Here, we have developed a simple hydrological model (DRYP) that incorporates the key processes of water partitioning in drylands. DRYP is a modular, versatile, and parsimonious model that can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions.
Daniel Sanchez-Rivas and Miguel A. Rico-Ramirez
Atmos. Meas. Tech., 14, 2873–2890, https://doi.org/10.5194/amt-14-2873-2021, https://doi.org/10.5194/amt-14-2873-2021, 2021
Short summary
Short summary
In our paper, we propose a robust and operational algorithm to determine the height of the melting level that can be applied to either quasi-vertical profiles (QVPs) or vertical profiles (VPs) of polarimetric radar variables. The algorithm is applied to 1 year of rainfall events that occurred over southeast England and validated using radiosonde data. The algorithm proves to be accurate as the errors (mean absolute error and root mean square error) are close to 200 m.
Isaac Kipkemoi, Katerina Michaelides, Rafael Rosolem, and Michael Bliss Singer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-48, https://doi.org/10.5194/hess-2021-48, 2021
Manuscript not accepted for further review
Short summary
Short summary
The work is a novel investigation of the role of temporal rainfall resolution and intensity in affecting the water balance of soil in a dryland environment. This research has implications for what rainfall data are used to assess the impact of climate and climate change on the regional water balance. This information is critical for anticipating the impact of a changing climate on dryland communities globally who need it to know when to plant their seeds or where livestock pasture is available.
Cited articles
Abe, C. A., de Lobo, F. L., Dibike, Y. B., de Costa, M. P. F., Dos Santos, V., and Novo, E. M. L. M.: Modelling the effects of historical and future land cover changes on the hydrology of an Amazonian basin, Water, 10, 932, https://doi.org/10.3390/w10070932, 2018.
Ashagrie, A. G., de Laat, P. J., de Wit, M. J., Tu, M., and Uhlenbrook, S.: Detecting the influence of land use changes on discharges and floods in the Meuse River Basin – the predictive power of a ninety-year rainfall-runoff relation?, Hydrol. Earth Syst. Sci., 10, 691–701, https://doi.org/10.5194/hess-10-691-2006, 2006.
Asokan, S. M. and Dutta, D.: Analysis of water resources in the Mahanadi
River Basin, India under projected climate conditions, Hydrol. Process. An
Int. J., 22, 3589–3603, 2008.
Babar, S. and Ramesh, H.: Streamflow response to land use-land cover change
over the Nethravathi River Basin, India, J. Hydrol. Eng., 20, 05015002,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001177, 2015.
Bao, Z., Liu, J., Zhang, J., Fu, G., Wang, G., Yan, X., Zhang, A., Xu, Q., and Shang, M.: Estimation of baseflow parameters of variable infiltration capacity model with soil and topography properties for predictions in ungauged basins, Hydrol. Earth Syst. Sci. Discuss., 8, 7017–7053, https://doi.org/10.5194/hessd-8-7017-2011, 2011.
Behera, M. D., Tripathi, P., Das, P., Srivastava, S. K., Roy, P. S., Joshi,
C., Behera, P. R., Deka, J., Kumar, P., Khan, M. L., Tripathi, O. P., Dash,
T., and Krishnamurthy, Y. V. N.: Remote sensing based deforestation analysis
in Mahanadi and Brahmaputra river basin in India since 1985, J. Environ.
Manage., 206, 1192–1203, https://doi.org/10.1016/j.jenvman.2017.10.015, 2018.
Berihun, M. L., Tsunekawa, A., Haregeweyn, N., Meshesha, D. T., Adgo, E.,
Tsubo, M., Masunaga, T., Fenta, A. A., Sultan, D., Yibeltal, M., and Ebabu,
K.: Hydrological responses to land use/land cover change and climate
variability in contrasting agro-ecological environments of the Upper Blue
Nile basin, Ethiopia, Sci. Total Environ., 689, 347–365,
https://doi.org/10.1016/j.scitotenv.2019.06.338, 2019.
Beven, K. and Binley, A.: The future of distributed models: Model
calibration and uncertainty prediction, Hydrol. Process., 6, 279–298,
https://doi.org/10.1002/hyp.3360060305, 1992.
Bhuvan: NRSC Open EO Data Archive | NOEDA | Ortho | DEM | Elevation | AWiFS | LISSIII | HySI | TCHP | OHC | Free GIS Data | Download, available at: https://bhuvan-app3.nrsc.gov.in/data/download/index.php, last access: 13 December 2021.
Bosch, J. M. and Hewlett, J. D.: A review of catchment experiments to
determine the effect of vegetation changes on water yield and
evapotranspiration, J. Hydrol., 55, 3–23,
https://doi.org/10.1016/0022-1694(82)90117-2, 1982.
Breuer, L., Huisman, J. A., and Frede, H. G.: Monte Carlo assessment of
uncertainty in the simulated hydrological response to land use change,
Environ. Model. Assess., 11, 209–218, https://doi.org/10.1007/s10666-006-9051-9,
2006.
Central Water Commission, Ministry of jal shakti, Department of Water Resources, River Development and Ganga Rejuvenation, GoI: http://www.cwc.gov.in/, last access: 13 December 2021.
Chaney, N. W., Herman, J. D., Reed, P. M., and Wood, E. F.: Flood and drought hydrologic monitoring: the role of model parameter uncertainty, Hydrol. Earth Syst. Sci., 19, 3239–3251, https://doi.org/10.5194/hess-19-3239-2015, 2015.
Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturvedi, R. K., Fuchs,
R., Brovkin, V., Ciais, P., Fensholt, R., Tømmervik, H., Bala, G., Zhu,
Z., Nemani, R. R., and Myneni, R. B.: China and India lead in greening of the
world through land-use management, Nat. Sustain., 2, 122–129,
https://doi.org/10.1038/s41893-019-0220-7, 2019a.
Chen, Y., Xu, C. Y., Chen, X., Xu, Y., Yin, Y., Gao, L., and Liu, M.:
Uncertainty in simulation of land-use change impacts on catchment runoff
with multi-timescales based on the comparison of the HSPF and SWAT models,
J. Hydrol., 573, 486–500, https://doi.org/10.1016/j.jhydrol.2019.03.091,
2019b.
Cherkauer, K. A. and Lettenmaier, D. P.: Hydrologic effects of frozen soils
in the upper Mississippi River basin, J. Geophys. Res.-Atmos., 104,
19599–19610, https://doi.org/10.1029/1999JD900337, 1999.
Chu, M. L., Knouft, J. H., Ghulam, A., Guzman, J. A., and Pan, Z.: Impacts of
urbanization on river flow frequency: A controlled experimental
modeling-based evaluation approach, J. Hydrol., 495, 1–12,
https://doi.org/10.1016/j.jhydrol.2013.04.051, 2013.
Cornelissen, T., Diekkrüger, B., and Giertz, S.: A comparison of
hydrological models for assessing the impact of land use and climate change
on discharge in a tropical catchment, J. Hydrol., 498, 221–236,
https://doi.org/10.1016/j.jhydrol.2013.06.016, 2013.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A Statistical
Exploration of the Relationships of Soil Moisture Characteristics to the
Physical Properties of Soils, Water Resour. Res., 20, 682–690,
https://doi.org/10.1029/WR020i006p00682, 1984.
Costa, M. H., Botta, A., and Cardille, J. A.: Effects of large-scale changes
in land cover on the discharge of the Tocantins River, Southeastern
Amazonia, J. Hydrol., 283, 206–217,
https://doi.org/10.1016/S0022-1694(03)00267-1, 2003.
Dadhwal, V. K., Mishra, N., and Aggarwal, S, P.: Hydrological Simulation of
Mahanadi River Basin and Impact of Land Use/Land Cover Change on Surface
Runoff Using a Macro Scale Hydrological Model, ISPRS TC VII Symp. – 100
Years ISPRS, Vienna, Austria, XXXVIII, 165–170, 2010.
Das, P., Behera, M. D., Patidar, N., Sahoo, B., Tripathi, P., Behera, P. R.,
Srivastava, S. K., Roy, P. S., Thakur, P., Agrawal, S. P., and Krishnamurthy,
Y. V. N.: Impact of LULC change on the runoff, base flow and
evapotranspiration dynamics in eastern Indian river basins during 1985–2005
using variable infiltration capacity approach, J. Earth Syst. Sci., 127,
1–19, https://doi.org/10.1007/s12040-018-0921-8, 2018.
Demaria, E. M., Nijssen, B., and Wagener, T.: Monte Carlo sensitivity
analysis of land surface parameters using the Variable Infiltration Capacity
model, J. Geophys. Res.-Atmos., 112, 1–15, https://doi.org/10.1029/2006JD007534,
2007.
Demaria, E. M. C., Maurer, E. P., Sheffield, J., Bustos, E., Poblete, D.,
Vicuña, S., and Meza, F.: Using a gridded global dataset to characterize
regional hydroclimate in central Chile, J. Hydrometeorol., 14, 251–265,
https://doi.org/10.1175/JHM-D-12-047.1, 2013.
Eum, H. Il, Dibike, Y., and Prowse, T.: Comparative evaluation of the effects
of climate and land-cover changes on hydrologic responses of the Muskeg
River, Alberta, Canada, J. Hydrol. Reg. Stud., 8, 198–221,
https://doi.org/10.1016/j.ejrh.2016.10.003, 2016.
Feng, D. and Beighley, E.: Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments, Hydrol. Earth Syst. Sci., 24, 2253–2267, https://doi.org/10.5194/hess-24-2253-2020, 2020.
Fohrer, N., Haverkamp, S., Eckhardt, K., and Frede, H. G.: Hydrologic
response to land use changes on the catchment scale, Phys. Chem. Earth Pt
B, 26, 577–582,
https://doi.org/10.1016/S1464-1909(01)00052-1, 2001.
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter,
S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J.
H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A.,
Prentice, I. C., Ramankutty, N., and Snyder, P. K.: Global consequences of
land use, Science, 309, 570–574,
https://doi.org/10.1126/science.1111772, 2005.
Franchini, M. and Pacciani, M.: Comparative analysis of several conceptual
rainfall-runoff models, J. Hydrol., 122, 161–219,
https://doi.org/10.1016/0022-1694(91)90178-K, 1991.
Gao, H., Tang, Q., Shi, X., Zhu, C., and Bohn, T.: Water budget record from Variable Infiltration Capacity (VIC) model, Algorithm Theor. Basis Doc. Terr. Water Cycle Data Rec., (Vic), 120–173, available at: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Water+Budget+Record+from+Variable+Infiltration+Capacity+(+VIC+)+Model#2 (last access: 20 June 2021), 2010.
Garg, V., Aggarwal, S. P., Gupta, P. K., Nikam, B. R., Thakur, P. K.,
Srivastav, S. K., and Senthil Kumar, A.: Assessment of land use land cover
change impact on hydrological regime of a basin, Environ. Earth Sci.,
76, 1–17, https://doi.org/10.1007/s12665-017-6976-z, 2017.
Garg, V., Nikam, B. R., Thakur, P. K., Aggarwal, S. P., Gupta, P. K., and
Srivastav, S. K.: Human-induced land use land cover change and its impact on
hydrology, HydroResearch, 1, 48–56, https://doi.org/10.1016/j.hydres.2019.06.001, 2019.
Gebremicael, T. G., Mohamed, Y. A., and Van der Zaag, P.: Attributing the
hydrological impact of different land use types and their long-term dynamics
through combining parsimonious hydrological modelling, alteration analysis
and PLSR analysis, Sci. Total Environ., 660, 1155–1167,
https://doi.org/10.1016/j.scitotenv.2019.01.085, 2019.
Ghosh, S., Raje, D., and Mujumdar, P. P.: Mahanadi streamflow: climate change
impact assessment and adaptive strategies, Curr. Sci. India, 98, 1084–1091,
2010.
Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019.
Gou, J., Miao, C., Duan, Q., Tang, Q., Di, Z., Liao, W., Wu, J., and Zhou,
R.: Sensitivity Analysis-Based Automatic Parameter Calibration of the VIC
Model for Streamflow Simulations Over China, Water Resour. Res., 56,
1–19, https://doi.org/10.1029/2019WR025968, 2020.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y.: The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481–3496, https://doi.org/10.5194/gmd-11-3481-2018, 2018.
Hengade, N., Eldho, T. I., and Ghosh, S.: Climate change impact assessment of
a river basin using CMIP5 climate models and the VIC hydrological model,
Hydrolog. Sci. J., 63, 596–614, https://doi.org/10.1080/02626667.2018.1441531, 2018.
Her, Y., Yoo, S. H., Cho, J., Hwang, S., Jeong, J., and Seong, C.:
Uncertainty in hydrological analysis of climate change: multi-parameter vs.
multi-GCM ensemble predictions, Sci. Rep.-UK, 9, 1–22,
https://doi.org/10.1038/s41598-019-41334-7, 2019.
Herman, J. D., Kollat, J. B., Reed, P. M., and Wagener, T.: Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models, Hydrol. Earth Syst. Sci., 17, 2893–2903, https://doi.org/10.5194/hess-17-2893-2013, 2013.
Huang, M. and Liang, X.: On the assessment of the impact of reducing
parameters and identification of parameter uncertainties for a hydrologic
model with applications to ungauged basins, J. Hydrol., 320, 37–61,
https://doi.org/10.1016/j.jhydrol.2005.07.010, 2006.
Hurkmans, R. T. W. L., Terink, W., Uijlenhoet, R., Moors, E. J., Troch, P.
A., and Verburg, P. H.: Effects of land use changes on streamflow generation
in the Rhine basin, Water Resour. Res., 45, 1–15,
https://doi.org/10.1029/2008WR007574, 2009.
Hurtt, G. C., Chini, L. P., Sahajpal, R., Frolking, S. E., Bodirsky, B.,
Calvin, K. V, Doelman, J. C., Fisk, J., Fujimori, S., Goldewijk, K., and
others: LUH2: Harmonization of global land-use scenarios for the period
850-2100, AGUFM, 2018, GC13A–01, 2018.
India Meteorological Department: https://www.imd.gov.in, last access: 13 December 2021.
IPCC: Special report on global warming of 1.5 ∘C (SR15), available at: https://www.ipcc.ch/sr15/ (last access: 30 November 2021), 2019.
Jin, L., Whitehead, P. G., Rodda, H., Macadam, I., and Sarkar, S.: Simulating
climate change and socio-economic change impacts on flows and water quality
in the Mahanadi River system, India, Sci. Total Environ., 637–638,
907–917, https://doi.org/10.1016/j.scitotenv.2018.04.349, 2018.
Joseph, J., Ghosh, S., Pathak, A., and Sahai, A. K.: Hydrologic impacts of
climate change: Comparisons between hydrological parameter uncertainty and
climate model uncertainty, J. Hydrol., 566, 1–22,
https://doi.org/10.1016/j.jhydrol.2018.08.080, 2018.
Kneis, D., Chatterjee, C., and Singh, R.: Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi), Hydrol. Earth Syst. Sci., 18, 2493–2502, https://doi.org/10.5194/hess-18-2493-2014, 2014.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Krause, A., Haverd, V., Poulter, B., Anthoni, P., Quesada, B., Rammig, A.,
and Arneth, A.: Multimodel Analysis of Future Land Use and Climate Change
Impacts on Ecosystem Functioning, Earth's Futur., 7, 833–851,
https://doi.org/10.1029/2018EF001123, 2019.
Kumar, N., Singh, S. K., Singh, V. G., and Dzwairo, B.: Investigation of
impacts of land use/land cover change on water availability of Tons River
Basin, Madhya Pradesh, India, Model. Earth Syst. Environ., 4, 295–310,
https://doi.org/10.1007/s40808-018-0425-1, 2018.
Kundu, S., Khare, D., and Mondal, A.: Individual and combined impacts of
future climate and land use changes on the water balance, Ecol. Eng., 105,
42–57, https://doi.org/10.1016/j.ecoleng.2017.04.061, 2017.
Land Use Harmonization: https://luh.umd.edu/data.shtml, last access: 13 December 2021.
Li, Z., Deng, X., Wu, F., and Hasan, S. S.: Scenario analysis for water
resources in response to land use change in the middle and upper reaches of
the heihe river Basin, Sustainability-Basel, 7, 3086–3108, https://doi.org/10.3390/su7033086,
2015.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A Simple
hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs,
J. Geophys. Res., 99, 14415–14428, 1994.
Lilhare, R., Pokorny, S., Déry, S. J., Stadnyk, T. A., and Koenig, K. A.:
Sensitivity analysis and uncertainty assessment in water budgets simulated
by the variable infiltration capacity model for Canadian subarctic
watersheds, Hydrol. Process., 34, 2057–2075, https://doi.org/10.1002/hyp.13711,
2020.
Lohmann, D. A. G., Nolte-Holube, R., and Raschke, E.: A large-scale
horizontal routing model to be coupled to land surface parametrization
schemes, Tellus A, 48, 708–721, 1996.
Ma, X., Xu, J., and van Noordwijk, M.: Sensitivity of streamflow from a
Himalayan catchment to plausible changes in land cover and climate, Hydrol.
Process., 24, 1379–1390, https://doi.org/10.1002/hyp.7602, 2010.
Mao, D. and Cherkauer, K. A.: Impacts of land-use change on hydrologic
responses in the Great Lakes region, J. Hydrol., 374, 71–82,
https://doi.org/10.1016/j.jhydrol.2009.06.016, 2009.
Matheussen, B., Goodman, I. A., Lettenmaier, D. P., Kirschbaum, R. L., and
O'Donnell, G. M.: Effects of land cover change on streamflow in the interior
Columbia River Basin (USA and Canada), Hydrol. Process., 14, 867–885,
https://doi.org/10.1002/(sici)1099-1085(20000415)14:5<867::aid-hyp975>3.0.co;2-5, 2002.
Mishra, N., Aggarwal, S. P., and Dadhwal, V. K.: Macroscale Hydrological
Modelling and Impact of land cover change on stream flows of the Mahanadi
River Basin, A Master thesis Submitt. to Andhra Univ. Indian Inst. Remote
Sens. (National Remote Sens. Agency) Dept. Space, Govt. India, 2008.
Mockler, E. M., Chun, K. P., Sapriza-Azuri, G., Bruen, M., and Wheater, H.
S.: Assessing the relative importance of parameter and forcing uncertainty
and their interactions in conceptual hydrological model simulations, Adv.
Water Resour., 97, 299–313, https://doi.org/10.1016/j.advwatres.2016.10.008, 2016.
Morris, M. D.: Factorial sampling plans for preliminary computational
experiments, Technometrics, 33, 161–174, 1991.
NOAA Physical Sciences Laboratory: NCEP/NCAR Reanalysis 1: Pressure, available at: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html, last access: 13 December 2021.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., and
Mukhopadhyay, B.: Development of a new high spatial resolution
(0.25∘ × 0.25∘) long period (1901–2010) daily
gridded rainfall data set over India and its comparison with existing data
sets over the region, Mausam, 65, 1–18, 2014.
Patidar, N. and Behera, M. D.: How Significantly do Land Use and Land Cover
(LULC) Changes Influence the Water Balance of a River Basin? A Study in
Ganga River Basin, India, P. Natl. A. Sci. India A,
89, 353–365, https://doi.org/10.1007/s40010-017-0426-x, 2019.
Rawls, W. J., Gimenez, D., and Grossman, R.: Use of soil texture, bulk density, and slope of the water retention curve to predict saturated hydraulic conductivity, T. ASAE, 41, 983–988, 1998.
Reynolds, C. A., Jackson, T. J., and Rawls, W. J.: Estimating soil
water-holding capacities by linking the Food and Agriculture Organization
soil map of the world with global pedon databases and continuous
pedotransfer functions, Water Resour. Res., 36, 3653–3662, 2000.
Rodriguez, D. A. and Tomasella, J.: On the ability of large-scale
hydrological models to simulate land use and land cover change impacts in
Amazonian basins, Hydrolog. Sci. J., 61, 1831–1846,
https://doi.org/10.1080/02626667.2015.1051979, 2016.
Rogger, M., Agnoletti, M., Alaoui, A., Bathurst, J. C., Bodner, G., Borga,
M., Chaplot, V., Gallart, F., Glatzel, G., Hall, J., Holden, J., Holko, L.,
Horn, R., Kiss, A., Quinton, J. N., Leitinger, G., Lennartz, B., Parajka,
J., Peth, S., Robinson, M., Salinas, J. L., Santoro, A., Szolgay, J., Tron,
S., and Viglione, A.: Water Resour. Res., 53, 5209–5219,
https://doi.org/10.1002/2017WR020723, 2016.
Rosolem, R., Gupta, H. V., Shuttleworth, W. J., Zeng, X., and De
Gonçalves, L. G. G.: A fully multiple-criteria implementation of the
Sobol' method for parameter sensitivity analysis, J. Geophys. Res.-Atmos.,
117, 1–18, https://doi.org/10.1029/2011JD016355, 2012.
Sarrazin, F., Pianosi, F., and Wagener, T.: Global Sensitivity Analysis of
environmental models: Convergence and validation, Environ. Model. Softw.,
79, 135–152, https://doi.org/10.1016/j.envsoft.2016.02.005, 2016.
Singh, R., Wagener, T., Crane, R., Mann, M. E., and Ning, L.: A vulnerability
driven approach to identify adverse climate and land use change combinations
for critical hydrologic indicator thresholds: Application to a watershed in
Pennsylvania, USA, Water Resour. Res., 50, 3409–3427,
https://doi.org/10.1002/2013WR014988, 2014.
Sivasena Reddy, A. and Janga Reddy, M.: Evaluating the influence of spatial
resolutions of DEM on watershed runoff and sediment yield using SWAT, J.
Earth Syst. Sci., 124, 1517–1529, 2015.
Tang, Y., Reed, P., Wagener, T., and van Werkhoven, K.: Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrol. Earth Syst. Sci., 11, 793–817, https://doi.org/10.5194/hess-11-793-2007, 2007.
UH-VIC: https://vic.readthedocs.io/en/vic.4.2.d/Documentation/Routing/UH/, last access: 13 December 2021.
UW-Hydro: VIC, GitHub [code], available at:
https://github.com/UW-Hydro/VIC/releases/tag/VIC.4.2.d, last access:
13 December 2021.
Vanrolleghem, P. A., Mannina, G., Cosenza, A., and Neumann, M. B.: Global
sensitivity analysis for urban water quality modelling: Terminology,
convergence and comparison of different methods, J. Hydrol., 522, 339–352,
https://doi.org/10.1016/j.jhydrol.2014.12.056, 2015.
Viglione, A., Merz, B., Viet Dung, N., Parajka, J., Nester, T., and
Blöschl, G.: Attribution of regional flood changes based on scaling
fingerprints, Water Resour. Res., 52, 5322–5340, 2016.
Viola, M. R., Mello, C. R., Beskow, S., and Norton, L. D.: Impacts of
Land-use Changes on the Hydrology of the Grande River Basin Headwaters,
Southeastern Brazil, Water Resour. Manag., 28, 4537–4550,
https://doi.org/10.1007/s11269-014-0749-1, 2014.
Wagner, P. D., Kumar, S., and Schneider, K.: An assessment of land use change impacts on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India, Hydrol. Earth Syst. Sci., 17, 2233–2246, https://doi.org/10.5194/hess-17-2233-2013, 2013.
Wang, A. and Solomatine, D. P.: Practical experience of sensitivity
analysis: Comparing six methods, on three hydrological models, with three
performance criteria, Water, 11, 1–26,
https://doi.org/10.3390/w11051062, 2019.
Wilk, J. and Hughes, D. A.: Simulating the impacts of land-use and climate
change on water resource availability for a large south Indian catchment,
Hydrolog. Sci. J., 47, 19–30, 2002.
Woldesenbet, T. A., Elagib, N. A., Ribbe, L., and Heinrich, J.: Hydrological
responses to land use/cover changes in the source region of the Upper Blue
Nile Basin, Ethiopia, Sci. Total Environ., 575, 724–741,
https://doi.org/10.1016/j.scitotenv.2016.09.124, 2017.
Xie, Z., Yuan, F., Duan, Q., Zheng, J., Liang, M., and Chen, F.: Regional
parameter estimation of the VIC land surface model: Methodology and
application to river basins in China, J. Hydrometeorol., 8, 447–468,
https://doi.org/10.1175/JHM568.1, 2007.
Yanto, Livneh, B., Rajagopalan, B., and Kasprzyk, J.: Hydrological model
application under data scarcity for multiple watersheds, Java Island,
Indonesia, J. Hydrol. Reg. Stud., 9, 127–139,
https://doi.org/10.1016/j.ejrh.2016.09.007, 2017.
Yeste, P., García-Valdecasas Ojeda, M., Gámiz-Fortis, S. R.,
Castro-Díez, Y., and Jesús Esteban-Parra, M.: Integrated Sensitivity
Analysis of a Macroscale Hydrologic Model in the North of the Iberian
Peninsula, J. Hydrol., 590, 125230,
https://doi.org/10.1016/j.jhydrol.2020.125230, 2020.
Zeng, X.: Global Vegetation Root Distribution for Land Modeling, J.
Hydrometeorol., 2, 525–530, https://doi.org/10.1175/1525-7541(2001)002<0525:gvrdfl>2.0.co;2, 2002.
Zhang, T., Zhang, X., Xia, D., and Liu, Y.: An analysis of land use change
dynamics and its impacts on hydrological processes in the Jialing River
Basin, Water, 6, 3758–3782, https://doi.org/10.3390/w6123758, 2014.
Zhao, R. J., Zhang, Y. L., Fang, L. R., Liu, X. R., and Zhang, Q. S.: The Xinanjiang model Hydrological Forecasting Proceedings Oxford Symposium, IASH 129, 351–356, 1980.
Short summary
Rapid growth in population in developing countries leads to an increase in food demand, and as a consequence, percentages of land are being converted to cropland which alters river flow processes. This study describes how the hydrology of a flood-prone river basin in India would respond to the current and future changes in land cover. Our findings indicate that the recurrent flood events occurring in the basin might be influenced by these changes in land cover at the catchment scale.
Rapid growth in population in developing countries leads to an increase in food demand, and as a...
