Al-Qurashi, A., McIntyre, N., Wheater, H., and Unkrich, C.: Application of the
Kineros2 rainfall-runoff model to an arid catchment in Oman, J. Hydrol., 355, 91–105, https://doi.org/10.1016/j.jhydrol.2008.03.022, 2008. a

Andreassian, V., Perrin, C., Michel, C., Usart-Sanchez, I., and Lavabre, J.:
Impact of imperfect rainfall knowledge on the efficiency and the parameters
of watershed models, J. Hydrol., 250, 206–223,
https://doi.org/10.1016/S0022-1694(01)00437-1, 2001. a

Bahat, Y., Grodek, T., Lekach, J., and Morin, E.: Rainfall-runoff modeling in
a small hyper-arid catchment, J. Hydrol., 373, 204–217,
https://doi.org/10.1016/j.jhydrol.2009.04.026, 2009. a

Bárdossy, A.: Copula-based geostatistical models for groundwater quality
parameters, Water Resour. Res., 42, W11416,
https://doi.org/10.1029/2005WR004754, 2006. a

Bárdossy, A.: Interpolation of groundwater quality parameters with some
values below the detection limit, Hydrol. Earth Syst. Sci., 15, 2763–2775,
https://doi.org/10.5194/hess-15-2763-2011, 2011. a

Bárdossy, A. and Hörning, S.: Random Mixing: An Approach to Inverse
Modeling for Groundwater Flow and Transport Problems, Transport Porous Med., 114, 241–259, https://doi.org/10.1007/s11242-015-0608-4, 2016a. a

Bárdossy, A. and Hörning, S.: Gaussian and non-Gaussian inverse
modeling of groundwater flow using copulas and random mixing, Water Resour. Res., 52, 4504–4526, https://doi.org/10.1002/2014WR016820, 2016b. a, b, c

Bell, T. L.: A space-time stochastic model of rainfall for satellite
remote-sensing studies, J. Geophys. Res.-Atmos., 92,
9631–9643, https://doi.org/10.1029/JD092iD08p09631, 1987. a

Beven, K. and Hornberger, G.: Assessing the effect of spatial pattern of
precipitation in modeling stream-flow hydrographs, Water Resour. Bull.,
18, 823–829, 1982. a

Casper, M. C., Herbst, M., Grundmann, J., Buchholz, O., and Bliefernicht, J.:
Influence of rainfall variability on the simulation of extreme runoff in
small catchments, Hydrol. Wasserbewirts., 53, 134–139,
2009. a

Chaubey, I., Haan, C., Grunwald, S., and Salisbury, J.: Uncertainty in the
model parameters due to spatial variability of rainfall, J. Hydrol., 220, 48–61, https://doi.org/10.1016/S0022-1694(99)00063-3, 1999. a

Del Giudice, D., Albert, C., Rieckermann, J., and Reichert, P.: Describing the
catchment-averaged precipitation as a stochastic process improves parameter
and input estimation, Water Resour. Res., 52, 3162–3186,
https://doi.org/10.1002/2015WR017871, 2016. a

Dyck, S. and Peschke, G.: Grundlagen der Hydrologie, Verlag für Bauwesen
Berlin, 1983. a

Faures, J., Goodrich, D., Woolhiser, D., and Sorooshian, S.: Impact of
small-scale spatial rainfall variability on runoff modeling, J. Hydrol., 173, 309–326, https://doi.org/10.1016/0022-1694(95)02704-S, 1995. a

Gerner, A.: A novel strategy for estimating groundwater recharge in arid
mountain regions and its application to parts of the Jebel Akhdar Mountains
(Sultanate of Oman), PhD thesis, Technische Universität Dresden, 2013. a

Golub, G. and Kahan, W.: Calculating the Singular Values and Pseudo-Inverse of
a Matrix, J. Soc. Ind. Appl. Math., 2, 205–224, 1965. a

Gunkel, A. and Lange, J.: New Insights Into The Natural Variability of Water
Resources in The Lower Jordan River Basin, Water Resour. Manage., 26,
963–980, https://doi.org/10.1007/s11269-011-9903-1, 2012. a

Haese, B., Horning, S., Chwala, C., Bardossy, A., Schalge, B., and Kunstmann,
H.: Stochastic Reconstruction and Interpolation of Precipitation Fields Using
Combined Information of Commercial Microwave Links and Rain Gauges, Water Resour. Res., 53, 10740–10756, 2017. a

Hörning, S.: Process-oriented modeling of spatial random fields using
copulas, Eigenverlag des Instituts für Wasser- und
Umweltsystemmodellierung der Universität Stuttgart, 2016. a, b, c

Hu, L.: Gradual deformation and iterative calibration of Gaussian-related
stochastic models, Math Geol., 32, 87–108, 2000. a

Journel, A.: Geostatistics for conditional simulation of ore bodies, Econ.
Geol., 69, 673–687, 1974. a

Kavetski, D., Kuczera, G., and Franks, S.: Bayesian analysis of input
uncertainty in hydrological modeling: 1. Theory, Water Resour. Res.,
42, W03407, https://doi.org/10.1029/2005WR004368, 2006. a

Kirchner, J. W.: Catchments as simple dynamical systems: Catchment
characterization, rainfall-runoff modeling, and doing hydrology backward,
Water Resour. Res., 45, W02429, https://doi.org/10.1029/2008WR006912, 2009. a

Krajewski, W. F., Lakshmi, V., Georgakakos, K. P., and Jain, S. C.: A Monte
Carlo Study of rainfall sampling effect on a distributed catchment model,
Water Resour. Res., 27, 119–128, https://doi.org/10.1029/90WR01977, 1991. a

Kretzschmar, A., Tych, W., and Chappell, N. A.: Reversing hydrology:
Estimation of sub-hourly rainfall time-series from streamflow,
Environ. Modell. Softw., 60, 290–301,
https://doi.org/10.1016/j.envsoft.2014.06.017, 2014. a

Leblois, E. and Creutin, J.-D.: Space-time simulation of intermittent rainfall
with prescribed advection field: Adaptation of the turning band method, Water Resour. Res., 49, 3375–3387, https://doi.org/10.1002/wrcr.20190, 2013. a

Le Ravalec, M., Noetinger, B., and Hu, L. Y.: The FFT Moving Average (FFT-MA)
Generator: An Efficient Numerical Method for Generating and Conditioning
Gaussian Simulations, Math. Geol., 32, 701–723, 2000. a

Li, J.: Application of Copulas as a New Geostatistical Tool, Eigenverlag des
Instituts für Wasser- und Umweltsystemmodellierung der Universität
Stuttgart, 2010. a

Lopes, V.: On the effect of uncertainty in spatial distribution of rainfall on
catchment modelling, Catena, 28, 107–119,
https://doi.org/10.1016/S0341-8162(96)00030-6, 1996. a

Mantoglou, A. and Wilson, J.: The Turning Bands Method for simulation of
random fields using line generation by a spectral method, Water Resour. Res., 18, 1379–1394, https://doi.org/10.1029/WR018i005p01379, 1982. a

McIntyre, N., Al-Qurashi, A., and Wheater, H.: Regression analysis of
rainfall-runoff data from an arid catchment in Oman, Hydrolog. Sci. J., 52,
1103–1118, International Conference on Future of
Drylands, Tunis, Tunisia, June 2006, https://doi.org/10.1623/hysj.52.6.1103, 2007. a

McMillan, H., Jackson, B., Clark, M., Kavetski, D., and Woods, R.: Rainfall
uncertainty in hydrological modelling: An evaluation of multiplicative error
models, J. Hydrol., 400, 83–94,
https://doi.org/10.1016/j.jhydrol.2011.01.026, 2011. a

Morin, E., Goodrich, D., Maddox, R., Gao, X., Gupta, H., and Sorooshian, S.:
Spatial patterns in thunderstorm rainfall events and their coupling with
watershed hydrological response, Adv. Water Resour., 29,
843–860, https://doi.org/10.1016/j.advwatres.2005.07.014, 2006. a

Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models
part I – A discussion of principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a

Nicotina, L., Celegon, E. A., Rinaldo, A., and Marani, M.: On the impact of
rainfall patterns on the hydrologic response, Water Resour. Res.,
44, W12401, https://doi.org/10.1029/2007WR006654, 2008.
a

Obled, C., Wendling, J., and Beven, K.: The sensitivity of hydrological models
to spatial rainfall patterns – an evaluation using observed data, J. Hydrol., 159, 305–333, https://doi.org/10.1016/0022-1694(94)90263-1, 1994. a

Paschalis, A., Molnar, P., Fatichi, S., and Burlando, P.: A stochastic model
for high-resolution space-time precipitation simulation, Water Resour. Res., 49, 8400–8417, https://doi.org/10.1002/2013WR014437, 2013. a

Paschalis, A., Fatichi, S., Molnar, P., Rimkus, S., and Burlando, P.: On the
effects of small scale space-time variability of rainfall on basin flood
response, J. Hydrol., 514, 313–327,
https://doi.org/10.1016/j.jhydrol.2014.04.014, 2014. a

Pegram, G. and Clothier, A.: High resolution space-time modelling of rainfall:
the “String of Beads” model, J. Hydrol., 241, 26–41,
https://doi.org/10.1016/S0022-1694(00)00373-5, 2001. a

Peleg, N., Fatichi, S., Paschalis, A., Molnar, P., and Burlando, P.: An
advanced stochastic weather generator for simulating 2-D high-resolution
climate variables, J. Adv. Model. Earth Sy., 9,
1595–1627, https://doi.org/10.1002/2016MS000854, 2017. a

Pilgrim, D., Chapman, T., and Doran, D.: Problems of rainfall-runoff modeling
in arid and semiarid regions, Hydrolog. Sci. J., 33, 379–400, https://doi.org/10.1080/02626668809491261,
1988. a, b

Renard, B., Kavetski, D., Leblois, E., Thyer, M., Kuczera, G., and Franks,
S. W.: Toward a reliable decomposition of predictive uncertainty in
hydrological modeling: Characterizing rainfall errors using conditional
simulation, Water Resour. Res., 47, W11516, https://doi.org/10.1029/2011WR010643,
2011. a

Shah, S., O'Connell, P., and Hosking, J.: Modelling the effects of spatial
variability in rainfall on catchment response. 2. Experiments with
distributed and lumped models, J. Hydrol., 175, 89–111,
https://doi.org/10.1016/S0022-1694(96)80007-2, 1996. a

Shinozuka, M. and Deodatis, G.: Simulation of stochastic processes by spectral
representation, Appl. Mech. Rev., 44, 191–204, https://doi.org/10.1115/1.3119501, 1991. a

Shinozuka, M. and Deodatis, G.: Simulation of multi-dimensional Gaussian
stochastic fields by spectral representation, Appl. Mech. Rev., 49, 29–53, https://doi.org/10.1115/1.3101883, 1996. a

Troutman, B.: Runoff prediction errors and bias in parameter-estimation
induced by spatial variability of precipitation, Water Resour. Res.,
19, 791–810, https://doi.org/10.1029/WR019i003p00791, 1983. a

Wilks, D.: Multisite generalization of a daily stochastic precipitation
generation model, J. Hydrol., 210, 178–191,
https://doi.org/10.1016/S0022-1694(98)00186-3, 1998. a

Wood, A.: When is a truncated covariance function on the line a covariance
function on the circle?, Stat. Probabil. Lett., 24, 157–164,
1995. a

Wood, A. and Chan, G.: Simulation of stationary Gaussian process in [0,1]^{d},
J. Comput. Graph. Stat., 3, 409–432, 1994. a