Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches.

Application of ANN and ANFIS models for reconstructing

Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow-only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias-insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (?7%) and terrestrial water storage (?6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme.

/pdf/improving-the-predictive-skill-of-a-distributed-hydrological-6zjsbicpei.pdf

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

https://serval.unil.ch/resource/serval:BIB_320A56663B51.P001/REF.pdf

Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies

. This study evaluates the ability of different gridded
rainfall datasets to plausibly represent the spatio-temporal patterns of
multiple hydrological processes (i.e. streamflow, actual evaporation, soil
moisture and terrestrial water storage) for large-scale hydrological
modelling in the predominantly semi-arid Volta River basin (VRB) in West
Africa. Seventeen precipitation products based essentially on
gauge-corrected satellite data (TAMSAT, CHIRPS, ARC, RFE, MSWEP, GSMaP,
PERSIANN-CDR, CMORPH-CRT, TRMM 3B42 and TRMM 3B42RT) and on reanalysis (ERA5,
PGF, EWEMBI, WFDEI-GPCC, WFDEI-CRU, MERRA-2 and JRA-55) are compared as
input for the fully distributed mesoscale Hydrologic Model (mHM). To assess
the model sensitivity to meteorological forcing during rainfall partitioning
into evaporation and runoff, six different temperature reanalysis datasets
are used in combination with the precipitation datasets, which results in
evaluating 102 combinations of rainfall–temperature input data. The model is
recalibrated for each of the 102 input combinations, and the model responses
are evaluated by using in situ streamflow data and satellite remote-sensing
datasets from GLEAM evaporation, ESA CCI soil moisture and GRACE
terrestrial water storage. A bias-insensitive metric is used to assess the
impact of meteorological forcing on the simulation of the spatial patterns
of hydrological processes. The results of the process-based evaluation show
that the rainfall datasets have contrasting performances across the four
climatic zones present in the VRB. The top three best-performing rainfall
datasets are TAMSAT, CHIRPS and PERSIANN-CDR for streamflow; ARC, RFE and
CMORPH-CRT for terrestrial water storage; MERRA-2, EWEMBI/WFDEI-GPCC and
PGF for the temporal dynamics of soil moisture; MSWEP, TAMSAT and ARC for
the spatial patterns of soil moisture; ARC, RFE and GSMaP-std for the
temporal dynamics of actual evaporation; and MSWEP, TAMSAT and MERRA-2 for the
spatial patterns of actual evaporation. No single rainfall or temperature
dataset consistently ranks first in reproducing the spatio-temporal
variability of all hydrological processes. A dataset that is best in
reproducing the temporal dynamics is not necessarily the best for the
spatial patterns. In addition, the results suggest that there is more
uncertainty in representing the spatial patterns of hydrological processes
than their temporal dynamics. Finally, some region-tailored datasets
outperform the global datasets, thereby stressing the necessity and
importance of regional evaluation studies for satellite and reanalysis
meteorological datasets, which are increasingly becoming an alternative to
in situ measurements in data-scarce regions.

/pdf/suitability-of-17-gridded-rainfall-and-temperature-datasets-3aggeygdi9.pdf

Jacob Tumbulto

Papers

Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings