Indian Forum for Water Adroit

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Previously reported trends in daily monsoon rainfall since 1950 have been estimated using interpolated weather station observations released by the India Meteorological Department. The number of reporting weather stations changes over time, and poor coverage by weather stations can overlook extreme rainfall events. By applying the interpolation of this changing network to satellite‐based rainfall data, we show that the changing coverage of weather stations in the Indian rainfall data leads to spurious increases in extreme rainfall. This suggests that previously reported trends of extreme rainfall are biased positive. Access to the raw weather station data would improve our ability to track changes in the Indian monsoon and assess modeled predictions given climate change.

Freshwater resources are of high societal relevance and understanding their past variability is vital to water management in the context of current and future climatic change. This study introduces a global gridded monthly reconstruction of runoff covering the period from 1902 to 2014. In-situ streamflow observations are used to train a machine learning algorithm that predicts monthly runoff rates based on antecedent precipitation and temperature from an atmospheric reanalysis. The accuracy of this reconstruction is assessed with cross-validation and compared with an independent set of discharge observations for large river basins. The presented dataset agrees on average better with the streamflow observations than an ensemble of 13 state-of-the art global hydrological model runoff simulations. We estimate a global long-term mean runoff of 37 419 km3 yr−1 in agreement with previous assessments. The temporal coverage of the reconstruction offers an unprecedented view on large-scale features of runoff variability also in regions with limited data coverage, making it an ideal candidate for large-scale hydro-climatic process studies, water resources assessments and for evaluating and refining existing hydrological models. The paper closes with example applications fostering the understanding of global freshwater dynamics, interannual variability, drought propagation and the response of runoff to atmospheric teleconnections.

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Widely recognised climate change is affecting our planet Earth, our Environment, and in the end, our lives extensively. This special issue reflects such pervasiveness, addressing various statistical problems that connect climate change and atmosphere, hydrological risks, and land changes. Moreover, it considers both the analysis and modelling of high‐level data or indicators and measurement uncertainty, which involves lower level data, often called Level 1 and Level 2 data.
Hydrological sciences / Machine Learning for Modeling Water Demand
« Last post by Pankaj Dey on March 13, 2019, 06:20:50 PM »
This work shows the application of machine learning (ML) methods to the modeling of water demand for the first time. Classification and regression trees (CART) and random forest (RF), a multivariate, spatially nonstationary and nonlinear ML approach, were used to build a predictive model of water demand in the city of Seville, Spain, at the census tract level. Regression trees (RT) allowed estimation of water demand with an error of 22  L/day/inhabitant and determination of the main driving variables. RF allowed estimation of water demand with error values ranging from 18.89 to 26.91  L/day/inhabitant. The RF method provided better predictions; however, the RT model facilitated better understanding of water demand. This research shows an alternative to the hitherto applied cluster and linear regression approaches for modeling water demand and paves the way for a new set of further scientific investigations based on ML methods.
Thermal infrared remote sensing observations have been widely used to provide useful information on surface energy and water stress for estimating evapotranspiration (ET). However, the revisit time of current high spatial resolution (<100 m) thermal infrared remote sensing systems, sixteen days for Landsat for example, can be insufficient to reliably derive ET information for water resources management. We used in situ ET measurements from multiple Ameriflux sites to (1) evaluate different scaling methods that are commonly used to derive daytime ET estimates from time-of-day observations; and (2) quantify the impact of different revisit times on ET estimates at monthly and seasonal time scales. The scaling method based on a constant evaporative ratio between ET and the top-of-atmosphere solar radiation provided slightly better results than methods using the available energy, the surface solar radiation or the potential ET as scaling reference fluxes. On average, revisit time periods of 2, 4, 8 and 16 days resulted in ET uncertainties of 0.37, 0.55, 0.73 and 0.90 mm per day in summer, which represented 13%, 19%, 23% and 31% of the monthly average ET calculated using the one-day revisit dataset. The capability of a system to capture rapid changes in ET was significantly reduced for return periods higher than eight days. The impact of the revisit on ET depended mainly on the land cover type and seasonal climate, and was higher over areas with high ET. We did not observe significant and systematic differences between the impacts of the revisit on monthly ET estimates that are based on morning or afternoon observations. We found that four-day revisit scenarios provided a significant improvement in temporal sampling to monitor surface ET reducing by around 40% the uncertainty of ET products derived from a 16-day revisit system, such as Landsat for instance.
Data / Free GIS Data
« Last post by Karthikeyan L on March 11, 2019, 11:38:41 PM »
The following contains information on variable wise as well as country wise free GIS datasets. A very good summary!
Interesting information / How to run Python in R
« Last post by Pankaj Dey on March 11, 2019, 08:26:08 PM »
As much as I love R, it’s clear that Python is also a great language—both for data science and general-purpose computing. And there can be good reasons an R user would want to do some things in Python. Maybe it’s a great library that doesn’t have an R equivalent (yet). Or an API you want to access that has sample code in Python but not R.
Thanks to the R reticulate package, you can run Python code right within an R script—and pass data back and forth between Python and R.
Interesting information / Human influence on winter precipitation trends
« Last post by Pankaj Dey on March 10, 2019, 06:52:35 PM »
It is difficult to isolate the anthropogenic influence on long‐term precipitation trends due to confounding effects from internal variability. Here we remove the influence of atmospheric circulation variability, which is primarily unforced, from observed precipitation trends using an empirical approach called "dynamical adjustment. This removal isolates the thermodynamic component of observed precipitation trends as a residual. We find that this thermodynamic component is in good agreement with the anthropogenic component determined from historical simulations from climate model simulations. Thus, we conclude that we are able to identify a human influence on observed century‐scale precipitation trends over North America and Eurasia.
Key Points

    1. Removing the influence of atmospheric circulation variability reveals the thermodynamic component of observed precipitation trends
    2. The observed thermodynamic trend is in good agreement with anthropogenically‐forced trends from historical climate model simulations
    3. Our approach provides an alternative to formal “detection and attribution” methods for revealing anthropogenic changes in observations
This paper considers distributed hydrological models in hydrology as an expression of a pragmatic realism. Some of the problems of distributed modelling are discussed including the problem of nonlinearity, the problem of scale, the problem of equifinality, the problem o f uniqueness and the problem of uncertainty. A structure for the application of distributed modelling is suggested based on an uncertain or fuzzy landscape space to model space mapping. This is suggested as the basis for an Alternative Blueprint for distributed modelling in the form of an application methodology. This Alternative Blueprint is scientific in that it allows for the formulation of testable hypotheses. It focuses attention on the prior evaluation of models in terms of physical realism and on the value of data in model rejection. Finally, some unresolved questions that distributed modelling must address in the future are outlined, together with a vision for distributed modelling as a means of learning about places.
Interesting information / Open Source Python Packages in Hydrology
« Last post by Pankaj Dey on March 09, 2019, 07:24:59 PM »
R.A. Collenteur of University of Graz has compiled available python packages that can help hydrological analysis. Please find the github link. Fork it to get notification for update in the repository.
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