Precipitation Trends over the Indus Basin

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

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Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-10719-2012 ◽

2012 ◽

Vol 9 (9) ◽

pp. 10719-10773 ◽

Cited By ~ 7

Author(s):

D. Duethmann ◽

J. Zimmer ◽

A. Gafurov ◽

A. Güntner ◽

B. Merz ◽

...

Keyword(s):

Reanalysis Data ◽

Hydrological Modelling ◽

Mean Annual Precipitation ◽

Model Parameters ◽

Data Sets ◽

Precipitation Data ◽

Data Set ◽

Station Data ◽

Precipitation Estimates ◽

Areal Precipitation

Abstract. In data sparse regions, as in many mountainous catchments, it is a challenge to generate suitable precipitation input fields for hydrological modelling, as station data do not provide enough information to derive areal precipitation estimates. This study presents a method using the spatial variation of precipitation from downscaled reanalysis data for the interpolation of gauge observations. The second aim of this study is the evaluation of different precipitation estimates by hydrological modelling. Study area is the Karadarya catchment in Central Asia (11 700 km2). ERA-40 reanalysis data are downscaled with the regional climate model Weather Research and Forecasting Model (WRF). Precipitation data from gauge observations are interpolated (i) using monthly accumulated WRF precipitation data, (ii) using monthly fields from multiple linear regression against topographical variables and (iii) with the inverse distance approach. These precipitation data sets are also compared to (iv) the direct use of the precipitation output from the WRF downscaled ERA-40 data and (v) precipitation from the APHRODITE data set. Our study suggests that using monthly fields from downscaled reanalysis data can be a good approach for the interpolation of station data in data sparse mountainous regions. Compared to mean annual precipitation from continental and global scale gridded data sets our precipitation estimates for the study area are considerably higher. The introduction of a calibrated precipitation bias factor for the comparison of different precipitation estimates by hydrological modelling allows for a more informed differentiation with regard to the temporal dynamics, on the one hand, and the overall bias, on the other hand. Uncertainty and sensitivity analyses suggest that our results are robust against uncertainties in the calibration parameters, other model parameters and inputs, and the selected calibration period.

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Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4747-2015 ◽

2015 ◽

Vol 19 (12) ◽

pp. 4747-4764 ◽

Cited By ~ 5

Author(s):

F. Alshawaf ◽

B. Fersch ◽

S. Hinz ◽

H. Kunstmann ◽

M. Mayer ◽

...

Keyword(s):

Remote Sensing ◽

Water Vapor ◽

Data Fusion ◽

Spatial Resolution ◽

Heterogeneous Data ◽

Spatial Density ◽

Data Sets ◽

Data Set ◽

Fixed Rank Kriging ◽

Fixed Rank

Abstract. Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging.

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Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-019-01724-9 ◽

2019 ◽

Vol 34 (9) ◽

pp. 1369-1383 ◽

Cited By ~ 1

Author(s):

Dirk Diederen ◽

Ye Liu

Keyword(s):

Large Scale ◽

Spatial Coherence ◽

Original Data ◽

Return Level ◽

Data Sets ◽

Large Set ◽

Precipitation Data ◽

Data Set ◽

Spatio Temporal ◽

Synthetic Precipitation

Abstract With the ongoing development of distributed hydrological models, flood risk analysis calls for synthetic, gridded precipitation data sets. The availability of large, coherent, gridded re-analysis data sets in combination with the increase in computational power, accommodates the development of new methodology to generate such synthetic data. We tracked moving precipitation fields and classified them using self-organising maps. For each class, we fitted a multivariate mixture model and generated a large set of synthetic, coherent descriptors, which we used to reconstruct moving synthetic precipitation fields. We introduced randomness in the original data set by replacing the observed precipitation fields in the original data set with the synthetic precipitation fields. The output is a continuous, gridded, hourly precipitation data set of a much longer duration, containing physically plausible and spatio-temporally coherent precipitation events. The proposed methodology implicitly provides an important improvement in the spatial coherence of precipitation extremes. We investigate the issue of unrealistic, sudden changes on the grid and demonstrate how a dynamic spatio-temporal generator can provide spatial smoothness in the probability distribution parameters and hence in the return level estimates.

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The detection of irrigation through remote sensing soil moisture and a land surface model: a case study in Spain

10.5194/egusphere-egu2020-3654 ◽

2020 ◽

Author(s):

Jacopo Dari ◽

Pere Quintana-Seguí ◽

María José Escorihuela ◽

Luca Brocca ◽

Renato Morbidelli ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Hydrological Cycle ◽

Land Surface Model ◽

Surface Model ◽

Data Sets ◽

Data Set ◽

Agricultural Areas ◽

Irrigation Practices

Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (Surface Externalis&#233;e - Interaction Sol Biosph&#232;re Atmosph&#232;re) land surface model is used as a support for the performed analyses.The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.

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Evaluation of two precipitation data sets for the Rhine River using streamflow simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-15-3355-2011 ◽

2011 ◽

Vol 15 (11) ◽

pp. 3355-3366 ◽

Cited By ~ 22

Author(s):

C. S. Photiadou ◽

A. H. Weerts ◽

B. J. J. M. van den Hurk

Keyword(s):

Annual Cycle ◽

Reanalysis Data ◽

Data Sets ◽

Precipitation Data ◽

Rhine River ◽

Extended Version ◽

Data Set ◽

Rhine Basin ◽

Temporal Correlations ◽

Lower Uncertainty

Abstract. This paper presents an extended version of a widely used precipitation data set and evaluates it along with a recently released precipitation data set, using streamflow simulations. First, the existing precipitation data set issued by the Commission for the Hydrology of the Rhine basin (CHR), originally covering the period 1961–1995, was extended until 2008 using a number of additional precipitation data sets. Next, the extended version of the CHR, together with E-OBS Version 4 (ECA & D gridded data set) were evaluated for their performance in the Rhine basin for extreme events. Finally, the two aforementioned precipitation data sets and a meteorological reanalysis data set were used to force a hydrological model, evaluating the influence of different precipitation forcings on the annual mean and extreme discharges compared to observational discharges for the period from 1990 until 2008. The extended version of CHR showed good agreement in terms of mean annual cycle, extreme discharge (both high and low flows), and spatial distribution of correlations with observed discharge. E-OBS performed well with respect to extreme discharge. However, its performance of the mean annual cycle in winter was rather poor and remarkably well in the summer. Also, CHR08 outperformed E-OBS in terms of temporal correlations in most of the analyzed sub-catchment means. The length extension for the CHR and the even longer length of E-OBS permit the assessment of extreme discharge and precipitation values with lower uncertainty for longer return periods. This assessment classifies both of the presented precipitation data sets as possible reference data sets for future studies in hydrological applications.

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Comparison of drought indicators derived from multiple data sets over Africa

Hydrology and Earth System Sciences ◽

10.5194/hess-18-1625-2014 ◽

2014 ◽

Vol 18 (5) ◽

pp. 1625-1640 ◽

Cited By ~ 40

Author(s):

G. Naumann ◽

E. Dutra ◽

P. Barbosa ◽

F. Pappenberger ◽

F. Wetterhall ◽

...

Keyword(s):

Eastern Africa ◽

Drought Monitoring ◽

Data Sets ◽

Monthly Precipitation ◽

Precipitation Data ◽

Data Set ◽

Precipitation Climatology ◽

Multiple Data Sets ◽

Drought Indicators ◽

Global Precipitation

Abstract. Drought monitoring is a key component to mitigate impacts of droughts. Lack of reliable and up-to-date precipitation data sets is a common challenge across the globe. This study investigates different data sets and drought indicators on their capability to improve drought monitoring in Africa. The study was performed for four river basins located in different climatic regions (the Oum er-Rbia in Morocco, the Blue Nile in eastern Africa, the Upper Niger in western Africa, and the Limpopo in southeastern Africa) as well as the Greater Horn of Africa. The five precipitation data sets compared are the ECMWF ERA-Interim reanalysis, the Tropical Rainfall Measuring Mission satellite monthly rainfall product 3B-43, the Global Precipitation Climatology Centre gridded precipitation data set, the Global Precipitation Climatology Project Global Monthly Merged Precipitation Analyses, and the Climate Prediction Center Merged Analysis of Precipitation. The set of drought indicators used includes the Standardized Precipitation Index, the Standardized Precipitation-Evaporation Index, and Soil Moisture Anomalies. A comparison of the annual cycle and monthly precipitation time series shows a good agreement in the timing of the rainy seasons. The main differences between the data sets are in the ability to represent the magnitude of the wet seasons and extremes. Moreover, for the areas affected by drought, all the drought indicators agree on the time of drought onset and recovery although there is disagreement on the extent of the affected area. In regions with limited rain gauge data the estimation of the different drought indicators is characterized by a higher uncertainty. Further comparison suggests that the main source of differences in the computation of the drought indicators is the uncertainty in the precipitation data sets rather than the estimation of the distribution parameters of the drought indicators.

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The Swiss snow climatology as seen by reanalyses and further gridded datasets

10.5194/ems2021-282 ◽

2021 ◽

Author(s):

Monika Goeldi ◽

Stefanie Gubler ◽

Christian Steger ◽

Simon C. Scherrer ◽

Sven Kotlarski

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Snow Water Equivalent ◽

Spatiotemporal Variability ◽

Data Sets ◽

Data Set ◽

Climate Services ◽

Snow Cover Extent ◽

Snow Water

Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations &#8211; e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.

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EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set

10.5194/essd-2021-222 ◽

2021 ◽

Author(s):

Jakob J. Assmann ◽

Jesper E. Moeslund ◽

Urs A. Treier ◽

Signe Normand

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Laser Scanning ◽

Three Dimensional ◽

Airborne Laser Scanning ◽

Data Sets ◽

Ecosystem Structure ◽

Data Set ◽

Vegetation Height ◽

Airborne Laser

Abstract. Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark's publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark's terrestrial surface. The resulting data set is comparatively small (~ 87 GB, compressed 16.4 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.

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Using high-resolution groundwater data for the validation of a global hydrological model: evaluating WaterGAP and calibration/data assimilation (C/DA) performance over France

10.5194/egusphere-egu21-1317 ◽

2021 ◽

Author(s):

Kuei-Hua Hsu ◽

Laurent Longuevergne ◽

Annette Eicker ◽

Mehedi Hasan ◽

Andreas Güntner ◽

...

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Hydrological Model ◽

Remote Sensing Data ◽

Data Sets ◽

Calibration Data ◽

Data Set ◽

Freshwater Resources ◽

Sensing Data

The dynamic global water cycle is of ecological and societal importance as it affects the availability of freshwater resources and influences extreme events such as floods and droughts. This work is set in the frame of the GlobalCDA Research Unit, which has the goal of developing a calibration/data assimilation approach (C/DA) to improve the quantification of freshwater resources by combining the global hydrological model WaterGAP with geodetic (GRACE, altimetry) and remote sensing data. This presentation focuses on the validation of the C/DA results using an independent in-situ groundwater data set based on ~1500 monitoring boreholes in France.The resulting validation data set is applied to independently assess the output of several C/DA experiments: data assimilation using different combinations of the available geodetic and remote sensing data sets and different methods of model calibration, based on either an ensemble Kalman filter approach or a Pareto-optimal calibration algorithm.To further understand in-situ groundwater and WaterGAP data set, we subtract the coherent signals using Empirical orthogonal function (EOF).&#160; Over 85% variances can be explained by the first 3 EOFs for both data sets.

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Structural and statistical properties of the collocation technique for error characterization

Nonlinear Processes in Geophysics ◽

10.5194/npg-19-69-2012 ◽

2012 ◽

Vol 19 (1) ◽

pp. 69-80 ◽

Cited By ~ 74

Author(s):

S. Zwieback ◽

K. Scipal ◽

W. Dorigo ◽

W. Wagner

Keyword(s):

Remote Sensing ◽

Expert Knowledge ◽

Simulated Data ◽

Data Sources ◽

Data Sets ◽

Data Set ◽

Formidable Challenge ◽

Error Characterization ◽

Collocation Technique ◽

The Impact

Abstract. The validation of geophysical data sets (e.g. derived from models, exploration techniques or remote sensing) presents a formidable challenge as all products are inherently different and subject to errors. The collocation technique permits the retrieval of the error variances of different data sources without the need to specify one data set as a reference. In addition calibration constants can be determined to account for biases and different dynamic ranges. The method is frequently applied to the study and comparison of remote sensing, in-situ and modelled data, particularly in hydrology and oceanography. Previous studies have almost exclusively focussed on the validation of three data sources; in this paper it is shown how the technique generalizes to an arbitrary number of data sets. It turns out that only parts of the covariance structure can be resolved by the collocation technique, thus emphasizing the necessity of expert knowledge for the correct validation of geophysical products. Furthermore the bias and error variance of the estimators are derived with particular emphasis on the assumptions necessary for establishing those characteristics. Important properties of the method, such as the structural deficiencies, dependence of the accuracy on the number of measurements and the impact of violated assumptions, are illustrated by application to simulated data.

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