Land cover and water yield: inference problems when comparing catchments with mixed land cover

Controlled experiments provide strong evidence that changing land cover (e.g. deforestation or afforestation) can affect mean catchment streamflow (<i>Q</i>). By contrast, a similarly strong influence has not been found in studies that interpret <i>Q</i> from multiple catchments with mixed land cover. One possible reason is that there are methodological issues with the way in which the Budyko framework was used in the latter type studies. We examined this using <i>Q</i> data observed in 278 Australian catchments and by making inferences from synthetic <i>Q</i> data simulated by a hydrological process model (the Australian Water Resources Assessment system Landscape model). The previous contrasting findings could be reproduced. In the synthetic experiment, the land cover influence was still present but not accurately detected with the Budyko- framework. Likely sources of interpretation bias demonstrated include: (i) noise in land cover, precipitation and <i>Q</i> data; (ii) additional catchment climate characteristics more important than land cover; and (iii) covariance between <i>Q</i> and catchment attributes. These methodological issues caution against the use of a Budyko framework to quantify a land cover influence in <i>Q</i> data from mixed land-cover catchments. Importantly, however, our findings do not rule out that there may also be physical processes that modify the influence of land cover in mixed land-cover catchments. Process model simulations suggested that lateral water redistribution between vegetation types and recirculation of intercepted rainfall may be important

TRMM-TMI satellite observed soil moisture and vegetation density (1998-2005) show strong connection with El Nino in eastern Australia

Spatiotemporal patterns in soil moisture and vegetation water content across mainland Australia were investigated from 1998 through 2005, using TRMM/TMI passive microwave observations. The Empirical Orthogonal Function technique was used to extract dominant spatial and temporal patterns in retrieved estimates of moisture content for the top 1-cm of soil (θ) and vegetation moisture content (via optical depth τ). The dominant temporal θ and τ patterns were strongly correlated to the El Niño Southern Oscillation Index (SOI) in spring (

Optimization of deep learning precipitation models using categorical binary metrics

This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm rate are popular metrics used in the verification of precipitation models. However, machine learning models trained using gradient descent cannot be optimized based on these metrics, as they are not differentiable. We propose an alternative formulation for these categorical indices that are differentiable and we demonstrate how they can be used to optimize the skill of precipitation neural network models defined as a multi-objective optimization problem. To our knowledge, this is the first proposal of a methodology for optimizing weather neural network models based on categorical indices.TIN2016-78365-

Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

Combining information derived from satellitebased passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 (“transitional regions”), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied Correspondence to: Y. Y. Liu ([email protected]) to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates

Improving drought simulations within the Murray-Darling Basin by combined calibration/assimilation of GRACE data into the WaterGAP Global Hydrology Model

Simulating hydrological processes within the (semi-)arid region of the Murray-Darling Basin (MDB), Australia, is very challenging specially during droughts. In this study, we investigate whether integrating remotely sensed terrestrial water storage changes (TWSC) from the Gravity Recovery And Climate Experiment (GRACE) mission into a global water resources and use model enables a more realistic representation of the basin hydrology during droughts. For our study, the WaterGAP Global Hydrology Model (WGHM), which simulates the impact of human water abstractions on surface water and groundwater storage, has been chosen for simulating compartmental water storages and river discharge during the so-called ‘Millennium Drought’ (2001–2009). In particular, we test the ability of a parameter calibration and data assimilation (C/DA) approach to introduce long-term trends into WGHM, which are poorly represented due to errors in forcing, model structure and calibration. For the first time, the impact of the parameter equifinality problem on the C/DA results is evaluated. We also investigate the influence of selecting a specific GRACE data product and filtering method on the final C/DA results. Integrating GRACE data into WGHM does not only improve simulation of seasonality and trend of TWSC, but also it improves the simulation of individual water storage components. For example, after the C/DA, correlations between simulated groundwater storage changes and independent in-situ well data increase (up to 0.82) in three out of four sub-basins. Declining groundwater storage trends - found mainly in the south, i.e. Murray Basin, at in-situ wells - have been introduced while simulated soil water and surface water storage do not show trends, which is in agreement with existing literature. Although GRACE C/DA in MDB does not improve river discharge simulations, the correlation between river storage simulations and gauge-based river levels increases significantly from 0.15 to 0.52. By adapting the C/DA settings to the basin-specific characteristics and reducing the number of calibration parameters, their convergence is improved and their uncertainty is reduced. The time-variable parameter values resulting from C/DA allow WGHM to better react to the very wet Australian summer 2009/10. Using solutions from different GRACE data providers produces slightly different C/DA results. We conclude that a rigorous evaluation of GRACE errors is required to realistically account for the spread of the differences in the results

Human-water interface in hydrological modeling: Current status and future directions

Over the last decades, the global population has been rapidly increasing and human activities have altered terrestrial water fluxes at an unprecedented scale. The phenomenal growth of the human footprint has significantly modified hydrological processes in various ways (e.g., irrigation, artificial dams, and water diversion) and at various scales (from a watershed to the globe). During the early 1990s, awareness of the potential water scarcity led to the first detailed global water resource assessments. Shortly thereafter, in order to analyse the human perturbation on terrestrial water resources, the first generation of large-scale hydrological models (LHMs) was produced. However, at this early stage few models considered the interaction between terrestrial water fluxes and human activities, including water use and reservoir regulation, and even fewer models distinguished water use from surface water and groundwater resources. Since the early 2000s, a growing number of LHMs are incorporating human impacts on hydrological cycle, yet human representations in hydrological models remain challenging. In this paper we provide a synthesis of progress in the development and application of human impact modeling in LHMs. We highlight a number of key challenges and discuss possible improvements in order to better represent the human-water interface in hydrological models

Human–water interface in hydrological modelling: current status and future directions

Over recent decades, the global population has been rapidly increasing and human activities have altered terrestrial water fluxes to an unprecedented extent. The phenomenal growth of the human footprint has significantly modified hydrological processes in various ways (e.g. irrigation, artificial dams, and water diversion) and at various scales (from a watershed to the globe). During the early 1990s, awareness of the potential for increased water scarcity led to the first detailed global water resource assessments. Shortly thereafter, in order to analyse the human perturbation on terrestrial water resources, the first generation of largescale hydrological models (LHMs) was produced. However, at this early stage few models considered the interaction between terrestrial water fluxes and human activities, including water use and reservoir regulation, and even fewer models distinguished water use from surface water and groundwater resources. Since the early 2000s, a growing number of LHMs have incorporated human impacts on the hydrological cycle, yet the representation of human activities in hydrological models remains challenging. In this paper we provide a synthesis of progress in the development and application of human impact modelling in LHMs. We highlight a number of key challenges and discuss possible improvements in order to better represent the human-water interface in hydrological models