Impacts of climate change on the hydrometeorological characteristics of the Soan River Basin, Pakistan

The global hydrological cycle is vulnerable to changing climatic conditions, especially in developing regions, which lack abundant resources and management of freshwater resources. This study evaluates the impacts of climate change on the hydrological regime of the Chirah and Dhoke Pathan sub catchments of the Soan River Basin (SRB), in Pakistan, by using the climate models included in the NEX-GDDP dataset and the hydrological model HBV-light. After proper calibration and validation, the latter is forced with NEX-GDDP inputs to simulate a historic and a future (under the RCP 4.5 and RCP 8.5 emission scenarios) streamflow. Multiple evaluation criteria were employed to find the best performing NEX-GDDP models. A different ensemble was produced for each sub catchment by including the five best performing NEX-GDDP GCMs (ACCESS1-0, CCSM4, CESM1-BGC, MIROC5, and MRI-CGCM3 for Chirah and BNU-ESM, CCSM4, GFDL-CM3. IPSL-CM5A-LR and NorESM1-M for Dhoke Pathan). Our results show that the streamflow is projected to decrease significantly for the two sub catchments, highlighting the vulnerability of the SRB to climate change

On the benefits of bias correction techniques for streamflow simulation in complex terrain catchments: a case-study for the Chitral River Basin in Pakistan

This work evaluates the suitability of linear scaling (LS) and empirical quantile mapping (EQM) bias correction methods to generate present and future hydrometeorological variables (precipitation, temperature, and streamflow) over the Chitral River Basin, in the Hindukush region of Pakistan. In particular, LS and EQM are applied to correct the high-resolution statistically downscaled dataset, NEX-GDDP, which comprises 21 state-of-the-art general circulation models (GCMs) from the coupled model intercomparison project phase 5 (CMIP5). Raw and bias-corrected NEX-GDDP simulations are used to force the (previously calibrated and validated) HBV-light hydrological model to generate long-term (up to 2100) streamflow projections over the catchment. Our results indicate that using the raw NEX-GDDP leads to substantial errors (as compared to observations) in the mean and extreme streamflow regimes. Nevertheless, the application of LS and EQM solves these problems, yielding much more realistic and plausible streamflow projections for the XXI century

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

Understanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological-hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought-related consequences

Estimating GRACE terrestrial water storage anomaly using an improved point mass solution

The availability of terrestrial water storage anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) supports many hydrological applications. Five TWSA products are operational and publicly available, including three based on mass concentration (mascon) solutions and two based on the synthesis of spherical harmonic coefficients (SHCs). The mascon solutions have advantages regarding the synthesis of SHCs since the basis functions are represented locally rather than globally, which allows geophysical data constraints. Alternative new solutions based on SHCs are, therefore, critical and warranted to enrich the portfolio of user-friendly TWSA data based on different algorithms. TWSA data based on novel processing protocols is presented with a spatial re-sampling of 0.25 arc-degrees covering 2002–2022. This approach parameterizes the improved point mass (IPM) and adopts the synthesized residual gravitational potential as observations. The assay indicates that the proposed Hohai University (HHU-) IPM TWSA data reliably agree with the mascon solutions. The presented HHU-IPM TWSA data set would be instrumental in regional hydrological applications, particularly enabling improved assessment of regional water budgets

Safe and just Earth system boundaries

The stability and resilience of the Earth system and human well-being are inseparably linked 1-3, yet their interdependencies are generally under-recognized; consequently, they are often treated independently 4,5. Here, we use modelling and literature assessment to quantify safe and just Earth system boundaries (ESBs) for climate, the biosphere, water and nutrient cycles, and aerosols at global and subglobal scales. We propose ESBs for maintaining the resilience and stability of the Earth system (safe ESBs) and minimizing exposure to significant harm to humans from Earth system change (a necessary but not sufficient condition for justice) 4. The stricter of the safe or just boundaries sets the integrated safe and just ESB. Our findings show that justice considerations constrain the integrated ESBs more than safety considerations for climate and atmospheric aerosol loading. Seven of eight globally quantified safe and just ESBs and at least two regional safe and just ESBs in over half of global land area are already exceeded. We propose that our assessment provides a quantitative foundation for safeguarding the global commons for all people now and into the future

Safe and just Earth system boundaries

The stability and resilience of the Earth system and human well-being are inseparably linked1-3, yet their interdependencies are generally under-recognized; consequently, they are often treated independently4,5. Here, we use modelling and literature assessment to quantify safe and just Earth system boundaries (ESBs) for climate, the biosphere, water and nutrient cycles, and aerosols at global and subglobal scales. We propose ESBs for maintaining the resilience and stability of the Earth system (safe ESBs) and minimizing exposure to significant harm to humans from Earth system change (a necessary but not sufficient condition for justice)4. The stricter of the safe or just boundaries sets the integrated safe and just ESB. Our findings show that justice considerations constrain the integrated ESBs more than safety considerations for climate and atmospheric aerosol loading. Seven of eight globally quantified safe and just ESBs and at least two regional safe and just ESBs in over half of global land area are already exceeded. We propose that our assessment provides a quantitative foundation for safeguarding the global commons for all people now and into the future

Tensor partial least squares for hyperspectral image classification

A hyperspectral image is classically a three-way (or tensor) block of data. In order to extract information from it, it has to be classified using image classifiers. Since classifiers are traditionally two-way classifiers, the hyperspectral data is unfolded into a two-way data. Processing the hyperspectral data as a two-way data often reduces the accuracy of the classification. This research explores the novel application of Tensor Partial Least Squares (TPLS) for hyperspectral image classification. TPLS has been proven to be more robust than the two-way PLS. Unlike the two-way classifiers, the TPLS utilises the hyperspectral data as a three-way (tensor) data. Two hyperspectral images of Indian Pines region in Northwest Indiana, USA and University of Pavia, Italy are used as test beds for the experiment. The results extracted by the model are the loadings, loadings, scores, and scores. The computed training mean r square values for Indian Pines and University of Pavia are and respectively. The results of the experiment show that the TPLS performed better than the unfolded PLS, but fell short of the notable traditional classifiers

Assessing Freshwater Changes over Southern and Central Africa (2002–2017)

In large freshwater river basins across the globe, the composite influences of large-scale climatic processes and human activities (e.g., deforestation) on hydrological processes have been studied. However, the knowledge of these processes in this era of the Anthropocene in the understudied hydrologically pristine South Central African (SCA) region is limited. This study employs satellite observations of evapotranspiration (ET), precipitation and freshwater between 2002 and 2017 to explore the hydrological patterns of this region, which play a crucial role in global climatology. Multivariate methods, including the rotated principal component analysis (rPCA) were used to assess the relationship of terrestrial water storage (TWS) in response to climatic units (precipitation and ET). The use of the rPCA technique in assessing changes in TWS is warranted to provide more information on hydrological changes that are usually obscured by other dominant naturally-driven fluxes. Results show a low trend in vegetation transpiration due to deforestation around the Congo basin. Overall, the Congo (r2 = 76%) and Orange (r2 = 72%) River basins maintained an above-average consistency between precipitation and TWS throughout the study region and period. Consistent loss in freshwater is observed in the Zambezi (−9.9 ± 2.6 mm/year) and Okavango (−9.1 ± 2.5 mm/year) basins from 2002 to 2008. The Limpopo River basin is observed to have a 6% below average reduction in rainfall rates which contributed to its consistent loss in freshwater (−4.6 ± 3.2 mm/year) from 2006 to 2012.Using multi-linear regression and correlation analysis we show that ET contributes to the variability and distribution of TWS in the region. The relationship of ET with TWS (r = 0.5) and rainfall (r = 0.8) over SCA provides insight into the role of ET in regulating fluxes and the mechanisms that drive precipitation in the region. The moderate ET–TWS relationship also shows the effect of climate and anthropogenic influence in their interactions

Impacts of climate change on the streamflow of a large river basin in the Australian tropics using optimally selected climate model outputs

Climate change affects natural systems, leading to increased acceleration of global water cycle and substantial impacts on the productivity of tropical rivers and the several ecosystem functions they provide. However, the anticipated impacts of climate change in terms of frequency and intensity of extreme events (e.g., droughts and floods) on hydrological systems across regions could be substantially different. This study therefore aims to assess the impacts of climate change on the streamflow of a large river basin located in central Australia (Cooper Creek- Bulloo River Basin). Modified version of the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) was used in this study to generate daily streamflow. This model was first calibrated (2001-2010) and then validated for two independent periods (1993-1997 and 2011-2015). The model depicted a good performance in simulating observed streamflow. Climate projection data from multiple general circulation models (ACCESS1.0, CanESM2, CESM1-CAM5, CNRM-CM5, GFDL-ESM2M, HadGEM2-CC, MIROC5, NorESM1-M, ACCESS1-0, ACCESS1-3, CCSM4, CNRM-CM5, CSIRO-Mk3.6, GFDL-CM3, GFDL-ESM2M, HadGEM2, MIROC5, MPI-ESM-LR, and NorESM1-M) in various forms (raw, statistically downscaled, dynamically downscaled, and bias adjusted) were considered in this study. Results showed that three high resolution dynamically downscaled and bias adjusted models (ACCESS1-3, CNRM-CM5, and MPI-ESM-LR) from Terrestrial Ecosystem Research Network (TERN) dataset (v1.0.2) have better performance than other models considered, that is, in terms of capturing observed precipitation over the basin. Future climate projections of ensemble of these three models forced with RCP 4.5 and RCP 8.5 emission scenarios were then used to generate streamflow for 2050s (2040-2069) and 2080s (2070-2099). Results of the study indicated that mean annual precipitation was projected to decrease by up to -8% in 2050s and temperature was projected to increase by up to 4.66 °C in 2080s under the average and extreme emission scenarios, respectively. Mean annual, mean seasonal (December-February, March-May, June-August, September-November), and mean monthly streamflow were projected to decrease under different emission scenarios in 2050s and 2080s. These results indicate decreased water availability in the future as well as water cycle intensification. These changes in streamflow might have impacts on agriculture, natural ecosystem, and could lead to water restrictions. The outcome of this study can directly feed into frameworks for sustainable management of water resources and support adaptation strategies that rely on science and policy to improve water resources allocation in the region

Impacts of Fully Coupling Land Surface and Flood Models on the Simulation of Large Wetlands' Water Dynamics: The Case of the Inner Niger Delta

Abstract It is known that representing wetland dynamics in land surface modeling improves models' capacity to reproduce fluxes and land surface boundary conditions for atmospheric modeling in general circulation models. This study presents the development of the full coupling between the Noah‐MP land surface model (LSM) and the Hydrological Modeling and Analysis Platform (HyMAP) flood model in the NASA Land Information System and its application over the Inner Niger Delta (IND), a well‐known hot‐spot of strong land surface‐atmosphere interactions in West Africa. Here, we define two experiments at 0.02° spatial resolution over 2002–2018 to quantify the impacts of the proposed developments on simulating IND dynamics. One represents the one‐way approach for simulating land surface and flooding processes (1‐WAY), that is, Noah‐MP neglects surface water availability, and the proposed two‐way coupling (2‐WAY), where Noah‐MP takes surface water availability into account in the vertical water and energy balance. Results show that accounting for two‐way interactions between Noah‐MP and HyMAP over IND improves simulations of all selected hydrological variables. Compared to 1‐WAY, evapotranspiration derived from 2‐WAY over flooding zones doubles, increased by 0.8 mm/day, resulting in an additional water loss rate of ∼18,900 km3/year, ∼40% drop of wetland extent during wet seasons, and major improvement in simulated water level variability at multiple locations. Significant soil moisture increase and surface temperature drop were also observed. Wetland outflows decreased by 35%, resulting in a substantial Nash‐Sutcliffe coefficient improvement, from −0.73 to 0.79. It is anticipated that future developments in water monitoring and water‐related disaster warning systems will considerably benefit from these findings