Comprehensive evaluation of high-resolution satellite-based precipitation products over China

Characterizing the errors in satellite-based precipitation estimation products is crucial for understanding their effects in hydrological applications. Six precipitation products derived from three algorithms are comprehensively evaluated against gauge data over mainland China from December 2006 to November 2010. These products include three satellite-only estimates: the Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK), the Climate Prediction Center (CPC) MORPHing (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), as well as their gauge-corrected counterparts: the GSMaP Gauge-calibrated Product (GSMaP_Gauge), bias-corrected CMORPH (CMORPH_CRT), and PERSIANN Climate Data Record (PERSIANN-CDR). Overall, the bias-correction procedures largely reduce various errors for the three groups of satellite-based precipitation products. GSMaP_Gauge produces better fractional coverage with the highest correlation (0.95) and the lowest RMSE (0.53 mm/day) but also high RB (15.77%). In general, CMORPH_CRT amounts are closer to the gauge reference. CMORPH shows better performance than GSMaP_MVK and PERSIANN with the highest CC (0.82) and the lowest RMSE (0.93 mm/day), but also presents a relatively high RB (-19.60%). In winter, all six satellite precipitation estimates have comparatively poor capability, with the IR-based PERSIANN_CDR exhibiting the closest performance to the gauge reference. Both satellite-only and gauge-corrected satellite products show poor capability in detecting occurrence of precipitation with a low POD (40%)

Inter-comparison of high-resolution satellite precipitation products over Central Asia

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between -57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%)

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Sustainable Reservoir Management Approaches under Impacts of Climate Change - A Case Study of Mangla Reservoir, Pakistan

Reservoir sedimentation is a major issue for water resource management around the world. It has serious economic, environmental, and social consequences, such as reduced water storage capacity, increased flooding risk, decreased hydropower generation, and deteriorated water quality. Increased rainfall intensity, higher temperatures, and more extreme weather events due to climate change are expected to exacerbate the problem of reservoir sedimentation. As a result, sedimentation must be managed to ensure the long-term viability of reservoirs and their associated infrastructure. Effective reservoir sedimentation management in the face of climate change necessitates an understanding of the sedimentation process and the factors that influence it, such as land use practices, erosion, and climate. Monitoring and modelling sedimentation rates are also useful tools for forecasting future impacts and making management decisions. The goal of this research is to create long-term reservoir management strategies in the face of climate change by simulating the effects of various reservoir-operating strategies on reservoir sedimentation and sediment delta movement at Mangla Reservoir in Pakistan (the second-largest dam in the country). In order to assess the impact of the Mangla Reservoir's sedimentation and reservoir life, a framework was developed. This framework incorporates both hydrological and morphodynamic models and various soft computing models. In addition to taking climate change uncertainty into consideration, the proposed framework also incorporates sediment source, sediment delivery, and reservoir morphology changes. Furthermore, the purpose of this study is to provide a practical methodology based on the limited data available. In the first phase of this study, it was investigated how to accurately quantify the missing suspended sediment load (SSL) data in rivers by utilizing various techniques, such as sediment rating curves (SRC) and soft computing models (SCMs), including local linear regression (LLR), artificial neural networks (ANN) and wavelet-cum-ANN (WANN). Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for SCMs development. Based on an evaluation of the outcomes of all leading models for SSL estimation, it can be concluded that SCMs are more effective than SRC approaches. Additionally, the results also indicated that the WANN model was the most accurate model for reconstructing the SSL time series because it is capable of identifying the salient characteristics in a data series. The second phase of this study examined the feasibility of using four satellite precipitation datasets (SPDs) which included GPM, PERSIANN_CDR, CHIRPS, and CMORPH to predict streamflow and sediment loads (SL) within a poorly gauged mountainous catchment, by employing the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANN), random forests (SWAT-RF), and support vector regression (SWAT-SVR). SCMs were developed using the outputs of un-calibrated SWAT hydrological models to improve the predictions. The results indicate that during the entire simulation, the GPM shows the best performance in both schemes, while PERSIAN_CDR and CHIRPS also perform well, whereas CMORPH predicts streamflow for the Upper Jhelum River Basin (UJRB) with relatively poor performance. Among the best GPM-based models, SWAT-RF offered the best performance to simulate the entire streamflow, while SWAT-ANN excelled at simulating the SL. Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating streamflow and SL, particularly in complex terrain where gauge network density is low or uneven. The third and last phase of this study investigated the impact of different reservoir operating strategies on Mangla reservoir sedimentation using a 1D sediment transport model. To improve the accuracy of the model, more accurate boundary conditions for flow and sediment load were incorporated into the numerical model (derived from the first and second phases of this study) so that the successive morphodynamic model could precisely predict bed level changes under given climate conditions. Further, in order to assess the long-term effect of a changing climate, a Global Climate Model (GCM) under Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 for the 21st century is used. The long-term modelling results showed that a gradual increase in the reservoir minimum operating level (MOL) slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of sustainable reservoir use for societal benefit. In summary, this study provides comprehensive insights into reservoir sedimentation phenomena and recommends an operational strategy that is both feasible and sustainable over the long term under the impact of climate change, especially in cases where a lack of data exists. Basically, it is very important to improve the accuracy of sediment load estimates, which are essential in the design and operation of reservoir structures and operating plans in response to incoming sediment loads, ensuring accurate reservoir lifespan predictions. Furthermore, the production of highly accurate streamflow forecasts, particularly when on-site data is limited, is important and can be achieved by the use of satellite-based precipitation data in conjunction with hydrological and soft computing models. Ultimately, the use of soft computing methods produces significantly improved input data for sediment load and discharge, enabling the application of one-dimensional hydro-morphodynamic numerical models to evaluate sediment dynamics and reservoir useful life under the influence of climate change at various operating conditions in a way that is adequate for evaluating sediment dynamics.:Chapter 1: Introduction Chapter 2:Reconstruction of Sediment Load Data in Rivers Chapter 3:Assessment of The Hydrological and Coupled Soft Computing Models, Based on Different Satellite Precipitation Datasets, To Simulate Streamflow and Sediment Load in A Mountainous Catchment Chapter 4:Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan Chapter 5:Conclusions and Recommendation

Advances in Modelling of Rainfall Fields

Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

Conservation biology of an apex predator in the Anthropocene : poaching, pastoralism and lions in multi-use landscapes, South-Eastern Africa

Many of the world’s apex predators are experiencing catastrophic declines as a result of competition with humans. Understanding the mechanisms and ecosystem impacts of apex predator declines is a fundamental ecological question crucial to conserving the Earth’s biodiversity and functioning ecosystems. In this thesis I used the African lion Panthera leo, as a model species to investigate the impacts of anthropogenic pressures on the conservation and ecology of an apex predator. Specifically, I investigated the relative and cumulative influences of pastoralism and poaching on lion occurrence, population connectivity, ecological role, space use, prey selection and viability across a 73 000 km2 multi-use landscape in southern Africa. Using landscape occupancy spoor surveys, I tested the hypotheses that lions were most limited by either interference or by exploitative competition with humans and identified thresholds of lion tolerance to human activities. My results showed that lions occupied only a fraction of the landscape and were limited by a combination of interference and exploitative competition with humans. Interference competition with pastoralism however was the biggest driver limiting lion occupancy, creating a clear disturbance threshold for lions cumulating in their near complete loss from the landscape. I employed call-up surveys, pride monitoring and mortality analysis to investigate the numerical impacts of anthropogenic pressures on the viability of a lion sub-population. I found that persecution by pastoralists was the greatest source of lion mortalities across the landscape. However, the targeted poaching of lions for body parts had emerged as the greatest threat to lions in a nominally protected National Park where I documented a steep population decline and collapse of lion prides. I used GPS tracking and diet analysis of lions at the human-wildlands interface to test if lions foraged optimally or were constrained by competition with humans. I fount that individual lions appeared to select for prey and habitat optimally, while also showing some level of risk avoidance towards anthropogenic pressures. I then considered landscape resistances to test if sink habitats may provide connectivity between sources or act as ecological traps. I found that potential connectivity for lions between the region’s two source populations was limited by a loss of habitat and prey. Furthermore, the impacts of by-catch in snares risked transforming the few remaining potential conservation corridors into ecological traps. Finally, I examined interactions between lions and syntopic mesopredators across gradients of anthropogenic pressures to test if the functional role of lions was affected by human pressures. I found that lions showed limited suppression of mesopredators, however, anthropogenic pressures increased lion’s interactions with syntopic predators. As an apex predator, lions have evolved limited capacity to mitigate top-down competitive pressures, however, like many of the world’s apex predators, they are becoming increasingly limited by anthropogenic pressures. This study provides a predictive understanding of an apex predator’s ecological responses to top-down anthropogenic pressures which can be applied globally to the question of conservation in the Anthropocene

Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia

This study presents three global precipitation products and their downscaled versions (CHIRPSv2, TAMSATv3, PERSIANN_CDR, CHIRPS_D, PERSIANNN_CDR_D, and TAMSAT_D) estimated with observed values from 1983 to 2014. Performance evaluation of global precipitation products and their downscaled versions is important for accurate use of those measured values in water resource management, climate, and hydrological applications, particularly in the data-sparse Wabi Shebelle River Basin, Ethiopia. Categorical and quantitative evaluation index techniques were applied. The spatial downscaled global precipitation products outperformed raw spatial resolution estimates in all statistical indicators. TAMSAT-D had acceptable performance ratings in terms of RMSE, CC, and scatter plots (R2). CHIRPSv2 showed the least performance at a daily timestep. Performance of global precipitation products and their downscaled versions increased when daily data were aggregated to the monthly data. CHIRPS-D performed better than other products with a minimum error value (RMSE) and higher CC at a monthly timestep. On the other hand, PERSIANN_CDR_D showed a relatively good performance with a lower, positive Pbias and higher POD values compared to other products for daily and monthly timescales. For spatial mismatch analysis, the bias and RMSE from reference data (individual rain gauge station vs. the average of all available eight stations) against satellite rainfall estimates (PERSIANN_CDR) had a significantly different weight, which could be related to the position of the gauge station to provide the “true” spatial rainfall amount. Overall, TAMSATv3 and CHIRPSv2 and their downscaled version satellite estimates showed good performance at daily and monthly timesteps, respectively. PERSIANN_CDR performed best with low Pbias and the highest POD values. Thus, this study decided that the downscaled version of CHIRPSv2 and PERSIANN_CDR-D satellite estimates could be applicable as an alternative to gauge data on a monthly timestep for hydrological and drought-monitoring applications, respectively

Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia

This study presents three global precipitation products and their downscaled versions (CHIRPSv2, TAMSATv3, PERSIANN_CDR, CHIRPS_D, PERSIANNN_CDR_D, and TAMSAT_D) estimated with observed values from 1983 to 2014. Performance evaluation of global precipitation products and their downscaled versions is important for accurate use of those measured values in water resource management, climate, and hydrological applications, particularly in the data-sparse Wabi Shebelle River Basin, Ethiopia. Categorical and quantitative evaluation index techniques were applied. The spatial downscaled global precipitation products outperformed raw spatial resolution estimates in all statistical indicators. TAMSAT-D had acceptable performance ratings in terms of RMSE, CC, and scatter plots (R2). CHIRPSv2 showed the least performance at a daily timestep. Performance of global precipitation products and their downscaled versions increased when daily data were aggregated to the monthly data. CHIRPS-D performed better than other products with a minimum error value (RMSE) and higher CC at a monthly timestep. On the other hand, PERSIANN_CDR_D showed a relatively good performance with a lower, positive Pbias and higher POD values compared to other products for daily and monthly timescales. For spatial mismatch analysis, the bias and RMSE from reference data (individual rain gauge station vs. the average of all available eight stations) against satellite rainfall estimates (PERSIANN_CDR) had a significantly different weight, which could be related to the position of the gauge station to provide the “true” spatial rainfall amount. Overall, TAMSATv3 and CHIRPSv2 and their downscaled version satellite estimates showed good performance at daily and monthly timesteps, respectively. PERSIANN_CDR performed best with low Pbias and the highest POD values. Thus, this study decided that the downscaled version of CHIRPSv2 and PERSIANN_CDR-D satellite estimates could be applicable as an alternative to gauge data on a monthly timestep for hydrological and drought-monitoring applications, respectively

Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia

This study presents three global precipitation products and their downscaled versions (CHIRPSv2, TAMSATv3, PERSIANN_CDR, CHIRPS_D, PERSIANNN_CDR_D, and TAMSAT_D) estimated with observed values from 1983 to 2014. Performance evaluation of global precipitation products and their downscaled versions is important for accurate use of those measured values in water resource management, climate, and hydrological applications, particularly in the data-sparse Wabi Shebelle River Basin, Ethiopia. Categorical and quantitative evaluation index techniques were applied. The spatial downscaled global precipitation products outperformed raw spatial resolution estimates in all statistical indicators. TAMSAT-D had acceptable performance ratings in terms of RMSE, CC, and scatter plots (R2). CHIRPSv2 showed the least performance at a daily timestep. Performance of global precipitation products and their downscaled versions increased when daily data were aggregated to the monthly data. CHIRPS-D performed better than other products with a minimum error value (RMSE) and higher CC at a monthly timestep. On the other hand, PERSIANN_CDR_D showed a relatively good performance with a lower, positive Pbias and higher POD values compared to other products for daily and monthly timescales. For spatial mismatch analysis, the bias and RMSE from reference data (individual rain gauge station vs. the average of all available eight stations) against satellite rainfall estimates (PERSIANN_CDR) had a significantly different weight, which could be related to the position of the gauge station to provide the “true” spatial rainfall amount. Overall, TAMSATv3 and CHIRPSv2 and their downscaled version satellite estimates showed good performance at daily and monthly timesteps, respectively. PERSIANN_CDR performed best with low Pbias and the highest POD values. Thus, this study decided that the downscaled version of CHIRPSv2 and PERSIANN_CDR-D satellite estimates could be applicable as an alternative to gauge data on a monthly timestep for hydrological and drought-monitoring applications, respectively

Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China

Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1⁻25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash⁻Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1⁻28.0%) and overestimated low flow (18.9⁻49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management