Precipitation Uncertainty Research Articles

Reducing the Uncertainty in the Tropical Precipitation through a Multi‐Criteria Decision‐Making Approach

ABSTRACTThe inherent model uncertainty in precipitation projections is found to be more dominant over tropical regions thereby reducing the reliability of using them in climate change impact assessment studies. To address such issues, a subset of well performing global climate models (GCMs) can provide narrow range of possible future outcomes, which can be helpful in formulating mitigation and adaptation strategies that are more targeted and efficient. In this study, climate models are selected based on their performance in simulating relative humidity and vertical velocity since these variables play an important role in precipitation simulation and significantly contribute toward the intermodel spread. The models are evaluated by using various statistical performance measures and ranked using multi‐criteria decision‐making approaches. Finally, based on Jenks natural breaks optimization algorithm, subset of GCMs consisting of ACCESS1.0, ACCESS1.3 and INM‐CM4 models, are considered as the best possible subset for precipitation simulation over tropical land regions. Two observational precipitation datasets are further considered to investigate the effectiveness of the proposed framework. The proposed methodology is validated to be effective in identifying the best climate models since the resulting subset is capable of both capturing observed precipitation and minimizing the uncertainty in future projections. Hence, this methodology can be utilized further for performance evaluation of GCMs focusing different geography and climatic drivers.

Evaluation and projection of changes in temperature and precipitation over Northwest China based on CMIP6 models

AbstractNorthwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI‐CM‐1‐1‐MR, BCC‐CSM2‐MR, FGOALS‐g3, INM‐CM4‐8, INM‐CM5‐0 and MRI‐ESM2‐0. The multi‐model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty.

Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures

Hydrological models are evaluated by comparisons with observed hydrological quantities such as streamflow. A model evaluation procedure should account for dominantly epistemic errors in hydrological data such as model input precipitation and streamflow and avoid type-2 errors (rejecting a good model). This study uses quantile random forest (QRF) to develop limits-of-acceptability (LoA) over streamflows that account for uncertainties in precipitation and streamflow values. A significant advantage of this method is that it can be used to evaluate models even at ungauged basins. This method was used to evaluate a hydrological model –Sacramento Soil Moisture Accounting (SAC-SMA) – over the St. Joseph River Watershed (SJRW) for both gauged and hypothetical ungauged scenarios. QRF defined wide LoAs that yielded a large number of models as behavioral, suggesting the need for additional measures to develop a more discriminating inference procedure. The paper discusses why the LoAs defined by QRF were wide, along with some ways to define more discriminating LoAs. To further constrain the model, five streamflow-based signatures (i.e., autocorrelation function, Hurst exponent, baseflow index, flow duration curve, and long-term runoff coefficient) were used. The combination of LoAs over streamflow and streamflow-based signatures helped constrain the set of behavioral models in both the gauged and the ungauged scenarios. Among the signatures used in this study, the Hurst exponent and baseflow index were the most useful ones. All the 1-million models evaluated in this study were eventually rejected as unfit-for-purpose.

Optimizing food crop layout considering precipitation uncertainty: Balancing regional water, carbon, and economic pressures with food security

As the global food supply chain becomes increasingly unstable, food security is gaining importance in China. Nevertheless, factors such as water scarcity, limited land resources, and global climate change pose great challenges to increasing food production. Therefore, it is crucial to explore sustainable development models for food systems. In this paper, a multi-objective crop layout model considering crop rotation is constructed and solved using the INSGA2-DS algorithm. Various precipitation scenarios were established for climate change. Data analysis and optimization were conducted under different precipitation scenarios. Applied in the plain area of southwestern Shandong Province, the model can increase soybean yield by 6.78%, net carbon sequestration intensity by 0.04%, net benefit by 2.09%, and crop water productivity by 3.91% during a median flow year. Optimization tends to increase the total area planted with food crops during wet years and decrease it during dry years. The optimization model can enhance water productivity and carbon sequestration intensity of crops, as well as prevent unreasonable fluctuations in the area planted with food crops. This enables the benefits of water conservation and yield increase to be fully utilized, and reduces yield and economic losses due to dry years. The models help to rationally allocate soil and water resources, effectively respond to climate change risks, and provide robust decision support for regional food crop planning.

The Inter‐Model Uncertainty of Projected Precipitation Change in Northern China: The Modulating Role of North Atlantic Sea Surface Temperature

AbstractPrecipitation changes in northern China are projected to increase in the Coupled Model Inter‐comparison Project Phase 6 (CMIP6) multi‐model ensemble. However, these projections are accompanied by notable inter‐model uncertainty, and the sources of this uncertainty remain largely unexplored. By analyzing 30 CMIP6 models, this research explores the source of inter‐model uncertainty in projected precipitation change and reveals the fundamental mechanism driving uncertainty spread. Following the empirical orthogonal function of inter‐model projected precipitation change, the leading mode displays a seesaw spatial pattern between northwest and north China. This phenomenon predominantly stems from the inter‐model divergence of projected sea surface temperature (SST) warming in the North Atlantic. Further scrutinizing the ocean mixed layer heat budget, we discover that the combined effect of surface sensible heat flux, net surface shortwave radiation flux, and ocean heat transport convergence influences heat flux and SST change of North Atlantic. The multi‐model projections indicate that localized increases in solar radiation and heat convergence warm sea surface, raising SST and initiating convective motion. This convective motion subsequently transforms the 200 hPa teleconnection wave train, leading to an anti‐phase pattern over northern China. This wave pattern modulates total cloud cover percentage, influences surface upward latent heat flux, and adjusts the top of atmosphere outgoing longwave radiation, collectively resulting in the seesaw pattern. Our study underscores the pivotal role of inter‐model disparities in North Atlantic SST warming projection, which is a primary driver of precipitation uncertainty in northern China. These insights offer an essential foundation for refining and diminishing inter‐model uncertainty.

On the Challenges of Simulating Streamflow in Glacierized Catchments of the Himalayas Using Satellite and Reanalysis Forcing Data

Abstract Hydrologic assessment of climate change impacts on complex terrains and data-sparse regions like High Mountain Asia is a major challenge. Combining hydrological models with satellite and reanalysis data for evaluating changes in hydrological variables is often the only available approach. However, uncertainties associated with the forcing dataset, coupled with model parameter uncertainties, can have significant impacts on hydrologic simulations. This work aims to understand and quantify how the uncertainty in precipitation and its interaction with the model uncertainty affect streamflow estimation in glacierized catchments. Simulations for four precipitation datasets [Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), ERA5-Land, and Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE)] and two glacio-hydrological models [Glacio-Hydrological Degree-Day Model (GDM) and Hydrological Model for Distributed Systems (HYMOD_DS)] are evaluated for the Marsyangdi and Budhigandaki River basins in Nepal. Temperature sensitivity of streamflow simulations is also investigated. Relative to APHRODITE, which compared well with ground stations, ERA5-Land overestimates the catchment average precipitation for both basins by more than 70%; IMERG and CHIRPS overestimate by ∼20%. Precipitation uncertainty propagation to streamflow exhibits strong dependencies to model structure and streamflow components (snowmelt, ice melt, and rainfall-runoff), but overall uncertainty dampens through precipitation-to-streamflow transformation. Temperature exerts a significant additional source of uncertainty in hydrologic simulations of such environments. GDM was found to be more sensitive to temperature variations, with >50% increase in total flow for 20% increase in actual temperature, emphasizing that models that rely on lapse rates for the spatial distribution of temperature have much higher sensitivity. Results from this study provide critical insight into the challenges of utilizing satellite and reanalysis products for simulating streamflow in glacierized catchments. Significance Statement This work investigates the uncertainty of streamflow simulations due to climate forcing and model parameter/structure uncertainty and quantifies the relative importance of each source of uncertainty and its impact on simulating different streamflow components in glacierized catchments of High Mountain Asia. Results highlight that in high mountain regions, temperature uncertainty exerts a major control on hydrologic simulations and models that do not adequately represent the spatial variability of temperature are more sensitive to bias in the forcing data. These findings provide guidance on important aspects to be considered when modeling glacio-hydrological response of catchments in such areas and are thus expected to impact both research and operation practice related to hydrologic modeling of glacierized catchments.

Implications of the transition towards water-wise approaches in urban areas: Elucidating the risk from micropollutants release

The transition towards circular and sustainable urban water systems, embracing a water-wise city approach, results in highly interconnected systems, introducing complexities in managing water quality due to the intricate interplay of pollutant sources, pathways, and receptors in urban environments. This study focuses on assessing the impact of transitioning to a water-wise city approach on water quality. The analysis employs an integrated model-based approach to evaluate the release of micropollutants (PFOA, PFOS, pyrene) in the urban water system, considering potential future changes. We considered Milan (Italy) as a case study and explored sustainable urban water management strategies related to the hydraulic reconnection of canals, including (i) the use of groundwater for energy purposes, (ii) the establishment of a separate stormwater sewer system, and (iii) the indirect reuse of reclaimed water for crop irrigation. Acute and chronic environmental risks were assessed at various locations of the recipients, under different water management and climate change scenarios. Uncertainties on projections related to rainfall and runoff concentrations were also considered. The model accurately predicted average micropollutants concentrations in the current situation. PFOA and PFOS exhibit chronic, but not acute risks in all scenarios; high removal efficiency is required to reduce the risk at acceptable levels. Pyrene poses higher risks for increasing separation fractions of the sewer system, with further increase for climate change, but concentration uncertainty has more influence than precipitation uncertainty on the risk extent. This study provides a water management framework, identifying critical sources and locations under current, future, and climate change scenarios.

Increases in Water Balance‐Derived Catchment Evapotranspiration in Germany During 1970s–2000s Turning Into Decreases Over the Last Two Decades, Despite Uncertainties

AbstractUnderstanding variations in catchment evapotranspiration (EC) is critical as it directly affects water availability for humans and ecosystems. Previous studies found increases in EC in Central Europe over recent decades, but fixed study periods may not fully reveal inter‐decadal hydroclimatological variability. We performed a multi‐temporal trend analysis of water balance‐derived EC for 461 German catchments and the period 1964–2019. We accounted for previously often neglected changes in storage and uncertainties in precipitation. EC generally increased throughout Germany during 1970s–2000s (>2 mm year−2), while it showed milder changes and decreases afterward. These variations were robust to uncertainties in precipitation (median relative uncertainty of 26%) and broadly coherent with sparse plot‐scale data. Variations in EC were related with variations in precipitation and radiation, with a potentially increasing influence of precipitation after 2000s. These findings provide a reference for synthesizing current knowledge on variations in EC and their uncertainties.

A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan

This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season.

Uncertainty of Coupled Model Intercomparison Projects 6 (CMIP6) in Indonesia’s maritime continental region for the historical period

Climate Model is a tool for studying climate and climate change, In climate models there is uncertainty that occurs due to the inability of the model to simulate climate in an area. This study aims to find the best CMIP6 model in the IMC and look at the uncertainty of the model. The data used in this study are precipitation data on the CMIP6 model, as well as the use of CRU data as reference data and observation data. Uncertainty is calculated for historical periods using CDF to see random values from the model. Validation of historical CRU and CMIP6 data is performed by performing statistical calculations that include Pearson correlation, standard deviation, RMSE, KGE, TSS, and relative bias. CRU model testing in Indonesia shows quite good performance at all points, the CRU model has a pattern that follows observational data based on correlation values. the uncertainty of CMIP6 is large in high precipitation and low in moderate precipitation, and there is moderate uncertainty in low precipitation, The analysis of historical data indicates that the EC-Earth3-Veg-LR model has greater performance in simulating precipitation within the IMC region, closely aligning with the values and patterns observed in the CRU model. Furthermore, the EC-Earth3-Veg-LR model displays a strong correlation, as well as good values for the KGE and relative Bias, measuring at 0.33, 0.175, and 1.101% respectively.

Satellite-based precipitation error propagation in the hydrological modeling chain across China

Satellite-based precipitation products have great potential to promote hydrological modeling skill owing to the spatially homogeneous measurements they provide. However, satellite-based precipitation uncertainties and their hydrological impacts are poorly understood for different products and hydroclimate regions in China. This study comprehensively evaluates the error properties of ten satellite-based precipitation products and the precipitation error propagation characteristics in the hydrological modeling chain during the period 2001–2018. The results indicate that precipitation error tends to become smaller during the routing process for most satellite precipitation products; e.g., αRMAE, an error propagation ratio based on relative mean absolute error, decreased about 26% (from 0.58 to 0.43) when considering routing processes during hydrological simulation by using the PERSIANN-CDR satellite precipitation product. The linear slope of simulated streamflow against precipitation error greatly decreases (compared to that of simulated runoff) after flow routing of 366 catchments across China. This study also found that satellite-based precipitation product errors in hydrological simulations for drier and water-limited small catchments are more likely for amplify in the streamflow simulations. These findings are essential to improving understanding of the interactions between satellite-derived precipitation forcing and hydrological processes, and thus for improving the data accuracy of precipitation forcing for hydrological simulation in China.

The Effect of Drainage and Subirrigation From a Small Drainage Water Recycling Reservoir on Corn and Soybean Yields in Eastern North Carolina

Highlights The Drainage Water Recycling (DWR) reservoir stored enough water to meet irrigation demand in three of four growing seasons. Subirrigation raised the groundwater table by an average of 15 cm. On average, DWR increased corn and soybean yields by 0.52 Mg ha-1 (8%) and 0.45 Mg ha-1 (17%), respectively. Subirrigation from the small size DWR reservoir did not protect corn from extended dry conditions during the growing season. Nutrients recycled back to the field via supplemental irrigation were not large enough to reduce fertilizer application rate. Abstract. Drainage water recycling (DWR) has been proposed as a source of supplemental irrigation to increase crop production resilience to extended and more frequent dry periods during the crop growing season; however, the system’s potential benefits have not been adequately quantified. The main objective of this study was to assess the performance of a DWR system for providing water for supplemental irrigation to corn and soybean at a research site in eastern North Carolina and quantify corn and soybean yield responses during 4 growing seasons (2018-2021) with varying weather conditions. Two treatments were implemented at the study site: DWR and the control (CT) treatment. The CT treatment was a 11.23 ha non-irrigated field that was primarily drained by a surface drainage system. The DWR treatment (11.48 ha) had a subsurface drainage system that provided drainage during the wet periods and subirrigation during the dry periods of the growing season. A small size reservoir (5,458 m3) was used to collect surface runoff and subsurface drainage and subirrigate the DWR treatment. Results showed that the DWR reservoir stored enough water to meet irrigation requirements in 3 of the four growing seasons and provided 5 to 73 mm of irrigation to the DWR treatment. Subirrigation raised the groundwater table by an average of 15 cm, which helped increase the upward movement of soil water to the root zone and meet crop evapotranspiration demand. DWR increased corn yields by 0.13 and 0.91 Mg ha-1 (1% and 79%) and soybean yields by 0.31 and 0.59 Mg ha-1 (9% and 30%). Subirrigation, which is generally less efficient than overhead irrigation methods, did not optimize the use of the limited water stored in the small reservoir and could not provide enough protection to corn against prolonged dry conditions in the 2019 growing season. The amount of nutrients recycled back to the field through subirrigation was not large enough to help reduce fertilizer application rate. Overall, the results demonstrated that DWR is a promising practice for increasing the resilience of crop production in the southeastern U.S. to the uncertainty in precipitation, which is expected to intensify by climate change. Monitoring the performance of DWR for longer periods with varying factors of weather, soil, and system design and management would help guide the design and management of the system to optimize the performance and minimize the implementation cost. Keywords: Drainage water management, Drainage water reuse, Irrigation reservoir, On-farm water storage, Subsurface drainage, Supplemental irrigation.

Process‐Based Intercomparison of Water Isotope‐Enabled Models and Reanalysis Nudging Effects

AbstractThe products from the Stable Water Isotope Intercomparison Group, Phase 2, are currently used for numerous studies, allowing water isotope model‐data comparisons with various isotope‐enabled atmospheric general circulation model (AGCMs) outputs. However, the simulations under this framework were performed using different parameterizations and forcings. Therefore, a uniform experimental design with state‐of‐the‐art AGCMs is required to interpret isotope observations rigorously. Here, we evaluate the outputs from three isotope‐enabled numerical models nudged by three different reanalysis products and investigate the ability of the isotope‐enabled AGCMs to reproduce the spatial and temporal patterns of water isotopic composition observed at the surface and in the atmospheric airborne water. Through correlation analyses at various spatial and temporal scales, we found that the model's performance depends on the model or reanalysis we use, the observations we compare, and the vertical levels we select. Moreover, we employed the stable isotope mass balance method to conduct decomposition analyses on the ratio of isotopic changes in the atmosphere. Our goal was to elucidate the spread in simulated atmospheric column δ18O, which is influenced by factors such as evaporation, precipitation, and horizontal moisture flux. Satisfying the law of conservation of water isotopes, this budget method is expected to explain various fractionation phenomena in atmospheric meteorological and climatic events. It also aims to highlight the spreads in modeled isotope results among different experiments using multiple models and reanalyses, which are primarily dominated by uncertainties in moisture flux and precipitation, respectively.

Impacts of precipitation uncertainty on hydrological ensemble simulations over the Ganjiang River basin

Study regionThe Ganjiang River basin (113°−117°E, 24°−30°N; 83,374 km2) is a large watershed with complex topography in the Poyang Lake basin in Jiangxi province, China. Study focusThis study evaluates three quantitative precipitation estimates (QPEs) over the Ganjiang River basin, namely the China Gauged-Based Daily Precipitation Analysis (CGDPA) data, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data, and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The study also investigates the impacts of precipitation uncertainty on hydrological ensemble streamflow simulations using the three QPEs as precipitation inputs of the Variable Infiltration Capacity (VIC) hydrological model. New hydrological insights for the regionAPHRODITE underestimates precipitation compared to CGDPA, while PERSIANN-CDR shows greater spatiotemporal variability. The ensemble mean streamflow demonstrates greater improvement compared to the results obtained from a single parameter set. Among the three QPEs, the simulations forced by CGDPA show the best deterministic and probabilistic verification scores, followed by APHRODITE. PERSIANN-CDR tends to underestimate evaporation and leads to the lowest score of ensemble streamflow simulations, but shows advantages in simulating extremely low streamflow. The study highlights that high-density gauge-based QPEs remain the most accurate source of precipitation inputs for reliable hydrological simulations, while satellite-gauge merged QPEs can provide valuable inputs for hydrological simulations over the basins where meteorological stations are scarce.

Spatiotemporal snow water storage uncertainty in the midlatitude American Cordillera

Abstract. This work quantifies the uncertainty of accumulation-season peak snow water storage in the portions of the midlatitude American Cordillera where snow is a dominant driver of hydrology. This is accomplished through intercomparison of commonly used global and regional products over the Western United States (WUS) and Andes domains, which have similar hydrometeorology but are disparate with respect to the amount of available in situ information. The recently developed WUS Snow Reanalysis (WUS-SR) and Andes Snow Reanalysis (Andes-SR) datasets, which have been extensively verified against in situ measurements, are used as baseline reference datasets in the intercomparison. Relative to WUS-SR climatological peak snow water equivalent (SWE) storage (269 km3), high- and moderate-resolution products (i.e., those with resolutions less than ∼10 km) are in much better agreement (284±14 km3; overestimated by 6 %) compared to low-resolution products (127±54 km3; underestimated by 53 %). In comparison to the Andes-SR peak snow storage (29 km3), all other products show large uncertainty and bias (19±16 km3; underestimated by 34 %). Examination of spatial patterns related to orographic effects showed that only the high- to moderate-resolution Snow Data Assimilation System (SNODAS) and University of Arizona (UA) products show comparable estimates of windward–leeward SWE patterns over a subdomain (Sierra Nevada) of the WUS. Coarser products distribute too much snow on the leeward side in both the Sierra Nevada and Andes, missing orographic and rain shadow patterns that have important hydrological implications. The uncertainty of peak seasonal snow storage is primarily explained by precipitation uncertainty in both the WUS (R2=0.55) and Andes (R2=0.84). Despite using similar forcing inputs, snow storage diverges significantly within the ECMWF Reanalysis v5 (ERA5) (i.e., ERA5 vs. ERA5-Land) products and the Global Land Data Assimilation System (GLDAS) (modeled with Noah, Variable Infiltration Capacity (VIC), and Catchment model) products due to resolution-induced elevation differences and/or differing model process representation related to rain–snow partitioning and accumulation-season snowmelt generation. The availability and use of in situ precipitation and snow measurements (i.e., in WUS) in some products adds value by reducing snow storage uncertainty; however, where such data are limited, i.e., in the Andes, significant biases and uncertainty exist.

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

AbstractPrecipitation is the primary driver of hydrological models, and its spatial and temporal variability have a great impact on water partitioning. However, in data‐sparse regions, uncertainty in precipitation estimates is high and the sensitivity of water partitioning to this uncertainty is unknown. This is a particular challenge in drylands (semi‐arid and arid regions) where the water balance is highly sensitive to rainfall, yet there is commonly a lack of in situ rain gauge data. To understand the impact of precipitation uncertainty on the water balance in drylands, here we have performed simulations with a process‐based hydrological model developed to characterize the water balance in arid and semi‐arid regions (DRYP: DRYland water Partitioning model). We performed a series of numerical analyses in the Upper Ewaso Ng'iro basin, Kenya driven by three gridded precipitation datasets with different spatio‐temporal resolutions (IMERG, MSWEP, and ERA5), evaluating simulations against streamflow observations and remotely sensed data products of soil moisture, actual evapotranspiration, and total water storage. We found that despite the great differences in the spatial distribution of rainfall across a climatic gradient within the basin, DRYP shows good performance for representing streamflow (KGE >0.6), soil moisture, actual evapotranspiration, and total water storage (r >0.5). However, the choice of precipitation datasets greatly influences surface (infiltration, runoff, and transmission losses) and subsurface fluxes (groundwater recharge and discharge) across different climatic zones of the Ewaso Ng'iro basin. Within humid areas, evapotranspiration does not show sensitivity to the choice of precipitation dataset, however, in dry lowland areas it becomes more sensitive to precipitation rates as water‐limited conditions develop. The analysis shows that the highest rates of precipitation produce high rates of diffuse recharge in Ewaso uplands and also propagate into runoff, transmission losses and, ultimately focused recharge, with the latter acting as the main mechanism of groundwater recharge in low dry areas. The results from this modelling exercise suggest that care must be taken in selecting forcing precipitation data to drive hydrological modelling efforts, especially in basins that span a climatic gradient. These results also suggest that more effort is required to reduce uncertainty between different precipitation datasets, which will in turn result in more consistent quantification of the water balance.

Uncertainty analysis of 100-year flood maps under climate change scenarios

This study presents an assessment of the uncertainty associated with 100-year flood maps under three scenarios: present, RCP4.5, and RCP8.5. Through intensive Monte Carlo simulations, the study focuses on evaluating the uncertainty introduced by key model input parameter, namely the roughness coefficient, runoff coefficient, and precipitation intensity. Notably, the precipitation intensity incorporates multiple sources of uncertainty, including RCP scenario, climate model, and probability distribution function. To analyze the uncertainties, a surrogate hydrodynamic/hydrologic model based on a physical 2D model is employed.The findings of this study challenge the traditional use of deterministic flood maps and climate factors, highlighting the necessity of employing probabilistic approaches for the development of accurate and secure flood maps. Moreover, the research findings indicate that the primary source of uncertainty in precipitation is the selection of the probability distribution, followed by the choice of climate model, and to a lesser extent, the specific RCP scenario.

Origins of Uncertainty in the Response of the Summer North Pacific Subtropical High to CO2 Forcing

AbstractThe variability of the summer North Pacific Subtropical High (NPSH) has substantial socioeconomic impacts. However, state‐of‐the‐art climate models significantly disagree on the response of the NPSH to anthropogenic warming. Inter‐model spread in NPSH projections originates from models' inconsistency in simulating tropical precipitation changes. This inconsistency in precipitation changes is partly due to inter‐model spread in tropical sea surface temperature (SST) changes, but it can also occur independently of uncertainty in SST changes. Here, we show that both types of precipitation uncertainty influence the NPSH via the Matsuno‐Gill wave response, but their relative impact varies by region. Through the modulation of low cloud fraction, inter‐model spread of the NPSH can have a further impact on extra‐tropical land surface temperature. The teleconnection between tropical precipitation and the NPSH is examined through a series of numerical experiments.

Examining the Implications of Climate Change and Adaptation Technologies on the Livelihood of Cocoa Farmers in Offinso Municipalities, Ghanas

Cocoa is highly sensitive to uncertainties in temperature and precipitation and is projected to be challenged by climate variability. This study examined the implications of climate change on the livelihood of cocoa farmers, and the adaptation technologies adopted to mitigate the impacts in the Offinso Municipalities. 210 cocoa farmers participated in the study, out of which 180 were selected randomly from the list of Kuapa Kokoo company. These people were interviewed with the help of questionnaires. The rest 30 were selected for focus group discussions held in two different communities. The study found deforestation, land degradation, and felling of trees for lumber and domestic fuel as the main causes of climate change. The socio-economic impacts were a reduction in crop yield and income, food insecurity, poor access to healthcare, poor water quality, drying up of streams, and reduction in labour supply. A positive revelation was that the farmers have realized a gradual increase in yield in the past three (3) years due to innovative measures adopted. Such innovations include agroforestry, good agricultural practices, crop diversification, frequent spraying, fertilizer application, adoption of hybrid varieties, and creation of fire belts. As a way of minimizing financial constraints, poor irrigation facilities, inadequate seed supply, and reduction in labour supply; the farmers receive some support from farmer organizations, the government, and cocoa companies. The study recommends that government institutions commit to the enforcement of laws on deforestation and illegal mining, ensure an effective supply of cocoa inputs, and promote adaptation measures in building farmer resilience in the study area.

Applicability of CMIP5 and CMIP6 Models in China: Reproducibility of Historical Simulation and Uncertainty of Future Projection

Abstract General circulation model (GCM) outputs archived by the Coupled Model Intercomparison Project (CMIP) are widely used for climate projection. A comprehensive evaluation of the applicability of GCMs is crucial for generating reliable projections. However, previous GCM evaluation studies mainly focused on the reproducibility of the historical simulation to the observation data, ignoring the uncertainty involved in the future climate projection. In this study, an innovative and comprehensive index, named the history–future applicability score (HFAS), is proposed for evaluating the applicability of GCMs. Through incorporating the Taylor skill score (S) and the analysis of variance (ANOVA) within a general framework, the HFAS can reflect both the historical reproducibility and future uncertainty of GCMs. The HFAS framework is applied to quantitatively evaluating the skills of 28 GCMs (14 from CMIP5 and 14 from CMIP6) in modeling temperature and precipitation at 631 reference sites across China. Results indicate that (i) both CMIP5 and CMIP6 models underestimate the historical temperature of China and overestimate the historical precipitation; (ii) the total uncertainties in future temperature and precipitation projections increase along with time; compared to CMIP5, temperature uncertainty of CMIP6 is narrowed, while the precipitation uncertainty is increased; (iii) taking into account historical reproducibility and future uncertainty, CMIP6 models generally have increased applicability compared to CMIP5; and (iv) the best-performing GCMs in modeling temperature and precipitation of China are ACCESS-CM2 and MPI-ESM-LR, respectively, with the HFAS being 68.4 and 63.5. The findings are helpful for researchers to select locally appropriate GCMs and generate reliable regional climate projection.