Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions

High-resolution assessment of the carrying capacity and utilization intensity in mountain rangelands with remote sensing and field data

Dry rangelands provide resources for half of the world's livestock, but degradation due to overgrazing is a major threat to system sustainability. Existing carrying capacity assessments are limited by low spatiotemporal resolution and high generalization, which hampers applied ecological management decisions. This paper provides an example for deriving the carrying capacity and utilization levels for cold drylands at a new level of detail by including major parts of the transhumance system. We combined field data on vegetation biomass and communities, forage quality, productivity, livestock species and quantities, grazing areas and their spatiotemporal variations with Sentinel-2 and MODIS snow cover satellite imagery to develop maps of forage requirements and availability. These products were used to calculate carrying capacity and grazing potential in the Pamir-Hindukush Mountains. Results showed high spatial variability of utilization rates between 5% and 77%. About 30% of the area showed unsustainable grazing above the carrying capacity. Utilization rates displayed strong spatial differences with unsustainable grazing in winter pastures and at lower elevations, and low rates at higher altitudes. The forage requirements of wild herbivores (ungulates and marmots) were estimated to be negligible compared to livestock, with one tenth of the biomass consumption and no increase in unsustainably grazed pastures due to the wider distribution of animals. The assessment was sensitive to model parameterization of forage requirements and demand, whereby conservative scenarios, i.e. lower fodder availability or higher fodder requirements of livestock due to climate and altitude effects, increased the area with unsustainable grazing practices to 50%. The presented approach enables an in-depth evaluation of the carrying capacity and corresponding management actions. It includes new variables relevant for transhumance systems, such as the combination of forage quantity and quality or accessibility restrictions due to snow, and shows utilization patterns at high spatial resolutions. Regional maps allow the identification of unsustainable utilization areas, such as winter pastures in this study.

CA-discharge: Geo-Located Discharge Time Series for Mountainous Rivers in Central Asia

We present a collection of 295 gauge locations in mountainous Central Asia with norm discharge as well as time series of river discharge from 135 of these locations collected from hydrological yearbooks in Central Asia. Time series have monthly, 10-day and daily temporal resolution and are available for different duration. A collection of third-party data allows basin characterization for all gauges. The time series data is validated using standard quality checks. Norm discharge is validated against literature values and by using a water balance approach. The novelty of the data consists in the combination of discharge time series and gauge locations for mountainous rivers in Central Asia which is not available anywhere else. The geo-located discharge time series can be used for water balance modelling and training of forecast models for river runoff in mountainous Central Asia.

Evaluation of IMERG and ERA5 precipitation products over the Mongolian Plateau

Precipitation is an important component of the hydrological cycle and has significant impact on ecological environment and social development, especially in arid areas where water resources are scarce. As a typical arid and semi-arid region, the Mongolian Plateau is ecologically fragile and highly sensitive to climate change. Reliable global precipitation data is urgently needed for the sustainable development over this gauge-deficient region. With high-quality estimates, fine spatiotemporal resolutions, and wide coverage, the state-of-the-art Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and European Center for Medium-range Weather Forecasts Reanalysis 5 (ERA5) have great potential for regional climatic, hydrological, and ecological applications. However, how they perform has not been well investigated on the Mongolian Plateau. Therefore, this study evaluated the performance of three IMERG V06 datasets (ER, LR and FR), two ERA5 products (ERA5-HRES and ERA5-Land), and their predecessors (TMPA-3B42 and ERA-Interim) over the region across 2001-2018. The results showed that all products broadly characterized seasonal precipitation cycles and spatial patterns, but only the three reanalysis products, IMERG FR and TMPA-3B42 could capture interannual and decadal variability. When describing daily precipitation, dataset performances ranked ERA5-Land > ERA5-HRES > ERA-Interim > IMERG FR > IMERG LR > IMERG ER > TMPA-3B42. All products showed deficiencies in overestimating weak precipitation and underestimating high-intensity precipitation. Besides, products performed best in agricultural lands and forests along the northern and south-eastern edges, followed by urban areas and grasslands closer to the center, and worst in the sparse vegetation and bare areas of the south-west. Due to a negative effect of topographic complexity, IMERG showed poor detection capabilities in forests. Accordingly, this research currently supports the applicability of reanalysis ERA5 data over the arid, topographically complex Mongolian Plateau, which can inform regional applications with different requirements.

Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea

There is a large number of grid-based climate datasets available which differ in terms of their data source, estimation procedures, and spatial and temporal resolutions. This study evaluates the performance of diverse meteorological datasets in terms of representing spatio-temporal climate variabilities based on a national-scale domain over South Korea. Eleven precipitation products, including six satellite-based data (CMORPH, MSWEP, MERRA, PERSIANN, TRMM, and TRMM-RT) and five reanalysis-based data (ERA5, JRA-55, CPC-U, NCEP-DOE, and K-Hidra) and four temperature products (MERRA, ERA5, CPC-U, and NCEP-DOE) are investigated. In addition, the hydrological performance of forty-four input combinations of climate datasets are explored by using the Variable Infiltration Capacity (VIC) macroscale model. For this analysis, the VIC model is independently calibrated for each combination of input and the response to each combination is then evaluated with in situ streamflow data. Our results show that the gridded datasets perform differently particularly in representing precipitation variability. When a diverse combination of the datasets are used to represent spatio-temporal variability of streamflow through the hydrological model, K-Hidra and CPC-U performed best for precipitation and temperature, followed by the MERRA and ERA5 datasets, respectively. Lastly, we obtain only marginal improvement in the hydrological performance when utilizing multiple climate datasets after comparing it to a single hydrological simulation with the best performing climate dataset. Overall, our results indicate that the hydrological performance may vary considerably based on the selection of climate datasets, emphasizing the importance of regional evaluation studies for meteorological datasets.

Reconstructed Centennial Mass Balance Change for Golubin Glacier, Northern Tien Shan

Mass balance measurements for Golubin glacier in Northern Tien Shan, Kyrgyzstan, have been discontinuous over the last century, with significant data gaps. We provide a unique over 100-year-long mass balance series on daily resolution. We applied a temperature index model calibrated with glaciological measurements and validated with secular mass balances derived from independent length change observations. A comparison with other recent geodetic studies reveals good agreement. Golubin lost −0.16 ± 0.45 m w.e. a−1 from 1900/1901 to 2020/2021. From the long-term mass balance time series, we identify a shift to a more negative/less positive regime with time, with a steepening of the ablation and accumulation gradients, especially for the past two decades. We observe a parallel shift of the mass balance gradient accompanied by a rotation of the ablation gradient due to increased ablation at the glacier tongue and accumulation above the equilibrium line altitude. This tendency is believed to intensify in the future, affecting glaciers’ mass balance sensitivity to changes in atmospheric conditions and year-to-year variability and resulting in irregular melt water release feeding the rivers that provide water to Bishkek. These kinds of datasets are sparse for Tien Shan and, yet, indispensable to enhancing our understanding of glacier changes in High Mountain Asia.

Prediction of Sediment Yield in a Data-Scarce River Catchment at the Sub-Basin Scale Using Gridded Precipitation Datasets

Water-related soil erosion is a major environmental concern for catchments with barren topography in arid and semi-arid regions. With the growing interest in irrigation infrastructure development in arid regions, the current study investigates the runoff and sediment yield for the Gomal River catchment, Pakistan. Data from a precipitation gauge and gridded products (i.e., GPCC, CFSR, and TRMM) were used as input for the SWAT model to simulate runoff and sediment yield. TRMM shows a good agreement with the data of the precipitation gauge (≈1%) during the study period, i.e., 2004–2009. However, model simulations show that the GPCC data predicts runoff better than the other gridded precipitation datasets. Similarly, sediment yield predicted with the GPCC precipitation data was in good agreement with the computed one at the gauging site (only 3% overestimated) for the study period. Moreover, GPCC overestimated the sediment yield during some years despite the underestimation of flows from the catchment. The relationship of sediment yields predicted at the sub-basin level using the gauge and GPCC precipitation datasets revealed a good correlation (R2 = 0.65) and helped identify locations for precipitation gauging sites in the catchment area. The results at the sub-basin level showed that the sub-basin located downstream of the dam site contributes three (3) times more sediment yield (i.e., 4.1%) at the barrage than its corresponding area. The findings of the study show the potential usefulness of the GPCC precipitation data for the computation of sediment yield and its spatial distribution over data-scarce catchments. The computations of sediment yield at a spatial scale provide valuable information for deciding watershed management strategies at the sub-basin level.

Catchment Scale Evaluation of Multiple Global Hydrological Models from ISIMIP2a over North America

A satisfactory performance of hydrological models under historical climate conditions is considered a prerequisite step in any hydrological climate change impact study. Despite the significant interest in global hydrological modeling, few systematic evaluations of global hydrological models (gHMs) at the catchment scale have been carried out. This study investigates the performance of 4 gHMs driven by 4 global observation-based meteorological inputs at simulating weekly discharges over 198 large-sized North American catchments for the 1971–2010 period. The 16 discharge simulations serve as the basis for evaluating gHM accuracy at the catchment scale within the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). The simulated discharges by the four gHMs are compared against observed and simulated weekly discharge values by two regional hydrological models (rHMs) driven by a global meteorological dataset for the same period. We discuss the implications of both modeling approaches as well as the influence of catchment characteristics and global meteorological forcing in terms of model performance through statistical criteria and visual hydrograph comparison for catchment-scale hydrological studies. Overall, the gHM discharge statistics exhibit poor agreement with observations at the catchment scale and manifest considerable bias and errors in seasonal flow simulations. We confirm that the gHM approach, as experimentally implemented through the ISIMIP2a, must be used with caution for regional studies. We find the rHM approach to be more trustworthy and recommend using it for hydrological studies, especially if findings are intended to support operational decision-making.

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

Hot Spots of Glacier Mass Balance Variability in Central Asia

The Tien Shan and Pamir mountains host over 28,000 glaciers providing essential water resources for increasing water demand in Central Asia. A disequilibrium between glaciers and climate affects meltwater release to Central Asian rivers, challenging the region's water availability. Previous research has neglected temporal variability. We present glacier mass balance estimates based on transient snowline and geodetic surveys with unprecedented spatiotemporal resolution from 1999/00 to 2017/18. Our results reveal spatiotemporal heterogeneity characterized by two mass balance clusters: (a) positive, low variability, and (b) negative, high variability. This translates into variable glacial meltwater release (≈1-16%) of annual river runoff for two watersheds. Our study reveals more complex climate forcing-runoff responses and importance of glacial meltwater variability for the region than suggested previously.

Multi-sensor analysis of monthly gridded snow precipitation on alpine glaciers

Abstract. Accurate and reliable solid precipitation estimates for high mountain regions are crucial for many research applications. Yet, measuring snowfall at high elevation remains a major challenge. In consequence, observational coverage is typically sparse, and the validation of spatially distributed precipitation products is complicated. This study presents a novel approach using reliable daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glacier sites to assess the performance of various gridded precipitation products. The ground observations are available during two and four winter seasons. The performance of three readily-available precipitation data products based on different data sources (gauge-based, remotely-sensed, and re-analysed) is assessed in terms of their accuracy compared to the ground reference. Furthermore, we include a data set, which corresponds to the remotely-sensed product with a local adjustment to independent SWE measurements. We find a large bias of all precipitation products at a monthly and seasonal resolution, which also shows a seasonal trend. Moreover, the performance of the precipitation products largely depends on in situ wind direction during snowfall events. The varying performance of the three precipitation products can be partly explained with their compilation background and underlying data basis.

Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia

Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world’s protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability, we analyzed the potential of several state-of-the-art climate datasets (CHIRPS, CRU, ERA5-Land, GPCC-Monitoring-Product, IMERG-GPM, MERRA-2, MODIS-MOD10A1) for assessing NDVI anomalies (MODIS-MOD13Q1) in two particularly suitable remote conservation areas. We calculated anomalies of 156 climate variables and seasonal periods during 2001–2018, correlated these with vegetation anomalies while taking the multiple comparison problem into consideration, and computed their spatial performance to derive suitable parameters. Our results showed that four datasets (MERRA-2, ERA5-Land, MOD10A1, CRU) were suitable for vegetation analysis in both regions, by showing significant correlations controlled at a false discovery rate < 5% and in more than half of the analyzed areas. Cross-validated variable selection and importance assessment based on the Boruta algorithm indicated high importance of the reanalysis datasets ERA5-Land and MERRA-2 in both areas but higher differences and variability between the regions with all other products. CHIRPS, GPCC and the bias-corrected version of MERRA-2 were unsuitable and not important in both regions. We provide evidence that reanalysis datasets are most suitable for spatiotemporally consistent environmental analysis whereas gauge- or satellite-based products and their combinations are highly variable and may not be applicable in peripheral areas.

Capacity of Satellite-Based and Reanalysis Precipitation Products in Detecting Long-Term Trends across Mainland China

Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms.

Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location

The number of global open-source hydrometeorological datasets and models is large and growing. However, with a constantly growing demand for services and tools from stakeholders, not only in the water sector, we still lack simple solutions, which are easy to use for nonexperts. The new R package incorporates the BROOK90 hydrologic model and global open-source datasets used for parameterization and forcing. The aim is to estimate the vertical water fluxes within the soil–water–plant system of a single site or of a small catchment (<100 km2). This includes data scarce regions where no hydrometeorological measurements or reliable site characteristics can be obtained. The end-user only needs to provide a location and the desired period. The package automatically downloads the necessary datasets for elevation (Amazon Web Service Terrain Tiles), land cover (Copernicus: Land Cover 100 m), soil characteristics (ISRIC: SoilGrids250), and meteorological forcing (Copernicus: ERA5 reanalysis). Subsequently these datasets are processed, specific hydrotopes are created, and BROOK90 is applied. In a last step, the output data of all desired variables on a daily scale as well as time-series plots are stored. A first daily and monthly validation based on five catchments within various climate zones shows a decent representation of soil moisture, evapotranspiration, and runoff components. A considerably better performance is achieved for a monthly scale.

Evaluation of MERRA-2 Precipitation Products Using Gauge Observation in Nepal

Precipitation is the most important variable in the climate system and the dominant driver of land surface hydrologic conditions. Rain gauge measurement provides precipitation estimates on the ground surface; however, these measurements are sparse, especially in the high-elevation areas of Nepal. Reanalysis datasets are the potential alternative for precipitation measurement, although it must be evaluated and validated before use. This study evaluates the performance of second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) datasets with the 141-gauge observations from Nepal between 2000 and 2018 on monthly, seasonal, and annual timescales. Different statistical measures based on the Correlation Coefficient (R), Mean Bias (MB), Root-Mean-Square Error (RMSE), and Nash–Sutcliffe efficiency (NSE) were adopted to determine the performance of both MERRA-2 datasets. The results revealed that gauge calibrated (MERRA-C) underestimated, whereas model-only (MERRA-NC) overestimated the observed seasonal cycle of precipitation. However, both datasets were able to reproduce seasonal precipitation cycle with a high correlation (R ≥ 0.95), as revealed by observation. MERRA-C datasets showed a more consistent spatial performance (higher R-value) to the observed datasets than MERRA-NC, while MERRA-NC is more reasonable to estimate precipitation amount (lower MB) across the country. Both MERRA-2 datasets performed better in winter, post-monsoon, and pre-monsoon than in summer monsoon. Moreover, MERRA-NC overestimated the observed precipitation in mid and high-elevation areas, whereas MERRA-C severely underestimated at most of the stations throughout all seasons. Among both datasets, MERRA-C was only able to reproduce the observed elevation dependency pattern. Furthermore, uncertainties in MERRA-2 precipitation products mentioned above are still worthy of attention by data developers and users.

Extreme Events of Precipitation over Complex Terrain Derived from Satellite Data for Climate Applications: An Evaluation of the Southern Slopes of the Pyrenees

Estimating extreme precipitation events over complex terrain is challenging but crucial for evaluating the performance of climate models for the present climate and expected changes of the climate in the future. New satellites operating in the microwave wavelengths have started to open new opportunities for performing such estimation at adequate temporal and spatial scales and within sensible error limits. This paper illustrates the feasibility and limits of estimating precipitation extremes from satellite data for climatological applications. Using a high-resolution gauge database as ground truth, it was found that global precipitation measurement (GPM) constellation data can provide valuable estimates of extreme precipitation over the southern slopes of the Pyrenees, a region comprising several climates and a very diverse terrain (a challenge for satellite precipitation algorithms). Validation using an object-based quality measure showed reasonable performance, suggesting that GPM estimates can be advantageous reference data for climate model evaluation.