Remote sensing-aided large-scale rainfall-runoff modelling in the humid tropics

Abstract. Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitively assess Costa Rica’s water resources at a national scale. Data scarcity was compensated using adjusted global topography and remotely-sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly MODIS (Moderate Resolution Imaging Spectroradiometer) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations. The calibration consisted in step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow was a better option instead of calibrating only with daily streamflow. Additionally, including PET and AET in the calibration improved the simulated water balance and better matched hydrological signatures. Thus, the constrained parameter uncertainty increased the confidence in the simulation results. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision making at relatively high spatial and temporal resolution.

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The role of climatic and terrain attributes in estimating baseflow recession in tropical catchments

Hydrology and Earth System Sciences ◽

10.5194/hess-14-2193-2010 ◽

2010 ◽

Vol 14 (11) ◽

pp. 2193-2205 ◽

Cited By ~ 34

Author(s):

J. L. Peña-Arancibia ◽

A. I. J. M. van Dijk ◽

M. Mulligan ◽

L. A. Bruijnzeel

Keyword(s):

Potential Evapotranspiration ◽

Aridity Index ◽

Global Scale ◽

Tree Cover ◽

Annual Rainfall ◽

Ungauged Catchments ◽

Ongoing Research ◽

Daily Streamflow ◽

Terrain Attributes ◽

Streamflow Data

Abstract. The understanding of low flows in rivers is paramount more than ever as demand for water increases on a global scale. At the same time, limited streamflow data to investigate this phenomenon, particularly in the tropics, makes the provision of accurate estimations in ungauged areas an ongoing research need. This paper analysed the potential of climatic and terrain attributes of 167 tropical and sub-tropical unregulated catchments to predict baseflow recession rates. Daily streamflow data (m3 s–1) from the Global River Discharge Center (GRDC) and a linear reservoir model were used to obtain baseflow recession coefficients (kbf) for these catchments. Climatic attributes included annual and seasonal indicators of rainfall and potential evapotranspiration. Terrain attributes included indicators of catchment shape, morphology, land cover, soils and geology. Stepwise regression was used to identify the best predictors for baseflow recession coefficients. Mean annual rainfall (MAR) and aridity index (AI) were found to explain 49% of the spatial variation of kbf. The rest of climatic indices and the terrain indices average catchment slope (SLO) and tree cover were also good predictors, but co-correlated with MAR. Catchment elongation (CE), a measure of catchment shape, was also found to be statistically significant, although weakly correlated. An analysis of clusters of catchments of smaller size, showed that in these areas, presumably with some similarity of soils and geology due to proximity, residuals of the regression could be explained by SLO and CE. The approach used provides a potential alternative for kbf parameterisation in ungauged catchments.

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Terrestrial Aridity and Its Response to Greenhouse Warming across CMIP5 Climate Models

Journal of Climate ◽

10.1175/jcli-d-14-00480.1 ◽

2015 ◽

Vol 28 (14) ◽

pp. 5583-5600 ◽

Cited By ~ 76

Author(s):

Jacob Scheff ◽

Dargan M. W. Frierson

Keyword(s):

Land Surface ◽

Large Scale ◽

Climate Models ◽

Potential Evapotranspiration ◽

Global Climate ◽

Aridity Index ◽

Global Climate Models ◽

Greenhouse Warming ◽

Dimensionless Ratio ◽

The Tropics

Abstract The aridity of a terrestrial climate is often quantified using the dimensionless ratio of annual precipitation (P) to annual potential evapotranspiration (PET). In this study, the climatological patterns and greenhouse warming responses of terrestrial P, Penman–Monteith PET, and are compared among 16 modern global climate models. The large-scale climatological values and implied biome types often disagree widely among models, with large systematic differences from observational estimates. In addition, the PET climatologies often differ by several tens of percent when computed using monthly versus 3-hourly inputs. With greenhouse warming, land P does not systematically increase or decrease, except at high latitudes. Therefore, because of moderate, ubiquitous PET increases, decreases (drying) are much more widespread than increases (wetting) in the tropics, subtropics, and midlatitudes in most models, confirming and expanding on earlier findings. The PET increases are also somewhat sensitive to the time resolution of the inputs, although not as systematically as for the PET climatologies. The changes in the balance between P and PET are also quantified using an alternative aridity index, the ratio , which has a one-to-one but nonlinear correspondence with . It is argued that the magnitudes of changes are more uniformly relevant than the magnitudes of changes, which tend to be much higher in wetter regions. The ratio and its changes are also found to be excellent statistical predictors of the land surface evaporative fraction and its changes.

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Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements

Remote Sensing ◽

10.3390/rs12142330 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2330

Author(s):

Yan Tong ◽

Lian Feng ◽

Kun Sun ◽

Jing Tang

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Radiative Forcing ◽

Large Scale ◽

Data Availability ◽

Temporal Scales ◽

Modis Aod ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

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Financial distress determinants among SMEs: empirical evidence from Sweden

Journal of Economic Studies ◽

10.1108/jes-01-2019-0030 ◽

2020 ◽

Vol 47 (3) ◽

pp. 547-560 ◽

Cited By ~ 1

Author(s):

Darush Yazdanfar ◽

Peter Öhman

Keyword(s):

Financial Crisis ◽

Financial Distress ◽

Large Scale ◽

Global Financial Crisis ◽

Binary Logistic Regression ◽

Data Availability ◽

Cross Sectional ◽

Data Set ◽

Content Type ◽

The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

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Contributions of Vegetation Greening and Climate Change to Evapotranspiration Trend after Large-Scale Vegetation Restoration on the Loess Plateau, China

Water ◽

10.3390/w13131755 ◽

2021 ◽

Vol 13 (13) ◽

pp. 1755

Author(s):

Shuo Wang ◽

Chenfeng Cui ◽

Qin Dai

Keyword(s):

Climate Change ◽

Loess Plateau ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Potential Evapotranspiration ◽

Development Trend ◽

Future Trend ◽

Natural Ecosystem ◽

The Loess Plateau

Since the early 2000s, the vegetation cover of the Loess Plateau (LP) has increased significantly, which has been fully recorded. However, the effects on relevant eco-hydrological processes are still unclear. Here, we made an investigation on the changes of actual evapotranspiration (ETa) during 2000–2018 and connected them with vegetation greening and climate change in the LP, based on the remote sensing data with correlation and attribution analysis. Results identified that the average annual ETa on the LP exhibited an obvious increasing trend with the value of 9.11 mm yr−1, and the annual ETa trend was dominated by the changes of ETa in the third quarter (July, August, and September). The future trend of ETa was predicted by the Hurst exponent. Partial correlation analysis indicated that annual ETa variations in 87.8% regions of the LP were controlled by vegetation greening. Multiple regression analysis suggested that the relative contributions of potential evapotranspiration (ETp), precipitation, and normalized difference vegetation index (NDVI), to the trend of ETa were 5.7%, −26.3%, and 61.4%, separately. Vegetation greening has a close relationship with the Grain for Green (GFG) project and acts as an essential driver for the long-term development trend of water consumption on the LP. In this research, the potential conflicts of water demanding between the natural ecosystem and social-economic system in the LP were highlighted, which were caused by the fast vegetation expansion.

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Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

Remote Sensing ◽

10.3390/rs13081563 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1563

Author(s):

Yuanyuan Tao ◽

Qianxin Wang

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Large Scale ◽

Remote Sensing Image ◽

Data Availability ◽

Function Evaluation ◽

Characteristic Analysis ◽

Accurate Identification ◽

Living Space ◽

Evolution Characteristics

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.

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Projecting Basin-Scale Distributed Irrigation and Domestic Water Demands and Values: A Generic Method for Large-Scale Modeling

Water Economics and Policy ◽

10.1142/s2382624x16500235 ◽

2016 ◽

Vol 02 (04) ◽

pp. 1650023 ◽

Cited By ~ 1

Author(s):

Noémie Neverre ◽

Patrice Dumas

Keyword(s):

Irrigation Water ◽

Large Scale ◽

Potential Evapotranspiration ◽

Demand Curve ◽

Economic Valuation ◽

Economic Value ◽

Expansion Method ◽

Domestic Water ◽

Water Demands ◽

Basin Scale

This paper presents a methodology to project irrigation and domestic water demands on a regional to global scale, in terms of both quantity and economic value. Projections are distributed at the water basin scale. Irrigation water demand is projected under climate change. It is simply computed as the difference between crop potential evapotranspiration for the different stages of the growing season and available precipitation. Irrigation water economic value is based on a yield comparison approach between rainfed and irrigated crops using average yields. For the domestic sector, we project the combined effects of demographic growth, economic development and water cost evolution on future demands. The method consists in building three-part inverse demand functions in which volume limits of the blocks evolve with the level of GDP per capita. The value of water along the demand curve is determined from price-elasticity, price and demand data from the literature, using the point-expansion method, and from water cost data. This generic methodology can be easily applied to large-scale regions, in particular developing regions where reliable data are scarce. As an illustration, it is applied to Algeria, at the 2050 horizon, for demands associated to reservoirs. Our results show that domestic demand is projected to become a major water consumption sector. The methodology is meant to be integrated into large-scale hydroeconomic models, to determine inter-sectorial and inter-temporal water allocation based on economic valuation.

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Global scale hydrological modelling at 100 m, 1 h resolution, in Python

10.5194/egusphere-egu21-7154 ◽

2021 ◽

Author(s):

Kor de Jong ◽

Marc van Kreveld ◽

Debabrata Panja ◽

Oliver Schmitz ◽

Derek Karssenberg

Keyword(s):

Large Scale ◽

Model Building ◽

Flow Direction ◽

Building Blocks ◽

Global Scale ◽

Data Availability ◽

Hydrological Models ◽

Continental Scale ◽

Modelling Framework ◽

Scale Models

Data availability at global scale is increasing exponentially. Although considerable challenges remain regarding the identification of model structure and parameters of continental scale hydrological models, we will soon reach the situation that global scale models could be defined at very high resolutions close to 100 m or less. One of the key challenges is how to make simulations of these ultra-high resolution models tractable ([1]).Our research contributes by the development of a model building framework that is specifically designed to distribute calculations over multiple cluster nodes. This framework enables domain experts like hydrologists to develop their own large scale models, using a scripting language like Python, without the need to acquire the skills to develop low-level computer code for parallel and distributed computing.We present the design and implementation of this software framework and illustrate its use with a prototype 100 m, 1 h continental scale hydrological model. Our modelling framework ensures that any model built with it is parallelized. This is made possible by providing the model builder with a set of building blocks of models, which are coded in such a manner that parallelization of calculations occurs within and across these building blocks, for any combination of building blocks. There is thus full flexibility on the side of the modeller, without losing performance.This breakthrough is made possible by applying a novel approach to the implementation of the model building framework, called asynchronous many-tasks, provided by the HPX C++ software library ([3]). The code in the model building framework expresses spatial operations as large collections of interdependent tasks that can be executed efficiently on individual laptops as well as computer clusters ([2]). Our framework currently includes the most essential operations for building large scale hydrological models, including those for simulating transport of material through a flow direction network. By combining these operations, we rebuilt an existing 100 m, 1 h resolution model, thus far used for simulations of small catchments, requiring limited coding as we only had to replace the computational back end of the existing model. Runs at continental scale on a computer cluster show acceptable strong and weak scaling providing a strong indication that global simulations at this resolution will soon be possible, technically speaking.Future work will focus on extending the set of modelling operations and adding scalable I/O, after which existing models that are currently limited in their ability to use the computational resources available to them can be ported to this new environment.More information about our modelling framework is at https://lue.computationalgeography.org.References[1] M. Bierkens. Global hydrology 2015: State, trends, and directions. Water Resources Research, 51(7):4923&#8211;4947, 2015. [2] K. de Jong, et al. An environmental modelling framework based on asynchronous many-tasks: scalability and usability. Submitted. [3] H. Kaiser, et al. HPX - The C++ standard library for parallelism and concurrency. Journal of Open Source Software, 5(53):2352, 2020.

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Hydrological evaluation of high-resolution precipitation estimates from the WRF model in the Third Pole river basins

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0272.1 ◽

2021 ◽

Author(s):

He Sun ◽

Fengge Su ◽

Zhihua He ◽

Tinghai Ou ◽

Deliang Chen ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Hydrological Modeling ◽

Climate Model ◽

Regional Climate ◽

Wrf Model ◽

Infiltration Capacity ◽

Flood Prediction ◽

Daily Streamflow ◽

Precipitation Estimates

AbstractIn this study, two sets of precipitation estimates based on the regional Weather Research and Forecasting model (WRF) –the high Asia refined analysis (HAR) and outputs with a 9 km resolution from WRF (WRF-9km) are evaluated at both basin and point scales, and their potential hydrological utilities are investigated by driving the Variable Infiltration Capacity (VIC) large-scale land surface hydrological model in seven Third Pole (TP) basins. The regional climate model (RCM) tends to overestimate the gauge-based estimates by 20–95% in annual means among the selected basins. Relative to the gauge observations, the RCM precipitation estimates can accurately detect daily precipitation events of varying intensities (with absolute bias < 3 mm). The WRF-9km exhibits a high potential for hydrological application in the monsoon-dominated basins in the southeastern TP (with NSE of 0.7–0.9 and bias of -11% to 3%), while the HAR performs well in the upper Indus (UI) and upper Brahmaputra (UB) basins (with NSE of 0.6 and bias of -15% to -9%). Both the RCM precipitation estimates can accurately capture the magnitudes of low and moderate daily streamflow, but show limited capabilities in flood prediction in most of the TP basins. This study provides a comprehensive evaluation of the strength and limitation of RCMs precipitation in hydrological modeling in the TP with complex terrains and sparse gauge observations.

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Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach

Nonlinear Processes in Geophysics ◽

10.5194/npg-18-223-2011 ◽

2011 ◽

Vol 18 (2) ◽

pp. 223-234 ◽

Cited By ~ 8

Author(s):

R. Haas ◽

K. Born

Keyword(s):

Complex Terrain ◽

Large Scale ◽

Distribution Functions ◽

Data Availability ◽

Cumulative Distribution ◽

Second Step ◽

Mountain Area ◽

Investigation Area ◽

Distribution Parameters ◽

Mountain Environment

Abstract. In this study, a two-step probabilistic downscaling approach is introduced and evaluated. The method is exemplarily applied on precipitation observations in the subtropical mountain environment of the High Atlas in Morocco. The challenge is to deal with a complex terrain, heavily skewed precipitation distributions and a sparse amount of data, both spatial and temporal. In the first step of the approach, a transfer function between distributions of large-scale predictors and of local observations is derived. The aim is to forecast cumulative distribution functions with parameters from known data. In order to interpolate between sites, the second step applies multiple linear regression on distribution parameters of observed data using local topographic information. By combining both steps, a prediction at every point of the investigation area is achieved. Both steps and their combination are assessed by cross-validation and by splitting the available dataset into a trainings- and a validation-subset. Due to the estimated quantiles and probabilities of zero daily precipitation, this approach is found to be adequate for application even in areas with difficult topographic circumstances and low data availability.

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